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4th International Conference on Artificial Intelligence and data sciences, AiDAS 2023

作者： Mahardika, Muhammad Razi Wijaya, I Putu Janardana Prayoga, Arvin Rayhandi Lucky, Henry Iswanto, Irene Anindaputri School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350318432

Due to its widespread distribution and the anonymity it offers users, hate speech on social media platforms, particularly Twitter, is a major problem. Because of Twitter's echo chamber algorithm and viral nature, hate speech can spread quickly and lead to societal unrest. The use of BERT-based models for hate speech identification in the Indonesian language has been studied in previous research. This study compares how well several pre-trained BERT models, IndoBERT and mBERT, perform in identifying hate speech on Twitter. The methodology includes gathering datasets, preparing the data, and utilizing the proper metrics to assess the models. IndoBERT successfully outperforms mBERT model according to the average of each evaluation metric shown. By carrying out this study, we hope to advance knowledge of techniques for identifying hate speech on Indonesian social media platforms and enhance the use of BERT models for such analysis. © 2023 IEEE.

关键词： Speech recognition

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A Systematic Literature Review of Cyber Security and Privacy in IoT Devices 6

A Systematic Literature Review of Cyber Security and Privacy...

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6th International Conference on computer and Informatics Engineering, IC2IE 2023

作者： Agustine, Karen Tati, Devy Ivana Andrea, Vivian Agatha Anggreainy, Maria Susan Kurniawan, Afdhal School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350345162

The Internet of Things (IoT) has been developing in recent years and a lot has been implemented inside peoples' lives. However, a significant concern of both the developer and user is about the security of these things. This literature review paper will mainly discuss security works and the importance of data privacy in the Internet of Things, as well as security attacks on some architecture in the Internet of Things devices. Furthermore, solutions that have been implemented in recent years from other studies will also be discussed. This mainly aims to better understand the structure and ways to protect and secure data for better privacy in IoT devices, as this would make more people trust and keep supporting the development of IoT technologies. © 2023 IEEE.

关键词： cyber attack cyber security data privacy internet of things technology devices

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Towards Better Accuracy-Efficiency Trade-Offs: Dynamic Activity Inference via Collaborative Learning From Various Width-Resolution Configurations

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第12期5卷 6723-6738页

作者： Qin, Lutong Zhang, Lei Li, Chengrun Song, Chaoda Cheng, Dongzhou Wang, Shuoyuan Wu, Hao Song, Aiguo Nanjing Normal University School of Electrical and Automation Engineering Nanjing210023 China Shanghai200240 China Case Western Reserve University Department of Computer and Data Sciences ClevelandOH44106 United States University of Macau Department of Computer and Information Science Taipa999078 China Yunnan University School of Information Science and Engineering Kunming650500 China Southeast University Department of Instrument Science and Engineering Nanjing210096 China

Recently, deep neural networks have triumphed over a large variety of human activity recognition (HAR) applications on resource-constrained mobile devices. However, most existing works are static and ignore the fact that the computational budget usually changes drastically across various devices, which prevent real-world HAR deployment. It still remains a major challenge: how to adaptively and instantly tradeoff accuracy and latency at runtime for on-device activity inference using time series sensor data? To address this issue, this article introduces a new collaborative learning scheme by training a set of subnetworks executed at varying network widths when fueled with different sensor input resolutions as data augmentation, which can instantly switch on-the-fly at different width-resolution configurations for flexible and dynamic activity inference under varying resource budgets. Particularly, it offers a promising performance-boosting solution by utilizing self-distillation to transfer the unique knowledge among multiple width-resolution configuration, which can capture stronger feature representations for activity recognition. Extensive experiments and ablation studies on three public HAR benchmark datasets validate the effectiveness and efficiency of our approach. A real implementation is evaluated on a mobile device. This discovery opens up the possibility to directly access accuracy-latency spectrum of deep learning models in versatile real-world HAR deployments. © 2020 IEEE.

