Reviewerly/exHarmony huggingface dataset:
Seeing Crucial Parts: Vehicle Model Verification via a Discriminative Representation Model

Widely used surveillance cameras have promoted large amounts of street scene data, which contains one important but long-neglected object: the vehicle. Here we focus on the challenging problem of vehicle model verification. Most previous works usually employ global features (e.g., fully connected features) to further perform vehicle-level deep metric learning (e.g., triplet-based network). However, we argue that it is noteworthy to investigate the distinctiveness of local features and consider vehicle-part-level metric learning by reducing the intra-class variance as much as possible. In this article, we introduce a simple yet powerful deep model—the enforced intra-class alignment network (EIA-Net)—which can learn a more discriminative image representation by localizing key vehicle parts and jointly incorporating two distance metrics: vehicle-level embedding and vehicle-part-sensitive embedding. For learning features, we propose an effective feature extraction module that is composed of two components: the regional proposal network (RPN)-based network and part-based CNN. The RPN is used to define key vehicle regions and aggregate local features on these regions, whereas part-based CNN offers supplementary global features for the RPN-based network. The fusion features learned by feature extraction module are cast into the deep metric learning module. Especially, we derived an enforced intra-class alignment loss by re-utilizing key vehicle part information to enhance reducing intra-class variance. Furthermore, we modify the coupled cluster loss to model the vehicle-level embedding by enlarging the inter-class variance while shortening intra-class variance. Extensive experiments over benchmark datasets VehicleID and CompCars have shown that the proposed EIA-Net significantly outperforms the state-of-the-art approaches for vehicle model verification. Furthermore, we also conduct comprehensive experiments on vehicle re-identification datasets (i.e., VehicleID and VeRi776) to validate the generalization ability effectiveness of our proposed method.
Multiview Hand Gesture Recognition using Deep Learning

Gesture recognition is a challenging research topic since different gestures have different sizes, poses, and sometimes face occlusion. In this paper, we tackle this problem by utilizing multiview gestures for recognition. We propose a hand gesture recognition system that learns from multiview gestures in a Convolutional neural network (Convnet) to tackle the problem of self-occlusion. Extensive experiments are performed on isolated gestures for different possible combinations of multiview training and testing sets. The proposed multiview recognition system is evaluated over the HGM-4 dataset. Evaluations are performed based on the system accuracy and found that as the number of views increases, the system more accurately recognizes the gestures. Also, our technique outperforms other methods with an overall accuracy of 94.71% on HGM-4 dataset.
Facing Muscular Dystrophy During Covid-19 Pandemic: The Role of Support Associations and Spirituality

Abstract Several researches in scientific literature analyze the theme of Muscular Dystrophy (MD), As well as many others focus on the theme of the Covid-19 pandemic; however, there is a rather limited number of studies that analyse how the pandemic has affected the life of people suffering from MD, especially during the time of the first lockdown in the spring of 2020. The present study has applied a qualitative research design with the aim to investigate how patients with MD have lived the social restrictions imposed for the contagion containment and whether the assistance of associations for their support has contributed to make the participants feel closer or more distant from the spiritual dimension. The analysis involved 12 participants, and they were presented with a semi-structured interview. The data obtained from the interviews have been analysed through a thematic analysis from which 4 thematic areas have emerged: (1) the impact of the pandemic on an emotional level; (2) the illness management and the role of family; (3) the role of the associations; (4) aspects related to spirituality. The crucial role that the closeness of family and the activities promoted remotely by the associations for patients’ support has emerged, since they have allowed the participants to feel united by something beyond, to discover new aspects of themselves, to give more value to Life and to move closer to their spiritual dimension.
VC-SLAM—A Handcrafted Data Corpus for the Construction of Semantic Models

Ontology-based data management and knowledge graphs have emerged in recent years as efficient approaches for managing and utilizing diverse and large data sets. In this regard, research on algorithms for automatic semantic labeling and modeling as a prerequisite for both has made steady progress in the form of new approaches. The range of algorithms varies in the type of information used (data schema, values, or metadata), as well as in the underlying methodology (e.g., use of different machine learning methods or external knowledge bases). Approaches that have been established over the years, however, still come with various weaknesses. Most approaches are evaluated on few small data corpora specific to the approach. This reduces comparability and also limits statements for the general applicability and performance of those approaches. Other research areas, such as computer vision or natural language processing solve this problem by providing unified data corpora for the evaluation of specific algorithms and tasks. In this paper, we present and publish VC-SLAM to lay the necessary foundation for future research. This corpus allows the evaluation and comparison of semantic labeling and modeling approaches across different methodologies, and it is the first corpus that additionally allows to leverage textual data documentations for semantic labeling and modeling. Each of the contained 101 data sets consists of labels, data and metadata, as well as corresponding semantic labels and a semantic model that were manually created by human experts using an ontology that was explicitly built for the corpus. We provide statistical information about the corpus as well as a critical discussion of its strengths and shortcomings, and test the corpus with existing methods for labeling and modeling.
Solving the TSP by the AALHNN algorithm

