Reviewerly/exHarmony huggingface dataset:
How has forking changed in the last 20 years?

The notion of forking has changed with the rise of distributed version control systems and social coding environments, like GitHub. Traditionally forking refers to splitting off an independent development branch (which we call hard forks); research on hard forks, conducted mostly in pre-GitHub days showed that hard forks were often seen critical as they may fragment a community. Today, in social coding environments, open-source developers are encouraged to fork a project in order to contribute to the community (which we call social forks), which may have also influenced perceptions and practices around hard forks. To revisit hard forks, we identify, study, and classify 15,306 hard forks on GitHub and interview 18 owners of hard forks or forked repositories. We find that, among others, hard forks often evolve out of social forks rather than being planned deliberately and that perception about hard forks have indeed changed dramatically, seeing them often as a positive non-competitive alternative to the original project.
Cross-Spectrum Thermal Face Pattern Generator

Conversion of a visible face image into a thermal face image (V2T), or one thermal face image into another one given a different target temperature (T2T), is required in applications such as thermography, human body thermal pattern analysis, and surveillance using cross-spectral imaging. In this work, we propose to use conditional generative adversarial networks (cGAN) with cGAN loss, perceptual loss, and temperature loss to solve the conversion tasks. In our experiment, we used Carl and SpeakingFaces Databases. Frèchet Inception Distance (FID) is used to evaluate the generated images. As well, face recognition was applied to assess the performance of our models. For the V2T task, the FID of the generated thermal images reached a low value of 57.3. For the T2T task, we achieved a rank-1 face recognition rate of 91.0% which indicates that the generated thermal images preserve the majority of the identity information.
Modeling of Supervised Machine Learning using Mechanism of Quantum Computing.

Abstract Mechanism of quantum computing helps to propose several task of machine learning in quantum technology. Quantum computing is enriched with quantum mechanics such as superposition and entanglement for making new standard of computation which will be far different than classical computer. Qubit is sole of quantum technology and help to use quantum mechanism for several tasks. Tasks which are non-computable by classical machine can be solved by quantum technology and these tasks are classically hard to compute and categorised as complex computations. Machine learning on classical models is very well set but it has more computational requirements based on complex and high-volume data processing. Supervised machine learning modelling using quantum computing deals with feature selection, parameter encoding and parameterized circuit formation. This paper highlights on integration of quantum computation and machine learning which will make sense on quantum machine learning modeling. Modelling of quantum parameterized circuit, Quantum feature set design and implementation for sample data is discussed. Supervised machine learning using quantum mechanism such as superposition and entanglement are articulated. Quantum machine learning helps to enhance the various classical machine learning methods for better analysis and prediction using complex measurement.
Drainage System of Tegalsari Polder for Handling Flood and Tide in Tegal City Indonesia

Abstract Although in 2019 the local government of Tegal city Indonesia had constructed a retention basin at drainage system of Siwatu, Tegal Barat, Tegal city with a catchment area of 226 ha, the areas around the system still experienced flood and inundation. This study belonged to a descriptive qualitative research aimed to evaluate the performances of Siwatu drainage system and Tegalsari retention basin. Data of the study included field data and technical data from institutions. Based on the 15-year rainfall data (2014 – 2018) from Pemali - Comal PSDA Office, Central Java Province, Indonesia, the statistical parameters of Cs: 0.0027, Ck: 1.904, Sd: 15.91, Cv: 0.144 were obtained and so Gumbel method distribution was applied in the study, the return period rainfall of 10 years was 138 mm, the flood discharge for Qr.10 years was 9.63 m 3 /sec., the addition of long storage was 8×2,50×500 m, and the combination of pump addition was of 1 m 3 /sec. with the long storage of 8×2.00×500 m. By implementing one of the alternative choices, either flood or inundation could be resolved.
Quantum Algorithm for Attacking RSA Based on Fourier Transform and Fixed-Point

Shor in 1994 proposed a quantum polynomial-time algorithm for finding the order r of an element a in the multiplicative group Z n * , which can be used to factor the integer n by computing [see formula in PDF] and hence break the famous RSA cryptosystem. However, the order r must be even. This restriction can be removed. So in this paper, we propose a quantum polynomial-time fixed-point attack for directly recovering the RSA plaintext M from the ciphertext C , without explicitly factoring the modulus n . Compared to Shor’s algorithm, the order r of the fixed-point C for RSA( e , n ) satisfying [see formula in PDF] does not need to be even. Moreover, the success probability of the new algorithm is at least [see formula in PDF] and higher than that of Shor’s algorithm, though the time complexity for both algorithms is about the same.
Securing Cloud Virtual Machine Image Using Ethereum Blockchain

