Reviewerly/exHarmony huggingface dataset:
Neural bases of learning and recognition of statistical regularities

Abstract Statistical learning is a set of cognitive mechanisms allowing for extracting regularities from the environment and segmenting continuous sensory input into discrete units. The current study used functional magnetic resonance imaging (fMRI) (N = 25) in conjunction with an artificial language learning paradigm to provide new insight into the neural mechanisms of statistical learning, considering both the online process of extracting statistical regularities and the subsequent offline recognition of learned patterns. Notably, prior fMRI studies on statistical learning have not contrasted neural activation during the learning and recognition experimental phases. Here, we found that learning is supported by the superior temporal gyrus and the anterior cingulate gyrus, while subsequent recognition relied on the left inferior frontal gyrus. Besides, prior studies only assessed the brain response during the recognition of trained words relative to novel nonwords. Hence, a further key goal of this study was to understand how the brain supports recognition of discrete constituents from the continuous input versus recognition of mere statistical structure that is used to build new constituents that are statistically congruent with the ones from the input. Behaviorally, recognition performance indicated that statistically congruent novel tokens were less likely to be endorsed as parts of the familiar environment than discrete constituents. fMRI data showed that the left intraparietal sulcus and angular gyrus support the recognition of old discrete constituents relative to novel statistically congruent items, likely reflecting an additional contribution from memory representations for trained items.
LIRAT: Layout and Image Recognition Driving Automated Mobile Testing of Cross-Platform

The fragmentation issue spreads over multiple mobile platforms such as Android, iOS, mobile web, and WeChat, which hinders test scripts from running across platforms. To reduce the cost of adapting scripts for various platforms, some existing tools apply conventional computer vision techniques to replay the same script on multiple platforms. However, because these solutions can hardly identify dynamic or similar widgets. It becomes difficult for engineers to apply them in practice. In this paper, we present an image-driven tool, namely LIRAT, to record and replay test scripts cross platforms, solving the problem of test script cross-platform replay for the first time. LIRAT records screenshots and layouts of the widgets, and leverages image understanding techniques to locate them in the replay process. Based on accurate widget localization, LIRAT supports replaying test scripts across devices and platforms. We employed LIRAT to replay 25 scripts from 5 application across 8 Android devices and 2 iOS devices. The results show that LIRAT can replay 88% scripts on Android platforms and 60% on iOS platforms. The demo can be found at: https: //github.com/YSC9848/LIRAT.
A CPU Real-Time Face Alignment for Mobile Platform

Face alignment is a common technology in face recognition and face verification field. Previous works mostly pay attention to improving the accuracy of prediction and ignored the practicability of the method. In this paper, we aim at providing a two-stage face alignment network for mobile platform. Firstly, the network was trained with residual label which is the difference between ground truth and mean shape. Secondly, the input data in the second stage is composed of the original data and generated heatmap which enriched the data types. Finally, a new loss function is used to enhance the convergence of local region. Experimental results show that our method not only provides high precision but also improve the real-time processing performance on the mobile platforms.
An automated assessment system for embodied cognition in children

We present our preliminary data analysis towards an automated assessment system for the Activate Test for Embodied Cognition (ATEC), a test which measures cognitive skills through physical activity. More specifically, we present two core ATEC tasks designed to assess attention, working memory, response inhibition, rhythm and coordination in children: the Sailor Step and the Ball-Drop-to-the-Beat task. These tasks are specifically designed to assess lower and upper body accuracy, response inhibition and rhythm. Motion data were collected through the Kinect camera. This paper presents an overview of the assessment tasks, the data collection, and annotation with a preliminary analysis towards an automated scoring system through machine learning and computer vision methods.
Visual Analytics of Anomalous User Behaviors: A Survey

With the pervasive use of information technologies, the increasing availability of data provides new opportunities for understanding user behaviors.Unearthing anomalies in user behavior is of particular importance as it helps signal harmful incidents such as network intrusions, terrorist activities, and financial frauds.In this article, we survey state-of-the-art research work in visual analytics of anomalous user behaviors and classify them into four application domains, which are social interaction, travel, network communication, and financial transaction.We further examine the research work in each category in terms of data types, visualization techniques, and interactive analysis methods.We hope that our survey can provide systematic guidelines for researchers and practitioners to find effective solutions to their research problems in specific application domains.Finally, we discuss trends of academic interest over the past decades and suggest potential directions across visual analytics of these user behaviors for future research.
Global relational reasoning with spatial temporal graph interaction networks for skeleton-based action recognition

