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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Chang, Ting-Jui Lin, Lydia Hsiao-Mei Tsai, Richard Tzong-Han National Central University Department of Computer Science and Information Engineering Taoyuan Taiwan National Taipei University of Technology Department of Interaction Design Taipei Taiwan Research Center for Humanities and Social Sciences Academia Sinica Taipei Taiwan

ISBN: (纸本)9798350360721

It is expected to deploy chatbots as sales assistants on e-commerce platforms soon. In recent years, the capabilities demonstrated by large language models (LLMs) indicate their suitability for this role. However, a dialogue process is not fully established. Most existing works concentrate on searching for ideal products, such as Retrieval-Augmented Generation (RAG) and product search. Many proposed methods rely on product reviews. However, in the case of some e-commerce platform products, valuable reviews are often lacking, rendering these approaches impractical. Thus, we studied how to assist users and select a set of candidate products. Accordingly, we simulated sales conversations based on the product features provided by the seller, using LLM and reliable content while addressing the cold-start problem. Specifically, we used product features to generate customer persona for user simulation. LLM was used to play the role of sales assistant in giving recommendations. The experimental results showed that practical conversation was created between customers and chatbots in Traditional Chinese. The code and results are available at: https://***/terryobe-ncu/CPR_LLM © 2024 IEEE.

关键词： Sales

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Enhancing Cancer Gene Prediction through Aligned Fusion of Multiple PPI Networks Using Graph Transformer Models

Enhancing Cancer Gene Prediction through Aligned Fusion of M...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Han, Zebei Yu, Gufeng Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China

ISBN: (纸本)9798350337488

Despite the considerable progress that has been made in cancer research, identifying cancer genes remains a significant challenge due to the intricate nature of the disease. Given the importance of incorporating gene interaction relationships in identifying potential cancer genes, Graph Neural Networks (GNNs) have garnered increasing attention for their ability to model gene associations effectively. Particularly, recent studies have demonstrated the superiority of GNNs in deciphering the knowledge embedded in Protein-Protein interaction (PPI) networks for cancer gene prediction. However, these studies primarily focused on a single PPI network, overlooking the valuable insights encapsulated within other PPI networks. Additionally, previous endeavors often centered around pan-cancer datasets, neglecting the importance of predicting specific cancer genes. To address these limitations, we present a novel method called MPIT, which employs Graph Transformer Networks (GTNs) to identify specific cancer driver genes. MPIT effectively integrates data from diverse PPI and multi-omics data via the alignment and fusion of gene representations learned from different PPI networks. We collect three distinct cancer cell line datasets to assess the model performance. Our experimental findings demonstrate the superiority of MPIT over the existing methods, achieving the state-of-the-art performance across all three datasets. © 2023 IEEE.

关键词： Cancer genes feature alignment feature fusion Graph Transformer Network specific cancer genes

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A DV-Hop dynamic weight positioning model with genetic algorithm

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International Journal of Wireless and Mobile Computing 2021年第2期20卷 139-145页

作者： Wang, Penghong Fan, Tian Cai, Xingjuan Li, Wuchao School of Computer Science and Technology Taiyuan University of Science and Technology Taiyuan Shanxi003024 China Research Centre of Intelligent Interface and Human Computer Interaction Department of Computer Science and Technology Harbin Institute of Technology Harbin Heilongjiang150006 China Jiaxing Vocational Technical College Jiaxing Zhejiang China

Wireless Sensor Networks (WSNs), as an important part of the Internet of Things (IoT), are widely deployed to the location estimation in WSNs. However, at present, the study of DV-Hop algorithm is still limited to the improvement of the sensor nodes in the ordinary region. In this paper, aiming at the complex areas under different application backgrounds, based on the analysis of the localisation principle of the traditional DV-Hop model based on optimisation algorithm, a dynamic weight positioning model based on genetic algorithm (DWMGA-DV-Hop) is proposed. The algorithm performs simulation experiments on three different types of test by adopting dynamic weight calculation model. And experimental results demonstrate that, under different test sets, compared with the standard DV-Hop algorithm, TW-PSODV-Hop and OCS-DV-Hop algorithm, this algorithm has the highest positioning accuracy and stability. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Genetic algorithms

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Rubikon: Intelligent Tutoring for Rubik’s Cube Learning Through AR-enabled Physical Task Reconfiguration

arXiv

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arXiv 2025年

作者： Ren, Haocheng Wu, Muzhe Croisdale, Gregory Guo, Anhong Wang, Xu University of Michigan Computer Science & Engineering Ann ArborMI United States Carnegie Mellon University Human-Computer Interaction Institute PittsburghPA United States

Learning to solve a Rubik’s Cube requires the learners to repeatedly practice a skill component, e.g., identifying a misplaced square and putting it back. However, for 3D physical tasks such as this, generating sufficient repeated practice opportunities for learners can be challenging, in part because it is difficult for novices to reconfigure the physical object to specific states. We propose Rubikon, an intelligent tutoring system for learning to solve the Rubik’s Cube. Rubikon reduces the necessity for repeated manual configurations of the Rubik’s Cube without compromising the tactile experience of handling a physical cube. The foundational design of Rubikon is an AR setup, where learners manipulate a physical cube while seeing an AR-rendered cube on a display. Rubikon automatically generates configurations of the Rubik’s Cube to target learners’ weaknesses and help them exercise diverse knowledge components. In a between-subjects experiment, we showed that Rubikon learners scored 25% higher on a post-test compared to baselines. © 2025, CC BY.

