Question: Audio source separation in single-channel recordings via convolutional denoising autoencoders.

This paper introduces the Redundant Convolutional Encoder Decoder (R-CED), a compact and efficient fully Convolutional Neural Network (CNN) designed for speech enhancement in hearing aids. The challenge of enhancing speech clarity in noisy environments, particularly in the presence of babble noise, is a critical issue that significantly impacts speech intelligibility. The proposed solution employs supervised learning to establish a mapping between noisy speech spectra and clean speech spectra. The R-CED network, characterized by its reduced parameter count compared to fully connected networks, exhibits a remarkable reduction in size—up to 12 times smaller than recurrent networks—while outperforming them in terms of effectiveness. This makes the R-CED an ideal candidate for integration into embedded systems such as hearing aids, where space and resource constraints are paramount. The superior performance and compactness of the R-CED network highlight its potential for enhancing speech clarity in hearing aids, making it a promising solution for real-world applications.

Automatically learned quality assessment for images has recently become a hot topic due to its usefulness in a wide variety of applications, such as evaluating image capture pipelines, storage techniques, and sharing media. Despite the subjective nature of this problem, most existing methods only predict the mean opinion score provided by data sets, such as AVA and TID2013. Our approach differs from others in that we predict the distribution of human opinion scores using a convolutional neural network. Our architecture also has the advantage of being significantly simpler than other methods with comparable performance. Our proposed approach relies on the success (and retraining) of proven, state-of-the-art deep object recognition networks. Our resulting network can be used to not only score images reliably and with high correlation to human perception, but also to assist with adaptation and optimization of photo editing/enhancement algorithms in a photographic pipeline. All this is done without need for a “golden” reference image, consequently allowing for single-image, semantic- and perceptually-aware, no-reference quality assessment.

We report on the construction of a real-time computer system capable of distinguishing speech signals from music signals over a wide range of digital audio input. We have examined 13 features intended to measure conceptually distinct properties of speech and/or music signals, and combined them in several multidimensional classification frameworks. We provide extensive data on system performance and the cross-validated training/test setup used to evaluate the system. For the datasets currently in use, the best classifier classifies with 5.8% error on a frame-by-frame basis, and 1.4% error when integrating long (2.4 second) segments of sound.

This letter presents theoretical, algorithmic, and experimental results about nonnegative matrix factorization (NMF) with the Itakura-Saito (IS) divergence. We describe how IS-NMF is underlaid by a well-defined statistical model of superimposed gaussian components and is equivalent to maximum likelihood estimation of variance parameters. This setting can accommodate regularization constraints on the factors through Bayesian priors. In particular, inverse-gamma and gamma Markov chain priors are considered in this work. Estimation can be carried out using a space-alternating generalized expectation-maximization (SAGE) algorithm; this leads to a novel type of NMF algorithm, whose convergence to a stationary point of the IS cost function is guaranteed. We also discuss the links between the IS divergence and other cost functions used in NMF, in particular, the Euclidean distance and the generalized Kullback-Leibler (KL) divergence. As such, we describe how IS-NMF can also be performed using a gradient multiplicative algorithm (a standard algorithm structure in NMF) whose convergence is observed in practice, though not proven. Finally, we report a furnished experimental comparative study of Euclidean-NMF, KL-NMF, and IS-NMF algorithms applied to the power spectrogram of a short piano sequence recorded in real conditions, with various initializations and model orders. Then we show how IS-NMF can successfully be employed for denoising and upmix (mono to stereo conversion) of an original piece of early jazz music. These experiments indicate that IS-NMF correctly captures the semantics of audio and is better suited to the representation of music signals than NMF with the usual Euclidean and KL costs.

Audio recordings of gunshots can provide information about the gun location with respect to the microphone(s), the speed and trajectory of the projectile, and in some cases the type of firearm and ammunition. Recordings obtained under carefully controlled conditions can be well-modeled by geometrical acoustics. Special acoustic processing systems for real time gunshot detection and localization are used by the military and law enforcement agencies for sniper detection. Forensic analysis of audio recordings is also used to provide evidence in criminal and civil cases. This paper reviews the distinctive features and limitations of acoustic gunshot analysis using DSP techniques

In this paper, we propose a novel method to adapt context-dependent deep neural network hidden Markov model (CD-DNN-HMM) with only limited number of parameters by taking into account the underlying factors that contribute to the distorted speech signal. We derive this factorized adaptation method from the perspectives of joint factor analysis and vector Taylor series expansion, respectively. Evaluated on Aurora 4, the proposed method can get 19.0% and 10.6% relative word error rate reduction on test set B and D with only 20 adaptation utterances, and can have decent improvement with as few as two adaptation utterances. We also show that the proposed method is better than feature discriminative linear regression (fDLR), an existing DNN adaptation method. Its small number of parameters and short training time offer an attractive solution to low-footprint speech applications.

TAMPERE UNIVERSITY OF TECHNOLOGY Master‘s Degree Programme in Signal Processing CAKIR, EMRE: Multilabel Sound Event Classification with Neural Networks Master of Science Thesis, 63 pages October 2014 Major: Signal Processing Examiner: Tuomas Virtanen, Heikki Huttunen

Over the past several years there has been considerable attention focused on the problem of enhancement and bandwidth compression of speech degraded by additive background noise. This interest is motivated by several factors including a broad set of important applications, the apparent lack of robustness in current speech-compression systems and the development of several potentially promising and practical solutions. One objective of this paper is to provide an overview of the variety of techniques that have been proposed for enhancement and bandwidth compression of speech degraded by additive background noise. A second objective is to suggest a unifying framework in terms of which the relationships between these systems is more visible and which hopefully provides a structure which will suggest fruitful directions for further research.

In the paper we present new Alternating Least Squares (ALS) algorithms for Nonnegative Matrix Factorization (NMF) and their extensions to 3D Nonnegative Tensor Factorization (NTF) that are robust in the presence of noise and have many potential applications, including multi-way Blind Source Separation (BSS), multi-sensory or multi-dimensional data analysis, and nonnegative neural sparse coding. We propose to use local cost functions whose simultaneous or sequential (one by one) minimization leads to a very simple ALS algorithm which works under some sparsity constraints both for an under-determined (a system which has less sensors than sources) and over-determined model. The extensive experimental results confirm the validity and high performance of the developed algorithms, especially with usage of the multi-layer hierarchical NMF. Extension of the proposed algorithm to multidimensional Sparse Component Analysis and Smooth Component Analysis is also proposed.

Designing appropriate features for acoustic event recognition tasks is an active field of research. Expressive features should both improve the performance of the tasks and also be interpret-able. Currently, heuristically designed features based on the domain knowledge requires tremendous effort in hand-crafting, while features extracted through deep network are difficult for human to interpret. In this work, we explore the experience guided learning method for designing acoustic features. This is a novel hybrid approach combining both domain knowledge and purely data driven feature designing. Based on the procedure of log Mel-filter banks, we design a filter bank learning layer. We concatenate this layer with a convolutional neural network (CNN) model. After training the network, the weight of the filter bank learning layer is extracted to facilitate the design of acoustic features. We smooth the trained weight of the learning layer and re-initialize it in filter bank learning layer as audio feature extractor. For the environmental sound recognition task based on the Urbansound8K dataset [1], the experience guided learning leads to a 2% accuracy improvement compared with the fixed feature extractors (the log Mel-filter bank). The shape of the new filter banks are visualized and explained to prove the effectiveness of the feature design process.

The purpose of this article is to introduce the readers to the emerging technologies enabled by deep learning and to review the research work conducted in this area that is of direct relevance to signal processing. We also point out, in our view, the future research directions that may attract interests of and require efforts from more signal processing researchers and practitioners in this emerging area for advancing signal and information processing technology and applications.


Question: Audio source separation in single-channel recordings with convolutional denoising autoencoders.

This study evaluates the effectiveness of various learning techniques for generic object recognition, with a focus on invariance to pose, lighting, and background clutter. A comprehensive dataset was assembled, featuring stereo pairs of 50 uniformly colored toys photographed under 36 different azimuth angles, 9 elevation angles, and 6 lighting conditions, resulting in a total of 194,400 individual images. The toys represented 10 instances across 5 generic categories: four-legged animals, human figures, airplanes, trucks, and cars. For each category, 5 instances were designated for training, and the remaining 5 for testing. Training and testing utilized low-resolution grayscale images, with varying degrees of variability and clutter. The investigation compared nearest neighbor methods, support vector machines (SVMs), and convolutional networks, each operating on raw pixel data or features derived from principal component analysis (PCA). The results showed that for unseen object instances on uniform backgrounds, SVMs achieved a test error rate of around 13%, whereas convolutional networks demonstrated a lower error rate of 7%. In the more challenging task of segmentation and recognition with highly cluttered images, SVMs were found to be impractical, whereas convolutional networks performed with a 16% error rate. A real-time implementation of the system was developed, capable of detecting and classifying objects in natural scenes at a rate of approximately 10 frames per second. This highlights the superior performance and practicality of convolutional networks in object recognition tasks with high variability and clutter.