With the prevalence of accessible depth sensors, dynamic skeletons have attracted much attention as a robust modality for action recognition. Convolutional neural networks (CNNs) excel at modeling local relations within local receptive fields and are typically inefficient at capturing global relations. In this article, we first view the dynamic skeletons as a spatio-temporal graph (STG) and then learn the localized correlated features that generate the embedded nodes of the STG by message passing. To better extract global relational information, a novel model called spatial–temporal graph interaction networks (STG-INs) is proposed, which perform long-range temporal modeling of human body parts. In this model, human body parts are mapped to an interaction space where graph-based reasoning can be efficiently implemented via a graph convolutional network (GCN). After reasoning, global relation-aware features are distributed back to the embedded nodes of the STG. To evaluate our model, we conduct extensive experiments on three large-scale datasets. The experimental results demonstrate the effectiveness of our proposed model, which achieves the state-of-the-art performance.
EVChain: A Blockchain-based Credit Sharing in Electric Vehicles Charging

The Digital economy is based on confidence in its trustworthiness. Blockchain distributed consensus provides a reliable and trustful network for financial and non-financial transactions. Blockchain-based electric vehicles (EVs) charging applications benefit blockchain features to provide automated and verifiable services for EV charging market. Requirements for feasible charging operation and privacy concerns are challenging issues with blockchain-based EV charging approaches. To provide a feasible charging ability and preserve EV owner's privacy, we introduce EVChain. The EVChain is a trustful and decentralized platform based on blockchain technology to share charging credits in the EV charging market. To share credits, the main blockchain in EVChain is connected to one or more subnetwork blockchains. We introduce an interconnection position to preserve EV owners' privacy with k-anonymity protection. We simulate and evaluate the privacy protection it provides, based on an example EV charging scenario.
Self-adaption neighborhood density clustering method for mixed data stream with concept drift

Clustering analysis is an important data mining method for data stream. In this paper, a self-adaption neighborhood density clustering method for mixed data stream is proposed. The method uses a significant metric criteria to make categorical attribute values become numeric and then the dimension of data is reduced by a nonlinear dimensionality reduction method. In the clustering method, each point is evaluated by neighborhood density. The k points are selected from the data set with maximum mutual distance after k is determined according to rough set. In addition, a new similarity measure based on neighborhood entropy is presented. The data points can be partitioned into the nearest cluster and the algorithm adaptively adjusts the clustering center points by clustering error. The experimental results show that the proposed method can obtain better clustering results than the comparison algorithms on the most data sets and the experimental results prove that the proposed algorithm is effective for data stream clustering.
Demystifying the Membership Inference Attack

The Membership Inference Attack (MIA) is the process of determining whether a sample comes from the training dataset (in) of a machine learning model or not (out). This attack makes use of a trained machine learning to expose confidential information about its training data. It is particularly alarming in cases where data is tightly linked to individuals like in the medical, financial and marketing domains. The underlying factors of the success of MIA are not well understood. The current theory explains its success by the difference in the confidence levels for in samples and out samples. In this article, we show that the confidence levels play little to no role in the MIA success in most of the cases. We propose a more general theory that explains previous results and some unexpected observations that have been made in the state-of-the-art. To back up our theory, we run MIA exneriments on MNIST, CIFAR-10 and Fashion-MNIST.
Unsupervised Drift Detector Ensembles for Data Stream Mining

Data stream mining is among the most contemporary branches of machine learning. The potentially infinite sources give us many opportunities and at the same time pose new challenges. To properly handle streaming data we need to improve our well-established methods, so they can work with dynamic data and under strict constraints. Supervised streaming machine learning algorithms require a certain number of labeled instances in order to stay up-to-date. Since high budgets dedicated for this purpose are usually infeasible, we have to limit the supervision as much as we can. One possible approach is to trigger labeling, only if a change is explicitly indicated by a detector. While there are several supervised algorithms dedicated for this purpose, the more practical unsupervised ones are still lacking a proper attention. In this paper, we propose a novel unsupervised ensemble drift detector that recognizes local changes in feature subspaces (EDFS) without additional supervision, using specialized committees of incremental Kolmogorov-Smirnov tests. We combine it with an adaptive classifier and update it, only if the drift detector signalizes a change. Conducted experiments show that our framework is able to efficiently adapt to various concept drifts and outperform other unsupervised algorithms.
Physical-based optimization for non-physical image dehazing methods