Virtual Machine Image (VMI) is the building block of cloud infrastructure. It encapsulates the various applications and data deployed at the Cloud Service Provider (CSP) end. With the leading advances of cloud computing, comes the added concern of its security. Securing the Cloud infrastructure as a whole is based on the security of the underlying Virtual Machine Images (VMI). In this paper an attempt has been made to highlight the various risks faced by the CSP and Cloud Service Consumer (CSC) in the context of VMI related operations. Later, in this article a formal model of the cloud infrastructure has been proposed. Finally, the Ethereum blockchain has been incorporated to secure, track and manage all the vital operations of the VMIs. The immutable and decentralized nature of blockchain not only makes the proposed scheme more reliable but guarantees auditability of the system by maintaining the entire VMI history in the blockchain.
Ancient Tibetan Word Segmentation based on Deep Learning

Tibetan ancient literature is an important literature material for the study of ancient Tibetan culture, history and language, which has important academic value for the study of the development of Sino Tibetan language family. However, the lack of research on ancient Tibetan word segmentation seriously restricts the research of ancient Tibetan literature. In view of this situation, this paper utilizes ancient Tibetan interlaced contrast tagging data to extract the ancient Tibetan word segmentation data set. Based on this dataset, this paper conduct the research on dictionary based word segmentation, statistics based word segmentation and deep learning based ancient Tibetan word segmentation. Experimental results show that BiLSTM + CRF word segmentation algorithm can achieve the best performance by a single model, and the effect of ancient Tibetan word segmentation can be further improved through model ensemble. And the results show that the unknown words, insufficient training data and word ambiguity also restrict the performance of ancient Tibetan word segmentation.
RvDfi: A RISC-V Architecture With Security Enforcement by High Performance Complete Data-Flow Integrity

With the rapid revolution of open-source hardware, RISC-V architecture has been prevalent in both academic research and industrial developments. Due to the increasing threats of information leakage, it is imperative to provide a secure RISC-V ecosystem to defend against malicious software exploits. Toward this goal, data-flow integrity (DFI) is employed as a strict security policy for enforcing the legitimacy of each data access, thereby filtering out most of the attack exploits. However, due to the intensive computations needed by DFI, there are only limited proposals successfully implementing partial DFI with low performance overhead. Moreover, all the previous studies failed to enforce the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">complete</i> DFI policy in a real hardware platform, while trading off security strength for performance efficiency. To provide RISC-V architecture with high security enforcement and low performance overhead, we leverage the open-source Rocket Chip and propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RvDfi</small> , the first complete DFI implementation based on RISC-V architecture with only 17.8% performance overhead on average and 3.9% in minimum, incurring much less performance loss compared to the 166.3% overhead caused by previous complete DFI implementation.
Decision Intelligence Analytics: Making Decisions Through Data Pattern and Segmented Analytics

Prescriptive analysis is regarded as the extensive probe with regard to the value added to the analysis. Panel data or longitudinal data can be regarded as a marriage between time-series and cross-sectional data. This kind of data is mostly used in econometrics, epidemiology, business, and social behavioral studies. The representation, analysis, and prediction of panel data are of utmost importance for decision making. Although traditional models are present for panel data analysis, there is no rigid model to represent panel data and then perform analysis. The representation gets tough due to the multidimensionality factor present. In this chapter, the author tries to represent panel data formally in terms of a lattice, and then exploiting the properties of concept lattice, various rule-based analyses are presented with support and confidence level. The algorithm is finally implemented in a data set, and various interesting observations are generated.
A Parallel Quasi-identifier Discovery Scheme for Dependable Data Anonymisation

Quasi-identifiers (QIDs) are attribute combinations that can be used to discover hidden personal identifying information from an anonymised dataset. Typically, the information drawn from such QIDs can then be combined with more publicly accessible datasets to discover sensitive information (e.g. medical conditions, financial status, criminal history, ...). Research on data anonymisation has therefore proposed various algorithms to discover and transform quasi-identifiers efficiently to prevent re-identification. However, all existing algorithms are inefficient and fail to prevent re-identification attacks on large real-world high dimensional datasets successfully. This paper presents a quasi-identifier discovery algorithm that combines parallelism with an efficient search technique to find all minimal quasi-identifiers in a given dataset. As a further step, we present an adversary model based on the enumeration problem of discovering unique column combinations in a dataset. We demonstrate that our quasi-identifier discovery algorithm is secure to re-identification attacks based on this adversarial model, even in the presence of large high-dimensional datasets that change dynamically. Our empirical results show that our algorithm not only scales well to large high-dimensional datasets but exploits its parallelisability on GPU (Graphics Processing Unit) architectures to prevent re-identification even in the presence of a powerful adversary equipped with similar high-performance computing processing power. Furthermore, our results show that the proposed GPU algorithm offers up to 100x times speedup over the algorithm's CPU version.
A Trajectory Evaluator by Sub-tracks for Detecting VOT-based Anomalous Trajectory

