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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Junfeng Guo Yiming Li Ruibo Chen Yihan Wu Chenxi Liu Heng Huang Department of Computer Science Institute of Health Computing University of Maryland College Park College of Computing and Data Science Nanyang Technology University

ISBN: (纸本)9798331314385

High-quality public datasets significantly prompt the prosperity of deep neural networks (DNNs). Currently, dataset ownership verification (DOV), which consists of dataset watermarking and ownership verification, is the only feasible solution to protect their copyright by preventing unauthorized use. In this paper, we revisit existing DOV methods and find that they all mainly focused on the first stage by designing different types of dataset watermarks and directly exploiting watermarked samples as the verification samples for ownership verification. As such, their success relies on an underlying assumption that verification is a onetime and privacy-preserving process, which does not necessarily hold in practice. To alleviate this problem, we propose ZeroMark to conduct ownership verification without disclosing dataset-specified watermarks. Our method is inspired by our empirical and theoretical findings of the intrinsic property of DNNs trained on the watermarked dataset. Specifically, ZeroMark first generates the closest boundary version of given benign samples and calculates their boundary gradients under the label-only black-box setting. After that, it examines whether the given suspicious method has been trained on the protected dataset by performing a hypothesis test, based on the cosine similarity measured on the boundary gradients and the watermark pattern. Extensive experiments on benchmark datasets verify the effectiveness of our ZeroMark and its resistance to potential adaptive attacks. The codes for reproducing our main experiments are publicly available at https://***/JunfengGo/***.

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Confidential Spatio-Temporal Graph Convolutional Networks with Differential Privacy for Traffic Forecasting 5

Confidential Spatio-Temporal Graph Convolutional Networks wi...

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5th International Conference on Electronic Communication and Artificial Intelligence, ICECAI 2024

作者： Chen, Xiangyang Xiao, Aiwen Pang, Weiling Jiang, Xinyi Ren, Zehua Tang, Min School of Mathematics and Computing Science Guangxi Colleges and Universities Guilin University of Electronic Technology Key Laboratory of Data Analysis and Computation Guilin541004 China Guilin541002 China

ISBN: (纸本)9798350386943

Traffic forecasting is essential for urban traffic management in intelligent transportation systems (ITS). However, it involves personal sensitive information, such as user location data. In this paper, we propose an innovative secure protocol for addressing the privacy challenges of traffic prediction in ITS. Our model integrates Spatio-Temporal Graph Convolutional Network (STGCN) and differential privacy (DP) to effectively protect user privacy on the premise of predictive performance. Our experimental results demonstrate that the model maintains a high level of prediction accuracy while offering privacy protection for the user data. On the PeMSD7 dataset, compared to the STGCN benchmark model, our model shows a slight increase of 0.5% in performance metrics (e.g., mean absolute error) while safeguarding the user data. Additionally, the model's average training time increases by roughly 8.4% in the ciphertext domain. © 2024 IEEE.

关键词： Differential privacy

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Exploring Multimodal Generative AI for Education through Co-design Workshops with Students 25

Exploring Multimodal Generative AI for Education through Co-...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Prasad, Prajish Balse, Rishabh Balchandani, Dhwani School of Computing and Data Sciences FLAME University Pune India Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom

ISBN: (纸本)9798400713941

Multimodal large language models (MLLMs) are Generative AI models that take different modalities such as text, audio, and video as input and generate appropriate multimodal output. Since such models will be integrated into future educational tools, a human-centered design approach that takes students' perspectives into account is essential while designing such *** paper describes two co-design workshops which were conducted with 79 student groups to examine how they design and prototype future educational tools integrated with MLLMs. Through various activities in the workshops, students discussed relevant educational problems, created journey maps, storyboards and low fidelity prototypes for their applications, and evaluated their applications based on relevant design principles. We found that students' applications used MLLMs for important learning environment design features such as multimodal content creation, personalization, and feedback. Based on these findings, we discuss future research directions for the design of multimodality in generative AI educational applications. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Students

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Evolutionary Multiobjective Feature Selection Assisted by Unselected Features 13

Evolutionary Multiobjective Feature Selection Assisted by Un...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Duan, Xuan Liu, Songbai Ji, Junkai Li, Lingjie Lin, Qiuzhen Tan, Kay Chen College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen China Shenzhen China The Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9798350308365

To enhance the generalization of multi-objective feature selection (MOFS) in classification, this paper proposes an evolutionary multitasking algorithm, diverging from previous approaches that exclusively target selected features. The algorithm integrates information from both selected and unselected features, introducing a novel objective to minimize the accuracy of unselected features. This objective, combined with the goal of minimizing classification errors for selected features, forms an auxiliary MOFS task. The paper presents a dual-population evolutionary multitasking framework that synergizes the main MOFS task with the auxiliary task. A knowledge transfer mechanism, based on accuracy preferences, seamlessly shares insights from the auxiliary to the main task, aiming to identify improved Pareto feature subsets. Empirical results demonstrate the superior performance of several state-of-the-art multi-objective algorithms within this framework, highlighting significant improvements across diverse datasets. © 2024 IEEE.

