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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Gong, Yunpeng Zhong, Zhun Qu, Yansong Luo, Zhiming Ji, Rongrong Jiang, Min School of Informatics Xiamen University China School of Computer Science and Information Engineering Hefei University of Technology China The Department of Artificial Intelligence Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Key Laboratory of Digital Protection and Intelligent Processing of Intangible CulturalHeritage of Fujian and Taiwan Ministry of Culture and Tourism Xiamen University Fujian Xiamen361005 China

In recent years, there has been significant research focusing on addressing security concerns in single-modal person re-identification (ReID) systems that are based on RGB images. However, the safety of cross-modality scenarios, which are more commonly encountered in practical applications involving images captured by infrared cameras, has not received adequate attention. The main challenge in cross-modality ReID lies in effectively dealing with visual differences between different modalities. For instance, infrared images are typically grayscale, unlike visible images that contain color information. Existing attack methods have primarily focused on the characteristics of the visible image modality, overlooking the features of other modalities and the variations in data distribution among different modalities. This oversight can potentially undermine the effectiveness of these methods in image retrieval across diverse modalities. This study represents the first exploration into the security of cross-modality ReID models and proposes a universal perturbation attack specifically designed for cross-modality ReID. This attack optimizes perturbations by leveraging gradients from diverse modality data, thereby disrupting the discriminator and reinforcing the differences between modalities. We conducted experiments on three widely used cross-modality datasets, namely RegDB, SYSU, and LLCM. The results not only demonstrate the effectiveness of our method but also provide insights for future improvements in the robustness of cross-modality ReID systems. © 2024 Neural information processing systems foundation. All rights reserved.

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Efficient Resource Management in IoTs Using Evolutionary and Swarm Intelligence Algorithms 7

Efficient Resource Management in IoTs Using Evolutionary and...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Gupta, Keshav Dev Cengiz, Korhan Awasthi, Charu Ramini, Siva Akash Chatterjee, Shyamapriya Pavithra, M. Poornima College of Engineering Department of Computer Science Engineering Jaipur India University of Fujairah College of Information Technology United Arab Emirates Istinye University Department of Computer Engineering Istanbul Turkey University of Hradec Kralove Faculty of Informatics and Management Department of Information Technologies Hradec Kralove500 03 Czech Republic JSS Academy of Technical Education Department of Information Technology Noida India Andhra Pradesh Amaravati India Narula Institute of Technology Department of Information Technology West Bengal Kolkata India Jansons Institute of Technology Karumathampatti Department of Computer Science and Engineering Coimbatore641659 India

ISBN: (纸本)9798350383065

The proliferation of the Internet of Things (IoT) has ushered in a transformative era of connected devices, emphasizing the critical need for effective resource management. This study introduces an innovative approach that harnesses Evolutionary and Swarm Intelligence algorithms for IoT Task Scheduling, addressing resource optimization challenges. The approach offers superior resource utilization through advanced optimization techniques, reducing energy consumption and enhancing efficiency. Furthermore, it significantly reduces task scheduling execution time, enabling prompt decision-making in dynamic IoT environments. This results in improved task allocation quality, meeting application-specific requirements, and prioritizing critical tasks. Notably, the approach excels in minimizing completion time, making it particularly advantageous for real-time IoT applications. This research contributes to the advancement of IoT resource management, offering an adaptive, efficient, and intelligent solution with broad applicability. In the evolving landscape of IoT, the findings presented here lay a solid foundation for future research and practical implementations, ultimately promoting more responsive, sustainable, and intelligent IoT ecosystems. © 2023 IEEE.

关键词： Swarm intelligence

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Label-Free Biomolecule Detection With InP/AlGaAs Charge Plasma Dielectric-Modulated Vertical TFET Using TaN as Metal Gate

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IEEE Access 2025年 13卷 89667-89684页

作者： Hashim Elshafie Abdulrahman Saad Alqahtani Azath Mubarakali Adarsh Rag S M. Venkatesh P. Parthasarathy Department of Computer Engineering College of Computer Science King Khalid University Abha Saudi Arabia College of Computing and Information Technology University of Bisha Bisha Saudi Arabia Department of Informatics and Computer Systems College of Computer Science King Khalid University Abha Saudi Arabia Department of Data Science and Computer Applications Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Electronics and Communication Engineering CMR Institute of Technology Bengaluru Karnataka India

