Reviewerly/exHarmony huggingface dataset:
ZhuSuan: design and implementation of differentiable probabilistic programming libraries

Probabilistic models provide a set of powerful tools to deal with uncertainty that is pervasive in machine learning applications. Probabilistic programming utilizes computer programs to represent probabilistic models, and it supports the functions of sampling as well as probabilistic inference conditioned on arbitrary observations. Traditionally, the dependency relationship in probabilistic programming is mainly linear or generalized linear, which serves as the basis of many successful models and inference algorithms. However, such linearity also limits the expressiveness and flexibility of probabilistic programs. Differentiable probabilistic programming allows probabilistic programs to have nonlinear dependency under a proper parameterization form (e.g., neural networks) and can learn unknown parameters from data via gradient-based methods. This programming paradigm is easy to extend, largely avoids the tedious model selection process, and makes the deployment of probabilistic models possible in an end-to-end manner. This article presents ZhuSuan, an open-source library for differentiable probabilistic programming. Taking ZhuSuan as an example, we discuss the design and implementation of differentiable probabilistic programming systems.
Past Event Recall Test for Mitigating Session Hijacking and Cross-Site Request Forgery

Authentication of user on a computer or network enable privacy protection and directing information to appropriate audience. Present authentication mechanisms only authenticate user once at the beginning of a communication session. The new wave of attacks that are used to steal information has made one-time authentication of users inadequate because the authenticated session can now be hijacked. Thus, it has become necessary for the communicating parties in a computer transaction session to reconfirm the other party on the other end periodically. Researchers have proposed different solutions to prevent or detect malicious taking over of a computer session. The solutions either work for particular types of attacks or only suitable for specific applications used to develop it. Others would fail in the face of spoofing attacks. This research proposed PERT for mitigating session hijacking and Cross-Site Request Forgery attacks. PERT ensure that a node is communicating only with a known system that it had previously successfully transacted with. The prototype was tested in NS-3 testbed. The tests were carried out to observe the proposed solution's performance against spoofing attacks and identity theft attacks. PERT performed satisfactorily better than two other solutions used to benchmark it as presented in the results section. It recorded 35% longer average execution time than the faster benchmark but 20.06% shorter average execution time than the slower benchmark. It prevented 97% and 95% of requests from an identity thief and a spoofing attacker respectively. The benchmark solutions recorded lower prevention efficiency.
The Role of Cybersecurity Certifications

The chapter looks at the burgeoning field of certification for individuals in the field of information security or cybersecurity. Individual information security certifications cover a wide range of topics from the deeply technical to the managerial. These certifications are used as a visible indication of an individual's status and knowledge, used to define experience and status, used in job descriptions and screening, and may define expectations placed on the individual. This chapter examines how these certifications are produced, the subjects they cover, and how they integrate and the various audiences to which the certifications are aimed. The role, the perceived and real value, and benefits of certification within the field of information security both from an individual and an organizational perspective are discussed. Finally, some conclusions on certification are presented.
Medical Mask Detection Using Various CNN Models

The world has suffered from the new Coronavirus pandemic (COVID-19). It came from Wuhan, China. The World Health Organization (WHO) declared it to be a fatal disease on 11 March 2020 that has taken roots worldwide with 114 countries seriously afflicted. At the end of May 2021, there were more than 100 million corona virus cases worldwide. Medical masks have been required to minimize the spread of the deadly infection and to sensitize people. Face mask detection is now a trend application because of the COVID-19 pandemic situation, which requires every person to medical masks, keep distance from others and wash hand sanitizers.
Cyclotomic constructions

In this chapter, we look at graphs defined by a difference set in a usually abelian group. Difference sets in a vector space that are invariant under multiplication by scalars are equivalent to two-weight codes and to two-character subsets of a projective space. We survey a lot of examples of such two-character sets (infinite families and sporadic ones, the latter summarised in a table). We review cyclic codes, in particular cyclic two-weight codes and introduce the related Van Lint-Schrijver graphs, the Hill graph, the De Lange graphs and the Peisert graphs. Then our attention goes to the one-dimensional affine rank 3 graphs, which we review in some detail, including proofs of the parameter restrictions that lead to the different cases: the Paley graphs, the Van Lint-Schrijver graphs and the Peisert graphs. We also discuss the Paley graphs in some detail and provide a table with small strongly regular power residue graphs. The penultimate section is dedicated to graphs related to the action of the alternating group Alt(5) and the symmetric group Sym(4) on a projective line. In the last section, we review strongly regular graphs constructed from bent functions.
Advanced ant colony algorithm for high dimensional abnormal data mining in Internet of things

