Question: Detecting shadows using conditional generative adversarial networks.

This paper presents a novel approach to training shadow detectors using a large-scale dataset, which was previously unfeasible due to the prohibitive cost of meticulously annotating shadows. Instead of relying on time-consuming and precise labeling, we propose utilizing images that are rapidly but imprecisely tagged. Our innovative label recovery technique autonomously rectifies a significant portion of the inaccurate annotations, enabling the trained classifiers to achieve state-of-the-art performance. To illustrate this, we enhance the label accuracy of a new dataset that is twenty times larger than existing ones and encompasses a diverse range of scenes and image types. Given its substantial size, this dataset is ideal for training deep learning models. Consequently, we introduce a semantic-aware patch-level Convolutional Neural Network (CNN) architecture designed to efficiently train on shadow patches while integrating image-level semantic information. This means that the detected shadow patches are refined in the context of the image's semantics. Our proposed method provides a robust foundation for future advancements in shadow detection technology.

CNNs have massively improved performance in object detection in photographs. However research into object detection in artwork remains limited. We show state-of-the-art performance on a challenging dataset, People-Art, which contains people from photos, cartoons and 41 different artwork movements. We achieve this high performance by fine-tuning a CNN for this task, thus also demonstrating that training CNNs on photos results in overfitting for photos: only the first three or four layers transfer from photos to artwork. Although the CNN’s performance is the highest yet, it remains less than 60% AP, suggesting further work is needed for the cross-depiction problem.

Robust scene understanding of outdoor environments using passive optical sensors is a onerous and essential task for autonomous navigation. The problem is heavily characterized by changing environmental conditions throughout the day and across seasons. Robots should be equipped with models that are impervious to these factors in order to be operable and more importantly to ensure safety in the real-world. In this paper, we propose a novel semantic segmentation architecture and the convoluted mixture of deep experts (CMoDE) fusion technique that enables a multi-stream deep neural network to learn features from complementary modalities and spectra, each of which are specialized in a subset of the input space. Our model adaptively weighs class-specific features of expert networks based on the scene condition and further learns fused representations to yield robust segmentation. We present results from experimentation on three publicly available datasets that contain diverse conditions including rain, summer, winter, dusk, fall, night and sunset, and show that our approach exceeds the state-of-the-art. In addition, we evaluate the performance of autonomously traversing several kilometres of a forested environment using only the segmentation for perception.

This paper presents a novel variational image inpainting method to solve the problem of generating, from 3-D lidar measures, a dense depth map coherent with a given color image, tackling visibility issues. When projecting the lidar point cloud onto the image plane, we generally obtain a sparse depth map, due to undersampling. Moreover, lidar and image sensor positions generally differ during acquisition, such that depth values referring to objects that are hidden from the image view point might appear with a naive projection. The proposed algorithm estimates the complete depth map, while simultaneously detecting and excluding those hidden points. It consists in a primal-dual optimization method, where a coupled total variation regularization term is included to match the depth and image gradients and a visibility indicator handles the selection of visible points. Tests with real data prove the effectiveness of the proposed strategy.

Distinguishing between natural images (NIs) and computer-generated (CG) images by naked human eyes is difficult. In this paper, we propose an effective method based on a convolutional neural network (CNN) for this fundamental image forensic problem. Having observed the rather limited performance of training existing CCNs from scratch or fine-tuning pre-trained network, we design and implement a new and appropriate network with two cascaded convolutional layers at the bottom of a CNN. Our network can be easily adjusted to accommodate different sizes of input image patches while maintaining a fixed depth, a stable structure of CNN, and a good forensic performance. Considering the complexity of training CNNs and the specific requirement of image forensics, we introduce the so-called local-to-global strategy in our proposed network. Our CNN derives a forensic decision on local patches, and a global decision on a full-sized image can be easily obtained via simple majority voting. This strategy can also be used to improve the performance of existing methods that are based on hand-crafted features. Experimental results show that our method outperforms existing methods, especially in a challenging forensic scenario with NIs and CG images of heterogeneous origins. Our method also has good robustness against typical post-processing operations, such as resizing and JPEG compression. Unlike previous attempts to use CNNs for image forensics, we try to understand what our CNN has learned about the differences between NIs and CG images with the aid of adequate and advanced visualization tools.

The ability to classify objects is fundamental for robots. Besides knowledge about their visual appearance, captured by the RGB channel, robots heavily need also depth information to make sense of the world. While the use of deep networks on RGB robot images has benefited from the plethora of results obtained on databases like ImageNet, using convnets on depth images requires mapping them into three-dimensional channels. This transfer learning procedure makes them processable by pretrained deep architectures. Current mappings are based on heuristic assumptions over preprocessing steps and on what depth properties should be most preserved, resulting often in cumbersome data visualizations, and in suboptimal performance in terms of generality and recognition results. Here, we take an alternative route and we attempt instead to learn an optimal colorization mapping for any given pretrained architecture, using as training data a reference RGB-D database. We propose a deep network architecture, exploiting the residual paradigm, that learns how to map depth data to three channel images. A qualitative analysis of the images obtained with this approach clearly indicates that learning the optimal mapping preserves the richness of depth information better than current hand-crafted approaches. Experiments on the Washington, JHUIT-50 and BigBIRD public benchmark databases, using CaffeNet, VGG-16, GoogleNet, and ResNet50 clearly showcase the power of our approach, with gains in performance of up to 16% compared to state of the art competitors on the depth channel only, leading to top performances when dealing with RGB-D data.

We propose a simple algorithm to train stochastic neural networks to draw samples from given target distributions for probabilistic inference. Our method is based on iteratively adjusting the neural network parameters so that the output changes along a Stein variational gradient (Liu & Wang, 2016) that maximumly decreases the KL divergence with the target distribution. Our method works for any target distribution specified by their unnormalized density function, and can train any black-box architectures that are differentiable in terms of the parameters we want to adapt. As an application of our method, we propose an amortized MLE algorithm for training deep energy model, where a neural sampler is adaptively trained to approximate the likelihood function. Our method mimics an adversarial game between the deep energy model and the neural sampler, and obtains realisticlooking images competitive with the state-of-the-art results.

We present multiresolution tree-structured networks to process point clouds for 3D shape understanding and generation tasks. Our network represents a 3D shape as a set of locality-preserving 1D ordered list of points at multiple resolutions. This allows efficient feed-forward processing through 1D convolutions, coarse-to-fine analysis through a multi-grid architecture, and it leads to faster convergence and small memory footprint during training. The proposed treestructured encoders can be used to classify shapes and outperform existing pointbased architectures on shape classification benchmarks, while tree-structured decoders can be used for generating point clouds directly and they outperform existing approaches for image-to-shape inference tasks learned using the ShapeNet dataset. Our model also allows unsupervised learning of point-cloud based shapes by using a variational autoencoder, leading to higher-quality generated shapes.

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.