With the popularization of visual object tracking (VOT), more and more trajectory data are obtained and have begun to gain widespread attention in the fields of mobile robots, intelligent video surveillance, and the like. How to clean the anomalous trajectories hidden in the massive data has become one of the research hotspots. Anomalous trajectories should be detected and cleaned before the trajectory data can be effectively used. In this article, a Trajectory Evaluator by Sub-tracks (TES) for detecting VOT-based anomalous trajectory is proposed. Feature of Anomalousness is defined and described as the Eigenvector of classifier to filter Track Lets anomalous trajectory and IDentity Switch anomalous trajectory, which includes Feature of Anomalous Pose and Feature of Anomalous Sub-tracks (FAS). In the comparative experiments, TES achieves better results on different scenes than state-of-the-art methods. Moreover, FAS makes better performance than point flow, least square method fitting and Chebyshev Polynomial Fitting. It is verified that TES is more accurate and effective and is conducive to the sub-tracks trajectory data analysis.
A novel realtime vision-based acupoint estimation for TCM massage robot

Traditional Chinese medicine(TCM) massage robot is a new technology that combines traditional Chinese medicine and modern robot. It plays a vital role in alleviating the shortage of doctor resources and achieving the clinical curative effect of traditional Chinese medicine. Self-localization of acupoint massage points is the key to realizing TCM massage robots' automatic and intelligent massage. A novel real-time 3D visual acupoint estimation method is proposed in this paper. Firstly, the neural network is used to obtain the information of human joints, then proportional geometry measurement is used to analyze the geometric relationship of acupoints, and finally, the accurate positioning of acupoints is realized combined with the 3D information. This method can realize fully automatic real-time acupoint 3D estimation. The research shows that the average positioning accuracy of this method is 2.36cm, the real-time performance can reach 14fps, and it has a good adaptive effect for acupoint positioning of people with different bodies, environments, and postures.
A Condition Evaluation Method of Isolation Circuit Breaker Based on Triangular Fuzzy Analytic Hierarchy Process

Compared with other open equipment, isolated circuit breaker has the characteristics of complex structure and difficult condition evaluation. In order to evaluate the operation condition of isolated circuit breaker comprehensively, objectively and accurately, a new state evaluation method of isolated circuit breaker based on fuzzy analytic hierarchy process (FAHP) is put forward to process data scientifically, which combines triangular fuzzy number with analytic hierarchy process. The problems of strong subjectivity of expert assignment and difficult consistency of judgment matrix are effectively sloved by the combination of objective data and subjective evaluation. The results show that TAHP can scientifically evaluate the operation state of the isolated circuit breaker, assist the operation personnel to grasp the equipment status in time, and provide reference for the management of substation equipment.
An Efficient Deep Neural Networks-Based Channel Estimation and Signal Detection in OFDM Systems

This article represents an improvement in the efficiency of Deep Neural Networks (DNN) employed in OFDM systems for estimation of channel, detection of symbols by using Hyperparameter tuning. Deep learning is increasing its popularity in each field and it's a set of Artificial Intelligence (AI). Deep Neural Network (DNN) model is completely different from traditional Orthogonal Frequency Division Multiplexing (OFDM) receiver in such a way that OFDM receiver has to estimate the channel state information separately then it is able to detect the transmitted signal, but DNN model will be trained offline by simulated data over various channel statistics then directly employed in online for detection of the transmitted signal directly. Deep learning gives a performance better than traditional methods, i.e., Minimum Mean Square Error (MMSE) of channel estimation and symbol detection in wireless communications. First, we have analyzed the performance of traditional methods, i.e., MMSE over Winner II channel without deep learning by observing BER with cyclic prefix and without Cyclic Prefix (CP) and with variation in number of pilot carriers. Thereafter we will deploy hyperparameter tuning with proposed DNN model in OFDM for estimation of channel and detection of symbols in the winner II channel mainly for the Urban power delay profile using QPSK modulation. Here, we have claimed that by reducing the no of epochs from 20,000 to 10,000, training period will be reduced from 52 to 26 h, i.e., 50% while maintaining the almost same BER performance.
Virtual climbing: An immersive upslope walking system using passive haptics