This paper proposes a deep mining method of high-dimensional abnormal data in Internet of things based on improved ant colony algorithm. Preprocess the high-dimensional abnormal data of the Internet of things and extract the data correlation feature quantity; The ant colony algorithm is improved by updating the pheromone and state transition probability; With the help of the improved ant colony algorithm, the feature response signal of high-dimensional abnormal data in Internet of things is extracted, the judgment threshold of high-dimensional abnormal data in Internet of things is determined, and the objective function is constructed to optimize the mining depth, so as to realize the deep data mining. The results show that the average error of the proposed method is only 0.48%.
Sports Simulation and Video Analysis System for Physical Training

The technology of computer aided movement (CAS) includes: intelligent test technology of optical mechatronics, computer graphics technology, video processing technology, large database management and Internet technology. Through computer simulation technology, the teaching experience of physical education teachers, the training intention of coaches, the organization plan of managers and the training process of athletes can be reproduced, so as to achieve the interpretation, analysis, prediction, organization and evaluation of sports system. The overall goal is to study the key technologies of three-dimensional human motion simulation and video analysis for sports training, aiming to achieve two progress and transformation of sports training methods: from the traditional method based on human eye observation to the method based on high-precision video capture and analysis of human motion measurement; From the experience based training analysis method to human motion simulation and simulation based human motion analysis method, so as to improve the training level and performance more quickly and effectively.
Analysis of Differences in Academic Stress, Self-esteem, and Depression of Adolescents through the Application of School Sports Activities Programs

Objectives This study was conducted to find out the differences in academic stress, self-esteem, and depression through the application of school sports activity programs. Methods To achieve this research purpose, 318 students enrolled in high school were selected as subjects. A questionnaire was used as a measurement tool for deriving research results. As for the data processing method, cross-analysis, exploratory factor analysis, reliability analysis, and covariate analysis (ANCOVA) were performed using IBM Statistics SPSS Win ver. 22.0 and AMOS 22.0 programs. Results The application of school sports activity programs showed positive differences in academic stress, self-esteem, and depression. Conclusions The conclusions derived by applying the above research method are as follows. First, the academic stress of students in the experimental group who participated in the school sports activity program decreased. Second, the self-esteem of students in the experimental group who participated in the school sports activity program improved. Third, the depression of students in the experimental group who participated in the school sports activity program decreased.
Towards Incorporating Extended Reality Technology in the Education of Mining Professionals

Extended reality (XR) technology in the pedagogy of the education of mining professionals such as mining engineers and mine surveyors is critical in the development of an education programme that will provide graduates with a competitive edge at the dawn of the Fourth Industrial Revolution. Virtual reality (VR) technologies have been introduced with varying degrees of success at some mining companies and universities in South Africa. To develop graduates who have an integrated understanding of the mining environment, university students are expected to complete several facilitated tours to mining operations during their studies. These visits and practical exposure for students are constrained by safety regulations, venue capacity and the goodwill of industry partners. It is argued that VR technology can support and enhance the learning experience of students and provide insights that would normally only be experienced through physical exposure to the mining environment. A review of how the various forms of VR technologies which are incorporated into training syllabi was made to identify successes and failures to develop a viable strategy in the deployment of VR technology in the support of undergraduate education of mining professionals at the University of Johannesburg. A mixed reality (MR), digital-twin simulacrum that combines a physical training mine environment with VR experiences is being developed in the university’s mining campus. The learnings gleaned from this research will be used to develop a successful model for incorporating virtual reality into a physical mining environment to establish the foundations for a fully integrated mixed reality environment.
Technology-Supported Individualized Learning and Middle School Students’ Achievement in English/Language Arts

Technology-Supported Individualized Learning and Middle School Students’ Achievement in English/Language Arts 의 이용 수, 등재여부, 발행기관, 저자, 초록, 목차, 참고문헌 등 논문에 관한 다양한 정보 및 관련논문 목록과 논문의 분야별 BEST, NEW 논문 목록을 확인 하실 수 있습니다.
Ontological principles and genesis of virtual assets (cryptocurrencies) in modern conditions of development of social and legal system

