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Assessing and prioritizing crucial drivers for CloudIoT-based healthcare adoption: an analytic hierarchy process approach

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International Journal of Information Technology (Singapore) 2024年 1-18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

The CloudIoT paradigm has profoundly transformed the healthcare industry, providing outstanding innovation and practical applications. However, despite its many advantages, the adoption of this paradigm in healthcare settings remains limited. There is a lack of comprehensive understanding of the factors that ensure successful implementation. To address this issue, we have used the Analytic Hierarchy Process to prioritize 26 factors that could potentially influence the adoption of CloudIoT-based healthcare. These factors were derived from existing literature and categorized into four main groups: personal-specific, technology-specific, environment-specific, and organization-specific factors. Our findings revealed that performance expectancy, trust, facilitating conditions, task complexity, and perceived security are the top five critical factors influencing CloudIoT-based healthcare adoption. Interestingly, our results indicated that environmental factors were less influential than those in the other categories, with personal, technological, and organizational factors dominating the top ten significant factors. The insights from this study can help policymakers, CloudIoT service providers, healthcare providers, designers, and developers better understand the factors contributing to the successful implementation of CloudIoT-based healthcare. This understanding can guide decision-making and inform the development of strategies to promote the broader adoption of this innovative technology. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adoption Analytic hierarchy process CloudIoT Factors Healthcare industry

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Motivating Users to Atend to Privacy: A Theory-Driven Design Study

Motivating Users to Atend to Privacy: A Theory-Driven Design...

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2024 ACM Designing Interactive Systems Conference, DIS 2024

作者： Shiri, Varun Xiong, Maggie Cheng, Jinghui Guo, Jin L.C. Department of Computer and Software Engineering Polytechnique Montreal MontrealQC Canada School of Computer Science McGill University MontrealQC Canada

ISBN: (纸本)9798400705830

In modern technology environments, raising users’ privacy awareness is crucial. Existing eforts largely focused on privacy policy presentation and failed to systematically address a radical challenge of user motivation for initiating privacy awareness. Leveraging the Protection Motivation Theory (PMT), we proposed design ideas and categories dedicated to motivating users to engage with privacy-related information. Using these design ideas, we created a conceptual prototype, enhancing the current App Store product page. Results from an online experiment and follow-up interviews showed that our design efectively motivated participants to attend to privacy issues, raising both the threat appraisal and coping appraisal, two main factors in PMT. Our work indicated that efective design should consider combining PMT components, calibrating information content, and integrating other design elements, such as visual cues and user familiarity. Overall, our study contributes valuable design considerations driven by the PMT to amplify the motivational aspect of privacy communication. © 2024 Copyright held by the owner/author(s).

关键词： Motivation

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Multi-layer composite autoencoders for semi-supervised change detection in heterogeneous remote sensing images

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science China(Information sciences) 2023年第4期66卷 124-125页

作者： Jiao SHI Tiancheng WU Hanwen YU A.K.QIN Gwanggil JEON Yu LEI School of Electronics and Information Northwestern Polytechnical University School of Resources and Environment University of Electronic Science and Technology of China Department of Computer Science and Software Engineering Swinburne University of Technology Department of Embedded Systems Engineering Incheon National University

With the increasing complexity of application scenarios, the fusion of different remote sensing data types has gradually become a trend, which can greatly improve the utilization of massive remote sensing *** the problem of change detection for heterogeneous remote images can be much more complicated than the traditional change detection for homologous remote sensing images,

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Sentiment analysis in social internet of things using contextual representations and dilated convolution neural network

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Neural Computing and Applications 2024年第20期36卷 12357-12370页

作者： Abid, Fazeel Rasheed, Jawad Hamdi, Mohammed Alshahrani, Hani Al Reshan, Mana Saleh Shaikh, Asadullah Department of Information Systems Dr. Hassan Murad School of Management University of Management and Technology Lahore Pakistan Department of Computer Engineering Istanbul Sabahattin Zaim University Istanbul34303 Turkey Department of Software Engineering Istanbul Nisantasi University Istanbul34394 Turkey Department Computer Science College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia Department Information Systems College of Computer Science and Information Systems Najran University Najran61441 Saudi Arabia

