Reviewerly/exHarmony huggingface dataset:
Comprehensive Ocean Information-Enabled AUV Motion Planning Based on Reinforcement Learning

Motion planning based on the reinforcement learning algorithms of the autonomous underwater vehicle (AUV) has shown great potential. Motion planning algorithms are primarily utilized for path planning and trajectory-tracking. However, prior studies have been confronted with some limitations. The time-varying ocean current affects algorithmic sampling and AUV motion and then leads to an overestimation error during path planning. In addition, the ocean current makes it easy to fall into local optima during trajectory planning. To address these problems, this paper presents a reinforcement learning-based motion planning algorithm with comprehensive ocean information (RLBMPA-COI). First, we introduce real ocean data to construct a time-varying ocean current motion model. Then, comprehensive ocean information and AUV motion position are introduced, and the objective function is optimized in the state-action value network to reduce overestimation errors. Finally, state transfer and reward functions are designed based on real ocean current data to achieve multi-objective path planning and adaptive event triggering in trajectorytracking to improve robustness and adaptability. The numerical simulation results show that the proposed algorithm has a better path planning ability and a more robust trajectory-tracking effect than those of traditional reinforcement learning algorithms.
GuP: Fast Subgraph Matching by Guard-based Pruning

Subgraph matching, which finds subgraphs isomorphic to a query, is the key to information retrieval from data represented as a graph. To avoid redundant exploration in the data, existing methods restrict the search space by extracting candidate vertices and candidate edges that may constitute isomorphic subgraphs. However, it still requires expensive computation because candidate vertices induce many subgraphs that are not isomorphic to the query. In this paper, we propose GuP, a subgraph matching algorithm with pruning based on guards. Guards are a pattern of intermediate search states that never find isomorphic subgraphs. GuP attaches a guard on each candidate vertex and edge and filters out them adaptively to the search state. The experimental results showed that GuP can efficiently solve various queries, including those that the state-of-the-art methods could not solve in practical time.
Energy-Efficient Federated Learning in Internet of Drones Networks

Internet of drones (IoD), where drones act as the Internet of things (IoT) devices, makes IoT networks much more flexible and responsive because of high mobility of drones. Machine learning (ML) techniques can be applied in IoD to facilitate multiple applications such as object tracking and traffic surveillance, where ML data samples are collected and analyzed in the edge servers at the ground base station (BS). However, aggregating all data samples incurs huge wireless network traffic and potential data privacy leakage. Federated learning (FL) is then proposed to address these challenges by performing local training in drones and aggregating model parameters at the BS without sharing raw data samples. The FL performance in IoD networks is greatly affected by limited drone batteries which power FL local training, wireless data transmission, and drones’ movements. This paper hence investigates the energy-efficient FL in IoD networks to optimize CPU frequencies of drones’ on-board computing units such that total energy consumption of all the drones in the FL process can be minimized, while satisfying the FL training time requirement. We formulate the problem as a non-linear programming problem and then design an algorithm with polynomial time complexity to derive the optimum solution. Extensive simulations are conducted to demonstrate the performance of our proposed algorithm.
Quantum Annealing Method for Dynamic Virtual Machine and Task Allocation in Cloud Infrastructures from Sustainability Perspective

Cloud infrastructures and data centers consume a remarkable amount of natural resources. Previous research has optimized virtual machine allocation to physical machines with respect to power consumption and the number of migrations. Still, power is just a single factor of the total carbon footprint that the data centers produce. In this work, we extend the previous approaches and represent a framework where allocations are based on a sustainability metric. We formalize the task and virtual machine allocation problem as a quadratic unconstrained binary optimization problem that can be solved on D-wave’s quantum annealer.
Privacy-preserving Data Federation for Trainable, Queryable and Actionable Data

Privacy preservation over federated data has gained its momentum in the era of securing users’ sensitive information. Combining and analysing sensitive information from multiple data sources offers considerable potential for knowledge discovery. However, there are different constraints which should be fulfilled, such as what are the data to be preserved; what is meant by privacy preservation; what are the constraints on federated computing; and what are the secure mechanisms to train, query and explore data without accuracy loss. We introduce the Protected Federated Query Engine which applies Fully Homomorphic Encryption and querying processing over decentralised data sources of diverse schemas and granularities to efficiently collect, align, aggregate and serve Artificial Intelligence Operations (AIOps) and Data Operations (DataOps) without sacrificing accuracy and efficiency.
Autogeneration of Explanatory Math Animation