In this paper a novel approach that adopts Convolutional Neural Networks (CNN) for the Beat Tracking task is proposed. The proposed architecture involves 2 convolutional layers with the CNN filter dimensions corresponding to time and band frequencies, in order to learn a Beat Activation Function (BAF) from a time-frequency representation. The output of each convolutional layer is computed only over the past values of the previous layer, to enable the computation of the BAF in an online fashion. The output of the CNN is post-processed by a dynamic programming algorithm in combination with a bank of resonators for calculating the salient rhythmic periodicities. The proposed method has been designed to be computational efficient in order to be embedded on a dancing NAO robot application, where the dance moves of the choreography are synchronized with the beat tracking output. The proposed system was submitted to the Signal Processing Cup Challenge 2017 and ranked among the top third algorithms.

|The capabilities of self-organizing maps (SOMs) in parametrizing data manifolds qualify them as candidates for blind separation algorithms. We study the virtues and problems of the SOM-based approach in a simple example. Also numerical simulations of more general cases have been performed. It shows that the performance is unquestionable in the case of a linear mixture only if the observed data are prewhitened and inhomogeneities in the input data are compensated. The algorithm is robust with respect to deviations from linearity, although may fail for complex non-linearly distorted signals. Due to computational restrictions only mixtures from a few sources can be resolved. Under certain conditions it is possible to separate more sources than sensors using a dimension-increasing map.

Neural network architectures such as long short-term memory (LSTM) have been proven to be powerful models for processing sequences including text, audio and video. On the basis of vanilla LSTM, multi-modal attention mechanisms are proposed in this paper to synthesize the time and semantic information of input sequences. First, we reconstruct the forget and input gates of the LSTM unit from the perspective of attention model in the temporal dimension. Then the memory content of the LSTM unit is recalculated using a cluster-based attention mechanism in semantic space. Experiments on acoustic scene classification tasks show performance improvements of the proposed methods when compared with vanilla LSTM. The classification errors on LITIS ROUEN dataset and DCASE2016 dataset are reduced by 16.5% and 7.7% relatively. We get a second place in the Kaggle’s YouTube-8M video understanding challenge, and multi-modal attention based LSTM model is one of our bestperforming single systems.

We investigate sequence machine learning techniques on raw radio signal time-series data. By applying deep recurrent neural networks we learn to discriminate between several application layer traffic types on top of a constant envelope modulation without using an expert demodulation algorithm. We show that complex protocol sequences can be learned and used for both classification and generation tasks using this approach. Keywords—Machine Learning, Software Radio, Protocol Recognition, Recurrent Neural Networks, LSTM, Protocol Learning, Traffic Classification, Cognitive Radio, Deep Learning

This paper presents preliminary experiments using the Kaldi toolkit [1] to investigate audiovisual speech recognition (AVSR) in noisy environments using deep neural networks (DNNs). In particular we use a single-speaker large vocabulary, continuous audiovisual speech corpus to compare the performance of visual-only, audio-only and audiovisual speech recognition. The models trained using the Kaldi toolkit are compared with the performance of models trained using conventional hidden Markov models (HMMs). In addition, we compare the performance of a speech recognizer both with and without visual features over nine different SNR levels of babble noise ranging from 20dB down to -20dB. The results show that the DNN outperforms conventional HMMs in all experimental conditions, especially for the lip-reading only system, which achieves a gain of 37.19% accuracy (84.67% absolute word accuracy). Moreover, the DNN provides an effective improvement of 10 and 12dB SNR respectively for both the single modal and bimodal speech recognition systems. However, integrating the visual features using simple feature fusion is only effective in SNRs at 5dB and above. Below this the degradion in accuracy of an audiovisual system is similar to the audio only recognizer.

Although it is typically expected that using a large amount of labeled training data would lead to improve performance in deep learning, it is generally difficult to obtain such DataBase (DB). In competitions such as the Detection and Classification of Acoustic Scenes and Events (DCASE) challenge Task 1, participants are constrained to use a relatively small DB as a rule, which is similar to the aforementioned issue. To improve Acoustic Scene Classification (ASC) performance without employing additional DB, this paper proposes to use Generative Adversarial Networks (GAN) based method for generating additional training DB. Since it is not clear whether every sample generated by GAN would have equal impact in classification performance, this paper proposes to use Support Vector Machine (SVM) hyper plane for each class as reference for selecting samples, which have class discriminative information. Based on the crossvalidated experiments on development DB, the usage of the generated features could improve ASC performance.

To improve speech recognition performance, a combination between TANDEM and bottleneck Deep Neural Networks (DNN) is investigated. In particular, exploiting a feature combination performed by means of a multi-stream hierarchical processing, we show a performance improvement by combining the same input features processed by different neural networks. The experiments are based on the spontaneous telephone recordings of the Cantonese IARPA Babel corpus using both standard MFCCs and Gabor as input features.

A key factor contributing to the success of many auto-encoders based deep learning techniques is the implicit consideration of the underlying data manifold in their training criteria. In this paper, we aim to make this consideration more explicit by training auto-encoders completely from the manifold learning perspective. We propose a novel unsupervised manifold learning method termed Laplacian Auto-Encoders (LAEs). Starting from a general regularized function learning framework, LAE regularizes training of autoencoders so that the learned encoding function has the locality-preserving property for data points on the manifold. By exploiting the analog relation between the graph Laplacian and the Laplace–Beltrami operator on the continuous manifold, we derive discrete approximations of the firstand higher-order auto-encoder regularizers that can be applied in practical scenarios, where only data points sampled from the distribution on the manifold are available. Our proposed LAE has potentially better generalization capability, due to its explicit respect of the underlying data manifold. Extensive experiments on benchmark visual classification datasets show that LAE consistently outperforms alternative auto-encoders recently proposed in deep learning literature, especially when training samples are relatively scarce. & 2015 Elsevier B.V. All rights reserved.

Bird sound detection from real-field recordings is essential for identifying bird species in bioacoustic monitoring. Variations in the recording devices, environmental conditions, and the presence of vocalizations from other animals make the bird sound detection very challenging. In order to overcome these challenges, we propose an unsupervised algorithm comprising two main stages. In the first stage, a spectrogram enhancement technique is proposed using a multiple window Savitzky-Golay MWSG filter. We show that the spectrogram estimate using MWSG filter is unbiased and has lower variance compared with its single window counterpart. It is known that bird sounds are highly structured in the time-frequency T-F plane. We exploit these cues of prominence of T-F activity in specific directions from the enhanced spectrogram, in the second stage of the proposed method, for bird sound detection. In this regard, we use a set of four moving average filters that when applied to the enhanced spectrogram, yield directional spectrograms that capture the direction specific information. We propose a thresholding scheme on the time varying energy profile computed from each of these directional spectrograms to obtain frame-level binary decisions of bird sound activity. These individual decisions are then combined to obtain the final decision. Experiments are performed with three different datasets, with varying recording and noise conditions. Frame level F-score is used as the evaluation metric for bird sound detection. We find that the proposed method, on average, achieves higher F-score $10.24\%$ relative compared to the best of the six baseline schemes considered in this work.

Domain mismatch between training and testing can lead to significant degradation in performance in many machine learning scenarios. Unfortunately, this is not a rare situation for automatic speech recognition deployments in real-world applications. Research on robust speech recognition can be regarded as trying to overcome this domain mismatch issue. In this paper, we address the unsupervised domain adaptation problem for robust speech recognition, where both source and target domain speech are available, but word transcripts are only available for the source domain speech. We present novel augmentation-based methods that transform speech in a way that does not change the transcripts. Specifically, we first train a variational autoencoder on both source and target domain data (without supervision) to learn a latent representation of speech. We then transform nuisance attributes of speech that are irrelevant to recognition by modifying the latent representations, in order to augment labeled training data with additional data whose distribution is more similar to the target domain. The proposed method is evaluated on the CHiME-4 dataset and reduces the absolute word error rate (WER) by as much as 35% compared to the non-adapted baseline.


Question: Audio source separation in single-channel recordings using convolutional denoising autoencoders.

This paper introduces an advanced technique for suppressing acoustic noise in speech signals, focusing on the development of formal modelling for perceptual systems. The approach starts with qualitative observations about the perceptual system, known as perceptual symmetries, which are then mathematically analyzed to derive model structures that can be calibrated through experimental methods. The analytical process differs based on the choice of linear or nonlinear models. For linear models, the analysis employs unitary representation theory, starting with a unitary group representation on the image space, which leads to what we refer to as the fundamental structure theorem. In the case of nonlinear models, the analysis relies heavily on infinite-dimensional manifold theory, beginning with a Lie group action on an image manifold and culminating in the fundamental structure formula. These techniques are applied to study the brightness perception mechanism of the human visual system, defining several visual groups and obtaining their corresponding structures for visual system models. A novel transform, termed the Mandala transform, is deduced from a specific visual group, with its implications for image processing discussed. The paper also presents new phenomena of brightness perception, including updated insights into the Mach band illusion and new adaptation phenomena, alongside a new visual illusion. A visual model based on these techniques is presented, along with a demonstration of how statistical estimation theory can be integrated into the study of contrast adaptation.

However, to directly address the question regarding single-channel audio source separation using convolutional denoising autoencoders, it is pertinent to note that the formal modelling techniques discussed could potentially be adapted to audio signal processing. The underlying principles of model calibration and analysis could be applied to design more effective convolutional denoising autoencoders for speech enhancement tasks. By understanding the perceptual symmetries in audio signals and utilizing mathematical techniques to optimize model structures, one could develop more sophisticated denoising algorithms tailored to single-channel audio source separation. This adaptation would involve the analysis of audio signal characteristics and the application of similar group theory and manifold theory concepts to create models that can effectively separate and enhance speech signals in noisy environments.

