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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Xiao Liu Yifan Zhou Fabian Weigend Shubham Sonawani Shuhei Ikemoto Heni Ben Amor Computing and Augmented Intelligence Arizona State University USA Department of Human Intelligence Systems Graduate School of Life Science and Systems Engineering Kyushu Institute of Technology

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

While imitation learning provides a simple and effective framework for policy learning, acquiring consistent action during robot execution remains a challenging task. Existing approaches primarily focus on either modifying the action representation at data curation stage or altering the model itself, both of which do not fully address the scalability of consistent action generation. To overcome this limitation, we introduce the Diff-Control policy, which utilizes a diffusion-based model to learn action representation from a state-space modeling viewpoint. We demonstrate that diffusion-based policies can acquire statefulness through a Bayesian formulation facilitated by ControlNet, leading to improved robustness and success rates. Our experimental results demonstrate the significance of incorporating action statefulness in policy learning, where Diff-Control shows improved performance across various tasks. Specifically, Diff-Control achieves an average success rate of 72% and 84% on stateful and dynamic tasks, respectively. Notably, Diff-Control also shows consistent performance in the presence of perturbations, outperforming other state-of-the-art methods that falter under similar conditions. Project page: https://***/

关键词： Training Torque Imitation learning Scalability Perturbation methods Process control Sensor systems and applications Robustness Intelligent sensors Intelligent robots

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Analyzing Deep Learning Techniques in Natural Scene Image Classification

Analyzing Deep Learning Techniques in Natural Scene Image Cl...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Kanavos, Athanasios Papadimitriou, Orestis Al-Hussaeni, Khalil Karamitsos, Ioannis Maragoudakis, Manolis University of the Aegean Department of Information and Communication Systems Engineering Samos Greece Rochester Institute of Technology Computing Sciences Department Dubai United Arab Emirates Rochester Institute of Technology Graduate and Research Department Dubai United Arab Emirates Ionian University Department of Informatics Corfu Greece

ISBN: (纸本)9798350362480

Image classification is a fundamental task in computer vision, with wide applications including autonomous navigation, content recommendation, and environmental monitoring. This paper offers a comprehensive comparative analysis of deep learning techniques using the Intel Natural Scenes Image dataset, which features diverse scenes such as forests, mountains, seas, streets, buildings, and glaciers. Our study evaluates the performance of various Convolutional Neural Network (CNN) architectures, focusing on aspects such as network design, hyperparameters, data augmentation, and transfer learning strategies. We describe our experimental setup in detail, including the CNN models used, preprocessing techniques applied, and evaluation metrics employed. The results and discussions present key findings, highlighting the strengths and limitations of the approaches studied and providing guidance for future research and practical applications. Our systematic analysis yields valuable insights into effective strategies for recognizing natural scenes. © 2024 IEEE.

关键词： Convolutional neural networks

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TERMS: textual emotion recognition in multidimensional space

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Applied Intelligence 2023年第3期53卷 2673-2693页

作者： Ghafoor, Yusra Jinping, Shi Calderon, Fernando H. Huang, Yen-Hao Chen, Kuan-Ta Chen, Yi-Shin Social Networks and Human-Centered Computing Taiwan International Graduate Program Institute of Information Science Academia Sinica. Institute of Information Systems and Applications National Tsing Hua University Hsinchu City Taiwan Institute of Information Systems and Applications National Tsing Hua University Hsinchu City Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science National Tsing Hua University Hsinchu City Taiwan

