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A systematic mapping to investigate the application of machine learning techniques in requirement engineering activities

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1412-1434页

作者： Shoaib Hassan Qianmu Li Khursheed Aurangzeb Affan Yasin Javed Ali Khan Muhammad Shahid Anwar School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjingJiangsuChina Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia School of Software Northwestern Polytechnical UniversityXianShaanxiChina Department of Computer Science School of PhysicsEngineering&Computer ScienceUniversity of HertfordshireHatfieldUK Department of AI and Software Gachon University Seongnam-siSeongnamSouth Korea

Over the past few years,the application and usage of Machine Learning(ML)techniques have increased exponentially due to continuously increasing the size of data and computing *** the popularity of ML techniques,only a few research studies have focused on the application of ML especially supervised learning techniques in Requirement engineering(RE)activities to solve the problems that occur in RE *** authors focus on the systematic mapping of past work to investigate those studies that focused on the application of supervised learning techniques in RE activities between the period of 2002–*** authors aim to investigate the research trends,main RE activities,ML algorithms,and data sources that were studied during this ***-five research studies were selected based on our exclusion and inclusion *** results show that the scientific community used 57 *** those algorithms,researchers mostly used the five following ML algorithms in RE activities:Decision Tree,Support Vector Machine,Naïve Bayes,K-nearest neighbour Classifier,and Random *** results show that researchers used these algorithms in eight major RE *** activities are requirements analysis,failure prediction,effort estimation,quality,traceability,business rules identification,content classification,and detection of problems in requirements written in natural *** selected research studies used 32 private and 41 public data *** most popular data sources that were detected in selected studies are the Metric Data Programme from NASA,Predictor Models in software engineering,and iTrust Electronic Health Care System.

关键词： data sources machine learning requirement engineering supervised learning algorithms

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Wireless Sensing for Material Identification: A Survey

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IEEE Communications Surveys and Tutorials 2024年第3期27卷 1598-1617页

作者： Chen, Yande Xu, Chongzhi Li, Kexin Zhang, Jia Guo, Xiuzhen Jin, Meng Zheng, Xiaolong He, Yuan Tsinghua University School of Software Beijing China Beijing University of Posts and Telecommunications School of Computer Science Beijing China Shanghai Jiao Tong University John Hopcroft Center for Computer Science Shanghai China Zhejiang University College of Control Science and Engineering Hangzhou China

As an application of fine-grained wireless sensing, RF-based material identification follows the paradigm of RF computing that fetches the information during RF signal propagation. Specifically, the RF signal accesses the objects' material-related information and carries the information with its electromagnetic properties. With a variety of important applications, research on RF-based material identification has gained significant progress in recent years. However, several fundamental problems remain insufficiently studied, such as the sensing models, signal processing approaches, performance and future extensions. This paper presents the first comprehensive survey of RF-based material identification. According to the basic sensing model used for sensing, we propose a taxonomy to classify the existing works into two categories: reflection-based and penetration-based. The works in each category are further grouped by the type of RF signals used, with elaborated discussion of the detailed approaches and the common challenges. We provide a framework that benchmarks the performance of the existing works, followed by a thorough discussion of future extensions. © 1998-2012 IEEE.

关键词： II-VI semiconductors

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EmoTracer: A User-independent Wearable Emotion Tracer with Multi-source Physiological Sensors Based on Few-shot Learning

EmoTracer: A User-independent Wearable Emotion Tracer with M...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Kuang, Wenting Jin, Shuo Wang, Danyang Zheng, Yuda Zou, Yongpan Wu, Kaishun Shenzhen University College of Computer Science and Software Engineering China Information Hub Hong Kong

ISBN: (纸本)9798350386226

The rising prevalence of mood disorders, including depression and anxiety, underscores the critical state of physical and mental health issues in contemporary society. Automatic emotion recognition technology emerges as a potential tool for monitoring mental health disorders, and offering valuable guidance through human-computer interfaces. However, existing technologies grapple with limitations, such as emotional concealment, potential privacy leakage, and specific device location restrictions. To overcome these limitations, we propose a mobile wearable emotion recognition system called EmoTracer incorporating ubiquitous multi-source sensors to measure physiology signals, and address challenges such as the intricate signal-emotion relationship, sparse data processing, and notable disparities among different subjects. We implement a real-time prototype and carry out comprehensive experiments to evaluate its performance. For six basic emotions, the results show that EmoTracer can achieve 95.6% accuracy in intra-subject emotion classification and 80.7% accuracy with 5 shots in cross-subject emotion classification. © 2024 IEEE.