Unsupervised image translation, which aims in translating two independent sets of images, is challenging in discovering the correct correspondences without paired data. Existing works build upon Generative Adversarial Networks (GANs) such that the distribution of the translated images are indistinguishable from the distribution of the target set. However, such set-level constraints cannot learn the instance-level correspondences (e.g. aligned semantic parts in object transfiguration task). This limitation often results in false positives (e.g. geometric or semantic artifacts), and further leads to mode collapse problem. To address the above issues, we propose a novel framework for instance-level image translation by Deep Attention GAN (DA-GAN). Such a design enables DA-GAN to decompose the task of translating samples from two sets into translating instances in a highly-structured latent space. Specifically, we jointly learn a deep attention encoder, and the instance-level correspondences could be consequently discovered through attending on the learned instances. Therefore, the constraints could be exploited on both set-level and instance-level. Comparisons against several state-of-the-arts demonstrate the superiority of our approach, and the broad application capability, e.g, pose morphing, data augmentation, etc., pushes the margin of domain translation problem.1

Facial landmarks constitute the most compressed representation of faces and are known to preserve information such as pose, gender and facial structure present in the faces. Several works exist that attempt to perform high-level face-related analysis tasks based on landmarks alone without the aid of face images. In contrast, in this work, an attempt is made to tackle the inverse problem of synthesizing faces from their respective landmarks. The primary aim of this work is to demonstrate that information preserved by landmarks (gender in particular) can be further accentuated by leveraging generative models to synthesize corresponding faces. Though the problem is particularly challenging due to its ill-posed nature, we believe that successful synthesis will enable several applications such as boosting performance of high-level face related tasks using landmark points and performing dataset augmentation. To this end, a novel face-synthesis method known as Gender Preserving Generative Adversarial Network (GP-GAN) that is guided by adversarial loss, perceptual loss and a gender preserving loss is presented. Further, we propose a novel generator sub-network UDeNet for GP-GAN that leverages advantages of U-Net and DenseNet architectures. Extensive experiments and comparison with recent methods are performed to verify the effectiveness of the proposed method. Our code is available at: https://github.com/DetionDXlGP-GAN-Gender-Preserving-GAN-for-Synthesizing-Faces-from-Landmarks


Question: Detecting shadows using conditional generative adversarial networks.

This paper presents an innovative method utilizing conditional generative adversarial networks (cGANs) for the detection of shadows in images. Adversarial networks have proven their efficacy in generating high-quality images, and we extend this concept to enhance the accuracy of semantic segmentation, specifically focusing on shadow detection. Our approach involves training a convolutional semantic segmentation network in tandem with a conditional adversarial network. This adversarial network distinguishes between segmentation maps derived from ground truth data and those produced by the segmentation network, aiming to rectify inconsistencies that are not easily captured by traditional methods. By focusing on these higher-order discrepancies, our method ensures that the segmentation network learns to accurately identify shadows, leading to more precise segmentation results. Experimental results on datasets like Cityscapes and KITTI demonstrate the effectiveness of our cGAN-based shadow detection method, outperforming conventional techniques in terms of segmentation accuracy.

The ability to classify objects is fundamental for robots. Besides knowledge about their visual appearance, captured by the RGB channel, robots heavily need also depth information to make sense of the world. While the use of deep networks on RGB robot images has benefited from the plethora of results obtained on databases like ImageNet, using convnets on depth images requires mapping them into three-dimensional channels. This transfer learning procedure makes them processable by pretrained deep architectures. Current mappings are based on heuristic assumptions over preprocessing steps and on what depth properties should be most preserved, resulting often in cumbersome data visualizations, and in suboptimal performance in terms of generality and recognition results. Here, we take an alternative route and we attempt instead to learn an optimal colorization mapping for any given pretrained architecture, using as training data a reference RGB-D database. We propose a deep network architecture, exploiting the residual paradigm, that learns how to map depth data to three channel images. A qualitative analysis of the images obtained with this approach clearly indicates that learning the optimal mapping preserves the richness of depth information better than current hand-crafted approaches. Experiments on the Washington, JHUIT-50 and BigBIRD public benchmark databases, using CaffeNet, VGG-16, GoogleNet, and ResNet50 clearly showcase the power of our approach, with gains in performance of up to 16% compared to state of the art competitors on the depth channel only, leading to top performances when dealing with RGB-D data.

It is very attractive to formulate vision in terms of pattern theory [26], where patterns are defined hierarchically by compositions of elementary building blocks. But applying pattern theory to real world images is currently less successful than discriminative methods such as deep networks. Deep networks, however, are black-boxes which are hard to interpret and can easily be fooled by adding occluding objects. It is natural to wonder whether by better understanding deep networks we can extract building blocks which can be used to develop pattern theoretic models. This motivates us to study the internal representations of a deep network using vehicle images from the PASCAL3D+ dataset. We use clustering algorithms to study the population activities of the features and extract a set of visual concepts which we show are visually tight and correspond to semantic parts of vehicles. To analyze this we annotate these vehicles by their semantic parts to create a new dataset, VehicleSemanticParts, and evaluate visual concepts as unsupervised part detectors. We show that visual concepts perform fairly well but are outperformed by supervised discriminative methods such as Support Vector Machines (SVM). We next give a more detailed analysis of visual concepts and how they relate to semantic parts. Following this, we use the visual concepts as building blocks for a simple pattern theoretical model, which we call compositional voting. In this model several visual concepts combine to detect semantic parts. We show that this approach is significantly better than discriminative methods like SVM and deep networks trained specifically for semantic part detection. Finally, we return to studying occlusion by creating an annotated dataset with occlusion, called VehicleOcclusion, and show that compositional voting outperforms even deep networks when the amount of occlusion becomes large.

Many machine learning image classifiers are vulnerable to adversarial attacks, inputs with perturbations designed to intentionally trigger misclassification. Current adversarial methods directly alter pixel colors and evaluate against pixel norm-balls: pixel perturbations smaller than a specified magnitude, according to a measurement norm. This evaluation, however, has limited practical utility since perturbations in the pixel space do not correspond to underlying real-world phenomena of image formation that lead to them and has no security motivation attached. Pixels in natural images are measurements of light that has interacted with the geometry of a physical scene. As such, we propose a novel evaluation measure, parametric normballs, by directly perturbing physical parameters that underly image formation. One enabling contribution we present is a physically-based differentiable renderer that allows us to propagate pixel gradients to the parametric space of lighting and geometry. Our approach enables physically-based adversarial attacks, and our differentiable renderer leverages models from the interactive rendering literature to balance the performance and accuracy trade-offs necessary for a memory-efficient and scalable adversarial data augmentation workflow.

Humans have the remarkable ability of imagination, where within the human mind virtual simulations are done of scenarios whether visual, auditory or any other senses. These imaginations are based on the experiences during interaction with the real world, where human senses help the mind understand their surroundings. Such level of imagination has not yet been achieved using current algorithms, but a current trend in deep learning architectures known as Generative Adversarial Networks (GANs) have proven capable of generating new and interesting images or videos based on the training data. In that way, GANs can be used to mimic human imagination, where the resulting generated visuals of GANs are based on the data used during training. In this paper, we use a combination of Long Short-Term Memory (LSTM) Networks and 3D GANs to generate videos. We use a 3D Convolutional GAN to generate new human action videos based on trained data. The generated human action videos are used to generate longer videos consisting of a sequence of short actions combined creating longer and more complex activities. To generate the sequence of actions needed we use an LSTM network to translate a simple input description text into the required sequence of actions. The generated chunks are then concatenated using a motion interpolation scheme to form a single video consisting of many generated actions. Hence a visualization of the input text description is generated as a video of a subject performing the activity described.

Undoing the image formation process and therefore decomposing appearance into its intrinsic properties is a challenging task due to the under-constrained nature of this inverse problem. While significant progress has been made on inferring shape, materials and illumination from images only, progress in an unconstrained setting is still limited. We propose a convolutional neural architecture to estimate reflectance maps of specular materials in natural lighting conditions. We achieve this in an end-to-end learning formulation that directly predicts a reflectance map from the image itself. We show how to improve estimates by facilitating additional supervision in an indirect scheme that first predicts surface orientation and afterwards predicts the reflectance map by a learning-based sparse data interpolation. In order to analyze performance on this difficult task, we propose a new challenge of Specular MAterials on SHapes with complex IllumiNation (SMASHINg) using both synthetic and real images. Furthermore, we show the application of our method to a range of image editing tasks on real images.

