Reviewerly/exHarmony huggingface dataset:
Understanding the Working Habits of GH-SO Users on GitHub Commit Activity and Stack Overflow Post Activity

GitHub and Stack Overflow are often used together for software development. GH-SO users, who use both GitHub and Stack Overflow, contribute to the development of various software projects in GitHub and share their knowledge and experience on software development in Stack Overflow. To widely understand the interests and working habits of GH-SO users on software development, it is important to investigate how GH-SO users utilize GitHub and Stack Overflow. In this paper, we present an exploratory study on GitHub commit and Stack Overflow post activities of GH-SO users. Specifically, we investigate the working habits of GH-SO users on GitHub commit and Stack Overflow post activities. We randomly selected 19,756 of GH-SO users as our target sample and collected 2,819,483 and 2,147,317 of commit activity data and post activity data of the GH-SO users. We then categorized the collected commit and post activity datasets into specific categories on programming languages and statistically analyzed the categorized commit and post activity datasets. As the results of our analysis, we found the following: (1) The overall commit and post activities of the GH-SO users share some similarity. (2) The commit activities gradually change while the post activities drastically change over time. (3) The commit activities of the GH-SO users are broadly distributed while the post activities are narrowly distributed and the commit activity can be better predictor for post activity. (4) The commit activity of the GH-SO users tends to be performed prior post activity. We believe that our findings can contribute to finding the ways to better support commit and post activities of GitHub and Stack Overflow users.
RLStereo: Real-Time Stereo Matching Based on Reinforcement Learning

Many state-of-the-art stereo matching algorithms based on deep learning have been proposed in recent years, which usually construct a cost volume and adopt cost filtering by a series of 3D convolutions. In essence, the possibility of all the disparities is exhaustively represented in the cost volume, and the estimated disparity holds the maximal possibility. The cost filtering could learn contextual information and reduce mismatches in ill-posed regions. However, this kind of methods has two main disadvantages: 1) cost filtering is very time-consuming, and it is thus difficult to simultaneously satisfy the requirements for both speed and accuracy; 2) thickness of the cost volume determines the disparity range which can be estimated, and the pre-defined disparity range may not meet the demand of practical application. This paper proposes a novel real-time stereo matching method called RLStereo, which is based on reinforcement learning and abandons the cost volume or the routine of exhaustive search. The trained RLStereo makes only a few actions iteratively to search the value of the disparity for each pair of stereo images. Experimental results show the effectiveness of the proposed method, which achieves comparable performances to state-of-the-art algorithms with real-time speed on the public large-scale testset, i.e., Scene Flow.
A Correlated Noise-Assisted Decentralized Differentially Private Estimation Protocol, and its Application to fMRI Source Separation

Blind source separation algorithms such as independent component analysis (ICA) are widely used in the analysis of neuroimaging data. To leverage larger sample sizes, different data holders/sites may wish to collaboratively learn feature representations. However, such datasets are often privacy-sensitive, precluding centralized analyses that pool the data at one site. In this work, we propose a differentially private algorithm for performing ICA in a decentralized data setting. Due to the high dimension and small sample size, conventional approaches to decentralized differentially private algorithms suffer in terms of utility. When centralizing the data is not possible, we investigate the benefit of enabling limited collaboration in the form of generating jointly distributed random noise. We show that such (anti) correlated noise improves the privacy-utility trade-off, and can reach the same level of utility as the corresponding non-private algorithm for certain parameter choices. We validate this benefit using synthetic and real neuroimaging datasets. We conclude that it is possible to achieve meaningful utility while preserving privacy, even in complex signal processing systems.
Detection and Following of Moving Target by an Indoor Mobile Robot using Multi-sensor Information

The following behavior is an important aspect in developing of mobile robot control algorithms. It is frequently a substantial part of the control strategies used to control robots that are designed to co-exist and collaborate with human beings in a shared working environment. In this research, an approach is proposed and investigated allowing a holonomic mobile platform to detect and follow targets, moving on the ground, in an indoor environment. Using multi-sensor data flow, the control algorithm is able to localize the target object by its shape and to start following it. The robot will maintain the desired orientation and distance while following the target. Additionally, the mobile platform has also a fully manual control mode option and is able to recognize human gesture commands.
Speech Command Recognition Using Deep Learning

