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Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, CHI EA 2023

作者： Sun, Yuqian Xu, Ying Cheng, Chenhang Li, Yihua Lee, Chang Hee Asadipour, Ali Computer Science Research Centre Royal College of Art London United Kingdom Department of Industrial Design School of Art and Design Wuhan University of Technology Wuhan China Shanghai China Department of Product Design College of Fashion and Design Donghua University Shanghai China Affective Systems and Cognition Lab Industrial Design Department College of Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of

ISBN: (纸本)9781450394222

People always envision the future of earth through science fiction (Sci-fi), so can we create a unique experience of "visiting the future earth"through the lens of artificial intelligence (AI)? We introduce Wander 2.0, an AI chatbot that co-creates sci-fi stories through knowledge-based story generation on daily communication platforms like WeChat and Discord. Using location information from Google Maps, Wander generates narrative travelogues about specific locations (e.g. Paris) through a large-scale language model (LLM). Additionally, using the large-scale text-to-image model (LTGM) Stable Diffusion, Wander transfers future scenes that match both the text description and location photo, facilitating future imagination. The project also includes a real-time visualization of the human-AI collaborations on a future map. Through journeys with visitors from all over the world, Wander demonstrates how AI can serve as a subjective interface linking fiction and reality. Our research shows that multi-modal AI systems have the potential to extend the artistic experience and creative world-building through adaptive and unique content generation for different people. Wander 2.0 is available at http://***/ © 2023 Owner/Author.

关键词： Knowledge based systems

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Visual-Language Collaborative Representation Network for Broad-Domain Few-Shot Image Classification 24

Visual-Language Collaborative Representation Network for Bro...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Guo, Qianyu Ren, Jieji Wang, Haofen Wu, Tianxing Ge, Weifeng Zhang, Wenqiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China College of Design and Innovation Tongji University Shanghai China School of Computer Science and Engineering Southeast University Nanjing China School of Computer Science Fudan University Shanghai China Engineering Research Center of AI & Robotics Ministry of Education Academy for Engineering & Technology Fudan University Shanghai China

ISBN: (纸本)9798400706868

Visual-language models based on CLIP have shown remarkable abilities in general few-shot image classification. However, their performance drops in specialized fields such as healthcare or agriculture, because CLIP's pre-training does not cover all category data. Existing methods excessively depend on the multi-modal information representation and alignment capabilities acquired from CLIP pre-training, which hinders accurate generalization to unfamiliar domains. To address this issue, this paper introduces a novel visual-language collaborative representation network (MCRNet), aiming at acquiring a generalized capability for collaborative fusion and representation of multi-modal information. Specifically, MCRNet learns to generate relational matrices from an information fusion perspective to acquire aligned multi-modal features. This relationship generation strategy is category-agnostic, so it can be generalized to new domains. A class-adaptive fine-tuning inference technique is also introduced to help MCRNet efficiently learn alignment knowledge for new categories using limited data. Additionally, the paper establishes a new broad-domain few-shot image classification benchmark containing seven evaluation datasets from five domains. Comparative experiments demonstrate that MCRNet outperforms current state-of-the-art models, achieving an average improvement of 13.06% and 13.73% in the 1-shot and 5-shot settings, highlighting the superior performance and applicability of MCRNet across various domains. © 2024 ACM.

关键词： Visual languages

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A trust evaluation fluctuation model in the e-commerce

A trust evaluation fluctuation model in the e-commerce

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第27届中国控制与决策会议

作者： Zhang Jing Yu Shengjun Fan Hongbo Rong Hui Computer Technology Application Key Lab of Yunnan Province Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming University of Science and Technology Faculty of Art and Design Kunming Metallurgy College

ISBN: (纸本)9781479970186

E-commerce is becoming the main shopping *** trust decision-making is the most important factor which decides whether the transaction is successful or *** to offer people an accurate and objective trust is a basic issue that scholars must face and *** can measure the stability of a set of data and help us avoid risk to some *** order to prevent fraud and deception,this paper proposes a trust evaluation fluctuation model based on historical rating data.A new reward and punishment mechanism is added into the *** results of simulation experiments prove the practicality and validity of the model.

关键词： decision-making trust volatility fraud and deception

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Data Quality Assessment of Substation Data in Bangladesh: Insights from Handwritten Data Digitization 10

Data Quality Assessment of Substation Data in Bangladesh: In...

