Reviewerly/exHarmony huggingface dataset:
Effectiveness of Parenting Styles on Personality Traits of Children at Secondary Level

Parenting style is basically a psychological term that leads us to the approaches that were used by the parents to bring up their childrens in their childhood. The main focus of the study was to explore the effectiveness of parenting styles on the personality of children at the secondary level. Objectives of the study were to explore the effectiveness of parenting styles on the personality of children at the secondary level and to recommend proposed parenting styles for the development of students personality of children at the secondary level. This study followed a mixed method in nature and followed survey design. All the children of class 10 and their parents were the population of the study from the government and private schools of Faisalabad city. It is highly recommended that parents may be provided awareness session to now the importance of their parenting styles and their impact on the personality traits of their children. It is recommended that the government may provide funds and also provide technical facilities. More researches may be conducted on the subject cited above. Curriculum agencies may develop a curriculum on the subject so everybody may have an idea about parenting style also.
Solving pictorial jigsaw puzzles via Internet-based collective intelligence

Pictorial jigsaw (PJ) puzzles are a well-known leisure game for humans. Usually, a PJ puzzle game is played by one or several players face-to-face in physical space. In this paper, we focus on how to solve PJ puzzles in cyberspace by a group of physically-distributed players. We propose an approach to solve PJ puzzles using stigmergy-inspired Internet-based human collective intelligence. The core of the approach is a continuously-executing loop, namely, the EIFloop, which consists of three activities: exploration, integration, and feedback. During the explorationactivity, each player tries to solve the PJ puzzle alone, without direct interaction with the other players. At any time, the result of a players exploration represents a partial solution to the PJ puzzle and a set of rejected neighboring relationships between the puzzle pieces. The results of all players explorations are integrated in real time through the integrationactivity, forming a continuously-updated collective opinion graphCOG). Through the feedbackactivity, each player is provided with personalized feedback information based on the current COG and the players current exploration results, in order to accelerate his/her puzzle-solving process. Exploratory experiments show that:(1) Supported by this approach, the time to solve PJ puzzles is nearly linear with respect to the reciprocal of the number of players. Furthermore, compared with the best single players in the experiments, the puzzle-solving time decreases by 31.36%–64.57% on average for groups composed of 2 to 10 players.linebreak(2) Supported by this approach, the feedback information received by the best player in a group has an average precision of 86.34%, and, as the group size increases, the feedback information in the puzzle-solving result of the best player increases gradually from 20% to 45%.(3) This approach exhibits a better scalability with puzzle size than that of a face-to-face collaboration among ten players. Additionally, it always leads to 100%-accurate puzzle-solving results, whereas the results of the automated PJ-puzzle solver have an average accuracy of only 52%.It is envisaged that these results will provide useful information or opinions to facilitate a broader application of human collective intelligence in the Internet environment.
Polynomial-time plaintext recovery attacks on the IKKR code-based cryptosystems

Recently, Ivanov et al. proposed a new approach to construct code-based cryptosystems, namely the $ {\sf IKKR} $ public-key encryptions (PKE) in the International Workshop on Code-Based Cryptography (CBCrypto 2020) [9]. Unlike the usual construction in code-based encryption schemes which has restrictions on the Hamming weight of the error introduced into the ciphertext, the $ {\sf IKKR} $ approach allows error vectors of arbitrary weight being introduced into the ciphertext. Using this new approach, Ivanov et al. constructed two cryptosystems, namely the modified and the upgraded $ {\sf IKKR} $-PKE. This paper aims to discuss the practical security of the $ {\sf IKKR} $-PKE. In particular, we describe the weaknesses in the design of the public key used in the $ {\sf IKKR} $-PKE. We exploit such weaknesses and propose two attacks to recover the plaintext in the $ {\sf IKKR} $-PKE. The approach of our first attack is similar to the LCKN attack [12], whilst our second attack is more efficient than the LCKN attack. Our experimental results show that we can recover the plaintext from a given ciphertext in less than 176 milliseconds for schemes based on random Goppa codes and BCH codes.
Strategic Alignment of Information Technology Initiatives in a Scaled Agile Environment

Scaled Agile introduces challenges that confront organisations. One of these challenges is how initiatives are aligned with the organisational strategies in frameworks such as SAFe. Nineten interviews were conducted within a case that has implemented a scaled Agile framework. The purpose was to determine how this framework was implemented and to assess how information technology (IT) initiatives are aligned with the corporate strategies. The results indicate that IT initiatives are aligned with the strategies but that two approaches are used. Future research will focus on benefits realisation within a scaled Agile environment.
Complex Events Processing on Live News Events Using Apache Kafka and Clustering Techniques