Speech Recognition Software is a computer program that is trained to take the input of human speech, interpret it, and transcribe it into text. Most recently, the field has benefited from advances in deep learning and big data. The advances are evidenced not only by the surge of academic papers published in the field, but more importantly by the worldwide industry adoption of a variety of deep learning methods in designing and deploying speech recognition systems. The objective of this paper is to propose an advanced and accurate end-user software system that is able to recognize specific commands to control a robot to perform specified tasks in a hospital. This model will be based on Deep Learning since it is effective in models having huge data as for the two versions of Google TensorFlow and AIY datasets used in our model. Convolutional neural network will be used since it is able to extract features from the dataset instead of traditional methods of feature extraction, thus saving training time and reducing the complexity of the system. With addition to that, NVIDIA CUDA will be also used to train the model with GPU to decrease the training time. During training, some experiments have been done to see the effect of some parameters on the results of the system, and to make sure that the chosen parameters in our model are the best. The results indicate that the training, validation, and testing accuracies of the proposed approach were high, the training duration reached very low values due to the innovation used (CUDA Toolkit) and the commands were successfully recognized by the model. These results outcome the results of the papers that developed similar work which will be presented in the coming sections.
Authentication of Quran Verses Sequences Using Deep Learning

The emergence of electronic copies of the Holy Quran has allowed an increase in the phenomenon of reading and sharing Arabic verses among internet users. The Quran has been brought together in one book to maintain the order and integrity of its content. Changing the order of verses is considered as a degradation of the Holy Quran, resulting in new surahs in the form of a new composition, contradicting the structure and meaning of what is in the Ottoman Quran. Similar to any verification methodology, this study aims to apply deep learning algorithms to automatically authenticate the integrity of the Quranic content arrangement. The Long Short-Term Memory (LSTM) algorithm was chosen in this work, and the results achieved a test accuracy of 99.98% on the dataset that we created using Tanzil website data.
Stabilized Nested Rollout Policy Adaptation

Nested Rollout Policy Adaptation (NRPA) is a Monte Carlo search algorithm for single player games. In this paper we propose to modify NRPA in order to improve the stability of the algorithm. Experiments show it improves the algorithm for different application domains: SameGame, Traveling Salesman with Time Windows and Expression Discovery.
VIP-Bench: A Benchmark Suite for Evaluating Privacy-Enhanced Computation Frameworks

Privacy-enhanced computation enables the processing of encrypted data without exposing underlying sensitive information. Such technologies are extremely promising for the advancement of data privacy, as they remove plaintexts from the attackers’ reach. However, each privacy technology provides varying degrees of computational capabilities and performance overheads, creating challenges for adoption. For example, some publicly available homomorphic encryption schemes are limited in expressiveness or cannot support deep computation without incurring significant overheads. This diversity warrants a benchmark suite that can adequately assess capability and performance while supporting a variety of privacy-enhanced software architectures. We propose VIP-Bench, a benchmark suite designed with varying operational complexity and computational depth to evaluate competing privacy frameworks fairly and directly. VIP-Bench defines a forward-looking privacy-enhanced computation model and then develops under that model an array of privacy-focused benchmarks. The benchmark set is designed to flexibly cover the whole range of expected computational power and capability, enabling VIP-Bench to evaluate the privacy-enhanced computation capabilities of both today and tomorrow.
ZKCPlus: Optimized Fair-exchange Protocol Supporting Practical and Flexible Data Exchange

Devising a fair-exchange protocol for digital goods has been an appealing line of research in the past decades. The Zero-Knowledge Contingent Payment (ZKCP) protocol first achieves fair exchange in a trustless manner with the aid of the Bitcoin network and zero-knowledge proofs. However, it incurs setup issues and substantial proving overhead, and has difficulties handling complicated validation of large-scale data. In this paper, we propose an improved solution ZKCPlus for practical and flexible fair exchange. ZKCPlus incorporates a new commit-and-prove non-interactive zero-knowledge (CP-NIZK) argument of knowledge under standard discrete logarithmic assumption, which is prover-efficient for data-parallel computations. With this argument we avoid the setup issues of ZKCP and reduce seller's proving overhead, more importantly enable the protocol to handle complicated data validations. We have implemented a prototype of ZKCPlus and built several applications atop it. We rework a ZKCP's classic application of trading sudoku solutions, and ZKCPlus achieves 21-67 times improvement in seller efficiency than ZKCP, with only milliseconds of setup time and 1 MB public parameters. In particular, our CP-NIZK argument shows an order of magnitude higher proving efficiency than the zkSNARK adopted by ZKCP. We also built a realistic application of trading trained CNN models. For a 3-layer CNN containing 8,620 parameters, it takes less than 1 second to prove and verify an inference computation, and also about 1 second to deliver the parameters, which is very promising for practical use.
Real time Arabic speech recognition based on convolution neural network