We propose the Adversarial Localization Network, a novel weakly supervised approach to generate object masks in an image. We train a corruption net to mask out regions of the image to reduce prediction confidence of the classifier. To avoid generating adversarial artifacts due to vulnerability of deep networks to adversarial perturbation, we also co-train the classifier to correctly predict the corrupted images. Our approach could efficiently produce reasonable object masks with a simple architecture and few hyper-parameters. We achieve competitive results on the ILSVRC2012 dataset for object localization with only image level labels and no bounding boxes for training.

We propose a novel training procedure for Generative Adversarial Networks (GANs) to improve stability and performance by using an adaptive hinge loss objective function. We estimate the appropriate hinge loss margin with the expected energy of the target distribution, and derive both a principled criterion for updating the margin and an approximate convergence measure. The resulting training procedure is simple yet robust on a diverse set of datasets. We evaluate the proposed training procedure on the task of unsupervised image generation, noting both qualitative and quantitative performance improvements.

Generative models have found their way to the forefront of deep learning the last decade and so far, it seems that the hype will not fade away any time soon. In this paper, we give an overview of the most important building blocks of most recent revolutionary deep generative models such as RBM, DBM, DBN, VAE and GAN. We will also take a look at three of state-of-the-art generative models, namely PixelRNN, DRAW and NADE. We will delve into their unique architectures, the learning procedures and their potential and limitations. We will also review some of the known issues that arise when trying to design and train deep generative architectures using shallow ones and how different models deal with these issues. This paper is not meant to be a comprehensive study of these models, but rather a starting point for those who bear an interest in the field.

We address the problem of fully automatic object localization and reconstruction from a single image. This is both a very challenging and very important problem which has, until recently, received limited attention due to difficulties in segmenting objects and predicting their poses. Here we leverage recent advances in learning convolutional networks for object detection and segmentation and introduce a complementary network for the task of camera viewpoint prediction. These predictors are very powerful, but still not perfect given the stringent requirements of shape reconstruction. Our main contribution is a new class of deformable 3D models that can be robustly fitted to images based on noisy pose and silhouette estimates computed upstream and that can be learned directly from 2D annotations available in object detection datasets. Our models capture top-down information about the main global modes of shape variation within a class providing a “low-frequency” shape. In order to capture fine instance-specific shape details, we fuse it with a high-frequency component recovered from shading cues. A comprehensive quantitative analysis and ablation study on the PASCAL 3D+ dataset validates the approach as we show fully automatic reconstructions on PASCAL VOC as well as large improvements on the task of viewpoint prediction.

Convolutional Neural Networks have been highly successful in performing a host of computer vision tasks such as object recognition, object detection, image segmentation and texture synthesis. In 2015, Gatys et. al [7] show how the style of a painter can be extracted from an image of the painting and applied to another normal photograph, thus recreating the photo in the style of the painter. The method has been successfully applied to a wide range of images and has since spawned multiple applications and mobile apps. In this paper, the neural style transfer algorithm is applied to fashion so as to synthesize new custom clothes. We construct an approach to personalize and generate new custom clothes based on a user’s preference and by learning the user’s fashion choices from a limited set of clothes from their closet. The approach is evaluated by analyzing the generated images of clothes and how well they align with the user’s fashion style.


Question: Shadow identification utilizing conditional generative adversarial networks.

This research delves into the impact of convolutional network depth on accuracy within the domain of large-scale image recognition. Our primary contribution lies in a comprehensive assessment of networks with escalating depth, which reveals that significant enhancements in performance over state-of-the-art configurations can be realized by increasing the depth to 16-19 weight layers. This discovery was the cornerstone of our submission to the ImageNet Challenge 2014, where our team secured the top positions in both the localization and classification categories. Moreover, we demonstrate that our models' representations are highly transferable to alternative datasets, where they outperform existing methods. Of notable importance, we have made our two leading ConvNet models openly accessible to encourage further investigation into the utilization of deep visual representations in the field of computer vision.

**Question**: An Empirical Study of Feature Selection Techniques for Intrusion Detection Systems

**Documents**: A comparative study of feature selection and classification methods in intrusion detection systems
This paper presents a comparative study of feature selection and classification methods for intrusion detection systems (IDSs). Feature selection is an important preprocessing step in the design of IDSs to reduce the dimensionality of the feature space and improve the performance of the classification algorithm. We compare the performance of various feature selection methods, including filter-based, wrapper-based, and embedded methods, using the KDD Cup 99 dataset. We also evaluate the performance of several classification algorithms, including decision trees, support vector machines, and neural networks. The results show that the wrapper-based feature selection method combined with the support vector machine classifier achieves the best performance in terms of detection rate and false alarm rate.

**Similar Documents**: This study offers an in-depth comparison of different feature selection and classification techniques employed in intrusion detection systems (IDSs). The process of feature selection is a critical phase in the development of IDSs, aimed at minimizing the feature space dimensionality and enhancing the efficacy of classification algorithms. Our investigation encompasses a range of feature selection methodologies, encompassing filter-based, wrapper-based, and embedded approaches, utilizing the KDD Cup 99 dataset as a benchmark. Additionally, we assess the proficiency of various classification algorithms, encompassing decision trees, support vector machines (SVMs), and neural networks. The findings indicate that the wrapper-based feature selection technique, when coupled with an SVM classifier, yields superior outcomes in terms of both detection and false alarm rates, outperforming alternative methods.

Many deployed learned models are black boxes: given input, returns output. Internal information about the model, such as the architecture, optimisation procedure, or training data, is not disclosed explicitly as it might contain proprietary information or make the system more vulnerable. This work shows that such attributes of neural networks can be exposed from a sequence of queries. This has multiple implications. On the one hand, our work exposes the vulnerability of black-box neural networks to different types of attacks – we show that the revealed internal information helps generate more effective adversarial examples against the black box model. On the other hand, this technique can be used for better protection of private content from automatic recognition models using adversarial examples. Our paper suggests that it is actually hard to draw a line between white box and black box models.

A recent technical breakthrough in the domain of machine learning is the discovery and the multiple applications of Generative Adversarial Networks (GANs). Those generative models are computationally demanding, as a GAN is composed of two deep neural networks, and because it trains on large datasets. A GAN is generally trained on a single server. In this paper, we address the problem of distributing GANs so that they are able to train over datasets that are spread on multiple workers. MD-GAN is exposed as the first solution for this problem: we propose a novel learning procedure for GANs so that they fit this distributed setup. We then compare the performance of MD-GAN to an adapted version of Federated Learning to GANs, using the MNIST and CIFAR10 datasets. MD-GAN exhibits a reduction by a factor of two of the learning complexity on each worker node, while providing better performances than federated learning on both datasets. We finally discuss the practical implications of distributing GANs.

Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer. However, their framework requires a slow iterative optimization process, which limits its practical application. Fast approximations with feed-forward neural networks have been proposed to speed up neural style transfer. Unfortunately, the speed improvement comes at a cost: the network is usually tied to a fixed set of styles and cannot adapt to arbitrary new styles. In this paper, we present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. At the heart of our method is a novel adaptive instance normalization (AdaIN) layer that aligns the mean and variance of the content features with those of the style features. Our method achieves speed comparable to the fastest existing approach, without the restriction to a pre-defined set of styles. In addition, our approach allows flexible user controls such as content-style trade-off, style interpolation, color & spatial controls, all using a single feed-forward neural network.

Supervised deep learning has been successfully applied to many recognition problems. Although it can approximate a complex many-to-one function well when a large amount of training data is provided, it is still challenging to model complex structured output representations that effectively perform probabilistic inference and make diverse predictions. In this work, we develop a deep conditional generative model for structured output prediction using Gaussian latent variables. The model is trained efficiently in the framework of stochastic gradient variational Bayes, and allows for fast prediction using stochastic feed-forward inference. In addition, we provide novel strategies to build robust structured prediction algorithms, such as input noise-injection and multi-scale prediction objective at training. In experiments, we demonstrate the effectiveness of our proposed algorithm in comparison to the deterministic deep neural network counterparts in generating diverse but realistic structured output predictions using stochastic inference. Furthermore, the proposed training methods are complimentary, which leads to strong pixel-level object segmentation and semantic labeling performance on Caltech-UCSD Birds 200 and the subset of Labeled Faces in the Wild dataset.

