Reviewerly/exHarmony huggingface dataset:
A novel deep LeNet-5 convolutional neural network model for image recognition

At present, the traditional machine learning methods and convolutional neural network (CNN) methods are mostly used in image recognition. The feature extraction process in traditional machine learning for image recognition is mostly executed by manual, and its generalization ability is not strong enough. The earliest convolutional neural network also has many defects, such as high hardware requirements, large training sample size, long training time, slow convergence speed and low accuracy. To solve the above problems, this paper proposes a novel deep LeNet-5 convolutional neural network model for image recognition. On the basis of Lenet-5 model with the guaranteed recognition rate, the network structure is simplified and the training speed is improved. Meanwhile, we modify the Logarithmic Rectified Linear Unit (L ReLU) of the activation function. Finally, the experiments are carried out on the MINIST character library to verify the improved network structure. The recognition ability of the network structure in different parameters is analyzed compared with the state-of-the-art recognition algorithms. In terms of the recognition rate, the proposed method has exceeded 98%. The results show that the accuracy of the proposed structure is significantly higher than that of the other recognition algorithms, which provides a new reference for the current image recognition.
Implementation of Trusted Traceability Query Using Blockchain and Deep Reinforcement Learning in Resource Management

To better track the source of goods and maintain the quality of goods, the present work uses blockchain technology to establish a system for trusted traceability queries and information management. Primarily, the analysis is made on the shortcomings of the traceability system in the field of agricultural products at the present stage; the study is conducted on the application of the traceability system to blockchain technology, and a new model of agricultural product traceability system is established based on the blockchain technology. Then, a study is carried out on the task scheduling problem of resource clusters in cloud computing resource management. The present work expands the task model and uses the deep Q network algorithm in deep reinforcement learning to solve various optimization objectives preset in the task scheduling problem. Next, a resource management algorithm based on a deep Q network is proposed. Finally, the performance of the algorithm is analyzed from the aspects of parameters, structure, and task load. Experiments show that the algorithm is better than Shortest Job First (SJF), Tetris ∗ , Packer, and other classic task scheduling algorithms in different optimization objectives. In the traceability system test, the traceability accuracy is 99% for the constructed system in the first group of samples. In the second group, the traceability accuracy reaches 98% for the constructed system. In general, the traceability accuracy of the system proposed here is above 98% in 8 groups of experimental samples, and the traceability accuracy is close for each experimental group. The resource management approach of the traceability system constructed here provides some ideas for the application of reinforcement learning technology in the construction of traceability systems.
Feature selection based on ACO and knowledge graph for Arabic text classification

Nowadays, multi-class learning processes have attracted a vast amount of attention due to their complexities and progressive development in various fields. This study focuses on the prediction of semantic categories of Arabic hadith texts by combining the Knowledge-Graph (KG) with the Ant Colony Optimisation (ACO) method. Knowledge-Graph is constructed based on the correlation between features and categories. The links’ weights depend on how frequently the features and the categories occur together, either directly or indirectly. Knowledge-Graph also directly gauges the weights of the links among features. Finally, the ants traverse the graph to select features the most prominent. Features are important whenever they are mentioned frequently in Hadith texts, and they belong to one or fewer categories. It is worth considering that the more relationships between one feature and multiple categories, the less likely it is to be selected. In our analytical experiments, six famous books of the Prophetic Sunnah were used, including more than thirty thousand hadiths with a number of categories slightly under 120. On the experimental side, we found promising results when the ACO-KG model has combined with machine learning classifiers, namely an increase of 3% in the classification task.
An Automated Proctor Assistant in Online Exams Using Computer Vision

Cheating or attempting to cheat in education has had the chance to increase in both number and complexity since the outbreak of the COVID-19 pandemic. With teaching and testing conducted online, learners can easily access prohibited materials without notice of a human proctor. Such problems raise the need for an automated intelligent system to help proctors in supervising test takers. Therefore, this work proposes a system that can automatically examine students’ behaviors through two main cameras. The first camera takes images of a student's frontal face and use them as input for a facial landmark model, detecting anomalies in student’s face movements. The second camera captures a student’s whole body and the surrounding environment, and by using a trained pose recognition model, it can efficiently classify student actions as suspicion or not. Results of this research show good remarks and can be applied in schools, universities experimentally in the future.
MEG: Multi-objective Ensemble Generation for Software Defect Prediction