The methodologies based on neural networks are substantial to accomplish sentiment analysis in the Social Internet of Things (SIoT). With social media sentiment analysis, significant insights can produce efficient and intelligent applications. Neural networks such as recurrent neural networks (RNNs) and convolution neural networks (CNNs) have been considered widely in many text classification tasks. However, RNNs are computationally expensive and require complex training to capture contextual information and long-term dependencies. Similarly, traditional CNNs must stack multiple convolutional layers, requiring massive computations and additional parameters. To address these problems, this work initialized the novel architecture, in which contextual representations (CRs) based on the textual framework are proposed at the initial step. In CRs, state-of-the-art word representation models, such as GloVe (global vectors) and FastText (subword information), collectively produce word representations upon the input sequence using a weight mechanism. Secondly, a unique way is introduced: a three-parallel layered dilated convolutional network with global mean pooling. The experimental results show that the proposed methods when compared with baseline methods, the dilation in CNNs following CRs significantly increases the accuracy from 72.45 to 98.98% and reduces computational resources. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Sentiment analysis

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Offshore software Maintenance Outsourcing Process Model Validation:A Case Study Approach

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computers, Materials & Continua 2023年第3期74卷 5035-5048页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Adel Sulaiman Muhammad Akram Ahmad Salman Khan Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Computer Science College of Computer Science and Information SystemsNajran UniversityNajranSaudi Arabia Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan

The successful execution and management of Offshore software Maintenance Outsourcing(OSMO)can be very beneficial for OSMO vendors and the OSMO *** a lot of research on software outsourcing is going on,most of the existing literature on offshore outsourcing deals with the outsourcing of software development *** frameworks have been developed focusing on guiding software systemmanagers concerning offshore software ***,none of these studies delivered comprehensive guidelines for managing the whole process of *** is a considerable lack of research working on managing OSMO from a vendor’s ***,to find the best practices for managing an OSMO process,it is necessary to further investigate such complex and multifaceted phenomena from the vendor’s *** study validated the preliminary OSMO process model via a case study research *** results showed that the OSMO process model is applicable in an industrial setting with few *** industrial data collected during the case study enabled this paper to extend the preliminary OSMO process *** refined version of the OSMO processmodel has four major phases including(i)Project Assessment,(ii)SLA(iii)Execution,and(iv)Risk.

关键词： Offshore outsourcing process model model validation vendor challenges case study

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Combatting the TrickBot Threat: Analysis, Impact, and Defensive Strategies in Cybersecurity 17

Combatting the TrickBot Threat: Analysis, Impact, and Defens...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Cao, Jintao Null, Allie Stewart, Marissa Bhunia, Suman Salman, Mohammad Miami University Department of Computer Science and Software Engineering OxfordOH United States University of Anbar College of Computer Anbar Iraq

ISBN: (纸本)9798350351637

The TrickBot Botnet, emerging in late 2016, has been a significant cybersecurity threat, leveraging sophisticated attack vectors such as phishing emails, network vulnerabilities, and secondary payloads. This paper provides a comprehensive analysis of TrickBot's evolution, attack methodologies, and widespread impact on individual users, businesses, and government entities. Despite some disruptions to its infrastructure, TrickBot has demonstrated remarkable resilience, continually adapting its tactics to evade detection and enhance its malicious capabilities. Our research offers detailed insights into the operational mechanisms of TrickBot, supported by extensive data on its propagation and impact. Furthermore, we evaluate various defense strategies, including advanced technical measures and human-centric approaches, to mitigate the ongoing threat posed by TrickBot and similar botnets. The findings underscore the critical need for continuous vigilance and innovation in cybersecurity practices to effectively counter such persistent threats. © 2024 IEEE.

关键词： Bot (Internet)

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Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT

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computers, Materials & Continua 2023年第11期77卷 2529-2544页

作者： Muhammad Tahir Mingchu Li Irfan Khan Salman AAl Qahtani Rubia Fatima Javed Ali Khan Muhammad Shahid Anwar School of Software Technology Dalian University of Technology(DUT)Dalian116621China School of Software Technology Key Laboratory for Ubiquitous Network and Service Software of Liaoning ProvinceDalian116650China Department of Computer Engineering King Saud UniversityRiyadh11543Saudi Arabia School of Software Tsinghua UniversityBeijing100084China Department of Software Engineering University of Science and TechnologyBanu28100Pakistan Department of AI and Software Gachon UniversitySeongnamsi13120Korea

Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)***,delays in data retrieval can markedly hinder the efficacy of big data awareness detection *** advocate for a collaborative caching approach involving edge devices and cloud networks to combat *** strategy is devised to streamline the data retrieval path,subsequently diminishing network *** an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation *** paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge *** system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health *** the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during *** system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response ***,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize *** the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a *** reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark *** empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring *** encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection *** methodology yields enh

关键词： Real-time health data monitoring Cache-Assisted Real-Time Detection(CARD) edge-cloud collaborative caching scheme hierarchical detection Internet of Health Things(IoHT)

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A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual Models 62

A Curious Case of Searching for the Correlation between Trai...

