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arXiv 2023年

作者： Huang, Jen-Tse Wang, Wenxuan Li, Eric John Lam, Man Ho Ren, Shujie Yuan, Youliang Jiao, Wenxiang Tu, Zhaopeng Lyu, Michael R. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Institute of Psychology Tianjin Medical University China Tencent AI Lab. China School of Data Science The Chinese University of Hong Kong Shenzhen China

Large Language Models (LLMs) have recently showcased their remarkable capacities, not only in natural language processing tasks but also across diverse domains such as clinical medicine, legal consultation, and education. LLMs become more than mere applications, evolving into assistants capable of addressing diverse user requests. This narrows the distinction between human beings and artificial intelligence agents, raising intriguing questions regarding the potential manifestation of personalities, temperaments, and emotions within LLMs. In this paper, we propose a framework, PsychoBench, for evaluating diverse psychological aspects of LLMs. Comprising thirteen scales commonly used in clinical psychology, PsychoBench further classifies these scales into four distinct categories: personality traits, interpersonal relationships, motivational tests, and emotional abilities. Our study examines five popular models, namely text-davinci-003, ChatGPT, GPT-4, LLaMA-2-7b, and LLaMA-2-13b. Additionally, we employ a jailbreak approach to bypass the safety alignment protocols and test the intrinsic natures of LLMs. We have made PsychoBench openly accessible via https://***/CUHK-ARISE/PsychoBench. Copyright © 2023, The Authors. All rights reserved.

关键词： Medicine

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Overcoming Heterogeneous data in Federated Medical Vision-Language Pre-training: A Triple-Embedding Model Selector Approach 39

Overcoming Heterogeneous Data in Federated Medical Vision-La...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Aowen Zhang, Zhiwang Wang, Dongang Wang, Fanyi Hu, Haotian Guo, Jinyang Zhou, Yipeng Pang, Chaoyi Wen, Shiting College of Computer Science and Technology Zhejiang University China School of Computing and Data Engineering NingboTech University China Brain and Mind Centre The University of Sydney Australia Zhejiang Leapmotor Technology Co. Ltd China Institute of Artificial Intelligence Beihang University China School of Computing Macquarie University Australia

ISBN: (纸本)157735897X

The scarcity of data in the medical field brings challenges to collaborative training in medical vision-language pre-training (VLP) across different clients Thus, collaborative training in medical VLP faces two significant challenges: First, the medical data requires privacy and therefore cannot be directly shared across different clients. Second, medical data distribution across institutes is typically heterogeneous, hindering local model alignment and representation capabilities. To simultaneously overcome these two challenges, we propose a framework called personalized model selector with fused multimodal information (PMS-FM). The contribution of PMS-FM is two-fold: 1) PMS-FM uses embeddings to represent information in different formats, allowing for the fusion of multimodal data. 2) PMS-FM adapts to personalized data distributions by training multiple models. A model selector then identifies and selects the best-performing model for each individual client. Extensive experiments with multiple real-world medical datasets demonstrate the superb performance of PMS-FM over existing federated learning methods on different zero-shot classification tasks. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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A convergence analysis for a class of practical variance-reduction stochastic gradient MCMC

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science China(Information sciences) 2019年第1期62卷 67-79页

作者： Changyou CHEN Wenlin WANG Yizhe ZHANG Qinliang SU Lawrence CARIN Department of Computer Science and Engineering Department of Electrical and Computer Engineering Duke University Microsoft Research School of Data and Computer Science Sun Yat-sen University

Stochastic gradient Markov chain Monte Carlo(SG-MCMC) has been developed as a flexible family of scalable Bayesian sampling algorithms. However, there has been little theoretical analysis of the impact of minibatch size to the algorithm's convergence rate. In this paper, we prove that at the beginning of an SG-MCMC algorithm, i.e., under limited computational budget/time, a larger minibatch size leads to a faster decrease of the mean squared error bound. The reason for this is due to the prominent noise in small minibatches when calculating stochastic gradients, motivating the necessity of variance reduction in SG-MCMC for practical use. By borrowing ideas from stochastic optimization, we propose a simple and practical variance-reduction technique for SG-MCMC, that is efficient in both computation and *** importantly, we develop the theory to prove that our algorithm induces a faster convergence rate than standard SG-MCMC. A number of large-scale experiments, ranging from Bayesian learning of logistic regression to deep neural networks, validate the theory and demonstrate the superiority of the proposed variance-reduction SG-MCMC framework.

