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2024 IEEE International Conference on Computing, ICOCO 2024

作者： Mansor, Mahayaudin M. Ibrahim, Nurain Radi, Noor Fadhilah Ahmad Rahim, Nadirah Abdul Yahaya, Syarul Heiry Bakar, Mohd Aftar Abu Zakaria, Roslinazairimah School of Mathematical Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Malaysia Institute of Big Data Analytics and Artificial Intelligence Universiti Teknologi MARA Malaysia Department of Electrical and Computer Engineering Kuliyyah of Engineering International Islamic University Malaysia Department of Science Computer and Mathematics Universiti Teknologi MARA Penang Campus Malaysia Department of Mathematical Sciences Faculty of Science & Technology Universiti Kebangsaan Malaysia Centre for Mathematical Sciences Universiti Malaysia Pahang Al-Sultan Abdullah Malaysia

ISBN: (纸本)9798331530303

As artificial intelligence (ai) advances, it is essential to continuously comprehend its limitations to optimise the integration of ai into autonomous systems that empower humans. The first objective of this study is to highlight the key capabilities of both humans and ai, focusing on the differences and identifying limitations of ai from the literature. Through discrepancy analysis, this study uses a Venn diagram to visualise the remaining limitations of ai into five key domains: (i) emotional intelligence, (ii) consciousness and awareness, (iii) creative imagination, (iv) communication, and (v) ethical decision-making. Furthermore, this study employs thematic bibliometric analysis to provide a more detailed examination of each ai limitation as the second objective. This study has identified underdeveloped and emerging research themes with potential for future development, such as emotion recognition, human-computer interaction, digital health, and situational awareness, which may require further research. Additionally, this study commends the ongoing efforts to harness ai’s computational power and algorithmic innovations to enhance ai’s overall performance and applicability. ©2024 IEEE.

关键词： Emotion Recognition

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Towards Seamless Connectivity: Implementing 6G Communication Technologies in Next-Generation Networks 3

Towards Seamless Connectivity: Implementing 6G Communication...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Kalaiselvi, K. Sankar, R. Supriya, S. Kaushik, G. Swamy, Hemanth Devika, M. Saveetha College of Liberal Arts and Sciences SIMATS Department of Data Analytics Tamilnadu Chennai India Chennai Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India MLR Institute of Technology Department of Electronics and Communication Engineering Hyderabad India Motorola Solutions United States Dhanalakshmi College of Engineering Department of AI& DS Tamil Nadu Chennai India

ISBN: (纸本)9798350354171

Advanced wireless networks are essential given the increase in demand for smart services enabled by the Internet of Everything (IoE). Despite the fact that 5G networks could enable Internet of Everything (IoE)-based services, they can't meet all of the requirements needed to create intelligent applications. In order to overcome the shortcomings of the current infrastructure, there is an increasing demand to investigate the potential of 6G wireless communication technologies. Complex network optimization problems can be resolved by integrating artificial intelligence into sixth generation networks. An increasing user base and support for large-scale data-driven applications are essential given the fast evolving 6G landscape. In addition to highlighting network needs, critical supporting technologies, and a comparison of 5G and 6G networks, this study looks at recent advancements and trends in 6G technology. The report also explores new approaches to 6G connectivity, such as edge computing, backscatter communications, ai-driven IoT networks, and holographic beamforming, all of which are meant to support intelligent communities. In order to achieve the goal of 6Gbased IoT networks, it concludes by outlining potential research avenues. © 2024 IEEE.

关键词： Internet of Everything

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Multi-agent Scheduling for Shared Manufacturing Systems

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Procedia computer Science 2025年 253卷 727-736页

作者： Ege Duran Cemalettin Ozturk Barry O’Sullivan SFI Centre for Research Training in AI School of Computer Science & IT University College Cork Ireland Department of Process Energy & Transport Engineering Munster Technological University Cork Ireland Insight SFI Research Centre for Data Analytics School of Computer Science & IT University College Cork Ireland

Advances in digitization and resource-sharing business models have created new opportunities for manufacturing companies, enhancing competitiveness and resilience. However, these benefits bring computational challenges in efficiently planning and scheduling shared resources. Therefore, there is a need for scalable and quick solutions for practical applications. Shared manufacturing systems share characteristics with parallel machine scheduling, allowing for the application of advancements from this domain. This research focuses on Multi-Agent Parallel Machine Scheduling (MAPMS) in shared manufacturing, specifically addressing scenarios involving two parallel machines and distinct agents managing exclusive, set of non-overlapping orders. The study introduces a novel multi-objective mixed integer programming (MIP) model for order scheduling across multiple facilities, accounting for sequence-dependent setup times between orders. It also proposes a new heuristic method designed for industrial use. Benchmark instances demonstrate the practicality of both the MIP model and heuristic, contributing valuable insights into MAPMS challenges in shared manufacturing environments.