关键词： Budget control

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Overcoming Spatial Constraints in VR:A Survey of Redirected Walking Techniques

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Journal of computer science & Technology 2024年第4期39卷 841-870页

作者： Jia-Hong Liu Yang-Fu Ren Qi Wen Gan Kui Huang Fiona Xiao Yu Chen Er-Xia Luo Khang Yeu Tang Yue-Yao Fu Cheng-Wei Fan Sen-Zhe Xu Song-Hai Zhang Academy of Arts and Design Tsinghua UniversityBeijing 100084China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China College of Computer Technology and Application Qinghai UniversityXining 810016China Zhili College Tsinghua UniversityBeijing 100084China School of Computer Beijing Information Science and Technology UniversityBeijing 100192China Department of Physics Tsinghua UniversityBeijing 100084China School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China

As the virtual reality(VR)technology strives to provide immersive and natural user experiences,the challenge of aligning vast virtual environments with limited physical spaces remains *** survey comprehensively explores the advancements in redirected walking(RDW)techniques aimed at overcoming spatial constraints in *** addresses this by subtly manipulating users’physical movements to allow for seamless navigation within constrained *** survey delves into gain perception mechanisms,detailing how slight discrepancies between virtual and real-world movements can be utilized without user awareness,thus extending the effective navigable *** RDW control algorithms for gain-based RDW are analyzed,highlighting their implementation and effectiveness in maintaining immersion and minimizing perceptual ***,novel methods extending beyond traditional gain-based techniques are discussed,showcasing innovative approaches that further refine VR *** practical implications of RDW in enhancing safety and reducing physical collisions in VR environments are underscored,alongside its potential to improve user experience by aligning virtual exploration more closely with natural human behavior *** a thorough review of existing literature and recent advancements,this survey provides a systematic understanding for researchers,developers,and industry *** underscores the importance of RDW in the future of VR,emphasizing RDW's role in making VR more accessible and practical across various applications,from education and training to therapy and *** paper concludes with a forward-looking perspective on the continued evolution and potential of RDW in revolutionizing virtual reality experiences.

关键词： virtual reality(VR) redirected walking(RDW) redirection gain perception

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Application of improved virtual sample and sparse representation in face recognition

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1391-1402页

作者： Yongjun Zhang Zewei Wang Xuexue Zhang Zhongwei Cui Bob Zhang Jinrong Cui Lamin LJanneh College of Computer Science and Technology Guizhou UniversityGuiyangChina Big Data Science and Intelligent Engineering Research Institute Guizhou Education UniversityGuiyangChina School of Mathematics and Big Data Guizhou Education UniversityGuiyangChina Department of Computer and Information Science University of MacaoAvenida da UniversidadeTaipaMacaoChina School of Mathematics and Information South China Agricultural UniversityGuangzhouChina

Sparse representation plays an important role in the research of face *** a deformable sample classification task,face recognition is often used to test the performance of classification *** face recognition,differences in expression,angle,posture,and lighting conditions have become key factors that affect recognition ***,there may be significant differences between different image samples of the same face,which makes image classification very ***,how to build a robust virtual image representation becomes a vital *** solve the above problems,this paper proposes a novel image classification ***,to better retain the global features and contour information of the original sample,the algorithm uses an improved non‐linear image representation method to highlight the low‐intensity and high‐intensity pixels of the original training sample,thus generating a virtual ***,by the principle of sparse representation,the linear expression coefficients of the original sample and the virtual sample can be calculated,*** obtaining these two types of coefficients,calculate the distances between the original sample and the test sample and the distance between the virtual sample and the test *** two distances are converted into distance ***,a simple and effective weight fusion scheme is adopted to fuse the classification scores of the original image and the virtual *** fused score will determine the final classification *** experimental results show that the proposed method outperforms other typical sparse representation classification methods.

