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31st acm International conference on Multimedia, MM 2023

作者： Jiang, Runhua Liu, Yongge Zhang, Boyuan Chen, Xu Li, Deng Han, Yahong College of Intelligence and Computing Tianjin University Tianjin China School of Computer and Information Engineering Anyang Normal University Anyang China Key Laboratory of Oracle Bone Inscriptions Information Processing Ministry of Education Anyang China

ISBN: (纸本)9798400701085

Oracle Bone Inscriptions (OBI) are ancient hieroglyphs originated in China and are considered one of the most famous writing systems in the world. Up to now, thousands of OBIs have been discovered, which require deciphering by experts to understand their contents. Experts typically need to restore, classify, and compare each character with previous inscriptions. Although existing research can assist with one of these operations, their performance falls short of practical requirements. In this work, we propose the OraclePoints framework, which represents OBI images as hybrid neural representations comprising features of images and point sets. The image representation provides inscription appearance and character structure, while the point representation makes it easy and effective to distinguish characters and noises. In addition, we demonstrate that OraclePoints can be easily integrated with existing models in a plug-and-play manner. Comprehensive experiments demonstrate that the proposed hybrid neural representation framework supports a range of OBI tasks, including character image retrieval, recognition, and denoising. It is also demonstrated that OraclePoints is helpful for deciphering OBIs by linking ancient characters to modern Chinese characters. Our codes are available at https://***/. © 2023 acm.

关键词： Geometry

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PacmHCI V7, CSCW2, October 2023 Editorial

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proceedings of the acm on Human-computer Interaction 2023年第CSCW2期7卷 1-1页

作者： De Choudhury, Munmun Ding, Xianghua Guha, Shion Pinatti De Carvalho, Aparecido Fabiano Georgia Institute of Technology AtlantaGA United States University of Glasgow Glasgow United Kingdom University of Toronto TorontoON Canada University of Oslo Oslo Norway

We are again thrilled to be able to present the computer-supported cooperative work and social computing (CSCW) community with an issue of the proceedings of the acm on Human-computer Interaction, containing very interesting and relevant scholarship from its members. This issue includes 138 papers, of which 82 were accepted from the July 2022 cycle, and 56 were accepted from the January 2023 cycle. It reflects great efforts and contributions from external reviewers, Associate Chairs and Editors, who together have conducted a rigorous review process to select contributions of the highest quality advancing the CSCW field. As Track Chairs, we are grateful for the community's collective efforts to continue shaping and sharing CSCW's tradition of high-quality scholarship across the years. © 2023 Owner/Author.

关键词： computer supported cooperative work

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A Few-shot Learning Approach for Anomaly Detection in cooperative Tracking Control System 21

A Few-shot Learning Approach for Anomaly Detection in Cooper...

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21st IEEE International Symposium on Parallel and Distributed Processing with Applications, 13th IEEE International conference on Big Data and Cloud computing, 16th IEEE International conference on social computing and Networking and 13th International conference on Sustainable computing and Communications, ISPA/BDCloud/socialCom/SustainCom 2023

作者： Rong, Jieqi Liu, Weirong Yu, Xiaoquan Ren, Wanwan Shu, Boyu Peng, Jun Central South University Computer Science and Engineering Changsha China Central South University School of Traffic and Transportation Engineering Changsha China Central South University School of Automation Changsha China

ISBN: (纸本)9798350329223

cooperative tracking control for multiple heavy-haul trains can improve the safety and efficiency of heavy-haul railway transportation. However, the false data injection attack for the tracking control system is a serious obstacle, which will lead to imprecise train operation control. To address the issue, a few-shot learning approach for false data injection attack (FDIA) detection for tracking control systems of heavy-haul trains is proposed. First, the physical-cyber model of the tracking control system is established, and the affects of the FDIA on the tracking control system is analyzed. Second, the heavy-haul train anomaly detection simulation model was built by Matlab/Simulink to obtain the dataset for training the GAN model. Third, the Generative Adversarial Network (GAN) method is introduced to augment the anomaly dataset, which can solve the problem of lack of actual operation fault data of heavy trains. Further, the Local Outlier Factor (LOF) algorithm is leveraged to achieve the false data injection attack detection. Simulation results show that the proposed method can effectively increase the anomaly detection accuracy than other supervised methods. © 2023 IEEE.

