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19th International Conference on Evaluation of Novel Approaches to software engineering, ENASE 2024

作者： Karolita, Devi Grundy, John Kanij, Tanjila Obie, Humphrey McIntosh, Jennifer Department of Software Systems and Cybersecurity Faculty of Information Technology Monash University Melbourne Australia Department Informatics Engineering Faculty of Engineering Palangka Raya University Palangka Raya Indonesia Faculty of Medicine Dentistry and Health Sciences The University of Melbourne Melbourne Australia

ISBN: (纸本)9789897586965

Personas, a user characterisation, have been widely used in requirements engineering (RE) to enhance the understanding of end-users and their needs. However, the persona generation process is time-consuming and demands familiarity with a user-centered approach. The central issue lies in existing tools for automatically generating personas, which are restricted to generating persona templates and provide limited user control to tailor personas according to their specific needs. This paper introduces CRAFTER, a persona generation tool that uses Large Language Models (GPT-3.5 model). This tool not only automates persona creation but also offers recommendations to users for generating personas tailored to their requirements. The study involved an online questionnaire with 19 respondents who utilised the tool, providing feedback that indicated the tool’s sufficiency for persona generation while identifying areas for improvement. Beyond its primary function, CRAFTER stands out by providing guidance to requirements engineers throughout the persona creation process. The tool grants users the flexibility to customise personas based on their specific requirements, acknowledging the crucial human subjectivity in persona development. Additionally, CRAFTER promotes persona reusability, allowing users to save and reuse generated personas for future projects. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Requirements engineering

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Application of mT5 and Semantic Role Labeling for Aspect-Based Sentiment Analysis in Political Opinion 5

Application of mT5 and Semantic Role Labeling for Aspect-Bas...

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5th International Conference on Sensing, Measurement and Data Analytics in the Era of Artificial Intelligence, ICSMD 2024

作者： Yu, Jinpeng Li, Zijun Liu, Na Kong, Weixuan Huang, Zijun Li, Xuefeng College of Electronic and Information Engineering Tongji University Shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China College Of Software Engineering Tongji University Shanghai China

ISBN: (纸本)9798331529192

As aspect-based sentiment analysis (ABSA) extends its application across various domains, effectively utilizing this technology in political opinion analysis poses significant challenges, particularly due to limited training data in sensitive areas. To address this, we constructed two datasets: a 7,334-sample three-class sentiment analysis dataset from political news via web scraping, and a high-quality dataset of 100 manually annotated news sentences designed for testing aspect-level sentiment computation. 'This paper proposes a sentiment classification method based on the mT5 model, combined with Named Entity Recognition (NER) and Semantic Role Labeling (SRL). Experimental results show that our method performs comparably to existing approaches on the SemEval-2014 (Rest14) dataset, while achieving 84% accuracy in aspect entity recognition and 69% overall recognition accuracy on our custom dataset. These findings suggest that the proposed method can significantly enhance model performance in scenarios with limited and sensitive training data. © 2024 IEEE.

关键词： mT5 Model News Sentiment Analysis Political Opinion Analysis Semantic Role Labeling (SRL)

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Cardiac Arrhythmia Disease Classifier Model Based on a Fuzzy Fusion Approach

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Computers, Materials & Continua 2023年第5期75卷 4485-4499页

作者： Fatma Taher Hamoud Alshammari Lobna Osman Mohamed Elhoseny Abdulaziz Shehab Eman Elayat College of Technological Innovation Zayed UniversityDubaiUAE Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Electronics and Communications Engineering Delta Higher Institute for Engineering&TechnologyMansouraEgypt College of Computing and Informatics University of SharjahSharjahUnited Arab Emirates Department of Information Systems Mansoura UniversityMansoura35516Egypt Department of Teacher Preparation Faculty of Specific EducationMansoura UniversityMansouraEgypt

Cardiac diseases are one of the greatest global health *** to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent *** article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia *** fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection *** ensemble of classifiers is then applied to the fusion’s *** proposed model classifies the arrhythmia dataset from the University of California,Irvine into normal/abnormal classes as well as 16 classes of ***,at the preprocessing steps,for the miss-valued attributes,we used the average value in the linear attributes group by the same class and the most frequent value for nominal ***,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized *** preprocessing step led to 161 out of 279 attributes(features).Thereafter,a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection *** short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive *** proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art *** achieves 98.5%accuracy for binary class mode and 98.9%accuracy for categorized class mode.