关键词： Emotion Recognition

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Reliable Service Recommendation: A Multi-modal Adversarial Method for Personalized Recommendation under Uncertain Missing Modalities

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IEEE Transactions on Services Computing 2025年第03期18卷 1724-1738页

作者： Chen, Junyang Yang, Ruohan Guo, Jingcai Wang, Huan Wu, Kaishun Zhang, Liangjie Shenzhen University College of Computer Science and Software Engineering China Huazhong Agricultural University College of Informatics Wuhan China The Hong Kong Polytechnic University Department of Computing Hong Kong DSA Thrust and IoT Thrust Center China

Personalized recommendation is of paramount importance in online content platforms like Kuai and Tencent. To ensure accurate recommendations, it is crucial to consider multi-modal information in both items and user-user/item interactions. While existing works on multimedia recommendation have made strides in leveraging multi-modal contents to enrich item representations, many of them overlook the practical scenario of multiple modality missing. As a result, the performance of recommendation systems can be significantly compromised in such cases. In this paper, we introduce a novel multi-modal adversarial method called MMAM, which aims to provide reliable personalized recommendation services even in the presence of uncertain missing modalities. The core idea behind MMAM is to design a generator that can effectively encode both user-user/item interactions and multi-modal contents, taking into account various missing cases. The generator is trained to learn transferable features from different combinations of missing modalities in order to deceive a discriminative classifier. Additionally, we propose a modal discriminator that can classify the missing cases of multi-modalities, further enhancing the capability of the model. Moreover, a well-equipped predictor utilizes the transferable features to predict potential user interests. To improve the prediction accuracy, we design a type discriminator that enhances the classification of link types. By employing a mini-max game between the generator and the discriminators, MMAM successfully obtains transferable features that encompass multi-modal contents, even when facing uncertain missing modalities. We conduct extensive experiments on industrial datasets, including Kuai and Tencent. Comparing with state-of-the-art approaches, MMAM achieves improvements in personalized recommendation tasks under uncertain missing modalities. MMAM holds promise for enhancing multi-modal personalized recommendations in real-world applications

关键词： Discriminators

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AoI Analysis for Automatic Repeat-Request in Vehicular Cooperative Perception Networks 100

AoI Analysis for Automatic Repeat-Request in Vehicular Coope...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Huang, Qinan Zeng, Jianhua Guo, Chongtao Liu, Jing Pan, Haoyuan Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China

ISBN: (纸本)9798331517786

Cooperative perception has been shown an effective approach to address the perception limitation problem faced by the individual perception in the era of autonomous driving. Considering a two-source two-hop cooperative perception vehicular network, we investigate the average age of information (AoI) with different automatic repeat-request (ARQ) strategies in this paper. We formulate the packet transmission process as a Markov chain, which allows us to establish linear equations about the first and second moments of all states' residual waiting time for the destination to receive a new update packet. Then, we derive the average AoI based on the analytical solution to the equations. Furthermore, the analysis is applied to a special case of the single-source two-hop cooperative perception system. Simulation results show that the optimal strategy is neither non-ARQ nor infinite-ARQ, but truncated ARQ, which means that there exists a finite retransmission limit to minimize the average AoI, implying that better cooperative perception can be achieved by optimizing the retransmission limit, especially in the single-source two-hop situation. © 2024 IEEE.

