Reviewerly/exHarmony huggingface dataset:
A Hardware-Oriented Algorithm for Ultra-High-Speed Object Detection

This paper describes a novel hardware-oriented algorithm that can be implemented on a field-programmable gate array in a high-speed vision platform for detection of multiple objects with clear texture information in images of 512×512 pixels at 10 000 frames per second (fps) under complex background. The proposed algorithm is specially designed for devices with limited hardware resource for high-frame-rate, high-data-throughput, and high-parallelism processing of video streams with low latency. The proposed algorithm is based on the conventional histograms of oriented gradient (HOG) descriptor and support vector machine classifier algorithms. Considering the trade-off between speed and accuracy, many hardware-based optimization operations were implemented. The data throughput is nearly 29.30 Gbps while the latency for feature extraction is 0.76 us (61 clock period). After hardware-based image processing, the source image and the detected object features can be transferred to a personal computer for recording or post-processing at 10 000 fps. Several experiments were done to demonstrate the performance of our proposed algorithm for ultra-high-speed moving object detection with clear texture information in images.
Understanding Test Takers' Choices in a Self-Adapted Test: A Hidden Markov Modeling of Process Data

With the rise of more interactive assessments, such as simulation- and game-based assessment, process data are available to learn about students' cognitive processes as well as motivational aspects. Since process data can be complicated due to interdependencies in time, our traditional psychometric models may not necessarily fit, and we need to look for additional ways to analyze such data. In this study, we draw process data from a study on self-adapted test under different goal conditions (Arieli-Attali, 2016) and use hidden Markov models to learn about test takers' choice making behavior. Self-adapted test is designed to allow test takers to choose the level of difficulty of the items they receive. The data includes test results from two conditions of goal orientation (performance goal and learning goal), as well as confidence ratings on each question. We show that using HMM we can learn about transition probabilities from one state to another as dependent on the goal orientation, the accumulated score and accumulated confidence, and the interactions therein. The implications of such insights are discussed.
Tracking multiple extended targets with multi‐Bernoulli filter

This study presents an improved multi-target multi-Bernoulli (IMeMBer) gamma Gaussian inverse Wishart (GGIW) filter for tracking multiple extended targets (ETs). The main contribution of this study consists of three parts, first, a novel method is proposed to obtain the unbiased cardinality estimation of multiple targets using the multi-Bernoulli recursion. As a variation of the existing cardinality-balanced MeMBer (CBMeMBer) filter, the presented filter is called the improved MeMBer filter, which overcomes the high detection probability limitation of the CBMeMBer filter. Second, based on the mathematical derivation, the IMeMBer filter is expanded to accommodate the characteristics of the ETs of which each target generates more than one measurement at each time step, and the GGIW method is used for its implementation. The resulting filter simultaneously provides the kinematic, extended and measurement rate states of ETs with an unknown and time-varying number. Third, the simulation results show that the presented filter achieves a considerable performance at the cost of less time, compared to the labelled multi-Bernoulli GGIW filter.
MCCH: A novel convex hull prior based solution for saliency detection

Salient object detection has received much attention in the past decades. One of the representative approaches is utilizing the convex hull prior to find the salient object in the image. Recently, researchers have proposed many variant methods, which are based the convex hull prior. Nevertheless, most of them used a single center to construct the convex hull prior (CHP) map, while few attention has been made on the use of multiple centers. In this paper, we propose a multi-center convex hull prior based solution for salient object detection. To strengthen our solution, we also integrate two non-trivial optimizations: the first one is used to obtain an enhanced global color distinction prior (GCDP) map, and the second one is used to refine the preliminary saliency map. We conduct extensive experiments based on several widely used benchmarking datasets. The experimental results demonstrate that our solution is effective and competitive, compared against state-of-the-art saliency detection algorithms.
Haze-removal polarimetric imaging schemes with the consideration of airlight's circular polarization effect

The polarimetric dehazing methods have been proven effective in enhancing the quality of images acquired in turbid media, including in hazy weather conditions. However, traditional polarimetric dehazing methods are all based on a hypothesis that the airlight only performs linear polarization property. The circular polarization effect is usually ignored although it may be an essential component of the airlight. In this paper, we report a new polarimetirc dehazing method considering the full polarization effects of the airlight (the linear and circular polarization effects of the airlight). In addition, an effective dehazing method only using the circular polarization effect of the airlight is given. Experimental results verify the effectiveness and the universality of the proposed method, and its dehazing capacity is obviously enhanced. Meanwhile, the circular polarization effect based method also can effectively achieve dehazing result only with one intensity image and one circular polarization image. This method is much easier to implement, although the dehazing results are not comparative to that of the full polarization effects based method. The proposed methods would advance the applications of the polarimetric dehazing methods in dehazing application filed.
Designing and implementing a collaborative health knowledge system

