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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Clear, Tony Clear, Alison Auckland University of Technology Department of Computer Science and Software Engineering Auckand New Zealand School of Computing Eastern Institute of technology Auckland New Zealand

ISBN: (纸本)9798350351507

This research paper describes and extends the outcomes from an in- depth study investigating the difference in the expected skills requirements from junior software engineers to senior software engineers, and reflections on the findings from that study. It is a given that senior software engineers have more experience and skills than junior software engineers. However, a focus on their differing competencies and dispositions provides an enhanced mechanism for comparison. Gaps were identified in assessing 'professional knowledge' as categorized by the IEEE/ACM Computing Curriculum Overview Report (CC2020), and in assessing 'dispositions'. It appeared that the specific scenario of comparing the expected competencies between junior and senior software engineers, tested the framework for assessing competencies developed in the CC2020 project and applied in its mapping to the IEEE/ACM computer Science (CS2013) approved curriculum. In this study into the difference between Junior and Senior software Engineers, an initial review of relevant literature was conducted. The review found that research analyzing job requirements for software engineers of different levels was limited;'experience' as a keyword was seldom mentioned;and a common distinction was made between 'soft' and 'hard' skills - the latter being skills that were 'technical', such as programming languages, frameworks, libraries, and tools, whereas soft skills referred to skills such as personality traits, attitudes, and teamwork skills. In our extension of that work the notion of soft skills was unpacked into professional skills and dispositions. The process of mapping from the CC2020 competency framework to the CS2013 curriculum had deliberately modelled how to represent a competency-based rather than a knowledge-based curriculum. The critical deficiency identified here was the limitation imposed by adopting a skills framework based on the cognitive taxonomy, and thereby unwittingly omitting the crucial compani

关键词： Mapping

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A blockchain-based framework for data quality in edge-computing-enabled crowdsensing

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Frontiers of computer Science 2023年第4期17卷 127-139页

作者： Jian AN Siyuan WU Xiaolin GUI Xin HE Xuejun ZHANG School of Computer Science and Technology Shaanxi Province Key Laboratory of Computer NetworkXi’an Jiaotong UniversityXi’an 710049China School of Software Henan UniversityKaifeng 475001China School of Electronic and Information Engineering Lanzhou Jiaotong UniversityLanzhou 730070China

With the rapid development of mobile technology and smart devices,crowdsensing has shown its large potential to collect massive *** the limitation of calculation power,edge computing is introduced to release unnecessary data *** edge-computing-enabled crowdsensing,massive data is required to be preliminary processed by edge computing devices(ECDs).Compared with the traditional central platform,these ECDs are limited by their own capability so they may only obtain part of relative factors and they can’t process data *** involved in one task are required to cooperate to process the task *** privacy of participants is important in crowdsensing,so blockchain is used due to its decentralization and *** crowdsensing tasks,it is usually difficult to obtain the assessment criteria in advance so reinforcement learning is *** mentioned before,ECDs can’t process task data comprehensively and they are required to cooperate quality ***,a blockchain-based framework for data quality in edge-computing-enabled crowdsensing(BFEC)is proposed in this ***(Delegated Proof of Reputation),which is proposed in our previous work,is improved to be suitable in ***,the final result is calculated without revealing the privacy of *** on the open datasets Adult,Blog,and Wine Quality show that our new framework outperforms existing methods in executing sensing tasks.

关键词： crowdsensing edge computing devices blockchain quality assessment reinforcement learning

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Dynamic Countdown Scheduling MAC Protocol in Underwater Acoustic Sensor Networks

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IEEE Sensors Journal 2025年第11期25卷 20797-20812页

作者： Feng, Libin Li, Zhe Mai, Haohao Chen, Yanxia Huang, Xiangdang Hainan University School of Computer Science and Technology Haikou570228 China Hainan University School of Software Engineering Haikou570228 China Hainan University School of Cyberspace Security Haikou570228 China Hainan University and the Innovation Platform for Academicians of Hainan Province School of Computer Science and Technology Haikou570228 China

