Reviewerly/exHarmony huggingface dataset:
Studying the effectiveness of deep active learning in software defect prediction

AbstractAccurate prediction of defective software modules is of great importance for prioritizing quality assurance efforts, reasonably allocating testing resources, reducing costs and improving software quality. Several studies have used machine learning to predict software defects. However, complex structures and imbalanced class distributions in software defect data make learning an effective defect prediction model challenging. In this article, two deep learning-based defect prediction models using static code metrics are proposed. In order to enhance the learning process and improve the performance of the proposed models, pool-based active learning is employed. In this regard, the possibility of using active learning to mitigate the need for a large amount of labeled data in the process of building deep learning models is investigated. To deal with imbalanced distribution of software modules between defective and non-defective classes, Near-Miss under-sampling and KNN, with different number of neighbors, are used. The reason for choosing them is their good performance in binary classification problems. Experiments are performed on two well-known, publicly available datasets, GitHub Bug Dataset and public Unified Bug Dataset for java projects. The evaluation results reveal the effectiveness of our proposed models in comparison to the traditional machine learning algorithms. In the conducted investigations on the Unified Bug Dataset, at the file level, the value of F-measure and AUC criteria have improved by 13 and 11 percent, respectively and at the class level, the values have improved by 14 and 11 percent, respectively.KEYWORDS: Bug predictioncode metricsactive learningdeep learning Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsFarid FeyziFarid Feyzi received his M.S. degree in Software Engineering from the Sharif University of Technology in 2012, and his Ph.D. in Software Engineering from the Iran University of Science and Technology in 2018. He is an assistant professor of Software Engineering at the University of Guilan. His research focus is on developing statistical algorithms to improve software quality with an emphasis on statistical fault localization and automated test data generation.Arman DaneshdoostArman Daneshdoost recieved his M.S. degree in Software Engineering from University of Guilan in 2020. His research focus is on designing, developing and fine tunning deep learning models and transformers to improve the performance of automated industrial and service based systems and applications.
Machine Learning-Based Garbage Detection and 3D Spatial Localization for Intelligent Robotic Grasp

Garbage detection and 3D spatial localization play a crucial role in industrial applications, particularly in the context of garbage trucks. However, existing approaches often suffer from limited precision and efficiency. To overcome these challenges, this paper presents an algorithmic architecture that leverages advanced techniques in computer vision and machine learning. The proposed approach integrates cutting-edge computer vision methodologies to improve the precision of waste classification and spatial localization. By utilizing RGB-D data captured by the RealSenseD415 camera, the algorithm incorporates state-of-the-art computer vision algorithms and machine learning models, including the Yolactedge model, for real-time instance segmentation of garbage objects based on RGB images. This enables the accurate prediction of garbage class and the generation of masks for each instance. Furthermore, the predicted masks are utilized to extract the point cloud corresponding to the garbage instances. The oriented bounding boxes of the segmented point cloud is calculated as the spatial location information of the garbage instances using the DBSCAN clustering algorithm to remove the interfering points. The findings indicate that the proposed approach can run at a maximum speed of 150 FPS. The usefulness of the proposed method in achieving accurate garbage recognition and spatial localization in a vision-driving robot grasp system has been tested experimentally on datasets that were custom-collected. The results demonstrate the algorithmic architecture’s ability to transform waste management procedures while also enabling intelligent garbage sorting and enabling robotic grasp applications.
Contrastive Learning Methods for Deep Reinforcement Learning

Deep reinforcement learning (DRL) has shown promising performance in various application areas (e.g., games and autonomous vehicles). Experience replay buffer strategy and parallel learning strategy are widely used to boost the performances of offline and online deep reinforcement learning algorithms. However, state-action distributions shift leads to bootstrap errors. Experience replay buffer learns policies with elder experience trajectories, limiting its application to off-policy algorithms. Balancing the new and the old experience is challenging. Parallel learning strategies can train policies with online experiences. However, parallel environmental instances organize the agent pool inefficiently with higher simulation or physical costs. To overcome these shortcomings, we develop four lightweight and effective DRL algorithms, instance-actor, parallel-actor, instance-critic, and parallel-critic methods, to contrast different-age trajectory experiences. We train the contrast DRL according to the received rewards and proposed contrast loss, which is calculated by designed positive/negative keys. Our benchmark experiments using PyBullet robotics environments show that our proposed algorithm matches or is better than the state-of-the-art DRL algorithms.
Robust Explanations for Human-Neural Multi-agent Systems with Formal Verification