关键词： Anomaly detection

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MINDNOTES : A Mobile Platform to enable users to break stigma around mental health and connect with therapists 21

MINDNOTES : A Mobile Platform to enable users to break stigm...

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24th acm conference on computer-supported cooperative work and social computing (CSCW)

作者： Chanda, Prateek Wagh, Amogh Johnson, Jemimah Renghe, Swaraj Chandramouli, Vageesh Mathews, George Behar, Sapna Bhola, Poornima Rao, Girish Sudhir, Paulomi Srikanth, T. K. Sharma, Amit Mehrotra, Seema Microsoft Res India Bangalore Karnataka India Natl Inst Mental Hlth & Neurosci Bangalore Karnataka India Icarus Nova Bangalore Karnataka India Int Inst Informat Technol Bangalore Karnataka India

ISBN: (纸本)9781450384797

We describe a mobile app for helping people manage their mental health, keeping in mind the unique challenges offered by the Indian context. One of the biggest challenges in India is the stigma around mental health and consequently the concerns and misconceptions about reaching out for professional help. Therefore, the proposed app focuses on tackling the barriers that people experience when reaching out for help and provides a free-of-cost platform for interactions with mental health professionals to alleviate any concerns and obtain an informed view. All content is developed by clinical psychologists and rather than replacing professional help, the app provides an example of a technology intervention for mental health that supplements existing infrastructures of mental healthcare.

关键词： Human centered computing mental health professional therapy barriers

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Integrating Individual and social Contexts into Self-Reflection Technologies

Integrating Individual and Social Contexts into Self-Reflect...

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Extended Abstracts of the 2023 CHI conference on Human Factors in computing Systems, CHI EA 2023

作者： Bhattacharjee, Ananya Kulzhabayeva, Dana Reza, Mohi Kumar, Harsh Seong, Eunchae Wu, Xuening Rifat, Mohammad Rashidujjaman Bowman, Robert Kornfield, Rachel Mariakakis, Alex Ahmed, Syed Ishtiaque De Choudhury, Munmun Doherty, Gavin Czerwinski, Mary P. Williams, Joseph Jay Department of Computer Science University of Toronto TorontoON Canada Department of Psychology University of Toronto TorontoON Canada School of Computer Science and Statistics Trinity College Dublin Dublin Ireland Department of Preventive Medicine Northwestern University Evanston United States School of Interactive Computing Georgia Institute of Technology Atlanta United States Microsoft Research Redmond United States

ISBN: (纸本)9781450394222

Technology for promoting reflection can help people better understand their emotions and thought patterns, eventually motivating them to take action to adopt healthy or productive behaviors. However, existing work has often viewed users individualistically, addressing people's behaviors and emotions rather than recognizing the external factors that shape them (e.g., economic status, culture). We envision that the individualistic approaches can be extended and reimagined in ways that can consider such broader contexts. We believe such a shift in the design of interventions will help individuals reflect in a more holistic manner, supporting collaborative reflection processes that involve more than one person. With these aims in mind, we will discuss the two questions in our workshop. First, what individual and social contexts should HCI researchers consider while promoting reflection? Second, what role can various forms of technology (e.g., just-in-time adaptive interventions, peer-support platforms) play in supporting and augmenting reflective practices? Through our workshop, we hope to bring together a community of multidisciplinary researchers and practitioners who aim to design and develop reflection interventions that are situated within the fabric of users' individual and social contexts. © 2023 Owner/Author.

关键词： Reflection

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Privacy Threats and Countermeasures in Federated Learning for Internet of Things: A Systematic Review 17

Privacy Threats and Countermeasures in Federated Learning fo...