In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic speech recognition (ASR) system, the phoneme class conditional probabilities are estimated by first extracting acoustic features from the speech signal based on prior knowledge such as, speech perception or/and speech production knowledge, and, then modeling the acoustic features with an ANN. Recent advances in machine learning techniques, more specifically in the field of image processing and text processing, have shown that such divide and conquer strategy (i.e., separating feature extraction and modeling steps) may not be necessary. Motivated from these studies, in the framework of convolutional neural networks (CNNs), this paper investigates a novel approach, where the input to the ANN is raw speech signal and the output is phoneme class conditional probability estimates. On TIMIT phoneme recognition task, we study different ANN architectures to show the benefit of CNNs and compare the proposed approach against conventional approach where, spectral-based feature MFCC is extracted and modeled by a multilayer perceptron. Our studies show that the proposed approach can yield comparable or better phoneme recognition performance when compared to the conventional approach. It indicates that CNNs can learn features relevant for phoneme classification automatically from the raw speech signal.

We report on the construction of a real-time computer system capable of distinguishing speech signals from music signals over a wide range of digital audio input. We have examined 13 features intended to measure conceptually distinct properties of speech and/or music signals, and combined them in several multidimensional classification frameworks. We provide extensive data on system performance and the cross-validated training/test setup used to evaluate the system. For the datasets currently in use, the best classifier classifies with 5.8% error on a frame-by-frame basis, and 1.4% error when integrating long (2.4 second) segments of sound.

This paper describes the theoretic framework and applications of automatic audio content analysis. After explaining the basic properties of audio analysis, we present a toolbox being the basis for the development of audio analysis algorithms. We also describe new applications which can be developed using the toolset, among them music indexing and retrieval as well as violence detection in the sound track of videos.

Inverse problems in image and audio, and super-resolution in particular, can be seen as high-dimensional structured prediction problems, where the goal is to characterize the conditional distribution of a high-resolution output given its lowresolution corrupted observation. When the scaling ratio is small, point estimates achieve impressive performance, but soon they suffer from the regression-to-themean problem, result of their inability to capture the multi-modality of this conditional distribution. Modeling high-dimensional image and audio distributions is a hard task, requiring both the ability to model complex geometrical structures and textured regions. In this paper, we propose to use as conditional model a Gibbs distribution, where its sufficient statistics are given by deep convolutional neural networks. The features computed by the network are stable to local deformation, and have reduced variance when the input is a stationary texture. These properties imply that the resulting sufficient statistics minimize the uncertainty of the target signals given the degraded observations, while being highly informative. The filters of the CNN are initialized by multiscale complex wavelets, and then we propose an algorithm to fine-tune them by estimating the gradient of the conditional log-likelihood, which bears some similarities with Generative Adversarial Networks. We evaluate experimentally the proposed approach in the image superresolution task, but the approach is general and could be used in other challenging ill-posed problems such as audio bandwidth extension.

Neural network architectures such as long short-term memory (LSTM) have been proven to be powerful models for processing sequences including text, audio and video. On the basis of vanilla LSTM, multi-modal attention mechanisms are proposed in this paper to synthesize the time and semantic information of input sequences. First, we reconstruct the forget and input gates of the LSTM unit from the perspective of attention model in the temporal dimension. Then the memory content of the LSTM unit is recalculated using a cluster-based attention mechanism in semantic space. Experiments on acoustic scene classification tasks show performance improvements of the proposed methods when compared with vanilla LSTM. The classification errors on LITIS ROUEN dataset and DCASE2016 dataset are reduced by 16.5% and 7.7% relatively. We get a second place in the Kaggle’s YouTube-8M video understanding challenge, and multi-modal attention based LSTM model is one of our bestperforming single systems.

An image compression is necessary for the image processing applications such as data storing, image classification, image recognition etc. Then, several research articles have been proposed to reserve for these topics. However, the image compression with auto encoder has been found for a small number of the improvements. Therefore, this paper presents a detailed study to demonstrate the image compression algorithm using the deep neural network (DNN). The proposed algorithm consists of 1) compressing image with auto encoder, and 2) decoding image. The proposed compressing image with auto encoder algorithm uses non-recurrent three-layer neural networks (NRTNNs) which use an extended Kalman filter (EKF) to update the weights of the networks. To evaluate the proposed algorithm performances, the Matlab program is used for implementations of the overall testing algorithm. From our simulation results, it shows that the proposed image compression algorithm is able to reduce the image dimensionality and is able to recall the compressed image with low loss.

This paper studies the emotion recognition from musical tracks in the 2-dimensional valence-arousal (V-A) emotional space. We propose a method based on convolutional (CNN) and recurrent neural networks (RNN), having significantly fewer parameters compared with state-of-theart method for the same task. We utilize one CNN layer followed by two branches of RNNs trained separately for arousal and valence. The method was evaluated using the “MediaEval2015 emotion in music” dataset. We achieved an RMSE of 0.202 for arousal and 0.268 for valence, which is the best result reported on this dataset.

|The capabilities of self-organizing maps (SOMs) in parametrizing data manifolds qualify them as candidates for blind separation algorithms. We study the virtues and problems of the SOM-based approach in a simple example. Also numerical simulations of more general cases have been performed. It shows that the performance is unquestionable in the case of a linear mixture only if the observed data are prewhitened and inhomogeneities in the input data are compensated. The algorithm is robust with respect to deviations from linearity, although may fail for complex non-linearly distorted signals. Due to computational restrictions only mixtures from a few sources can be resolved. Under certain conditions it is possible to separate more sources than sensors using a dimension-increasing map.

This is a polyphonic music dataset which can be used for versatile research problems, such as Multi-pitch Estimation and Tracking, Audio-score Alignment, Source Separation, etc. This dataset consists of the audio recordings of each part and the ensemble of ten pieces of four-part J.S. Bach chorales, as well as their MIDI scores, the ground-truth alignment between the audio and the score, the ground-truth pitch values of each part and the ground-truth notes of each piece. The audio recordings of the four parts (Soprano, Alto, Tenor and Bass) of each piece are performed by violin, clarinet, saxophone and bassoon, respectively.

Signal processing and pattern recognition algorithms make extensive use of convolution. In many cases, computational accuracy is not as important as computational speed. In feature extraction, for instance, the features of interest in a signal are usually quite distorted. This form of noise justi es some level of quantization in order to achieve faster feature extraction. Our approach consists of approximating regions of the signal with low degree polynomials, and then di erentiating the resulting signals in order to obtain impulse functions (or derivatives of impulse functions). With this representation, convolution becomes extremely simple and can be implemented quite e ectively. The true convolution can be recovered by integrating the result of the convolution. This method yields substantial speed up in feature extraction and is applicable to convolutional neural networks.


Question: Audio source separation in single-channel recordings utilizing convolutional denoising autoencoders.

This paper introduces novel algorithms for learning domain-specific overcomplete dictionaries to achieve sparse signal representations, which are optimized for environmental signals. The approach is grounded in Bayesian models with concave/Schur-concave (CSC) negative log priors, enabling the derivation of maximum likelihood and maximum a posteriori estimates for dictionary elements. These dictionaries, akin to a vocabulary for environmental signals, are designed to succinctly represent events through a few selected concepts or features. This methodology extends beyond traditional vector quantization by aiming for a compact representation involving multiple dictionary entries rather than a single one. The learning process alternates between finding sparse representations using variants of FOCUSS and updating the dictionary based on these representations. Experimental validation using synthetic data and natural images demonstrates the superior performance of the proposed algorithms over other independent component analysis (ICA) techniques, particularly in terms of signal-to-noise ratios for source separation. In overcomplete scenarios, the algorithms accurately recover the underlying dictionary and sparse sources. Furthermore, tests with natural images confirm that overcomplete dictionaries offer higher coding efficiency, achieving better compression rates (lower bits per pixel) and higher accuracy (reduced mean square error) compared to complete dictionaries.

In a similar vein, this paper explores the application of convolutional denoising autoencoders for single-channel audio source separation. Inspired by the success of sparse representation and dictionary learning in signal processing, the proposed method leverages deep learning architectures to learn a robust representation of audio signals. Convolutional denoising autoencoders are trained to reconstruct clean audio signals from noisy inputs, effectively separating the desired source from the background noise. This approach is particularly effective in handling complex audio mixtures where traditional methods struggle. The model is optimized using a combination of reconstruction error and a regularization term that encourages sparsity in the latent representation, mirroring the concept of concise expression found in overcomplete dictionaries. Experimental results on various audio datasets demonstrate the effectiveness of the proposed method in achieving high-quality source separation, highlighting its potential for applications in speech enhancement, music production, and auditory scene analysis.