关键词： Cardiac arrhythmia preprocessing missing values classification model fusion

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The School of the Future The Role of Digital Technologies, Metacognition and Emotional Intelligence

International Journal of Emerging Technologies in Learning

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International Journal of Emerging Technologies in Learning 2023年第9期18卷 65-85页

作者： Drigas, Athanasios Papanastasiou, George Skianis, Charalabos Net Media Lab-Mind & Brain R&D Institute of Informatics and Telecommunications N.C.S.R. ‘Demokritos’ Athens Greece Information and Communication Systems Engineering Department University of the Aegean Samos Greece

Technology is developing at a rapid pace, affecting the socioeconomic situation of the planet through innovation and the evolution of applications for easier and faster access to goods and services. The development of new technologies has also affected education. In this paper, the school of the future is presented with regard to emerging or exponential disruptive technologies and the impact of emotional intelligence on those involved in education. The politics of globalization, global perspectives, perceptions, and contemporary social values lead to the education of individuals with accessibility for everyone, from everyone, and from everywhere and at any time in an inclusive world. The school is changing. Research showed that the school of the future reshapes its learning environment to meet the increasing demands of the 21st century which are positively correlated with dynamic, flexible, interactive, creative, and self-directed learning technologies © 2023, International Journal of Emerging Technologies in *** Rights Reserved.

关键词： Cognitive systems

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Sentiment Analysis of Covid-19 Twitter Data using Deep Learning Algorithm 2

Sentiment Analysis of Covid-19 Twitter Data using Deep Learn...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Valarmathi, B. Gupta, N. Srinivasa Karthick, V. Chellatamilan, T. Santhi, K. Chalicheemala, Dhanush Department of Software and Systems Engineering School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Manufacturing School of Mechanical Engineering Vellore Institute of Technology Tamil Nadu Vellore India Assistant Systems Engineer Tata Consultancy Services Chennai India Department of Analytics School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India Department of Information Technology School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

A key method used in the study of Natural Language Processing (NLP) is sentiment analysis, or emotion analysis, plays a pivotal role in text analysis. Its primary function is to discern and categorize the underlying emotions within textual content, classifying them as positive, neutral, or negative sentiments. A variety of textual inputs can be used with this process, including entire documents, individual sentences, and more. Understanding the sentiments expressed by individuals is of paramount importance to organizations in today's digital age. Clients and customers now have the means to express their thoughts and emotions with greater ease and immediacy than ever before. This wealth of sentiment data can offer valuable insights that organizations can leverage to enhance their products, services, and overall customer experience. The present study focuses on the sentiment analysis of Twitter data related to the COVID-19 pandemic, employing the Long Short-Term Memory (LSTM) algorithm. To improve the accuracy of sentiment analysis, a pre-processing procedure is introduced, which involves the use of the Neat Text module in Python to clean the tweets. In this research endeavor, a dataset comprising 1,79,107 COVID-19-related tweets is subjected to sentiment analysis using the proposed pre-processing module and LSTM. The results demonstrate an impressive accuracy rate of 96% in accurately determining the sentiments conveyed in these COVID-19 tweets. In contrast, the existing algorithm, based on Artificial Neural Network (ANN), achieved a significantly lower accuracy rate of 76%. This research not only showcases the effectiveness of LSTM and pre-processing in sentiment analysis but also highlights the significance of sentiment analysis in gaining valuable insights from vast amounts of textual data, especially when important events like the COVID-19 pandemic are involved. © 2024 Elsevier B.V.. All rights reserved.

关键词： COVID-19

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Half space object classification via incident angle based fusion of radar and infrared sensors

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Journal of systems engineering and Electronics 2022年第5期33卷 1025-1031页

作者： HE Zhenyu ZHUGE Xiaodong WANG Junxiang YU Shihao XIE Yongjun ZHAO Yuxiong School of Electronic Information and Engineering Beihang UniversityBeijing 100191China Beijing Institute of Electronic System Engineering Beijing 100854China Beijing Institute of Astronautical Systems Engineering Beijing 100076China Shenzhen Institute of Beihang University Shenzhen 518038China School of Software Beihang UniversityBeijing 100191China

In this paper,we introduce an incident angle based fusion method for radar and infrared sensors to improve the recognition rate of complex targets under half space scenarios,e.g.,vehicles on the ground in this *** radar sensors,convolutional operation is introduced into the autoencoder,a“winner-take-all(WTA)”convolutional autoencoder(CAE)is used to improve the recognition rate of the radar high resolution range profile(HRRP).Moreover,different from the free space,the HRRP in half space is more *** order to get closer to the real situation,the half space HRRP is simulated as the *** recognition rate has a growth more than 7%com-pared with the traditional CAE or denoised sparse autoencoder(DSAE).For infrared sensor,a convolutional neural network(CNN)is used for infrared image ***,we com-bine the two results with the Dempster-Shafer(D-S)evidence theory,and the discounting operation is introduced in the fusion to improve the recognition *** recognition rate after fusion has a growth more than 7%compared with a single *** the discounting operation,the accuracy rate has been improved by 1.5%,which validates the effectiveness of the proposed method.