It is prone to get stuck in a local minimum when solving the Traveling Salesman Problem (TSP) by the traditional Hopfield neural network (HNN) and hard to converge to an efficient solution, resulting from the defect of the penalty method used by the HNN. In order to mend this defect, an accelerated augmented Lagrangian Hopfield neural network (AALHNN) algorithm was proposed in this paper. This algorithm gets out of the dilemma of penalty method by Lagrangian multiplier method, ensuring that the solution to the TSP is undoubtedly efficient. The second order factor added in the algorithm stabilizes the neural network dynamic model of the problem, thus improving the efficiency of solution. In this paper, when solving the TSP by AALHNN, some changes were made to the TSP models of Hopfield and Tank. Say, constraints of TSP are multiplied by Lagrange multipliers and augmented Lagrange multipliers respectively, The augmented Lagrange function composed of path length function can ensure robust convergence and escape from the local minimum trap. The Lagrange multipliers are updated by using nesterov acceleration technique. In addition, it was theoretically proved that the extremum obtained by this improved algorithm is the optimal solution of the initial problem and the approximate optimal solution of the TSP was successfully obtained several times in the simulation experiment. Compared with the traditional HNN, this method can ensure that it is effective for TSP solution and the solution to the TSP obtained is better.
Semantic Residual Pyramid Network for Image Inpainting

Existing image inpainting methods based on deep learning have made great progress. These methods either generate contextually semantically consistent images or visually excellent images, ignoring that both semantic and visual effects should be appreciated. In this article, we propose a Semantic Residual Pyramid Network (SRPNet) based on a deep generative model for image inpainting at the image and feature levels. This method encodes a masked image by a residual semantic pyramid encoder and then decodes the encoded features into a inpainted image by a multi-layer decoder. At this stage, a multi-layer attention transfer network is used to gradually fill in the missing regions of the image. To generate semantically consistent and visually superior images, the multi-scale discriminators are added to the network structure. The discriminators are divided into global and local discriminators, where the global discriminator is used to identify the global consistency of the inpainted image, and the local discriminator is used to determine the consistency of the missing regions of the inpainted image. Finally, we conducted experiments on four different datasets. As a result, great performance was achieved for filling both the regular and irregular missing regions.
Feature Selection Technique for improving classification performance in the web-phishing detection process

Web phishing is a type of cybercrime that occasionally threatens the online activities of website visitors. Web phishing uses a phoney website page that closely mimics the legitimate Website in order to fool its target into providing crucial information. Web phishing attacks also continue to grow in popularity year after year. As a result, it is vital to design a web phishing detection system in order to reduce the number of victims and financial losses caused by web phishing attacks. The development of a web phishing detection system continues to this day, with machine learning being the most often used model. Unfortunately, the construction of a machine learning-based web phishing detection system frequently employs only a single classification step; however, the feature selection process enables an increase in the performance of the resultant classification. Thus, an experiment was conducted in this paper by using a feature selection procedure based on the Pearson correlation algorithm prior to doing machine learning modelling utilizing popular algorithms such as Naive Bayes, Decision Tree, and Random Forest. As a result, using a web phishing dataset from the UCI Machine Learning Repository, it was determined that the addition of the feature selection process based on the use of decision tree and random forest algorithms resulted in an increase in accuracy of up to 94.60 percent and 95.50 percent, respectively, and a slight decrease in accuracy of 0.4 percent when implemented in the Naive Bayes algorithm.
CFL-HC: A Coded Federated Learning Framework for Heterogeneous Computing Scenarios