Microblogs generate a vast amount of data in which users express their emotions regarding almost all aspects of everyday life. Capturing affective content from these context-dependent and subjective texts is a challenging task. We propose an intelligent probabilistic model for textual emotion recognition in multidimensional space (TERMS) that captures the subjective emotional boundaries and contextual information embedded in a text for robust emotion recognition. It is implausible with discrete label assignment;therefore, the model employs a soft assignment by mapping varying emotional perceptions in a multidimensional space and generates them as distributions via the Gaussian mixture model (GMM). To strengthen emotion distributions, TERMS integrates a probabilistic emotion classifier that captures the contextual and linguistic information from texts. The integration of these aspects, the context-aware emotion classifier and the learned GMM parameters provide a complete coverage for accurate emotion recognition. The large-scale experimentation shows that compared to baseline and state-of-the-art models, TERMS achieved better performance in terms of distinguishability, prediction, and classification performance. In addition, TERMS provide insights on emotion classes, the annotation patterns, and the models application in different scenarios. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Emotion Recognition

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Comparative Study of Machine Learning Algorithms and Text Vectorization Methods for Fake News Detection 14

Comparative Study of Machine Learning Algorithms and Text Ve...

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14th International Conference on Information, Intelligence, systems and Applications, IISA 2023

作者： Kanavos, Andreas Karamitsos, Ioannis Mohasseb, Alaa Gerogiannis, Vassilis C. Ionian University Department of Informatics Corfu Greece Rochester Institute of Technology Department of Graduate and Research Dubai United Arab Emirates School of Computing University of Portsmouth Portsmouth United Kingdom University of Thessaly Department of Digital Systems Larissa Greece

ISBN: (纸本)9798350318067

The detection of fake news is a crucial task in today's society, given the widespread use of social media and online platforms. In this study, we investigate the application of Machine Learning (ML) algorithms for the detection of fake news. We consider two different datasets of categorized news articles of various sizes and apply various ML algorithms, along with two methods of text vectorization. Specifically, we examine Bag of Words and Tf-Idf, with the use of stemming and with different n-gram values. The resulting vectors are processed by Naive Bayes algorithms, Linear algorithms, Support Vector Machines, and Random Forest classifiers. F1-score and computational time for each algorithm-vectorization combination were recorded. Our results have shown that Linear Algorithms and Support Vector Machines combined with Tf-Idf vectors and n-gram value of (1,2) produced the highest accuracies, with an F1-score up to 96.8%. © 2023 IEEE.

关键词： Support vector machines

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A Survey of Blockchain Based systems: Scalability Issues and Solutions, Applications and Future Challenges

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IEEE Access 2024年 12卷 79626-79651页

作者： Alghamdi, Turki Ali Khalid, Rabiya Javaid, Nadeem Umm Al-Qura University College of Computer and Information Systems Department of Computer Science Makkah21955 Saudi Arabia University of Leeds School of Computing LeedsLS2 9JT United Kingdom COMSATS University Islamabad Department of Computer Science Islamabad44000 Pakistan National Yunlin University of Science and Technology International Graduate School of Artificial Intelligence Douliu64002 Taiwan

Blockchain technology originated alongside Bitcoin as a novel method of conducting financial transactions. It has garnered significant attention from both industry and academia in recent years, emerging as a prominent area of research. It is a decentralized record-keeping system that holds transactional information. The scale of a blockchain network expands according to the growth in the number of nodes and transactions, resulting in issues related to storage capacity, data processing speed, and time delay. These issues have a direct impact on the scalability of a blockchain network. Currently, scalability is one of the prominent concerns in the field of blockchain technology and an active research area. This study conducts a comprehensive survey of the scalability challenges faced by blockchain technology in several sectors. It also examines potential solutions based on consensus mechanisms, smart contracts and directed acyclic graph (DAG). It is observed that the proposed scalability solutions target enhancing system throughput, reducing costs, and improving blockchain efficiency. Therefore, we examine, compare, and evaluate the literature using these specific criteria. Moreover, a survey of existing blockchain based survey papers is presented. A comparative analysis of these survey papers is presented along with their recency score, which is determined by the number of recent publications reviewed in a survey paper. By 'recent,' we mean the current year (or the publication year of a survey paper) and the three years prior to it. Additionally, this paper offers an elaborate discussion on the forthcoming open research challenges and applications of blockchain. © 2013 IEEE.

