Reviewerly/exHarmony huggingface dataset:
Practices for Effective Software Project Management in Global Software Development: A Systematic Literature Review

Effective software project management plays an important role during requirements collection and implementation for any software system.In Global Software Development (GSD), its significance increase more as stakeholders are far away across the globe.In GSD, challenges such as language differences and time zone differences cause significant barrier during requirements collection and thus need of effective project management increase more and more to handle challenges of GSD.This study address possible solutions and practices for effective global software project management.Through Systematic Literature Review (SLR), 25 practices are identified.These practices will help software vendors to better manage software projects in GSD.
Smart Assistive Glasses for Alzheimer's Patients

Alzheimer's patients require special care and constant monitoring. This paper utilizes smart glasses equipped with Augmented Reality (AR) screen to perform the basic functions of a caregiver and provide the patients with features that increase the independence and reduce caregiving costs. It targets patients during the early and middle stages of the disease who still possess control over their mental and physical behaviours. The core features are detecting the location of misplaced objects and guiding the patient to their last seen location, identifying and displaying the names of friends and relatives on the AR display, monitoring if the patient has lost the way and accordingly displaying the way home on the AR display while simultaneously sending the patient's location to the caregiver as an SMS, detecting if the patient removed the glasses, predicting the cause of removal, then notifying the caregiver with the predicted cause and the patient's location.
The Impact of Surface Features on Choice of (in)Secure Answers by Stackoverflow Readers

Existing research has shown that developers will use StackOverflow to answer programming questions: but what draws them to one particular answer over any other? The choice of answer they select can mean the difference between a secure application and insecure one, as the quality of supposedly secure answers can vary. Prior work has studied people posting on Stack Overflow—a two-way communication between the original poster and the Stack Overflow community. Instead, we study the situation of one-way communication, where people only read a Stack Overflow thread without being actively involved in it, sometimes long after a thread has closed. We report on a mixed-method study including a controlled between-groups experiment and qualitative analysis of participants’ rationale (N=1188), investigating whether explanation detail, answer scoring, accepted answer marks, as well as the security of the code snippet itself affect the answers participants accept. Our findings indicate that explanation detail affects what answers participants reading a thread select (p<0.01), while answer score and acceptance do not (p>0.05)—the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inverse</i> of what research has shown for those asking and answering questions. The qualitative analysis of participants’ rationale further explains how several cognitive biases underpin these findings. Correspondence bias, in particular, plays an important role in instilling readers with a false sense of confidence in an answer through the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">way it looks</i> , regardless of whether it works, is secure, or if the community agrees with it. As a result, we argue that StackOverflow's use as a knowledge base by people not actively involved in threads—when there is only one-way-communication—may inadvertently contribute to the spread of insecure code, as the community's voting mechanisms hold little power to deter them from answers.
The application of scam compliance models to investment fraud offending

Purpose The purpose of this paper is to undertake an exploratory study on mapping the investment fraud methods and tactics used by scammers against the emerging literature on scam compliance. Design/methodology/approach Qualitative interviews were conducted with victims of investment fraud supported by the engagement of specialist counsellors and allied health professionals who specialise in scam victim support (including investment fraud). Findings Investment fraud offending in the cases sampled exhibited a number of dominant offending traits and methodological themes. These included a strong reliance or dependency on legitimate service provisioning on the part of the fraudster and the use of key trust measures to lure the victim. The empirical data revealed the presence of a number of scam compliance influences captured in the literature, including trust, social influence and urgency, as well as others not previously documented that pave the way for further research attention. Research limitations/implications The research only examined a sample of investment fraud victim experiences that engaged a national victim support service immediately following detection over a 24 month period. Practical implications The research found that offending relied upon the participation of trust-building signals and measures. Legitimate economy participants appear to play a dominant role in enabling investment scam activities, further creating efficiencies for criminals. The offending tended to follow a number of distinct but connected phases. Impacts were influenced by specific offending attributes, such as whether remote access was given to offenders of a victim’s device, as well as the nature of the identity credentials access. Originality/value The research has practically applied an emerging view of scam compliance influences and vulnerabilities within an investment fraud context. The study is novel in its thematic analysis of the distinct phases and tactics used by scammers.
A Poisson random walk model of response times.

