Question: Recognition of events and actions using 3D thermal depth sensing.

This paper outlines a novel method for object and scene retrieval in videos, inspired by text retrieval techniques, which enables the efficient localization of all instances of a user-specified object within a video. The object is characterized through a set of region descriptors that are robust to variations in viewpoint, lighting conditions, and partial occlusions, ensuring a high degree of recognition accuracy. The temporal coherence inherent in video sequences is leveraged to track these regions, facilitating the elimination of unstable regions and mitigating descriptor noise. The text retrieval analogy is realized in the practical implementation, where descriptor matches are pre-calculated using vector quantization. This process is further enhanced by the use of inverted file systems and document ranking algorithms, akin to those employed by Google, to provide instant retrieval results. The efficacy of this method is demonstrated through its application in matching objects within two full-length feature films, showcasing its capability to return a prioritized list of key frames and shots.

In this paper we propose a new method for detecting multiple specific 3D objects in real time. We start from the template-based approach based on the LINE2D/LINEMOD representation introduced recently by Hinterstoisser et al., yet extend it in two ways. First, we propose to learn the templates in a discriminative fashion. We show that this can be done online during the collection of the example images, in just a few milliseconds, and has a big impact on the accuracy of the detector. Second, we propose a scheme based on cascades that speeds up detection. Since detection of an object is fast, new objects can be added with very low cost, making our approach scale well. In our experiments, we easily handle 10-30 3D objects at frame rates above 10fps using a single CPU core. We outperform the state-of-the-art both in terms of speed as well as in terms of accuracy, as validated on 3 different datasets. This holds both when using monocular color images (with LINE2D) and when using RGBD images (with LINEMOD). Moreover, we propose a challenging new dataset made of 12 objects, for future competing methods on monocular color images.

We are developing a personal activity recognition system that is practical, reliable, and can be incorporated into a variety of health-care related applications ranging from personal fitness to elder care. To make our system appealing and useful, we require it to have the following properties: (i) data only from a single body location needed, and it is not required to be from the same point for every user; (ii) should work out of the box across individuals, with personalization only enhancing its recognition abilities; and (iii) should be effective even with a cost-sensitive subset of the sensors and data features. In this paper, we present an approach to building a system that exhibits these properties and provide evidence based on data for 8 different activities collected from 12 different subjects. Our results indicate that the system has an accuracy rate of approximately 90% while meeting our requirements. We are now developing a fully embedded version of our system based on a cell-phone platform augmented with a Bluetooth-connected sensor board.

We propose an intelligent visuomotor system that interacts with the environment and memorizes the consequences of actions. As more memories are recorded and more interactions are observed, the agent becomes more capable of predicting the consequences of actions and is, thus, better at planning sequences of actions to solve tasks. In previous work, we introduced the aspect transition graph (ATG) which represents how actions lead from one observation to another using a directed multi-graph. In this work, we propose a novel aspect representation based on hierarchical CNN features, learned with convolutional neural networks, that supports manipulation and captures the essential affordances of an object based on RGB-D images. In a traditional planning system, robots are given a pre-defined set of actions that take the robot from one symbolic state to another. However symbolic states often lack the flexibility to generalize across similar situations. Our proposed representation is grounded in the robot's observations and lies in a continuous space that allows the robot to handle similar unseen situations. The hierarchical CNN features within a representation also allow the robot to act precisely with respect to the spatial location of individual features. We evaluate the robustness of this representation using the Washington RGB-D Objects Dataset and show that it achieves state of the art results for instance pose estimation. We then test this representation in conjunction with an ATG on a drill grasping task on Robonaut-2. We show that given grasp, drag, and turn demonstrations on the drill, the robot is capable of planning sequences of learned actions to compensate for reachability constraints.

Recent introduction of low-cost 3D sensing and affordable immersive virtual reality have lowered the barriers for creating and maintaining 3D virtual worlds. In this paper, we propose a way to combine these technologies with discrete-event simulation to improve the use of simulation in decision making in manufacturing. This work will describe how feedback is possible from real world systems directly into a simulation model to guide smart behaviors. Technologies included in the research include feedback from RGBD images of shop floor motion and human interaction within full immersive virtual reality that includes the latest headset technologies.

Ubisense has developed a platform for building Smart Space applications. The platform addresses the key requirements for building Smart Spaces: accurate 3D positioning, scalable realtime performance, development and deployment tools. This paper deepens the key requirements and describes how the Ubisense platform components meets them. The demonstration exemplifies the smart space platform by tracking players in a game application. The Ubisense Smart Space Platform Ubisense has developed a platform for building Smart Space applications. Our platform addresses the key requirements for building Smart Spaces: Accurate 3D positioning supports applications that can perceive the physical relationships between people and objects in the environment Scalable real-time performance enables arbitrary numbers of applications, used by arbitrary numbers of people over an arbitrarily-large area Development and deployment tools make it easy to design, implement, and manage Smart Space applications. The demonstration shows a Smart Space containing applications that integrate with external software (a simple game that users control by moving around), and devices (a PTZ camera that keeps users in shot while they are playing the game) Fig. 1 Smart space demonstration setup 1 Ubisense, St Andrews House, 90 St Andrews Road, Chesterton, CB4 1DL, United Kingdom, http://www.ubisense.net

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.

I ncreasingly powerful integrated circuits are making an entire range of new applications possible. Complementary metal-oxide semiconductor (CMOS) sensors, for example, have made the digital camera a commonplace consumer item. These light-sensitive chips, positioned where film would normally be, capture images as reusable digital files that users can upload to their computer, manipulate with software, and distribute electronically. Recent technological advances are enabling a new generation of smart cameras that represent a quantum leap in sophistication. While today's digital cameras capture images, smart cameras capture high-level descriptions of the scene and analyze what they see. These devices could support a wide variety of applications including human and animal detection, surveillance, motion analysis, and facial identification. Video processing has an insatiable demand for real-time performance. Fortunately, Moore's law provides an increasing pool of available computing power to apply to real-time analysis. Smart cameras leverage very large-scale integration (VLSI) to provide such analysis in a low-cost, low-power system with substantial memory. Moving well beyond pixel processing and compression, these systems run a wide range of algorithms to extract meaning from streaming video. Because they push the design space in so many dimensions, smart cameras are a leading-edge application for embedded system research. has developed a first-generation smart camera system that can detect people and analyze their movement in real time. Although there are many approaches to real-time video analysis, we chose to focus initially on human gesture recognition—identifying whether a subject is walking, standing, waving his arms, and so on. Because much work remains to be done on this problem, we sought to design an embedded system that can incorporate future algorithms as well as use those we created exclusively for this application. As Figure 1 shows, our algorithms use both low-level and high-level processing. The low-level component identifies different body parts and categorizes their movement in simple terms. The high-level component, which is application-dependent, uses this information to recognize each body part's action and the person's overall activity based on scenario parameters. The system captures images from the video input, which can be either uncompressed or compressed (MPEG and motion JPEG), and applies four different algorithms to detect and identify human body parts. Region extraction. The first algorithm transforms the pixels of an image, like that shown in Figure 2a, into an M × N bitmap and eliminates the background. It then detects the body part's skin area using a YUV …

Action recognition in videos remains a challenging problem in the machine learning community. Particularly challenging is the differing degree of intra-class variation between actions: While background information is enough to distinguish certain classes, many others are abstract and require fine-grained knowledge for discrimination. To approach this problem, in this work we evaluate different modalities on the recently published Moments in Time dataset, a collection of one million videos of short length.

This paper presents an unsupervised deep learning framework that derives spatio-temporal features for human-robot interaction. The respective models extract high-level features from low-level ones through a hierarchical network, viz. the Hierarchical Temporal Memory (HTM), providing at the same time a solution to the curse of dimensionality in shallow techniques. The presented work incorporates the tensor-based framework within the operation of the nodes and, thus, enhances the feature derivation procedure. This is due to the fact that tensors allow the preservation of the initial data format and their respective correlation and, moreover, attain more compact representations. The computational nodes form spatial and temporal groups by exploiting the multilinear algebra, subsequently express the samples according to those groups in terms of proximity. This generic framework may be applied in a diverse of visual data, whilst it has been examined on sequences of color and depth images, exhibiting remarkable performance.

