Reviewerly/exHarmony huggingface dataset:
Composite refactoring: Representations, characteristics and effects on software projects

code refactoring is a code transformation that aims to improve software quality. A composite refactoring (or, simply, composite) is defined by two or more interrelated refactorings, which is often applied by developers. Each composite needs to be somehow represented and has its own characteristics (e.g., code scope) as well as its effects on software quality. However, these basic elements of composites are rarely studied systematically. The lack of systematic knowledge also misguides the design of automated support tools for supporting composite refactoring. Thus, researchers might have controversial views about basic elements of composite refactorings. An example of these literature conflicts concerns the effect of composites: while some studies suggest composites more often remove code smells, other studies indicate composites often introduce code smells. in this sense, our study aims at analyzing the technical literature of composite refactoring and building a conceptual framework of the representation models, characteristics, and the effect of composite refactoring. we conducted a systematic mapping with 140 primary empirical studies about refactoring. Our systematic mapping summarizes the current knowledge on composites and also presents a conceptual framework intended to characterize composite refactoring. our conceptual framework presents seven representation models, nine characteristics, and thirteen effects of composites. We found out that studies used multidimensional representations, like graphs, to determine what refactoring(s) may be suggested and combined. On composite characteristics, studies mentioned developers often finish a composite in up to a month. However, these studies do not detail why and when composites span for several weeks. Then, we discussed other existing gaps on the current literature of composites. For instance, while most of the studies report the effect of composites on internal software quality, e.g., code smells, their effect on external software quality is little explored. our results can motivate future studies to more deeply investigate composite refactoring applications, and the improvement of tooling support for composite refactorings.
Pay Attention to Hard Trials

Performance of speaker recognition systems is evaluated on test trials. Although as crucial as rulers for tailors, trials have not been carefully treated so far, and most existing benchmarks compose trials by naive cross-pairing. In this paper, we argue that the cross-pairing approach produces overwhelming easy trials, which in turn leads to potential bias in system and technique comparison. To solve the problem, we advocate more attention to hard trials. We present an SVM-based approach to identifying hard trials and use it to construct new evaluation sets for VoxCeleb1 and SITW. With the new sets, we can re-evaluate the contribution of some recent technologies. The code and the identified hard trials will be published online at http://project.cslt.org.
Replay Attack Detection Based on High Frequency Missing Spectrum

Automatic Speaker Verification (ASV) has its benefits compared to other biometric verification methods, such as face recognition. It is convenient, low cost, and more privacy protected, so it can start being used for various practical applications. However, voice verification systems are vulnerable to unknown spoofing attacks, and need to be upgraded with the pace of forgery techniques. This paper investigates a low-cost attacking scenario in which a playback device is used to impersonate the real speaker. The replay attack only needs a recording and playback device to complete the process, so it can be one of the most widespread spoofing methods. In this paper, we explore and investigate some spectral clues in the high sampling rate recording signals, and utilize this property to effectively detect the replay attack. First, a small scale genuine-replay dataset of high sample rates are constructed using some low-cost mobile terminals; then, the signal features are investigated by comparing their spectra; machine learning models are also applied for evaluation. The experimental results verify that the high frequency spectral clue in the replay signal provides a convenient and reliable way to detect the replay attack.
Towards Open Science within Health Care Technology and Management Education

Lack of research reproducibility, restricted access to scientific knowledge to citizens, fake news (on social media) and limited citizens’ involvement during scientific knowledge generation are phenomena which negatively affect the relationship between Science and society. Unfortunately, students, who will be the next scientists, practitioners and citizens, are not educated to address these scientific and societal challenges. Teaching Open Science (OS) principles to students may equip them to deal with these challenges during their future career. OS refers to any endeavour aiming to make one’s research more open, inclusive, accessible, reproducible and replicable. Therefore, OS is expected to address these challenges by promoting: open and reproducible research, publicly available scientific knowledge, public engagement of (scientific) experts within society and greater citizens’ involvement in Science. OS consequently promises to transform the relationship between Science and society. This teaching innovation will introduce Health Care Technology and Management students with the theoretical underpinning of OS and will let students practise OS during a group project. The teaching activities will comprise a series of interactive lectures and practical assignments. Key topics of this teaching innovation are an introduction to OS and FAIR principles, pre-registration, performing open reproducible research, open peer-review and practise public outreach. The teaching materials and activities will be co-created with students who already participated in this course to ensure the OS content matches students’ knowledge, interests and needs. Dissemination efforts will be undertaken during the entire duration of the project to increase awareness concerning the importance of teaching OS within educational curricula.
Cloud computing usage by governmental organizations in Saudi Arabia based on Vision 2030