We propose a modeland exemplar-based approach for face recognition. This problem has been previously tackled using either models or exemplars, with limited success. Our idea uses models to synthesize many more exemplars, which are then used in the learning stage of a face recognition system. To demonstrate this, we develop a statistical shape-fromshading model to recover face shape from a single image, and to synthesize the same face under new illumination. We then use this to build a simple and fast classifier that was not possible before because of a lack of training data.

In this paper, a novel visual secret sharing (VSS) scheme based on QR code (VSSQR) with (k, n) threshold is investigated. Our VSSQR exploits the error correction mechanism in the QR code structure, to generate the bits corresponding to shares (shadow images) by VSS from a secret bit in the processing of encoding QR. Each output share is a valid QR code that can be scanned and decoded utilizing a QR code reader, which may reduce the likelihood of attracting the attention of potential attackers. Due to different application scenarios, two different recovered ways of the secret image are given. The proposed VSS scheme based on QR code can visually reveal secret image with the abilities of stacking and XOR decryptions as well as scan every shadow image, i.e., a QR code, by a QR code reader. The secret image could be revealed by human visual system without any computation based on stacking when no lightweight computation device. On the other hand, if the lightweight computation device is available, the secret image can be revealed with better visual quality based on XOR operation and could be lossless revealed when sufficient shares are collected. In addition, it can assist alignment for VSS recovery. The experiment results show the effectiveness of our scheme.

Top-performing deep architectures are trained on massive amounts of labeled data. In the absence of labeled data for a certain task, domain adaptation often provides an attractive option given that labeled data of similar nature but from a different domain (e.g. synthetic images) are available. Here, we propose a new approach to domain adaptation in deep architectures that can be trained on large amount of labeled data from the source domain and large amount of unlabeled data from the target domain (no labeled targetdomain data is necessary). As the training progresses, the approach promotes the emergence of “deep” features that are (i) discriminative for the main learning task on the source domain and (ii) invariant with respect to the shift between the domains. We show that this adaptation behaviour can be achieved in almost any feed-forward model by augmenting it with few standard layers and a simple new gradient reversal layer. The resulting augmented architecture can be trained using standard backpropagation. Overall, the approach can be implemented with little effort using any of the deep-learning packages. The method performs very well in a series of image classification experiments, achieving adaptation effect in the presence of big domain shifts and outperforming previous state-ofthe-art on Office datasets.

Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as highly unstable and prone to miss modes. We argue that these bad behaviors of GANs are due to the very particular functional shape of the trained discriminators in high dimensional spaces, which can easily make training stuck or push probability mass in the wrong direction, towards that of higher concentration than that of the data generating distribution. We introduce several ways of regularizing the objective, which can dramatically stabilize the training of GAN models. We also show that our regularizers can help the fair distribution of probability mass across the modes of the data generating distribution, during the early phases of training and thus providing a unified solution to the missing modes problem.

This work addresses the problem of semantic foggy scene understanding (SFSU). Although extensive research has been performed on image dehazing and on semantic scene understanding with clear-weather images, little attention has been paid to SFSU. Due to the difficulty of collecting and annotating foggy images, we choose to generate synthetic fog on real images that depict clear-weather outdoor scenes, and then leverage these partially synthetic data for SFSU by employing state-of-the-art convolutional neural networks (CNN). In particular, a complete pipeline to add synthetic fog to real, clear-weather images using incomplete depth information is developed. We apply our fog synthesis on the Cityscapes dataset and generate Foggy Cityscapes with 20,550 images. SFSU is tackled in two ways: (1) with typical supervised learning, and (2) with a novel type of semi-supervised learning, which combines (1) with an unsupervised supervision transfer from clear-weather images to their synthetic foggy counterparts. In addition, we carefully study the usefulness of image dehazing for SFSU. For evaluation, we present Foggy Driving, a dataset with 101 real-world images depicting foggy driving scenes, which come with ground truth annotations for semantic segmentation and object detection. Extensive experiments show that (1) supervised learning with our synthetic data significantly improves the performance of state-of-the-art CNN for SFSU on Foggy Driving; (2) our semi-supervised learning strategy further improves performance; and (3) image dehazing marginally advances SFSU with our learning strategy. The datasets, models and code are made publicly available.

While the costs of human violence have attracted a great deal of attention from the research community, the effects of the network-on-network (NoN) violence popularised by Generative Adversarial Networks have yet to be addressed. In this work, we quantify the financial, social, spiritual, cultural, grammatical and dermatological impact of this aggression and address the issue by proposing a more peaceful approach which we term Generative Unadversarial Networks (GUNs). Under this framework, we simultaneously train two models: a generator G that does its best to capture whichever data distribution it feels it can manage, and a motivator M that helps G to achieve its dream. Fighting is strictly verboten and both models evolve by learning to respect their differences. The framework is both theoretically and electrically grounded in game theory, and can be viewed as a winner-shares-all two-player game in which both players work as a team to achieve the best score. Experiments show that by working in harmony, the proposed model is able to claim both the moral and log-likelihood high ground. Our work builds on a rich history of carefully argued position-papers, published as anonymous YouTube comments, which prove that the optimal solution to NoN violence is more GUNs. Takes skill to be real, time to heal each other Tupac Shakur, Changes, 1998


Question: 7 critical obstacles for visual representation in cyber network defense strategies.

Creating Effective Visual Workspaces for Cybersecurity Professionals

The objective of cybersecurity visualization is to empower analysts to enhance the safety and robustness of digital infrastructures through the development of efficient tools and workspaces. Visualization experts are tasked with making visual tools more user-friendly and engaging compared to the predominantly text-based tools currently favored by cybersecurity analysts. An ideal cyber analytics work environment should facilitate concurrent investigations and information retrieval, while also offering a structured space for data organization. This paper outlines our investigation into the practices of cybersecurity professionals and the application of visualizations within a large, high-resolution display work environment, highlighting the types of analytical tasks such an environment can accommodate. We distill a series of design guidelines for the creation of user-friendly cyber analytic workspaces, informed by our research. Additionally, we showcase prototypes developed in accordance with these principles and present the results of a usability assessment of the workspace environment.

Network data is ubiquitous; e-mail traffic between persons, telecommunication, transport and financial networks are some examples. Often these networks are large and multivariate, besides the topological structure of the network, multivariate data on the nodes and links is available. Currently, exploration and analysis methods are focused on a single aspect; the network topology or the multivariate data. In addition, tools and techniques are highly domain specific and require expert knowledge. We focus on the non-expert user and propose a novel solution for multivariate network exploration and analysis that tightly couples structural and multivariate analysis. In short, we go from Detail to Overview via Selections and Aggregations (DOSA): users are enabled to gain insights through the creation of selections of interest (manually or automatically), and producing high-level, infographic-style overviews simultaneously. Finally, we present example explorations on real-world datasets that demonstrate the effectiveness of our method for the exploration and understanding of multivariate networks where presentation of findings comes for free.

