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Informative and Long-Term Response Generation using Multiple Suggestions and User Persona Retrieval in a Dialogue System

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APSIPA Transactions on Signal and information Processing 2024年第2期13卷

作者： Hsu, Jia-Hao Chen, Tsai-Yi Wu, Chung-Hsien Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Graduate Program of Artificial Intelligence National Cheng Kung University Tainan Taiwan

Enhancing user satisfaction in dialogue systems relies on their ability to understand users and generate responses that meet their expectations. This study proposes a dialogue system that incorporates the Multi-Suggestions Transformer (MST) to generate informative and long-term responses. The MST combines empathy suggestions, system persona suggestions, and knowledge suggestions to produce comprehensive and informative responses. Additionally, the system employs a persona detection model and a persona extraction model to extract the user persona from current sentences and retrieve the most suitable user persona from the dialogue history. This facilitates long-term conversations by enabling the system to remember and respond to sentences relevant to the user persona. The proposed MST-based dialogue system outperforms the baseline in terms of informativeness, as evidenced by higher scores in BLEU, BERT-score, Distinct-n, and Perplexity on the Blended Skill Talk and Multi Session Chat datasets. Furthermore, two novel evaluation metrics, PerP and PerB, introduced in this study demonstrate the system’s effective utilization of the user persona for achieving long-term dialogue. Human subjective evaluation indicates that our model consistently outperforms the baseline, achieving superior scores of 68%, 56%, 52%, and 64% in the four subjective metrics. © 2024 J.-H. Hsu, T.-Y. Chen and C.-H. Wu.

关键词： Speech processing

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Optimal Timeslot Assignment in a Fully-connected Unmanned Aerial Vehicles Network

Optimal Timeslot Assignment in a Fully-connected Unmanned Ae...

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IEEE International Conference on Communications in China (ICCC)

作者： Jing Zhang Zixuan Huang Bingyu Sun Science Island Branch of Graduate School University of Science and Technology of China Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

This paper considers a fully-connected communication network composed of multiple unmanned aerial vehicles (UAVs) that are within communication range of each other. Due to the severe self-interference of the wireless transceiver, we assume each UAV works in half-duplex mode where it cannot transmit and receive simultaneously. At the MAC layer we adopt the TDMA technique in which the time is discretized into fixed timeslots. Taking into account the constraint of maximum communication interval in terms of timeslots between any two UAVs, we formulate the timeslot assignment problem as a matching problem of the transmit and receive states of each UAV, with the target of maximizing the communication capacity of the entire network. Based on the concept of communication coding, a clique-based analytical method is provided to determine the lower bound of the maximum communication time interval, and the upper bound of the communication capacity of the UAV network. Furthermore, we design an optimal matching scheme for the assignment of the transmit and receive states of each UAV to achieve the capacity upper bound. Finally, numerical examples are provided to validate the designed models and schemes.

关键词： Wireless communication Time division multiple access Upper bound Network topology Autonomous aerial vehicles Encoding Transceivers

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Preferences for Using Canva Apps as a Presentation Media Creation Tool Among Gen Z Students: SEM Analysis with TAM and UTAUT2 Approaches 25

Preferences for Using Canva Apps as a Presentation Media Cre...

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25th International Seminar on Intelligent Technology and Its Applications, ISITIA 2024

作者： Maulana, Fairuz Iqbal Lestari, Dian Purnomo, Agung Arifuddin, Rahman Widartha, Vandha Pradwiyasma Adi, Puput Dani Prasetyo Bina Nusantara University School of Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Medicine Airlangga Surabaya60132 Indonesia Bina Nusantara University Binus Business School Undergraduate Program Entrepreneurship Department Jakarta11480 Indonesia University of Merdeka Malang Department of Electrical Engineering Malang Indonesia Telkom University Information System of Industrial Engineering Faculty Bandung Indonesia Research Center for Telecommunication Bandung Indonesia

ISBN: (纸本)9798350378573

The purpose of this study is to find out what makes Generation Z students accept and use Canva as a tool for making presentation materials. The conceptual framework of this study is the combination of "Technology Acceptance Model (TAM)"and the "Unified Theory of Acceptance and Use of Technology (UTAUT2)". "Structural Equation Modeling (SEM)"used for data analyzation. This research aims to understand the dynamics of Canva acceptance among students. The findings show that enjoyment, fun, and pleasure in using Canva, combined with its creative tools and design-centric approach, significantly resonate with Gen Z's preference for aesthetics. This research highlights that Canva is favored by students for creating engaging presentations. © 2024 IEEE.