The huge diversity and quantity of data and information, and the requirements for knowledge extraction out of them put new challenges for knowledge management, synthesis, conflict detection and reasoning This paper elaborates on the design and development of COSMOS, an intelligent system which supports a) collaboration of experts for developing common knowledge bases and b) diagnosis derivation which takes into account time and uncertainty. The health domain is used for illustration and discussion of the features of our approach. Initially, we present the syntax and the semantics of the rules which incorporate time (temporal rules) and the data items upon which reasoning is performed. Then we introduce its inference engine, able to perform reasoning on top of rules and data, handling also the embedded time and uncertainty. We proceed further to define a conflict detection policy for supporting the difficult and error prone task of rule generation. The complexities of the aforementioned tasks are hidden from users via a well-designed and user friendly web interface that possesses strong collaboration features enabling multiple experts to work on defining a rule and on developing a common knowledge base. Evaluation of COSMOS has been performed using a) students studying expert systems and b) health experts in order to demonstrate the usability of the approach and the considerable advantages gained. To the best of our knowledge, COSMOS is one of the very few systems combining temporal rules, a powerful inference engine handling uncertainty and conflict detection and progresses beyond state of the art by adopting strong collaboration features and paradigms.
Understanding Center Loss Based Network for Image Retrieval with Few Training Data

Performance of convolutional neural network based image retrieval depends on the characteristics and statistics of the data being used for training. We show that for training datasets with a large number of classes but small number of images per class, the combination of cross-entropy loss and center loss works better than either of the losses alone. While cross-entropy loss tries to minimize misclassification of data, center loss minimizes the embedding space distance of each point in a class to its center, bringing together data-points belonging to the same class.
moDNN: Memory Optimal Deep Neural Network Training on Graphics Processing Units

Graphics processing units (GPUs) have been widely adopted to accelerate the training of deep neural networks (DNNs). Although the computational performance of GPUs has been improving steadily, the memory size of modern GPUs is still quite limited, which restricts the sizes of DNNs that can be trained on GPUs, and hence raises serious challenges. This paper introduces a framework, referred to as moDNN (memory optimal DNN training on GPUs), to optimize the memory usage in DNN training. moDNN supports automatic tuning of DNN training code to match any given memory budget (not smaller than the theoretical lower bound). By taking full advantage of overlapping computations and data transfers, we develop new heuristics to judiciously schedule data offloading and prefetching transfers, together with convolution algorithm selection, to optimize memory usage. We further devise a new sub-batch size selection method which also greatly reduces memory usage. moDNN can save memory usage up to 59×, compared with an ideal case which assumes that the GPU memory is sufficient to hold all data. When executing moDNN on a GPU with 12 GB memory, the training time is increased by only 3 percent, which is much shorter than that incurred by the best known approach, vDNN. Furthermore, we propose an optimization strategy for moDNN on multiple GPUs again by utilizing the idea of overlapping data transfers and GPU computations. The results show that 3.7× speedup is attained on four GPUs.
An Agile Approach to Improve the Usability of a Physical Telerehabilitation Platform

The goal of a telerehabilitation platform is to safely and securely facilitate the rehabilitation of patients through the use of telecommunication technologies complemented with the use of biomedical smart sensors. The purpose of this study was to perform a usability evaluation of a telerehabilitation platform. To improve the level of usability, the researchers developed and proposed an iterative process. The platform uses a digital representation of the patient which duplicates the therapeutic exercise being executed by the patient; this is detected by a Kinect camera and sensors in real time. This study used inspection methods to perform a usability evaluation of an exploratory prototype of a telerehabilitation platform. In addition, a cognitive workload assessment was performed to complement the usability evaluation. Users were involved through all the stages of the iterative refinement process. Usability issues were progressively reduced from the first iteration to the fourth iteration according to improvements which were developed and applied by the experts. Usability issues originally cataloged as catastrophic were reduced to zero, major usability problems were reduced to ten (2.75%) and minor usability problems were decreased to 141 (38.74%). This study also intends to serve as a guide to improve the usability of e-Health systems in alignment with the software development cycle.
Explicit criteria for mean square exponential stability of stochastic differential equations

