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An Adaptive Decision-Making Approach for Better Selection of a Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and Performance Evaluation

arXiv

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arXiv 2023年

作者： Kaafarani, Rima Ismail, Leila Zahwe, Oussama ICCS-Lab Computer Science Department American University of Culture and Education Beirut1507 Lebanon Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to developers and business owners requirements. Therefore, several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using two blockchain platforms selected based on our proposed decision-making map approach for the selection of the top two suitable platforms for healthcare insurance frauds detection application, followed by an evaluation of their performances. Our classification shows that the largest percentage of blockchain platforms could be used for all types of application domains, and the second biggest percentage is to develop financial services only, even though generic platforms can be used, while a small number is for developing in other specific application domains. Our decision-making map revealed that Hyperledger Fabric is the best blockchain platform for detecting healthcare insurance frauds. The performance evaluation of the top two selected platforms indicates that Fabric surpassed Neo in all *** Codes 6804, 68M11, 68U35 © 2023, CC BY-NC-ND.

关键词： Smart contract

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SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrieval

arXiv

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arXiv 2022年

作者： Shen, Yang Sun, Xuhao Wei, Xiu-Shen Jiang, Qing-Yuan Yang, Jian School of Computer Science and Engineering Nanjing University of Science and Technology China State Key Laboratory of Integrated Services Networks Xidian University China State Key Laboratory for Novel Software Technology Nanjing University China Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security Nanjing University of Science and Technology China

In this paper, we propose Suppression-Enhancing Mask based attention and Interactive Channel transformatiON (SEMICON) to learn binary hash codes for dealing with large-scale fine-grained image retrieval tasks. In SEMICON, we first develop a suppression-enhancing mask (SEM) based attention to dynamically localize discriminative image regions. More importantly, different from existing attention mechanism simply erasing previous discriminative regions, our SEM is developed to restrain such regions and then discover other complementary regions by considering the relation between activated regions in a stage-by-stage fashion. In each stage, the interactive channel transformation (ICON) module is afterwards designed to exploit correlations across channels of attended activation tensors. Since channels could generally correspond to the parts of fine-grained objects, the part correlation can be also modeled accordingly, which further improves fine-grained retrieval accuracy. Moreover, to be computational economy, ICON is realized by an efficient two-step process. Finally, the hash learning of our SEMICON consists of both global- and local-level branches for better representing fine-grained objects and then generating binary hash codes explicitly corresponding to multiple levels. Experiments on five benchmark fine-grained datasets show our superiority over competing methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Image retrieval

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software engineering for Autonomous Robot: Challenges, Progresses and Opportunities

Software Engineering for Autonomous Robot: Challenges, Progr...

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Asia-Pacific Conference on software engineering

作者： Xinjun Mao Hao Huang Shuo Wang College of Computer Key Lab. of Software Engineering for Complex Systems National University of Defense Technology Changsha China

ISBN: (数字)9781728195537

ISBN: (纸本)9781728195544

software engineering for autonomous robot (SE4AR) is an emerging interdisciplinary field with the aims to support the development, running and evolution of software systems for autonomous robot - a safety and mission critical cyber-physical system. Such field has recently gained increasing attentions from both academia and industry and made rapid progresses in the past years. However, many challenges pose on the SE4AR due to the specific features and complexities of autonomous robot software (ARS) and several open problems should be tackled in the future researches. This paper aims to present a comprehensive investigation on the researches and practices of SE4AR. Our contributions are three-fold as follows: an in-depth analysis on the development challenges, a systematic review on the current progresses, and an open discussion of weaknesses in current researches and opportunities in future researches.

关键词： Industries Systematics Service robots software systems Complexity theory software engineering Autonomous robots

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Identifiability of car-following dynamics

arXiv

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arXiv 2021年

作者： Wang, Yanbing Delle Monache, Maria Laura Work, Daniel B. Department of Civil and Environmental Engineering Institute for Software Integrated Systems Vanderbilt University NashvilleTN37240 United States NeCS team Univ. Grenoble Alpes Inria CNRS Grenoble INP GIPSA-Lab Grenoble38000 France

The advancement of in-vehicle sensors provides abundant datasets to estimate parameters of car-following models that describe driver behaviors. The question of parameter identifiability of such models (i.e., whether it is possible to infer its unknown parameters from the experimental data) is a central system analysis question, and yet still remains open. This article presents both structural and practical parameter identifiability analysis on four common car-following models: i) the constant-time headway relative-velocity (CTH-RV) model, ii) the optimal velocity model (OV), iii) the follow-the-leader model (FTL) and iv) the intelligent driver model (IDM). The structural identifiability analysis is carried out using a differential geometry approach, which confirms that, in theory, all of the tested car-following systems are structurally locally identifiable, i.e., the parameters can be uniquely inferred under almost all initial condition and admissible inputs by observing the space gap alone. In a practical setting, we propose an optimization-based numerical direct test to determine parameter identifiability given a specific experimental setup (the specific initial conditions and input are known). The direct test conclusively finds distinct parameters under which the CTH-RV and FTL are not identifiable under the given initial condition and input trajectory. Copyright © 2021, The Authors. All rights reserved.