The article considers the principles of the emergence of virtual assets (cryptocurrencies) and the impact on these events of economic, socio-political relations through technological features. The paper considers the influence of the Austrian School of Economics and its achievements on the emergence and development of modern virtual assets, namely they considered the impact on the choice of available currencies: 1) as the purchase of goods and services, 2) as a reserve for future needs; 3) as deferred payments; 4) as a unit of account. According to Hayek's theoretical framework, market forces will determine the relative values of different competing currencies. As a result, exchange rates between competing currencies will float freely. The ideas of scientists such as Hayek and Mises and the ideas of the Austrian school of economics formed the basis of such a political trend as cryptoanarchism. Cryptoanarchism can be described as the realization of cyberspace and the manifestation of anarchism, a philosophy whose main goal is to respect human freedoms in cyberspace, regardless of status. Keywords: bitcoin, cryptocurrency, virtual asset, cryptoanarchism, blockchain, Austrian school of economics.
Integrating Local Visual Representation and Feature Information for Vehicle Re-identification

Vehicle re-identification (“vehicle Re-ID”) is a tough task due to the various appearance for one vehicle across multiple cameras and the similar visual representations for different vehicles with the same brand. This paper explores local visual representations and temporal information for vehicle Re-ID task. We propose to leverage two discriminative local areas called “vehicle face” and “vehicle windshield” incorporated with a dual-branch deep network to extract robust visual feature, which has been proved to be effective and efficient for vehicle Re-ID. On this basis, we design a spatial-temporal constraint search model to measure visual and temporal similarities for the desired vehicles with respect to a query. We conduct extensive experiments on three vehicle datasets, the experimental results demonstrate that the proposed two local features and the spatial-temporal constraint search model are both effective for vehicle Re-ID task.
Identity-Based Online Hate Content

The content which is expressed over the internet and its associated social media based on any of the protected characteristics like gender, religion, race, and disability is referred to as online hate content. This article aims to examine the user responses on online hate content and determine the predictors of online hate content. With an objective to identify the determinants of online hate content, the data has been collected from 716 internet users using a research instrument designed for the purpose. Both online and offline modes are used for collecting the data. A comprehensive analysis is made using partial least squares path modeling (PLS-PM) package from an open-source software R. The analysis of structural model revealed that the attitude and opinions towards different protected characteristics such as gender, religion, race, and disability are influencers of online hate content. Further, the analysis of measurement models revealed the role of measuring indicators.
Visualization and debugging on internal representation graph of Cloud-Sisal programs

This paper describes the solution of the tasks of visualizing the graphs of the internal representation of Cloud Sisal programs, visualizing the process of computing and debugging Cloud Sisal programs. The formal definitions of the graph with ports and the graph model with ports and attributes are shown. A model of the visualization of the graph model with ports and attributes is described using static images in the SVG format. A model of displaying changes in the graph model with ports and attributes using animations supported by the SVG vector graphics format is described. The connection of graphic animations displaying changes in visual styles and changes in the attributes of the graph model with ports is implemented using Petri nets. It describes the modeling of calculations corresponding to the functions of a given Cloud Sisal program using the hierarchical Petri nets, where the transitions correspond to the functions, and the places of arguments and the parameters of the corresponding functions. Also described modifications of hierarchical Petri nets, ensuring the functionality of the breakpoints and editing the arguments or the results of functions at activated breakpoints for debugging purposes by adding additional places and transitions. Thus, for the obtained Petri nets, the possibility of changing markup of places in the process of functioning is considered.
A Study of 2D Multi-person Pose Estimation Using Distance Scaling on Images

The innovation of convolutional networks in artificial intelligence led to many studies generally on image processing and object detection. This study aims to make use of human detection in 2D images to correctly estimate human pose. In addition, due to recent advances in pose estimation, we aim to apply multi-person human detection with distance scaling in 2D images. The proposed system makes use of multi-person 2D human detection with pose detection and convert the image into a 3D model which estimates the distance between human poses to create a 3D scale. The system is divided into three modules: 2D image multi-person human detection, 2D to 3D pose estimation, and 3D pose distance scaling.
MENA Countries Face the Challenge of the Knowledge-Based Economy

The growing interest in the knowledge economy raises many questions about its effect on economic growth. The study aims to position a set of MENA countries in the context of the knowledge economy compared to developed countries. It also detects theoretically and empirically the knowledge effect on economic growth. To do this, the authors have estimated an endogenous growth model, using the dynamic panel data technique, for a sample of 16 MENA countries over 1995-2014. The results show that, despite the significant improvements that have registered in the knowledge economy pillars, the selected countries are still lagging compared to developed countries. Far from international comparisons, the internal effects of these knowledge pillars (education, innovation, ICT, institutional regime) on growth are positive and highly significant.
Towards Decentralized and Provably Secure Cross-Domain Solutions