The explosive growth of news and news content generated worldwide, coupled with the expansion through online media and rapid access to data, has made trouble and screening of news tedious. An expanding need for a model that can reprocess, break down, and order main content to extract interpretable information, explicitly recognizing subjects and content-driven groupings of articles. This paper proposed automated analyzing heterogeneous news through complex event processing (CEP) and machine learning (ML) algorithms. Initially, news content streamed using Apache Kafka, stored in Apache Druid, and further processed by a blend of natural language processing (NLP) and unsupervised machine learning (ML) techniques.
Fast Light-Weight Network for Extreme Image Inpainting Challenge

Image inpainting has a wide range of applications. However, to this challenge existing inpainting models that usually have a large model size can hardly run fast, as memory and supported operations are much limited. In this paper, we propose a novel light-weight inpainting model in which we design three novel operations named Equilibrium Conv Mask-wise Gated Conv, Difference Conv and define a new loss function based on SN-patchGAN. In specific, the incorporation of Equilibrium Conv and Mask-wise Gated Conv not only reduces the model size and improve the efficiency, but also keeps comparative performance. For Difference Conv, it is benefit to handle big mask problem. Moreover, our proposed loss results in a better performance in recovering images with rich textures. Experimental results demonstrate our model is 1.43 $$\times $$ speeding up and reduces the size by 2.37 $$\times $$ compared with the state-of-the-art model.
Research on Arm TrustZone and Understanding the Security Vulnerability in Its Cache Architecture

Arm TrustZone technology is the most widely used system-level security framework, which provides a trusted execution environment (TEE) for embedded system SoC. This paper introduces the technical principle of TrustZone in detail, explains how to extend the security features from CPU to the whole system through various security components, and briefly introduces the secure boot, the application of TrustZone in mobile devices and the variant technology based on TrustZone. At the same time, there are many attacks on TrustZone. According to the cache architecture of TrustZone, each cache line uses a NS bit to indicate whether the line belongs to secure world or normal world. The purpose is to avoid refreshing when switching the two worlds, thus reducing the performance loss. However, it supports cache lines in the two worlds to compete and evict each other, this provides an opportunity for cache attacks, and this article details this security Vulnerability.
Towards Higher Code Quality in Scientific Computing

In scientific computing and data science, computer programs employing mathematical and statistical models are used for obtaining knowledge in different application domains. The results of these pro ...
Shapley Explanation Networks

Shapley values have become one of the most popular feature attribution explanation methods. However, most prior work has focused on post-hoc Shapley explanations, which can be computationally demanding (exponential time complexity) and preclude model regularization based on Shapley explanations during training. Thus, we propose to incorporate Shapley values themselves as latent representations in deep models---thereby making Shapley explanations first-class citizens in the modeling paradigm. This intrinsic explanation approach enables layer-wise explanations, explanation regularization of the model during training, and fast explanation computation at test time. We define the Shapley transform that transforms the input into a Shapley representation given a specific function. We operationalize the Shapley transform as a neural network module and construct both shallow and deep networks, called ShapNets, by composing Shapley modules. We prove that our Shallow ShapNets compute the exact Shapley values and our Deep ShapNets maintain the missingness and accuracy properties of Shapley values. We demonstrate on synthetic and real-world datasets that our ShapNets enable layer-wise Shapley explanations, novel Shapley regularizations during training, and fast computation while maintaining reasonable performance. Code is available at https://www.github.com/RuiWang1998/ShapleyExplanationNetworks.
ONLINE CONSULTS AS A COMPONENT PART OF DISTANCE EDUCATION IN HIGH SCHOOL