This study introduces a label-free biosensing method for biomolecule detection utilizing an InP/AlGaAs charge plasma dielectric-modulated vertical tunnel field-effect transistor (InP/AlGaAs VTFET) featuring TaN as the metal gate. The device comprises a nanocavity beneath the gate metal adjacent to the tunnelling junction, where biomolecules engage with the dielectric material, resulting in fluctuations in the drain current. A twin metal gate architecture with laterally split dielectrics is employed to eliminate short-channel effects. The biosensor has exceptional sensitivity, attaining a peak drain current sensitivity of $2.5 \times 10$ cm2 for biomolecules such as albumin (k = 7). The gadget proficiently identifies biomolecules with varying dielectric constants and charge distributions, enabling adaptable label-free detection of diverse targets. The study examines the influence of dielectric constant on critical metrics such as Energy Band Diagrams, Drain Current, Drain Sensitivity, and subthreshold swing (SS). The overlap between the source and pocket regions, along with the introduction of an auxiliary gate, optimizes the electrical characteristics. Simulation results show that the proposed InP/AlGaAs VTFET achieves a maximum sensitivity of $3.5 \times 10 ^{3}$ , outperforming configurations without overlap ( $2 \times 10 ^{3}$ ), highlights the potential of proposed InP/AlGaAs VTFET for scalable, high-sensitivity, label-free biomolecule detection.

关键词： Biosensors Logic gates Sensitivity Tunneling Dielectric constant Performance evaluation Metals Biological system modeling TFETs Electrostatics

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A Machine-Learning-Based Sentiment Classification Approach for Gojek Application

A Machine-Learning-Based Sentiment Classification Approach f...

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International Conference of computer and informatics engineering (IC2IE)

作者： I Putu Edy Suardiyana Putra Indra Hermawan I Komang Arya Adi Kusuma I Gede Deindra Dwija Puridiasta Dewa Gede Bhaskara Pramudya Clerencia Isabell Kowaas Digital Business Department Institut Pariwisata dan Bisnis Internasional Denpasar Indonesia Informatics and Computer Engineering Department Politeknik Negeri Jakarta Depok Indonesia Computer Science Department Faculty of Engineering and Vocational Ganesha University of Education Singaraja Indonesia

ISBN: (数字)9798331505530

ISBN: (纸本)9798331505547

This study proposes a machine-learning-based sentiment classification approach for Bahasa Indonesia, where Gojek App’s reviews are used for a case study. The sentiment classification is important since users often give 5-star ratings but leave negative reviews. A total of 19,772 reviews from The Google Play Store are used in this study. We use five machine learning algorithms to train the model, such as Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Naive Bayes (NB), and Stochastic Gradient Descent (SGD). The best results of our approach are 66.6% precision, 88.2% recall, and 69.2% F-score, where an SVM-based classifier is used. These results show a promising future for using machine-learning algorithms for sentiment classification.

关键词： Support vector machines Machine learning algorithms Reviews Stochastic processes Internet Classification algorithms Bayes methods informatics Random forests Classification tree analysis

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Blockchain-Based Secure and Efficient Secret Image Sharing With Outsourcing Computation in Wireless Networks

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IEEE Transactions on Wireless Communications 2024年第1期23卷 423-435页

作者： Zhou, Zhili Wan, Yao Cui, Qi Yu, Keping Mumtaz, Shahid Yang, Ching-Nung Guizani, Mohsen Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou510006 China Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China Graduate School of Science and Engineering Hosei University Tokyo184-8584 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Department of Applied Informatics Silesian University of Technology Gliwice44-100 Poland Department of Engineering Nottingham Trent University NottinghamNG1 4FQ United Kingdom Department of Computer Science and Information Engineering National Dong Hwa University Hualien97401 Taiwan Abu Dhabi United Arab Emirates

Secret Image Sharing (SIS) is the technology that shares any given secret image by generating and distributing n shadow images in the way that any subset of k shadow images can restore the secret image. However, in the existing SIS schemes, the shadow images will be easily tampered and corrupted during the communication, which will pose serious security issues. Recently, blockchain has emerged as a promising paradigm in the field of data communication and information security. To securely communicate and effectively protect the secret image data in wireless networks, we propose a Blockchain-based Secure and Efficient Secret Image Sharing (BC-SESIS) scheme with outsourcing computation in wireless networks. In the proposed BC-SESIS scheme, the shadow images are encrypted and stored in the blockchain to prevent them from being tampered and corrupted. The identity authentication-enabled smart contract is deployed to achieve the (k,n) threshold for secret image restoring. Furthermore, to reduce the computational burden of smart contract and users, an efficient outsourcing computation method is designed to outsource the restoring task, which is securely implemented by agent miners in the encryption domain. Theoretical analysis and extensive experiments demonstrate that the BC-SESIS scheme can achieve desirable communication security and high computational efficiency in the wireless networks. © 2023 IEEE.