关键词： Geometry

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Distributed nonlinear model predictive control and metric learning for heterogeneous vehicle platooning with cut-in/cut-out maneuvers

arXiv

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arXiv 2020年

作者： Basiri, Mohammad Hossein Ghojogh, Benyamin Azad, Nasser L. Fischmeister, Sebastian Karray, Fakhri Crowley, Mark The Department of Systems Design Engineering University of Waterloo ON Canada The Department of Electrical and Computer Engineering University of Waterloo ON Canada The Smart Hybrid and Electric Vehicles Systems Lab The Real-Time Embedded Software Lab The Machine Learning Lab The Centre for Pattern Analysis and Machine Intelligence

Vehicle platooning has been shown to be quite fruitful in the transportation industry to enhance fuel economy, road throughput, and driving comfort. Model Predictive Control (MPC) is widely used in literature for platoon control to achieve certain objectives, such as safely reducing the distance among consecutive vehicles while following the leader vehicle. In this paper, we propose a Distributed Nonlinear MPC (DNMPC), based upon an existing approach, to control a heterogeneous dynamic platoon with unidirectional topologies, handling possible cut-in/cut-out maneuvers. The introduced method addresses a collision-free driving experience while tracking the desired speed profile and maintaining a safe desired gap among the vehicles. The time of convergence in the dynamic platooning is derived based on the time of cut-in and/or cut-out maneuvers. In addition, we analyze the improvement level of driving comfort, fuel economy, and absolute and relative convergence of the method by using distributed metric learning and distributed optimization with Alternating Direction Method of Multipliers (ADMM). Simulation results on a dynamic platoon with cut-in and cut-out maneuvers and with different unidirectional topologies show the effectiveness of the introduced method. Copyright © 2020, The Authors. All rights reserved.

关键词： Topology

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FEDCSD: A FEDERATED LEARNING BASED APPROACH FOR CODE-SMELL DETECTION

arXiv

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arXiv 2023年

作者： Alawadi, Sadi Alkharabsheh, Khalid Alkhabbas, Fahed Kebande, Victor Awaysheh, Feras M. Palomba, Fabio Awad, Mohammed Center for Applied Intelligent Systems Research School of Information Technology Halmstad University Halmstad Sweden Department of Software Engineering Prince Abdullah bin Ghazi Faculty of Information and Communication Technology Al-Balqa Applied University Jordan Internet of Things and People Research Center Malmö University Sweden Department of Computer Science Blekinge Institute of Technology Sweden Institute of Computer Science Delta Research Centre University of Tartu Estonia SeSa Lab University of Salerno Italy Arab American University Palestine Palestine

software quality is critical, as low quality, or "Code smell," increases technical debt and maintenance costs. There is a timely need for a collaborative model that detects and manages code smells by learning from diverse and distributed data sources while respecting privacy and providing a scalable solution for continuously integrating new patterns and practices in code quality management. However, the current literature is still missing such capabilities. This paper addresses the previous challenges by proposing a Federated Learning Code Smell Detection (FedCSD) approach, specifically targeting "God Class," to enable organizations to train distributed ML models while safeguarding data privacy collaboratively. We conduct experiments using manually validated datasets to detect and analyze code smell scenarios to validate our approach. Experiment 1, a centralized training experiment, revealed varying accuracies across datasets, with dataset two achieving the lowest accuracy (92.30%) and datasets one and three achieving the highest (98.90% and 99.5%, respectively). Experiment 2, focusing on cross-evaluation, showed a significant drop in accuracy (lowest: 63.80%) when fewer smells were present in the training dataset, reflecting technical debt. Experiment 3 involved splitting the dataset across 10 companies, resulting in a global model accuracy of 98.34%, comparable to the centralized model’s highest accuracy. The application of federated ML techniques demonstrates promising performance improvements in code-smell detection, benefiting both software developers and researchers. Copyright © 2023, The Authors. All rights reserved.