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10th IEEE International Conference on Power Systems, ICPS 2023

作者： Al Amin, Murshed Jawad-Al-Mursalin Hoque, M.D. Nazzum, Zakia Sayed, Md Abu Tanveer Ahmed Rumee, Sarker Zaber, Moinul Islam Military Institute of Science and Technology Bangladesh Independent University Bangladesh Data and Design Lab Dhaka1000 Bangladesh University of Dhaka Department of Computer Science & Engineering Dhaka1000 Bangladesh United Nations University Guimarães Portugal

ISBN: (纸本)9798350318739

The assessment of power quality is vital for evaluating the existing state of the electricity supply, pinpointing its deficiencies, and guiding enhancements to guarantee a stable and consistent power supply, particularly in the face of rising demands from both industrial and residential sectors. In the context of Bangladesh, however, most substation data are still recorded manually, despite ongoing digitization efforts. This paper focuses on the quality of digitized transcription of handwritten substation data to determine its suitability for further analytical use. Our analysis revealed that the paper-based data from the substations exhibited significant gaps, undermining its reliability and accuracy. Through comprehensive evaluation, we concluded that the data, in its current state, is inadequate for in-depth analysis without the application of robust imputation methods. By tackling these challenges, our research aims to contribute to more informed decision-making and the efficient management of Bangladesh's electrical infrastructure. This study not only diagnoses the current shortcomings in data quality but also lays the groundwork for future improvements in the country's approach to energy data management. © 2023 IEEE.

关键词： Power quality

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Wireless Federated Learning With Hybrid Local and Centralized Training: A Latency Minimization design

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IEEE Journal on Selected Topics in Signal Processing 2023年第1期17卷 248-263页

作者： Huang, Ning Dai, Minghui Wu, Yuan Quek, Tony Q. S. Shen, Xuemin State Key Lab of Internet of Things for Smart City China University of Macau Department of Computer and Information Science China University of Macau State Key Laboratory of Internet of Things for Smart City The Department of Computer and Information Science China Zhuhai Um Science and Technology Research Institute Zhuhai519031 China Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore National Cheng Kung University Taiwan University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada

Wireless federated learning (FL) is a collaborative machine learning (ML) framework in which wireless client-devices independently train their ML models and send the locally trained models to the FL server for aggregation. In this paper, we consider the coexistence of privacy-sensitive client-devices and privacy-insensitive yet computing-resource constrained client-devices, and propose an FL framework with a hybrid centralized training and local training. Specifically, the privacy-sensitive client-devices perform local ML model training and send their local models to the FL server. Each privacy-insensitive client-device can have two options, i.e., (i) conducting a local training and then sending its local model to the FL server, and (ii) directly sending its local data to the FL server for the centralized training. The FL server, after collecting the data from the privacy-insensitive client-devices (which choose to upload the local data), conducts a centralized training with the received datasets. The global model is then generated by aggregating (i) the local models uploaded by the client-devices and (ii) the model trained by the FL server centrally. Focusing on this hybrid FL framework, we firstly analyze its convergence feature with respect to the client-devices' selections of local training or centralized training. We then formulate a joint optimization of client-devices' selections of the local training or centralized training, the FL training configuration (i.e., the number of the local iterations and the number of the global iterations), and the bandwidth allocations to the client-devices, with the objective of minimizing the overall latency for reaching the FL convergence. Despite the non-convexity of the joint optimization problem, we identify its layered structure and propose an efficient algorithm to solve it. Numerical results demonstrate the advantage of our proposed FL framework with the hybrid local and centralized training as well as our proposed alg

关键词： Information management

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Bring game characters to the social space: Developing storytelling community AI agents driven by LLMs

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Entertainment Computing 2025年

作者： Yuqian Sun Hanyi Wang Pok Man Chan Morteza Tabibi Yan Zhang Huan Lu Yuheng Chen Chang Hee Lee Ali Asadipour Computer Science Research Centre Royal College of Art Rausing Research & Innovation Building 15 Parkgate Road London SW11 4NL United Kingdom Independent United States Independent Hong Kong China Independent China rct.ai US Affective Systems and Cognition Lab Industrial Design Department College of Engineering KAIST Daejeon South Korea

AI agents driven by Large Language Models (LLMs) are now ubiquitous in our daily life, so can they bring characters from games into reality? We address the integration of storytelling and AI agents driven by LLMs to develop engaging and believable fictional characters in community settings. Motivated by the potential of fictional characters to enhance social interactions, we introduce Storytelling Community AI Agents (SCAs) and the concept of story engineering to transform fictional game characters into ”live” social entities within player communities. Our story engineering process includes three steps: (1) Character and story creation, defining the SC’s personality and worldview, (2) Presenting Live Stories to the Community, allowing the agent to recount challenges and seek suggestions, and (3) Communication with community members, enabling interaction between the agent and users. We employed the LLM GPT-3 to drive our SCAs, “David” and “Catherine,” and evaluated their performance in an online gaming community, “DE (Alias),” on Discord. Our mixed-method analysis, based on questionnaires (N=15) and interviews (N=8) with community members, reveals that storytelling significantly enhances the engagement and believability of AI agents in community settings.