关键词： Smart contract

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Utilization of Linguistic Data for Learner Assessment on e-Learning: Instrument and Processing 7

Utilization of Linguistic Data for Learner Assessment on e-L...

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7th International Conference on informatics and Computing, ICIC 2022

作者： Yuniarti, Wenty Dwi Hartati, Sri Priyanta, Sigit Surjono, Herman Dwi Doctoral Program of Computer Science Universitas Gadjah Mada Yogyakarta Indonesia Universitas Islam Negeri Walisongo Semarang Indonesia Universitas Gadjah Mada Department of Computer Science and Electronics Yogyakarta Indonesia Universitas Negeri Yogyakarta Faculty of Engineering Department of Informatics Engineering Education Yogyakarta Indonesia

ISBN: (纸本)9798350345711

The increasingly massive use of e-Learning illustrates the speed and need for innovation in learning. According to the National Higher education Standards (SN-Dikti), constructive alignment is required between learning outcomes, processes, and assessments to properly implement learning in e-Learning. Assessment is an essential component of e-Learning. Unfortunately, the problem of assessment in e-Learning is still found. One of them is the limitations of e-Learning in accommodating and processing various assessment data, primarily linguistic. Even though the variety of assessment data, both numerical, linguistic, and a combination of the two, supports a comprehensive assessment. On the other hand, the accommodation of linguistic data raises problems regarding how to process of unifying linguistic data is carried out. Research related to linguistic data using computing with words has been carried out, but it still needs more precise results from the unification of the linguistic data. Therefore, this study proposes providing an assessment instrument to accommodate linguistic data in e-Learning, as well as showing how to process of unifying linguistic data is carried out using 2-Tuple Fuzzy Linguistic. This approach can avoid the loss of assessment information by presenting more informative and precise results in a 2-tuple (s, α) where s indicates the ability level, and α shows a comparison of abilities with other learners and the potential of the learner to achieve higher abilities. This proposal has the potential to be applied in a learner assessment system for higher education e-Learning. © 2022 IEEE.

关键词： Linguistics

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Enhancing Restaurant Customer Review Analysis: Multi-Class Text Classification with BERT

Enhancing Restaurant Customer Review Analysis: Multi-Class T...

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International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

作者： Budi Sunarko Uswatun Hasanah Syahroni Hidayat Department of Informatics and Computer Engineering Education Universitas Negeri Semarang Semarang Indonesia Department of Computer Engineering Universitas Negeri Semarang Semarang Indonesia Department of Electrical Engineering Universitas Negeri Semarang Semarang Indonesia

Online customer reviews can be valuable information when processed with the right techniques. Businesses aim to enhance their strategies in managing operations by leveraging sentiment analysis from their clientele. In a business context, the primary focus of SA is to capture individual perspectives on products. Presently, sentiment analysis not only deals with emotional polarity but also classifies sentiments towards various product-related aspects. This research aims to classify restaurant customer review data on Google Maps into categories such as “menu,” “taste,” “indoor atmosphere,” and “outdoor atmosphere”. We utilize 450 entries for this dataset. The model used is Bidirectional Encoder Representation from Transformers (BERT), a technique based on transformer-based neural networks, capable of learning grammatical structures, interpreting semantics, and deriving relationships among contextual entities. The results show a less satisfying accuracy score of 41.11 0 /0. This is due to several inaccuracies in the label annotations of the utilized data. Further analysis is discussed in the Discussion and Conclusion section.

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Mqtt Routing Optimizing Based Intrusion Detection for Internet of Things Using Hybrid Machine Learning

Mqtt Routing Optimizing Based Intrusion Detection for Intern...

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2024 International Conference on Decision Aid Sciences and Applications, DASA 2024

作者： Hoomod, Haider K. Al-Hamami, Mohammad Alaa Kadhim, Aqeel Kamil Mohammed, Qutaiba Humadi Niaf, Jolan Rokan College of Education Mustansiriyah University Baghdad Iraq Applied Science University Manama Department of Management Information Systems Manam Bahrain Imam Al-Kadhim College Computer Techniques Engineering Department Baghdad Iraq College of Nursing University of Baghdad Baghdad Iraq Informatics Institute for Postgraduate Studies University of Information Technology and Communications Baghdad Iraq

