Reviewerly/exHarmony huggingface dataset:
Differential effects of aging and cognitive decline on visual exploration behavior in the elderly

Visual exploration disturbance has been examined in the elderly, mainly from the perspective of associations with cognitive function. However, it remains unknown whether this is a consequence of cognitive decline, age-related changes independent of cognitive decline, or both. In this study, 15 healthy elderly individuals were evaluated using two figure-matching tasks representing visual information processing (clock-matching and inverted clock-matching tasks). Cognitive functions were evaluated for each subject using the Mini-Mental State Examination (MMSE). Infrared eye-movement assessments were used to analyze eye movements during task performance. Behavioral analyses showed that age was associated with longer reaction time, while MMSE score was associated with higher accuracy on the inverted clock-matching task. Analyses of eye-movement parameters showed that MMSE score was negatively associated with a parameter indicating difficulty in the efficiency of visual exploration planning, while age was tended to be positively associated with the sum of saccade times in each trial, both predominantly on the inverted clock-matching task. Our approaches highlighted that age and cognitive decline are separately associated with eye-movement characteristics: cognitive decline is associated with difficulty in visual exploration planning, particularly in situations that require substantial visual working memory resources, whereas aging may be associated with oculomotor dysfunction.
Thread Quality Classification of a Tapping Machine Based on Machine Learning

This study discusses the quality analysis of a new tapping machine. By measuring the torque and speed of the tapping machine, the correlation between the quality of the nut and the tapping process is analyzed. Using various regression trees and learning methods, the results show that the magnitude of the tapping torque is an essential indicator of the quality of the thread. In addition, through such methods, the appropriate operation boundary of the tapper torque and tapper speed can be determined. Thread, processed in the two boundaries, can have a high probability of being a Class 1 thread. Furthermore, the tapping torque is also used for quality classification by a one-dimensional convolutional neural network (1-D CNN). The 1-D CNN model performs high classification accuracy and has the ability to localize the important area of the signal. The experimental results show that the regression tree can effectively find the operation boundary, providing information for users to adjust their machine settings, and improve the thread quality. In addition, the off-line classification of thread done by humans can be replaced by the high-accuracy 1-D CNN model predicting the thread quality in real-time. The detection speed can be accelerated, and labor costs can be reduced.
Scale‐adaptive Structure‐preserving Texture Filtering

Abstract This paper proposes a scale‐adaptive filtering method to improve the performance of structure‐preserving texture filtering for image smoothing. With classical texture filters, it usually is challenging to smooth texture at multiple scales while preserving salient structures in an image. We address this issue in the concept of adaptive bilateral filtering, where the scales of Gaussian range kernels are allowed to vary from pixel to pixel. Based on direction‐wise statistics, our method distinguishes texture from structure effectively, identifies appropriate scope around a pixel to be smoothed and thus infers an optimal smoothing scale for it. Filtering an image with varying‐scale kernels, the image is smoothed according to the distribution of texture adaptively. With commendable experimental results, we show that, needing less iterations, our proposed scheme boosts texture filtering performance in terms of preserving the geometric structures of multiple scales even after aggressive smoothing of the original image.
Blockchain Technology and Its Applications

Blockchain is an information technology solution for distributed digital record. Its main properties are secure, traceable, and transparent. Blockchain was first proposed for bitcoin, a decentralized virtual digital currency. Recently, blockchain draws attentions of researchers, entrepreneurs, and investors, because people found that blockchain technology can revolutionize many business practices. This paper describes the fundamentals of blockchain technology, including data structure, data distribution, and transaction execution and validation. This paper also discusses the representative blockchain protocols and development platforms. In addition, this paper outlines the potential applications of blockchain technology and the broader impact it might bring to industry, business, and beyond.
A Diversity Model Based on Dimension Entropy and Its Application to Swarm Intelligence Algorithm

The swarm intelligence algorithm has become an important method to solve optimization problems because of its excellent self-organization, self-adaptation, and self-learning characteristics. However, when a traditional swarm intelligence algorithm faces high and complex multi-peak problems, population diversity is quickly lost, which leads to the premature convergence of the algorithm. In order to solve this problem, dimension entropy is proposed as a measure of population diversity, and a diversity control mechanism is proposed to guide the updating of the swarm intelligence algorithm. It maintains the diversity of the algorithm in the early stage and ensures the convergence of the algorithm in the later stage. Experimental results show that the performance of the improved algorithm is better than that of the original algorithm.
Reinforcement Learning: A Survey