The development of voice-activated devices to perform handless operation makes modern systems like consumer electronics, audio systems, automobile electronics, household appliances and televisions are smarter. Real-time speech recognition is a major element in such applications, and research is ongoing to improve the functioning of these systems in Arabic languages as well. This paper proposes an Arabic speech recognition system dependent on the Convolution neural networks. The speech recognition model using Mel-Frequency Cepstrum Coefficients (MFCCs) for feature extraction after being processed to reduce noise and remove silence from speech signals. The performance of the proposed system is evaluated on dataset content 12000 samples Arabic spoken digits and commands from various dialects. The proposed system is a better accuracy of recognition 100% during training phases, accuracy of validity 93, and 91.625 % accuracy of the CNN model testing.
Exorcising Spectres with Secure Compilers

ttackers can access sensitive information of programs by exploiting the side-effects of speculatively-executed instructions using Spectre attacks. To mitigate these attacks, popular compilers deployed a wide range of countermeasures whose security, however, has not been ascertained: while some are believed to be secure, others are known to be insecure and result in vulnerable programs. This paper develops formal foundations for reasoning about the security of these defenses. For this, it proposes a framework of secure compilation criteria that characterise when compilers produce code resistant against Spectre v1 attacks. With this framework, this paper performs a comprehensive security analysis of countermeasures against Spectre v1 attacks implemented in major compilers, deriving the first security proofs of said countermeasures
Integration of Business Applications with the Blockchain: Odoo and Hyperledger Fabric Open Source Proof of Concept

Since its revolution in the financial sector, the blockchain technology disrupted the majority of collaboration-based applications including supply chain management. The supply chain is one of the most important sectors benefiting from all the advantages of blockchain. Through this paper, we are mainly focusing on the practical aspects of integrating blockchain technology with traditional and existing business applications. Indeed, most businesses and corporations will find it hard to shift from traditional architectures to a decentralized one with fears related to upgrading risks, unknown tools, and resistance to change. Thus, we mainly propose several scenarios for blockchain integration focusing on the most used Enterprise Resource Planning (ERP) platforms. Besides, we present our proof of concept integration that uses the HyperLedger Fabric blockchain platform and the Odoo ERP framework. The selection of these two solutions for our study was mainly influenced by the fact that Hyperledger is the most used open-source blockchain platform in the industry and that Odoo is the most used open-source ERP framework. Nevertheless, we believe that our proposed solution can easily be used with proprietary ERP platforms and business applications.
App identification based on encrypted multi-smartphone sources traffic fingerprints

The smartphone app identification technology based on traffic fingerprints has begun to play an important role in monitoring malware and assisting network management with the smartphone security issues getting more attention. Smartphones connected to the Internet are often produced by multiple manufacturers, belong to multiple models and use a variety of operating systems. But current researches cannot achieve high-accuracy app identification while using datasets collected from traffic sources of different smartphone. In this paper, we propose an app identification system which use frequency distribution fingerprints to identify apps from encrypted multi-smartphone source traffic. The key idea of our work is to convert the occurrence frequency of traffic stream attribute values (e.g. TCP stream length and SSL/TLS handshake message type) into the format of frequency distribution. We use the random forest algorithm to identify apps from the frequency distributions extracted from the encrypted traffic collected from different three smartphones. In addition, we explore the impact of the app browsing contents, app behaviors, individual differences between smartphones of the same model and brand differences on the performance of our identification system. Our work achieves 99.3% TPR and 0.2% FPR performance on the datasets collected from two different brands of smartphones. Additionally, we show that the variety of app behaviors has the most significant impact on our identification performance, even more than the brand differences.
Mission Planning for Unmanned Aerial Vehicles Based on Voronoi Diagram-Tabu Genetic Algorithm

Unmanned aerial vehicles (UAVs) are increasingly used in different military missions. In this paper, we focus on the autonomous mission allocation and planning abilities for the UAV systems. Such abilities enable adaptation to more complex and dynamic mission environments. We first examine the mission planning of a single unmanned aerial vehicle. Based on that, we then investigate the multi-UAV cooperative system under the mission background of cooperative target destruction and show that it is a many-to-one rendezvous problem. A heterogeneous UAV cooperative mission planning model is then proposed where the mission background is generated based on the Voronoi diagram. We then adopt the tabu genetic algorithm (TGA) to obtain multi-UAV mission planning. The simulation results show that the single-UAV and multi-UAV mission planning can be effectively realized by the Voronoi diagram-TGA (V-TGA). It is also shown that the proposed algorithm improves the performance by 3% in comparison with the Voronoi diagram-particle swarm optimization (V-PSO) algorithm.
Adaptive Computation Offloading for Mobile Augmented Reality