In this paper, we present a new database acquired with three different sensors (visible, near infrared and thermal) under different illumination conditions. This database consists of 41 people acquired in four different acquisition sessions, five images per session and three different illumination conditions. The total amount of pictures is 7,380 pictures. Experimental results consist of single sensor experiments as well as the combination of two and three sensors under different illumination conditions (natural, infrared and artificial illumination). We have found that the three studied spectral bands contribute in a nearly equal proportion to a combined system. Experimental results show a significant improvement combining the three spectrums, even when using a simple classifier and feature extractor. In six of the nine studied scenarios, we obtained identification rates higher or equal to 98 %, when using a trained combination rule, and two cases of nine when using a fixed rule.


Question: CUDT: A CUDA-based algorithm for constructing decision trees.

This paper introduces SPRINT, a novel decision-tree-based classification algorithm specifically designed for addressing the challenges of data mining in large databases. Unlike traditional classification algorithms, which often require significant portions of the dataset to be stored in memory, SPRINT eliminates all memory restrictions, making it a fast and scalable solution. This makes it particularly suitable for mining extensive datasets where memory limitations can hinder the effectiveness of other algorithms. Moreover, SPRINT has been architected with parallel processing in mind, enabling multiple processors to collaboratively construct a unified and consistent model. The paper also discusses the parallelization strategy employed by SPRINT, which exhibits remarkable scalability. The amalgamation of these features positions SPRINT as an optimal tool for data mining applications, especially in environments where large-scale data processing is required.

Lately, programmers have started to take advantage of GPU capabilities of cloud-based machines. Using the GPUs can decrease the number of nodes required to perform the computation by increasing the productivity per node. We combine Hadoop, a widely-used MapReduce framework, with Aparapi, a new Java-to-OpenCL conversion tool from AMD. We propose an easy-to-use API which allows easy implementation of MapReduce algorithms that make use of the GPU. Our API improves upon Hadoop by further hiding the complexity of GPU programming, thus allowing the programmer to concentrate on her algorithm. We also propose an accompanying refactoring that allows the programmer to specify the GPU part of their map computation by using very lightweight annotation.

Despite of the success of convolutional neural networks for semantic image segmentation, CNNs cannot be used for many applications due to limited computational resources. Even efficient approaches based on random forests are not efficient enough for real-time performance in some cases. In this work, we propose an approach based on superpixels and label propagation that reduces the runtime of a random forest approach by factor 192 while increasing the segmentation accuracy.

Cloud computing is the one of the admired paradigms of current era, which facilitates the users with on demand services and pay as you use services. It has tremendous applications in almost every sphere such as education, gaming, social networking, transportation, medical, business, stock market, pattern matching, etc. Stock market is such an industry where lots of data is generated and benefits are reaped on the basis of accurate prediction. So prediction is a vital part of stock market. Therefore, an attempt is being made to predict the stock market based on the given data set of stock market along with some features; using the techniques available for predictive data mining. Machine learning is one of the upcoming trends of data mining; hence few machine learning algorithms have been used such as Decision tree, Linear model, Random forest and further their results have been compared using the classification evaluation parameters such as H, AUC, ROC, TPR, FPR, etc. Random forest have been consider as the most effective model as it yield the highest accuracy of 54.12% whereas decision tree and linear model gives the accuracy of 51.87% and 52.83% respectively.

This article discusses the capabilities of state-of-the art GPU-based high-throughput computing systems and considers the challenges to scaling single-chip parallel-computing systems, highlighting high-impact areas that the computing research community can address. Nvidia Research is investigating an architecture for a heterogeneous high-performance computing system that seeks to address these challenges.

Decision-making is a process of choosing among alternative courses of action for solving complicated problems where multi-criteria objectives are involved. The past few years have witnessed a growing recognition of Soft Computing (SC) technologies that underlie the conception, design and utilization of intelligent systems. In this paper, we present different SC paradigms involving an artificial neural network trained using the scaled conjugate gradient algorithm, two different fuzzy inference methods optimised using neural network learning/evolutionary algorithms and regression trees for developing intelligent decision support systems. We demonstrate the efficiency of the different algorithms by developing a decision support system for a Tactical Air Combat Environment (TACE). Some empirical comparisons between the different algorithms are also provided.

This paper presents the design of an MPI-based parallel and distributed machine learning platform on large-scale HPC clusters. Researchers and practitioners can implement easily a class of parallelizable machine learning algorithms on the platform, or port quickly an existing non-parallel implementation of a parallelizable algorithm to the platform with only minor modifications. Complicated functions in parallel programming such as scheduling, caching and load balancing are handled automatically by the platform. The platform performance was evaluated in a series of stress tests by using a k-means clustering task on 7,500 hours of speech data (about 2.7 billion 52dimensional feature vectors). Good scalability is demonstrated on an HPC cluster with thousands of CPU cores.

The use of cone beam computed tomography (CBCT) is growing in the clinical arena due to its ability to provide 3D information during interventions, its high diagnostic quality (sub-millimeter resolution), and its short scanning times (60 s). In many situations, the short scanning time of CBCT is followed by a time-consuming 3D reconstruction. The standard reconstruction algorithm for CBCT data is the filtered backprojection, which for a volume of size 256(3) takes up to 25 min on a standard system. Recent developments in the area of Graphic Processing Units (GPUs) make it possible to have access to high-performance computing solutions at a low cost, allowing their use in many scientific problems. We have implemented an algorithm for 3D reconstruction of CBCT data using the Compute Unified Device Architecture (CUDA) provided by NVIDIA (NVIDIA Corporation, Santa Clara, California), which was executed on a NVIDIA GeForce GTX 280. Our implementation results in improved reconstruction times from minutes, and perhaps hours, to a matter of seconds, while also giving the clinician the ability to view 3D volumetric data at higher resolutions. We evaluated our implementation on ten clinical data sets and one phantom data set to observe if differences occur between CPU and GPU-based reconstructions. By using our approach, the computation time for 256(3) is reduced from 25 min on the CPU to 3.2 s on the GPU. The GPU reconstruction time for 512(3) volumes is 8.5 s.

Previous studies propose that associative classification has high classification accuracy and strong flexibility at handling unstructured data. However, it still suffers from the huge set of mined rules and sometimes biased classification or overfitting since the classification is based on only single high-confidence rule. In this study, we propose a new associative classification method, CMAR, i.e., Classification based on Multiple Association Rules. The method extends an efficient frequent pattern mining method, FP-growth, constructs a class distribution-associated FP-tree, and mines large database efficiently. Moreover, it applies a CR-tree structure to store and retrieve mined association rules efficiently, and prunes rules effectively based on confidence, correlation and database coverage. The classification is performed based on a weighted analysis using multiple strong association rules. Our extensive experiments on databases from UCI machine learning database repository show that CMAR is consistent, highly effective at classification of various kinds of databases and has better average classification accuracy in comparison with CBA and C4.5. Moreover, our performance study shows that the method is highly efficient and scalable in comparison with other reported associative classification methods.

The amount of data in the world and in our lives seems everincreasing and there’s no end to it. The Weka workbench is an organized collection of state-of-the-art machine learning algorithms and data pre-processing tools. The basic way of interacting with these methods is by invoking them from the command line. However, convenient interactive graphical user interfaces are provided for data exploration, for setting up largescale experiments on distributed computing platforms, and for designing configurations for streamed data processing. These interfaces constitute an advanced environment for experimental data mining. Classification is an important data mining technique with broad applications. It classifies data of various kinds. This paper has been carried out to make a performance evaluation of REPTree, Simple Cart and RandomTree classification algorithm. The paper sets out to make comparative evaluation of classifiers REPTree, Simple Cart and RandomTree in the context of dataset of Indian news to maximize true positive rate and minimize false positive rate. For processing Weka API were used. The results in the paper on dataset of Indian news also show that the efficiency and accuracy of RandomTree is good than REPTree, and Simple Cart. Keywords— Simple Cart, RandomTree, REPTree, Weka, WWW

Decision trees and ensembles of decision trees are very popular in machine learning and often achieve state-of-the-art performance on black-box prediction tasks. However, popular variants such as C4.5, CART, boosted trees and random forests lack a probabilistic interpretation since they usually just specify an algorithm for training a model. We take a probabilistic approach where we cast the decision tree structures and the parameters associated with the nodes of a decision tree as a probabilistic model; given labeled examples, we can train the probabilistic model using a variety of approaches (Bayesian learning, maximum likelihood, etc). The probabilistic approach allows us to encode prior assumptions about tree structures and share statistical strength between node parameters; furthermore, it offers a principled mechanism to obtain probabilistic predictions which is crucial for applications where uncertainty quantification is important. Existing work on Bayesian decision trees relies on Markov chain Monte Carlo which can be computationally slow and suffer from poor mixing. We propose a novel sequential Monte Carlo algorithm that computes a particle approximation to the posterior over trees in a top-down fashion. We also propose a novel sampler for Bayesian additive regression trees by combining the above top-down particle filtering algorithm with the Particle Gibbs (Andrieu et al., 2010) framework. Finally, we propose Mondrian forests (MFs), a computationally efficient hybrid solution that is competitive with non-probabilistic counterparts in terms of speed and accuracy, but additionally produces well-calibrated uncertainty estimates. MFs use the Mondrian process (Roy and Teh, 2009) as the randomization mechanism and hierarchically smooth the node parameters within each tree (using a hierarchical probabilistic model and approximate Bayesian updates), but combine the trees in a non-Bayesian fashion. MFs can be grown in an incremental/online fashion and remarkably, the distribution of online MFs is the same as that of batch MFs.


