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IEEE Open Journal of the Communications Society 2024年 5卷 3568-3580页

作者： Zahoor, Amina Mahmood, Khalid Saleem, Muhammad Asad Badar, Hafiz Muhammad Sanaullah Le, Tuan-Vinh Das, Ashok Kumar Department of Computer Science Sahiwal57000 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Emerson University Multan Department of Emerging Computing Technologies Multan61000 Pakistan Fu Jen Catholic University Bachelor's Program of Artificial Intelligence and Information Security New Taipei City24206 Taiwan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India

The Smart Power Grid (SPG) is pivotal in orchestrating and managing demand response in contemporary smart cities, leveraging the prowess of Information and Communication Technologies (ICTs). Within the immersive SPG environment, the ubiquitous deployment of smart meters stands as a testament to their paramount importance in the realm of vigilant monitoring and oversight. These smart meters are installed on high-tension electricity lines and transmit information about electricity outages and other issues to the utility center. To access services from utility centers, smart meters need to communicate securely over a public channel, even though the network itself is insecure. However, potential attacks from adversaries ( Ad) can exploit this communication. Therefore, protecting this communication is of utmost importance. Several privacy-preserving authentication protocols designed for SPG have been introduced in the literature. Nevertheless, a significant number of these protocols exhibit vulnerabilities to diverse security attacks. This article introduces a lightweight and anonymous authentication protocol specifically designed to address these concerns in the SPG environment. Our protocol ensures both security and efficiency, surpassing other comparable protocols in terms of its lightweight nature. By employing both formal and informal analysis, we showcase the robustness of our protocol against significant attacks while remaining lightweight. The proposed protocol provides added security features and incurs 23.8879% lower computation cost than related protocols. Consequently, our protocol is highly suitable for implementation in the SPG system. © 2020 IEEE.

关键词： Hash functions

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Evaluation of Healthprompt for Zero-shot Clinical Text Classification

Evaluation of Healthprompt for Zero-shot Clinical Text Class...

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Sonish Sivarajkumar Yanshan Wang School of Computing and Information Intelligent Systems Program University of Pittsburgh Pittsburgh USA Department of Health Information Management School of Health and Rehabilitation Sciences University of Pittsburgh Pittsburgh USA

This paper presents an evaluation of the Healthprompt, a prompt-based zero-shot clinical text classification framework. The lack of publicly available datasets and the expensive data annotation in the clinical domain make traditional NLP models difficult to train. To overcome this issue, Healthprompt utilizes Pre-trained Language Models (PLMs) and prompt-based Zero-Shot Learning (ZSL) to perform clinical NLP tasks without additional training data. However, the original Healthprompt paper missed the error analysis and ablation study of the framework. This paper conducts an error analysis and an ablation study on Electronic Health Records(EHR) notes to understand the capabilities and limitations of the Healthprompt framework for clinical text classification. The results provide insight into the potential and limitations of prompt-based zeroshot learning for clinical NLP tasks and offer suggestions for improvements to the Healthprompt framework, and for the future development of prompt-based ZSL.

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AI Recommendations: Friend or Foe? Unraveling the Impact on Consumer Benefits and Privacy

AI Recommendations: Friend or Foe? Unraveling the Impact on ...

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International Conference on ICT For Smart Society (ICISS)