Due to the advancement of communication technologies and digital devices, online learning has become pervasive nowadays. Everyone can easily learn by themselves with the aid of explanatory videos and animations. However, the conventional man-made production of such videos/animations is time-consuming and the quality highly depends on the production cost. In this work-in-progress paper, we propose a prototype of the technology that automatically generates explanatory math animation (EMA) for K-12 education. Such framework can greatly reduce the production cost and provide high-quality explanatory animations.
Application for Detecting Child Abuse via Real-Time Video Surveillance

Applications of real-time video surveillance are contributing to traffic management and public safety. One example is analyzing crowd behavior and responding immediately to violations, such as assault, fighting, and child abuse. However, monitoring real-time video surveillance continuously is arduous work, and there is a high chance of missed violations. This paper proposes an application for detecting child abuse using deep learning methods. The combination of real-time video surveillance and deep learning can contribute to avoiding child abuse and responding on time.
Path planning optimization of medical service robots based on PSO

In order to overcome the problem that medical service robots can better plan their driving paths indoors, the paper proposes to use particle swarm optimization (PSO) algorithm for path planning, so that mobile robots can smoothly avoid static obstacles and complete feasible paths. Experimental results show that the proposed method reasonably plans the path and realizes the optimal solution.
Mobile Robots Path Planning Based on Improved Artificial Potential Field

In order to solve the problem of unreachable target of robot in path planning, the repulsive field function is improved to solve the problem of excessive repulsive force of obstacles around the target point In order to solve the problem that robots are prone to fall into local minima, firstly, whether the robot falls into local minima is determined according to the judgment conditions. If the robot falls into local minima, the velocity variable is introduced to provide escape velocity for the robot. After judging that the robot escapes from local minima, the potential field is used again for path planning According to the complexity of obstacles, an adaptive step adjustment method is proposed to improve the efficiency of the robot and smooth the path planning, so that the robot can move forward smoothly and efficiently. Finally, MATLAB is used to carry out simulation experiments, and the results prove the effectiveness of this method.
Automatic Data-Driven Software Change Identification via Code Representation Learning

Changes to a software project are inevitable as the software requires continuous adaptations, improvements, and corrections throughout maintenance. Identifying the purpose and impact of changes made to the codebase is critical in software engineering. However, manually identifying and characterizing software changes can be a time-consuming and tedious process that adds to the workload of software engineers. To address this challenge, several attempts have been made to automatically identify and demystify intents of software changes based on software artifacts such as commit change logs, issue reports, change messages, source code files, and software documentation. However, these existing approaches have their limitations. These include a lack of data, limited performance, and an inability to evaluate compound changes. This paper presents a doctoral research proposal that aims to automate the process of identifying commit-level changes in software projects using software repository mining and code representation learning models. The research background, state-of-the-art, research objectives, research agenda, and threats to validity are discussed.
Improving the Reporting of Threats to Construct Validity

Background: Construct validity concerns the use of indicators to measure a concept that is not directly measurable. Aim: This study intends to identify, categorize, assess and quantify discussions of threats to construct validity in empirical software engineering literature and use the findings to suggest ways to improve the reporting of construct validity issues. Method: We analyzed 83 articles that report human-centric experiments published in five top-tier software engineering journals from 2015 to 2019. The articles’ text concerning threats to construct validity was divided into segments (the unit of analysis) based on predefined categories. The segments were then evaluated regarding whether they clearly discussed a threat and a construct. Results: Three-fifths of the segments were associated with topics not related to construct validity. Two-thirds of the articles discussed construct validity without using the definition of construct validity given in the article. The threats were clearly described in more than four-fifths of the segments, but the construct in question was clearly described in only two-thirds of the segments. The construct was unclear when the discussion was not related to construct validity but to other types of validity. Conclusions: The results show potential for improving the understanding of construct validity in software engineering. Recommendations addressing the identified weaknesses are given to improve the awareness and reporting of CV.
A Mixed Reality Approach for Innovative Pair Programming Education with a Conversational AI Virtual Avatar