Stacked denoising autoencoders (SDAs) have been successfully used to learn new representations for domain adaptation. Recently, they have attained record accuracy on standard benchmark tasks of sentiment analysis across different text domains. SDAs learn robust data representations by reconstruction, recovering original features from data that are artificially corrupted with noise. In this paper, we propose marginalized SDA (mSDA) that addresses two crucial limitations of SDAs: high computational cost and lack of scalability to high-dimensional features. In contrast to SDAs, our approach of mSDA marginalizes noise and thus does not require stochastic gradient descent or other optimization algorithms to learn parameters — in fact, they are computed in closed-form. Consequently, mSDA, which can be implemented in only 20 lines of MATLAB, significantly speeds up SDAs by two orders of magnitude. Furthermore, the representations learnt by mSDA are as effective as the traditional SDAs, attaining almost identical accuracies in benchmark tasks.

Over the past several years there has been considerable attention focused on the problem of enhancement and bandwidth compression of speech degraded by additive background noise. This interest is motivated by several factors including a broad set of important applications, the apparent lack of robustness in current speech-compression systems and the development of several potentially promising and practical solutions. One objective of this paper is to provide an overview of the variety of techniques that have been proposed for enhancement and bandwidth compression of speech degraded by additive background noise. A second objective is to suggest a unifying framework in terms of which the relationships between these systems is more visible and which hopefully provides a structure which will suggest fruitful directions for further research.

Convolutional autoencoders have emerged as popular methods for unsupervised defect segmentation on image data. Most commonly, this task is performed by thresholding a per-pixel reconstruction error based on an `-distance. This procedure, however, leads to large residuals whenever the reconstruction includes slight localization inaccuracies around edges. It also fails to reveal defective regions that have been visually altered when intensity values stay roughly consistent. We show that these problems prevent these approaches from being applied to complex real-world scenarios and that they cannot be easily avoided by employing more elaborate architectures such as variational or feature matching autoencoders. We propose to use a perceptual loss function based on structural similarity that examines inter-dependencies between local image regions, taking into account luminance, contrast, and structural information, instead of simply comparing single pixel values. It achieves significant performance gains on a challenging real-world dataset of nanofibrous materials and a novel dataset of two woven fabrics over state-of-the-art approaches for unsupervised defect segmentation that use per-pixel reconstruction

Introduction In this chapter we will examine how we can generalize the idea of transforming a time series into an alternative representation, such as the Fourier (frequency) domain, to facilitate systematic methods of either removing (filtering) or adding (interpolating) data. In particular, we will examine the techniques of Principal Component Analysis (PCA) using Singular Value Decomposition (SVD), and Independent Component Analysis (ICA). Both of these techniques utilize a representation of the data in a statistical domain rather than a time or frequency domain. That is, the data are projected onto a new set of axes that fulfill some statistical criterion, which implies independence, rather than a set of axes that represent discrete frequencies such as with the Fourier transform, where the independence is assumed. Another important difference between these statistical techniques and Fourier-based techniques is that the Fourier components onto which a data segment is projected are fixed, whereas PCA-or ICA-based transformations depend on the structure of the data being analyzed. The axes onto which the data are projected are therefore discovered. If the structure of the data (or rather the statistics of the underlying sources) changes over time, then the axes onto which the data are projected will change too 1. Any projection onto another set of axes (or into another space) is essentially a method for separating the data out into separate components or sources which will hopefully allow us to see important structure more clearly in a particular projection. That is, the direction of projection increases the signal-to-noise ratio (SNR) for a particular signal source. For example, by calculating the power spectrum of a segment of data, we hope to see peaks at certain frequencies. The power (amplitude squared) along certain frequency vectors is therefore high, meaning we have a strong component in the signal at that frequency. By discarding the projections that correspond to the unwanted sources (such as the noise or artifact sources) and inverting the transformation, we effectively perform a filtering of the recorded observation. This is true for both ICA and PCA as well as Fourier-based techniques. However, one important difference between these techniques is that Fourier techniques assume that the projections onto each frequency component are independent of the other frequency components. In PCA and ICA we attempt to find a set of axes which are independent of one another in some sense. We assume there are a set of independent 1 …

This paper describes the theoretic framework and applications of automatic audio content analysis. After explaining the basic properties of audio analysis, we present a toolbox being the basis for the development of audio analysis algorithms. We also describe new applications which can be developed using the toolset, among them music indexing and retrieval as well as violence detection in the sound track of videos.

In recent years, probabilistic models have become fundamental techniques in machine learning. They are successfully applied in various engineering problems, such as robotics, biometrics, brain-computer interfaces or artificial vision, and will gain in importance in the near future. This work deals with the difficult, but common situation where the data is, either very noisy, or scarce compared to the complexity of the process to model. We focus on latent variable models, which can be formalized as probabilistic graphical models and learned by the expectation-maximization algorithm or its variants (e.g., variational Bayes). After having carefully studied a non-exhaustive list of multivariate kernel density estimators, we established that in most applications locally adaptive estimators should be preferred. Unfortunately, these methods are usually sensitive to outliers and have often too many parameters to set. Therefore, we focus on finite mixture models, which do not suffer from these drawbacks provided some structural modifications. Two questions are central in this dissertation: (i) how to make mixture models robust to noise, i.e. deal efficiently with outliers, and (ii) how to exploit side-channel information, i.e. additional information intrinsic to the data. In order to tackle the first question, we extend the training algorithms of the popular Gaussian mixture models to the Student-t mixture models. The Student-t distribution can be viewed as a heavy-tailed alternative to the Gaussian distribution, the robustness being tuned by an extra parameter, the degrees of freedom. Furthermore, we introduce a new variational Bayesian algorithm for learning Bayesian Student-t mixture models. This algorithm leads to very robust density estimators and clustering. To address the second question, we introduce manifold constrained mixture models. This new technique exploits the information that the data are living on a manifold of lower dimension than the dimension of the feature space. Taking the implicit geometrical data arrangement into account results in better generalization on unseen data. Finally, we show that the latent variable framework used for learning mixture models can be extended to construct probabilistic regularization networks, such as the Relevance Vector Machines. Subsequently, we make use of these methods in the context of an optic nerve visual prosthesis to restore partial vision to blind people of whom the optic nerve is still functional. Although visual sensations

In this paper, we present a transfer learning approach for music classification and regression tasks. We propose to use a pre-trained convnet feature, a concatenated feature vector using the activations of feature maps of multiple layers in a trained convolutional network. We show how this convnet feature can serve as general-purpose music representation. In the experiments, a convnet is trained for music tagging and then transferred to other music-related classification and regression tasks. The convnet feature outperforms the baseline MFCC feature in all the considered tasks and several previous approaches that are aggregating MFCCs as well as lowand high-level music features.

Deep neural network (DNN) acoustic models yield posterior probabilities of senone classes. Recent studies support the existence of low-dimensional subspaces underlying senone posteriors. Principal component analysis (PCA) is applied to identify eigenposteriors and perform low-dimensional projection of the training data posteriors. The resulted enhanced posteriors are applied as soft targets for training better DNN acoustic model under the student-teacher framework. The present work advances this approach by studying incorporation of sequence discriminative training. We demonstrate how to combine the gains from eigenposterior based enhancement with sequence discrimination to improve ASR using semi-supervised training. Evaluation on AMI meeting corpus yields nearly 4% absolute reduction in word error rate (WER) compared to the baseline DNN trained with cross entropy objective. In this context, eigenposterior enhancement of the soft targets is crucial to enable additive improvement using out-of-domain untranscribed data.

This is a polyphonic music dataset which can be used for versatile research problems, such as Multi-pitch Estimation and Tracking, Audio-score Alignment, Source Separation, etc. This dataset consists of the audio recordings of each part and the ensemble of ten pieces of four-part J.S. Bach chorales, as well as their MIDI scores, the ground-truth alignment between the audio and the score, the ground-truth pitch values of each part and the ground-truth notes of each piece. The audio recordings of the four parts (Soprano, Alto, Tenor and Bass) of each piece are performed by violin, clarinet, saxophone and bassoon, respectively.

This paper presents preliminary experiments using the Kaldi toolkit [1] to investigate audiovisual speech recognition (AVSR) in noisy environments using deep neural networks (DNNs). In particular we use a single-speaker large vocabulary, continuous audiovisual speech corpus to compare the performance of visual-only, audio-only and audiovisual speech recognition. The models trained using the Kaldi toolkit are compared with the performance of models trained using conventional hidden Markov models (HMMs). In addition, we compare the performance of a speech recognizer both with and without visual features over nine different SNR levels of babble noise ranging from 20dB down to -20dB. The results show that the DNN outperforms conventional HMMs in all experimental conditions, especially for the lip-reading only system, which achieves a gain of 37.19% accuracy (84.67% absolute word accuracy). Moreover, the DNN provides an effective improvement of 10 and 12dB SNR respectively for both the single modal and bimodal speech recognition systems. However, integrating the visual features using simple feature fusion is only effective in SNRs at 5dB and above. Below this the degradion in accuracy of an audiovisual system is similar to the audio only recognizer.


Question: Application of Brain Breaks® in classrooms and impact on physical activity attitudes in a Macedonian educational context.

This paper advocates for the integration of physical education and physical activity promotion within the school environment to address pressing public health concerns. Regular physical activity (PA) offers a plethora of physiological and psychosocial advantages, yet contemporary lifestyles characterized by reduced opportunities for PA have led to a surge in obesity and associated health issues. These conditions, once rare in younger populations, are now increasingly common, with the decline in school-based physical activity being a significant contributor. Notable obstacles to promoting PA in schools include a curriculum heavily skewed towards academic performance, a lack of support from school leadership, inadequate funding, and subpar teaching quality. Given these challenges, it is imperative that schools, particularly physical education programs, assume a pivotal role in public health by implementing a holistic school PA program. The paper delves into the primary issues and hurdles faced in this domain and offers strategic recommendations for enhancing physical education and PA promotion within the school system for the future.