关键词： Augmented reality

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Auto-PIP: Real-time Identification of Critical Performance Inflection Points in Software Stress Testing 35

Auto-PIP: Real-time Identification of Critical Performance I...

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35th IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2024

作者： Zhang, Shenglin Xiong, Xiao Li, Mengyao Sun, Yongqian Zhao, Yongxin Chen, Xia Deng, Bowen Pei, Dan Nankai University China Haihe Laboratory of Information Technology Application Innovation China Tianjin Key Laboratory of Software Experience and Human Computer Interaction China Huawei Cloud Tsinghua University Beijing National Research Center for Information Science and Technology China

ISBN: (纸本)9798350367041

Conducting stress testing is essential to ensure the stability and performance of software systems post-launch. Among its various aspects, the real-time identification of the "Smooth Load Area"(SLA) is crucial for establishing performance benchmarks, identifying bottlenecks, guiding optimization strategies, and strategically allocating resources. However, existing methods are incapable of real-time inflection point detection or require manual intervention. This paper proposes Auto-PIP, an automated identification framework for performance inflection points based on key performance indicators (KPIs) during performance stress tests. Auto-PIP integrates trend testing algorithms with unsupervised anomaly detection algorithms to identify optimal operating points and estimate maximum capacity. Auto-PIP has been deployed in Huawei Cloud, and the evaluation results show that Auto-PIP demonstrated 100% accuracy in optimal inflection point detection, a 41.7% leap over baselines, and 83.9% accuracy in maximum point identification, a 10.7% gain. Additionally, we have released the dataset to the public to promote ongoing research. © 2024 IEEE.

关键词： Software testing

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AI-Driven Relocation Tracking in Dynamic Kitchen Environments

arXiv

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arXiv 2025年

作者： Esfahani, Arash Nasr Hosseini, Hamed Masouleh, Mehdi Tale Kalhor, Ahmad Sajedi, Hedieh Human and Robot Interaction Lab School of Electrical and Computer Engineering University of Tehran Tehran Iran School of Mathematics Statistics and Computer Science University of Tehran Tehran Iran

As smart homes become more prevalent in daily life, the ability to understand dynamic environments is essential which is increasingly dependent on AI systems. This study focuses on developing an intelligent algorithm which can navigate a robot through a kitchen, recognizing objects, and tracking their relocation. The kitchen was chosen as the testing ground due to its dynamic nature as objects are frequently moved, rearranged and replaced. Various techniques, such as SLAM feature-based tracking and deep learning-based object detection (e.g., Faster R-CNN), are commonly used for object tracking. Additionally, methods such as optical flow analysis and 3D reconstruction have also been used to track the relocation of objects. These approaches often face challenges when it comes to problems such as lighting variations and partial occlusions, where parts of the object are hidden in some frames but visible in others. The proposed method in this study leverages the YOLOv5 architecture, initialized with pre-trained weights and subsequently fine-tuned on a custom dataset. A novel method was developed, introducing a frame-scoring algorithm which calculates a score for each object based on its location and features within all frames. This scoring approach helps to identify changes by determining the best-associated frame for each object and comparing the results in each scene, overcoming limitations seen in other methods while maintaining simplicity in design. The experimental results demonstrate an accuracy of 97.72%, a precision of 95.83% and a recall of 96.84% for this algorithm, which highlights the efficacy of the model in detecting spatial changes. © 2025, CC BY.

关键词： Optical flows

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Robust Phishing Detection in Consumer IoT Devices with ANOVA F-Test and Satin Bowerbird Optimization of Deep Learning Model

Robust Phishing Detection in Consumer IoT Devices with ANOVA...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Gupta, Brij B. Gaurav, Akshat Chui, Kwok Tai Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan University of Economics and Human Science Warsaw Poland Ronin Institute MontclairNJ United States Hong Kong

ISBN: (纸本)9798331521165

Combining ANOVA F-Test for feature selection with Satin Bowerbird Optimization (SBO) for hyperparameter tuning of a deep learning model, our proposed model delivers a strong phishing detection model for consumer IoT devices. In terms of accuracy and loss, the suggested CNN model, optimized using SBO, outfits GRU, LSTM, and RNN models. With a significant loss decrease, the model had a high accuracy of 92% and proved effective in spotting phishing attempts. Comprehensive assessments including feature selection, correlation analysis, and performance comparisons indicate the model's excellence in both training and testing stages, therefore providing an efficient means of improving security in IoT systems. This solution offers a scalable and effective means of phishing detection for smart home appliances. © 2025 IEEE.