Background: Defect Prediction research aims at assisting software engineers in the early identification of software defect during the development process. A variety of automated approaches, ranging from traditional classification models to more sophisticated learning approaches, have been explored to this end. Among these, recent studies have proposed the use of ensemble prediction models (i.e., aggregation of multiple base classifiers) to build more robust defect prediction models. Aims: In this paper, we introduce a novel approach based on multi-objective evolutionary search to automatically generate defect prediction ensembles. Our proposal is not only novel with respect to the more general area of evolutionary generation of ensembles, but it also advances the state-of-the-art in the use of ensemble in defect prediction. Method: We assess the effectiveness of our approach, dubbed as Multi-objectiveEnsembleGeneration (MEG), by empirically benchmarking it with respect to the most related proposals we found in the literature on defect prediction ensembles and on multi-objective evolutionary ensembles (which, to the best of our knowledge, had never been previously applied to tackle defect prediction). Result: Our results show that MEG is able to generate ensembles which produce similar or more accurate predictions than those achieved by all the other approaches considered in 73% of the cases (with favourable large effect sizes in 80% of them). Conclusions: MEG is not only able to generate ensembles that yield more accurate defect predictions with respect to the benchmarks considered, but it also does it automatically, thus relieving the engineers from the burden of manual design and experimentation.
Identifying Source Code File Experts

Background: In software development, the identification of source code file experts is an important task. Identifying these experts helps to improve software maintenance and evolution activities, such as developing new features, code reviews, and bug fixes. Although some studies have proposed repository-mining techniques to automatically identify source code experts, there are still gaps in this area that can be explored. For example, investigating new variables related to source code knowledge and applying machine learning aiming to improve the performance of techniques to identify source code experts. Aim: The goal of this study is to investigate opportunities to improve the performance of existing techniques to recommend source code files experts. Method: We built an oracle by collecting data from the development history and surveying developers of 113 software projects. Then, we use this oracle to: (i) analyze the correlation between measures extracted from the development history and the developers' source code knowledge and (ii) investigate the use of machine learning classifiers by evaluating their performance in identifying source code files experts. Results:First Authorship and Recency of Modification are the variables with the highest positive and negative correlations with source code knowledge, respectively. Machine learning classifiers outperformed the linear techniques (F-Measure = 71% to 73%) in the public dataset, but this advantage is not clear in the private dataset, with F-Measure ranging from 55% to 68% for the linear techniques and 58% to 67% for ML techniques. Conclusion: Overall, the linear techniques and the machine learning classifiers achieved similar performance, particularly if we analyze F-Measure. However, machine learning classifiers usually get higher precision while linear techniques obtained the highest recall values. Therefore, the choice of the best technique depends on the user's tolerance to false positives and false negatives.
An Experience Report on Technical Debt in Pull Requests: Challenges and Lessons Learned

Background: GitHub is a collaborative platform for global software development, where Pull Requests (PRs) are essential to bridge code changes with version control. However, developers often trade software quality for faster implementation, incurring Technical Debt (TD). When developers undertake reviewers' roles and evaluate PRs, they can often detect TD instances, leading to either PR rejection or discussions. Aims: We investigated whether Pull Request Comments (PRCs) indicate TD by assessing three large-scale repositories: Spark, Kafka, and React. Method: We combined manual classification with automated detection using machine learning and deep learning models. Results: We classified two datasets and found that 37.7 and 38.7% of PRCs indicate TD, respectively. Our best model achieved F1 = 0.85 when classifying TD during the validation phase. Conclusions: We faced several challenges during this process, which may hint that TD in PRCs is discussed differently from other software artifacts (e.g., code comments, commits, issues, or discussion forums). Thus, we present challenges and lessons learned to assist researchers in pursuing this area of research.
Android API Field Evolution and Its Induced Compatibility Issues