Cross-Domain Solutions (CDS) are widely deployed today for secure and timely sharing of information across security domains. Content filters are a key function of the CDS used to mitigate data threats. CDS’s today are centralized and trusted and their deployments are being increasingly consolidated at the enterprise. This centralization and reliance on always-on connectivity to the enterprise introduces risk to timely and secure information sharing at the tactical edge. In this work, we take a step towards decentralizing the CDS functionality by distributing its security relevant components across untrusted tactical edge devices while still providing guarantees on the integrity of the end-to-end filtering pipeline. We instantiate a proof-of-concept decentralized CDS for bitmap image filtering and we demonstrate two alternative designs with similar trust assumptions but different performance tradeoffs. Both designs are based on verifiable computation. Our most performant system is able to filter a \(250\times 250\) pixel image in 15 s, \(20\times \) faster than a strong baseline, and is able to scale to much larger images (\(13\times \) larger scale than baseline within the available memory budget). We discuss ongoing and future work enhancing the expressiveness, performance, and security of the design.
No-Reference Stereoscopic Image Quality Assessment Based on The Visual Pathway of Human Visual System

With the development of stereoscopic imaging technology, stereoscopic image quality assessment (SIQA) has gradually been more and more important, and how to design a method in line with human visual perception is full of challenges due to the complex relationship between binocular views. In this article, firstly, convolutional neural network (CNN) based on the visual pathway of human visual system (HVS) is built, which simulates different parts of visual pathway such as the optic chiasm, lateral geniculate nucleus (LGN), and visual cortex. Secondly, the two pathways of our method simulate the ‘what’ and ‘where’ visual pathway respectively, which are endowed with different feature extraction capabilities. Finally, we find a different application way for 3D-convolution, employing it fuse the information from left and right view, rather than just extracting temporal features in video. The experimental results show that our proposed method is more in line with subjective score and has good generalization.
Scalable Inference for Hybrid Bayesian Hidden Markov Model Using Gaussian Process Emission

The hidden Markov model (HMM), used with Gaussian Process (GP) as an emission model, has been widely used to model sequential data in complex form. This study introduces the hybrid Bayesian HMM with GP emission using SM kernel (HMM-GPSM) to estimate the hidden state of each time-series observation, that is, sequentially observed from a single channel. We then propose a scalable inference method to train the HMM-GPSM using large-scale sequences of time-series dataset that has (1) a large number of sequences for state transitions and (2) a large number of data points in a time-series observation for each hidden state. For state transitions with a large number of sequences, we employ stochastic variational inference (SVI) to update the parameters of HMM-GPSM efficiently. Also, for each time-series observation that has a large number of data points, we propose the approximate GP emission using the Random Fourier Feature (RFF), which is constructed by using the spectral points that are sampled from the spectral density of SM kernel. We propose the efficient inference of the kernel hyperparameters of the approximate GP emission and corresponding HMM-GPSM. Specifically, we derive the training loss, that is, the evidence lower bound of the HMM-GPSM that can be scalably computed for a large number of time-series observations by employing the regularized lower bound of GP emission likelihood with KL divergence. The proposed methods can be used together to train HMM-GPSM with the sequential time-series dataset that contains both (1) and (2). We validate the proposed method on the synthetic and real datasets using the clustering accuracy, marginal likelihood, and training time as the performance metrics.
Saliency-Based Deep Learning Framework for Histopathological Image Classification

In health care, analysis of cancer has notable importance since it is one of the main causes of death globally. In the histopathological era, the treatment is carried out by using biopsy, autopsy etc which requires a lot of pathologist’s effort to diagnose different abnormalities in the human body. All the treatment and medical prescriptions are based on several medical reports and physical examinations from hospitals. To overcome these problems, in our proposed system, a novel saliency-based classification is introduced such that the whole diagnosis is carried out only with the tissue images of the patient. Firstly saliency of the histopathological images is detected, then specific features like SURF (Speeded-Up Robust Feature), ORB (Oriented FAST and Rotated BRIEF), HOG (Histogram of Oriented Gradient), BRISK (Binary Robust Invariant Scalable Keypoints) are extracted and are fused. By using a Deep Convolutional Neural network (CNN) they are classified and finally, abnormalities are detected. Data augmentation is done to the dataset images to overwhelm laboratory faults while capturing histopathological images. The proposed framework has also been compared with conventional methods like Saliency-based Dictionary learning (SDL), Region-based CNN (R-CNN), Dual Layer CNN (DL-CNN), and Auto Encoder-based CNN (AECNN).