关键词： Smart contract

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Minimizing the Number of Stunting Prevalence Using the Euclid Algorithm Clustering Approach 7

Minimizing the Number of Stunting Prevalence Using the Eucli...

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7th IEEE International Conference of Computer Science and Information Technology, ICOSNIKOM 2023

作者： Zarlis, Muhammad Oktavia, Tanty Buaton, Relita Ernawan, Ferda Andrian, Kevin Bina Nusantara University Information System Management Department BINUS Graduate Program - Master of Information System Management Jakarta11480 Indonesia STMIK Kaputama Informatics Engineering Binjai20714 Indonesia Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Pekan26600 Malaysia BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Department of Information Systems Management Jakarta Indonesia

ISBN: (纸本)9798350360752

Superior and quality human resources are based on healthy human resources with indicators of adequate nutritional intake according to age development. However, the world still faces the problem of hunger and malnutrition today. According to a UNICEF report, the number of people suffering from malnutrition in the world will reach 767.9 million people in 2021. The World Health Organization (WHO) said that malnutrition is a dangerous threat to the health of the world's population. Stunting also has an impact in Indonesia, the prevalence of toddlers experiencing stunting in Indonesia is 24.4% in 2021. The solution created is to classify and cluster the prevalence of stunting to produce a pattern that can be used as best practice to be transmitted to other affected areas. The algorithm used is Euclid. The Euclid algorithm can cluster stunting prevalence data into 4 clusters with the very little category at 79%, the little category at 67%, the many categories at 51%, and the very much category at 21%. The results of the classification and clustering of the best stunting prevalence in cluster one with a very small number, can be used as a source of accurate and updated information that can be used by the government in its efforts to optimize stunting handling in each district/city based on artificial intelligence which can provide handling and optimization patterns. stunting in every district/city. © 2023 IEEE.

关键词： Classification (of information)

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Privacy-Preserving Spatial Crowdsourcing in Smart Cities Using Federated and Incremental Learning Approach

Privacy-Preserving Spatial Crowdsourcing in Smart Cities Usi...

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IEEE Conference on Vehicular Technology (VTC)

作者： Md Mujibur Rahman Quazi Mamun Jun Wu School of Computing Mathematics and Engineering Charles Sturt University NSW Australia Graduate School of Information Production and Systems Waseda University Tokyo Japan

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

Spatial crowdsourcing (SC) systems have emerged as an advanced crowdsourcing paradigm to revolutionise the efficient development of smart city services. SC engages participants and their sensitive data to accomplish spatiotemporal tasks on platforms. However, revealing sensitive data in SC for smart cities exacerbates cybersecurity and privacy concerns, especially Membership Inference Attacks (MIA). To address the problems, this research proposes a Federated Learning (FL) and Incremental Learning (IL) based framework in SC that integrates advanced privacy-preserving techniques. By leveraging FL and adaptive differential privacy, sensitive data remains in decentralised devices while local models are trained without exchanging raw data to a server. We integrate additive secret sharing, a secure multi-party computation technique to protect data during transmission and aggregation. IL enhances the framework using a generative replay approach to ensure continuous adaptation to new data without forgetting existing knowledge to overcome catastrophic forgetting. We broadly evaluate our work against MIA and catastrophic forgetting using Yelp datasets. Compared with other baseline approaches, our experimental results demonstrate that the proposed framework significantly mitigates the risk of MIAs by around 50% and improves forgetting accuracy by up to 13%, thereby providing robust privacy-preserving mechanisms.

关键词： Crowdsourcing Vehicular and wireless technologies Differential privacy Incremental learning Smart cities Multi-party computation Data models Spatiotemporal phenomena Servers Computer security

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Multimodal Large Language Models Driven Privacy-Preserving Wireless Semantic Communication in 6G

Multimodal Large Language Models Driven Privacy-Preserving W...