Images captured under hazy conditions (e.g. fog, air pollution) usually present faded colors and loss of contrast. To improve their visibility, a process called image dehazing can be applied. Some of the most successful image dehazing algorithms are based on image processing methods but do not follow any physical image formation model, which limits their performance. In this paper, we propose a post-processing technique to alleviate this handicap by enforcing the original method to be consistent with a popular physical model for image formation under haze. Our results improve upon those of the original methods qualitatively and according to several metrics, and they have also been validated via psychophysical experiments. These results are particularly striking in terms of avoiding over-saturation and reducing color artifacts, which are the most common shortcomings faced by image dehazing methods.
Game theoretical study on client-controlled cloud data deduplication

Data deduplication eliminates redundant data and is receiving increasing attention in cloud storage services due to the proliferation of big data and the demand for efficient storage. Data deduplication not only requires a consummate technological designing, but also involves multiple parties with conflict interests. Thus, how to design incentive mechanisms and study their acceptance by all relevant stakeholders remain important open issues. In this paper, we detail the payoff structure of a client-controlled deduplication scheme and analyze the feasibilities of unified discount and individualized discount under this structure. Through game theoretical study, a privacy-preserving individualized discount-based incentive mechanism is further proposed with detailed implementation algorithms for choosing strategies, setting parameters and granting discounts. After theoretical analysis on the requirements of individual rationality, incentive compatibility, and profitability, we conduct extensive experiments based on a real-world dataset to demonstrate the effectiveness of the proposed incentive mechanism.
Image-based characterization of fibre waviness in a representative vacuum-bagged corner laminate

The ability to fully characterize design-induced defects in complex-shape laminates is indispensable to improving the design and out-of-autoclave processing of large integrated structures. To this end, two automated methods are proposed to characterize fibre waviness in a representative vacuum-bagged corner laminate. The first relies on optical microscopy of corner-flange sections to estimate the extent to which corner waviness progresses into flanges. The second relies on X-ray micro computed tomography of corner sections and advanced image-based techniques to measure the waviness magnitude in terms of representative, in- and out-of-plane angular components. The 3D structure of the waviness is then ascertained based on the examination of the combined imaging results. Finally, the usefulness of the magnitude measurements is illustrated by estimating the knockdown in corner stiffness via an Euler angle transformation. Both characterization methods can be adapted to other, more complex-shape cases and alternate processing routes.
Quality Control for Medium Voltage Insulator via a Knowledge-Informed SPSA Based on Historical Gradient Approximations

Medium voltage insulators are essential and versatile components in electrical engineering. Quality control of the manufacturing process for the insulators has a significant role in their economic production and reliable operation. As the quality of medium voltage insulator is mainly affected by the process parameters of the automatic pressure gelation process (APG), the optimal process settings are required to achieve a satisfactory quality target. However, traditional process parameters’ optimization methods are often cumbersome and cost-consuming. Moreover, the operational cost of APG for insulator production is relatively high. Therefore, the determination of the optimal settings becomes a significant challenge for the quality control of insulators. To address the above issues, an idea of knowledge-informed optimization was proposed in this study. Based on the above idea, a knowledge-informed simultaneous perturbation stochastic approximation (SPSA) methodology was formulated to reduce the optimization costs, and thus improve the efficiency of quality control. Considering the characteristics of SPSA, the historical gradient approximations generated during the optimization process were utilized to improve the accuracy of gradient estimations and to tune the iteration step size adaptively. Therefore, an implementation of a quality control strategy of knowledge-informed SPSA based on historical gradient approximations (GK-SPSA) was thus constructed. In this paper, the GK-SPSA-based quality control method was applied to the weight control of a kind of post insulators. The experimental simulation results showed that the revised knowledge-informed SPSA was effective and efficient on quality control of medium voltage insulators.
A Comparative Study of Hand Gestures Recognition Based on MobileNetV2 and ConvNet Models