关键词： representation sample image

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Demonstration of high-performance STO-based WORM devices controlled by oxygen-vacancies and metal filament growth

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Journal of Materials Chemistry C 2024年第23期12卷 8418-8425页

作者： Hsu, Chih-Chieh Wen, Xiu-Ming Xiao, Kai-Zer Jhang, Wun-Ciang Joodaki, Mojtaba Department of Electronic Engineering National Yunlin University of Science and Technology Douliu64002 Taiwan Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Douliu64002 Taiwan School of Computer Science and Engineering Constructor University Bremen28759 Germany

Herein, we propose high-performance Ti/STO/n+-Si and Ag/STO/n+-Si write-once-read-many-times devices, where the resistance transition mechanisms are controlled by oxygen vacancies in the STO layer and metal atoms from the electrochemically active Ag electrode, respectively. The as-deposited STO films have a low oxygen-vacancy content of 12.2%. When the Ti electrode is deposited, the formation of a thin TiOx interfacial layer at the Ti/STO interface is achieved, which leads to an increase of oxygen vacancy content in the STO layer to 28.6%. The relatively higher oxygen vacancy density means that the Ti/STO/n+-Si device can provide a voltage-polarity-independent memory function. In contrast, the resistance transition of the Ag/STO/n+-Si device can be limited in the positive voltage region because Ag cations can only be driven into the STO layer using a positive bias. Both devices show high ILRS/IHRS ratios, superior data retention capability, and fast transition speeds. © 2024 The Royal Society of Chemistry.

关键词： Oxygen vacancies

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Design of an Automatic Price Tag System for Web-Based Retail Business 9

Design of an Automatic Price Tag System for Web-Based Retail...

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9th International Conference on Information Management and Technology, ICIMTech 2024

作者： Liawatimena, Suryadiputra Gunawan, Devina Bina Nusantara University Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Computer Science Deparment BINUS Graduate Program Master of Computer Science Jakarta11480 Indonesia Bina Nusantara University Automotive & Robotics Program Computer Engineering Department BINUS ASO School of Engineering Jakarta11480 Indonesia

ISBN: (纸本)9798350390025

Modern retail businesses face a significant challenge with the inefficiency of manually changing price labels on shelves. This manual process not only consumes valuable time and resources but also increases the likelihood of errors, leading to potential inaccuracies in pricing and a less streamlined shopping experience for customers. The proposed solution is the implementation of an Electronic Shelf Label (ESL) system that automatically displays the prices of goods on retail business shelves. This system connects a website (front end and back end) to e-paper via a Wi-Fi network and microcontroller, allowing retail business owners to update prices more easily. Additionally, buyers can search for desired items through the website. The results are the time to send data from the website to the e-paper, namely, to know the performance of the e-paper used. The average time required from 10 attempts to send data from the website to the e-paper is 20.935 seconds. Since the data is connected to the server and will be updated automatically, using this system will be more efficient than manually changing the price label, although it takes time to transfer data from the website to the e-paper. © 2024 IEEE.

关键词： Sales

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Comparison of Machine Learning Algorithms for Diabetes Classification 9

Comparison of Machine Learning Algorithms for Diabetes Class...

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9th IEEE International Conference on Computing, Engineering and Design, ICCED 2023

作者： Irmawan, Frederick Nathan Muliawan, Nicholas Hans Angky, Edbert Valencio Setiawan, Karli Eka Hasani, Muhammad Fikri Bina Nusantara University School of Computer Science Computer Science Department Jakarta Indonesia

ISBN: (纸本)9798350370126

Diabetes is a chronic disease that influences how the human body turns food into energy and affects a significant portion of the global population. Diabetes prediction plays a major role in helping diagnose diabetes, which can help prevent complications and improve patient outcomes. This paper presents a machine learning approach for predicting the onset of diabetes using clinical and demographic data. The dataset used is from the Pima Indian Diabetes database (PIDD), which was gathered from the National Institute of Diabetes and Digestive and Kidney Diseases. This dataset contains various variables such as pregnancies, glucose, blood pressure, skin thickness, insulin, body mass index (BMI), diabetes pedigree function, and age. Some preprocessing methods, such as oversampling and removing zero values to impossible data, was done to the dataset in order to balance and improve the input data. Then the paper compares four different machine learning algorithms, which are the K-Nearest Neighbors Algorithm (KNN), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). These models are then built to be predictive. The model's performance is assessed using a number of metrics, including the F1-score, accuracy, precision, and recall. The Random Forest algorithm works better than other models, according to the results by achieving an accuracy of 89.5%, a precision of 97%, a recall of 83%, and an f1-score of 89%. This study shows the potential of machine learning approaches in diabetes prediction as well as the efficacy of the suggested preprocessing strategy which hopefully help clinicians identify individuals at risk of developing diabetes for early intervention. © 2023 IEEE.