Cloud computing (CC) has been used in several industries and domains. However, the use of CC in governmental organization is still limited. The purpose of this study is to examine the CC usage among governmental organizations in Saudi Arabia. The population of this study are Information technology (IT) professionals working for governmental organizations in Saudi Arabia. Purposive sampling was used to collect the data from the respondents. The questionnaire was distributed, and 211 valid responses were collected. The analysis was conducted using Smart PLS 4.0. The findings showed that perceived usefulness (PU), perceived ease of use (PEOU), external influence, security but not privacy have significant effects on CC usage. The findings also showed that technological readiness moderated the effect of PU, external influence, and security on CC usage. Decision makers are recommended to enhance the perception of the benefit of CC and conduct more training courses to ease the usage of CC.
Galaxy image classification using hierarchical data learning with weighted sampling and label smoothing

With the development of a series of Galaxy sky surveys in recent years, the observations increased rapidly, which makes the research of machine learning methods for galaxy image recognition a hot topic. Available automatic galaxy image recognition researches are plagued by the large differences in similarity between categories, the imbalance of data between different classes, and the discrepancy between the discrete representation of Galaxy classes and the essentially gradual changes from one morphological class to the adjacent class (DDRGC). These limitations have motivated several astronomers and machine learning experts to design projects with improved galaxy image recognition capabilities. Therefore, this paper proposes a novel learning method, ``Hierarchical Imbalanced data learning with Weighted sampling and Label smoothing" (HIWL). The HIWL consists of three key techniques respectively dealing with the above-mentioned three problems: (1) Designed a hierarchical galaxy classification model based on an efficient backbone network; (2) Utilized a weighted sampling scheme to deal with the imbalance problem; (3) Adopted a label smoothing technique to alleviate the DDRGC problem. We applied this method to galaxy photometric images from the Galaxy Zoo-The Galaxy Challenge, exploring the recognition of completely round smooth, in between smooth, cigar-shaped, edge-on and spiral. The overall classification accuracy is 96.32\%, and some superiorities of the HIWL are shown based on recall, precision, and F1-Score in comparing with some related works. In addition, we also explored the visualization of the galaxy image features and model attention to understand the foundations of the proposed scheme.
A Robust Method: Arbitrary Shape Text Detection Combining Semantic and Position Information

There is a growing interest in scene text detection for arbitrary shapes. The effectiveness of text detection has also evolved from horizontal text detection to the ability to perform text detection in multiple directions and arbitrary shapes. However, scene text detection is still a challenging task due to significant differences in size and aspect ratio and diversity in shape, as well as orientation, coarse annotations, and other factors. Regression-based methods are inspired by object detection and have limitations in fitting the edges of arbitrarily shaped text due to the characteristics of their methods. Segmentation-based methods, on the other hand, perform prediction at the pixel level and thus can fit arbitrarily shaped text better. However, the inaccuracy of image text annotations and the distribution characteristics of text pixels, which contain a large number of background pixels and misclassified pixels, degrades the performance of segmentation-based text detection methods to some extent. Usually, considering whether a pixel belongs to a text region is highly dependent on the strength of the semantic information it has and the position of the pixel in the text area. Based on the above two points, we propose an innovative and robust method for scene text detection combining position and semantic information. First, we add position information to the images using a position encoding module (PosEM) to help the model learn the implicit feature relationships associated with the position. Second, we use the semantic enhancement module (SEM) to enhance the model's focus on the semantic information in the image during feature extraction. Then, to minimize the effect of noise due to inaccurate image text annotations and the distribution characteristics of text pixels, we convert the detection results into a probability map that can more reasonably represent the text distribution. Finally, we reconstruct and filter the text instances using a post-processing algorithm to reduce false positives. The experimental results show that our model improves significantly on the Total-Text, MSRA-TD500, and CTW1500 datasets, outperforming most previous advanced algorithms.
The Effects of Digital Drama-Based Instruction on Developing Receptive and Expressive Language among Kindergarten Children

Young children need to mentally retrieve and express words aloud.They also need understood words when they are heard or read.The purpose of this study was to gain insight into the potential effects of digital drama -based instruction on developing receptive and expressive language among kindergarten children.This study employed a quantitative approach using a quasi-experimental, pre-test and post-test and follow up design.Sixty students were randomly chosen from public kindergartens.The kindergartens were randomly selected from the total number of public kindergartens in the city.They were divided into two groups: experimental group, where they received drama -based instruction for 6 weeks.While children in the control group did not receive such an instruction.The researcher has chosen a number of study topics that are the main topics of the curriculum approved in Kindergarten stage (e.g.Read facial expressions, Natural phenomena, and the Four Seasons etc.).They were formulated in the form of dramatic representations in which movement and action are acted by children at this stage.Using a pre-testintervention-post-test, and follow up design, it has been shown that digital dramabased instruction was effective in developing receptive and expressive language among kindergarten children.
A study on relationship between crypto currency, commodity and foreign exchange rate