Question: CUDT: A CUDA-driven decision tree algorithm implementation.

This paper delves into the challenges and strategies involved in developing a scalable classifier, particularly for large-scale data mining tasks, and introduces SLIQ, a novel classifier designed to address these challenges. Most existing classification algorithms are tailored for in-memory data, which can be restrictive when dealing with the vast datasets encountered in data mining. SLIQ, a decision tree classifier, is engineered to manage both numeric and categorical attributes effectively. A key innovation in SLIQ is its pre-sorting technique, which is seamlessly integrated into the tree-growing phase. This sorting mechanism, combined with a breadth-first tree growth strategy, facilitates the classification of datasets that reside on disk. Moreover, SLIQ incorporates an advanced tree-pruning algorithm that is both cost-effective and efficient, leading to compact yet accurate decision trees. These features collectively empower SLIQ to handle large datasets, making it a versatile tool for data mining applications, regardless of the number of classes, attributes, or records.

**Question**: An Algorithm for the Design of a Multistage Interconnection Network for an Optical Transputer System

**Documents**: The Design of a Multistage Interconnection Network for an Optical Transputer System
This paper presents the design of a multistage interconnection network (MIN) for an optical transputer system. The proposed MIN is based on a binary tree structure, where each node represents an optical transputer and is connected to its parent and children nodes using optical fibers. The design of the MIN is optimized for the number of optical fibers used and the number of stages required to achieve a given connectivity. The proposed MIN is shown to have a lower number of optical fibers and stages compared to existing MIN designs, making it suitable for large-scale optical transputer systems. The design is also shown to have a lower latency and higher throughput compared to existing MIN designs, making it suitable for high-speed optical transputer systems. The design is validated using simulations and experimental results.

**Similar Documents**: This paper outlines the development of a multistage interconnection network (MIN) specifically tailored for an optical transputer system. The proposed MIN architecture is centered around a binary tree framework, where each node signifies an optical transputer, interconnected with its parent and child nodes through optical fibers. The MIN design has been meticulously optimized to minimize the number of optical fibers utilized and the stages needed to attain a specified level of connectivity. Notably, the proposed MIN demonstrates a reduction in both the number of optical fibers and stages compared to conventional MIN designs, rendering it particularly advantageous for expansive optical transputer systems. Additionally, the design

Machine learning and data mining are gaining increasing attentions of the computing society. FPGA provides a highly parallel, low power, and flexible hardware platform for this domain, while the difficulty of programming FPGA greatly limits its prevalence. MapReduce is a parallel programming framework that could easily utilize inherent parallelism in algorithms. In this paper, we describe FPMR, a MapReduce framework on FPGA, which provides programming abstraction, hardware architecture, and basic building blocks to developers. An on-chip processor scheduler is implemented to maximize the utilization of computation resources and achieve better load balancing. An efficient data access scheme is carefully designed to maximize data reuse and throughput. Meanwhile, the FPMR framework hides the task control, synchronization, and communication away from designers so that more attention can be paid to the application itself. A case study of RankBoost acceleration based on FPMR demonstrates that FPMR efficiently helps with the development productivity; and the speedup is 31.8x versus CPU-based implementation. This performance is comparable to a fully manually designed version, which achieves 33.5x speedup. Two other applications: SVM, PageRank are also discussed to show the generalization of the framework.

Basic Linear Algebra Subprograms (BLAS) are a set of low level linear algebra kernels widely adopted by applications involved with the deep learning and scientific computing. The massive and economic computing power brought forth by the emerging GPU architectures drives interest in implementation of compute-intensive level 3 BLAS on multi-GPU systems. In this paper, we investigate existing multi-GPU level 3 BLAS and present that 1) issues, such as the improper load balancing, inefficient communication, insufficient GPU stream level concurrency and data caching, impede current implementations from fully harnessing heterogeneous computing resources; 2) and the inter-GPU Peer-to-Peer(P2P) communication remains unexplored. We then present BLASX: a highly optimized multi-GPU level-3 BLAS. We adopt the concepts of algorithms-by-tiles treating a matrix tile as the basic data unit and operations on tiles as the basic task. Tasks are guided with a dynamic asynchronous runtime, which is cache and locality aware. The communication cost under BLASX becomes trivial as it perfectly overlaps communication and computation across multiple streams during asynchronous task progression. It also takes the current tile cache scheme one step further by proposing an innovative 2-level hierarchical tile cache, taking advantage of inter-GPU P2P communication. As a result, linear speedup is observable with BLASX under multi-GPU configurations; and the extensive benchmarks demonstrate that BLASX consistently outperforms the related leading industrial and academic implementations such as cuBLAS-XT, SuperMatrix, MAGMA.

Support Vector Machines (SVM) are the classifiers which were originally designed for binary classification. The classification applications can solve multi-class problems. Decision-tree-based support vector machine which combines support vector machines and decision tree can be an effective way for solving multi-class problems. This method can decrease the training and testing time, increasing the efficiency of the system. The different ways to construct the binary trees divides the data set into two subsets from root to the leaf until every subset consists of only one class. The construction order of binary tree has great influence on the classification performance. In this paper we are studying an algorithm, Tree structured multiclass SVM, which has been used for classifying data. This paper proposes the decision tree based algorithm to construct multiclass intrusion detection system.

This paper presents an algorithm for incremental induction of decision trees that is able to handle both numeric and symbolic variables. In order to handle numeric variables, a new tree revision operator called`slewing' is introduced. Finally, a non-incremental method is given for nding a decision tree based on a direct metric of a candidate tree.

With the emergence of the big data age, the issue of how to obtain valuable knowledge from a dataset efficiently and accurately has attracted increasingly attention from both academia and industry. This paper presents a Parallel Random Forest (PRF) algorithm for big data on the Apache Spark platform. The PRF algorithm is optimized based on a hybrid approach combining data-parallel and task-parallel optimization. From the perspective of data-parallel optimization, a vertical data-partitioning method is performed to reduce the data communication cost effectively, and a data-multiplexing method is performed is performed to allow the training dataset to be reused and diminish the volume of data. From the perspective of task-parallel optimization, a dual parallel approach is carried out in the training process of RF, and a task Directed Acyclic Graph (DAG) is created according to the parallel training process of PRF and the dependence of the Resilient Distributed Datasets (RDD) objects. Then, different task schedulers are invoked for the tasks in the DAG. Moreover, to improve the algorithm's accuracy for large, high-dimensional, and noisy data, we perform a dimension-reduction approach in the training process and a weighted voting approach in the prediction process prior to parallelization. Extensive experimental results indicate the superiority and notable advantages of the PRF algorithm over the relevant algorithms implemented by Spark MLlib and other studies in terms of the classification accuracy, performance, and scalability. With the expansion of the scale of the random forest model and the Spark cluster, the advantage of the PRF algorithm is more obvious.