作者： Desyra Sukma Dewanthi Hari Kristopo Nunung Nurul Qomariyah Muhammad Axel Management Department Business Management & Marketing Program BINUS Business School International Undergraduate Program Bina Nusantara University Jakarta Indonesia Food Technology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computing and Creative Arts Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Artificial Intelligence (AI) and marketing have transformed consumer behavior and shopping experiences, especially through Recommender Systems (RSs) in e-commerce. RSs use algorithms to provide personalized recommendations, enhancing shopping convenience. However, this raises data privacy and ethical concerns, as RSs rely on user data. This research explores the quality determinants of RSs and their impact on consumer behavior, focusing on privacy awareness and its effect on repurchase intentions. Using a qualitative approach with semi-structured interviews, the study targets Indonesian respondents aged 18–40 with e-commerce experience. Findings reveal that recommendation quality, especially self-reference and vividness, significantly influences purchasing decisions. Respondents prefer recommendations that match their preferences and are visually appealing. Positive shopping experiences are linked to efficient and accurate RSs, but there's a need for improvements in transparency, novelty, diversity, and advanced technologies like AR. Respondents are aware of digital data storage but have limited understanding of privacy concerns. They trust e-commerce platforms with basic personal details but are hesitant to share sensitive information. Despite privacy issues, e-commerce remains vital, with respondents emphasizing the need for transparency, robust security, and clear explanations for data use to maintain trust. Addressing these issues not only enhances customer experience but also demonstrates that consumer trust directly impacts the sustainability and success of the business.

关键词： Data privacy Privacy Consumer behavior Focusing Electronic commerce Security Artificial intelligence Sustainable development Interviews Recommender systems

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The Syncretic Effect of Dual-Source Data on Affective computing in Online Learning Contexts: A Perspective From Convolutional Neural Network With Attention Mechanism

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Journal of Educational computing Research 2023年第2期61卷 466-493页

作者： Zhai, Xuesong Xu, Jiaqi Chen, Nian-Shing Shen, Jun Li, Yan Wang, Yonggu Chu, Xiaoyan Zhu, Yumeng College of Education Zhejiang University Zhejiang Hangzhou China Anhui Xinhua University Anhui Hefei China Anhui Jianzhu University Anhui Hefei China Program of Learning Sciences National Taiwan Normal University Yünlin Taiwan School of Computing and Information Technology University of Wollongong Wollongong NSW Australia College of Educational Science and Technology Zhejiang University of Technology Zhejiang Hangzhou China

Affective computing (AC) has been regarded as a relevant approach to identifying online learners’ mental states and predicting their learning performance. Previous research mainly used one single-source data set, typically learners’ facial expression, to compute learners’ affection. However, a single facial expression may represent different affections in various head poses. This study proposed a dual-source data approach to solve the problem. Facial expression and head pose are two typical data sources that can be captured from online learning videos. The current study collected a dual-source data set of facial expressions and head poses from an online learning class in a middle school. A deep learning neural network using AlexNet with an attention mechanism was developed to verify the syncretic effect on affective computing of the proposed dual-source fusion strategy. The results show that the dual-source fusion approach significantly outperforms the single-source approach based on the AC recognition accuracy between the two approaches (dual-source approach using Attention-AlexNet model 80.96%;single-source approach, facial expression 76.65% and head pose 64.34%). This study contributes to the theoretical construction of the dual-source data fusion approach, and the empirical validation of the effect of the Attention-AlexNet neural network approach on affective computing in online learning contexts. © The Author(s) 2022.

关键词： affective computing attention mechanism dual-source data fusion method multimodal

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Defect Recognition on Single Layer Bare Printed Circuit Boards for Quality Control and Visual Inspection: A Low-Sample-Size Deep Transfer Learning Approach

Defect Recognition on Single Layer Bare Printed Circuit Boar...

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International Conference of Signal Processing and Intelligent Systems (ICSPIS)

作者： Yolanda D. Austria Arnel C. Fajardo Graduate School Program Technological Institute of the Philippines Quezon City Philippines College of Computing Science Information and Communication Technology Isabela State University Cauayan City Isabela Philippines

Defect detection is a critical component in ensuring the highest quality of printed circuit board manufacture. The work investigates the application of transfer learning to identify YOLOv8 design flaws. The YOLOv8 model performs exceptionally well, with mean Average Precision (mAP) of 98.3%, Precision of 98.6%, and Recall of 96.0%. Numerous datasets provide high detection rates across a range of fault types following thorough testing and inference. This extensive evaluation demonstrates the robustness of the model by reliably identifying Fissure, Mousebite, EC-Short, MC-Spur, Breakout, Pinhole, and Copper defects. The study emphasizes how the YOLOv8 design can identify flaws, providing a dependable and effective way to guarantee the dependability and caliber of printed circuit boards throughout the production process. The foundation for utilizing YOLOv8 in industrial quality control is established by this study, which holds significant potential for improving defect detection and quality control in the manufacturing industry.