At the request of NSF's Interactive Systems Program, a two-day invitational workshop was held March 23-24, 1992 at UNC Chapel Hill to identify and recommend future research directions in the area of "virtual environments" (VE) *. Workshop participants included some 18 experts (plus 4 NSF officials) from universities, industry, and other leading technical organizations. The two-day schedule alternated between sessions of the entire group and sessions in the following specialty areas, around which the recommendations came to be organized: 1) Perception , 2) Human-Machine Software Interface , 3) Software, 4) Hardware, and 5) Applications. Also, two participants developed a taxonomy of VE applications that is included as an appendix to the report. 1. Collaborative science-technology development programs should be established at several sites around the country to encourage closer collaboration between developers and scientists. 2. Theoretical research should focus on development of metrics of performance and task demands in VE. 3. Paradigmatic applications and theoretical questions that illustrate the science-technology synergy need identification. Audition Spatial Sound 1.Theoretical research should emphasize the role of individual differences in Head-Related Transfer Functions (HRTF's), critical cues for distance and externalization, spectral cues for enhancing elevation and disambiguating the cone-of-confusion, head-motion, and intersensory interaction and adaptation in the accurate perception of virtual acoustic sources. The notion of artificially enhanced localization cues is also a promising area. 2. A fruitful area for joint basic and applied research is the development of perceptually-viable methods of simplifying the synthesis technique to maximize the efficiency of algorithms for complex room modeling. 3. Future effort should still be devoted to developing more realistic models of acoustic environments with implementation on more powerful hardware platforms. Nonspeech Audio 1. Theoretical research should focus on lower-level sensory and higher-level cognitive determinants of acoustic perceptual organization, with particular emphasis on how acoustic parameters interact to determine the identification, segregation, and localization of multiple, simultaneous sources. 2. Technology development should focus on hardware and software systems specifically aimed at real-time generation and control for acoustic information display. Haptics 1. Development should be encouraged of a variety of computer-controlled mechanical devices for either basic scientific investigation of the human haptic system or to serve as haptic interfaces for virtual environments and teleloperation. 2. Research programs should be initiated to encourage collaboration among engineers who are capable of building high precision robotic devices and scientists who can conduct biomechanical and perceptual experiments with the devices. 3. Research …

Attack models and attack graphs are efficient tools to describe and analyse attack scenarios aimed at computer networks. More precisely, attack graphs give all possible scenarios for an attacker to reach a certain goal, exploiting vulnerabilities of the targeted network. Nevertheless they give no information about the damages induced by these attacks, nor about the probability of exploitation of these scenarios. In this paper, we propose to combine attack graphs and CVSS framework, in order to add damage and exploitability probability information. Then, we define a notion of risk for each attack scenario, which is based on quantitative information added to attack graphs.

Addressing unresolved questions concerning computational thinking.

The design of office technology relies upon underlying conceptions of human organization and action. The goal of building office information systems requires a representation of office work and its relevant objects. The concern of this paper is that although system designers recognize the centrality of procedural tasks in the office, they tend to ignore the actual work involved in accomplishing those tasks. A perspicuous instance of work in an accounting office is used to recommend a new line of research into the practical problems of office work, and to suggest preliminary implications of that research for office systems design.

Reviewing literatures for a certain research field is always important for academics. One could use Google-like information seeking tools, but oftentimes he/she would end up obtaining too many possibly related papers, as well as the papers in the associated citation network. During such a process, a user may easily get lost after following a few links for searching or cross-referencing. It is also difficult for the user to identify relevant/important papers from the resulting huge collection of papers. Our work, called PaperVis, endeavors to provide a user-friendly interface to help users quickly grasp the intrinsic complex citation-reference structures among a specific group of papers. We modify the existing Radial Space Filling (RSF) and Bullseye View techniques to arrange involved papers as a node-link graph that better depicts the relationships among them while saving the screen space at the same time. PaperVis applies visual cues to present node attributes and their transitions among interactions, and it categorizes papers into semantically meaningful hierarchies to facilitate ensuing literature exploration. We conduct experiments on the InfoVis 2004 Contest Dataset to demonstrate the effectiveness of PaperVis.

Visualisation and interpretation of architectural spaces are intellectually challenging and multifaceted exercises with fairly wide ranging applications and implications. In the context of urban planning, they are most commonly undertaken to evaluate the usage of the architectural space to ensure efficient navigation and accessibility [1]. These exercises clearly assume a certain influence of the built structures on the human cognition. However, what aspect of architectural space affects the human behaviour still remains an open debate. In this respect, it is closely similar to an exercise to identify the unknown visual variables in information visualization. Since a quantitative analysis of the architectural geometric structure on a large scale will be a daunting computational task, the open space bounded by the built structures is studied instead [2]. The study of architectural open spaces essentially involves the computation of the visibility polygon or isovist (space visible all around a viewpoint, see Figure 1a) from a viewpoint and calculating various shape measures of the visibility polygon. The isovist computation involves drawing rays

It is perhaps unsurprising to find much of the focus in IT and computer security being drawn towards the technical aspects of the discipline. However, it is increasingly recognised that technology alone cannot deliver a complete solution, and there is also a tangible need to address human aspects. At the core, people must understand the threats they face and be able to use the protection available to them, and although this has not been entirely ignored, it has not received the level of attention that it merits either. Indeed, security surveys commonly reveal that the more directly user-facing aspects such as policy, training and education are prone to receiving significantly less attention than technical controls such as firewalls, antivirus and intrusion detection. The underlying reason for such disparity is that the human aspects are in many ways a more challenging problem to approach, not least because they cannot be easily targeted with a product-based solution. There is also a direct overlap into the technical area, with issues such as the usability and acceptability of technology solutions having a direct impact upon the actual protection that they are able to deliver. This paper explores these themes, highlighting the need for human aspects to form part of a holistic security strategy alongside the necessary technologies. Taking the specific examples of security awareness and two user-facing technical controls (user authentication and antivirus), the discussion examines how things have evolved to the present day and considers how they need to be positioned for the future. a 2012 Elsevier Ltd. All rights reserved.

We outline the design of a visualizer, named Vishit, for texts in the Hindi language. The Hindi language is lingua franca in many states of India where people speak different languages. The visualized text serves as a universal language where seamless communication is needed by many people who speak different languages and have different cultures. Vishit consists of the following three major processing steps: language processing, knowledge base creation and scene generation. Initial results from the Vishit prototype are encouraging.

Data analysis often involves the comparison of complex objects. With the ever increasing amounts and complexity of data, the demand for systems to help with these comparisons is also growing. Increasingly, information visualization tools support such comparisons explicitly, beyond simply allowing a viewer to examine each object individually. In this paper, we argue that the design of information visualizations of complex objects can, and should, be studied in general, that is independently of what those objects are. As a first step in developing this general understanding of comparison, we propose a general taxonomy of visual designs for comparison that groups designs into three basic categories, which can be combined. To clarify the taxonomy and validate its completeness, we provide a survey of work in information visualization related to comparison. Although we find a great diversity of systems and approaches, we find that all designs are assembled from the building blocks of juxtaposition, superposition, and explicit encodings. This initial exploration shows the power of our model, and suggests future challenges in developing a general understanding of comparative visualization and facilitating the development of more comparative visualization tools.


Question: 7 primary obstacles in utilizing visualization for cyber network defense.

In the contemporary era, data is being generated at an exponential pace, outstripping our ability to analyze these vast datasets effectively. This disparity presents significant hurdles for analysts, decision-makers, and operational teams, who rely on extracting valuable insights from hidden data patterns. Visual analytics emerges as a solution, bridging the gap between the overwhelming data volumes and human cognitive capabilities. By merging human intuition with visual representations and interactive tools, visual analytics enables the exploration of complex, diverse, and time-sensitive information. This interdisciplinary field synthesizes elements from visualization, data mining, and statistics, positioning itself as a fertile ground for innovation. This article endeavors to encapsulate the essence of visual analytics, outlining its boundaries, foundational concepts, and highlighting key research challenges. Moreover, it showcases a spectrum of application scenarios, underscoring the versatility and potential impact of visual analytics across diverse domains.