The Signature Quadratic Form Distance on feature signatures represents a flexible distance-based similarity model for effective content-based multimedia retrieval. Although metric indexing approaches are able to speed up query processing by two orders of magnitude, their applicability to large-scale multimedia databases containing billions of images is still a challenging issue. In this paper, we propose a parallel approach that balances the utilization of CPU and many-core GPUs for efficient similarity search with the Signature Quadratic Form Distance. In particular, we show how to process multiple distance computations and other parts of the search procedure in parallel, achieving maximal performance of the combined CPU/GPU system. The experimental evaluation demonstrates that our approach implemented on a common workstation with 2 GPU cards outperforms traditional parallel implementation on a high-end 48-core NUMA server in terms of efficiency almost by an order of magnitude. If we consider also the price of the high-end server that is ten times higher than that of the GPU workstation then, based on price/performance ratio, the GPU-based similarity search beats the CPU-based solution by almost two orders of magnitude. Although proposed for the SQFD, our approach of fast GPU-based similarity search is applicable for any distance function that is efficiently parallelizable in the SIMT execution model.

Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging. Existing high-level parallel abstractions like MapReduce are insufficiently expressive while low-level tools like MPI and Pthreads leave ML experts repeatedly solving the same design challenges. By targeting common patterns in ML, we developed GraphLab, which improves upon abstractions like MapReduce by compactly expressing asynchronous iterative algorithms with sparse computational dependencies while ensuring data consistency and achieving a high degree of parallel performance. We demonstrate the expressiveness of the GraphLab framework by designing and implementing parallel versions of belief propagation, Gibbs sampling, Co-EM, Lasso and Compressed Sensing. We show that using GraphLab we can achieve excellent parallel performance on large scale real-world problems.

The work presented here is driven by two observations. First, heterogeneous architectures that integrate a CPU and a GPU on the same chip are emerging, and hold much promise for supporting power-efficient and scalable high performance computing. Second, MapReduce has emerged as a suitable framework for simplified parallel application development for many classes of applications, including data mining and machine learning applications that benefit from accelerators.
 This paper focuses on the challenge of scaling a MapReduce application using the CPU and GPU together in an integrated architecture. We develop different methods for dividing the work, which are the map-dividing scheme, where map tasks are divided between both devices, and the pipelining scheme, which pipelines the map and the reduce stages on different devices. We develop dynamic work distribution schemes for both the approaches. To achieve high load balance while keeping scheduling costs low, we use a runtime tuning method to adjust task block sizes for the map-dividing scheme. Our implementation of MapReduce is based on a continuous reduction method, which avoids the memory overheads of storing key-value pairs.
 We have evaluated the different design decisions using 5 popular MapReduce applications. For 4 of the applications, our system achieves 1.21 to 2.1 speedup over the better of the CPU-only and GPU-only versions. The speedups over a single CPU core execution range from 3.25 to 28.68. The runtime tuning method we have developed achieves very low load imbalance, while keeping scheduling overheads low. Though our current work is specific to MapReduce, many underlying ideas are also applicable towards intra-node acceleration of other applications on integrated CPU-GPU nodes.

Since the Hoeffding tree algorithm was proposed in the literature, decision trees became one of the most popular tools for mining data streams. The key point of constructing the decision tree is to determine the best attribute to split the considered node. Several methods to solve this problem were presented so far. However, they are either wrongly mathematically justified (e.g., in the Hoeffding tree algorithm) or time-consuming (e.g., in the McDiarmid tree algorithm). In this paper, we propose a new method which significantly outperforms the McDiarmid tree algorithm and has a solid mathematical basis. Our method ensures, with a high probability set by the user, that the best attribute chosen in the considered node using a finite data sample is the same as it would be in the case of the whole data stream.

Faced with continuously increasing scale of data, original back-propagation neural network based machine learning algorithm presents two non-trivial challenges: huge amount of data makes it difficult to maintain both efficiency and accuracy; redundant data aggravates the system workload. This project is mainly focused on the solution to the issues above, combining deep learning algorithm with cloud computing platform to deal with large-scale data. A MapReduce-based handwriting character recognizer will be designed in this project to verify the efficiency improvement this mechanism will achieve on training and practical large-scale data. Careful discussion and experiment will be developed to illustrate how deep learning algorithm works to train handwritten digits data, how MapReduce is implemented on deep learning neural network, and why this combination accelerates computation. Besides performance, the scalability and robustness will be mentioned in this report as well. Our system comes with two demonstration software that visually illustrates our handwritten digit recognition/encoding application. 1


Question: CUDT: A CUDA-driven algorithm for constructing decision trees.

This paper explores the parallelization of classification decision tree algorithms, which are pivotal in data mining applications across various sectors, including retail marketing and fraud detection. The demand for highly parallel algorithms is driven by the need to process massive datasets efficiently. Although these algorithms exhibit inherent concurrency, their parallelization is complicated by the dynamic nature of the computations involved. Here, we introduce two parallel formulations for classification decision tree learning based on induction: the Synchronous Tree Construction Approach and the Partitioned Tree Construction Approach. Each method has its own merits and limitations, which are discussed in detail. To capitalize on the strengths of both approaches, we propose a hybrid method that integrates the best features of each. We also conduct a thorough analysis of the computational and communication costs associated with our hybrid method. The efficacy of this approach is substantiated by experimental results on an IBM SP-2, which reveal significant speedups and scalability.

Most statistical and machine-learning algorithms assume that the data is a random sample drawn from a stationary distribution. Unfortunately, most of the large databases available for mining today violate this assumption. They were gathered over months or years, and the underlying processes generating them changed during this time, sometimes radically. Although a number of algorithms have been proposed for learning time-changing concepts, they generally do not scale well to very large databases. In this paper we propose an efficient algorithm for mining decision trees from continuously-changing data streams, based on the ultra-fast VFDT decision tree learner. This algorithm, called CVFDT, stays current while making the most of old data by growing an alternative subtree whenever an old one becomes questionable, and replacing the old with the new when the new becomes more accurate. CVFDT learns a model which is similar in accuracy to the one that would be learned by reapplying VFDT to a moving window of examples every time a new example arrives, but with O(1) complexity per example, as opposed to O(w), where w is the size of the window. Experiments on a set of large time-changing data streams demonstrate the utility of this approach.

In this paper we work on the balance between hardware and software implementation of a machine learning algorithm, which belongs to the area of statistical learning theory. We use system-on-chip technology to demonstrate the potential usefulness of moving the critical sections of an algorithm into HW: the so-called hardware/software balance. Our experiments show that the approach can achieve speedups using a complex machine learning algorithm called a support vector machine. The experiments are conducted on a real-time Java virtual machine named Java optimized processor

The main objective of this study is to create a fast, easy and an efficient algorithm for disease prediction , with less error rate and can apply with even large data sets and show reasonable patterns with dependent variables. For disease identification and prediction in data mining a new hybrid algorithm was constructed. The Disease Identification and Prediction(DIP) algorithm, which is the combination of decision tree and association rule is used to predict the chances of some of the disease hits in some particular areas. It also shows the relationship between different parameters for the prediction. To implement this algorithm using vb.net software was also developed.

A growing number of commercial and enterprise systems increasingly rely on compute-intensive Machine Learning (ML) algorithms. While the demand for these compute-intensive applications is growing, the performance benefits from general-purpose platforms are diminishing. Field Programmable Gate Arrays (FPGAs) provide a promising path forward to accommodate the needs of machine learning algorithms and represent an intermediate point between the efficiency of ASICs and the programmability of general-purpose processors. However, acceleration with FPGAs still requires long development cycles and extensive expertise in hardware design. To tackle this challenge, instead of designing an accelerator for a machine learning algorithm, we present TABLA, a framework that generates accelerators for a class of machine learning algorithms. The key is to identify the commonalities across a wide range of machine learning algorithms and utilize this commonality to provide a high-level abstraction for programmers. TABLA leverages the insight that many learning algorithms can be expressed as a stochastic optimization problem. Therefore, learning becomes solving an optimization problem using stochastic gradient descent that minimizes an objective function over the training data. The gradient descent solver is fixed while the objective function changes for different learning algorithms. TABLA provides a template-based framework to accelerate this class of learning algorithms. Therefore, a developer can specify the learning task by only expressing the gradient of the objective function using our high-level language. Tabla then automatically generates the synthesizable implementation of the accelerator for FPGA realization using a set of hand-optimized templates. We use Tabla to generate accelerators for ten different learning tasks targeted at a Xilinx Zynq FPGA platform. We rigorously compare the benefits of FPGA acceleration to multi-core CPUs (ARM Cortex A15 and Xeon E3) and many-core GPUs (Tegra K1, GTX 650 Ti, and Tesla K40) using real hardware measurements. TABLA-generated accelerators provide 19.4x and 2.9x average speedup over the ARM and Xeon processors, respectively. These accelerators provide 17.57x, 20.2x, and 33.4x higher Performance-per-Watt in comparison to Tegra, GTX 650 Ti and Tesla, respectively. These benefits are achieved while the programmers write less than 50 lines of code.