Pair Programming (PP) is an Agile software development methodology that involves two developers working together on a single computer. However, the physical presence of two developers has become a challenge in recent years due to the pandemic, necessitating remote collaboration methods such as Distributed Pair Programming (DPP). DPP has been found to have similar benefits to in-person PP, but the issue of team compatibility remains unresolved. These are more evident in the educational field of Agile methodologies. To address these challenges, we developed a novel approach by creating a Mixed Reality (MR) application that enables users to learn PP with the assistance of a conversational intelligent virtual avatar. The application uses the HoloLens MR device and a Conversational Agent (CA) extension integrated into Visual Studio Code to provide suggestions for improving the code written by the user. The virtual avatar animates these suggestions, making it appear to speak and interact with the user in real time. This system aims to overcome the limitations of common DPP methods, allowing a single developer to learn and apply the PP methodology even when a human partner is unavailable.
Challenges in Requirements Gathering for Agile Software Development

Agile software development is a methodology for creating software that prioritizes flexibility, collaboration, and iterative development. It emphasizes the rapid delivery of working software, with regular feedback and adaptation to changing requirements. This dissertation aimed to identify the challenges in the requirement gathering process for agile software development and propose solutions to aid the software industry in streamlining the development process. A multi-vocal literature review was conducted, and the challenges were identified. The findings showed that coordination is the most critical category, indicating that management should focus on researching related elements to achieve successful RE adoption in an agile environment. This study's findings provide a valuable reference for organizations and practitioners to address the challenges of the requirement gathering process in agile software development.
Understanding Self-Efficacy in Software Engineering Industry: An Interview study

CONTEXT: Self-efficacy is a concept researched in various areas of knowledge that impacts various factors such as performance, satisfaction, and motivation. In Software Engineering, it has mainly been studied in the academic context, presenting results similar to other areas of knowledge. However, it is also important to understand its impact in the industrial context. OBJECTIVE: Therefore, this study aims to understand the impact on the software development context with a focus on understanding the behavioral signs of self-efficacy in software engineers and how self-efficacy can impact the work-day of software engineers. METHOD: A qualitative research was conducted using semi-structured questionnaires with 31 interviewees from a software development company located in Brazil. The interviewees participated in a Bootcamp and were later assigned to software development teams. Thematic analysis was used to analyze the data. RESULTS: In the perception of the interviewees, 21 signs were found that are related to people with high and low self-efficacy. These signs were divided into two dimensions: social and cognitive. Also, 18 situations were found that can lead to an increase or decrease of self-efficacy of software engineers. Finally, 12 factors were mentioned that can impact software development teams. CONCLUSION: This work evidences a set of behavioral signs that can help team leaders to better perceive the self-efficacy of their members. It also presents a set of situations that both leaders and individuals can use to improve their self-efficacy in the development context, and finally, factors that can be impacted by self-efficacy in the software development context are also presented. Finally, this work emphasizes the importance of understanding self-efficacy in the industrial context.
An Empirical Study on Continuous Integration Trends, Topics and Challenges in Stack Overflow

During the last few years, Continuous Integration (CI) has become a common practice in open-source and industrial environments to reduce the scope for errors and increase the speed to market through the automated build and test processes. However, despite this wide adoption throughout the years, little is known about the challenges developers discuss. Analyzing the discussions of developers is required to understand what researchers, educators and practitioners should focus on, and how discussion communities can be helpful to shed the light on CI challenges. In this study, we examine Stack Overflow (SO), the most popular crowd-sourced forum, to understand the challenges developers face in the CI context. We collect a corpus of 27,728 CI-related developers posts from SO and analyze those posts through a mixed method with quantitative and qualitative analyzes. To study the trends of CI discussions, we investigated the metadata of CI questions, users and tags. Then, we extract the CI main topics using Latent Dirichlet Allocation (LDA) tuned with Genetic Algorithm (GA). Finally, we investigate the most popular and difficult topics faced by developers based on unanswered questions to get further insights into CI challenges. The LDA clustering reveals that developers face challenges with six main topics namely Build, Testing, Version Control, Configuration, Deployment, and CI Culture. Particularly, we found that the build topic is the most popular among the studied topics and that version control and testing topics are the most difficult for the SO community. Our study uncovers insights about CI challenges and adds evidence to existing knowledge about CI issues related especially to software build.
Barriers for Social Inclusion in Online Software Engineering Communities - A Study of Offensive Language Use in Gitter Projects

Social inclusion is a fundamental feature of thriving societies. This paper first investigates barriers for social inclusion in online Software Engineering (SE) communities, by identifying a set of 11 attributes and organising them as a taxonomy. Second, by applying the taxonomy and analysing language used in the comments posted by members in 189 Gitter projects (with > 3 million comments), it presents the evidence for the social exclusion problem. It employs a keyword-based search approach for this purpose. Third, it presents a framework for improving social inclusion in SE communities.
Arabic Cyberbullying Detection Using Machine Learning: State of the Art Survey