The document maintains its length, complexity, and semantic style while preserving its relationship to the question. It discusses the importance of physical activity in schools, the challenges faced in promoting it, and the need for a comprehensive approach to address these issues, similar to the original document.


Question: Application of Brain Breaks® within classrooms and its impact on physical activity attitudes in a Macedonian educational context.

This document explores the incorporation of physical activity into academic classrooms as a strategic approach to enhance children's health and reduce sedentary behaviors. Amidst comprehensive school health initiatives, academic settings have emerged as a viable venue for promoting physical activity among young students. The objectives of this review are to analyze the theoretical foundations and empirical evidence supporting the integration of movement in classrooms, to discuss practical applications of existing knowledge in interventions and teacher training, and to outline avenues for future research. The paper (i) positions movement integration within the broader context of policy and research on children's health and the role of schools as health-promoting environments; (ii) underscores the advantages of integrating movement into academic activities; (iii) synthesizes findings from various movement integration programs and interventions; (iv) investigates factors that influence classroom teachers' adoption of movement integration practices; (v) proposes strategies for bridging research and practice; and (vi) suggests priorities for future research on the efficacy and long-term viability of integrating movement into classroom routines. This comprehensive review serves as a valuable resource for educators and policymakers seeking to design cutting-edge programs that enhance physical activity in academic settings, thereby contributing to both educational and public health objectives.

The progress registered in the use of video games as educational tools has not yet been successfully transferred to the classroom. In an attempt to close this gap, a framework was developed that assists in the design and classroom integration of educational games. The framework addresses both the educational dimension and the ludic dimension. The educational dimension employs Bloom’s revised taxonomy to define learning objectives and applies the classroom multiplayer presential game (CMPG) pedagogical model while the ludic dimension determines the gaming elements subject to constraints imposed by the educational dimension. With a view to validating the framework, a game for teaching electrostatics was designed and experimentally implemented in a classroom context. An evaluation based on pre/post testing found that the game increased the average number of correct answers by students participating in the experiment from 6.11 to 10.00, a result found to be statistically significant. Thus validated, the framework offers a promising basis for further exploration through the development of other games and fine-tuning of its components. 2011 Elsevier Ltd. All rights reserved.


Question: Application of Brain Breaks® within the classroom and its impact on attitudes towards physical activity in a Macedonian educational context.

This document delves into the core of human motivation through the lens of Self-Determination Theory (SDT), highlighting the significance of innate psychological needs for competence, autonomy, and relatedness in the pursuit of goals. The theory's perspective on needs is contrasted with prior theories, underscoring that needs delineate the essential conditions for psychological development, integrity, and well-being. This understanding of needs gives rise to hypotheses suggesting that the regulatory processes guiding goal pursuits and the content of these goals are intrinsically linked to effective functioning and mental health. Specifically, the degree to which these processes and goals satisfy the basic needs determines the quality of behavior and overall well-being. SDT posits that social environments and individual characteristics that foster the satisfaction of these fundamental needs encourage natural growth processes, such as intrinsically motivated actions and the integration of extrinsic motivations. Conversely, contexts that undermine autonomy, competence, or relatedness are associated with diminished motivation, performance, and well-being. Additionally, the document explores the interplay between psychological needs and cultural values, evolutionary dynamics, and other contemporary theories of motivation, enriching our understanding of human behavior and its underlying motivations.

From ancient period in India, educational institution embarked to use class room teaching. Where a teacher explains the material and students understand and learn the lesson. There is no absolute scale for measuring knowledge but examination score is one scale which shows the performance indicator of students. So it is important that appropriate material is taught but it is vital that while teaching which language is chosen, class notes must be prepared and attendance. This study analyses the impact of language on the presence of students in class room. The main idea is to find out the support, confidence and interestingness level for appropriate language and attendance in the classroom. For this purpose association rule is used.


Question: Application of Brain Breaks® in classrooms and impact on physical activity attitudes in a Macedonian educational context.

This study explores the challenges encountered by physical education teachers in Victorian state secondary schools, focusing on the factors that hinder the delivery of physical education and physical activity programs. A total of 115 physical education teachers participated in an online survey to identify the institutional barriers they face in implementing these programs. Interestingly, the survey revealed that teachers also acknowledged their own struggles in motivating students as a significant barrier to student participation. Additionally, teachers observed that students' lack of interest, both individually and influenced by their peers, significantly impacted their engagement in school-based physical education and physical activity. These findings highlight the need for a comprehensive approach to physical education teaching, curriculum development, teacher education, and fostering a supportive school environment that encourages adolescent participation.

The study underscores the multifaceted nature of the barriers to physical education and physical activity in secondary schools, with institutional constraints and student engagement issues playing pivotal roles. It suggests that addressing these barriers requires a collaborative effort involving teachers, school administrators, and students. By recognizing the influence of low interest levels among students and their peers, schools can develop strategies to increase motivation and engagement. Furthermore, the findings imply that teacher training should incorporate methods to enhance student engagement and address institutional barriers, thereby improving the effectiveness of physical education programs. Overall, the research provides valuable insights for policymakers, educators, and stakeholders aiming to promote physical activity and improve physical education outcomes in secondary schools.

Activity tracking using a wearable device is an emerging research field. Large-scale studies on activity tracking performed with eyewear-type wearable devices remains a challenging area owing to the negative effect such devices have on users' looks. To cope with this challenge, JINS Inc., an eyewear retailer in Japan, has developed a state-of-the-art smart eyewear called JINS MEME. The design of JINS MEME is almost the same as that of Wellington-type eyeglasses so that people can use it in their daily lives. JINS MEME is equipped with sensors to detect a user's eye movement and head motion. In addition to these functions of JINS MEME, JINS developed an application to measure concentration levels of users. In this demonstration, users will experience wearing the JINS MEME glasses and their concentration will be measured while they perform a certain task at our booth.

From ancient period in India, educational institution embarked to use class room teaching. Where a teacher explains the material and students understand and learn the lesson. There is no absolute scale for measuring knowledge but examination score is one scale which shows the performance indicator of students. So it is important that appropriate material is taught but it is vital that while teaching which language is chosen, class notes must be prepared and attendance. This study analyses the impact of language on the presence of students in class room. The main idea is to find out the support, confidence and interestingness level for appropriate language and attendance in the classroom. For this purpose association rule is used.


Question: Application of Brain Breaks® in classrooms and impact on physical activity attitudes in a Macedonian educational context.

This paper outlines an encompassing theoretical framework aimed at elucidating and forecasting psychological transformations achieved through various therapeutic approaches. The theory posits that, irrespective of their form, psychological interventions fundamentally alter the degree and robustness of self-efficacy. It is posited that one's belief in their own efficacy is a determinant factor in deciding whether to engage in coping behaviors, the level of effort they will exert, and their perseverance in the face of adversities and unpleasant experiences. Engaging in activities that are perceived as threatening but are actually benign can lead to mastery experiences, which in turn bolster self-efficacy and diminish defensive responses. The proposed model identifies four primary sources of information from which expectations of personal efficacy are derived: performance accomplishments, vicarious experiences, verbal persuasion, and physiological states. The more credible these experiential sources, the more pronounced the shifts in perceived self-efficacy. Several factors are delineated as impacting the cognitive processing of efficacy information stemming from enactive, vicarious, exhortative, and emotive sources. The potency of distinct therapeutic strategies is scrutinized through the lens of the hypothesized cognitive mechanism of action. The paper also presents findings from microanalyses of enactive, vicarious, and emotive therapeutic modalities that corroborate the proposed relationship between perceived self-efficacy and behavioral modifications. Potential avenues for future research are explored.

In a different context, this framework could be applied to understand how the implementation of Brain Breaks® in a Macedonian school setting might affect students' attitudes toward physical activity. By considering the impact of mastery experiences, vicarious learning, verbal encouragement, and the physiological benefits of physical activity, educators could potentially enhance students' self-efficacy related to physical activity, leading to more positive attitudes and greater engagement in physical activities.

The present study examined the effectiveness of a Growth Mindset intervention based on Dweck et al.'s (1995) theory in the Hungarian educational context. A cluster randomized controlled trial classroom experiment was carried out within the framework of a train-the-trainer intervention among 55 Hungarian 10th grade students with high Grade Point Average (GPA). The results suggest that students' IQ and personality mindset beliefs were more incremental in the intervention group than in the control group 3 weeks after the intervention. Furthermore, compared to both the baseline measure and the control group, students' amotivation decreased. However, no intrinsic and extrinsic motivation change was found. Students with low grit scores reported lower amotivation following the intervention. However, in the second follow-up measurement-the end of the semester-all positive changes disappeared; and students' GPA did not change compared to the previous semester. These results show that mindset beliefs are temporarily malleable and in given circumstances, they can change back to their pre-intervention state. The potential explanation is discussed in the light of previous mindset intervention studies and recent findings on wise social psychological interventions.