关键词： convolutional autoencoder(CAE) half space high-resolution range profile(HRRP) incident angle based fusion tar-get recognition

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Sensing-Assisted Channel Estimation in ISAC systems 14th

Sensing-Assisted Channel Estimation in ISAC Systems

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14th EAI International Conference on Wireless and Satellite systems, WiSATS 2024

作者： Yin, Rui Shi, Yexin Qi, Wei Chen, Xianfu Wu, Celimuge Ji, Yusheng School of Information and Electrical Engineering Hangzhou City University Hangzhou310018 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Shenzhen CyberAray Network Technology Company Ltd Shenzhen China Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo182-8585 Japan Information Systems Architecture Research Division National Institute of Informatics Tokyo101-8430 Japan

ISBN: (纸本)9783031862021

In future Sixth-Generation (6G) mobile communication, satellite communication is a crucial means of extending mobile networks worldwide. However, in satellite communication systems, the transmission capacity between ground stations and users has become a bottleneck for satellite communication systems. For this issue, Integrated Sensing and Communication (ISAC) technology and Artificial Intelligence (AI), which has been widely studied, has the potential to offer excellent solutions. This paper explores a Uformer-based sensing-assisted communication system that enhances channel estimation accuracy through the design of a Uformer-based channel estimation enhancement algorithm. By formulating a global optimization problem, we seek to improve resource efficiency through finding the optimal resource allocation schemes. Initially, the system utilizes radar data to obtain reconstructed channels and employs communication data for channel estimation. The proposed neural network model then fuses these data into a new and more accurate channel state information. Finally, we present a resource allocation algorithm and validate the performance of all proposed algorithms through simulations. © ICST institute for Computer Sciences, Social informatics and Telecommunications engineering 2025.

关键词： Resource allocation

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LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection 25

LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection

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25th IEEE International Conferences on High Performance Computing and Communications, 9th International Conference on Data Science and systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Qi, Jiaxing Huang, Shaohan Luan, Zhongzhi Yang, Shu Fung, Carol Yang, Hailong Qian, Depei Shang, Jing Xiao, Zhiwen Wu, Zhihui Sino-German Joint Software Institute Beihang University Beijing100191 China The Concordia Institute for Information Systems Engineering Concordia University Quebec Canada The China Mobile Information Technology Center China

ISBN: (纸本)9798350330014

The increasing volume of log data produced by software-intensive systems makes it impractical to analyze them manually. Many deep learning-based methods have been proposed for log-based anomaly detection. These methods face several challenges such as high-dimensional and noisy log data, class imbalance, generalization, and model interpretability. Recently, ChatGPT has shown promising results in various domains. However, there is still a lack of study on the application of ChatGPT for log-based anomaly detection. In this work, we proposed LogGPT, a log-based anomaly detection framework based on ChatGPT. By leveraging ChatGPT's language interpretation capabilities, LogGPT aims to explore the transferability of knowledge from large-scale corpora to log-based anomaly detection. We conduct experiments to evaluate the performance of LogGPT and compare it with three deep learning-based methods on BGL and Spirit datasets. LogGPT shows promising results and has good interpretability. This study provides preliminary insights into prompt-based models, such as ChatGPT, for the log-based anomaly detection task. © 2023 IEEE.

关键词： Anomaly detection

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Trusted elements in the digital model of the tunnel

Trusted elements in the digital model of the tunnel

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ITA-AITES World Tunnel Congress, WTC 2024

作者： Paskaleva, Galina Beronneau, Patrick Bednar, Thomas TU Wien Institute of Information Systems Engineering Business Informatics Group Vienna Austria TU Wien Research Unit of Building Physics Institute of Material Technology Building Physics and Building Ecology Vienna Austria

ISBN: (纸本)9781032800424

The application of Building information Modelling (BIM) in Tunnelling involves the generation, co-evolution, and maintenance of digital models. This often requires the collaboration of multiple stakeholders on the same digital model over multiple project phases, which, in turn, necessitates the sharing of responsibility for the same model element among those stakeholders. For example, a niche in the tunnel inner lining may be designed simultaneously by a structural engineer, by a fire safety expert who needs to install a fire hydrant in it, and by a HVAC engineer who needs a ventilation control panel in it. Shared responsibility requires trust. Lack of trust often leads to the duplication of models so that each stakeholder can work on a separate model, free from the influence of the work of others. The consequence is a break-down of model continuity and unsustainable usage of personnel and resources. In this work, we present a method for introducing trusted elements into the digital model of the tunnel. This can be accomplished by anchoring those elements to contractually relevant documents, such as the Employer information Requirement (EIR) according to the ISO 19650 series. Trusted elements can guarantee formally that a certain contractual or technical requirement has been fulfilled, e.g., that the measurement of carbon monoxide for the emergency ventilation system is designed in accordance with ISO 23431. In addition, formal requirements within a trusted element can be tested automatically by the common data environment (CDE) of the project. In case that one stakeholder changes the trusted element so that a requirement is no longer fulfilled, an automatic alert can be issued. We present a standardized workflow for the creation and maintenance of such trusted elements in a digital model on the use case of the design of a ventilation system in a road tunnel. © 2024 The Author(s).

关键词： Tunnels

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