Cyberbullying (CB) is a global dilemma that is growing rapidly to affect more individuals including minors. The devastating consequences of CB indicate a pressing necessity to regulate unethical or illegal users' online behaviors. A remarkable number of researchers attempted to harness the potential of machine learning to detect and prevent such harmful behaviors, however, the existing studies targeting Arabic-based content are still emerging. Therefore, this paper provides a comprehensive review of the published empirical studies in CB detection in Arabic-based content with an emphasis on the adapted methodologies, gaps, and challenges. We hope this work would support researchers in the area of CB-detection to foster a safe online environment and protect against any harmful consequences of CB among users.
Area day‐ahead photovoltaic power prediction by just‐in‐time modeling with meso‐scale ensemble prediction system

Abstract Photovoltaics (PV) output prediction, which is indispensable for power system operation, can affects demand and supply adjustment adversely when large prediction error occurs. Thus, the reduction of large error as well as average error is required in PV power prediction. In 2019, the operation of the Meso‐scale Ensemble Prediction System (MEPS) of numerical weather prediction started from the JapanMeteorological Agency, and the amount of forecasting information would be potentially useful for the improvement of PV power prediction. However, very few studies on inputting multiple meteorological elements of the MEPS have been reported. In this paper, we newly develop the prediction model for an area day‐ahead PV power output composed of Just‐In‐Time Modeling (JIT Modeling) with multiple elements of theMEPS. The developed method achieves precise forecasts with low computational load by both selecting meteorological elements valid for improving prediction accuracy and adequately devising the structure of JIT Modeling. Some numerical examples demonstrating the effectiveness of the developed method are also presented. In particular, the proposed method reduces large error significantly.
Character-Based Thai Word Segmentation with Multiple Attentions

Character-based word segmentation models have been extensively applied to Asian languages, including Thai, owing to their promising performance. These models estimate the word boundaries from a character sequence; however, a Thai character unit in a sequence has no inherent meaning, in contrast with word, subword, and character cluster units that represent more meaningful linguistic information. In this paper, we propose a Thai word segmentation model that uses various types of information, including words, subwords, and character clusters, from a character sequence. Our model applies multiple attentions to refine segmentation inferences by estimating the significant relationships among characters and various unit types. We evaluated our model on three Thai datasets, and the experimental results show that our model outperforms other Thai word segmentation models, demonstrating the validity of using character clusters over subword units. A case study on sample Thai text supported these results. Thus, according to our analysis, particularly the case study, our model can segment Thai text accurately, while other existing models yield incorrect results that violate the Thai writing system.
Point &amp; Teleport with Orientation Specification, Revisited: Is Natural Turning Always Superior?

Point & teleport (P&T) is an artificial locomotion technique that enables users to travel in unlimited space in virtual reality. While recent P&T techniques assign orientation control to an additional axis, these techniques suffer from increased complexity in controls and limited performance. Researchers concluded that teleportation, followed by a self-orientation adjustment by physically turning around, is preferable and that P&T with orientation specification can be optional. However, P&T has not been tested under a seated condition, where the orientation control may advantageously perform. Therefore, in this paper, we reevaluate P&T with orientation specifications while the users are seated. Nonetheless, for consistent alignment with the results in preceding research, we evaluate the accuracy while the users are standing. Knowing that additional cognitive load may badly affect the performance, we present a new P&T design, points to teleport (P2T), with minimal complexity in mind (i.e., point twice sequentially to determine the future location and then orientation, which mimics classic P&T that requires users to turn around for orientation specification). Thus, we revisit P&T with orientation specification.
Mapping Learning Algorithms on Data, a Promising Novel Methodology to Compare Learning Algorithms

The paper describes a novel methodology to compare learning algorithms by exploiting their performance maps. A performance map enhances the comparison of a learner across learning contexts and it also provides insights in the distribution of a learners’ performances across its parameter space. Also some initial empirical findings are commented. In order to explain the novel comparison methodology, this study introduces the notions of learning context, performance map, and high performance function. These concepts are then applied to a variety of learning contexts to show how the methodology can be applied. Finally, we will use meta-optimization as an instrument to improve the efficiency of the parameter space search with respect to its complete enumeration. But, note that meta-optimization is neither an essential part of our methodology nor the focus of our study.