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Blending Lossy and Lossless Data Compression Methods to Support Health Data Streaming in Smart Cities

SSRN

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SSRN 2024年

作者： Andrade, Alexandre da Costa, Cristiano André Roehrs, Alex Muchaluat-Saade, Debora Christina Righi, Rodrigo da Rosa Applied Computing Program Universidade do Rio dos Sinos RS São Leopoldo Brazil Computer Science Department Universidade Federal Fluminense RJ Niterói Brazil Software Innovation Laboratory - SOFTWARELAB

The digital transformation process has significantly boosted the widespread adoption of telemedicine and the utilization of wearable devices for monitoring vital signs. However, implementing a system for continuous monitoring of the population's vital signs, with data being streamed from various locations within a smart city context, faces significant challenges. These challenges are related to bandwidth, latency, and storage capacity due to the large volume of data. To overcome these challenges, a common practice consists in modeling an edge-fog-cloud layered architecture. The literature lacks software solutions capable of managing the simultaneous transmission of various vital signs data, coming from individuals geographically distributed, while maintaining the ability to promptly issue alerts and generate health notifications. In this context, we propose the VSAC (Vital Sign Adaptive Compressor) model, which combines lossy and lossless data compression algorithms in a layered architecture to support healthcare demands in a smart city. The main contribution is how we blend both strategies: we first use lossy compression to collect only valuable vital sign data for each individual, applying lossless algorithms afterwards to reduce the number of bytes before sending it to higher layers. We provide a protocol that facilitates the collection of heterogeneous data distributed across different city regions, providing real-time processing. A prototype was developed to evaluate the model and its performance in various load scenarios. The results indicate that orchestrating two data compression algorithms is more efficient than conventional data reduction methods, showing gains up to 46\% when measuring the compression rate metric. © 2024, The Authors. All rights reserved.

关键词： Electronic health record

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Identifying Physical Unclonable Functions Using Classifier Algorithms

Identifying Physical Unclonable Functions Using Classifier A...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Caio B. Viana Carlos J. M. Araújo Danilo R. B. de Araújo Post Graduated Program in Applied Informatics Federal Rural University of Pernambuco (UFRPE) Recife Brazil Department of Computing (DC) Federal Rural University of Pernambuco (UFRPE) Recife Brazil

Industrial IoT systems are constantly increasing security to mitigate failures and unallowed access. Nowadays, enterprise systems can handle large amounts of sensitive information that can be received from hundreds of tiny devices such as sensors, fog devices, gateways, etc. However, the lower processing capacity associated with tiny devices due to manufacturing decisions has contributed to their lack of defensive measures and hardware security support. Those devices performance usually restricts advanced software security usage and results in several vulnerabilities in the device’s authentication. RFID tags have been used in the IoT industry to create a variety of applications for identification, authentication, or even virtual proof of reality, but RFID tags are still susceptible to serious attacks since fake RFID devices or other attacks are trying to find ways of bypassing the security. In this paper, 5 (five) machine learning algorithms have been used to classify a dataset of read-time operations from blocks in Mifare RFID and fake tags, aiming to identify different behaviors between dataset’s variables, which can be used for a Physical Unclonable Functions (PUF). According to our results, K-Nearest Neighbor (KNN) offered the best performance for the considered problem. The KNN obtained a root mean squared error equal to 0.058 and an mean absolute error equal to 0.003. The obtained results indicate that operation times have variables inherent to the manufacturing process and this can be useful for future identity management schemes.