Based on the simple what first comes to mind rule, the theory of visual attention (TVA; Bundesen, 1990) provides a comprehensive account of visual attention that has been successful in explaining performance in visual categorization for a variety of attention tasks. If the stimuli to be categorized are mutually confusable, a response rule based on the amount of evidence collected over a longer time seems more appropriate. In this paper, we extend the idea of a simple race to continuous sampling of evidence in favor of a certain response category. The resulting Poisson random walk model is a TVA-based response time model in which categories are reported based on the amount of evidence obtained. We demonstrate that the model provides an excellent account for response time distributions obtained in speeded visual categorization tasks. The model is mathematically tractable, and its parameters are well founded and easily interpretable. We also provide an extension of the Poisson random walk to any number of response alternatives. We tested the model in experiments with speeded and nonspeeded binary responses and a speeded response task with multiple report categories. The Poisson random walk model agreed very well with the data. A thorough investigation of processing rates revealed that the perceptual categorizations described by the Poisson random walk were the same as those obtained from TVA. The Poisson random walk model could therefore provide a unifying account of attention and response times. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
Human Motion Transfer With 3D Constraints and Detail Enhancement

We propose a new method for realistic human motion transfer using a generative adversarial network (GAN), which generates a motion video of a target character imitating actions of a source character, while maintaining high authenticity of the generated results. We tackle the problem by decoupling and recombining the posture information and appearance information of both the source and target characters. The innovation of our approach lies in the use of the projection of a reconstructed 3D human model as the condition of GAN to better maintain the structural integrity of transfer results in different poses. We further introduce a detail enhancement net to enhance the details of transfer results by exploiting the details in real source frames. Extensive experiments show that our approach yields better results both qualitatively and quantitatively than the state-of-the-art methods.
Deep Convolutional Neural Networks

This chapter discusses different software programs that are available for deep neural network learning. Training a deep learning model with a limited number of training images is a challenging task due to the number of learnable parameters. The chapter also discusses convolutional neural networks (CNNs) because these models are more applicable to the computer vision field. It aims to serve as a guide for specialists starting in deep learning/computer vision. The chapter provides a comprehensive foundation for deep CNNs. It presents some later developments in various aspects of CNNs, including the convolutional layer, pooling layer, activation function, fully connected layers, regularization, optimization, normalization, and network depth, and we present the advances in each phase. The chapter describes the network design of the proposed system and provides simulation results and evaluation criteria. It focuses on different types of CNNs, which are region-based CNNs, fully convolutional networks, and hybrid learning networks.
Evaluating the Impact of a Two-Stage Multivariate Data Cleansing Approach to Improve to the Performance of Machine Learning Classifiers: A Case Study in Human Activity Recognition

Human activity recognition (HAR) is a popular field of study. The outcomes of the projects in this area have the potential to impact on the quality of life of people with conditions such as dementia. HAR is focused primarily on applying machine learning classifiers on data from low level sensors such as accelerometers. The performance of these classifiers can be improved through an adequate training process. In order to improve the training process, multivariate outlier detection was used in order to improve the quality of data in the training set and, subsequently, performance of the classifier. The impact of the technique was evaluated with KNN and random forest (RF) classifiers. In the case of KNN, the performance of the classifier was improved from 55.9% to 63.59%.
Benchmarking JPEG XL image compression

JPEG XL is a practical, royalty-free codec for scalable web distribution and efficient compression of high-quality photographs. It also includes previews, progressiveness, animation, transparency, high dynamic range, wide color gamut, and high bit depth. Users expect faithful reproductions of ever higher-resolution images. Experiments performed during standardization have shown the feasibility of economical storage without perceptible quality loss, lossless transcoding of existing JPEG, and fast software encoders and decoders. We analyse the results of subjective and objective evaluations versus existing codecs including HEVC and JPEG. New image codecs have to co-exist with their previous generations for several years. JPEG XL is unique in providing value for both existing JPEGs as well as new users. It includes coding tools to reduce the transmission and storage costs of JPEG by 22% while allowing byte-for-byte exact reconstruction of the original JPEG. Avoiding transcoding and additional artifacts helps to preserve our digital heritage. Applications require fast and low-power decoding. JPEG XL was designed to benefit from multicore and SIMD, and actually decodes faster than JPEG. We report the resulting speeds on ARM and x86 CPUs. To enable reproduction of these results, we open sourced the JPEG XL software in 2019.
Concept of smart pedagogy for learning in a digital world