**Question**: A Framework for Developing a Theory of Service Learning

**Documents**: A Model for Developing Theory in Service-Learning
Service-learning is an educational approach that integrates community service with academic learning to enhance student development and contribute to community needs. Despite the growth in service-learning programs, theory development in this area has lagged behind practice. The purpose of this paper is to provide a model for developing theory in service-learning. The model includes four stages: (1) problem identification, (2) literature review, (3) data collection, and (4) theory generation. The model is based on the assumption that theory development is an iterative process that involves both deductive and inductive reasoning. Deductive reasoning is used to identify gaps in existing theories and to generate hypotheses that can be tested. Inductive reasoning is used to generate new theories based on data collected from service-learning programs. The model also emphasizes the importance of collaboration between researchers and practitioners in the theory development process. By working together, researchers and practitioners can ensure that the theories developed are relevant to practice and can be used to improve service-learning programs.

**Similar Documents**: This article presents a structured approach to foster theoretical advancements in the realm of service-learning, an educational methodology that intertwines community service with academic instruction to promote student growth and address societal needs. Acknowledging the disparity between the proliferation of service-learning initiatives and the evolution of underlying theories, this paper introduces a systematic framework for theory development. The proposed model encompasses four critical phases: (1) pinpointing research questions, (2) conducting a comprehensive literature review, (3) gathering empirical data, and (4) synthesizing theoretical insights. This iterative process hinges on

We present the novel high-level visualization taxonomy. Our taxonomy classifies visualization algorithms rather than data. Algorithms are categorized based on the assumptions they make about the data being visualized; we call this set of assumptions the design model. Because our taxonomy is based on design models, it is more flexible than existing taxonomies and considers the user's conceptual model, emphasizing the human aspect of visualization. Design models are classified according to whether they are discrete or continuous and by how much the algorithm designer chooses display attributes such as spatialization, timing, colour, and transparency. This novel approach provides an alternative view of the visualization field that helps explain how traditional divisions (e.g., information and scientific visualization) relates and overlap, and that may inspire research ideas in hybrid visualization areas

The paper describes an approach to IV that involves spatializing text content for enhanced visual browsing and analysis. The application arena is large text document corpora such as digital libraries, regulations and procedures, archived reports, etc. The basic idea is that text content from these sources may be transformed to a spatial representation that preserves informational characteristics from the documents. The spatial representation may then be visually browsed and analyzed in ways that avoid language processing and that reduce the analysts mental workload. The result is an interaction with text that more nearly resembles perception and action with the natural world than with the abstractions of written language.

In the study of image and vision computing, the generalization capability of an algorithm often determines whether it is able to work well in complex scenes. The goal of this review article is to survey the use of photorealistic image synthesis methods in addressing the problems of visual perception and understanding. Currently, the ACP Methodology comprising artificial systems, computational experiments, and parallel execution is playing an essential role in modeling and control of complex systems. This paper extends the ACP Methodology into the computer vision field, by proposing the concept and basic framework of Parallel Vision. In this paper, we first review previous works related to Parallel Vision, in terms of synthetic data generation and utilization. We detail the utility of synthetic data for feature analysis, object analysis, scene analysis, and other analyses. Then we propose the basic framework of Parallel Vision, which is composed of an ACP trilogy (artificial scenes, computational experiments, and parallel execution). We also present some in-depth thoughts and perspectives on Parallel Vision. This paper emphasizes the significance of synthetic data to vision system design and suggests a novel research methodology for perception and understanding of complex scenes.

Augmented Reality (AR) is an emerging visualization technology that promises to change the way people see the world. The main goal of AR is to “enhance” a person’s vision of the real world with useful information about the surrounding environment. Primarily this can be done through the use of a portable computer such as a laptop and a head-mounted display (HMD) unit. When such technology is made available to scientists and engineers, Augmented Reality can provide them with the freedom to do scientific analysis at the actual location of an experiment. With this idea in mind, the goal of this paper is to investigate the development of an AR system as a visualization tool for structural engineers. This particular application will focus on monitoring the “health” of the Kings Stormwater Bridge structure. Unfortunately, since AR is still in its infancy, much of the research devoted to it has focused mainly on technical issues rather than on the development of how this technology would be used by scientists. This has led to the development of “functional” AR systems that primarily can only be operated by the developer of the system ([1], [2], [3], [12], [14]). This paper avoids this trend by focusing on the design of the visualization system so that it is useful for structural engineers. As a result, this should produce a more robust Augmented Reality system that can be used by many different users. Thus, it is the goal of this paper to show how scientists would benefit by designing a visualization system with their needs in mind.

Classifications of visualization are often based on technical criteria, and leave out artistic ways of visualizing information. Understanding the differences between information visualization and other forms of visual communication provides important insights into the way the field works, though, and also shows the path to new approaches. We propose a classification of several types of information visualization based on aesthetic criteria. The notions of artistic and pragmatic visualization are introduced, and their properties discussed. Finally, the idea of visualization criticism is proposed, and its rules are laid out. Visualization criticism bridges the gap between design, art, and technical/pragmatic information visualization. It guides the view away from implementation details and single mouse clicks to the meaning of a visualization.

Rogue devices are an increasingly dangerous reality in the insider threat problem domain. Industry, government, and academia need to be aware of this problem and promote state-of-the-art detection methods.

We propose a characterization of distributed denial-of-s ervice (DDOS) defenses where reaction points are network-based and attack responses are active. The purpose is to provide a framework for comparing the performance and deployment of DDOS defenses. We identify the characteristics in attack detection algorithms and a ttack responses by reviewing defenses that have appeared in the literature. We expect that this char a terization will provide practitioners and academia insights into deploying DDOS defense as network services.

The ability to mine data represented as a graph has become important in several domains for detecting various structural patterns. One important area of data mining is anomaly detection, but little work has been done in terms of detecting anomalies in graph-based data. In this paper we present graph-based approaches to uncovering anomalies in applications containing information representing possible insider threat activity: e-mail, cell-phone calls, and order processing.

The proliferation of the video surveillance cameras, which accounts for the vast majority of cameras worldwide, has resulted in the need to nd methods and algorithms for dealing with the huge amount of information that is gathered every second. This encompasses processing tasks, such as raising an alarm or detouring moving objects, as well as some semantic tasks like event monitoring, trajectory or ow analysis, counting people, etc.

Cyber-physical systems U+0028 CPS U+0029 are complex systems with organic integration and in-depth collaboration of computation, communications and control U+0028 3C U+0029 technology. Subject to the theory and technology of existing network systems and physical systems, the development of CPS is facing enormous challenges. This paper first introduces the concept and characteristics of CPS and analyzes the present situation of CPS researches. Then the development of CPS is discussed from perspectives of system model, information processing technology and software design. At last it analyzes the main obstacles and key researches in developing CPS.


Question: 7 critical obstacles in the field of cyber network defense visualization.

In the realm of information visualization, the "Eyes Have It" framework presents a task by data type taxonomy that serves as a foundational guide for creating sophisticated graphical user interfaces. Beyond the initial Visual Information Seeking Mantra, which advocates for an overview-first approach followed by zooming, filtering, and on-demand details, this taxonomy delves deeper into the complexity of contemporary information visualizations. It categorizes visualizations by seven distinct data types: one-dimensional, two-dimensional, three-dimensional, temporal, multi-dimensional, tree, and network data. Correspondingly, seven visualization tasks are outlined: generating an overview, zooming, filtering, providing details on demand, establishing relationships, tracking history, and data extraction. This comprehensive taxonomy facilitates a more nuanced understanding of how visualizations can be tailored to specific data types and tasks, thereby enhancing their effectiveness in conveying complex information.