Using a novel approach, we present new explicit criteria for the mean square exponential stability of general non-linear stochastic differential equations. An application to stochastic neural networks is given.
RealGraph: A Graph Engine Leveraging the Power-Law Distribution of Real-World Graphs

As the size of real-world graphs has drastically increased in recent years, a wide variety of graph engines have been developed to deal with such big graphs efficiently. However, the majority of graph engines have been designed without considering the power-law degree distribution of real-world graphs seriously. Two problems have been observed when existing graph engines process real-world graphs: inefficient scanning of the sparse indicator and the delay in iteration progress due to uneven workload distribution. In this paper, we propose RealGraph, a single-machine based graph engine equipped with the hierarchical indicator and the block-based workload allocation. Experimental results on real-world datasets show that RealGraph significantly outperforms existing graph engines in terms of both speed and scalability.
Mining software repositories for adaptive change commits using machine learning techniques

Version Control Systems, such as Subversion, are standard repositories that preserve all of the maintenance changes undertaken to source code artifacts during the evolution of a software system. The documented data of the version history are organized as commits; however, these commits do not keep a tag that would identify the purpose of the relevant undertaken change of a commit, thus, there is rarely enough detail to clearly direct developers to the changes associated with a specific type of maintenance. This work examines the version histories of an open source system to automatically classify version commits into one of two categories, namely adaptive commits and non-adaptive commits. We collected the commits from the version history of three open source systems, then we obtained eight different code change metrics related to, for example, the number of changed statements, methods, hunks, and files. Based on these change metrics, we built a machine learning approach to classify whether a commit was adaptive or not. It is observed that code change metrics can be indicative of adaptive maintenance activities. Also, the classification findings show that the machine learning classifier developed has approximately 75% prediction accuracy within labeled change histories. The proposed method automates the process of examining the version history of a software system and identifies which commits to the system are related to an adaptive maintenance task. The evaluation of the method supports its applicability and efficiency. Although the evaluation of the proposed classifier on unlabeled change histories shows that it is not much better than the random guessing in terms of F-measure, we feel that our classifier would serve as a better basis for developing advanced classifiers that have predictive power of adaptive commits without the need of manual efforts.
Multi-directional local gradient descriptor: A new feature descriptor for face recognition

The performance of the face recognition systems is vulnerable to occlusion, light and expression changes and such constraints need to be handled effectively in a robust face recognition system. This paper presents a new multi-directional local gradient descriptor (MLGD) method for face recognition based on local directional gradient features that exploit the edges/line information in multiple directions. The proposed technique exploits advantage of similarity of a face image in small blocks. The weighted gradient features of face images in different directions and zones are computed based on co-relation between pixel elements. These features referred to as multi-directional local gradient descriptor (MLGD), which capture adequate edge information by integrating different directional gradients. Further, the directional gradient features extracted through MLGD operator are represented as a symbolic data object. The face identification is carried out by using the symbolic object representation of test image and employing a symbolic similarity measure. The experimental results on AR (97.33%) and LFW (97.25%) benchmark face databases demonstrate that the symbolic data representation of the new directional gradient magnitude of face image significantly improves the recognition performance as compared to local gradient descriptors and other state-of-the-art methods.
Maximizing Marginal Utility per Dollar for Economic Recommendation

Understanding the economic nature of consumer decisions in e-Commerce is important to personalized recommendation systems. Established economic theories claim that informed consumers always attempt to maximize their utility by choosing the items of the largest marginal utility per dollar (MUD) within their budgets. For example, gaining 5 dollars of extra benefit by spending 10 dollars makes a consumer much more satisfied than having the same amount of extra benefit by spending 20 dollars, although the second product may have higher absolute utility value. Meanwhile, making purchases online may be risky decisions that could cause dissatisfaction. For example, people may give low ratings towards purchased items that they thought they would like when placing the order. Therefore, the design of recommender systems should also take users' risk attitudes into consideration to better learn consumer behaviors.
Signal Processing on Static and Dynamic 3D Meshes: Sparse Representations and Applications