Bitcoin is one of the crypto currencies which is increasingly popular as a digital currency. Bitcoin is a digital asset that can be accepted by global modern society nowadays. Bitcoin can be used in online transactions either for the purchase of goods or services. This study aims to find out the significant relationship between Bitcoin, gold, crude oil, and USD. In addition, this study also examines the causal relationship that exists between Bitcoin with each variable, whether bitcoin affects the variable or independent variable affects bitcoin. The data used is secondary data obtained from historical data in investing.com starting from September 2015 until September 2020. This data was analyzed using Regression and Correlation, Unit Root Test and Pairwise Granger Causality to see the relationship and influence between variables. The results show that there is a significant relationship between bitcoin, gold, and crude oil. While there is an insignificant relationship with the USD. The study also shows that there is a positive correlation impact between Bitcoin, gold, and crude oil. There is a negative correlation impact between Bitcoin and the USD. That is, when there is an increase in gold and crude oil, it will also increase the Bitcoin. As for the USD currency, if the USD increases, Bitcoin will decrease and vice versa. Based on the analysis of granger causality shows that gold, crude oil, and USD do not cause Bitcoin, but Bitcoin, gold and crude oil cause the USD.
Image‐based fall detection in bus compartment scene

Bus, as an important means of public transportation, has always received attention for its safety. When an emergency occurs, fall behaviour is one of representative features to help alarm. However, compared with other open and fixed scenes, the bus compartment has its own characteristics, such as crowded, closed, in moving, and so on. Considering the problems of people blocked as well as light changes, this paper proposes a new image-based fall detection method in bus compartment scene, which can be divided into three parts: (1) Human detection, where object detection and pose estimation algorithms are combined, and then both global and local human features can be obtained; (2) fall discrimination, where both of the fall discrimination conditions and fall discrimination network are designed; (3) alert, where an alarm strategy is designed. Experiments are done in a real bus, and the Nvidia Jetson Xavier NX module is used to analyse the videos and images. Results finally show that the proposed fall detection method in bus compartment scenes can achieve 90% accuracy.
Blockchain-Based Decentralized Federated Learning

Federated learning (FL) has gained great traction in recent years. It can provide a privacy-preserving mechanism to train machine learning models on hidden data. However, most of today's FL systems use a centralized server to build the global FL model. Such centralization raises trust and fairness issues stemming from the fact that the FL server may have the ability to reconstruct the original data successfully. In this paper, we propose a blockchain-based decentralized FL system. The FL process in the proposed system is composed of two stages. In the first stage, FL nodes reach a consensus on the training configurations using smart contracts. In the second stage, nodes aggregate the model updates using a novel decentralized aggregation method. The proposed system efficiently schedules aggregation tasks between decentralized nodes, handles dropouts, and detects malicious acts. We test and analyze our solution using Ethereum smart contracts and Python. The implementation and testing details of the algorithms are presented, and all codes are publicly available on GitHub.
Multi-applicable text classification based on deep neural network

Most long text classification methods based on deep learning have problems such as semantics sparsity and long-distance dependence. To tackle these problems, a novel multi-applicable text classification based on deep neural network (MTDNN) is proposed, which contains a bidirectional encoder representation from transformer (BERT), a dimension reduction layer, and the bidirectional long short-term memory (Bi-LSTM) combining attention mechanism. BERT is used to pre-train the words into the word embedding vectors. The dimension reduction layer extracts the feature phrase representations with higher weight from the word embedding vectors. The Bi-LSTM captures both the forward and backward context representations. An attention mechanism is employed to focus on the information outputted from the Bi-LSTM. The experimental results illustrate that the accuracy of the MTDNN for long text, short text classification, and sentiment analysis reaches 94.95%, 93.53% and 92.32%, respectively. The results show that our method outperforms the other state-of-the-art text classification methods.
SQL Injection Attack Detection Using Machine Learning Algorithm