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Findings of the 62nd Annual Meeting of the Association for Computational Linguistics, ACL 2024

作者： Dang, Cuong Le, Dung D. Le, Thai FPT Software AI Center Viet Nam College of Engineering and Computer Science VinUniversity Viet Nam Department of Computer Science Indiana University United States

ISBN: (纸本)9798891760998

Existing works have shown that fine-tuned textual transformer models achieve state-of-the-art prediction performances but are also vulnerable to adversarial text perturbations. Traditional adversarial evaluation is often done only after fine-tuning the models and ignoring the training data. In this paper, we want to prove that there is also a strong correlation between training data and model robustness. To this end, we extract 13 different features representing a wide range of input fine-tuning corpora properties and use them to predict the adversarial robustness of the fine-tuned models. Focusing mostly on encoder-only transformer models BERT and RoBERTa with additional results for BART, ELECTRA, and GPT2, we provide diverse evidence to support our argument. First, empirical analyses show that (a) extracted features can be used with a lightweight classifier such as Random Forest to predict the attack success rate effectively, and (b) features with the most influence on the model robustness have a clear correlation with the robustness. Second, our framework can be used as a fast and effective additional tool for robustness evaluation since it (a) saves 30x-193x runtime compared to the traditional technique, (b) is transferable across models, (c) can be used under adversarial training, and (d) robust to statistical randomness. Our code is publicly available at https://***/CaptainCuong/RobustText_ACL2024. © 2024 Association for Computational Linguistics.

关键词： Adversarial machine learning

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Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models 38

Reference Trustable Decoding: A Training-Free Augmentation P...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Shi, Luohe Yao, Yao Li, Zuchao Zhang, Lefei Zhao, Hai National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Department of Computer Science and Engineering Shanghai Jiao Tong University China

Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and Parameter-Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting LLMs to downstream tasks. ICL typically constructs a few-shot learning scenario, either manually or by setting up a Retrieval-Augmented Generation (RAG) system, helping models quickly grasp domain knowledge or question-answering patterns without changing model parameters. However, this approach involves trade-offs, such as slower inference speed and increased space occupancy. PEFT assists the model in adapting to tasks through minimal parameter modifications, but the training process still demands high hardware requirements, even with a small number of parameters involved. To address these challenges, we propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning, maintaining low inference costs. RTD constructs a reference datastore from the provided training examples and optimizes the LLM's final vocabulary distribution by flexibly selecting suitable references based on the input, resulting in more trustable responses and enabling the model to adapt to downstream tasks at a low cost. Experimental evaluations on various LLMs using different benchmarks demonstrate that RTD establishes a new paradigm for augmenting models to downstream tasks. Furthermore, our method exhibits strong orthogonality with traditional methods, allowing for concurrent usage. Our code can be found at https://***/ShiLuohe/ReferenceTrustableDecoding. © 2024 Neural information processing systems foundation. All rights reserved.

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Analyzing the Impact of Preprocessing Techniques on the Efficiency and Accuracy of Sentiment Classification Algorithms 15

Analyzing the Impact of Preprocessing Techniques on the Effi...

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15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Nikhila Kanigiri, Sai Mekuriyaw, Chachi Goodman, Garrett Alexiou, Michail S. Kennesaw State University Department of Computer Science MariettaGA United States Miami University Computer Science and Software Engineering OxfordOH United States

ISBN: (纸本)9798350368833

In the era of data-driven decision-making, the ability to rapidly and accurately analyze vast amounts of textual data is essential. This paper examines the role of text pre-processing techniques in enhancing the effectiveness of sentiment classification algorithms. Such algorithms are pivotal in interpreting the emotional tone of texts and serving as a preliminary step towards reverse-engineering complex data systems. Through comprehensive analysis, we assess the impact of various preprocessing strategies such as keyword removal, lemmatization, and stemming and their combination on the performance of both traditional and advanced machine learning models, including Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Bidirectional Encoder Representations from Transformers (BERT). The current study evaluates the balance between efficiency and accuracy in sentiment analysis algorithms, illustrating the impact of preprocessing techniques. In addition, it aims to serve as a guideline for selecting preprocessing methods that optimize model performance in both sentiment analysis and textual data reverse-engineering. © 2024 IEEE.

关键词： Adversarial machine learning

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