Hand gestures offer humans a natural way to interact with computers for a variety of applications. However, factors such as the complexity of hand gesture patterns, size differences, hand posture, and environmental lighting can affect the performance of hand gesture recognition algorithms. Recent advances in deep learning have greatly improved the performance of image recognition systems. In particular, the deep convolutional neural network has demonstrated superior performance in terms of image representation and classification, compared to conventional machine learning methods. This article proposes a two-technique comparison, a proposed deep-convolution neural network and transfer learning with the pre-trained model mobilnetv2 , for hand gestures recognition of American sign language. Both models are trained and tested using 1815 images segmented by colour and with black background, and its static hand gestures for five volunteers, which incorporate variations in features such as scale, lighting, and noise. The results show that,the proposed CNN model achieved a classification recognition accuracy of 98.9%. with a 2% improvement over the convolutional neural network model enriched using transfer learning techniques 97.06%.
Syntax Trees and Information Retrieval to Improve Code Similarity Detection

In dealing with source code plagiarism and collusion, automated code similarity detection can be used to filter student submissions and draw attention to pairs of programs that appear unduly similar. The effectiveness of the detection process can be improved by considering more structural information about each program, but the ensuing computation can increase the processing time. This paper proposes a similarity detection technique that uses richer structural information than normal while maintaining a reasonable execution time. The technique generates the syntax trees of program code files, extracts directly connected n-gram structure tokens from them, and performs the subsequent comparisons using an algorithm from information retrieval, cosine correlation in the vector space model. Evaluation of the approach shows that consideration of the program structure (i.e., syntax tree) increases the recall and f-score (measures of effectiveness) at the expense of execution time (a measure of efficiency). However, the use of an information retrieval comparison process goes some way to offsetting this loss of efficiency.
Visual Tracking With Long-Short Term Based Correlation Filter

Visual object tracking is a fundamental problem in computer vision, and has been greatly improved with the rapid development of deep learning. However, existing tracking methods with single model update strategy cannot guarantee the robustness of tracker in complex scenes. In this paper, we innovatively propose a novel real-time long-short-term multi-model based tracking method. For the fusion of long-term and short-term features contain more spatiotemporal information, three models with different update periods are designed to learn the long-term and short-term features to improve the tracking robustness. Besides, the hierarchical feature contain deep convolution features and handcraft features are used to represent the current object, which can further improve the tracking accuracy with richer semantic information. Finally, to solve the inaccurate prediction of object position due to the cosine window in the correlation filter, the bounding-box regression strategy is introduced to optimize the final object position. Extensive experiments on OTB, VOT, TC128, and UAV123 datasets demonstrate that the proposed method performs favorably against state-of-the-art algorithms while running at 24 fps.
Backdoor Suppression in Neural Networks using Input Fuzzing and Majority Voting

While inference is needed at the edge, training is typically done at the cloud. Therefore, data necessary for training a model, as well as the trained model, have to be transmitted back and forth between the edge and the cloud training infrastructure. This creates significant security issues, including the inclusion of a backdoor sent to the user without the user's knowledge. This article presents an approach where a trained model can still operate as expected, irrespective of the presence of such a backdoor.
The enhancement of catenary image with low visibility based on multi-feature fusion network in railway industry

In the Industrial Internet of Things (IIoT), the security and efficiency are indispensable. For the railway industry, the video from inspection vehicle would be influenced by various factors with low visibility and hard for high level vision task, such as the fault diagnosis of catenary system. In this paper, we propose a method based on the multi-feature fusion network to improve the quality and visual effect of the catenary images. The transmission map is learned from the multi-scale and multi-feature fusion network, which would learn coarse and fine details and combine the latent features. In the catenary image, the sky and non-sky regions are segmented through multiple accommodative thresholds to estimate the atmospheric light value. With the refinement of transmission map, the restored catenary image is obtained through the atmospheric scattering model. In the experimental results, it can be seen that the proposed method can improve the clarity of catenary image in haze. The quantitative evaluation shows that it has better visual effect compared with the other traditional methods.
Improved Center and Scale Prediction-Based Pedestrian Detection Using Convolutional Block

Pedestrian detection is an important computer vision algorithm in advanced driver assistance systems (ADAS). In this paper, we propose a deep learning-based pedestrian detection method that is specialized for ADAS. The proposed model improves both accuracy and processing speed by applying a convolutional block attention module (CBAM) to center and scale prediction (CSP)-based detector. To evaluate the detection performance, we tested the proposed method using Caltech dataset, which is one of the most commonly used datasets for pedestrian detection challenges. Our model achieves 7% miss rate and takes 40.5 ms to inference an image.