Background: The continuous evolution of the Android operating system necessitates regular API updates, which may affect the functionality of Android apps. Recent studies investigated API evolution to ensure the reliability of Android apps; however, they focused on API methods alone. Aim: We aim to empirically investigate how Android API fields evolve, and how this evolution affects the compatibility of Android apps. Method: We conducted a study based on real-world app development history data involving 11098 tags out of 105 popular open-source Android apps. Results: Our study yields interesting findings, e.g., on average two API field compatibility issues exist per app, different types of checks are preferred when addressing different types of compatibility issues, and fixing compatibility issues induced by API field evolution takes more time than fixing compatibility issues induced by API method evolution. Conclusion: These findings will help developers and researchers better understand, detect, and handle Android compatibility issues induced by API field evolution.
Robot Path Planning Based on Improved Particle Swarm Optimization

Path planning is one of the research hotspots of today's robotics. This paper makes some improvements in allusion to the issue that particle swarm has poor local search ability and sinks into local optimum easily in robot path planning. In this method, the inflection point factor is introduced into the fitness function to cut down the number of turns and accelerate the convergence of the algorithm, and a new adaptive and dynamically adjusted inertia weight is adopted to adjust the local and global search capabilities of particles, so as to avert sinking into the suboptimization prematurely and improve the search quality. Experimental comparison results indicate that the algorithm can improve the quality of particle search in the early and late stage, so that an optimal path can be planned more quickly and effectively.
Chinese Sentiment Analysis Using Regularized Extreme Learning Machine and Stochastic Optimization

In Chinese sentiment analysis, accuracy and execution speed are a comprehensive performance evaluation of an algorithm. This paper proposes an efficient method for sentiment analysis. Firstly, the classification data set is directly used to construct the corpus and word2vec for conversion of processed corpus. Secondly, regularized extreme learning machine(RLM) for sentiment analysis. Considering that the single objective only considers one objective (precision), the particle swarm algorithm is used to solve a multi-objective function for the non-dominated Pareto front optimal value. Experiments were performed on the ChnSentiCorp, and compared with other mainstream models, better classification results and faster execution speed were achieved.
Research on Readability Grade Formula Based on HSK Compositions

The Chinese language proficiency test (Hanyu Shuiping Kaoshi, HSK) is an international Chinese proficiency standardized test. Composition research has always been a hotspot in HSK research. However, the readability of HSK compositions has not been systematically researched. In this paper, 5498 compositions were selected from the HSK examination papers over the years, and then rated by experts according to the differences of readability grades. A standard corpus of HSK compositions marked by experts was constructed. By means of multiple linear regression, the readability grade formula of HSK compositions was derived. The formula shows that the predictive ability of vocabulary features is stronger than that of character features, and the predictive abilities of Chinese character writing features, word length features, syntactic features and emotional features are poor.
Development of Effective Electronic Customer Relationship Management (ECRM) Model by the Applications of Web Intelligence Analytics

Analysis of customer relationships based on their satisfaction is a practical and motivating success factor for the growth of every company. Web intelligence describes the scientific development that uses information technology and artificial intelligence for new frameworks, services, and products provided by the web. This chapter aims to present the model of analyzing the users' sentiments from their online reviews on an e-commerce platform using machine-learning classifiers, namely naive bayes, logistic regression, support vector machine, and neural network. For data analysis, latent semantic analysis has been applied to examine the most frequent words used in online reviews. Finally, customers' interest in online shopping analysis has been performed to classify the customers' sentiments from their posted reviews on the e-commerce platform. In addition, the authors compared the performance results of these classifiers on the e-commerce dataset. The results reveal that the naive bayes classifier has performed better than all the other three classifiers.
Ship Detection in Inland Rivers Based on Multi-Head Self-Attention

Ship object detection is becoming a key technology for unmanned ships and plays a crucial role in ship safety. Aiming at the characteristics of a large difference in scale between classes and many small objects in inland rivers and ports, this paper proposes MHSA-YOLO (Multi-Head Self-Attention-You Only Look Once) based on Multi-Head Self-Attention (MHSA) and You Only Look Once (YOLO) network to implement the ship object detection in inland rivers. More specifically, this proposed method introduces MHSA in the feature extraction process to weaken the interference of complex background information and strengthen the feature information of the ship objects. Further, a simplified two-way feature pyramid is developed in the feature fusion process so as to strengthen the fusion of feature information and the representation ability. The experimental results on the Seaships dataset show that the proposed MHSA-YOLO method achieves 97.59% mAP@.5, and is more effective compared with the state-of-the-art (SOTA) methods.
Non-cooperative Space Target High-Speed Tracking Measuring Method Based on FPGA