关键词： Multiagent systems Scheduling planning parallel machine

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RefXVC: Cross-Lingual Voice Conversion with Enhanced Reference Leveraging

arXiv

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arXiv 2024年

作者： Zhang, Mingyang Zhou, Yi Ren, Yi Zhang, Chen Yin, Xiang Li, Haizhou The Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China The Department of Electrical and Computer Engineering National University of Singapore Singapore Speech & Audio Team ByteDance AI Lab

This paper proposes RefXVC, a method for cross-lingual voice conversion (XVC) that leverages reference information to improve conversion performance. Previous XVC works generally take an average speaker embedding to condition the speaker identity, which does not account for the changing timbre of speech that occurs with different pronunciations. To address this, our method uses both global and local speaker embeddings to capture the timbre changes during speech conversion. Additionally, we observed a connection between timbre and pronunciation in different languages and utilized this by incorporating a timbre encoder and a pronunciation matching network into our model. Furthermore, we found that the variation in tones is not adequately reflected in a sentence, and therefore, we used multiple references to better capture the range of a speaker’s voice. The proposed method outperformed existing systems in terms of both speech quality and speaker similarity, highlighting the effectiveness of leveraging reference information in cross-lingual voice conversion. The converted speech samples can be found on the website: http://*** © 2024, CC BY-NC-SA.

关键词： Embeddings

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Scenario, Role, and Persona: A Scoping Review of Design Strategies for Socially Intelligent ai Agents

Scenario, Role, and Persona: A Scoping Review of Design Stra...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Niu, Ruowen Hu, Jiaxiong Peng, Siyu Cao, Caleb Chen Liu, Chengzhong Han, Sirui Guo, Yike Hong Kong Generative AI Research and Development Center Hong Kong Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Hong Kong University of Science and Technology Hong Kong Big Data Institute Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9798400713958

As artificial intelligence (ai) agents have often been perceived as social actors, there is a growing expectation for them to demonstrate social intelligence. Social intelligence encompasses the ability of ai agents to align their actions with human intentions and behave in socially and culturally appropriate ways. This study conducts a scoping review to analyze the design strategies employed in current ai agent designs that support the exhibition of social intelligence. Our findings reveal three design strategy themes that interdependently and collectively shape the capabilities and behaviors of ai agents: scenario, role, and persona. These findings provide a structured perspective and actionable insights for designing ai agents that effectively integrate social intelligence into human-ai interactions. © 2025 Copyright held by the owner/author(s).

关键词： ai Agents Design Strategy Scoping Review Social Intelligence

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3D Landmark Detection on Human Point Clouds: A Benchmark and A Dual Cascade Point Transformer Framework

arXiv

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arXiv 2024年

作者： Zhang, Fan Mao, Shuyi Li, Qing Peng, Xiaojiang College of Big Data and Internet Shenzhen Technology University Shenzhen China Department of Electrical and Computer Engineering Georgia Institute of Technology Shenzhen China AI Lab Lenovo Research Shenzhen China

3D landmark detection plays a pivotal role in various applications such as 3D registration, pose estimation, and virtual try-on. While considerable success has been achieved in 2D human landmark detection or pose estimation, there is a notable scarcity of reported works on landmark detection in unordered 3D point clouds. This paper introduces a novel challenge, namely 3D landmark detection on human point clouds, presenting two primary contributions. Firstly, we establish a comprehensive human point cloud dataset, named HPoint103, designed to support the 3D landmark detection community. This dataset comprises 103 human point clouds created with commercial software and actors, each manually annotated with 11 stable landmarks. Secondly, we propose a Dual Cascade Point Transformer (D-CPT) model for precise point-based landmark detection. D-CPT gradually refines the landmarks through cascade Transformer decoder layers across the entire point cloud stream, simultaneously enhancing landmark coordinates with a RefineNet over local regions. Comparative evaluations with popular point-based methods on HPoint103 and the public dataset DHP19 demonstrate the dramatic outperformance of our D-CPT. Additionally, the integration of our RefineNet into existing methods consistently improves performance. Code and data will be released soon. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Mental Healthcare Analysis using Power BI & Machine Learning

Mental Healthcare Analysis using Power BI & Machine Learning

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International Conference on Signal Processing and Communication (ICSPC)

作者： Jash Virani Nikita Daredi Aayush Bhanushali Madhu Shukla Pooja Shah Computer Engineering Big-Data Analytics Department Marwadi University Rajkot Gujarat India Computer Engineering Artificial Intelligence Department Marwadi University Rajkot Gujarat India CSE Department Pandit Deendayal Energy University Gandhinagar Gujarat India

The rise in mental health problems among people and the need for effective solutions for mental health have led to a lot of research in the machine learning domain for its applications in mental healthcare Many people’s lives have been affected by the exponential rise in mental health and depression, and various factors must be revised. Different situations can have an impact on one’s mental stability. So, taking the factors in mind like gender, age, insomnia, loneliness, depression level, aggressiveness, stress level etc. we are going to analyze the most affecting factors for poor mental health. Nowadays mostly people are facing some kind of mental health problems but very few people are giving attention to their mental health. So, our main purpose is to analyze the factors and find those factors which are responsible for a person’s poor mental health. Those factors are also anonymous by the person itself and They don’t even know that they have weak mental health. For that we have taken the initiative to analyze and point out the main causes for poor mental health by circulating a google form Which has questions regarding regular lifestyle and from that we have collected data of different types of persons who are from different professions like students, employees, professors etc. After collecting the data, we had done preprocessing on that data and then we applied machine learning algorithms for classification and prediction. By applying machine learning algorithms, we found different types of outputs and by taking those outputs we can say which are more affecting factors on a person’s mental health and we can suggest how people should improve their mental health and what type of things they should avoid for their good mental health.