关键词： Markov chains

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A spatio-temporal multi-scale fusion algorithm for pine wood nematode disease tree detection

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Journal of Forestry Research 2024年第6期35卷 267-278页

作者： Chao Li Keyi Li Yu Ji Zekun Xu Juntao Gu Weipeng Jing College of Computer and Control Engineering Northeast Forestry UniversityHarbin 150040People’s Republic of China Heilongjiang Province Cyberspace Research Center Harbin 150090People’s Republic of China

Pine wood nematode infection is a devastating *** aerial vehicle(UAV)remote sensing enables timely and precise ***,UAV aerial images are challenged by small target size and complex sur-face backgrounds which hinder their effectiveness in *** address these challenges,based on the analysis and optimization of UAV remote sensing images,this study developed a spatio-temporal multi-scale fusion algorithm for disease *** multi-head,self-attention mechanism is incorporated to address the issue of excessive features generated by complex surface backgrounds in UAV *** enables adaptive feature control to suppress redundant information and boost the model’s feature extraction *** SPD-Conv module was introduced to address the problem of loss of small target feature information dur-ing feature extraction,enhancing the preservation of key ***,the gather-and-distribute mechanism was implemented to augment the model’s multi-scale feature fusion capacity,preventing the loss of local details during fusion and enriching small target feature *** study established a dataset of pine wood nematode disease in the Huangshan area using DJI(DJ-Innovations)*** results show that the accuracy of the proposed model with spatio-temporal multi-scale fusion reached 78.5%,6.6%higher than that of the benchmark *** upon the timeliness and flexibility of UAV remote sensing,the pro-posed model effectively addressed the challenges of detect-ing small and medium-size targets in complex backgrounds,thereby enhancing the detection efficiency for pine wood nematode *** facilitates early preemptive preser-vation of diseased trees,augments the overall monitoring proficiency of pine wood nematode diseases,and supplies technical aid for proficient monitoring.

关键词： Pine wood nematode disease UAV remote sensing Object detection Deep learning YOLOv8

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Framework for Effective Utilization of Distributed Scrum in software Projects

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computer Systems Science & engineering 2023年第1期44卷 407-422页

作者： Basit Shahzad Wardah Naeem Awan Fazal-e-Amin Ahsanullah Abro Muhammad Shoaib Sultan Alyahya Department of Software Engineering National University of Modern LanguagesIslamabad44000Pakistan Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computer Science Sukkur IBA UniversitySindh65200Pakistan Department of Information Systems College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

There is an emerging interest in using agile methodologies in Global software Development(GSD)to get the mutual benefits of both *** is currently admired by many development teams as an agile most known meth-odology and considered adequate for collocated *** the same time,stake-holders in GSD are dispersed by geographical,temporal,and socio-cultural *** to the controversial nature of Scrum and GSD,many significant challenges arise that might restrict the use of Scrum in *** conducted a Sys-tematic Literature Review(SLR)by following Kitchenham guidelines to identify the challenges that limit the use of Scrum in GSD and to explore the mitigation strategies adopted by practitioners to resolve the *** validate our reviewfindings,we conducted an industrial survey of 305 *** results of our study are consolidated into a research *** framework represents current best practices and recommendations to mitigate the identified distributed scrum challenges and is validated byfive experts of distributed *** of the expert review were found supportive,reflecting that the framework will help the stakeholders deliver sustainable products by effectively mitigating the identified challenges.

关键词： Global software development distributed Scrum sustainable development challenges mitigation strategies

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mmHRR: Monitoring Heart Rate Recovery with Millimeter Wave Radar 30

mmHRR: Monitoring Heart Rate Recovery with Millimeter Wave R...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Mao, Ziheng He, Yuan Zhang, Jia Sun, Yimiao Xie, Yadong Guo, Xiuzhen Tsinghua University School of Software & BNRist China Zhejiang University College of Control Science and Engineering China