The quality of explanations in human-agent interactions is fundamental to the development of trustworthy AI systems. In this paper we study the problem of generating robust contrastive explanations for human-neural multi-agent systems and introduce two novel verification-based algorithms to (i) identify non-robust explanations generated by other methods and (ii) generate contrastive explanations equipped with formal robustness certificates. We present an implementation and evaluate the effectiveness of the approach on two case studies involving neural agents trained on credit scoring and traffic sign recognition tasks.
Comparison of Prostate Cell Image Classification Using CNN: ResNet-101 and VGG-19

Being the most common disease in men, prostate cancer attacks the urinary system. Men with higher androgen levels have a greater risk of developing prostate cancer. This cancer occurs in the prostate gland of the male reproductive tract. This cancer appears when it begins to mutate and reproduce uncontrollably. Risk factors for prostate cancer include age, race and family history. This study classified prostate cell images based on their severity. Along with today’s technological advancement, especially research on image classification, it will be simpler for medical personnel to educate the public on how to recognize the severity of prostate cancer through a system. This image classification system utilized the preserved models of the deep learning method from the Convolutional Neural Network (CNN) algorithms: ResNet-101 and VGG-19. It aims to discover the most appropriate algorithm in image classification, determined from the training graph and confusion matrix calculation results. The measurements of the models’ reliability encompassed accuracy, computation time, graph stability, and confusion matrix calculation results. ResNet-101 appeared as the model with the highest training data accuracy and fastest computation time. The confusion matrix calculation also unveiled that ResNet-101 acquired the greatest results with an average accuracy of 97.70%, precision of 93.19%, recall of 93.25%, specificity of 98.62% and F-score of 93.11%, demonstrating its superiority over VGG-19 in classifying prostate cell images based on testing data.
Fourier expansion in variational quantum algorithms

The Fourier expansion of the loss function in variational quantum algorithms (VQAs) contains a wealth of information yet is generally hard to access. We focus on the class of variational circuits where constant gates are Clifford gates and parametrized gates are generated by Pauli operators, which covers most practical cases while allowing much control due to the properties of stabilizer circuits. We give a classical algorithm that, for an $N$-qubit circuit and a single Pauli observable, computes coefficients of all trigonometric monomials up to a degree $m$ in time bounded by $O(N{2}^{m})$. Using the general structure and implementation of the algorithm, we reveal several aspects of Fourier expansions in Clifford plus Pauli VQAs such as (i) reformulating the problem of computing the Fourier series as an instance of the multivariate Boolean quadratic system, (ii) showing that the approximation given by a truncated Fourier expansion can be quantified by the ${L}^{2}$-norm and evaluated dynamically, (iii) the tendency of Fourier series to be rather sparse and Fourier coefficients to cluster together, and (iv) the possibility to compute the full Fourier series for circuits of nontrivial sizes, featuring tens to hundreds of qubits and parametric gates.
The Inverse of the Discrete Momentum Operator

In the search of a quantum momentum operator with discrete spectrum, we obtain some properties of the discrete momentum operator for nonequally spaced spectrum. We find the inverse operator. We use the matrix representation of these operators, and we find that there is one more eigenvalue and eigenfunction than the dimension of the matrix. We apply the results to obtain the discrete adjoint of the momentum operator. We conclude that we can have discrete operators which can be self-adjoint and that it is possible to define a self-adjoint extension of the corresponding Hilbert space. These results help us understand the quantum time operator.
Lessons Learnt from a Multimodal Learning Analytics Deployment In-the-Wild

Multimodal Learning Analytics (MMLA) innovations make use of rapidly evolving sensing and artificial intelligence algorithms to collect rich data about learning activities that unfold in physical spaces. The analysis of these data is opening exciting new avenues for both studying and supporting learning. Yet, practical and logistical challenges commonly appear while deploying MMLA innovations “in-the-wild”. These can span from technical issues related to enhancing the learning space with sensing capabilities, to the increased complexity of teachers’ tasks. These practicalities have been rarely investigated. This article addresses this gap by presenting a set of lessons learnt from a 2-year human-centred MMLA in-the-wild study conducted with 399 students and 17 educators in the context of nursing education. The lessons learnt were synthesised into topics related to (i) technological/physical aspects of the deployment; (ii) multimodal data and interfaces; (iii) the design process; (iv) participation, ethics and privacy; and (v) sustainability of the deployment.
Spectral Clustering Approximation For Large Scale Crew Disruption Data Of An Airline Company For Intelligent Crew Recovery