Adaboost is a machine learning algorithm that builds a series of small decision trees, adapting each tree to predict difficult cases missed by the previous trees and combining all trees into a single model. We will discuss the AdaBoost methodology and introduce the extension called Real AdaBoost. Real AdaBoost comes from a strong academic pedigree: its authors are pioneers of machine learning and the method has well-established empirical and theoretical support spanning 15 years. Practically speaking, Real AdaBoost is able to produce readable credit scorecards and offers attractive features including variable interaction and adaptive, stage-wise binning. We will contrast Real AdaBoost to the dominant methodology for creating credit scorecards: stepwise weight of evidence logistic regression (SWOELR). Real AdaBoost is remarkably similar to SWOELR and is well positioned to serve as a benchmark for SWOELR models; it may even offer a statistical framework by which we can understand the power of SWOELR. We offer a macro to generate Real AdaBoost models in SAS. INTRODUCTION Financial institutions (FIs) must develop a wide range of models for marketing, fraud detection, loan adjudication, etc. Modeling has undergone a recent renaissance as machine learning has exploded – spurned by the availability of advanced statistical techniques, the ubiquity of powerful computers to execute these techniques, and the well-publicized successes of the companies who have embraced these methods (Parloff 2016). Modeling departments within some FIs face opposing demands: executives want some of the famed value of advanced methods, while government regulators, internal deployment teams and front-line staff want models that are easy to implement, interpret and understand. In this paper we review Real AdaBoost, a machine learning technique that may offer a middle-ground between powerful, but opaque machine learning methods and transparent conventional methods. CONSUMER RISK MODELS One field of modeling where FIs must often strike a balance between power and transparency is consumer risk modeling. Consumer risk modeling involves ranking customers by their credit worthiness (the likelihood they will repay a loan): first by identifying customer characteristics that indicate risk of delinquency, and then combining them mathematically to calculate a relative risk score for each customer (common characteristics include: past loan delinquency, high credit utilization, etc.). CREDIT SCORECARDS In order to keep the consumer risk models as transparent as possible, many FIs require that the final output of the model be in the form of a scorecard (an example is shown in Table 1). Credit scorecards are a popular way to represent customer risk models due to their simplicity, readability, and the ease with which business expertise can be incorporated during the modeling process (Maldonado et al. 2013). A scorecard lists a number of characteristics that indicate risk and each characteristic is subdivided into a small number of bins defined by ranges of values for that characteristic (e.g., credit utilization: 30-80% is a bin for the credit utilization characteristic). Each bin is assigned a number of score points, a value derived from a statistical model and proportional to the risk of that bin (SAS 2012). A customer will fall into one and only one bin per characteristic and the final score of the applicant is the sum of the points assigned by each bin (plus an intercept). This final score is proportional to consumer risk. The procedure for developing scorecards is termed stepwise weight of evidence logistic regression (SWOELR) and is implemented in the Credit Scoring add-on in SAS® Enterprise MinerTM.

Reinforcement learning aims to determine an optimal contro l policy from interaction with a system or from observations gathered from a system. In batch mode, i t can be achieved by approximating the so-calledQ-function based on a set of four-tuples (xt ,ut , rt ,xt+1) wherext denotes the system state at timet, ut the control action taken, rt the instantaneous reward obtained and xt+1 the successor state of the system, and by determining the contro l policy from this Q-function. The Q-function approximation may be obtained from the limit of a s equence of (batch mode) supervised learning problems. Within this framework we describe the use of several classical tree-based supervised learning methods (CART, Kd-tree, tree bagging) a d two newly proposed ensemble algorithms, namelyextremelyandtotally randomized trees. We study their performances on several examples and find that the ensemble methods based on regressi on trees perform well in extracting relevant information about the optimal control policy from sets of four-tuples. In particular, the totally randomized trees give good results while ensuring the convergence of the sequence, whereas by relaxing the convergence constraint even better accurac y results are provided by the extremely randomized trees.

The production environment for analytical data management applications is rapidly changing. Many enterprises are shifting away from deploying their analytical databases on high-end proprietary machines, and moving towards cheaper, lower-end, commodity hardware, typically arranged in a shared-nothing MPP architecture, often in a virtualized environment inside public or private “clouds”. At the same time, the amount of data that needs to be analyzed is exploding, requiring hundreds to thousands of machines to work in parallel to perform the analysis. There tend to be two schools of thought regarding what technology to use for data analysis in such an environment. Proponents of parallel databases argue that the strong emphasis on performance and efficiency of parallel databases makes them wellsuited to perform such analysis. On the other hand, others argue that MapReduce-based systems are better suited due to their superior scalability, fault tolerance, and flexibility to handle unstructured data. In this paper, we explore the feasibility of building a hybrid system that takes the best features from both technologies; the prototype we built approaches parallel databases in performance and efficiency, yet still yields the scalability, fault tolerance, and flexibility of MapReduce-based systems.

Random Forest is a computationally efficient technique that can operate quickly over large datasets. It has been used in many recent research projects and real-world applications in diverse domains. However, the associated literature provides almost no directions about how many trees should be used to compose a Random Forest. The research reported here analyzes whether there is an optimal number of trees within a Random Forest, i.e., a threshold from which increasing the number of trees would bring no significant performance gain, and would only increase the computational cost. Our main conclusions are: as the number of trees grows, it does not always mean the performance of the forest is significantly better than previous forests (fewer trees), and doubling the number of trees is worthless. It is also possible to state there is a threshold beyond which there is no significant gain, unless a huge computational environment is available. In addition, it was found an experimental relationship for the AUC gain when doubling the number of trees in any forest. Furthermore, as the number of trees grows, the full set of attributes tend to be used within a Random Forest, which may not be interesting in the biomedical domain. Additionally, datasets’ density-based metrics proposed here probably capture some aspects of the VC dimension on decision trees and low-density datasets may require large capacity machines whilst the opposite also seems to be true.

GPUs are an attractive target for data parallel stencil computations prevalent in scientific computing and image processing applications. Many tiling schemes, such as overlapped tiling and split tiling, have been proposed in past to improve the performance of stencil computations. While effective for 2D stencils, these techniques do not achieve the desired improvements for 3D stencils due to the hardware constraints of GPU.
 A major challenge in optimizing stencil computations is to effectively utilize all resources available on the GPU. In this paper we develop a tiling strategy that makes better use of resources like shared memory and register file available on the hardware. We present a systematic methodology to reason about which strategy should be employed for a given stencil and also discuss implementation choices that have a significant effect on the achieved performance. Applying these techniques to various 2D and 3D stencils gives a performance improvement of 200-400% over existing tools that target such computations.

Distributed computing is essential for handling very large datasets. Online learning is also promising for learning from rapid data streams. However, it is still an unresolved problem how to combine them for scalable learning and prediction on big data streams. We propose a general computational framework called loose model sharing for online and distributed machine learning. The key is to share only models rather than data between distributed servers. We also introduce Jubatus, an open source software platform based on the framework. Finally, we describe the details of implementing classifier and nearest neighbor algorithms, and discuss our experimental evaluations.


Question: CUDT: A CUDA-based algorithm for constructing decision trees.

This paper presents an innovative algorithm, Classification and Regression Trees (CART), tailored for decision tree analysis. Unlike traditional decision tree methods, CART is optimized for both classification and regression tasks, providing a versatile tool for data analysis. The algorithm is designed to handle complex datasets, enabling accurate predictions and classifications. CART employs a binary tree structure, where each internal node represents a test on a feature, and each leaf node represents a class label for classification or a numerical value for regression. The decision tree is built by recursively partitioning the data into subsets based on the feature values, aiming to minimize impurity at each node. This process ensures that the tree can effectively distinguish between different classes or predict continuous outcomes. CART's adaptability to various types of data and its ability to provide clear, interpretable models make it a valuable addition to the field of machine learning, particularly for tasks requiring both classification and regression capabilities.