Underwater acoustic sensor networks (UASNs) are highly sensitive to collisions due to the unique characteristics of underwater acoustic signal propagation. Thus, developing medium access control (MAC) protocols that can effectively avoid collisions has become particularly important. This paper proposes a dynamic countdown scheduling MAC protocol (DCS-MAC) to achieve collision-free transmission for UASNs. First, transmission schedules of nodes are integrated directly into data frames, eliminating the need for control frames, thereby reducing latency and energy consumption. Second, considering defined collision constraint, a dynamic countdown strategy is introduced where the transmission schedule of each node dynamically adjusts according to collected collision constraints, reducing the probability of data frame collisions. Then, interval trees are used to dynamically update collision constraints, maximizing the coverage of collision time periods and selecting the optimal transmission schedule. Finally, simulation results show that DCS-MAC excels in data frame delivery rate, network throughput, throughput fairness, and energy consumption. © 2001-2012 IEEE.

关键词： Sensor nodes

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Machine Learning Model for Recipe Optimization of College Students Based on Simulated Annealing 3

Machine Learning Model for Recipe Optimization of College St...

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3rd International Conference on Data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Sheng, Peizhi Chen, Xuhao School of Computer and Information Science Software Southwest University Chongqing China School of Engineering and Technology Southwest University Chongqing China

ISBN: (纸本)9798331519032

This paper first made a comprehensive dietary nutrition evaluation of the two recipes provided, and conducted a visual analysis of the differences of various nutrient components by querying the "Table of Chinese Food Ingredients". The nutrients involved included major daily productivity nutrients such as protein and fat, and non-productivity nutrients such as calcium, iron and zinc, which were used as collaborative evaluation indicators. Secondly, in order to specifically evaluate the daily diet of two male and female students, we pointed out the problems in the diet according to the Basic requirements for Analysis, Evaluation and Optimal Design of Dietary Recipes, and then adjusted and improved them, and made a comprehensive dietary nutrition evaluation again. Finally, the diet after adjustment has been improved to basically meet the nutritional requirements. Then, two models are established. Firstly, a model based on linear programming algorithm is established to solve the problem, and a model based on simulated annealing algorithm is established as a backup scheme and innovative ideas. Then, we used Model 1 to optimize male and female college birthday meal recipes with different goals (most economical, highest AAS, and dual goals). Finally, we also compare these three kinds of daily diet recipes with different objective optimization, and get the conclusion that the daily diet recipes with dual objective optimization have the highest cost performance. Finally, in order to realize the optimal design of weekly balanced diet, the paper extends the optimization model of daily diet based on problem 2. In order to solve the problem that the extended optimization model is difficult to solve due to too many decision variables, we propose a random elimination strategy to solve the weekly recipe scheme step by step according to the time granularity. After the design of daily recipes for each day is completed, the food of the day is eliminated from the daily recipes of the n

关键词： Simulated annealing

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Demystifying AI Advances in Explainable AI (XAI) 3

Demystifying AI Advances in Explainable AI (XAI)

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3rd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2024

作者： Kumar Pandiya, Dileep Ramrao Joshi, Vilas Tripathi, Kailash Nath ZoomInfo Technology Inc Principal Software Engineer BostonMA United States ISBM College of Engineering Department of Computer Engineering Maharashtra Pune India

ISBN: (纸本)9798350361391

In today's dynamic landscape, the integration of artificial intelligence (AI) has revolutionized operations across diverse domains. However, the enigmatic nature of many AI algorithms presents formidable obstacles, spanning from a dearth of confidence to potential ethical quandaries. Enter Explainable AI (XAI), a burgeoning field that serves as a beacon of clarity, providing to augment transparency and comprehension. This manuscript embarks on a comprehensive exploration of the strides made in XAI, meticulously dissecting its significance, fundamental methodologies, practical applications, hurdles, ethical dimensions, and trajectories for future advancement. By delving into these multifaceted dimensions, this endeavor aims to illuminate the trajectory towards closing the chasm between intricate AI frameworks and human cognition. © 2024 IEEE.