In the airline industry, after fuel costs, the crew costs con- stitute airlines’ second-highest cost items. For this reason, an airline needs to manage the valuable crew resource effi- ciently. Deviations from plans are fact in airline business and fixing deviations from crew schedules that occurred during operations by minimizing the crew-related delays and associated costs is one of the most important opera- tional burdens of airlines. In this context, the analysis of crew disruption data is vital in order to find disruption characteristics. Clustering analysis is one of the key meth- ods for analyzing the disruption characteristics. In this context, although there have been satisfactory studies in the literature and applications in the industry for small and medium-sized airlines, there is no good solution or industry practice for airlines with extensive networks and fleets. This study aims to analyze and categorize large- scale crew disruption data of a European airline. The relationship between categories of crew disruption and variables such as flight and crew types etc., are determined, and the disruption characteristics are revealed. For this purpose, clusters hidden in the large data set are extracted by spectral clustering. Due to the large size of the input data, a new approximation approach for spectral clustering is introduced. With the help of this new approximation approach, spectral clustering techniques are applied within a limited computational power and time frame as most real world scenario require. Even if the data set is gathered from one airline, the characteristics that are derived from the data is representing most of the cases an airline may face today. and will serve as a basis for further estimation and analysis of crew disruption.
Quantum machine learning‐based framework to detect heart failures in Healthcare 4.0

Abstract Quantum machine learning (QML) is an emerging field that combines the power of quantum computing with machine learning (ML) techniques to solve complex problems. In recent years, QML algorithms have shown tremendous potential in various applications such as image recognition, natural language processing, health care, finance, and drug discovery. QML algorithms aim to reduce computation costs and solve complex problems beyond the scope of classical machine learning algorithms. In this article, we study the performance of two QML algorithms, that is, quantum support vector classifiers (QSVC) and variational quantum classifiers (VQC), for chronic heart disease prediction in Healthcare 4.0. The performance of the two classifiers is assessed using different evaluation metrics like accuracy, precision, recall, and F1 score. The authors concluded the superior performance of QSVC over VQC with an accuracy of 82%.
Locomotion techniques with EEG signals in a virtual reality environment

The popularity of virtual reality (VR) experiences has been driven by a combination of technological advances, increased accessibility, and increased interest from both consumers and industries. VR applications are a great example of how immersive environments like Metaverse can take our perception to a new level. Locomotion is the most critical component in VR applications. Locomotion means movement from one place to another in a virtual reality environment. However, VR locomotion can have physical and psychological effects on users. These include potential adverse effects such as cybersickness (CS), eyestrain, disorientation, and psychological problems such as addiction or detachment from reality. Predicting potential problems is essential to understanding and mitigating them. A VREEG dataset was obtained to predict VR locomotion effects with thirty-two participants who completed ten locomotion techniques. This dataset used objective and subjective measures for VR locomotion tasks to assess physiological, usability, and cybersickness/predictability. A predictive model has used EEG features extracted from time, frequency, and time–frequency domains. This model achieved an accuracy of 99% from the dataset for nausea, oculomotor, and disorientation levels. It has also attracted attention due to its teleportation techniques (such as shift and blink locomotion), fast transitions, and low CS. Predictive goals provide insight into potential interests and areas where proactive action can be taken to reduce risks and maximize the benefits of virtual reality technology.
Incentive mechanism design for Federated Learning with Stackelberg game perspective in the industrial scenario

Federated Learning (FL) is a typical decentralized Machine Learning framework in which clients invest resources to train their local models without sharing their data and then transmit the model parameters to the server for parameter aggregation. Therefore, Incentive Mechanism Design (IMD) is a basic and important research direction in FL that stimulates clients to invest more resources in model training. In this paper, we designed the incentive mechanism as a Stackelberg game in which the server acts as the leader and the clients act as followers. We leveraged modified NSGA-II to find the Nash equilibrium. A real industrial scenario of pre-baked carbon anode quality prediction is applied to verify the high performance of the proposed method.
Privacy-preserving healthcare monitoring for IoT devices under edge computing

With the rapid development of the Internet of Things (IoT) technology, e-healthcare has received extensive attention because it is able to provide real-time health status feedback for users. Due to the exponential growth of health data and the expensive cost of training the healthcare monitoring model, outsourcing the healthcare monitoring service to the edge servers becomes a popular way in e-healthcare. How to accomplish healthcare monitoring without leaking users' privacy to the edge servers is the key element of e-healthcare. In this paper, we propose a privacy-preserving healthcare monitoring framework based on long short-term memory (LSTM) neural network for IoT devices, which is performed on edge servers. In our framework, to reduce the computational burden, we outsource the complicated computations to the edge servers. To protect the privacy of health data and healthcare monitoring model, we design a series of secure interactive protocols based on replicated secret sharing and use three edge servers to collaboratively implement secure inference and model training for LSTM. We demonstrate the security of the proposed protocols and give the performance evaluation. Numerical experiments show that the proposed protocols are able to produce results with fine accuracy while requiring low computation and communication overheads.
Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain

We consider a project (model) owner that would like to train a model by utilizing the local private data and compute power of interested data owners, i.e., trainers. Our goal is to design a data marketplace for such decentralized collaborative/federated learning applications that simultaneously provides i) proof-of-contribution based reward allocation so that the trainers are compensated based on their contributions to the trained model; ii) privacy-preserving decentralized model training by avoiding any data movement from data owners; iii) robustness against malicious parties (e.g., trainers aiming to poison the model); iv) verifiability in the sense that the integrity, i.e., correctness, of all computations in the data market protocol including contribution assessment and outlier detection are verifiable through zero-knowledge proofs; and v) efficient and universal design. We propose a blockchain-based marketplace design to achieve all five objectives mentioned above. In our design, we utilize a distributed storage infrastructure and an aggregator aside from the project owner and the trainers. The aggregator is a processing node that performs certain computations, including assessing trainer contributions, removing outliers, and updating hyper-parameters. We execute the proposed data market through a blockchain smart contract. The deployed smart contract ensures that the project owner cannot evade payment, and honest trainers are rewarded based on their contributions at the end of training. Finally, we implement the building blocks of the proposed data market and demonstrate their applicability in practical scenarios through extensive experiments.
A Robust Front-Running Methodology for Malicious Flash- Loan DeFi Attacks

This paper presents FrontDef, a security system to detect and front-run malicious transactions to mitigate financial loss caused by smart contract attacks. FrontDef monitors each transaction in the pending transaction pool to detect potential attacks. For each suspicious transaction, FrontDef analyzes the bytecode of the contract the transaction attempts to interact and assembles a sequence of mimic transactions to replicate the attack strategy. FrontDef then uses the assembled transactions to front- run the suspicious attack transaction to prevent financial loss. Empirical results show that FrontDef can successfully detect and assemble mimic transactions for all of the 24 benchmark cases that includes 21 historical attacks that occurred on Ethereum and Binance Smart Chain (BSC). They also confirm that FrontDef can process up to 1230 transactions per second, which currently is greater than the maximum throughput of Ethereum and BSC.
Beneath the surface: a bibliometric analysis of the hidden risks and costs of blockchain technology

Purpose The purpose of this paper is to identify the negative aspects of blockchain technology and to shed the light on most productive years, countries, authors, sources and frequent keywords. Design/methodology/approach A Web of Science bibliographic data set containing 209 journal articles was evaluated using descriptive and network analytics. A two-step process is adopted in this study; descriptive analysis is initially carried out using RStudio to determine the most productive years, nations, sources and authors, and using co-occurrence of keyword analysis in VOSviewer, the most influential keywords are determined. Findings The findings reveal that 2022 is the most prolific year in terms of number of publications. It is discovered that China tops the list for having published the most articles. Similarly, the most productive authors are Kumar A and Abhishek K. Originality/value To the best of the authors’ knowledge, this bibliometric analysis is unique in that it takes a thorough approach to examine the negative aspects of blockchain technology and identify research trends and offer insights that might guide future research and practical solutions.
Empowering small businesses with the force of big data analytics and AI: A technological integration for enhanced business management

Small and medium-sized businesses (SMEs) in developing economies still face a number of obstacles that prevent them from adopting digital technologies. In contrast, SMEs have achieved greater success in emerging markets. Due to its potential benefits for numerous businesses, machine learning (ML) has become a hot topic in recent years. Particularly a few major organizations, for example, Amazon, Google and Microsoft have shown a few effective cases on coordinating simulated intelligence capacity in their own organizations. This research suggests a fresh method for bettering private companies by combining large-scale data analysis with artificial intelligence and enhanced safety measures. Here, cloud edge administration with task planning using a dynamic joined real channel Kubernetes obstruction task scheduler improves business for the executives. Then, the organization's security is bolstered by a form of differential encryption on the blockchain that takes into account the need for security. We also propose assigning new jobs to the load node with the lightest workload. Experiments show that our strategy shortens job completion times and distributes work evenly across edge nodes. The experimental investigation is conducted in terms of latency, quality of service, energy efficiency, data integrity, and scalability. The proposed technique attained latency of 0.8354, QoS of 0.9395, energy efficiency of 0.9879, data integrity of 0.1189, scalability of 0.8400.