Nowadays, real-time 3D scanning and reconstruction becomes a requirement for a variety of interactive applications in various fields, including heritage science, gaming, engineering, landscape topography, and medicine. From the introduction of 3D scanning, which allowed the representation of real world or synthetic objects into the virtual world, hardware and software advances have seen tremendous progress. However, despite the continuous improvement of the new generation image sensors and acquisition techniques, the acquired data are often corrupted by the low-frequency noise, outliers, misalignment, missing data, and variations in point density. These effects are amplified if the low-cost sensors and hardware are being used (e.g., mobile devices); thus, the acquisition and communication cost per datum is driven to a minimum. This paper provides a comprehensive review of the ongoing efforts in geometry and signal processing, describing several models from a wide range of signal processing relevant tasks, such as robust principal component analysis, compressive sampling, and matrix completion. Various scalable architectures and optimization algorithms are analyzed and reviewed, revealing significant insights into the fundamental processing operations and the involved implementation tradeoffs. Moreover, the impact of sparse modeling and optimization tools to several 3D mesh processing tasks, such as completion of missing data, feature preserving noise removal, and rejection of outliers, is illustrated via test cases with several constraints posed by the arbitrarily complex animated scenarios. Finally, the identified limitations together with the potential open research directions are also presented for future research efforts toward modeling and optimization for static and dynamic 3D models.
Dataset bias exposed in face verification

Most facial verification methods assume that training and testing sets contain independent and identically distributed samples, although, in many real applications, this assumption does not hold. Whenever gathering a representative dataset in the target domain is unfeasible, it is necessary to choose one of the already available (source domain) datasets. Here, a study was performed over the differences among six public datasets, and how this impacts on the performance of the learned methods. In the considered scenario of mobile devices, the individual of interest is enrolled using a few facial images taken in the operational domain, while training impostors are drawn from one of the public available datasets. This work tried to shed light on the inherent differences among the datasets, and potential harms that should be considered when they are combined for training and testing. Results indicate that a drop in performance occurs whenever training and testing are done on different datasets compared to the case of using the same dataset in both phases. However, the decay strongly depends on the kind of features. Besides, the representation of samples in the feature space reveals insights into what extent bias is an endogenous or an exogenous factor.
Domain independent static video summarization using sparse autoencoders and K-means clustering

The ever growing video data over the internet has raised the challenge of efficient storage and retrieval of multimedia data. Video Summarization is one of the solutions to the problem which extracts interesting parts of a video. These summaries capture the essential content of the original video and enable the viewers to pick out interesting videos from huge video repositories by viewing its compact representation. To address the challenge of managing the video data, a new method for static video summarization is proposed here which is capable of describing input videos using a precise yet meaningful subset of frames. The method utilizes HOG descriptors of Gabor maps of input frames. A high-level representation of HOG descriptors is created using sparse auto-encoders (SAE). The final summary of the video is formed using candidate frames obtained after K-means clustering of these feature vectors. The summarization step is preceded by redundancy elimination to overcome computational burden. The method provides better results compared to the other state-of-the-art video summarization methods.
Estimation algorithm for system with non‐Gaussian multiplicative/additive noises based on variational Bayesian inference

Summary This paper considers estimation algorithms for linear and nonlinear systems contaminated by non‐Gaussian multiplicative and additive noises. Based on the variational idea, in order to derive optimal estimation algorithms, we combine the multiplicative noise with states as the joint parameters to estimate. The application of variational Bayesian inference to joint estimation of the state and the multiplicative noise is established. By treating the states as unknown quantities as well as the multiplicative noise, there are now correlations between the states and multiplicative noise in the posterior distribution. There are two main goals in Bayesian learning. The first is approximating the marginal likelihood (PDF of multiplicative noise) to perform model comparison. The second is approximating the posterior distribution over the states (also called a system model), which can then be used for prediction. The two goals constitute the iterative algorithm. The rules for determining the loop is the Kullback‐Leibler divergence between the true distribution of state and a chosen fixed tractable distribution, which is used to approximate the true one. The iterative algorithm is deduced, which is initialized based on the idea of sampling. Meanwhile, the convergence analysis of the proposed iterative algorithm is presented. The numerical simulation results in a comparison between the proposed method and these existing classic algorithms in the context of nonlinear hidden Markov models, state‐space models, and target‐tracking models with non‐Gaussian multiplicative noise demonstrate the superiorities, not only in speed, precision, and computation load but also in the ability to process non‐Gaussian complex noise.