The structured query language injection attack (SQLIA) is a well-known cyberattack targeting vulnerabilities in web-based applications; it is used to carry out illegal information control language, bypass confirmation measures, and get access to restricted data. There was some consideration given to existing systematic reviews in the literature. Contemporary systematic reviews frequently incorporate both older and more contemporary works in the topic. Therefore, we restricted ourselves to recently published works. For the current study, I used information from 2012 through 2020. Encryption, XML, design coordination, parsing, and machine learning are just some of the methods and systems that can be used to spot and prevent SQL injection attacks. The Machine Learning (ML) process, which has been proved to be important for SQLIA relief, is applied with the help of guarded coding. Machine learning approaches require a large amount of data for model preparation and only handle a few number of attack types. The use of ML methods may alleviate a particularly challenging vision impairment SQL injection attack. In the Waikato Climate for Data Exploration study, we looked at the following methods: Logistic Regression (LRN), Stochastic Gradient Descent (SDG), Sequential Minimal Optimization (SMO), Bayes Network (BNK), Instance-Based Learner (IBK), Multilayer Perceptron (MLP), Naive Bayes (NB), and J48. Wait (70%) and 10-fold Cross Validation assessment procedures were used to survey the presentation of the regulated learning grouping calculations to choose the optimal calculation. Accuracy values for SMO, IBK, and J48 were found to be 98.7785%, 98.4285%, and 98.2985% using the Cross Validation method, and 98.7956%, 98.1526%, and 100 using the Hold-Out method. Using the Cross Validation method SMO took IBK and J48 10.15 seconds, 0.06 seconds, and 14.12 seconds, whereas using the Hold-Out method SMO took 9.71 seconds, 0.16 seconds, and 14.28 seconds to construct their models. Based on the findings, IBK was chosen as the classifier for SQLIA detection and prevention since it was the fastest to train a model using the Cross Validation strategy and had the best overall performance. Not only is accuracy essential when choosing an algorithm for predictive analytics, but also a variety of performance assessment indicators.
Multi-Agent Path Finding with Temporal Jump Point Search

Temporal Jump Point Search (JPST) is a recently introduced algorithm for grid-optimal pathfinding among dynamic temporal obstacles. In this work we consider JPST as a low-level planner in Multi-Agent Path Finding (MAPF). We investigate how the canonical ordering of JPST can negatively impact MAPF performance and we consider several strategies which allow us to overcome these limitations. Experiments show our new CBS/JPST approach can substantially improve on CBS/SIPP, a contemporary and leading method from the area.
A Novel Blockchain Based Approach to Exchanging Information and Data in Power Systems

Exchanging information and data using cloud computing in power systems is now becoming common practice. However, there are still many challenges such as data security, interoperability, and scalability that need to be considered. In this paper, the authors have exploited blockchain technology by integrating it with Hadoop to enhance interoperability concerning power system operators at both transmission and distribution levels. A blockchain based approach is developed to exchange information and data within power systems. This approach enables users to exchange information and data without losing ownership or control of the data. The proposed approach provides the solutions to three important issues: scalability, data ownership, and interoperability. The case studies as presented in this paper evaluate the effectiveness of the proposed novel approach for enhancing information and data exchange. The paper specifically evaluates enhanced performance with regard to scalability, latency, and computational time.
Distributed Data-Sharing Consensus in Cooperative Perception of Autonomous Vehicles

To enable self-driving without a human driver, an autonomous vehicle needs to perceive its surrounding obstacles using onboard sensors, of which the perception accuracy might be limited by their own sensing range. An effective way to improve vehicles’ perception accuracy is to let nearby vehicles exchange their sensor data so that vehicles can detect obstacles beyond their own sensing ranges, called cooperative perception. The shared sensor data, however, might disclose the sensitive information of vehicles’ passengers, raising privacy and safety concerns (e.g. stalking or sensitive location leakage).In this paper, we propose a new data-sharing policy for the cooperative perception of autonomous vehicles, of which the objective is to minimize vehicles’ information disclosure without compromising their perception accuracy. Considering vehicles usually have different desires for data-sharing under different traffic environments, our policy provides vehicles autonomy to determine what types of sensor data to share based on their own needs. Moreover, given the dynamics of vehicles’ data-sharing decisions, the policy can be adjusted to incentivize vehicles’ decisions to converge to the desired decision field, such that a healthy cooperation environment can be maintained in a long term. To achieve such objectives, we analyze the dynamics of vehicles’ data-sharing decisions by resorting to the game theory model, and optimize the data-sharing ratio in the policy based on the analytic results. Finally, we carry out an extensive trace-driven simulation to test the performance of the proposed data-sharing policy. The experimental results demonstrate that our policy can help incentivize vehicles’ data-sharing decisions to the desired decision fields efficiently and effectively.