Reinforcement learning is one of the fields of study in machine learning. Reinforcement learning is ready to improve the work which is being done in the AI domain and represents a step toward building autonomous frameworks with a more impressive level of understanding of the visual world. Reinforcement learning is about taking appropriate measures to maximize reward in a particular situation. Behavioral psychology has a major influence on reinforcement learning. The main objective is to observe the behavior of an agent's interaction with the given environment in order to increase the value of a reward. This survey will focus on the basics of reinforcement learning and was intended to help in the understanding of reinforcement learning. The survey starts with giving an introduction to the general field of reinforcement learning, how it is different from different machine learning paradigms and the types of reinforcement learning. Our survey will also cover the challenges which come with reinforcement learning.
Complexes of Low Dimensional Linear Classifiers with L1 Margins

Large data sets consisting of a relatively small number of multidimensional feature vectors are often encountered. Such a structure of data sets appears, inter alia, in in the case of genetic data. Various types of classifiers are designed on the basis of such data sets. Small number of multivariate feature vectors are almost always linearly separable. For this reason, the linear classifiers play a fundamental role in the case of small samples of multivariate vectors. The maximizing margins is one of fundamental principles in classification. The Euclidean (L2) margins is a basic concept in the support vector machines (SVM) method of classifiers designing. An alternative approach to designing classifiers is linked to the local maximization of the margins of the L1 norm. This approach allows also designing complexes of linear classifiers on the basis of small samples of multivariate vectors.
Cybersecurity Hiring in Response to Data Breaches

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Clickstream Knowledge Tracing: Modeling How Students Answer Interactive Online Questions

Knowledge tracing (KT) is a research topic which seeks to model the knowledge acquisition process of students by analyzing their past performance in answering questions, based on which their performance in answering future questions is predicted. However, existing KT models only consider whether a student answers a question correctly when the answer is submitted but not the in-question activities. We argue that the interaction involved in the in-question activities can at least partially reveal the thinking process of the student, and hopefully even the competence of acquiring or understanding each piece of the knowledge required for the question.
Small Leaks Sink a Great Ship: An Evaluation of Key Reuse Resilience of PQC Third Round Finalist NTRU-HRSS

NTRU is regarded as an appealing finalist due to its long history against all known attacks and relatively high efficiency. In the third round of NIST competition, the submitted NTRU cryptosystem is the merger of NTRU-HPS and NTRU-HRSS. In 2019, Ding et al. have analyzed the case when the public key is reused for the original NTRU scheme. However, NTRU-HRSS selects coefficients in an arbitrary way, instead of fixed-weight sample spaces in the original NTRU and NTRU-HPS. Therefore, their method cannot be applied to NTRU-HRSS. To address this problem, we propose a full key mismatch attack on NTRU-HRSS. Firstly, we find a longest chain which helps us in recovering the following coefficients. Next, the most influential interference factors are eliminated by increasing the weight of targeted coefficients. In this step, we adaptively select the weights according to the feedbacks of the oracle to avoid errors. Finally, experiments show that we succeed in recovering all coefficients of the secret key in NTRU-HRSS with a success rate of \(93.6\%\). Furthermore, we illustrate the trade-off among the success rate, average number of queries, and average time. Particularly, we show that when the success rate is 93.6%, it has the minimum number of queries at the same time.
Learning the structure of Bayesian Networks via the bootstrap

Learning the structure of dependencies among multiple random variables is a problem of considerable theoretical and practical interest. Within the context of Bayesian Networks, a practical and surprisingly successful solution to this learning problem is achieved by adopting score-functions optimisation schema, augmented with multiple restarts to avoid local optima. Yet, the conditions under which such strategies work well are poorly understood, and there are also some intrinsic limitations to learning the directionality of the interaction among the variables. Following an early intuition of Friedman and Koller, we propose to decouple the learning problem into two steps: first, we identify a partial ordering among input variables which constrains the structural learning problem, and then propose an effective bootstrap-based algorithm to simulate augmented data sets, and select the most important dependencies among the variables. By using several synthetic data sets, we show that our algorithm yields better recovery performance than the state of the art, increasing the chances of identifying a globally-optimal solution to the learning problem, and solving also well-known identifiability issues that affect the standard approach. We use our new algorithm to infer statistical dependencies between cancer driver somatic mutations detected by high-throughput genome sequencing data of multiple colorectal cancer patients. In this way, we also show how the proposed methods can shade new insights about cancer initiation, and progression. Code: https://github.com/caravagn/Bootstrap-based-Learning
Scaling the Growing Neural Gas for Visual Cluster Analysis