关键词： Forecasting

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Optimizing resource utilization in cloud and fog computing environments using Hidden Markov Model 2

Optimizing resource utilization in cloud and fog computing e...

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2nd International Conference on computer Technology and Information science, CTIS 2024

作者： Hussein Awlla, Ardalan Barzan Abdalla, Hemn Department of Computer Science Cihan University Sulaimaniya Iraq Department of Computer Science Wenzhou-Kean University Wenzhou China School of Computer Science and Technology Kean University UnionNJ United States

The massive volumes of data IoT devices generate are processed on the cloud. Fog computing, a hybrid cloud, and IoT solution, is used to distribute workloads and allocate resources efficiently, but it requires much research to implement. The study investigates load balancing in fog and cloud computing environments, which is crucial for managing IoT data. Cloud computing centralizes data processing and storage in remote storage facilities, whereas fog computing decentralizes functions to intermediary nodes closer to data generation and consumption. This close proximity facilitates optimal bandwidth usage, decrease delay, and faster computing. This study uses Hidden Markov Model (HMM) to examine task behaviour's across diverse computing nodes and consider resource requirements. This study captures the probabilistic nature of load distribution by constructing emission and transition matrices from observed task dynamics and node-specific information. This research demonstrates the effectiveness of HMM in describing and optimizing load-balancing tactics, HMM play a vital role in optimizing load balancing tactics, as they model resource utilization, providing insights into enhancing resource allocation efficiency within complex computing infrastructures. The accuracy of the proposed method is approximately 92%. © Published under licence by IOP Publishing Ltd.

关键词： Fog computing

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MediServe:An IoT-Enhanced Deep Learning Framework for Personalized Medication Management for Elderly Care

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computers, Materials & Continua 2025年第4期83卷 935-976页

作者： Smita Kapse Ganesh Yenurkar Vincent Omollo Nyangaresi Gunjan Balpande Shravani Kale Manthan Jadhav Sahil Lawankar Vikrant Jaunjale Department of Computer Technology Yeshwantrao Chavan College of EngineeringWanadongariNagpur441110MaharashtraIndia Computer Science&Engineering Jaramogi Oginga Odinga University of Science&TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennai602105TamilnaduIndia

In today’s fast-paced world,many elderly individuals struggle to adhere to their medication schedules,especially those with memory-related conditions like Alzheimer’s disease,leading to serious health risks,hospital-izations,and increased healthcare *** reminder systems often fail due to a lack of personalization and real-time *** address this critical challenge,we introduce MediServe,an advanced IoT-enabled medication management system that seamlessly integrates deep learning techniques to provide a personalized,secure,and adaptive *** features a smart medication box equipped with biometric authentication,such as fingerprint recognition,ensuring authorized access to prescribed medication while preventing misuse.A user-friendly mobile application complements the system,offering real-time notifications,adherence tracking,and emergency alerts for caregivers and healthcare *** system employs predictive deep learning models,achieving an impressive classification accuracy of 98%,to analyze user behavior,detect anomalies in medication adherence,and optimize scheduling based on an individual’s habits and health ***,MediServe enhances accessibility by employing natural language processing(NLP)models for voice-activated interactions and text-to-speech capabilities,making it especially beneficial for visually impaired users and those with cognitive ***-based data analytics and wireless connectivity facilitate remote monitoring,ensuring that caregivers receive instant alerts in case of missed doses or medication ***,machine learning-based clustering and anomaly detection refine medication reminders by adapting to users’changing health *** combining IoT,deep learning,and advanced security protocols,MediServe delivers a comprehensive,intelligent,and inclusive solution for medication *** innovative approach not only improves the quality of life for elderly

关键词： MediServe medication health risks smart medication box

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