**Question**: The Role of Blockchain in Enhancing Cybersecurity

**Documents**: Blockchain: A Distributed Database for Secure and Transparent Data Sharing
Blockchain technology has been hailed as a revolutionary approach to data sharing and management, offering unparalleled security and transparency. At its core, a blockchain is a decentralized and distributed digital ledger that records transactions across multiple computers in such a way that the registered transactions cannot be altered retroactively. This technology ensures that data is not only secure but also tamper-proof, making it an ideal solution for enhancing cybersecurity in various sectors, including finance, healthcare, and supply chain management. By eliminating the need for centralized authorities to validate transactions, blockchain technology significantly reduces the risk of data breaches and cyber attacks.

**Similar Documents**: Blockchain technology represents a transformative paradigm in data management and sharing, distinguished by its robust security features and transparent operational framework. Essentially, a blockchain is a decentralized and distributed digital ledger designed to record transactions across a network of computers, ensuring that these records are immutable and unalterable once verified. This characteristic makes blockchain a formidable tool in bolstering cybersecurity across diverse industries, such as finance, healthcare, and logistics. By decentralizing the validation process for transactions, blockchain minimizes the reliance on centralized authorities, thereby drastically lowering the vulnerability to data breaches and cyber threats. Its inherent transparency and security mechanisms render it an increasingly popular solution for securing sensitive data and facilitating secure transactions.

Attack models and attack graphs are efficient tools to describe and analyse attack scenarios aimed at computer networks. More precisely, attack graphs give all possible scenarios for an attacker to reach a certain goal, exploiting vulnerabilities of the targeted network. Nevertheless they give no information about the damages induced by these attacks, nor about the probability of exploitation of these scenarios. In this paper, we propose to combine attack graphs and CVSS framework, in order to add damage and exploitability probability information. Then, we define a notion of risk for each attack scenario, which is based on quantitative information added to attack graphs.

We introduce a problem class which we term activity monitoring. Such problems involve monitoring the behavior of a large population of entities for interesting events requiring action. We present a framework within which each of the individual problems has a natural expression, as well as a methodology for evaluating performance of activity monitoring techniques. We show that two superficially different tasks, news story monitoring and intrusion detection, can be expressed naturally within the framework, and show that key differences in solution methods can be compared.

In this article, we first present the benefit of combing text analysis (topic models in particular) with interactive visualization. We then highlight examples from prior work on topic and time-oriented visual text analysis. Lastly, we present four challenges that warrant additional future research in the field of visual text analysis.

The pervasiveness of Cyber-Physical Systems (CPS) in various aspects of the modern society grows rapidly. This makes CPS to increasingly attractive targets for various kinds of attacks. We consider cyber-security as an integral part of CPS security. Additionally, the necessity exists to investigate the CPS-specific aspects which are out of scope of cyber-security. Most importantly, attacks capable to cross the cyber-physical domain boundary should be analyzed. The vulnerability of CPS to such cross-domain attacks has been practically proven by numerous examples, e.g., by the currently most famous Stuxnet attack. In this paper, we propose taxonomy for description of attacks on CPS. The proposed taxonomy is capable of representing both conventional cyber-attacks as well as cross-domain attacks on CPS. Furthermore, based on the proposed taxonomy, we define the attack categorization. Several possible application areas of the proposed taxonomy are extensively discussed. Among others, it can be used to establish a knowledge base about attacks on CPS known in the literature. Furthermore, the proposed description structure will foster the quantitative and qualitative analysis of these attacks, both of which are necessarily to improve CPS security.

Many methods have been developed to recognize those progresses of technologies, and one of them is to analyze patent information. And visualization methods are considered to be proper for representing patent information and its analysis results. However, current visualization methods for patent analysis patent maps have some drawbacks. Therefore, we propose an alternative visualization method in this paper. With colleted keywords from patent documents of a target technology field, we cluster patent documents by the k-Means algorithm. With the clustering results, we form a semantic network of keywords without respect of filing dates. And then we build up a patent map by rearranging each keyword node of the semantic network according to its earliest filing date and frequency in patent documents. Our approach contributes to establishing a patent map which considers both structured and unstructured items of a patent document. Besides, differently from previous visualization methods for patent analysis, ours is based on forming a semantic network of keywords from patent documents. And thereby it visualizes a clear overview of patent information in a more comprehensible way. And as a result of those contributions, it enables us to understand advances of emerging technologies and forecast its trend in the future. 2007 Elsevier Ltd. All rights reserved.

Recently, deep learning has been advancing the state of the art in artificial intelligence to a new level, and humans rely on artificial intelligence techniques more than ever. However, even with such unprecedented advancements, the lack of explanation regarding the decisions made by deep learning models and absence of control over their internal processes act as major drawbacks in critical decision-making processes, such as precision medicine and law enforcement. In response, efforts are being made to make deep learning interpretable and controllable by humans. In this paper, we review visual analytics, information visualization, and machine learning perspectives relevant to this aim, and discuss potential challenges and future research directions.

Software Defined Networks (SDN) technology can alternate the network, unlock critical intelligence, and help deliver the new services and analytics needed to run on-demand applications for today's businesses and consumers. So, SDN has been much request for adoption in mostly data centre. SDN provides network administrators to clear overview of the entire network architecture. It decouples a network's control and forwarding functions such that the physical and logical networks can be treated independently. This method allows network traffic flows to be programmatically and effectively reallocated to meet changing demands. That's what SDN does. Software defined network have too many scarcity like throughput, packet loss and network attack defense. Software defined firewalls helps to overcome lack of SDN and also reduce ratios of attack traffic. Here we analyze some SDF techniques and challenges related to software defined network.

Threat classification is extremely important for organizations, as it is an important step towards implementation of information security. Most of the existing threat classifications listed threats in static ways without linking threats to information system areas. The aim of this paper is to design a methodology that can classify deliberate threats in a dynamic way to represent each threat in different areas of the information system. This technique is based on the following factors: the attacker's prior knowledge (i. e. the knowledge hold by the source of the threat) about the system, loss of security information and the criticality of the area that might be affected by that threat.

Convolutional neural network (CNN)-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation) algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of an agriculture field is homogeneous, and obstacles, like people, animals and other obstacles, occur rarely and are of distinct appearance compared to the field. This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. We demonstrate DeepAnomaly as a fast state-of-the-art detector for obstacles that are distant, heavily occluded and unknown. DeepAnomaly is compared to state-of-the-art obstacle detectors including "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" (RCNN). In a human detector test case, we demonstrate that DeepAnomaly detects humans at longer ranges (45-90 m) than RCNN. RCNN has a similar performance at a short range (0-30 m). However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms =) a 7.28-times faster processing time per image. Unlike most CNN-based methods, the high accuracy, the low computation time and the low memory footprint make it suitable for a real-time system running on a embedded GPU (Graphics Processing Unit).

Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. • Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. May not music be described as the mathematics of the sense, mathematics as music of the reason? The musician feels mathematics, the mathematician thinks music: music the dream, mathematics the working life. Summary The increasing usage of computer-based systems in almost every aspect of our daily life makes more and more dangerous the threat posed by potential attackers , and more and more rewarding a successful attack. Moreover, the complexity of these systems is also increasing, including physical devices, software components and human actors interacting with each other to form so-called socio-technical systems. The importance of socio-technical systems to modern societies requires verifying their security properties formally, while their inherent complexity makes manual analyses impracticable. Graphical models for security offer an unrivalled opportunity to describe socio-technical systems, for they allow to represent different aspects like human behaviour, computation and physical phenomena in an abstract yet uniform manner. Moreover, these models can be assigned a formal semantics, thereby allowing formal verification of their properties. Finally, their appealing graphical notations enable to communicate security concerns in an understandable way also to non-experts, often in charge of the decision making. This dissertation argues that automated techniques can be developed on graphical security models to evaluate qualitative and quantitative security properties of socio-technical systems and to synthesise optimal attack and defence strategies. In support to this claim we develop analysis techniques for widely-used graphical security models such as attack trees and attack-defence trees. Our analyses cope with the optimisation of multiple parameters of an attack and defence scenario. Improving on the literature, in case of conflicting parameters such as probability and cost we compute the set of optimal solutions …