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Quantum Wrapper Networking: Investigating the Noise Impact of Classical Headers on the Quantum Payload

Quantum Wrapper Networking: Investigating the Noise Impact o...

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2024 Frontiers in Optics, FiO 2024

作者： Gül, Gamze On, Mehmet Berkay Tan, Shannon G. Proietti, Roberto Kanter, Gregory S. Yoo, S.J. Ben Kumar, Prem Graduate Program in Applied Physics Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Department of Electrical and Computer Engineering University of California Davis DavisCA95616 United States Center for Photonic Communication and Computing Dept. of Electrical and Computer Eng. Northwestern University EvanstonIL60208 United States Department of Electronics and Telecommunications Engineering Politecnico di Torino Italy

We study the impact in time and frequency domains of classical headers on quantum payloads in quantum wrapper networking. We identify and characterize in-fiber scattering processes that produce noise photons degrading...

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Drawing Lines for Measurement-Based Probabilistic Timing Analysis

Drawing Lines for Measurement-Based Probabilistic Timing Ana...

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Real-Time Systems Symposium (RTSS)

作者： Tadeu Nogueira C. Andrade George Lima Verônica Maria Cadena Lima Graduate Program in Computer Science Federal University of Bahia Salvador Bahia Brazil Institute of Computing Federal University of Bahia Salvador Bahia Brazil Institute of Mathematics and Statistics Federal University of Bahia Salvador Bahia Brazil

ISBN: (数字)9798331540265

ISBN: (纸本)9798331540272

We describe DBL-MBPTA, a new approach for measurement-based probabilistic timing analysis (MBPTA). Unlike the usual MBPTA, which treats the time a program executes as a random variable, we consider both the number n of instructions executed in each measurement and the time $T(n)$ they took to execute. By taking tuples $(n, T(n))$, for various values of n, DBL-MBPTA allows for multiple execution path analysis. We show that $(n, T(n))$ can be bounded from below and above by two reference lines. The modeled random variable is the relative distance ($n, T(n)$) from these lines, which explains the term distance between lines (DBL) given to the approach. According to DBL-MBPTA, samples can be analyzed and improved, for which we employ deep neural networks. Once sample coverage is deemed representative, probabilistic bounds on execution time are estimated via the modeled relative distance. We evaluate our approach using both synthetic data and data collected through measurements on a multi-core platform. The results obtained demonstrate the effectiveness of DBL-MBPTA.

关键词： Lower bound Codes Processor scheduling Bandwidth Artificial neural networks Probabilistic logic Real-time systems Timing Random variables Synthetic data

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Influence of land-sea breeze on PM _2.5 prediction in central and southern Taiwan using composite neural network

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Scientific Reports 2023年第1期13卷 1-9页

作者： Kibirige, George William Huang, Chiao Cheng Liu, Chao Lin Chen, Meng Chang Institute of Information Science Academia Sinica New Taipei Taiwan Social Networks and Human Centered Computing Program Taiwan International Graduate Program New Taipei Taiwan National Chengchi University New Taipei Taiwan

PM2.5 prediction plays an important role for governments in establishing policies to control the emission of excessive atmospheric pollutants to protect the health of citizens. However, traditional machine learning me...

PM_2.5 prediction plays an important role for governments in establishing policies to control the emission of excessive atmospheric pollutants to protect the health of citizens. However, traditional machine learning methods that use data collected from ground-level monitoring stations have reached their limit with poor model generalization and insufficient data. We propose a composite neural network trained with aerosol optical depth (AOD) and weather data collected from satellites, as well as interpolated ocean wind features. We investigate the model outputs of different components of the composite neural network, concluding that the proposed composite neural network architecture yields significant improvements in overall performance compared to each component and the ensemble model benchmarks. The monthly analysis also demonstrates the superiority of the proposed architecture for stations where land-sea breezes frequently occur in the southern and central Taiwan in the months when land-sea breeze dominates the accumulation of PM_2.5. © 2023, The Author(s).

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