关键词： Computer software selection and evaluation

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Self-supervised contrastive learning for code retrieval and summarization via semantic-preserving transformations

arXiv

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arXiv 2020年

作者： Bui, Nghi D.Q. Yu, Yijun Jiang, Lingxiao Trustworthy Software Engineering and Open Source Lab Huawei Ireland Research Center Trustworthy Software Engineering and Open Source Lab Huawei Ireland Research Center Open University United Kingdom School of Computing and Information Systems Singapore Management University Singapore

We propose Corder, a self-supervised contrastive learning framework for source code model. Corder is designed to alleviate the need of labeled data for code retrieval and code summarization tasks. The pre-trained model of Corder can be used in two ways: (1) it can produce vector representation of code which can be applied to code retrieval tasks that do not have labeled data;(2) it can be used in a fine-tuning process for tasks that might still require label data such as code summarization. The key innovation is that we train the source code model by asking it to recognize similar and dissimilar code snippets through a contrastive learning objective. To do so, we use a set of semantic-preserving transformation operators to generate code snippets that are syntactically diverse but semantically equivalent. Through extensive experiments, we have shown that the code models pretrained by Corder substantially outperform the other baselines for code-to-code retrieval, text-to-code retrieval, and code-to-text summarization tasks. Copyright © 2020, The Authors. All rights reserved.

关键词： Semantics

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Towards a circular economy of industrial software 27

Towards a circular economy of industrial software

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27th CIRP Life Cycle engineering Conference, LCE 2020

作者： Kutscher, Vladimir Ruland, Sebastian Müller, Patrick Wasser, Nathan Lochau, Malte Anderl, Reiner Schürr, Andy Mezini, Mira Hähnle, Reiner Department of Computer Integrated Design TU Darmstadt Otto-Berndt-Str. 2 Darmstadt64287 Germany Real-Time Systems Lab TU Darmstadt Magdalenenstr. 4 Darmstadt64289 Germany Software Technology Group TU Darmstadt Hochschulstr. 10 Darmstadt64287 Germany Software Engineering Group TU Darmstadt Hochschulstr. 10 Darmstadt64287 Germany

software has become an indispensable part of industrial production and thus influences the life cycle of manufacturing systems, as many of these systems have to be replaced or evolved due to changing requirements. software adaptation through continuous evolution extends the service time of these systems and thus saves valuable resources. In this paper we present an interdisciplinary methodology for reengineering legacy software to increase the productive lifetime. Our proposed approach systematically reuses implicit domain knowledge to evolve, validate and commission new software from legacy code. The approach is evaluated on a CNC machine as a special type of industrial system. © 2020 The Author(s). Published by Elsevier B.V.

关键词： Reengineering

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Challenges and Opportunities in Open Data Collaboration – a focus group study

Challenges and Opportunities in Open Data Collaboration – a...

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Euromicro Conference on software engineering and Advanced Applications (SEAA)

作者： Per Runeson Thomas Olsson Dept. of Computer Science Lund University Lund Sweden Software and Systems Engineering Lab RISE Research Institutes of Sweden Lund Sweden

ISBN: (数字)9781728195322

ISBN: (纸本)9781728195339

Data-driven software is becoming prevalent, especially with the advent of machine learning and artificial intelligence. With data-driven systems come both challenges - to keep collecting and maintaining high quality data - and opportunities - open innovation by sharing data with others. We propose Open Data Collaboration (ODC) to describe pecuniary and non-pecuniary sharing of open data, similar to Open Source software (OSS) and in contrast to Open Government Data (OGD), where public authorities share data. To understand challenges and opportunities with ODC, we organized five focus groups with in total 27 practitioners from 22 companies, public organizations, and research institutes. In the discussions, we observed a general interest in the subject, both from private companies and public authorities. We also noticed similarities in attitudes to open innovation practices, i.e. initial resistance which gradually turned into interest. While several of the participants were experienced in open source software, no had shared data openly. Based on the findings, we identify challenges which we set out to continue addressing in future research.

关键词： Organizations Technological innovation Ecosystems Conferences software Collaboration Government

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Incorporating Multiple Features to Predict Bug Fixing Time with Neural Networks

Incorporating Multiple Features to Predict Bug Fixing Time w...

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International Conference on software Maintenance (ICSM)

作者： Wei Yuan Yuan Xiong Hailong Sun Xudong Liu SKLSDE Lab School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China SKLSDE Lab School of Software Beihang University Beijing China

Debugging is a well-known time-consuming task, and knowing how long it would take to resolve bugs is of great importance for allocating the limited resources in a software development team. However, it is challenging to predict bug fixing time since fixing bugs is subject to a plethora of uncertain factors such as types of bugs, program complexity and developers' abilities. Existing work mainly focuses on developers' activities in a bug lifecycle and ignores other important factors. In light of the limitations of existing work, we propose a novel approach to predicting the bug fixing time by incorporating a comprehensive set of relevant features. Specifically, we consider four types of features including developers' activities, developers' sentiments, semantics of bugs, and efforts caused by understanding and analyzing source code, and design particular neural networks to take advantage of these features and make them work efficiently. Experimental results on four real application datasets demonstrate that on the one hand, our approach outperforms the state-of-the-art by over 5% in accuracy and 7.3% in F1-score on average; on the other hand, each type of the features considered in our approach plays an important part in the prediction.

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