Question: 7 Critical Hurdles for Visual Representation in Cyber Network Protection Efforts

This paper explores the critical challenge of false alarm rates in the context of intrusion detection systems, emphasizing the base-rate fallacy as a significant obstacle to their effectiveness. Intrusion detection systems are tasked with a dual mandate: to accurately identify a significant proportion of intrusions into the monitored system while simultaneously maintaining a manageable level of false alarms. The study reveals that, under a reasonable set of assumptions, the false alarm rate emerges as the pivotal factor constraining the performance of intrusion detection systems. This is attributed to the base-rate fallacy, which suggests that to achieve high Bayesian detection rates (P(Intrusion|Alarm)), it is imperative to attain an exceptionally low false alarm rate, potentially unachievable in certain scenarios. The paper further reviews a collection of reports on the performance of intrusion detection systems, leading to the observation that several types of these systems are yet to reach the necessary low false alarm rates required for optimal functionality. This underscores the pressing need for advancements in intrusion detection methodologies to mitigate the base-rate fallacy and enhance detection accuracy.

As part of the information fusion task we wish to automatically fuse information derived from the text extraction process with data from a structured knowledge base. This process will involve resolving, aggregating, integrating and abstracting information via the methodologies of Knowledge Representation and Reasoning into a single comprehensive description of an individual or event. This report surveys the key principles underlying research in the field of Knowledge Representation and Reasoning. It represents an initial step in deciding upon a Knowledge Representation and Reasoning system for our information fusion task. APPROVED FOR PUBLIC RELEASE

This paper contains a survey on aspects of visual event computing. We start by presenting events and their classifications, and continue with discussing the problem of capturing events in terms of photographs, videos, etc, as well as the methodologies for event storing and retrieving. Later, we review an extensive set of papers taken from well-known conferences and journals in multiple disciplines. We analyze events, and summarize the procedure of visual event actions. We introduce each component of a visual event computing system, and its computational aspects, we discuss the progress of each component and review its overall status. Finally, we suggest future research trends in event computing and hope to introduce a comprehensive profile of visual event computing to readers.

The physical and cyber worlds are increasingly intertwined and exposed to cyber attacks. The KYPO cyber range provides complex cyber systems and networks in a virtualized, fully controlled and monitored environment. Time-efficient and cost-effective deployment is feasible using cloud resources instead of a dedicated hardware infrastructure. This paper describes the design decisions made during it’s development. We prepared a set of use cases to evaluate the proposed design decisions and to demonstrate the key features of the KYPO cyber range. It was especially cyber training sessions and exercises with hundreds of participants which provided invaluable feedback for KYPO platform development.

The importance of network security has grown tremendously and a collection of devices have been introduced, which can improve the security of a network. Network intrusion detection systems (NIDS) are among the most widely deployed such system; popular NIDS use a collection of signatures of known security threats and viruses, which are used to scan each packet's payload. Today, signatures are often specified as regular expressions; thus the core of the NIDS comprises of a regular expressions parser; such parsers are traditionally implemented as finite automata. Deterministic Finite Automata (DFA) are fast, therefore they are often desirable at high network link rates. DFA for the signatures, which are used in the current security devices, however require prohibitive amounts of memory, which limits their practical use.
 In this paper, we argue that the traditional DFA based NIDS has three main limitations: first they fail to exploit the fact that normal data streams rarely match any virus signature; second, DFAs are extremely inefficient in following multiple partially matching signatures and explodes in size, and third, finite automaton are incapable of efficiently keeping track of counts. We propose mechanisms to solve each of these drawbacks and demonstrate that our solutions can implement a NIDS much more securely and economically, and at the same time substantially improve the packet throughput.

In this paper we discuss a software design framework that is capable of realizing network-centricity and the rising multicore technology. Instead of producing static design documents in the form of UML diagrams, we propose automatic generation of a visual simulation model, which represents the target system design. We discuss a design environment that is responsible for the generation and execution of the simulation model.

A computer vision system has been implemented that can recognize threedimensional objects from unknown viewpoints in single gray-scale images. Unlike most other approaches, the recognition is accomplished without any attempt to reconstruct depth information bottom-up from the visual input. Instead, three other mechanisms are used that can bridge the gap between the two-dimensional image and knowledge of three-dimensional objects. First, a process of perceptual organization is used to form groupings and structures in the image that are likely to be invariant over a wide range of viewpoints. Second, a probabilistic ranking method is used to reduce the size of the search space during model based matching. Finally, a process of spatial correspondence brings the projections of three-dimensional models into direct correspondence with the image by solving for unknown viewpoint and model parameters. A high level of robustness in the presence of occlusion and missing data can be achieved through full application of a viewpoint consistency constraint. It is argued that similar mechanisms and constraints form the basis for recognition in human vision. This paper has been published in Artificial Intelligence, 31, 3 (March 1987), pp. 355–395.

Machine learning is often used to automatically solve human tasks. In this paper, we look for tasks where machine learning algorithms are not as good as humans with the hope of gaining insight into their current limitations. We studied various Human Interactive Proofs (HIPs) on the market, because they are systems designed to tell computers and humans apart by posing challenges presumably too hard for computers. We found that most HIPs are pure recognition tasks which can easily be broken using machine learning. The harder HIPs use a combination of segmentation and recognition tasks. From this observation, we found that building segmentation tasks is the most effective way to confuse machine learning algorithms. This has enabled us to build effective HIPs (which we deployed in MSN Passport), as well as design challenging segmentation tasks for machine learning algorithms.

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

Network intrusion detection systems are faced with the challenge of identifying diverse attacks, in extremely high speed networks. For this reason, they must operate at multi-Gigabit speeds, while performing highly-complex per-packet and per-flow data processing. In this paper, we present a multi-parallel intrusion detection architecture tailored for high speed networks. To cope with the increased processing throughput requirements, our system parallelizes network traffic processing and analysis at three levels, using multi-queue NICs, multiple CPUs, and multiple GPUs. The proposed design avoids locking, optimizes data transfers between the different processing units, and speeds up data processing by mapping different operations to the processing units where they are best suited. Our experimental evaluation shows that our prototype implementation based on commodity off-the-shelf equipment can reach processing speeds of up to 5.2 Gbit/s with zero packet loss when analyzing traffic in a real network, whereas the pattern matching engine alone reaches speeds of up to 70 Gbit/s, which is an almost four times improvement over prior solutions that use specialized hardware.

In this paper we show how attackers can covertly leak data (e.g., encryption keys, passwords and files) from highly secure or air-gapped networks via the row of status LEDs that exists in networking equipment such as LAN switches and routers. Although it is known that some network equipment emanates optical signals correlated with the information being processed by the device ('side-channel'), intentionally controlling the status LEDs to carry any type of data ('covert-channel') has never studied before. A malicious code is executed on the LAN switch or router, allowing full control of the status LEDs. Sensitive data can be encoded and modulated over the blinking of the LEDs. The generated signals can then be recorded by various types of remote cameras and optical sensors. We provide the technical background on the internal architecture of switches and routers (at both the hardware and software level) which enables this type of attack. We also present amplitude and frequency based modulation and encoding schemas, along with a simple transmission protocol. We implement a prototype of an exfiltration malware and discuss its design and implementation. We evaluate this method with a few routers and different types of LEDs. In addition, we tested various receivers including remote cameras, security cameras, smartphone cameras, and optical sensors, and also discuss different detection and prevention countermeasures. Our experiment shows that sensitive data can be covertly leaked via the status LEDs of switches and routers at a bit rates of 10 bit/sec to more than 1Kbit/sec per LED. Keywords— ezfiltration; air-gap; network; optical; covertchannel (key words)