Federated learning (FL) is a promising machine learning paradigm because it allows distributed edge devices to collaboratively train a model without sharing their raw data. In practice, a major challenge to FL is that edge devices are heterogeneous, so slow devices may compromise the convergence of model training. To address such a challenge, several recent studies have suggested different solutions, in which a promising scheme is to utilize coded computing to facilitate the training of linear models. Nevertheless, the existing coded FL (CFL) scheme is limited by a fixed coding redundancy parameter, and a weight matrix used in the existing design may introduce unnecessary errors. In this paper, we tackle these issues and propose a novel framework, namely CFL-HC, to facilitate CFL in heterogeneous computing scenarios. In our framework, we consider a computing system consisting of a central server and multiple computing devices with original or coded datasets. Then we specify an expected number of input-output pairs that are used in one round. Within such a framework, we formulate an optimization problem to find the best deadline of each training round and the optimal size of the computing task allocated to each computing device. We then design a two-step optimization scheme to obtain the optimal solution. To evaluate the proposed framework, we develop a real CFL system using the message passing interface platform. Based on this system, we conduct numerical experiments, which demonstrate the advantages of the proposed framework, in terms of both accuracy and convergence speed.
Perception of Japanese Students in Using Online Video as A Learning Media

The purpose of this research is to examine students' perceptions towards the use of YouTube as a teaching medium to increase students’ interest and motivation in learning Japanese. The method used is descriptive qualitative with survey techniques and literature research. The descriptive analysis is used to describe the percentage of each variable, namely the use of YouTube and Japanese students' interest and learning motivation. The results of this study indicate that YouTube offers a significant effect on students in the use of online video as a learning medium and the research findings show that the participants positively view the use of YouTube in their lessons. The result also revealed a significant increase in increasing student interest and motivation in learning Japanese. The results of this study can be an illustration to encourage students, especially in learning Japanese, to make the most of the online media or video channels on YouTube related to Japanese language learning. This research also can be an online learning system in technological development.
Future Trends in Digital Face Manipulation and Detection

Abstract Recently, digital face manipulation and its detection have sparked large interest in industry and academia around the world. Numerous approaches have been proposed in the literature to create realistic face manipulations, such as DeepFakes and face morphs. To the human eye manipulated images and videos can be almost indistinguishable from real content. Although impressive progress has been reported in the automatic detection of such face manipulations, this research field is often considered to be a cat and mouse game . This chapter briefly discusses the state of the art of digital face manipulation and detection. Issues and challenges that need to be tackled by the research community are summarized, along with future trends in the field.
Visual Sensor Image Analysis and Massage Techniques to Prevent and Treat Common Injuries of Sports Dance Practitioners

In recent years, the performance of sports dance in China has become better and better. Naturally, the technical requirements for this dance are getting higher and higher, and the number and intensity of training have also increased, which has led to increasing injuries in sports dance. This article is based on visual sensor images to analyze and study the common injuries and prevention of sports dance practitioners. It is aimed at providing a certain reference basis for athletes’ injuries, so that dance practitioners and coaches can better master sports dance training and teaching. Injury-related rules and prevention reduce the injury rate. This article puts forward the related technology of a visual sensor image and applies its technology to the prevention and research of common injuries in sports dance. At the same time, it analyzes the causes of sports dance practitioners’ injuries and seeks economical and affordable massage techniques for prevention, and the method of treatment provides protection for dance practitioners. The experimental results in this article show that the Tuina group cured 15 subjects, 41 subjects were markedly effective, 13 subjects were improved, and 6 subjects were unhealed. The total effective rate was 92%.
Information Hiding in the DICOM Message Service and Upper Layer Service with Entropy-Based Detection

The DICOM (Digital Imaging and COmmunication in Medicine) standard provides a framework for a diagnostically-accurate representation, processing, transfer, storage and display of medical imaging data. Information hiding in DICOM is currently limited to the application of digital media steganography and watermarking techniques on the media parts of DICOM files, as well as text steganographic techniques for embedding information in metadata of DICOM files. To improve the overall security of the DICOM standard, we investigate its susceptibility to network steganographic techniques. To this aim, we develop several network covert channels that can be created by using a specific transport mechanism - the DICOM Message Service and Upper Layer Service. The bandwidth, undetectability and robustness of the proposed covert channels are evaluated, and potential countermeasures are suggested. Moreover, a detection mechanism leveraging entropy-based metrics is introduced and its performance has been assessed.
Robust active learning with binary responses