Education as a provider of changes is always challenged to prepare the next generation for the competencies that will be needed when entering the labour market. To a certain extent, the educational system is constantly operating in reform mode because its actors have the threefold desire to preserve the knowledge they have accumulated over the course of their evolution for future generations, to anticipate future development scenarios, and to provide opportunities for future competencies. This chapter proposes ideas of perspectives on how to evaluate the enhancement of the use of technology, what aspects should be taken into account when organizing a technology-enhanced learning process, and what educators should be aware of while creating digital learning materials. One of the suggestions for the educational environment is to develop smart pedagogical principles that include not only technological pedagogical content competencies but also predictive analytical competence to operate in a smart educational environment and to promote the development of a smart society.
Improvised number identification using SVM and random forest classifiers

Abstract The classification of any dataset is the common task of any algorithm. In machine learning, data pre-processing is essential steps for quality of data and information. The ability of classifier to learn directly depends on quality of data. Therefore, in some case, it is very important that the data need to pre-process before feeding it to the classifier. Normalization and Standardization are the pre-processing methods which we have used. The paper deals with the methods for improvised number identification analyse the pre-processing methods on Support Vector Machine (SVM) and Random Forest (RF) on the handwritten digit dataset. It is important to identify appropriate pre-processing method for specific data sets and algorithms. As a result, it is shown that using pre-processing we can improve the accuracy of SVM for hand written digit classification.
Assessing the Factors Associated With the Detection of Juvenile Hacking Behaviors

Research on delinquency reduction often highlights the importance of identifying and sanctioning antisocial and illegal activities so as to reduce the likelihood of future offending. The rise of digital technology complicates the process of detecting cybercrimes and technology enabled offenses, as individuals can use devices from anywhere to engage in various harmful activities that may appear benign to an observer. Despite the growth of cybercrime research, limited studies have examined the extent to which technology enabled offenses are detected, or the behavioral and attitudinal factors associated with being unobserved or caught for one's actions. The current study addresses this gap in the literature by estimating a multinomial regression model for self-reported computer hacking behavior and the likelihood of those actions being detected in a large international sample of juveniles (N = 51,059). The findings demonstrate significant differences between youth who hack without detection compared to those who are caught. The implications of this analysis for our understanding of cybercrime and its relationship to traditional delinquency are explored in depth.
Design and implementation of pipelined and parallel AES encryption systems using FPGA

&lt;p&gt;&lt;span&gt;The information security is one of the most important issues in the design of any communication network.One of the most common encryption algorithms is the Advanced Encryption Standard (AES).The main problem facing the AES algorithm is the high time consumption due to the large number of rounds used for performing the encryption operation. The more time the encryption system consumes to encrypt the data, the more chances the hackers have to break the system.This paper presents two effective algorithms that can be used to solve the mentioned problem and to achieve an effective processing time reduction using pipelined and parallel techniques to perform the encryption steps. These algorithms are based on using certain techniques to make the system able to encrypt many different states (the data will be encrypted) in the same time with no necessity to wait for the previous encryption operation to be completed. These two algorithms are very effective especially for big data size. This paper describes in detail the AES encryption system algorithm and a detailed explanation for the proposed algorithms. Moreover, the research shows the implementation of the three algorithms: the traditional, the pipelined, and the parallel algorithms, and finally a comparison between them.&lt;/span&gt;&lt;/p&gt;
Making Civic Initiatives Last

HCI and design research has turned toward exploring methods, technologies, processes for societal impact, and do so by intervening in the real world and designing for, with and by people and existing citizen initiatives. A key issue faced is enabling efforts to continue beyond the project or grant timeline. For this reason, there is a growing need to create a forum where researchers and practitioners can share their approaches to shed light on opportunities and challenges of supporting lasting civic initiatives moving forward. This workshop aims to bring together researchers and practitioners interested in how to make civic initiatives have lasting impact: either by supporting and sustaining such initiatives or by focusing on how their outcomes increase people's capacity to act on their ideas and wishes.
Privacy Protection Algorithm For The Internet Of Vehicles Based On Local Differential Privacy And Game Model

In recent years, with the continuous advancement of the intelligent process of the Internet of Vehicles (IoV), the problem of privacy leakage in IoV has become increasingly prominent. The research on the privacy protectio... | Find, read and cite all the research you need on Tech Science Press
An Online Learning Framework for Energy-Efficient Navigation of Electric Vehicles