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IEEE Congress on Cybermatics: 17th IEEE International conference on Internet of Things, iThings 2024, 20th IEEE International conference on Green computing and Communications, GreenCom 2024, 17th IEEE International conference on Cyber, Physical and social computing, CPSCom 2024, 10th IEEE International conference on Smart Data, SmartData 2024

作者： Elzemity, Adel Arief, Budi University of Kent School of Computing Canterbury United Kingdom

ISBN: (纸本)9798350351637

Federated Learning (FL) in the Internet of Things (IoT) environments can enhance machine learning by utilising decentralised data, but at the same time, it might introduce significant privacy and security concerns due to the constrained nature of IoT devices. This represents a research challenge that we aim to address in this paper. We systematically analysed recent literature to identify privacy threats in FL within IoT environments, and evaluate the defensive measures that can be employed to mitigate these threats. Using a Systematic Literature Review (SLR) approach, we searched five publication databases (Scopus, IEEE Xplore, Wiley, acm, and Science Direct), collating relevant papers published between 2017 and April 2024, a period which spans from the introduction of FL until now. Guided by the PRISMA protocol, we selected 49 papers to focus our systematic review on. We analysed these papers, paying special attention to the privacy threats and defensive measures - specifically within the context of IoT - using inclusion and exclusion criteria tailored to highlight recent advances and critical insights. We identified various privacy threats, including inference attacks, poisoning attacks, and eavesdropping, along with defensive measures such as Differential Privacy and Secure Multi-Party Computation. These defences were evaluated for their effectiveness in protecting privacy without compromising the functional integrity of FL in IoT settings. Our review underscores the necessity for robust and efficient privacy-preserving strategies tailored for IoT environments. Notably, there is a need for strategies against replay, evasion, and model stealing attacks. Exploring lightweight defensive measures and emerging technologies such as blockchain may help improve the privacy of FL in IoT, leading to the creation of FL models that can operate under variable network conditions. © 2024 IEEE.

关键词： Federated learning

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Embedding an Ethical Mind: Aligning Text-to-Image Synthesis via Lightweight Value Optimization 24

Embedding an Ethical Mind: Aligning Text-to-Image Synthesis ...

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32nd acm International conference on Multimedia, MM 2024

作者： Wang, Xingqi Yi, Xiaoyuan Xie, Xing Jia, Jia Department of Computer Science and Technology Tsinghua University Beijing China Microsoft Research Asia Beijing China Dcst BNRist Tsinghua University Key Laboratory of Pervasive Computing Beijing China

ISBN: (纸本)9798400706868

Recent advancements in diffusion models trained on large-scale data have enabled the generation of indistinguishable human-level images, yet they often produce harmful content misaligned with human values, e.g., social bias, and offensive content. Despite extensive research on Large Language Models (LLMs), the challenge of Text-to-Image (T2I) model alignment remains largely unexplored. Addressing this problem, we propose LiVO (Lightweight Value Optimization), a novel lightweight method for aligning T2I models with human values. LiVO only optimizes a plug-and-play value encoder to integrate a specified value principle with the input prompt, allowing the control of generated images over both semantics and values. Specifically, we design a diffusion model-tailored preference optimization loss, which theoretically approximates the Bradley-Terry model used in LLM alignment but provides a more flexible trade-off between image quality and value conformity. To optimize the value encoder, we also develop a framework to automatically construct a text-image preference dataset of 86k (prompt, aligned image, violating image, value principle) samples. Without updating most model parameters and through adaptive value selection from the input prompt, LiVO significantly reduces harmful outputs and achieves faster convergence, surpassing several strong baselines and taking an initial step towards ethically aligned T2I models. Warning: This paper involves descriptions and images depicting discriminatory, pornographic, bloody, and horrific scenes. © 2024 acm.

关键词： Semantics

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A Sample-driven Selection Framework: Towards Graph Contrastive Networks with Reinforcement Learning 24

A Sample-driven Selection Framework: Towards Graph Contrasti...

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32nd acm International conference on Multimedia, MM 2024

作者： Zheng, Xiangping Hao, Xiuxin Wu, Bo Bao, Xigang Zhang, Xuan Li, Wei Liang, Xun College of Computer Science and Technology Harbin Engineering University Heilongjiang Harbin China Modeling and Emulation in E-Government National Engineering Laboratory Heilongjiang Harbin China Qingdao Innovation and Development Base of Harbin Engineering University Shandong Qindao China R&d and External Relations Department Xiangjiang Laboratory Hunan Changsha China School of Artificial Intelligence and Advanced Computing Hunan University of Technology and Business Hunan Changsha China Changsha Artificial Intelligence Social Laboratory Hunan Changsha China School of Information Renmin University of China Beijing China Guanghua School of Management Peking University Beijing China Harvest Fund Management Co. Ltd. Beijing China