关键词： Advancements Artificial Intelligence Ethical implications Explainable AI (XAI) Transparency

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AI-Driven Mycology: Transfer Learning for Automated Mushroom Species Identification

AI-Driven Mycology: Transfer Learning for Automated Mushroom...

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2024 IEEE International Conference on Computing, Applications and Systems, COMPAS 2024

作者： Ahmad, Md Hasan Rahman Sohag, Sohanur Rimon Bormon, Sajib Shishir, Hasanur Rahman Hossain Antar, Sabbir Daffodil International University Department of Computer Science and Engineering Ashulia Dhaka Bangladesh Noakhali Science and Technology University Department of Software Engineering Noakhali Bangladesh

ISBN: (纸本)9798331529765

Mushroom categorization is a difficult process since there are so many different species and they all have different aesthetic qualities. In this paper, we are to investigate the use of transfer learning techniques for precise mushroom classification. We have examined the performance of EfficientNetB0, ResNet50, InceptionV3, and MobileNet, four well-known transfer learning models. The mushroom dataset is first preprocessed to make sure that the images are properly scaled and normalized. After initializing and fine-tuning the pre-trained weights of the above-mentioned models on our mushroom dataset, we then apply transfer learning techniques. Each model is trained using a carefully chosen training set, and its performance is assessed using a different test set. Our findings reveal that all four transfer learning models successfully categorize mushrooms, achieving outstanding classification accuracies. With a 97% accuracy rate, EfficientNetB0 surpasses the other models when we compare them. Its sophisticated design, which combines compound scaling and AutoML approaches to attain the best accuracy and efficiency, is responsible for its exceptional performance. The results of this study demonstrate the value of transfer learning in tasks involving mushroom categorization. Additionally, they stress the benefit of using cutting-edge models like EfficientNetB0, which produce greater accuracy compared to conventional transfer learning architectures. The development of automated methods for mushroom identification and subsequent breakthroughs in the mycology field can be facilitated by the use of these findings by academics and professionals working on mushroom classification. © 2024 IEEE.

关键词： Adversarial machine learning

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PredRacer: Predictively Detecting Data Races in Android Applications 31

PredRacer: Predictively Detecting Data Races in Android Appl...

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31st IEEE International Conference on software Analysis, Evolution and Reengineering, SANER 2024

作者： Guo, Xin Qi, Xiaofang Li, Yanhui Wu, Chao School of Computer Science and Engineering Southeast University China Nanjing University State Key Laboratory for Novel Software Technology China

ISBN: (纸本)9798350330663

Android platform offers a hybrid concurrency model encompassing multi-threading and asynchronous messaging for concurrent programming. The model is powerful but complex, making it difficult for developers to analyze concurrent behaviors. Data race, a prevalent concurrency defect in real-world Android applications, often results in abnormal executions of mobile applications, even crashes. Despite quite a few studies on detecting data races, it still suffers from high false positives with static analysis techniques and high false negatives with dynamic analysis techniques. To address this issue, this paper presents a predictive approach, PredRacer, for detecting data races in Android applications. It first captures an execution trace of an Android application, and then reorders the events within the trace based on partial orders. Finally, it checks the feasibility of the generated event sequence, which contains a potential data race. PredRacer increases the search scope and reduces false negatives while mitigating false positives by incorporating the happen-before relations specific to the Android concurrency model. The effectiveness of PredRacer is evaluated using the BenchERoid data set. Experimental results demonstrate that PredRacer achieves high precision, recall, and F1 score, outperforming the state-of-the-art techniques. A collection of 20 open-source Android applications is further utilized to assess the effectiveness of PredRacer, and an evaluation of 300 wild apps is conducted to assess its efficiency and scalability. © 2024 IEEE.