ISBN: (纸本)9798331515966

Heart rate recovery (HRR) within the initial minute following exercise is a widely utilized metric for assessing cardiac autonomic function in individuals and predicting mortality risk in patients with cardiovascular disease. However, prevailing solutions for HRR monitoring typically involve the use of specialized medical equipment or contact wearable sensors, resulting in high costs and poor user experience. In this paper, we propose a contactless HRR monitoring technique, mmHRR, which achieves accurate heart rate (HR) estimation with a commercial mmWave radar. Unlike HR estimation at rest, the HR varies quickly after exercise and the heartbeat signal entangles with the respiration harmonics. To overcome these hurdles and effectively estimate the HR from the weak and non-stationary heartbeat signal, we propose a novel signal processing pipeline, including dynamic target tracking, adaptive heartbeat signal extraction, and accurate HR estimation with composite sliding windows. Real-world experiments demonstrate that mmHRR exhibits exceptional robustness across diverse environmental conditions, and achieves an average HR estimation error of 3.31 bpm (beats per minute), 71% lower than that of the state-of-the-art method. © 2024 IEEE.

关键词： Heart

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A Review of Security and Information control in Blockchain-Integrated IoT Applications 3

A Review of Security and Information Control in Blockchain-I...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Cholke, Abhijeet Mandhare, V.V. Vikhe, P.S. Matoshri College of Engineering & Research Centre Department of Computer Engineering Nashik India Pravara Rural Engineering College Department of Computer Engineering Loni Ahmednagar India Pravara Rural Engineering College Department of Instrumentation and Control Engineering Loni Ahmednagar India

ISBN: (纸本)9798350354348

The convergence of the Internet of Things (IoT) and blockchain technologies has gained substantial traction in recent years, owing to the rapid advancement of communication technologies worldwide. This research introduces a prototype model that seamlessly integrates an IoT platform with blockchain technology to ensure the integrity of data. The prototype establishes a decentralized framework for IoT devices, underpinned by a private blockchain infrastructure capable of replicating device data and validating transactions among devices through a consensus mechanism. Nevertheless, the implementation of this concept necessitates a closer examination of various considerations. One crucial aspect to address is transforming data generated by diverse IoT devices into a format compatible with the blockchain. This research takes a proactive approach to effective data management within the blockchain. Initially, we collect data from IoT devices and systematically store it in a blockchain-compatible manner. The custom blockchain and smart contract components define the data access policies for end-users. Within this model, we categorize IoT appliances responsible for different services into distinct sets of chains. These sets of chains collaboratively exchange data while adhering to a comprehensive access control *** research endeavors to harmonize IoT and blockchain technologies through a prototype that safeguards data integrity. It tackles the challenge of data conversion from various IoT devices into a blockchain-compatible format and outlines a sophisticated data management strategy. By delineating access policies and segmenting IoT appliances into specialized sets of chains, this model aims to facilitate secure and efficient data sharing among decentralized IoT devices. © 2024 IEEE.

关键词： Data integrity

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Timely Status Update in TDMA: Round-Based or User-Based? 4

Timely Status Update in TDMA: Round-Based or User-Based?

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4th International Conference on Information Science, Parallel and Distributed Systems, ISPDS 2023

作者： Han, Xinhui Feng, Jian College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350337181

This paper investigates the information freshness of two time division multiple access (TDMA) systems: round-based TDMA and user-based TDMA. The age of information (AoI) metric is used to represent the freshness of information, measured by the time since the generation of the most recently received packet. In a conventional round-based TDMA system, multiple users send their packets in rounds, regardless of whether their packets are successfully received by the receiver, possibly leading to a high average AoI of the system. In contrast, we propose a user-based TDMA system that guarantees successful packet reception for each user through feedback before proceeding to the next user. We evaluate the average AoI and average Peak AoI (PAoI) of both TDMA systems. Our theoretical and simulation results indicate that while both systems exhibit the same average PAoI, the user-based TDMA system has a lower average AoI compared with its round-based counterpart. Our findings provide new insights into the design of TDMA systems with different information freshness requirements. © 2023 IEEE.

关键词： Time division multiple access

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