A reported weakness of C4.5 in domains with continuous attributes is addressed by modifying the formation and evaluation of tests on continuous attributes. An MDL-inspired penalty is applied to such tests, eliminating some of them from consideration and altering the relative desirability of all tests. Empirical trials show that the modi cations lead to smaller decision trees with higher predictive accuracies. Results also con rm that a new version of C4.5 incorporating these changes is superior to recent approaches that use global discretization and that construct small trees with multi-interval splits.

The watershed transform is a popular image segmentation procedure from mathematical morphology used in many applications of computer vision. This paper proposes a novel parallel watershed procedure designed for GPU implementation. Our algorithm constructs paths of steepest descent and reduces these paths into direct pointers to catchment basin minima in logarithmic time, also crucially incorporating successful resolution of plateaux. Three implementation variants and their parameters are analysed through experiments on 2D and 3D images; a comparison against the state-of-the-art shows a runtime improvement of around 30%. For 3D images of 128 megavoxels execution times of approximately 1.5–2 seconds are achieved.

Cervical cancer threatens the lives of many women in our world today. In 2014, the number of women infected with this disease in the United States was 12,578, of which 4,115 died, with a death rate of nearly 32%. Cancer data, including cervical cancer datasets, represent a significant challenge data mining techniques because absence of different costs for error cases. The proposed model present a cost sensitive classifiers that has three main stages; the first stage is prepressing the original data to prepare it for classification model which is build based on decision tree classifier with cost selectivity and finallyevaluation the proposed model based on many metrics in addition to apply a cross validation.The proposed model provides more accurate result in both binary class and multi class classification. It has a TP rate (0.429) comparing with (0.160) for typical decision tree in binary class task.

The increasing main memory capacity and the explosion of big data have fueled the development of in-memory big data management and processing. By offering an efficient in-memory parallel execution model which can eliminate disk I/O bottleneck, existing in-memory cluster computing platforms (e.g., Flink and Spark) have already been proven to be outstanding platforms for big data processing. However, these platforms are merely CPU-based systems. This paper proposes GFlink, an in-memory computing architecture on heterogeneous CPU-GPU clusters for big data. Our proposed architecture extends the original Flink from CPU clusters to heterogeneous CPU-GPU clusters, greatly improving the computational power of Flink. Furthermore, we have proposed a programming framework based on Flink's abstract model, i.e., DataSet (DST), hiding the programming complexity of GPUs behind the simple and familiar high-level interfaces. To achieve high performance and good load-balance, an efficient JVM-GPU communication strategy, a GPU cache scheme, and an adaptive locality-aware scheduling scheme for three-stage pipelining execution are proposed. Extensive experiment results indicate that the high computational power of GPUs can be efficiently utilized, and the implementation on GFlink outperforms that on the original CPU-based Flink.

The emerging high efficiency video coding standard (HEVC) adopts the quadtree-structured coding unit (CU). Each CU allows recursive splitting into four equal sub-CUs. At each depth level (CU size), the test model of HEVC (HM) performs motion estimation (ME) with different sizes including 2N × 2N, 2N × N, N × 2N and N × N. ME process in HM is performed using all the possible depth levels and prediction modes to find the one with the least rate distortion (RD) cost using Lagrange multiplier. This achieves the highest coding efficiency but requires a very high computational complexity. In this paper, we propose a fast CU size decision algorithm for HM. Since the optimal depth level is highly content-dependent, it is not efficient to use all levels. We can determine CU depth range (including the minimum depth level and the maximum depth level) and skip some specific depth levels rarely used in the previous frame and neighboring CUs. Besides, the proposed algorithm also introduces early termination methods based on motion homogeneity checking, RD cost checking and SKIP mode checking to skip ME on unnecessary CU sizes. Experimental results demonstrate that the proposed algorithm can significantly reduce computational complexity while maintaining almost the same RD performance as the original HEVC encoder.

MassiveOnline Analysis (MOA) is a software environment for implementing al orithms and running experiments for online learning from evolving data str eams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Na ı̈ve Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and is released under the GNU GPL license.

Corresponding Author: Vanaja, S., Research Scholar and Research guide, Research and Development, Bharathiar University, Coimbatore, Tamil Nadu, India Email: vanajasha@yahoo.com Abstract: The aim of this research paper is to study and discuss the various classification algorithms applied on different kinds of medical datasets and compares its performance. The classification algorithms with maximum accuracies on various kinds of medical datasets are taken for performance analysis. The result of the performance analysis shows the most frequently used algorithms on particular medical dataset and best classification algorithm to analyse the specific disease. This study gives the details of different classification algorithms and feature selection methodologies. The study also discusses about the data constraints such as volume and dimensionality problems. This research paper also discusses the new features of C5.0 classification algorithm over C4.5 and performance of classification algorithm on high dimensional datasets. This research paper summarizes various reviews and technical articles which focus on the current research on Medical diagnosis.

-The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. The Healthcare industry is generally “information rich”, which is not feasible to handle manually. These large amounts of data are very important in the field of data mining to extract useful information and generate relationships amongst the attributes. Kidney disease is a complex task which requires much experience and knowledge. Kidney disease is a silent killer in developed countries and one of the main contributors to disease burden in developing countries. In the health care industry the data mining is mainly used for predicting the diseases from the datasets. The Data mining classification techniques, namely Decision trees, ANN, Naive Bayes are analyzed on Kidney disease data set. Keywords--Data Mining, Kidney Disease, Decision tree, Naive Bayes, ANN, K-NN, SVM, Rough Set, Logistic Regression, Genetic Algorithms (GAs) / Evolutionary Programming (EP), Clustering

Learning general functional dependencies between arbitra ry input and output spaces is one of the key challenges in computational intelligence. While recent progress in machine learning has mainly focused on designing flexible and powerful input representa tions, this paper addresses the complementary issue of designing classification algorithms that c an deal with more complex outputs, such as trees, sequences, or sets. More generally, we consider pr oblems involving multiple dependent output variables, structured output spaces, and classifica tion problems with class attributes. In order to accomplish this, we propose to appropriately generalize the well-known notion of a separation margin and derive a corresponding maximum-margin formulat ion. While this leads to a quadratic program with a potentially prohibitive, i.e. exponential, number of constraints, we present a cutting plane algorithm that solves the optimization problem i n polynomial time for a large class of problems. The proposed method has important applications i n areas such as computational biology, natural language processing, information retrieval/extr action, and optical character recognition. Experiments from various domains involving different types o f output spaces emphasize the breadth and generality of our approach.

We introduce a parallel GPU implementation of the Simple Linear Iterative Clustering (SLIC) superpixel segmentation. Using a single graphic card, our implementation achieves speedups of up to 83× from the standard sequential implementation. Our implementation is fully compatible with the standard sequential implementation and the software is now available online and is open source.


Question: CUDT: A CUDA-driven algorithm for decision tree construction.

In the realm of computational intelligence, Genetic Algorithms (GAs) have emerged as a potent force in search, optimization, and machine learning. This comprehensive resource provides an accessible and practical guide to GAs, designed to equip both learners and professionals with the skills and knowledge necessary to harness the power of genetic algorithms in tackling complex problems. By blending computer science methodologies, mathematical foundations, and cutting-edge research findings, this book offers a holistic approach to understanding and implementing GAs across various domains. Whether you're a student seeking to grasp the fundamentals or a practitioner looking to apply GAs in real-world scenarios, this guide ensures you're well-prepared to leverage genetic algorithms for enhanced search, optimization, and machine learning capabilities.

**Question**: A Comparative Study of Heuristic Algorithms for the Vehicle Routing Problem

**Documents**: An improved genetic algorithm for the vehicle routing problem
The vehicle routing problem (VRP) is a well-known combinatorial optimization problem. In this paper, we propose an improved genetic algorithm (GA) to solve the VRP. The proposed GA incorporates a new crossover operator and a local search procedure. The crossover operator, which we call the "two-point crossover with path exchange," is designed to maintain the feasibility of the offspring. The local search procedure, which we call the "two-opt star," is used to improve the quality of the solutions. We compare the performance of the proposed GA with that of the standard GA and other heuristic algorithms for the VRP. The results show that the proposed GA outperforms the standard GA and other heuristic algorithms in terms of solution quality and computational time.