ISBN: (纸本)9798350369106

With the advent of the Internet of Things, a world was born in which everything could be uniquely identified and monitored, tracked, and managed by computer programs. Items can self-configure using a predefined communication protocol. Other software programs can be linked to operate together using Message Queue Telemetry Transport Protocol (MQTT) or MQSeries components. Business integration software and middleware are common terms for this category of applications. With its suggested lightweight, easy-to-implement, and lowbandwidth system for billions of smart items, the MQTT protocol offers a wide range of potential applications, including home automation. In order to enhance the power savings in MQservice routing, we presented an intelligent assistant for optimizing routing model. For Internet of Things (IoT) purposes, it introduces an optimal routing model based on optimized hybrid machine learning-based anomaly detection (OHMLA). In order to extend the reach of a network, the offered approach allows Internet of Things devices to make efficient use of energy. To further enhance Intrusion Detection identification and get optimum network selection routes, the moth Salp Swarm technique (SSA) technique is used for the optimized hyperparameters. The model is capable of accurately detecting attacks that result in network congestion, causing delays in connection response and data transfer in IoT network. © 2024 IEEE.

关键词： Middleware

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Performance Evaluation of Raspberry Pi Pico for Internet of Things and Its Analysis

Performance Evaluation of Raspberry Pi Pico for Internet of ...

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Electronic and Electrical engineering and Intelligent System (ICE3IS), International Conference on

作者： Puput Dani Prasetyo Adi Arip Kristiyanto Ajang Sopandi Irma Nirmala Muhlis Tahir Etistika Yuni Wijaya Department of Telecommunication National Research and Innovation Agency (BRIN) Bandung Indonesia Department of Information System Faculty of Computer Sciences Pamulang University Department of Informatics Engineering University of Muhammadiyah Tangerang Department of Computer Systems Tanjungpura University Pontianak West Borneo Department of Informatics Education Trunojoyo University Madura

ISBN: (数字)9798350378368

ISBN: (纸本)9798350378375

The massive development of the Internet of Things to date continues to develop a lot of small devices that are integrated such as the Wireless Sensor Network, several experts have conducted research using simulation and practice of WSN, one of the powerful embedded system hardware is the Raspberry Pi Pico, which is a small-sized Raspberry Pi such as Mini Arduino or Micro Arduino. The purpose of developing research in the field of Embedded Systems is to strive in terms of power consumption and dynamism in research, which can be placed on drones and also in various research ideas such as medical for patient comfort. Raspberry Pi RP2040 microcontroller uses an ARM Cortex-M0+ dual-core processor, Raspberry Pi Pico works on a voltage of 1.8 – 5.5 V DC, and can be handled with a 3.7 V DC battery. With its small dimensions, Raspberry Pi pico can be applied to various applicative research.

关键词： Performance evaluation Wireless sensor networks Embedded systems Power demand Voltage Real-time systems Internet of Things Servers Intelligent sensors Wireless fidelity

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Adaptive AI-Based Personalized Learning for Accelerated Vocabulary and Syntax Mastery in Young English Learners

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 678-687页

作者： Aravind, Angalakuduru Durairaj, M. Chitkara, Preeti Baker El-Ebiary, Yousef A. Muniyandy, Elangovan Ushasree, Linginedi Ammar, Mohamed Ben Kodad Telangana 508206 India Dept.of English Panimalar Engineering College Poonamallee Chennai600123 India Head PR & International Relations Department of Applied Sciences KIET Group of Institutions Delhi-NCR Ghaziabad India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur Dist. 522302 India Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia

Language acquisition is an integral part of early schooling, but young English language learners struggle to learn vocabulary and syntax since they are not provided with specialized instruction. Conventional teaching may vary according to different learning speeds and it leads to unbalanced levels of proficiency among students and possibly leading to disengagement among slow learners. The present computer-assisted learning aids provide practice interactively but without real-time adaptation and personalized feedback, limiting their capacity to address learners’ unique problems. To overcome these constraints, this study suggests an Artificial Intelligence based personalized learning system that supports vocabulary and syntax learning via adaptive learning models, NLP-based chatbots and gamified interactive lessons. The system dynamically adapts content according to students’ most recent performance in real time to enable a personalized learning experience, which results in efficient Learning. The research has experimental study design, and two groups are considered, an AI-supported learning group and a traditional learning group. Pre-test and post-test design measures the effects of the system on vocabulary recall and syntax correctness. Other learner engagement rates like survey results and qualitative feedback inform learner experience and learning efficacy. Initial results indicate that learners working with the Artificial Intelligence powered learning system gained 25percent in recalling vocabulary and 30percent in syntax accuracy over the control group. Further, learner engagement rates are elevated because of real-time feedback and gamification components. These results emphasize the promise of AI-based personalized learning to boost language acquisition and lay the basis for further effective innovations in adaptive education technologies. © (2025), (Science and Information Organization). All rights reserved.

关键词： Students

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