ISBN: (纸本)9798400706868

Graph Contrastive Learning (GCL) aims to address the issue of label scarcity by leveraging graph structures to propagate labels from a limited set of labeled data to a broader range of unlabeled data. However, recent GCL methods often rely on uniform negative sample selection schemes, such as random sampling, which results in suboptimal performance. To tackle this challenge, we present GraphSaSe, a tailored approach specifically designed for graph contrastive learning. Our method introduces an innovative reinforcement learning strategy that translates the divergence between positive pairs into a reinforcement reward mechanism. This mechanism generates selection probabilities to dynamically guide the selection of negative samples during training. We explore the impact of negative sample selection at different stages in graph contrastive learning and analyze how the discount factor affects the reward mechanism in reinforcement learning. These studies enhance the overall performance of the model. Comprehensive experimentation across diverse real-world datasets validates the effectiveness of our algorithm, positioning it favorably against contemporary state-of-the-art methodologies. © 2024 acm.

关键词： Contrastive Learning

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Edu-larp @ CHI

Edu-larp @ CHI

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Extended Abstracts of the 2023 CHI conference on Human Factors in computing Systems, CHI EA 2023

作者： Robinson, Raquel B. Johansson, Karin Fey, James Collin Márquez Segura, Elena Back, Jon Waern, Annika Bowman, Sarah Lynne Isbister, Katherine Social Emotional Technology Lab University of California Santa Cruz United States Uppsala University Sweden Department of Computer Science and Engineering Universidad Carlos Iii de Madrid Madrid Spain

ISBN: (纸本)9781450394222

Role-play has long been used as an active learning technique in educational and training contexts. In particular, Edu-larp (a structured, live action roleplay experience that teaches through social enactment and reflection [1]) has been used in therapeutic contexts, training courses, and even an entire course curriculum [4]. We propose to host a workshop at CHI 2023 which will connect CHI attendees of various disciplines interested in this topic, with the outcome to understand how edu-larp might be an effective way of augmenting existing teaching and research within HCI. During the workshop, attendees will participate in numerous edu-larp exercises designed to introduce and orient them to the concept, and facilitate discussion about the different ways edu-larp can be leveraged in the broad domain of HCI. © 2023 Owner/Author.

关键词： Human computer interaction

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Distributed Ensemble Hyper-Heuristic Algorithm for Aircraft Arrival Sequencing and Scheduling Problem

Distributed Ensemble Hyper-Heuristic Algorithm for Aircraft ...

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2024 Genetic and Evolutionary Computation conference Companion, GECCO 2024 Companion

作者： Wang, Xian-Ping Xu, Xin-Xin Jiang, Yi Bi, Sheng Liang, Yan-Chun Zhan, Zhi-Hui School of Computer Science and Engineering South China University of Technology Guangzhou510006 China School of Computer Science and Technology Ocean University of China Qingdao266100 China Zhuhai College of Science and Technology Zhuhai519041 China College of Artificial Intelligence Nankai University Tianjin300350 China

ISBN: (纸本)9798400704956

Aircraft arrival sequencing and scheduling (ASS) is a challenging NP-hard problem, which aims to arrange the order of aircraft landing at an airport to optimize the use of runway capacity and reduce delays. As air traffic continues to grow, airports face the constant challenge of efficiently managing the flow of incoming flights, emphasizing the need for a fast and efficient algorithm. However, existing algorithms are often extremely time-consuming to construct a near-optimal solution for ASS. To overcome the deficiency in existing algorithms, this paper proposes a fast and efficient distributed ensemble hyper-heuristic algorithm (DEHHA). To fast construct an optimal solution for ASS, DEHHA proposes an ensemble hyper-heuristic-based solution construct (EHHSC) strategy and a distributed cooperative evolution (DCE) strategy. The EHHSC strategy ensembles multiple effective hyper-heuristic optimizers to find diverse solutions and enables the communication of diverse optimizers to effectively find global optimal solution. The DCE strategy integrates the strengths of distributed computing, which enables multiple hyper-heuristic optimizers to cooperatively evolve in parallel to reduce running time. Extensive experiments results indicate DEHHA is not only capable of achieving superior performance in terms of solution quality but also requires a mere 42.9% of the time needed by the state-of-the-art algorithms. © 2024 held by the owner/author(s).

关键词： Heuristic algorithms

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