Augmented reality (AR) underpins many emerging mobile applications, but it increasingly requires more computation power for better machine understanding and user experience. While computation offloading promises a solution for high-quality and interactive mobile AR, existing methods work best for high-definition videos but cannot meet the real-time requirement for emerging 4K videos due to the long uploading latency. We introduce ACTOR, a novel computation-offloading framework for 4K mobile AR. To reduce the uploading latency, ACTOR dynamically and judiciously downscales the mobile video feed to be sent to the remote server. On the server-side, it leverages image super-resolution technology to scale back the received video so that high-quality object detection, tracking and rendering can be performed on the full 4K resolution. ACTOR employs machine learning to predict which of the downscaling resolutions and super-resolution configurations should be used, by taking into account the video content, server processing delay, and user expected latency. We evaluate ACTOR by applying it to over 2,000 4K video clips across two typical WiFi network settings. Extensive experimental results show that ACTOR consistently and significantly outperforms competitive methods for simultaneously meeting the latency and user-perceived video quality requirements.
Solar energy toroidal concentrators

This article deals with toroidal concentrators of solar radiation. The developed design configurations have been described. The base model is a semi-torus obtained by cutting full torus along the symmetry plane, the inner surface of semi-toroidal surface is of specular type. The inner surface of semi-toroidal surface is of specular type. The semi-circles of the concentrator cross-section are the generatrices of the surfaces. The radiation receiver is mounted in the midsection, and the radiation receiver symmetry point coincides with that of the concentrator. Results of experimental and theoretical studies of their characteristics have been discussed. Various models employing the advantages of toroidal concentrators have been considered. Analysis results of the most preferable radiation receivers have been presented. Layout options of integration of photoelectric plants equipped with toroidal concentrators of solar radiation into buildings and landscape design objects have been described. Physical limitations of application of installations based on concentrators of this type have been defined and substantiated. Methods for evaluating coast-related characteristics of toroidal concentrators depending on values of their output parameters and on those of design have been presented.
Identifying User Personas for Engagement with a COVID-19 Health Service Delivery Relational Agent

User personas play a crucial role in user-centered design (UCD) methodology-based technology development. In this way, appropriate user personas improve the design of interaction interfaces between a relational agent (RA) and its users, particularly when the RA is about delivering health services to the general population. This article discusses the approach for establishing patient personas to help in designing and developing interaction scenarios and dialogues between a health service RA and its target users during various COVID-19 stages. We employed a mixed-method research that was completed in two phases involving both healthcare professionals (HCPs) and individuals who have encountered COVID-19. Our approach resulted in the identification of three COVID-19 related user personas i.e., experiencing symptoms, COVID-19 positive with mild symptoms, and recovering from COVID-19.
A Meta-analysis of Big Data Security: How the Government Formulates a Model of Public Information and Security Assurance into Big Data

This study aims to find a model for guaranteeing the security of big data information using a bibliometric analysis approach. The data of this research are 710 international indexed scientific articles (Scopus) in the form of (Article, Book Chapter and Conference Paper). Data was collected using the keyword (“security information”) AND (“assurance big data”) and was limited to 2016 to 2020. Bibliometric indicators, such as citations, were used to identify the overall theme structure. The data analysis phase was carried out with the analysis tools of VOSviewer and NVivo Plus 12 software. The use of VOSviewer will map the main theme trends in the research area so that it can find new literature. The main theme mappings are then integrated in the Nvivo Plus12 software analysis tool, to produce large theme visualizations. The results show that the author, institution, and theme keywords infographics that: Research studies on information security and assurance for big data have an increasing trend in the number of articles in the last five years; The study of information security and assurance for big data based on network analysis reveals four important themes of concern to the authors, meaning intelligence, privacy, access, and smart cities. In addition, the Government’s big data information security model pays more attention to data security and privacy, public, social, and trends through a cyber physical social system.
The System for Detecting Vietnamese Mispronunciation

The deepening international integration of Vietnam is open the occupation opportunity for more and more foreigners. The demand for learning Vietnamese, therefore, is rising. However, Vietnamese can be difficult to pronounce for some who just getting started due to its complex vowels and tone marks. A Computer-Assisted Pronunciation Training system can significantly improve the capability of pronouncing for learners. In particular, a mispronunciation model plays an important role in this type of system. While there is still a limited number of researches for Vietnamese, this research introduces a process to build the new mispronunciation detection for the language. Besides, comparing some of the current advanced techniques in this task such as Goodness of Pronunciation for scoring and deep neural network for classifying, a process to build the Vietnamese vowels - tones test set is also introduced to evaluate the performance of the system. The best performance of the proposed model achieved a 0.54 F1 score and 0.46 PCC score on the built test set. This is a reasonable result, compare with a 0.45 PCC score on another English test set.