关键词： Feature Selection

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Pushing AI to wireless network edge: an overview on integrated sensing, communication, and computation towards 6G

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science China(Information sciences) 2023年第3期66卷 7-25页

作者： Guangxu ZHU Zhonghao LYU Xiang JIAO Peixi LIU Mingzhe CHEN Jie XU Shuguang CUI Ping ZHANG Shenzhen Research Institute of Big Data Future Network of Intelligence Institute (FNii) The Chinese University of Hong Kong (Shenzhen) School of Science and Engineering (SSE) The Chinese University of Hong Kong (Shenzhen) State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Peking University Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Peng Cheng Laboratory State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications

Pushing artificial intelligence(AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things(AIoT) in the sixth-generation(6G) era. This gives rise to an emerging research area known as edge intelligence, which concerns the distillation of human-like intelligence from the vast amount of data scattered at the wireless network edge. Typically, realizing edge intelligence corresponds to the processes of sensing, communication,and computation, which are coupled ingredients for data generation, exchanging, and processing, ***, conventional wireless networks design the three mentioned ingredients separately in a task-agnostic manner, which leads to difficulties in accommodating the stringent demands of ultra-low latency, ultra-high reliability, and high capacity in emerging AI applications like auto-driving and metaverse. This thus prompts a new design paradigm of seamlessly integrated sensing, communication, and computation(ISCC) in a taskoriented manner, which comprehensively accounts for the use of the data in downstream AI tasks. In view of its growing interest, this study provides a timely overview of ISCC for edge intelligence by introducing its basic concept, design challenges, and enabling techniques, surveying the state-of-the-art advancements, and shedding light on the road ahead.

关键词： sixth-generation (6G) edge intelligence artificial intelligence of things (AIoT) integrated sensing communication and computation (ISCC)

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Predicting Protein-Protein Interactions Using Self-Attention-Based Deep Neural Networks and FastText Embeddings 15

Predicting Protein-Protein Interactions Using Self-Attention...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Oviya, I.R. Shanmukha Sravya, N. Raja, Kalpana Amrita Vishwa Vidyapeetham Amrita School Of Computing Department of Computer Science Engineering Chennai601103 India Yale University Section for Biomedical Informatics and Data Science School of Medicine New HavenCT United States

ISBN: (纸本)9798350370249

Predicting Protein-protein interactions are central to understanding the intricate mechanisms governing cellular functions, as proteins seldom act in isolation. While current machine learning and deep learning models focus on encoding protein sequences based on residue composition or physicochemical properties, they often overlook the complex relationships between amino acid segments within lengthy sequences. To address these limitations, we propose a novel hybrid model that combines a self-attention to enhance Deep neural network feature extraction with an with FastText Protein embeddings for predicting PPI. Utilizing transfer learning via the unsupervised FastText embedding technique to derive statistical representations of protein sequences, offers advantages over traditional protein encoding techniques in the context of analysing k-Mer sequences. Our model's performance is assessed through 5-fold cross-validation on datasets of *** and Human-Bacillus Anthracis protein interactions, resulting in accuracies of 92.28% and 94.08%, respectively. Compared to existing machine learning models, our model demonstrates superior performance on both datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Machine Learning Models through Hyperparameter Optimization with Particle Swarm Optimization 10

Enhancing Machine Learning Models through Hyperparameter Opt...

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10th International Conference on Computer, Control, Informatics and its Applications, IC3INA 2023

作者： Indrawati, Ariani Wahyuni, Intan N. Research Center for Data and Information Science National Research and Innovation Agency Indonesia Research Center for Computing National Research and Innovation Agency Indonesia

ISBN: (纸本)9798350394870

Hyperparameter tuning plays a crucial role in optimizing the performance of machine learning algorithms. This study explores the effectiveness of Particle Swarm optimization (PSO) in fine-Tuning the hyperparameters of three popular machine learning algorithms, including Random Forest, K-Nearest Neighbors, and Support Vector Machine. The studies use two publicly available datasets: The Heart Failure Clinical Records dataset and the Iris dataset. For additional comparative analysis, we also compare with the common optimization technique using Random Search. The result reveals that PSO and Random Search have varying impacts on the performance of different algorithms and datasets. The findings provide valuable insights into the behavior of different optimization techniques and their effect on model performance, paving the way for better and more reliable machine learning models in various domains. Through effective hyperparameter optimization, machine learning models can achieve enhanced accuracy. © 2023 IEEE.