Energy-efficient navigation constitutes an important challenge in electric vehicles, due to their limited battery capacity. We employ a Bayesian approach to model the energy consumption at road segments for efficient navigation. In order to learn the model parameters, we develop an online learning framework and investigate several exploration strategies such as Thompson Sampling and Upper Confidence Bound. We then extend our online learning framework to multi-agent setting, where multiple vehicles adaptively navigate and learn the parameters of the energy model. We analyze Thompson Sampling and establish rigorous regret bounds on its performance. Finally, we demonstrate the performance of our methods via several real-world experiments on Luxembourg SUMO Traffic dataset.
Dynamic Online Conversation Recommendation

Trending topics in social media content evolve over time, and it is therefore crucial to understand social media users and their interpersonal communications in a dynamic manner.In this research we study dynamic online conversation recommendation, to help users engage in conversations that satisfy their evolving interests.Different from works in conversation recommendation which assume static user interests, our model captures the temporal aspects of user interests.Moreover, our model can cater for cold start problem where conversations are new and unseen in training.We propose a neural architecture to analyze changes of user interactions and interests over time, whose result is used to predict which discussions the users are likely to enter.We conduct experiments on large-scale collections of Reddit conversations.Results on three subreddits show that our model significantly outperforms state-of-the-art models based on static assumption of user interests.We further evaluate performance in cold start, and observe consistently better performance by our model when considering various degrees of sparsity of user's chatting history and conversation contexts.Lastly, our analysis also confirms the change of user interests.This further justify the advantage and efficacy of our model.
A Web Service for Video Summarization

This paper presents a Web service that supports the automatic generation of video summaries for user-submitted videos. The developed Web application decomposes the video into segments, evaluates the fitness of each segment to be included in the video summary and selects appropriate segments until a pre-defined time budget is filled. The integrated deep-learning-based video analysis and summarization technologies exhibit state-of-the-art performance and, by exploiting the processing capabilities of modern GPUs, offer faster than real-time processing. Configurations for generating video summaries that fulfill the specifications for posting on the most common video sharing platforms and social networks are available in the user interface of this application, enabling the one-click generation of distribution-channel-specific summaries.
Efficient Estimation of Graph Signals With Adaptive Sampling

We propose two new least mean squares (LMS)-based algorithms for adaptive estimation of graph signals that improve the convergence speed of the LMS algorithm while preserving its low computational complexity. The first algorithm, named extended least mean squares (ELMS), extends the LMS algorithm by virtue of reusing the signal vectors of previous iterations alongside the signal available at the current iteration. Utilizing the previous signal vectors accelerates the convergence of the ELMS algorithm at the expense of higher steady-state error compared to the LMS algorithm. To further improve the performance, we propose the fast ELMS (FELMS) algorithm in which the influence of the signal vectors of previous iterations is controlled by optimizing the gradient of the mean-square deviation (GMSD). The FELMS algorithm converges faster than the ELMS algorithm and has steady-state errors comparable to that of the LMS algorithm. We analyze the mean-square performance of ELMS and FELMS algorithms theoretically and derive the respective convergence conditions as well as the predicted MSD values. In addition, we present an adaptive sampling strategy in which the sampling probability of each node is changed according to the GMSD of the node. Computer simulations using both synthetic and real data validate the theoretical results and demonstrate the merits of the proposed algorithms.
Multi-granularity pruning for deep residual networks

Network pruning has been widely used to reduce the high computational cost of deep convolutional neural networks(CNNs). The dominant pruning methods, channel pruning, removes filters in layers based on their importance or sparsity training. But these methods often give limited acceleration ratio and encounter difficulties when pruning CNNs with skip connections. Block pruning methods take a sequence of consecutive layers (e.g., Conv-BN-ReLu) as a block and remove entire block each time. However, previous methods usually introduce new parameters to help pruning and lead additional parameters and extra computations. This work proposes a novel multi-granularity pruning approach that combines block pruning with channel pruning (BPCP). The block pruning (BP) module remove blocks by directly searches the redundant blocks with gradient descent and leaves no extra parameters in final models, which is friendly to hardware optimization. The channel pruning (CP) module remove redundant channels based on importance criteria and handles CNNs with skip connections properly, which further improves the overall compression ratio. As a result, for CIFAR10, BPCP reduces the number of parameters and MACs of a ResNet56 model up to 78.9% and 80.3% respectively with &lt;3% accuracy drop. In terms of speed, it gives a 3.17 acceleration ratio. Our code has been made available at https://github.com/Pokemon-Huang/BPCP.