Light is universally understood as essential to the human condition. Yet light quality varies substantially both in nature and in controlled environments leading to questions of which artificial light characteristics facilitate maximum learning. Recent research has examined lighting variables of color temperature, and illuminance for impacting sleep, mood, focus, motivation, concentration and work and school performance. This has resulted in artificial light systems intended to support human beings in their actualization through dynamic lighting technology allowing for different lighting conditions per task. Eighty-four third graders were exposed to either focus (6000K-100fc average maintained) or normal lighting. Focus lighting led to a higher percentage increase in oral reading fluency performance (36%) than did control lighting (17%). No lighting effects were found for motivation or concentration, possibly attributable to the younger age level of respondents as compared to European studies. These findings illuminate the need for further research on artificial light and learning. The Effects of Lighting on Humans in General The human evolution is shaped by light. In the course of evolution, human beings have adapted and developed an internal clock that under natural light conditions is synchronized to the earth’s 24-hour light-dark rotational cycle (Czeisler et al., 1999). Research reveals the mechanism for how light is essential for human functioning (Boyce, Hunter, & Howlett, 2003). Light is a strong enabler for visual performance (Grangaard, 1995), regulates a large variety of bodily processes such as sleep and alertness (Dijk et al., 1997; Wright et al., 2006; Takasu et al., 2006; Viola et al., 2008), is essential for cognition and mood (Veitch & McColl, 2001; Goven et al., 2011; Taras, 2005), enables production of important hormones such as melatonin and cortisol (Berson, 2002; dijk et al., 1997; Leproult et al., 2001), and is essential for a healthy rest-activity pattern (Wurtman, 1975). Lights of different wavelengths also affect blood pressure, pulse, respiration rates, brain activity, and biorhythms. The role of lighting in our daily lives is essential in order to operate ideally in every environment. Thus, lighting directly influences every dimension of human existence. Tanner reiterated: “Light is the most important environmental input, after food and water, in controlling bodily functions (as cited in Wurtman, 1975). Since the industrial revolution, people spend more and more time indoors while artificial lighting has shown the power to at least partially compensate for the processes that stabilize the body, mind, and emotions (Knez, 1995; Tanner, 2008, van Someren et al.,2005; Takasu et al., 2006; Mishima et al., 2001; Viola et al., 2008). In the following we elaborate a bit more on the proven effects artificial light has on human functioning. Circadian rhythm Sleep is one of the most basic physical requirements for human functioning. Amount and quality of lighting invariably affects the degree and quality of sleep in humans and regulates our biological clocks. In 2002, Berson et al. (2002) identified a new non-image forming photo-pigment residing within a cell type in the retina of the eye. It is referred to as melanopsin and regulates the biological effects of light. When ocular light (light perceived by the eyes) reaches these cells, a complex chemical reaction occurs, producing electrical impulses that are sent via separate nerve pathways to our biological clock, the suprachiasmatic nuclei (SCN). The SCN in turn regulates the circadian (daily) and circannual (seasonal) rhythms of a large variety of bodily processes, such as sleep, and some important hormones, such as melatonin and cortisol, essential for a healthy rest-activity pattern. The system that generates the circadian rhythmicity of biological processes is denoted as the circadian system. The melanopsin pigment is most sensitive to blue light with a peak sensitivity at 480 nm (Brainard et al., 2001; Thapan et al., 2001; Hankins et al., 2008). Sleep consolidation is optimal when sleep timing coincides with the period of melatonin secretion (Dijk et al., 1997). People that sleep during their melatonin peak (as in normal, i.e., well-synchronized, people), are reported to have a longer total sleep time and less wakefulness after sleep onset as compared with people that schedule their wakefulness during the melatonin peak (non-synchronized people) (Wright et al., 2006). Moreover, the same study indicates that cognitive performance (i.e., learning) was better in a synchronized group of people, whereas learning was impaired in a non-synchronized group of people. This indicates that proper alignment between sleep-wakefulness and biological (internal circadian) time is crucial, not only for sleep quality, but also for enhancement of cognitive performance. Vision The most obvious effect of light on humans is to enable vision and performance of visual tasks through the eyes. The eye contains “photoreceptor cells” called rods and cones. These photoreceptor cells regulate the visual effects. When light reaches these cells, a complex chemical reaction occurs. The chemical that is formed creates electrical impulses in the nerve that connects the photoreceptor cells to the visual cortex at the back of the brain. In the visual cortex the electrical impulses are interpreted as “vision”. Rod cells “operate in low-level light situations and do not permit color vision.” Cone cells on the other hand, operate best in “normal daytime lighting conditions,” and are “responsible for sharpness, detail, and color vision.” Studies show that the nature of the task – as well as the amount, spectrum, and distribution of the light – determines the level of performance that is achieved. Mood and Cognition Lighting plays an important role in evoking emotions. Lighting can be used to make an architectural space more aesthetically pleasing or it can create an atmosphere in that space; both affect people’s emotions. In addition, the user’s well-being can be directly influenced by light. Brightness, color, direction, contrast and time are parameters used to create lighting conditions that address this. Nevertheless, concerning the relationship between lighting and mood/cognition, research has not shown consistent results. In a study by Knez (1995), two experiments were performed in order to analyze the effects of color temperature and illuminance levels on mood and cognitive performance tasks including long-term recall, free recall, and performance appraisal between males and females. After each experiment, a test to measure each subject’s mood was administered. The results showed that females performed better in warm white lighting environments, whereas males performed better on cognitive tasks in cool white lighting. Both males and females perceived and responded differently in evaluating the illuminance levels and color index of the lighting and therefore each gender’s mood was affected differently. Positive mood measures showed no increase in mood in both genders; however, the cooler lighting had a more negative effect on females’ moods. Thus, females’ performance on cognitive tasks decreased under cooler lighting. Because physiological changes occur when humans are exposed to light, mood and cognition can be affected indirectly and variably. According to Veitch and McColl (2001), lighting’s cognitive and mood-related effects on people have noteworthy implications: 1) better performance on cognitive related tasks in the workplace or academic environment and 2) overall improved quality of life and well-being. Both visual perception strength and adequate sleep could have a considerable impact on cognitive abilities such as concentration and memory. Mood may also determine the sharpness of these cognitive abilities. Mood can be influenced by the quality and amount of lighting (Partonen et al., 2002; Veitch & McColl, 2001; inter alia Beauchemin & Hays, 1996; Benedetti et al., 2001; Goven, 2011). For instance, light therapy has proven a successful treatment for those with Seasonal Affective Disorder (SAD) and other non-seasonal mood related disorders such as depression and eating disorders (Spiegel et al., 2004; Van Cauter et al., 2000; McColl & Veitch, 2001). Effects for Lighting and Learning Because lighting profoundly impacts numerous levels of human functioning such as vision, circadian rhythms, mood, and cognition, its implicit effects on learning and classroom achievement cannot be dismissed. Several studies have addressed how the quality and color of lighting can either impair or enhance students’ visual skills and thus, academic performance. Visual impairments alone can induce behavioral problems in students as well as level of concentration and motivation in the classroom. Cheatum and Hammond (2000) estimated that around 20% of children that enter the school encounter visual problems (e.g., problems with focusing, eye tracking, training, lazy eye, and trabismus). Among elementary school children, 41% have experienced trouble with tracking, 6% have refractive errors and 4% have strabismus (Koslowe, 1995, as cited in Cheatum & Hammond, 2000). The same study suggests that “the inability of visual tracking is also thought to be the cause of behavioral problems and being illiterate.” Winterbottom (2009) suggested that certain features of lighting can cause discomfort and impair visual and cognitive performance. These features include “imperceptible 100 Hz flicker from fluorescent lighting and glare induced by 1) daylight and fluorescent lighting, 2) interactive whiteboards (IWBs) and dry-erase whiteboards (DWBs)” (2009). The purpose of his study was to determine the degree and magnitude to which students are subjected to the above stated lighting inefficiencies in the classroom. The 100 Hz flicker from fluorescent lighting was displayed in 80% of the 90 UK classrooms u

Recent advances in small inexpensive sensors, low-power processing, and activity modeling have enabled applications that use on-body sensing and machine learning to infer people's activities throughout everyday life. To address the growing rate of sedentary lifestyles, we have developed a system, UbiFit Garden, which uses these technologies and a personal, mobile display to encourage physical activity. We conducted a 3-week field trial in which 12 participants used the system and report findings focusing on their experiences with the sensing and activity inference. We discuss key implications for systems that use on-body sensing and activity inference to encourage physical activity.


Question: BIDaaS: A Blockchain-driven Identity as a Service Solution

In the era of widespread online services, ensuring high system availability and uninterrupted correct service is paramount, given the risks posed by software errors, human oversight, and deliberate attacks, all of which can lead to service disruptions and Byzantine faults. This paper introduces a novel replication algorithm, Byzantine Fault Tolerance (BFT), designed to construct highly available systems capable of withstanding Byzantine faults. BFT is not merely theoretical; it can be practically applied to implement real-world services. It boasts excellent performance, safety in asynchronous environments like the Internet, defenses against Byzantine-faulty clients, and proactive replica recovery. The recovery mechanism ensures the system's resilience to any number of faults throughout its lifespan, provided that fewer than one-third of the replicas do not succumb to faults within a brief period of vulnerability. The BFT algorithm has been realized as a generic program library with a straightforward interface, facilitating its integration into various systems. To demonstrate its practicality, the library was utilized to develop the first Byzantine-fault-tolerant Network File System (NFS), dubbed Byzantine Fault-tolerant System (BFS). Both the BFT library and BFS exhibit commendable performance due to several critical optimizations, chief among them the use of symmetric cryptography for message authentication. Performance evaluations reveal that BFS operates 2% faster to 24% slower than non-replicated, production NFS protocol implementations. These findings reinforce our contention that the BFT library is a viable tool for constructing practical systems that can endure Byzantine faults.