关键词： Students

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UPMatch: Enhancing Semi-Supervised Medical Image Classification through Contrastive Learning with Unreliable Pseudo Labels

UPMatch: Enhancing Semi-Supervised Medical Image Classificat...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Jihong Hu Yinhao Li Lanfen Lin Yen-Wei Chen The Graduate School of Information Science and Engineering Ritsumeikan University Osaka Japan The College of Information Science and Engineering Ritsumeikan University Osaka Japan The College of Computer Science and Technology Zhejiang University Hangzhou China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Pseudo-labeling based semi-supervised learning (SSL) framework has proven highly successful in medical image analysis (MIA) by addressing the problem of a shortage of labeled samples. However, the existing SSL methods use a fixed or flexible confidence threshold to filter reliable samples, leaving large number of unlabeled samples unused. This is a more serious issue in MIA because of the low inter-class distance and imbalanced categories. We argue that effectively mining useful information hidden in ambiguous unlabeled sample is the key to improve model performance, so we propose UPmatch, a new pseudo labeling-based SSL framework. Our framework introduces a contrastive unreliable pseudo label learning module (CUPM) that incorporates unreliable pseudo label samples into the training process. Additionally, we propose an informative sample selection strategy (ISSS) that selects samples used in contrastive learning iteratively in each mini-batch. Our experiments on TissueMNIST and ISIC2019 dataset with various training settings demonstrate the effectiveness of our proposed strategy.

关键词： Training Contrastive learning Semisupervised learning information filters Reliability Biomedical imaging Image classification

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Fostering Digital Innovation in Indonesia: The Role of STEM-AI Skills in Overcoming Talent Deficits and Gender Stereotypes

Fostering Digital Innovation in Indonesia: The Role of STEM-...

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Intelligent Cybernetics Technology & Applications (ICICyTA), International Conference on

作者： Erwin Halim Amalia Zahra Maryani Lili Ayu Wulandhari Devi Fitrianah Virienia Puspita Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Communication Department BINUS Graduate Program Master of Strategic Marketing Communication Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506490

ISBN: (纸本)9798331506506

This research explores the optimization of digital talent in advanced industries, particularly in the context of rapid digital transformation. Despite the increasing importance of digital talent for gaining a competitive edge, several challenges persist. In Indonesia, for example, there is a significant shortage of digitally skilled workers, with the World Bank reporting a gap of 9 million skilled individuals over the past 15 years. Additionally, artificial intelligence (AI) is expected to impact 9.5 million jobs, further exacerbating the demand for digital skills. Furthermore, the employability of graduates remains low, largely due to inadequate training, poor language proficiency, and limited cultural sensitivity. Many companies face difficulties in finding qualified individuals, quickly upskilling their workforce, and fostering an innovative organizational culture. This study seeks to evaluate the factors influencing digital talent performance in the context of these challenges. It examines seven key variables: digital readiness, digital technology adoption, relevance of AI, digital skills, individual performance, problem-solving ability, and overall digital talent performance. Hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM). Data was collected from 378 participants, primarily students with STEM backgrounds, in the Jabodetabek area of Indonesia in November 2024. The findings reveal that all hypotheses are significant, with results indicating that user satisfaction and the perceived value of digital talent in Indonesia have a notable impact on continued usage intentions in digital roles.

关键词： Training Industries Technological innovation Sensitivity Mathematical models Problem-solving Artificial intelligence Optimization Faces Gender issues

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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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Open Wireless Digital Twin: End-to-End 5G Mobility Emulation in O-RAN Framework

arXiv

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arXiv 2025年

作者： Iye, Tetsuya Sakamoto, Masaya Takaya, Shohei Sato, Eisaku Susukida, Yuki Nagaoka, Yu Maruta, Kazuki Nakazato, Jin Kozo Keikaku Engineering Inc. Tokyo Japan Department of Electronics and Computer Systems Graduate School of Engineering Takushoku University Tokyo Japan Department of Electrical Engineering Graduate School of Engineering Tokyo University of Science Tokyo Japan Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan

This paper presents an end-to-end wireless digital twin platform constructed using open-source software and open data to enhance the evaluation of mobile communication systems. The proposed open wireless digital twin (OWDT) integrates OpenAirInterface (OAI) for 5G NR protocol stack emulation and NVIDIA Sionna RT for high-resolution ray tracing based radio propagation modeling. This integration enables realistic emulation of vehicular mobility scenarios, leveraging real-world geographic and building data to bridge the gap between theoretical simulations and real-world deployments. The platform utilizes ORAN’s Near-RT RIC via OAI FlexRIC to dynamically monitor key performance indicators (KPIs) such as RSRP, MCS, BLER, and throughput in real time. Through extensive evaluation in urban environments, this study demonstrates the validity of the emulation framework, revealing its capability to replicate real-world communication dynamics with high fidelity. The results underscore the potential of OWDT in accelerating wireless system development, reducing experimental costs, and optimizing network configurations. Copyright © 2025, The Authors. All rights reserved.