The distance learning in the higher education has been characterized as one of the most appropriate forms in the current conditions of globalization and the spread of quarantine measures in Ukraine and in the whole world. Is has been determined that there are numerous advantages of distance learning on the one hand, but there are significant risks of reducing the quality of such learning in comparison to traditional education. In this connection, the expediency of introducing of synchronous online consultations in the distance education as a compulsory component has been substantiated. The experience of giving synchronous online consultations in the conditions of distance learning at the Petro Mohyla Black Sea National University during the teaching of normative professional subjects of Ukrainian language and Literature magisters has been analyzed. The proposed system of synchronous online consultations has been designed to perform the following educational functions such as a function of motivation of students' learning activities, a function of assimilation of acquired knowledge, a corrective function, a function of developing professionally oriented competence, a function of generalization and systematization of knowledge. It has been emphasized that synchronous online consultations will be effective in case they are systematic and they are given due to psychological and didactic requirements, in particular, taking into account the psychological characteristics of the current generation of students as “digital”. And as the result there is a requirement to visualize learning material, its complete systematization and generalization. The virtual environment for synchronous online consultations can be the online conference platform Zoom Video Communications, which technical features provide their full implementation. Confirming the effectiveness of synchronous online consultations, it has been conducted an anonymous survey of students, as a result of which most students recognize the importance and necessity of this form for distance learning. The proposed system of synchronous online consultations contributes to the effective study of the relevant discipline and improves the quality of education in general. The development of online courses in the disciplines has been provided by the relevant educational programs. Keywords: distance education, synchronous online consultations, organization of synchronous online consultations, digital generation of students, virtual environment of distance education, quality of education.
Know thy Context: Parsing Contextual Information from User Reviews for Recommendation Purposes

In this paper, we study an important problem of parsing contextual information from user reviews for recommendation purposes. First, we study the ways contextual information is expressed in user reviews and obtain novel insights about it. Among other things, we demonstrate that such type of information tends to appear at the beginning of the review, in longer sentences, in the sentences written in the past tense or using gerund form, and in the sentences referring to some points in time. Second, we propose a novel Context Parsing method for systematically extracting contextual information from user-generated reviews that relies on the insights obtained in our study. We apply the proposed method to three different Yelp applications (restaurants, hotels, and beauty & spas) and demonstrate that it works well and leads to better recommendation performance than the baseline approaches. Our method systematically extracts more comprehensive sets of relevant contextual variables and corresponding phrases than the baselines. Our analysis also shows the importance of the newly discovered contextual information for recommendation purposes. The obtained results and the proposed method can help to get more comprehensive knowledge about contextual variables in a given application that leads to better recommendations.
Strong Tracking PHD Filter Based on Variational Bayesian with Inaccurate Process and Measurement Noise Covariance

Assuming that the measurement and process noise covariances are known, the probability hypothesis density (PHD) filter is effective in real-time multi-target tracking; however, noise covariance is often unknown and time-varying for an actual scene. To solve this problem, a strong tracking PHD filter based on Variational Bayes (VB) approximation is proposed in this paper. The measurement noise covariance is described in the linear system by the inverse Wishart (IW) distribution. Then, the fading factor in the strong tracking principle uses the optimal measurement noise covariance at the previous moment to control the state prediction covariance in real-time. The Gaussian IW (GIW) joint distribution adopts the VB approximation to jointly return the measurement noise covariance and the target state covariance. The simulation results show that, compared with the traditional Gaussian mixture PHD (GM-PHD) and the VB-adaptive PHD, the proposed algorithm has higher tracking accuracy and stronger robustness in a more reasonable calculation time.
Individual Action and Group Activity Recognition in Soccer Videos from a Static Panoramic Camera

Data and statistics are key to soccer analytics and have important roles in player evaluation and fan engagement.Automatic recognition of soccer events -such as passes and corners -would ease the data gathering process, potentially opening up the market for soccer analytics at non professional clubs.Existing approaches extract events on group level only and rely on television broadcasts or recordings from multiple camera viewpoints.We propose a novel method for the recognition of individual actions and group activities in panoramic videos from a single viewpoint.Three key contributions in the proposed method are (1) player snippets as model input, (2) independent extraction of spatio-temporal features per player, and (3) feature contextualisation using zero-padding and feature suppression in graph attention networks.Our method classifies video samples in eight action and eleven activity types, and reaches accuracies above 75% for ten of these classes.
Data Mining Technology Application in False Text Information Recognition

False information on the Internet is being heralded as serious social harm to our society. To recognize false text information, in this paper, an effective method for mining text features is proposed in the field of false drug advertisements. Firstly, the data of false drug advertisements and real drug advertisements were collected from the official websites to build a database of false and real drug advertisements. Secondly, by performing feature extraction on the text of drug advertisements, this work built a characteristic matrix based on the effective features and assigned positive or negative labels to the feature vector of the matrix according to whether it is a fake medical advertisement or not. Thirdly, this study trained and tested several different classifiers, selected the classification model with the best performance in identifying false drug advertisements, and found the key characteristics that can determine the classification. Finally, the model with the best performance was used to predict new false drug advertisements collected from Sina Weibo. In the case of identifying false drug advertisements, the classification effect of the support vector machine (SVM) classifier established on the feature set after feature selection was the most effective. The findings of this study can provide an effective method for the government to identify and combat false advertisements. This study has a certain reference significance in demonstrating the use of text data mining technology to identify and detect information fraud behavior.
CNN Hyperparameter Optimization using Random Grid Coarse-to-fine Search for Face Classification