We introduce a method of Robust Learning (‘robl’) for binary data, and propose its use in situations where Active Learning is appropriate, and where sampling the predictors is easy and cheap, but learning the responses is hard and expensive. We seek robustness against both modelling errors and the mislabelling of the binary responses. Thus we aim to sample effectively from the population of predictors, and learn the responses only for an ‘influential’ sub-population. This is carried out by probability weighted sampling, for which we derive optimal ‘unbiased’ sampling weights, and weighted likelihood estimation, for which we also derive optimal estimation weights. The robustness issues can lead to biased estimates and classifiers; it is somewhat remarkable that our weights eliminate the mean of the bias – which is a random variable as a result of the sampling – due to both types of errors mentioned above. These weights are then tailored to minimize the mean squared error of the predicted values. Simulation studies indicate that when bias is of significant concern, robl allows for substantial reductions, relative to Passive Learning, in the prediction errors. The methods are then illustrated in real-data analyses.
A New ℓ-step Neighbourhood Distributed Moving Horizon Estimator

This paper focuses on Distributed State Estimation over a peer-to-peer sensor network composed by possible low-computational sensors. We propose a new ℓ-step Neighbourhood Distributed Moving Horizon Estimation technique with fused arrival cost and pre-estimation, improving the accuracy of the estimation, while reducing the computation time compared to other approaches from the literature. Simultaneously, convergence of the estimation error is improved by means of spreading the information amongst neighbourhoods, which comes natural in the sliding window data present in the Moving Horizon Estimation paradigm.
A content analysis of studies published in the field of augmented reality in education

This review study was carried out using content analysis and it was aimed to analyze the articles published in national journals in the field of augmented reality. The sample of this study, in which the content analysis method was used, consists of 87 academic studies conducted in the field of augmented reality in education in Turkey between 2012 and 2020. Studies obtained as a result of scanning were analyzed according to some variables such as the publication year, purpose, subject area, approach / method, sample group, sample number, data collection tool of study. As a result of the analysis of the research, it was observed that the first studies started in 2012 and increased with each year. It has been determined that most of the studies are aimed at determining the effect of augmented reality on students' achievement. In addition, it was seen that the vast majority of researches were carried out in the field of science education, using the experimental research method with 40-60 university students. Furthermore; it has been determined that the achievement test in quantitative studies and interview form in qualitative research is more preferred as a data collection tool.
The XMUSPEECH System for Accented English Automatic Speech Recognition

In this paper, we present the XMUSPEECH systems for Track 2 of the Interspeech 2020 Accented English Speech Recognition Challenge (AESRC2020). Track 2 is an Automatic Speech Recognition (ASR) task where the non-native English speakers have various accents, which reduces the accuracy of the ASR system. To solve this problem, we experimented with acoustic models and input features. Furthermore, we trained a TDNN-LSTM language model for lattice rescoring to obtain better results. Compared with our baseline system, we achieved relative word error rate (WER) improvements of 40.7% and 35.7% on the development set and evaluation set, respectively.
Autonomous Vehicle Path Planning Using MPC and APF

Autonomous vehicles have been at the forefront of academic and industrial research in recent decades. This study’s aim is to reduce traffic congestion, improve safety, and accidents. Path planning algorithms are one of the main elements in autonomous vehicles that make critical decisions. Motion planning methods are required when transporting passengers from one point to another. These methods have incorporated several methods such as generating the best trajectory while considering the constraints of vehicle dynamics and obstacles, searching a path to follow, and avoiding obstacles that guarantee comfort, safety, and efficiency. We suggested an effective path planning algorithm based on Model Predictive Controller that determines the maneuvers mode such as lane-keeping and lane-changing automatically. We utilized two different artificial potential field functions for the road boundary, obstacles, and lane center to ensure safety. On the four scenarios, we examined the proposed path planning controller. The obtained results show that when a path planning controller is used, the vehicle avoids colliding with obstacles and follows the rules of the road by adjusting the vehicle’s dynamics. An autonomous vehicle’s safety is ensured by the path planning controller.
Sampling blockchain-enabled smart city applications among South Korea, the United States and China

Blockchain technology is valuable due to the renowned characteristics, such as immutability, security, and transparency. Although blockchain’s effectiveness and efficiency are yet to be proven, its potential for resolving social issues is attracting the attention of public administrations, private entities, and individuals to adopt or develop this technology. In this paper, some of the most high-profile blockchain-enabled smart city applications in South Korea, the United States and China are sampled and reviewed. The goal is to explore blockchain-related regulations or legislation in each country, which is the underlying basis to allow the blockchain pilots to proceed. Then, each country’s key blockchain initiatives are explored using a top-down framework (i.e., central/federal government – provincial/state governments – city governments). Lastly, by summarizing those blockchain-based smart city projects, the challenges and observations are discussed.