关键词： Machine learning algorithms data visualization Mental health Medical services Machine learning Signal processing Depression

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An Encrypted Abnormal Stream Detection Method Based on Improved Skyline Computation

An Encrypted Abnormal Stream Detection Method Based on Impro...

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2022 International Conference on Networking and Network Applications, NaNA 2022

作者： Jiang, Xinghong Li, Xuan Lv, Chenyang Ma, Yong Shen, Yulong He, Meibin Li, Guozheng School of Computer and Information Engineering Jiangxi Normal University Nanchang China School of Computer Xidian University Xi'an China Jiangxi Telecom Department of Big Data and Ai Nanchang China Guizhou Broadcasting and Tv Information Network Co. Ltd. Guiyang China

ISBN: (数字)9781665461313

ISBN: (纸本)9781665461313

With the development of a new generation of mobile communication technology and the enhancement of user security awareness, a large amount of data containing private information generated by users every day will be transmitted in an encrypted form in the network, and it is difficult for traditional abnormal stream detection methods to detect encrypted data, which will increase the likelihood of DDoS attacks on servers that store user information. In response to this problem, this paper proposes a method called detection of encrypted abnormal stream based on improved skyline(DEF-IS). First of all, the Order-Revealing Encryption algorithm is used to encrypt the data stream to ensure the security of the data stream;Then, efficient encrypted abnormal data stream detection is carried out based on reservoir sampling algorithm and improved skyline algorithm;Finally, the performance of the DEF-IS algorithm is verified in the simulation environment. The experimental results show that DEF-IS algorithm can quickly and accurately detect abnormal data while ensuring the safety of data. © 2022 IEEE.

关键词： Denial-of-service attack

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A Review of Spam Detection in Social Media 6

A Review of Spam Detection in Social Media

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6th International Conference on computer Science and engineering, UBMK 2021

作者： Yurtseven, Ilke Bagriyanik, Selami Ayvaz, Serkan Big Data Analytics Bahçeşehir University Istanbul Turkey Software Engineering Department Nisantasi University Istanbul Turkey Computer Engineering Department Yildiz Technical University Istanbul Turkey

ISBN: (纸本)9781665429085

With significant usage of social media to socialize in virtual environments, bad actors are now able to use these platforms to spread their maUcious activities such as hate speech, spam, and even phishing to very large crowds. Especially, Twitter is suitable for these types of activities because it is one of the most common social media platforms for microblogging with millions of active users. Moreover, since the end of 2019, Covid-19 has changed the lives of individuals in many ways. While it increased social media usage due to free time, the number of cyber-attacks soared too. To prevent these activities, detection is a very crucial phase. Thus, the main goal of this study is to review the state-of-art in the detection of malicious content and the contribution of ai algorithms for detecting spam and scams effectively in social media. © 2021 IEEE

关键词： Network security

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Few-Shot Learning-Based Machine Fault Diagnosis Using EMD-Gammatone Spectrogram with Limited Labeled Audio dataset

Few-Shot Learning-Based Machine Fault Diagnosis Using EMD-Ga...

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2025 IEEE International Conference on big data and Smart Computing, bigComp 2025

作者： Zabin, Mahe Ayon, Syed Tasnimul Karim Siraj, Farhan Md Shuvo, Mehedi Hasan Choi, Ho-Jin Uddin, Jia Korea Advanced Institute of Science and Technology School of Computing Daejeon Korea Republic of BRAC University Department of Computer Science and Engineering Dhaka Bangladesh Dhaka University of Engineering & Technology Department of Computer Science and Engineering Dhaka Gazipur Bangladesh Woosong University AI and Big Data Department Endicott College Daejeon Korea Republic of

The deep learning models are computationally burdensome, and their accuracy depends on the number of labeled datasets used in training. The scarcity of the labeled dataset in industrial machine diagnosis is a significant concern as it is pretty impossible to collect data from faulty machines in industrial environments. To address this issue, in this paper, we presented a few-shot learning-based machine fault diagnosis model on a lightweight SqueezeNet architecture using generated texture images from audio sensor data. Subsets of two public audio datasets, MIMII and ToyADMOS, containing limited data- 10 images per class were used to validate the model. The experimental results demonstrate that the model has exhibited 89.6% and 98.3% accuracy for MIMII and ToyADMOS, respectively, with only ten labeled images of each fault class. As the model has only 73 thousand trainable parameters and requires only 300 KB of memory, it is expected to be deployable in a portal device for machine fault diagnosis. © 2025 IEEE.

关键词： Deep learning

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