关键词： Static analysis

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Self-adaptive label filtering learning for unsupervised domain adaptation

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Frontiers of computer Science 2023年第1期17卷 225-227页

作者： Qing TIAN Heyang SUN Shun PENG Tinghuai MA School of Computer and Software Nanjing University of Information Science and TechnologyNanjing 210044China Engineering Research Center of Digital Forensics Ministry of EducationNanjing University of Information Science and TechnologyNanjing 210044China

1 Introduction As an emerging machine learning paradigm,unsupervised domain adaptation(UDA)aims to train an effective model for unlabeled target domain by leveraging knowledge from related but distribution-inconsistent source *** of the existing UDA methods[2]align class-wise distributions resorting to target domain pseudo-labels,for which hard labels may be misguided by misclassifications while soft labels are confusing with trivial noises so that both of them tend to cause frustrating *** overcome such drawbacks,as shown in Fig.1,we propose to achieve UDA by performing self-adaptive label filtering learning(SALFL)from both the statistical and the geometrical perspectives,which filters out the misclassified pseudo-labels to reduce negative ***,the proposed SALFL firstly predicts labels for the target domain instances by graph-based random walking and then filters out those noise labels by self-adaptive learning strategy.

关键词： filtering resort overcome

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A three-step SEM-Bayesian network approach for predicting the determinants of CloudIoT-based healthcare adoption

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International Journal of Information technology (Singapore) 2024年 1-21页

作者： Altawaiha, Iyad Atan, Rodziah Yaakob, Razali Bin Abdullah, Rusli Bin Hj Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia

Adopting the CloudIoT-based healthcare paradigm provides various prospects for medical IT and considerably enhances healthcare services. However, compared to the advanced development of CloudIoT-based healthcare systems, their usage is relatively low among healthcare professionals in hospitals. Thus, a study on the healthcare professionals acceptance and adoption of CloudIoT-based healthcare is critical. This study explores factors influencing healthcare professionals adoption of CloudIoT-based healthcare solutions by extending the Unified Theory of Acceptance and Use of technology 2 model. Data was collected from 300 healthcare professionals in Jordan through an online questionnaire developed using the Google^® form application. The Bayesian Network and Structural Equation Modeling techniques were used to analyze and validate the proposed model. The results revealed that the healthcare professional’s behavioral intention is directly affected by seven factors: performance expectancy, effort expectancy, facilitating conditions, habit, trust, privacy, and security. The results also indicate that trust mediates the influence of privacy, facilitating conditions, and performance expectancy on behavioral intention. The research results will aid CloudIoT service providers, healthcare organizations, designers, and developers by offering comprehensive knowledge of the significant factors of CloudIoT-based healthcare adoption. A better understanding of these factors will help these stakeholder groups enhance their services and speed up the adoption of CloudIoT in the healthcare area. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adoption Bayesian network CloudIoT Healthcare professionals Structural equation modeling UTAUT2

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MemSpate: Memory Usage Protocol Guided Fuzzing 1

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25th International Conference on Formal engineering Methods, ICFEM 2024

作者： Fu, Zhiyuan Jiang, Jiacheng Wen, Cheng Xu, Zhiwu Qin, Shengchao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangzhou Institute of Technology Xidian University Xi’An China

ISBN: (数字)9789819606177

ISBN: (纸本)9789819606160

Memory safety vulnerabilities are high-risk and common vulnerabilities in software testing, often leading to a series of system errors. Fuzz testing is widely recognized as one of the most effective methods for detecting vulnerabilities, including memory safety ones. However, current fuzzing solutions typically only partially address memory usage, limiting their ability to detect memory safety vulnerabilities. In this paper, we introduce MemSpate, a dedicated fuzzer designed to detect memory safety vulnerabilities. Utilizing a more comprehensive memory usage protocol, MemSpate identifies the memory operation sequences that may violate the protocol and estimates the overall memory consumption to exceed an acceptable limit. It then monitors the coverage of these operation sequences and tracks the maximum memory consumption, both of which are used as a new feedback mechanism to guide the fuzzing process. We evaluated MemSpate on 12 real-world open-source programs and compared its performance with 5 state-of-the-art fuzzers. The results demonstrate that MemSpate surpasses all other fuzzers in terms of discovering memory safety vulnerabilities. Furthermore, our experiments have led to the discovery of 4 previously unknown vulnerabilities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Safety testing

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