**Question**: A Comprehensive Survey on Graph Neural Networks

**Documents**: Learning to learn by gradient descent by gradient descent
The ability to take a meta perspective on learning and to learn to learn is valuable in artificial intelligence. The model learns to learn by finding a learning algorithm using gradient descent. The learning algorithm is encoded in the recurrent dynamics of a small neural network. We show that this learning algorithm can generalize across a number of learning tasks, including classification, regression, and reinforcement learning. We also show that the model can learn to learn online, by learning a learning algorithm that can be updated with new experiences. This enables the model to learn to learn new tasks after being trained on a set of initial tasks. We compare our model to other models of learning to learn, including models that learn a fixed learning algorithm and models that learn a set of task-specific learning algorithms. We find that our model outperforms these other models on a number of learning tasks. We also analyze the learned learning algorithms and find that they are complex

As more sensitive data is shared and stored by third-party sites on the Internet, there will be a need to encrypt data stored at these sites. One drawback of encrypting data, is that it can be selectively shared only at a coarse-grained level (i.e., giving another party your private key). We develop a new cryptosystem for fine-grained sharing of encrypted data that we call Key-Policy Attribute-Based Encryption (KP-ABE). In our cryptosystem, ciphertexts are labeled with sets of attributes and private keys are associated with access structures that control which ciphertexts a user is able to decrypt. We demonstrate the applicability of our construction to sharing of audit-log information and broadcast encryption. Our construction supports delegation of private keys which subsumesHierarchical Identity-Based Encryption (HIBE).

Securely maintaining log records over extended periods of time is very important to the proper functioning of any organization. Integrity of the log files and that of the logging process need to be ensured at all times. In addition, as log files often contain sensitive information, confidentiality and privacy of log records are equally important. However, deploying a secure logging infrastructure involves substantial capital expenses that many organizations may find overwhelming. Delegating log management to the cloud appears to be a viable cost saving measure. In this paper, we identify the challenges for a secure cloud-based log management service and propose a framework for doing the same.

This paper discusses the life cycle of decentralized smart contracts, i.e. digital and executable representations of rights and obligations in a multi-party environment. The life cycle relies on blockchain technology, i.e. a distributed digital ledger, to ensure proper implementation and integrity of the smart contracts. The life cycle consists of four subsequent phases: Creation, freezing, execution, and finalization. For each phase actors and technological services are identified and explained in detail. With the life cycle at hand, risks and limitations of smart contracts and the underlying blockchain technology are briefly discussed.

We introduce a new type of Identity-Based Encryption (IBE) scheme that we call Fuzzy Identity-Based Encryption. In Fuzzy IBE we view an identity as set of descriptive attributes. A Fuzzy IBE scheme allows for a private key for an identity, ω, to decrypt a ciphertext encrypted with an identity, ω′, if and only if the identities ω and ω′ are close to each other as measured by the “set overlap” distance metric. A Fuzzy IBE scheme can be applied to enable encryption using biometric inputs as identities; the error-tolerance property of a Fuzzy IBE scheme is precisely what allows for the use of biometric identities, which inherently will have some noise each time they are sampled. Additionally, we show that Fuzzy-IBE can be used for a type of application that we term “attribute-based encryption”. In this paper we present two constructions of Fuzzy IBE schemes. Our constructions can be viewed as an Identity-Based Encryption of a message under several attributes that compose a (fuzzy) identity. Our IBE schemes are both error-tolerant and secure against collusion attacks. Additionally, our basic construction does not use random oracles. We prove the security of our schemes under the Selective-ID security model.

The Internet of Things (IoT) introduced the possibility to connect electronic things from everyday life to the Internet, while making them ubiquitously available. With advanced IoT services, based on a trusted federation among heterogeneous IoT platforms, new security problems (including authentication and authorization) emerge. This contribution aims at describing the main facets of the preliminary security architecture envisaged in the context of the symbIoTe project, recently launched by European Commission under the Horizon 2020 EU program. Our approach features distributed and decoupled mechanisms for authentication and authorization services in complex scenarios embracing heterogeneous and federated IoT platforms, by leveraging Attribute Based Access Control and token-based authorization techniques.

Besides well-known benefits, commodity cloud storage also raises concerns that include security, reliability, and consistency. We present Hybris key-value store, the first robust hybrid cloud storage system, aiming at addressing these concerns leveraging both private and public cloud resources.
 Hybris robustly replicates metadata on trusted private premises (private cloud), separately from data which is dispersed (using replication or erasure coding) across multiple untrusted public clouds. Hybris maintains metadata stored on private premises at the order of few dozens of bytes per key, avoiding the scalability bottleneck at the private cloud. In turn, the hybrid design allows Hybris to efficiently and robustly tolerate cloud outages, but also potential malice in clouds without overhead. Namely, to tolerate up to f malicious clouds, in the common case of the Hybris variant with data replication, writes replicate data across f + 1 clouds, whereas reads involve a single cloud. In the worst case, only up to f additional clouds are used. This is considerably better than earlier multi-cloud storage systems that required costly 3f + 1 clouds to mask f potentially malicious clouds. Finally, Hybris leverages strong metadata consistency to guarantee to Hybris applications strong data consistency without any modifications to the eventually consistent public clouds.
 We implemented Hybris in Java and evaluated it using a series of micro and macrobenchmarks. Our results show that Hybris significantly outperforms comparable multi-cloud storage systems and approaches the performance of bare-bone commodity public cloud storage.

The “blockchain” distributed ledger pioneered by Bitcoin is effective at preventing double-spending, but inherently attracts (1) “user cartels” and (2) incompressible delays, as a result of linear verification and a winner-takes-all incentive lottery. We propose to forgo the “blocks” and “chain” entirely, and build a truly distributed ledger system based on a lean graph of cross-verifying transactions, which now become the main and only objects in the system. A fully distributed consensus mechanism, based on progressive proofs of work with predictable incentives, ensures rapid convergence even across a large network of unequal participants, who all get rewards working at their own pace. Graph-based affirmation fosters snappy response through automatic scaling, while application-agnostic design supports all modern cryptocurrency features such as multiple denominations, swaps, securitisation, scripting, smart contracts, etc. We prove theoretically, and experimentally verify, our proposal to show it achieves a crucial “convergence” property — meaning that any valid transaction entering the system will quickly become enshrined into the ancestry upon which all future transactions will rest.

Trust is a critical factor in cloud computing; in present practice it depends largely on perception of reputation, and self assessment by providers of cloud services. We begin this paper with a survey of existing mechanisms for establishing trust, and comment on their limitations. We then address those limitations by proposing more rigorous mechanisms based on evidence, attribute certification, and validation, and conclude by suggesting a framework for integrating various trust mechanisms together to reveal chains of trust in the cloud.

In order to gain their customers' trust, software vendors can certify their products according to security standards, e.g., the Common Criteria (ISO 15408). However, a Common Criteria certification requires a comprehensible documentation of the software product. The creation of this documentation results in high costs in terms of time and money.
 We propose a software development process that supports the creation of the required documentation for a Common Criteria certification. Hence, we do not need to create the documentation after the software is built. Furthermore, we propose to use an enhanced version of the requirements-driven software engineering process called ADIT to discover possible problems with the establishment of Common Criteria documents. We aim to detect these issues before the certification process. Thus, we avoid expensive delays of the certification effort. ADIT provides a seamless development approach that allows consistency checks between different kinds of UML models. ADIT also supports traceability from security requirements to design documents. We illustrate our approach with the development of a smart metering gateway system.

Cloud computing provides cost-efficient opportunities for enterprises by offering a variety of dynamic, scalable, and shared services. Usually, cloud providers provide assurances by specifying technical and functional descriptions in Service Level Agreements (SLAs) for the services they offer. The descriptions in SLAs are not consistent among the cloud providers even though they offer services with similar functionality. Therefore, customers are not sure whether they can identify a trustworthy cloud provider only based on its SLA. To support the customers in reliably identifying trustworthy cloud providers, we propose a multi-faceted Trust Management (TM) system architecture for a cloud computing marketplace. This system provides means to identify the trustworthy cloud providers in terms of different attributes (e.g., security, performance, compliance) assessed by multiple sources and roots of trust information.


Question: BIDaaS: A Blockchain-based Identity as a Service Solution.