**Similar Documents**: This study introduces an enhanced genetic algorithm (GA) tailored specifically for addressing the vehicle routing problem (VRP), a significant challenge in combinatorial optimization. The proposed GA features a novel crossover operator, termed the "two-point crossover with path exchange," meticulously engineered to preserve the feasibility of the offspring solutions. Additionally, an innovative local search strategy, known as the "two-opt star," is incorporated to refine solution quality. Comparative evaluations against the conventional GA and other heuristic methods for the VRP demonstrate the superiority of the enhanced GA in terms of solution excellence and computational efficiency. This research underscores the potential of advanced GA modifications in overcoming the intricacies of the VRP, offering a promising avenue for future developments in the field of logistics optimization.

In this paper we present a new algorithm suitable for matching discrete objects such as strings and trees in linear time, thus obviating dynamic programming with quadratic time complexity. Furthermore, prediction cost in many cases can be reduced to linear cost in the length of the sequence to be classified, regardless of the number of support vectors. This improvement on the currently available algorithms makes string kernels a viable alternative for the practitioner.

Random Forest is a computationally efficient technique that can operate quickly over large datasets. It has been used in many recent research projects and real-world applications in diverse domains. However, the associated literature provides almost no directions about how many trees should be used to compose a Random Forest. The research reported here analyzes whether there is an optimal number of trees within a Random Forest, i.e., a threshold from which increasing the number of trees would bring no significant performance gain, and would only increase the computational cost. Our main conclusions are: as the number of trees grows, it does not always mean the performance of the forest is significantly better than previous forests (fewer trees), and doubling the number of trees is worthless. It is also possible to state there is a threshold beyond which there is no significant gain, unless a huge computational environment is available. In addition, it was found an experimental relationship for the AUC gain when doubling the number of trees in any forest. Furthermore, as the number of trees grows, the full set of attributes tend to be used within a Random Forest, which may not be interesting in the biomedical domain. Additionally, datasets’ density-based metrics proposed here probably capture some aspects of the VC dimension on decision trees and low-density datasets may require large capacity machines whilst the opposite also seems to be true.

Emerging mobile applications, such as augmented reality, demand robust feature detection at high frame rates. We present an implementation of the popular Scale-Invariant Feature Transform (SIFT) feature detection algorithm that incorporates the powerful graphics processing unit (GPU) in mobile devices. Where the usual GPU methods are inefficient on mobile hardware, we propose a heterogeneous dataflow scheme. By methodically partitioning the computation, compressing the data for memory transfers, and taking into account the unique challenges that arise out of the mobile GPU, we are able to achieve a speedup of 4-7x over an optimized CPU version, and a 6.4x speedup over a published GPU implementation. Additionally, we reduce energy consumption by 87 percent per image. We achieve near-realtime detection without compromising the original algorithm.

This article discusses the capabilities of state-of-the art GPU-based high-throughput computing systems and considers the challenges to scaling single-chip parallel-computing systems, highlighting high-impact areas that the computing research community can address. Nvidia Research is investigating an architecture for a heterogeneous high-performance computing system that seeks to address these challenges.

Cervical cancer threatens the lives of many women in our world today. In 2014, the number of women infected with this disease in the United States was 12,578, of which 4,115 died, with a death rate of nearly 32%. Cancer data, including cervical cancer datasets, represent a significant challenge data mining techniques because absence of different costs for error cases. The proposed model present a cost sensitive classifiers that has three main stages; the first stage is prepressing the original data to prepare it for classification model which is build based on decision tree classifier with cost selectivity and finallyevaluation the proposed model based on many metrics in addition to apply a cross validation.The proposed model provides more accurate result in both binary class and multi class classification. It has a TP rate (0.429) comparing with (0.160) for typical decision tree in binary class task.

Search-based graph queries, such as finding short paths and isomorphic subgraphs, are dominated by memory latency. If input graphs can be partitioned appropriately, large cluster-based computing platforms can run these queries. However, the lack of compute-bound processing at each vertex of the input graph and the constant need to retrieve neighbors implies low processor utilization. Furthermore, graph classes such as scale-free social networks lack the locality to make partitioning clearly effective. Massive multithreading is an alternative architectural paradigm, in which a large shared memory is combined with processors that have extra hardware to support many thread contexts. The processor speed is typically slower than normal, and there is no data cache. Rather than mitigating memory latency, multithreaded machines tolerate it. This paradigm is well aligned with the problem of graph search, as the high ratio of memory requests to computation can be tolerated via multithreading. In this paper, we introduce the multithreaded graph library (MTGL), generic graph query software for processing semantic graphs on multithreaded computers. This library currently runs on serial machines and the Cray MTA-2, but Sandia is developing a run-time system that will make it possible to run MTGL-based code on symmetric multiprocessors. We also introduce a multithreaded algorithm for connected components and a new heuristic for inexact subgraph isomorphism We explore the performance of these and other basic graph algorithms on large scale-free graphs. We conclude with a performance comparison between the Cray MTA-2 and Blue Gene/Light for s-t connectivity.

In recent years, deep neural networks (DNN) have demonstrated significant business impact in large scale analysis and classification tasks such as speech recognition, visual object detection, pattern extraction, etc. Training of large DNNs, however, is universally considered as time consuming and computationally intensive task that demands datacenter-scale computational resources recruited for many days. Here we propose a concept of resistive processing unit (RPU) devices that can potentially accelerate DNN training by orders of magnitude while using much less power. The proposed RPU device can store and update the weight values locally thus minimizing data movement during training and allowing to fully exploit the locality and the parallelism of the training algorithm. We evaluate the effect of various RPU device features/non-idealities and system parameters on performance in order to derive the device and system level specifications for implementation of an accelerator chip for DNN training in a realistic CMOS-compatible technology. For large DNNs with about 1 billion weights this massively parallel RPU architecture can achieve acceleration factors of 30, 000 × compared to state-of-the-art microprocessors while providing power efficiency of 84, 000 GigaOps∕s∕W. Problems that currently require days of training on a datacenter-size cluster with thousands of machines can be addressed within hours on a single RPU accelerator. A system consisting of a cluster of RPU accelerators will be able to tackle Big Data problems with trillions of parameters that is impossible to address today like, for example, natural speech recognition and translation between all world languages, real-time analytics on large streams of business and scientific data, integration, and analysis of multimodal sensory data flows from a massive number of IoT (Internet of Things) sensors.

In this paper we work on the balance between hardware and software implementation of a machine learning algorithm, which belongs to the area of statistical learning theory. We use system-on-chip technology to demonstrate the potential usefulness of moving the critical sections of an algorithm into HW: the so-called hardware/software balance. Our experiments show that the approach can achieve speedups using a complex machine learning algorithm called a support vector machine. The experiments are conducted on a real-time Java virtual machine named Java optimized processor

A similarity join operation A BOWTIEepsiv B takes two sets of points A, B and a value epsiv isin Ropf, and outputs pairs of points p isin A,q isin B, such that the distance D(p, q) les epsiv. Similarity joins find use in a variety of fields, such as clustering, text mining, and multimedia databases. A novel similarity join algorithm called LSS is presented that executes on a graphics processing unit (GPU), exploiting its parallelism and high data throughput. As GPUs only allow simple data operations such as the sorting and searching of arrays, LSS uses these two operations to cast a similarity join operation as a GPU sort-and-search problem. It first creates, on the fly, a set of space-filling curves on one of its input datasets, using a parallel GPU sort routine. Next, LSS processes each point p of the other dataset in parallel. For each p, it searches an interval of one of the space-filling curves guaranteed to contain all the pairs in which p participates. Using extensive theoretical and experimental analysis, LSS is shown to offer a good balance between time and work efficiency. Experimental results demonstrate that LSS is suitable for similarity joins in large high-dimensional datasets, and that it performs well when compared against two existing prominent similarity join methods.

Multidisciplinary dataflow computing is a powerful approach to scientific computing that has led to orders-of-magnitude performance improvements for a wide range of applications.


Question: IRSTLM: an open-source toolkit for managing large-scale language models.

Exploring the Utility of Web-based Frequencies for Natural Language Processing Tasks

This study delves into the effectiveness of utilizing web-based frequencies in a variety of Natural Language Processing (NLP) tasks, expanding the scope of previous research which demonstrated the potential of web counts as proxies for bigram counts. While earlier studies were limited in the range of tasks and data sets explored, this article comprehensively evaluates the performance of models relying on web-based frequencies across a spectrum of NLP tasks, encompassing both syntax and semantics, generation and analysis. It also investigates a broader range of n-grams and parts of speech than previously considered.