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IEEE International Conference on Communications Workshops, ICC

作者： Daipeng Cao Jun Wu Ali Kashif Bashir Graduate School of Information Production and Systems Waseda University Japan Department of Computing and Mathematics Manchester Metropolitan University Manchester U.K.

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

In the context of 6G and artificial intelligence, the amount of data on the internet is rapidly increasing. However, traditional syntactic communication encoding techniques are approaching the Shannon limit and have reached a bottleneck. Semantic communication can significantly reduce data traffic and enhance communication efficiency by converting multimodal data, such as images, audio, and video, into semantic information for transmission. However, current semantic communication technology is still in its early stages and faces many challenges. The main challenges in semantic communication currently include data heterogeneity, semantic consistency, and privacy issues. Multimodal large language models (MLLM) could perform semantic understanding of multimodal data and semantic-based multimodal data generation, which offer a unified and consistent multimodal semantic conversion. Therefore, we propose a Privacy-Preserving Semantic Communication scheme based on MLLM (MLLM-PSC). The semantic interpretation ability of MLLM-PSC is cultivated based on the pre-training and fine-tuning of MLLM, without the need for additional knowledge base (KB) alignment. We take textual semantics as a medium for consistently conversion of multimodal data, while safeguarding sensitive semantic information based on few-shot learning. Our simulation results demonstrate the superior performance of MLLM-PSC.

关键词： 6G mobile communication Wireless communication Wireless sensor networks Large language models Conferences Simulation Semantics

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A Study of Interaction Design for E-learning System Based on Distributed Asynchronous Communication 22

A Study of Interaction Design for E-learning System Based on...

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Proceedings of the Tenth International Symposium of Chinese CHI

作者： Xiaonan Wang Yi Sun Yancong Su School of Design and Art Xiamen University of Technology China Department of Information Systems Graduate School of Information Technology Kobe Institute of Computing Japan

ISBN: (纸本)9781450398695

This paper discusses insufficient teacher-student communication in online courses for asynchronous computer programming. In such an online course, communication between students and teachers is significantly reduced compared with that in offline courses. Hence, teachers seldom receive questions and feedback from students because in asynchronous online learning, questions are mostly asked through email, chat, and forum. This makes it difficult for students to describe the questions clearly, resulting in a decrease in students’ motivation to ask questions. Furthermore, it is difficult for teachers to statistically analyze the collected questions to improve the quality of the learning materials. In this study, we designed an online learning system according to the learning habits in computer science, which allows students to ask questions without too much description and to review more easily. In addition, teachers are provided with tools for statistical analysis.

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ICT for remote data collection: A study on hydrological and weather stations in Afghanistan 7

ICT for remote data collection: A study on hydrological and ...

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7th International Conference on Applied System Innovation, ICASI 2021

作者： Esmat, Ahmad Tamim Graduate School of Information Technology Kobe Institute of Computing Kobe Department of Information Systems Hyogo Japan

ISBN: (纸本)9781665441438

Collecting data from remote hydrological and weather stations in Afghanistan has been a severe challenge to the relevant departments, as the lack of internet infrastructures is evident across the country. The staff members commute to the stations regularly to collect the data manually. This manual practice requires constant human labor and substantial financial resources and is inefficient in accessing timely data and managing the massive amount of collected data afterward. Until recent years, some stations were accessible through a satellite link;however, they are now disconnected due to high cost of the service. On the other hand, telecommunication services in Afghanistan had a rapid growth in the last two decades. People even in rural areas now have access to phone call and SMS;however, internet access has remained limited to the cities and populated areas. Hence, this research proposes the SMS solution to address the challenges of collecting data from remote hydrological and weather stations. A survey was conducted to evaluate the coverage of SMS vs. mobile data on 183 stations. A set of lab experiments were also conducted as a proof of concept. The results show that SMS is an applicable solution, considering current conditions in the region. © 2021 IEEE.

关键词： Data acquisition

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