Convolutional Neural Network (CNN) is a recently used popular machine learning technique to classify images. However, choosing an optimum and efficient architecture is an inevitable challenge. The research goal was to implement CNN on face classification from low quality CCTV footage. The best model was gained from the hyperparameter optimization process used on CNN structure. The optimized hyperparameters were those connected to the structure network including activation function, the number of kernel, the size of kernel, and the number of nodes on the fully connected layers. Hyperparameter optimization strategy used was random grid coarse-to-fine search optimization approach. This approach combined random search, grid search, and coarse-to-fine technique that was easily and efficiently applied, yet worked well. Exhaustive-random search process was done by evaluating all selected activation functions and choosing another hyperparameters randomly. This was based on the assumption that activation functions were the most related hyperparameter to the model. The SELU activation function used in the next step was the one with the best average performance. Grid coarse-to-fine was conducted to optimize the number of kernel and the number of node on fully connected layer, while grid search was conducted to optimize the kernel size. This process aimed to locate optimal value gradually in hyperparameter which had high-dimensional space. Evaluation of the model resulted from the optimum hyperparameter was 97,56%.
Predicting Schoolteachers' Intention and Behaviour of Promoting Cyber-Safety Awareness

School learners are attractive candidates for cyber-safety attacks. Advances in Information and Communications Technology (ICT) devices, and the surge in Internet usage, poses significant risks to users with poor cyber-safety awareness and security practices. An example of such users are school learners. Therefore, school learners should be made aware of cyber-safety or safe Internet usage, through awareness programmes as early as possible. Applying the classical Theory of Planned Behaviour in a survey of 109 schoolteachers across 18 schools in 2 districts, the results of a multiple regression analysis unveil that: 1) attitude does not predict the schoolteachers’ intention to raise cyber-safety awareness at their schools in the next 12 months, nor does it predict the schoolteachers’ behaviour of raising cyber-safety awareness in their teaching; and 2) subjective norms and perceived behavioural control predict the schoolteachers’ intention and behaviour. In light of this, the South African Department of Basic Education (DBE) is encouraged to identify influential schoolteachers (i.e. phase heads, sports and cultural coordinators, principals and their deputies, etc.), and to groom them into cyber-safety awareness advocates, for the purposes of influencing other teachers or their peers. Also, the DBE should provide teachers with the requisite resources, opportunities, and support which facilitates cyber-safety awareness in schools, to empower teachers to adopt this highly desired behaviour.
Study of Sub-Nyquist Sampling Techniques for Multi-band Signals

Shannon-Nyquist sampling theorem is the most fundamental and well known for transforming a continuous-time band-limited signal into its discrete-time form. Over the years, many other methods have been developed for the same. The sampling frequency i.e. the number of samples taken per second, is one of the important characteristics of the sampling process. Since high sampling frequency is very tedious to achieve in the practical world, one desire to have a sampling frequency as low as possible. This paper investigates the classical Sub-Nyquist sampling methods named as Demodulation, Undersampling and Periodically Non-uniform Sampling which reduces the sampling frequency below the Nyquist rate, assuming a multiband or bandpass signal with known frequency support. It will present a comparative study of these methods in terms of the sampling frequency with respect to both the Nyquist rate and the minimal sampling frequency possible, additional hardware circuitry needed and their suitability to multi-band inputs.
In-game Residential Home Planning via Visual Context-aware Global Relation Learning

In this paper, we propose an effective global relation learning algorithm to recommend an appropriate location of a building unit for in-game customization of residential home complex. Given a construction layout, we propose a visual context-aware graph generation network that learns the implicit global relations among the scene components and infers the location of a new building unit. The proposed network takes as input the scene graph and the corresponding top-view depth image. It provides the location recommendations for a newly added building units by learning an auto-regressive edge distribution conditioned on existing scenes. We also introduce a global graph-image matching loss to enhance the awareness of essential geometry semantics of the site. Qualitative and quantitative experiments demonstrate that the recommended location well reflects the implicit spatial rules of components in the residential estates, and it is instructive and practical to locate the building units in the 3D scene of the complex construction.