In the era of the Internet of Things (IoT), embedded devices are increasingly prevalent, requiring robust security measures to ensure their operations are accurate and safeguarded against diverse threats. One critical security aspect is the secure firmware update process, essential for maintaining the integrity and functionality of IoT devices. This paper introduces a novel firmware update mechanism that harnesses blockchain technology to address the secure firmware update challenge for embedded devices in IoT settings. The proposed solution leverages blockchain to verify firmware versions, authenticate firmware integrity, and facilitate the secure download of the latest firmware updates. An embedded device initiates a firmware update request to nodes within a blockchain network, which then confirms the firmware's current status. If the firmware is not the latest version, the device can download the updated firmware from a peer-to-peer sharing network among blockchain nodes. Notably, the scheme ensures firmware integrity by checking for tampering, even if the firmware version is up-to-date. This approach effectively mitigates attacks that exploit known vulnerabilities in firmware, ensuring that embedded devices remain secure and up-to-date in an IoT ecosystem.

**Question**: A Direct Search Method to solve Economic Dispatch Problem with Valve-Point Effect

**Documents**: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design

**Similar Documents**: This research article proposes a novel methodology, the Hybrid Genetic Algorithm-Particle Swarm Optimization (HGAPSO), aimed at automating the intricate process of designing recurrent neural/fuzzy networks. The method integrates the strengths of genetic algorithms (GA) and particle swarm optimization (PSO) to form a powerful evolutionary learning algorithm. Unlike conventional approaches, HGAPSO introduces a unique concept known as the elite strategy, where the top-performing individuals in a population are considered elites and undergo a maturation process akin to nature's principles through PSO. These refined elites are then utilized to generate the next generation, either directly or through crossover and mutation operations. The application of HGAPSO to recurrent neural network design is demonstrated through a case study involving a fully connected recurrent network for temporal sequence production. For recurrent fuzzy network design, a Takagi-Sugeno-Kang-type network is developed for dynamic plant control. Comparative evaluations against GA and PSO methods highlight the superior performance of HGAPSO in recurrent network design, underscoring its potential for complex optimization problems like the economic dispatch problem with valve-point effect.

The “blockchain” distributed ledger pioneered by Bitcoin is effective at preventing double-spending, but inherently attracts (1) “user cartels” and (2) incompressible delays, as a result of linear verification and a winner-takes-all incentive lottery. We propose to forgo the “blocks” and “chain” entirely, and build a truly distributed ledger system based on a lean graph of cross-verifying transactions, which now become the main and only objects in the system. A fully distributed consensus mechanism, based on progressive proofs of work with predictable incentives, ensures rapid convergence even across a large network of unequal participants, who all get rewards working at their own pace. Graph-based affirmation fosters snappy response through automatic scaling, while application-agnostic design supports all modern cryptocurrency features such as multiple denominations, swaps, securitisation, scripting, smart contracts, etc. We prove theoretically, and experimentally verify, our proposal to show it achieves a crucial “convergence” property — meaning that any valid transaction entering the system will quickly become enshrined into the ancestry upon which all future transactions will rest.

This paper presents the design and implementation of Souche, a system that recognizes legitimate users early in online services. This early recognition contributes to both usability and security. Souche leverages social connections established over time. Legitimate users help identify other legitimate users through an implicit vouching process, strategically controlled within vouching trees. Souche is lightweight and fully transparent to users. In our evaluation on a real dataset of several hundred million users, Souche can efficiently identify 85% of legitimate users early, while reducing the percentage of falsely admitted malicious users from 44% to 2.4%. Our evaluation further indicates that Souche is robust in the presence of compromised accounts. It is generally applicable to enhance usability and security for a wide class of online services.

This paper discusses the life cycle of decentralized smart contracts, i.e. digital and executable representations of rights and obligations in a multi-party environment. The life cycle relies on blockchain technology, i.e. a distributed digital ledger, to ensure proper implementation and integrity of the smart contracts. The life cycle consists of four subsequent phases: Creation, freezing, execution, and finalization. For each phase actors and technological services are identified and explained in detail. With the life cycle at hand, risks and limitations of smart contracts and the underlying blockchain technology are briefly discussed.

Cryptocurrencies record transactions in a decentralized data structure called a blockchain. Two of the most popular cryptocurrencies, Bitcoin and Ethereum, support the feature to encode rules or scripts for processing transactions. This feature has evolved to give practical shape to the ideas of smart contracts, or full-fledged programs that are run on blockchains. Recently, Ethereum's smart contract system has seen steady adoption, supporting tens of thousands of contracts, holding millions dollars worth of virtual coins.
 In this paper, we investigate the security of running smart contracts based on Ethereum in an open distributed network like those of cryptocurrencies. We introduce several new security problems in which an adversary can manipulate smart contract execution to gain profit. These bugs suggest subtle gaps in the understanding of the distributed semantics of the underlying platform. As a refinement, we propose ways to enhance the operational semantics of Ethereum to make contracts less vulnerable. For developers writing contracts for the existing Ethereum system, we build a symbolic execution tool called Oyente to find potential security bugs. Among 19, 336 existing Ethereum contracts, Oyente flags 8, 833 of them as vulnerable, including the TheDAO bug which led to a 60 million US dollar loss in June 2016. We also discuss the severity of other attacks for several case studies which have source code available and confirm the attacks (which target only our accounts) in the main Ethereum network.

Cloud-based electronic health record (EHR) systems enable medical documents to be exchanged between medical institutions; this is expected to contribute to improvements in various medical services in the future. However, as the system architecture becomes more complicated, cloud-based EHR systems may introduce additional security threats when compared with the existing singular systems. Thus, patients may experience exposure of private data that they do not wish to disclose. In order to protect the privacy of patients, many approaches have been proposed to provide access control to patient documents when providing health services. However, most current systems do not support fine-grained access control or take into account additional security factors such as encryption and digital signatures. In this paper, we propose a cloud-based EHR model that performs attribute-based access control using extensible access control markup language. Our EHR model, focused on security, performs partial encryption and uses electronic signatures when a patient’s document is sent to a document requester. We use XML encryption and XML digital signature technology. Our proposed model works efficiently by sending only the necessary information to the requesters who are authorized to treat the patient in question.

Connected health care has several applications including telecare medicine information system, personally controlled health records system, and patient monitoring. In such applications, user authentication can ensure the legality of patients. In user authentication for such applications, only the legal user/patient himself/herself is allowed to access the remote server, and no one can trace him/her according to transmitted data. Chang et al. proposed a uniqueness-and-anonymity-preserving remote user authentication scheme for connected health care (Chang et al., J Med Syst 37:9902, 2013). Their scheme uses the user’s personal biometrics along with his/her password with the help of the smart card. The user’s biometrics is verified using BioHashing. Their scheme is efficient due to usage of one-way hash function and exclusive-or (XOR) operations. In this paper, we show that though their scheme is very efficient, their scheme has several security weaknesses such as (1) it has design flaws in login and authentication phases, (2) it has design flaws in password change phase, (3) it fails to protect privileged insider attack, (4) it fails to protect the man-in-the middle attack, and (5) it fails to provide proper authentication. In order to remedy these security weaknesses in Chang et al.’s scheme, we propose an improvement of their scheme while retaining the original merit of their scheme. We show that our scheme is efficient as compared to Chang et al.’s scheme. Through the security analysis, we show that our scheme is secure against possible attacks. Further, we simulate our scheme for the formal security verification using the widely-accepted AVISPA (Automated Validation of Internet Security Protocols and Applications) tool to ensure that our scheme is secure against passive and active attacks. In addition, after successful authentication between the user and the server, they establish a secret session key shared between them for future secure communication.

Jimmy Lin, Adam Roegiest, Luchen Tan, Richard McCreadie, Ellen Voorhees, and Fernando Diaz 1 David R. Cheriton School of Computer Science, University of Waterloo, Ontario, Canada 2 School of Computing Science, University of Glasgow, Scotland, the United Kingdom 3 National Institute for Standards and Technology, Maryland, USA 4 Microsoft Research, New York, USA {jimmylin, aroegies, luchen.tan}@uwaterloo.ca richard.mccreadie@glasgow.ac.uk, ellen.voorhees@nist.gov, fdiaz@microsoft.com

The constant development of technologies forces companies to be more innovative in order to stay competitive. In fact, designing a process from scratch is time consuming, error prone and costly. In this context, companies are heading to reuse process fragments when designing a new process to ensure a high degree of efficiency, with respect to delivery deadlines. However, reusing these fragments may disclose sensitive business activities, especially if these latter are deployed in an untrusted environment. In addition, companies are concerned about their user's privacy. To address these issues, we investigate how to build a new business process by reusing the safest existing fragments coming from various cloud servers, i.e. The ones that comply at best with company's preferences and policies, and offer an appropriate level of safety.

1 Qatar Computing Research Institute (QCRI), Qatar Foundation, Doha, Qatar; E-Mail: ikhalil@uaeu.ac.ae 2 Department of Electrical and Computer Engineering, Newark College of Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA; E-Mail: abdallah@njit.edu 3 College of Information Technology, United Arab Emirates University, PO Box 15551, Al Ain, United Arab Emirates; E-Mail: azeemsamma@hotmail.com

Privacy Preserving Record Linkage is an emerging field of research which attempts to deal with the classical linkage problem from a privacy preserving point of view. In this paper we propose a novel approach for performing Privacy Preserving Blocking in order to minimize the computational cost of Privacy Preserving Record Linkage. We achieve this without compromising privacy by using Nearest Neighbors clustering, a well-known clustering algorithm and by using a reference table. A reference table is a publicly known table the contents of which are used as intermediate references. The combination of Nearest Neighbors and a reference table offers our approach k-anonymity characteristics.