关键词： Analysis of variance (ANOVA)

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Enhancing Consumer Electronics Security in Smart Homes with t-SNE Feature Selection and Seagull Optimized Deep Learning Model

Enhancing Consumer Electronics Security in Smart Homes with ...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Gaurav, Akshat Gupta, Brij B. Chui, Kwok Tai Ronin Institute MontclairNJ United States Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan University of Economics and Human Science Warsaw Poland Hong Kong

ISBN: (纸本)9798331521165

This work presents a t-SNE-based feature selection and Seagull Optimized Deep Learning Model to improve consumer electronics security in smart homes. Optimized using the Seagull Optimization Algorithm (SOA), the suggested Convolutional Neural Network (CNN) model showed exceptional performance with an accuracy of 92% in identifying cyber risks. The model routinely exceeded in both accuracy and loss measures when tested against conventional machine learning and deep learning models. T-SNE helped to choose important criteria that provide strong identification of harmful activity. Our method presents a major development in smart home security and a dependable instrument for reducing cybersecurity threats in a linked world. © 2025 IEEE.

关键词： Convolutional neural networks

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"it Might be Technically Impressive, but It's Practically Useless to us": Motivations, Practices, Challenges, and Opportunities for Cross-Functional Collaboration around AI within the News Industry 25

"it Might be Technically Impressive, but It's Practically Us...

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2025 CHI Conference on human Factors in Computing Systems, CHI 2025

作者： Xiao, Qing Fan, Xianzhe Simon, Felix Marvin Zhang, Bingbing Eslami, Motahhare Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Tsinghua University Beijing China Reuters Institute for the Study of Journalism University of Oxford Oxford United Kingdom School of Journalism and Mass Communication University of Iowa Iowa CityIA United States School of Computer Science Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9798400713941

Recently, an increasing number of news organizations have integrated artificial intelligence (AI) into their workflows, leading to a further influx of AI technologists and data workers into the news industry. This has initiated cross-functional collaborations between these professionals and journalists. Although prior research has explored the impact of AI-related roles entering the news industry, there is a lack of studies on how internal cross-functional collaboration around AI unfolds between AI professionals and journalists within the news industry. Through interviews with 17 journalists, six AI technologists, and three AI workers with cross-functional experience from leading Chinese news organizations, we investigate the practices, challenges, and opportunities for internal cross-functional collaboration around AI in news industry. We first study how these journalists and AI professionals perceive existing internal cross-collaboration strategies. We explore the challenges of cross-functional collaboration and provide recommendations for enhancing future cross-functional collaboration around AI in the news industry. © 2025 Copyright held by the owner/author(s).

关键词： Motivation

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Movement Sonification of Familiar Music to Support the Agency of People with Chronic Pain 25

Movement Sonification of Familiar Music to Support the Agenc...

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2025 CHI Conference on human Factors in Computing Systems, CHI 2025

作者： Potapov, Kyrill Gold, Nicolas Olugbade, Temitayo De C Williams, Amanda C Overbeck, Christopher Dieter Lynch, Danielle Nygren, Minna Berthouze, Nadia UCL Interaction Centre University College London London United Kingdom UCL Computer Science University College London London United Kingdom School of Engineering and Informatics University of Sussex Brighton United Kingdom Department of Clinical Educational & Health Psychology University College London London United Kingdom UCL Computer Science London United Kingdom His Majesty's Civil Service London United Kingdom UCL Interaction Centre UCL London United Kingdom

ISBN: (纸本)9798400713941

FFAME (Filtering Familiar Audio for Movement Exploration) is a novel sonification framework aiming to facilitate movement in individuals with chronic back pain. Our personalised, music-based approach contrasts and extends prior work with predetermined tonal sonification. FFAME progressively filters selected music based on angles of the trunk. Through a qualitative analysis of reported experience of 15 participants with chronic pain and 5 physiotherapists, we identify how sonification parameters and musical characteristics affect movement and meaning-making. Music-based movement sonification proved impactful across multiple dimensions: (1) encouraging movement, (2) escaping pain-related rumination, (3) externalizing pain experiences, and (4) scaffolding physical activities. Drawing on enactivism and related philosophies, the study highlights how the semantic indeterminacy of music, combined with real-time movement sonification, created a rich, open-ended environment that supported user agency and exploration. Sonification for pain management can be creative and expressive, enabling people with pain to extend challenging movements and build movement confidence. © 2025 Copyright held by the owner/author(s).

关键词： Time and motion study

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