关键词： Support vector machines

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Artificial intelligence algorithms for object detection and recognition in video and images

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Multimedia Tools and Applications 2025年 1-18页

作者： Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Department of Computer Science Engineering Saveetha School Of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai6021055 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India

The usage of machine learning and deep learning algorithms have necessitated Artificial Intelligence'. AI is aimed at automating things by limiting human interference. It is widely used in IT, healthcare, finance, and agriculture. It is achieved through several deep learning algorithms that reflect the human brain's intelligence. These AI algorithms can be manipulated according to changing needs and improved efficiency. This paper tries to utilize the developments made in AI technology to classify the images and recognize the objects present in them. One widely used AI algorithm is CNN (Convolutional Neural Networks). The CNN is a deep learning-based algorithm that consists of various layers that extract and filters the parameters present in the images. Some additional layers of ResNet50 and the CNN algorithm are used to extract the parameters to improve image recognition accuracy. The image dataset taken for training and testing the proposed model is imageNet. The images are initially processed before sending them to the proposed model. The proposed model is trained, validated, and tested through the images obtained after the initial processing. The same process is repeated several times until getting the maximum accuracy. The accuracy of the proposed model in terms of image recognition is recorded. The obtained results are compared with other image classification algorithms like VGG16 and VGG19. It is concluded that the proposed model outperforms other traditional methods in terms of accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Offline behavior distillation 24

Offline behavior distillation

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Shiye Lei Sen Zhang Dacheng Tao School of Computer Science The University of Sydney College of Computing & Data Science Nanyang Technological University

ISBN: (纸本)9798331314385

Massive reinforcement learning (RL) data are typically collected to train policies offline without the need for interactions, but the large data volume can cause training inefficiencies. To tackle this issue, we formulate offline behavior distillation (OBD), which synthesizes limited expert behavioral data from sub-optimal RL data, enabling rapid policy learning. We propose two naive OBD objectives, DBC and PBC, which measure distillation performance via the decision difference between policies trained on distilled data and either offline data or a near-expert policy. Due to intractable bi-level optimization, the OBD objective is difficult to minimize to small values, which deteriorates PBC by its distillation performance guarantee with quadratic discount complexity O(1/(1 - γ)2). We theoretically establish the equivalence between the policy performance and action-value weighted decision difference, and introduce action-value weighted PBC (Av-PBC) as a more effective OBD objective. By optimizing the weighted decision difference, Av-PBC achieves a superior distillation guarantee with linear discount complexity O(1/(1 - γ)). Extensive experiments on multiple D4RL datasets reveal that Av-PBC offers significant improvements in OBD performance, fast distillation convergence speed, and robust cross-architecture/optimizer generalization. The code is available at https://***/LeavesLei/OBD.

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Finding Top-k Important Edges on Bipartite Graphs: Ego-betweenness Centrality-based Approaches 39

Finding Top-k Important Edges on Bipartite Graphs: Ego-betwe...

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39th IEEE International Conference on data Engineering, ICDE 2023

作者： Weng, Tongfeng Zhou, Xu Fang, Yixiang Tan, Kian-Lee Li, Kenli Hunan University College of Computer Science and Electronic Engineering China The Chinese University of HongKong School of Data Science Shenzhen China National University of Singapore School of Computing Singapore

ISBN: (纸本)9798350322279

Bipartite graph is an important data structure that widely exists in disease prevention and control, community detection, and other real-life applications. In a bipartite graph, edges not only connect entries of different types but also are bridges of different communities in the above applications. However, research to date has not yet focused on edge importance in bipartite graphs. Inspired by this, we study a new problem of top-k edge search in bipartite graphs with the goal of finding k most important edges for a given bipartite graph;these edges are crucial bridges among communities. In particular, we introduce the measure of ego-betweenness for evaluating the importance of edges. To handle this problem effectively, a lazy bound-based algorithm is first proposed by integrating an upper bound pruning strategy. After that, to further get better efficiency, a greedy bound-based heuristic algorithm is explored on the basis of a tighter upper bound which contributes to reducing redundant computation for calculating ego-betweenness. Last but not least, two parallel techniques with different levels of granularity, called P -src and P -task, are respectively introduced to further improve the search efficiency. The experimental results on both real-world and synthetic graphs demonstrate the efficiency and scalability of the proposed algorithms. © 2023 IEEE.

关键词： Heuristic algorithms

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