Our findings indicate that for the majority of tasks assessed, unsupervised models leveraging web-based n-gram counts outperform those using counts from large corpora. In certain instances, performance can be further enhanced by incorporating backoff or interpolation techniques that merge web counts with corpus counts. Nonetheless, these unsupervised web-based models do not typically surpass the accuracy of supervised state-of-the-art models trained on smaller corpora. We conclude that web-based models should be regarded as a baseline to compare against, rather than a substitute for, conventional supervised models. This perspective underscores the complementary nature of web-based models in the NLP toolkit, offering a valuable starting point for further refinement and enhancement through supervised learning techniques.

Recently, many NLP tasks have benefited from distributed word representation. However, it remains unknown whether embedding models are really immune to the typological diversity of languages, despite the language-independent architecture. Here we investigate three representative models on a large set of language samples by mapping dense embedding to sparse linguistic property space. Experiment results reveal the language universal and specific properties encoded in various word representation. Additionally, strong evidence supports the utility of word form, especially for inflectional languages.

Sophisticated gated recurrent neural network architectures like LSTMs and GRUs have been shown to be highly effective in a myriad of applications. We develop an un-gated unit, the statistical recurrent unit (SRU), that is able to learn long term dependencies in data by only keeping moving averages of statistics. The SRU’s architecture is simple, un-gated, and contains a comparable number of parameters to LSTMs; yet, SRUs perform favorably to more sophisticated LSTM and GRU alternatives, often outperforming one or both in various tasks. We show the efficacy of SRUs as compared to LSTMs and GRUs in an unbiased manner by optimizing respective architectures’ hyperparameters in a Bayesian optimization scheme for both synthetic and realworld tasks.

Previous work demonstrated that Web counts can be used to approximate bigram counts, suggesting that Web-based frequencies should be useful for a wide variety of Natural Language Processing (NLP) tasks. However, only a limited number of tasks have so far been tested using Web-scale data sets. The present article overcomes this limitation by systematically investigating the performance of Web-based models for several NLP tasks, covering both syntax and semantics, both generation and analysis, and a wider range of n-grams and parts of speech than have been previously explored. For the majority of our tasks, we find that simple, unsupervised models perform better when n-gram counts are obtained from the Web rather than from a large corpus. In some cases, performance can be improved further by using backoff or interpolation techniques that combine Web counts and corpus counts. However, unsupervised Web-based models generally fail to outperform supervised state-of-the-art models trained on smaller corpora. We argue that Web-based models should therefore be used as a baseline for, rather than an alternative to, standard supervised models.

Long Short-Term Memory (LSTM) is a recurrent neural network (RNN) architecture specializing in modeling long-range temporal dynamics. On acoustic modeling tasks, LSTM-RNNs have shown better performance than DNNs and conventional RNNs. In this paper, we conduct an extensive study on speaker adaptation of LSTM-RNNs. Speaker adaptation helps to reduce the mismatch between acoustic models and testing speakers. We have two main goals for this study. First, on a benchmark dataset, the existing DNN adaptation techniques are evaluated on the adaptation of LSTM-RNNs. We observe that LSTMRNNs can be effectively adapted by using speaker-adaptive (SA) front-end, or by inserting speaker-dependent (SD) layers. Second, we propose two adaptation approaches that implement the SD-layer-insertion idea specifically for LSTM-RNNs. Using these approaches, speaker adaptation improves word error rates by 3-4% relative over a strong LSTM-RNN baseline. This improvement is enlarged to 6-7% if we exploit SA features for further adaptation.

We present a parser for Abstract Meaning Representation (AMR). We treat Englishto-AMR conversion within the framework of string-to-tree, syntax-based machine translation (SBMT). To make this work, we transform the AMR structure into a form suitable for the mechanics of SBMT and useful for modeling. We introduce an AMR-specific language model and add data and features drawn from semantic resources. Our resulting AMR parser improves upon state-of-the-art results by 7 Smatch points.

Rule-based information extraction is an important approach for processing the increasingly available amount of unstructured data. The manual creation of rule-based applications is a time-consuming and tedious task, which requires qualified knowledge engineers. The costs of this process can be reduced by providing a suitable rule language and extensive tooling support. This paper presents UIMA Ruta, a tool for rule-based information extraction and text processing applications. The system was designed with focus on rapid development. The rule language and its matching paradigm facilitate the quick specification of comprehensible extraction knowledge. They support a compact representation while still providing a high level of expressiveness. These advantages are supplemented by the development environment UIMA Ruta Workbench. It provides, in addition to extensive editing support, essential assistance for explanation of rule execution, introspection, automatic validation, and rule induction. UIMA Ruta is a useful tool for academia and industry due to its open source license. We compare UIMA Ruta to related rule-based systems especially concerning the compactness of the rule representation, the expressiveness, and the provided tooling support. The competitiveness of the runtime performance is shown in relation to a popular and freelyavailable system. A selection of case studies implemented with UIMA Ruta illustrates the usefulness of the system in real-world scenarios.

The Alexa Meaning Representation Language (AMRL) is a compositional graph-based semantic representation that includes fine-grained types, properties, actions, and roles and can represent a wide variety of spoken language. AMRL increases the ability of virtual assistants to represent more complex requests, including logical and conditional statements as well as ones with nested clauses. Due to this representational capacity, the acquisition of large scale data resources is challenging, which limits the accuracy of resulting models. This paper has two primary contributions. The first contribution is a linearization of the AMRL parses that aligns it to a related task of spoken language understanding (SLU) and a deep neural network architecture that uses multi-task learning to predict AMRL fine-grained types, properties and intents. The second contribution is a deep neural network architecture that leverages embeddings from the large-scale data resources that are available for SLU. When combined, these contributions enable the training of accurate models of AMRL parsing, even in the presence of data sparsity. The proposed models, which use the linearized AMRL parse, multi-task learning, residual connections and embeddings from SLU, decrease the error rates in the prediction of the full AMRL parse by 3.56%

We present a framework for information retrieval that combines document models and query models using a probabilistic ranking function based on Bayesian decision theory. The framework suggests an operational retrieval model that extends recent developments in the language modeling approach to information retrieval. A language model for each document is estimated, as well as a language model for each query, and the retrieval problem is cast in terms of risk minimization. The query language model can be exploited to model user preferences, the context of a query, synonomy and word senses. While recent work has incorporated word translation models for this purpose, we introduce a new method using Markov chains defined on a set of documents to estimate the query models. The Markov chain method has connections to algorithms from link analysis and social networks. The new approach is evaluated on TREC collections and compared to the basic language modeling approach and vector space models together with query expansion using Rocchio. Significant improvements are obtained over standard query expansion methods for strong baseline TF-IDF systems, with the greatest improvements attained for short queries on Web data.

Recurrent neural network language models (RNNLMs) have recently demonstrated state-of-the-art performance across a variety of tasks. In this paper, we improve their performance by providing a contextual real-valued input vector in association with each word. This vector is used to convey contextual information about the sentence being modeled. By performing Latent Dirichlet Allocation using a block of preceding text, we achieve a topic-conditioned RNNLM. This approach has the key advantage of avoiding the data fragmentation associated with building multiple topic models on different data subsets. We report perplexity results on the Penn Treebank data, where we achieve a new state-of-the-art. We further apply the model to the Wall Street Journal speech recognition task, where we observe improvements in word-error-rate.

In this paper, we introduce a Web-scale linguistics search engine, Linggle, that retrieves lexical bundles in response to a given query. The query might contain keywords, wildcards, wild parts of speech (PoS), synonyms, and additional regular expression (RE) operators. In our approach, we incorporate inverted file indexing, PoS information from BNC, and semantic indexing based on Latent Dirichlet Allocation with Google Web 1T. The method involves parsing the query to transforming it into several keyword retrieval commands. Word chunks are retrieved with counts, further filtering the chunks with the query as a RE, and finally displaying the results according to the counts, similarities, and topics. Clusters of synonyms or conceptually related words are also provided. In addition, Linggle provides example sentences from The New York Times on demand. The current implementation of Linggle is the most functionally comprehensive, and is in principle language and dataset independent. We plan to extend Linggle to provide fast and convenient access to a wealth of linguistic information embodied in Web scale datasets including Google Web 1T and Google Books Ngram for many major languages in the world.