关键词： 5G mobile communication systems

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Enhancing Parkinson’s Disease Diagnosis Accuracy Through Speech Signal Algorithm Modeling

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computers, Materials & Continua 2022年第2期70卷 2953-2969页

作者： Omar M.El-Habbak Abdelrahman M.Abdelalim Nour H.Mohamed Habiba M.Abd-Elaty Mostafa A.Hammouda Yasmeen Y.Mohamed Mohanad A.Taifor Ali W.Mohamed School of Information Technology and Computer Science Nile UniversityGiza12677Egypt Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGiza12677Egypt

Parkinson’s disease(PD),one of whose symptoms is dysphonia,is a prevalent neurodegenerative *** use of outdated diagnosis techniques,which yield inaccurate and unreliable results,continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical *** solve this issue,the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’voice *** of these processed speech signals were obtained and experimented on by random forest and logistic regression *** were highly successful,with 90%accuracy produced by the random forest classifier and 81.5%by the logistic regression ***,a deep neural network was implemented to investigate if such variation in method could add to the *** proved to be effective,as the neural network yielded an accuracy of nearly 92%.Such results suggest that it is possible to accurately diagnose early-stage PD through merely testing patients’*** research calls for a revolutionary diagnostic approach in decision support systems,and is the first step in a market-wide implementation of healthcare software dedicated to the aid of clinicians in early diagnosis of PD.

关键词： Early diagnosis logistic regression neural network Parkinson’s disease random forest speech signal processing algorithms

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Asynchronous Remote Distributed Key Generation Method for Securing User Data in the Metaverse

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IEEE Transactions on Consumer Electronics 2024年 1-1页

作者： Wang, Yintong Fang, Guowei Huang, Shitao Lian, Zhuotao Ren, Yongjun School of Information Engineering Nanjing Xiaozhuang University Nanjing Jiangsu China School of Computer Engineering Research Center of Digital Forensics of Ministry of Education Nanjing University of Information Science and Technology Nanjing Jiangsu China JSPS International Research Fellow Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Japan

The rapid development of consumer electronics technology has greatly promoted the progress of the metaverse. However, as a digitized virtual environment, the metaverse imposes high demands on the security of user identities and assets. In this context, asynchronous remote distributed key generation has become one of the key technologies to ensure the security of the metaverse. Unlike traditional key generation methods, the asynchronous nature makes the distributed key generation process more flexible, helping to address the dynamic and heterogeneous network environment within the metaverse. This paper proposes an asynchronous remote key generation method. This method employs public-key cryptography and basic asynchronous primitives to accomplish remote key generation and exchange through encryption and decryption operations between different nodes. In comparison to traditional methods, the asynchronous remote key generation approach provides higher security and reliability, while also showcasing increased efficiency and flexibility. IEEE

关键词： Authentication Protocol

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AI-Enhanced Resource Allocation for LPWAN-based LoRaWAN:A Hybrid TinyML and Deep Learning Approach

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IEEE Internet of Things Journal 2025年

作者： Lodhi, Muhammad Ali Sun, Xiaobing Mahmood, Khalid Lodhi, Anum Park, Youngho Hussain, Majid Yangzhou University School of Information Engineering Yangzhou China 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 China Kyungpook National University School of Electronics Engineering Daegu41566 Korea Republic of The University of Faisalabad Department of Computer Science Faisalabad38850 Pakistan

The integration of Artificial Intelligence (AI) with Low Power Wide Area Networks (LPWAN) offers a promising approach to address resource constraints and dynamic network conditions inherent in these networks. However, deploying complex AI algorithms on resource-limited edge devices presents significant challenges due to their limited computational capabilities. In this study, we propose a hybrid Tiny Machine Learning (TinyML) and Deep Neural Network (DNN)-based solution for optimizing resource allocation in LPWAN-based LoRaWAN networks, targeting both static and mobile applications. Our approach leverages the strengths of a 1-D Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model implemented on the network server, combined with TinyML models deployed on edge devices. The CNN-LSTM model predicts optimal spreading factor and transmission power by analyzing spatial and temporal patterns from real-time data, while the TinyML models enable edge devices to autonomously adjust communication parameters in resource-constrained and disconnected scenarios. This hybrid framework enhances network performance by improving the packet success ratio (PSR), maximizing energy efficiency, and addressing the challenges posed by dynamic IoT environments. © 2014 IEEE.

关键词： Deep neural networks

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