Based on the analysis of visible image features of space targets, a high-speed tracking and measurement method is proposed for space non-cooperative targets in a starry background. The interference of background stars on the real target detection results is excluded by using algorithms such as clustering analysis of target-oriented graphical features, and the tracking detection of the target is completed by the shape center extraction algorithm. The algorithm is finally applied to an FPGA-based space embedded system. Finally, the software operation efficiency is improved by optimizing the clustering algorithm and setting the region of interest, and the parallel processing capability of FPGA is used to realize the processing of image data while reading. The software image processing speed is tested to reach 50Hz for targets with imaging scales between 3x3 pixels and 500x500 pixels. Currently, the design has been applied to a practical system with good application results.
Fortuna: A Novel Staked Voting System for Distributed Pari-Mutuel Gaming

In 2008, Bitcoin was introduced to the public and became the first widely accepted, fully decentralized cryptocurrency. However, as effective as blockchain technology has proven to be, one challenge that still exists is the ability to authenticate real-world event outcomes. Previous approaches rely on “trusted” feeds, self-reporting, and semi-centralized oracles - all of which have deficiencies. What is needed is a system and method of bringing real-world data into a decentralized network in a robust and low-cost way. One application for such a system is gaming. Casinos offer many benefits to a bettor. However, players often face large fees and unfavorable odds in order to participate in this centralized process. These high fees and unfavorable odds provide both an opportunity and a need for a decentralized platform with the ability to provide players with a more favorable means to engage in gambling. We seek to solve this problem by implementing a novel method of staked voting, enabling a network of otherwise trustless users to reliably agree on the outcome of a real-world event, such as the 2022 World Cup. We require winning bettors to vote on the outcome of another event in order to redeem their winnings. If they vote with the consensus, their winnings will be released. Otherwise, their winnings will be forfeited. This method of staked voting creates a fully distributed, self-regulated, pari-mutuel betting platform.
Near-Infrared Image Colorization with Weighted UNet++ and Auxiliary Color Enhancement GAN

We propose a novel GAN-based method for near-infrared image colorization. This method innovatively rebalances the color of the colorization image by importing a luminance channel and a feature weight-driven color generator. We set the weighted UNet++ structure in the generator for colorization results with the detail of focal objects. A color enhancement network composed of a deeper luminance network and a colorimetric network is used for global color balance to improve the color quality of the generated color images. Our network is trained and evaluated on two datasets. According to the FID, SSIM and PSNR results, our network performs well, with good recovery effects for both overall color and detailed color and outperforming the current state-of-the-art methods.
Analyzing Soft and Hard Partitions of Global-Scale Blockchain Systems

Partitioning attacks on blockchain systems are a serious threat, with the potential to cause significant harm on the individual level and to the system as a whole. Deliberate partitions can be used by attackers to defraud merchants using crypto-currencies and enrich themselves, while natural disasters and wars could disrupt blockchain systems for months, potentially destroying them entirely. Unfortunately, the exact effects of these partitions at large scale are unknown and difficult to model, making it challenging to implement preventative measures or plan for partition recovery. In this work, we examine a variety of real-world global scale partitioning events to develop a framework for categorizing and analyzing different partition types. We use this framework, along with a new metric introduced to finely measure the global coherence of a given blockchain, to quantify the impacts of soft and hard network partitions at varying scales. Our goal is to lay the groundwork for the introduction of new blockchain architectures to minimize the harm caused during partitions and facilitate rapid and uncontroversial recovery from them.
Generating Adversarial Examples in Audio Classification with Generative Adversarial Network

To improve the performance of acoustic adversarial examples, this paper proposes an adversarial generation model based on Generative Adversarial Network (GAN) for audio classification. By introducing the classification model into GAN, this paper proposes a general GAN framework to execute adversarial attacks for audio classification. Then we propose a Short-time Synthesis GAN-based (SSGAN) attack method, which can reduce the complexity of audio adversarial example generation, and further improve the generality and performance of the GAN-based audio adversarial example generation. Experiments on audio classification datasets such as UrbanSound8k and ESC50 show that compared with existing audio adversarial example generation methods, the proposed method generates adversarial examples with lower perceptibility, and has a higher attack success rate and attack efficiency for typical audio classification models.