The growing neural gas (GNG) is an unsupervised topology learning algorithm that models a data space through interconnected units that stand on the most populated areas of that space. Its output is a graph that can be visually represented on a two-dimensional plane, disclosing cluster patterns in datasets. It is common, however, for GNG to result in highly connected graphs when trained on high-dimensional data, which in turn leads to highly cluttered 2D representations that may fail to disclose meaningful patterns. Moreover, its sequential learning limits its potential for faster executions on local datasets, and, more importantly, its potential for training on distributed datasets while leveraging from the computational resources of the infrastructures in which they reside. This paper presents two methods that improve GNG for the visualization of cluster patterns in large-scale and high-dimensional datasets. The first one focuses on providing more accurate and meaningful 2D visual representations for cluster patterns of high-dimensional datasets, by avoiding connections that lead to high-dimensional graphs in the modeled topology which may, in turn, result in overplotting and clutter. The second method presented in this paper enables the use of GNG on big and distributed datasets with faster execution times, by modeling and merging separate parts of a dataset using the MapReduce model. Quantitative and qualitative evaluations show that the first method leads to the creation of lower-dimensional graph structures that provide more meaningful (and sometimes more accurate) cluster representations with less overplotting and clutter; and that the second method preserves the accuracy and meaning of the cluster representations while enabling its execution in large-scale and distributed settings.
The SLT 2021 Children Speech Recognition Challenge: Open Datasets, Rules and Baselines

Automatic speech recognition (ASR) has been significantly advanced with the use of deep learning and big data. How-ever improving robustness, including achieving equally good performance on diverse speakers and accents, is still a challenging problem. In particular, the performance of children speech recognition (CSR) still lags behind due to 1) the speech and language characteristics of children's voice are substantially different from those of adults and 2) sizable open dataset for children speech is still not available in the research community. To address these problems, we launch the Children Speech Recognition Challenge (CSRC), as a flagship satellite event of IEEE SLT 2021 workshop. The challenge will release about 400 hours of Mandarin speech data for registered teams and set up two challenge tracks and provide a common testbed to benchmark the CSR performance. In this paper, we introduce the datasets, rules, evaluation method as well as baselines.
Sentiment analysis in higher education: a systematic mapping review

Abstract In the last years, sentiment analysis (SA) has attracted increasing interest in the text mining area. It increasingly becomes a popular research area for opinion mining in education that analyses and understands students’ opinions toward their institutions for improving the quality of decision-making. In this study, a systematic review was conducted to explore the recent application of sentiment analysis in higher education; to classify SA techniques and methods commonly and successfully used in the higher education domains. A systematic mapping review was applied to 840 articles, and 22 related studies are selected based on the study’s criteria. The findings revealed that the prior studies mainly focus on six domains for applying SA in the higher education context and the teaching quality evaluation was the most addressed domain. The study also found that applying specific SA techniques could be the best tool for institutions to solve particular learning problems and a useful tool for improving higher education institutions’ quality and evaluating the teaching process as well as teachers’ performance. This study’s main contribution is the new categorizations of the SA applications techniques in higher education to provide a closely full image of SA-related tools and areas.
Multifeature Selective Fusion Network for Real-Time Driving Scene Parsing

Real-time driving scene parsing using semantic segmentation is an essential yet challenging task for an autonomous driving system, where both efficiency and accuracy need to be considered simultaneously. In this article, we propose an efficient and high-performance deep neural network called feature selective fusion network (FSFnet) for robust semantic segmentation of road scenes. Since the complex driving scene parsing usually requires the fusion of features in different levels or scales, we propose a feature selective fusion module (FSFM) to adaptively merge these features by generating correlated weight maps in both spatial and channelwise. Furthermore, a multiscale context enhancement module is designed based on an asymmetric nonlocal neural network to aggregate both multiscale and global context information. The proposed FSFnet obtains precise segmentation results in real time on Cityscapes and CamVid data sets. Specifically, the architecture achieves 77.1% mean pixel intersection-over-union (mIoU) on the Cityscapes test set at a speed of 53 frames per second (FPS) for a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1024\times 2048$ </tex-math></inline-formula> input and 75.1% mIoU on the CamVid test set at a speed of 123 FPS for a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$960\times 720$ </tex-math></inline-formula> input on a single NVIDIA 2080 TI GPU.
A Discreet Wearable Long-Range Emergency System Based on Embedded Machine Learning