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Socially engaged arts practice in HCI

Socially engaged arts practice in HCI

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32nd Annual ACM Conference on Human Factors in Computing Systems, CHI EA 2014

作者： Clarke, Rachel Heitlinger, Sara Briggs, Jo Crivellaro, Clara Light, Ann Culture Lab. School of Computing Science Newcastle University United Kingdom Media and Art Technology Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Media and Communication Design Northumbria University United Kingdom

ISBN: (纸本)9781450324748

Socially engaged methods are increasingly being used within HCI research, yet arts practice in this context has been little explored. HCI research that aligns with socially engaged arts practices encourages debate around societal challenges;for example discussion of issues surrounding the role of digital technology in sustainability, inclusion, community, identity and the politics of participation. Building on existing research, this workshop will bring together a diverse group of HCI researchers, artists and other creators whose work or interests align with socially engaged arts practice, to foster critical exploration and creative collaboration.

关键词： Human computer interaction

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Gameplay design patterns for believable non-player characters

Gameplay design patterns for believable non-player character...

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3rd Digital Games Research Association International Conference: "Situated Play", DiGRA 2007

作者： Lankoski, Petri Björk, Staffan Media Lab. University of Art and Design Helsinki Hämeentie 135 C FI-00560 Helsinki Finland Interaction Design Collegium Computer Science and Engineering Chalmers University of Technology and Göteborg University SE-412 96 Göteborg Sweden

Descriptions of humans require several qualities for people to experience them as believable: human body;selfawareness, intentional states, and self impelled actions;expression of emotions;ability to use natural language;and persistent traits. Based on these we analyze non-player character Claudette Perrick in The Elders Scroll IV: Oblivion to detect how these qualities can be created in the interactive environment of a game. We derive the gameplay design patterns Awareness of Surrounding, Visual Body Damage, Dissectible Bodies, Initiative, Own Agenda, Sense of Self, Emotional Attachment, Contextual Conversational Responses, and Goal-Driven Personal Development, which point to design choices that can be made when designing believable non-player characters in games. © 2007 Authors & Digital Games Research Association (DiGRA).

关键词： Game design

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Collaborative Learning of Gesture Recognition and 3D Hand Pose Estimation with Multi-order Feature Analysis 16th

Collaborative Learning of Gesture Recognition and 3D Hand Po...

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16th European Conference on computer Vision, ECCV 2020

作者： Yang, Siyuan Liu, Jun Lu, Shijian Er, Meng Hwa Kot, Alex C. Lab Interdisciplinary Graduate Programme Nanyang Technological University Singapore Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Singapore School of Computer Science and Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9783030585792

Gesture recognition and 3D hand pose estimation are two highly correlated tasks, yet they are often handled separately. In this paper, we present a novel collaborative learning network for joint gesture recognition and 3D hand pose estimation. The proposed network exploits joint-aware features that are crucial for both tasks, with which gesture recognition and 3D hand pose estimation boost each other to learn highly discriminative features. In addition, a novel multi-order multi-stream feature analysis method is introduced which learns posture and multi-order motion information from the intermediate feature maps of videos effectively and efficiently. Due to the exploitation of joint-aware features in common, the proposed technique is capable of learning gesture recognition and 3D hand pose estimation even when only gesture or pose labels are available, and this enables weakly supervised network learning with much reduced data labeling efforts. Extensive experiments show that our proposed method achieves superior gesture recognition and 3D hand pose estimation performance as compared with the state-of-the-art. © 2020, Springer Nature Switzerland AG.

关键词： Gesture recognition

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Memetic three-dimensional gabor feature extraction for hyperspectral imagery classification

Memetic three-dimensional gabor feature extraction for hyper...

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3rd International Conference on Swarm Intelligence, ICSI 2012

作者： Zhu, Zexuan Shen, Linlin Sun, Yiwen He, Shan Ji, Zhen City Key Laboratory of Embedded System Design College of Computer Science and Software Engineering Shenzhen University Shenzhen 518060 China Shenzhen Key Lab. of Biomedical Engineering School of Medicine Shenzhen University 518060 China School of Computer Science University of Birmingham Edgbaston Birmingham B15 2TT United Kingdom

ISBN: (纸本)9783642309755

This paper proposes a three-dimensional Gabor feature extraction for pixel-based hyperspectral imagery classification using a memetic algorithm. The proposed algorithm named MGFE combines 3-D Gabor wavelet feature generation and feature selection together to capture the signal variances of hyperspectral imagery, thereby extracting the discriminative 3-D Gabor features for accurate classification. MGFE is characterized with a novel fitness evaluation function based on independent feature relevance and a pruning local search for eliminating redundant features. The experimental results on two real-world hyperspectral imagery datasets show that MGFE succeeds in obtaining significantly improved classification accuracy with parsimonious feature selection. © 2012 Springer-Verlag.

关键词： Feature Selection

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