In virtual environments (VEs), users can explore a large virtual scene through the viewpoint operation of a head-mounted display (HMD) and movement gains combined with redirected walking technology. The existing redirection methods and viewpoint operations are effective in the horizontal direction; however, they cannot help participants experience immersion in the vertical direction. To improve the immersion of upslope walking, this study presents a virtual climbing system based on passive haptics. This virtual climbing system uses the tactile feedback provided by sponges, a commonly used flexible material, to simulate the tactile sense of a user's soles. In addition, the visual stimulus of the HMD, the tactile feedback of the flexible material, and the operation of the user's walking in a VE combined with redirection technology are all adopted to enhance the user's perception in a VE. In the experiments, a physical space with a hard-flat floor and three types of sponges with thicknesses of 3, 5, and 8 cm were utilized. We recruited 40 volunteers to conduct these experiments, and the results showed that a thicker flexible material increases the difficulty for users to roam and walk within a certain range. The virtual climbing system can enhance users' perception of upslope walking in a VE.
Eye Fixation Versus Pupil Diameter as Eye-Tracking Features for Virtual Reality Emotion Classification

The usage of eye-tracking technology is becoming increasingly popular in machine learning applications, particularly in the area of affective computing and emotion recognition. Typically, emotion recognition studies utilize popular physiological signals such as electroencephalography (EEG), while the research on emotion detection that relies solely on eye-tracking data is limited. In this study, an empirical comparison of the accuracy of eye-tracking-based emotion recognition in a virtual reality (VR) environment using eye fixation versus pupil diameter as the classification feature is performed. We classified emotions into four distinct classes according to Russell's four-quadrant Circumplex Model of Affect. 360° videos are presented as emotional stimuli to participants in a VR environment to evoke the user's emotions. Three separate experiments were conducted using Support Vector Machines (SVMs) as the classification algorithm for the two chosen eye features. The results showed that emotion classification using fixation position obtained an accuracy of 75% while pupil diameter obtained an accuracy of 57%. For four-quadrant emotion recognition, eye fixation as a learning feature produces better classification accuracy compared to pupil diameter. Therefore, this empirical study has shown that eye-tracking-based emotion recognition systems would benefit from using features based on eye fixation data rather than pupil size.
Learning Cooperative Multi-Agent Policies With Partial Reward Decoupling

One of the preeminent obstacles to scaling multi-agent reinforcement learning to large numbers of agents is assigning credit to individual agents' actions. In this paper, we address this credit assignment problem with an approach that we call \textit{partial reward decoupling} (PRD), which attempts to decompose large cooperative multi-agent RL problems into decoupled subproblems involving subsets of agents, thereby simplifying credit assignment. We empirically demonstrate that decomposing the RL problem using PRD in an actor-critic algorithm results in lower variance policy gradient estimates, which improves data efficiency, learning stability, and asymptotic performance across a wide array of multi-agent RL tasks, compared to various other actor-critic approaches. Additionally, we relate our approach to counterfactual multi-agent policy gradient (COMA), a state-of-the-art MARL algorithm, and empirically show that our approach outperforms COMA by making better use of information in agents' reward streams, and by enabling recent advances in advantage estimation to be used.
Accelerate Graph Neural Network Training by Reusing Batch Data on GPUs

With the increasing adoption of graph neural networks (GNNs) in the graph-based deep learning community, various graph programming frameworks and models have been developed to improve the productivity of GNNs. The current GNN frameworks choose GPU as an essential tool to accelerate GNN training. However, it is still challenging to train GNNs on large graphs with limited GPU memory. Unlike traditional neural networks, generating mini-batch data by sampling in GNNs requires some complicated tasks such as traversing the graph to select neighboring nodes and gathering their features. This process takes up most of the training and we find the main bottleneck comes from transferring nodes features from CPU to GPU through limited bandwidth. In this paper, We propose a method Reusing Batch Data for the problem of data transmission. This method utilizes the similarity between adjacent mini-batches to reduce repeated data transmission from CPU to GPU. Furthermore, to reduce the overhead introduced by this method, we design a fast algorithm based on GPU to detect repeated nodes’ data and achieve shorter additional computation time. Evaluations on three representative GNN models show that our method can reduce transmission time by up to 60% and speed the end-to-end GNN training by up to 1.79× over the state-ofthe-art baselines. Besides, Reusing Batch Data can effectively save GPU memory footprint by about 19% to 40% while still reducing the training time compared to the static cache strategy.