Low-Power Wide Area Networks have contributed in several parts of the Internet of Things ecosystem during the last years by enabling long range, robust and low power communication. Machine Learning for embedded systems has also assisted the advancement of the Internet of Things by identifying patterns and increasing the accuracy of predicting events and behaviours. At the same time, wearable and mobile systems are less obtrusive, consuming less energy and have more computing resources. In this paper we combine these three components and propose a low cost wearable system based on a regular shoe and off-the-shelf electronics which is able to recognize foot gestures and transmit messages over long range, in cases of emergency. The evaluation considers an application scenario where the user performs specific foot gestures to trigger the transmission of an emergency message, during other activities (e.g., walking). The proposed wearable system would benefit a user who is in danger and attempts to notify her/his emergency contacts in a discreet manner. Results show that the proposed system is able to identify the intended foot gestures with 98% accuracy.
Trace Me If You Can: An Unlinkability Approach for Privacy-Preserving in Social Networks

Privacy in social networks has been a vast active area of research due to the enormous increase in privacy concerns with social networking services.Social networks contain sensitive information of individuals, which could be leaked due to insecure data sharing.To enable a secure social network data publication, several privacy schemes were proposed and built upon the anonymity of users.In this paper, we incorporate unlinkability in the context of weighted network data publication, which has not been addressed in prior work.Two key privacy models are defined, namely edge weight unlinkability and node unlinkability to obviate the linking of auxiliary information to a targeted individual with high probability.Two new schemes satisfying these unlinkability notions, namely MinSwap and δ-MinSwapX are proposed to address edge weight disclosure, link disclosure and identity disclosure problems in publishing weighted network data.The edge weight is modified based on minimum value change in order to preserve the usefulness and properties of the edge weight data.In addition, edge randomization is performed to minimally modify the structural information of a user.Experimental results on real data sets show that our schemes efficiently achieve data utility preservation and privacy protection simultaneously.
Uncertainty Quantification of Hyperspectral Image Denoising Frameworks Based on Sliding-Window Low-Rank Matrix Approximation

Sliding-window-based low-rank matrix approximation (LRMA) is a technique widely used in hyperspectral images (HSIs) denoising or completion. However, the uncertainty quantification of the restored HSI has not been addressed to date. Accurate uncertainty quantification of the denoised HSI facilitates applications such as multisource or multiscale data fusion, data assimilation, and product uncertainty quantification since these applications require an accurate approach to describe the statistical distributions of the input data. Therefore, we propose a prior-free closed-form element-wise uncertainty quantification method for LRMA-based HSI restoration. Our closed-form algorithm overcomes the difficulty of handling uncertainty in HSI patch mixing caused by the sliding-window strategy used in the conventional LRMA process. The proposed approach only requires the uncertainty of the observed HSI and provides the uncertainty result relatively rapidly and with similar computational complexity as the LRMA technique. We conduct extensive experiments to validate the estimation accuracy of the proposed closed-form uncertainty approach. The method is robust to at least 10% random impulse noise at the cost of 10%–20% of additional processing time compared to the LRMA. The experiments indicate that the proposed closed-form uncertainty quantification method is more applicable to real-world applications than the baseline Monte Carlo test, which is computationally expensive.
Stein’s method for the Poisson–Dirichlet distribution and the Ewens sampling formula, with applications to Wright–Fisher models

We provide a general theorem bounding the error in the approximation of a random measure of interest—for example, the empirical population measure of types in a Wright–Fisher model—and a Dirichlet process, which is a measure having Poisson–Dirichlet distributed atoms with i.i.d. labels from a diffuse distribution. The implicit metric of the approximation theorem captures the sizes and locations of the masses, and so also yields bounds on the approximation between the masses of the measure of interest and the Poisson–Dirichlet distribution. We apply the result to bound the error in the approximation of the stationary distribution of types in the finite Wright–Fisher model with infinite-alleles mutation structure (not necessarily parent independent) by the Poisson–Dirichlet distribution. An important consequence of our result is an explicit upper bound on the total variation distance between the random partition generated by sampling from a finite Wright–Fisher stationary distribution, and the Ewens sampling formula. The bound is small if the sample size n is much smaller than N1/6log(N)−1/2, where N is the total population size. Our analysis requires a result of separate interest, giving an explicit bound on the second moment of the number of types of a finite Wright–Fisher stationary distribution. The general approximation result follows from a new development of Stein's method for the Dirichlet process, which follows by viewing the Dirichlet process as the stationary distribution of a Fleming–Viot process, and then applying Barbour's generator approach.