关键词： Markov chain Monte Carlo SG-MCMC variance reduction deep neural networks

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MCRec: Multi-channel Gated Gifts Recommendation

MCRec: Multi-channel Gated Gifts Recommendation

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IEEE International Conference on data Mining (ICDM)

作者： Ting-Ting Su Wu-Dong Xi Xing-Xing Xing Chang-Dong Wang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China UX Center NetEase Games Guangzhou China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China

In recent years, various recommendation methods are proposed to capture user preferences more accurately, with the assumption that different types of records reflects the positive intention of users to buy items in different degree. However, the records of different channels may denote positive or negative impacts on users’ willingness to buy items in multi-channel scenario, which is a salient features of games. Making recommendation only with the records of buy channel makes it difficult to capture cross-channel impact of items. To solve the issue, this paper proposes a multi-channel gated gifts recommendation method, named MCRec, which is able to mine the impact of acquisition in different channels on buy channel from multi-channel records and generate personalized gifts for users. The MCRec method extracts channel-aware correlation of items from channel-aware item-item graphs. The contribution of different items in various sessions is distinguished with item gate, and the impact of the context of different channels on buy channel is measured with channel gate. Finally, personalized gifts will be generated hierarchically with different purposes. Extensive experiments are conducted on two datasets that are constructed with the data collected from two massively multiplayer online (MMO) games. The results demonstrate the superiority of our MCRec over state-of-the-art recommendation methods in gifts recommendation. Further ablation studies validate the effectiveness of the design of MCRec in modeling cross-channel impact of items.

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Converter Networks for Multi-Scale Tokens-Aware Multi-Region and Multi-Sequence MR-to-CT Synthesis in a Single Model

Converter Networks for Multi-Scale Tokens-Aware Multi-Region...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Parikshit N. Mahalle Manoj Chandra Lohani S. Hemelatha Divya Sharma Chitra S Sujayaraj Samuel Jayakumar Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune India School of Computing Graphic Era Hill University Haldwani India Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai School of Sciences Forensic Science JAIN (Deemed to be University) Bangalore Karnataka

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Synthetic computed tomography (sCT) based on magnetic resonance imaging (MRI) streamlines the planning of radiation therapy treatments by doing away with CT simulation and error-prone picture registration, which in turn reduces radiation dosage for patients and setup uncertainty. To create sCT images that matched the MRI anatomy, an optimally trained MultiHead Attention was used in conjunction with the reverse diffusion process. We generated sCT using MRI on two datasets—one for the brain and one for the prostate—to test the suggested strategy. The following metrics were used for qualitative evaluation: ground truth CTs and sCTs normalized cross correlation (NCC) indices, multi-scale Structure Similarity index (MS-SSIM), peak signal-to-noise ratio (PSNR), and mean absolute error (MAE) of Hounsfield unit (HU). A new method for creating CT images from standard MRIs utilizing a transformer-based convolutional neural network (CNN) has been designed and verified by us. Synthetic CT (sCT) pictures of excellent quality and robustness may be produced in a few minutes using this model, which successfully depicts the intricate interaction among CT and MRI images. By cutting down on patient time spent on treatment planning, improving accuracy of treatment delivery, and doing away with the necessity for extra CT scans, this method may considerably streamline radiation treatment planning.

关键词： PSNR Uncertainty Magnetic resonance imaging Computed tomography Diffusion processes Streaming media Transformers Planning Convolutional neural networks Standards

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Prediction Analysis for Diabetic Disease Using Machine Learning Model

Prediction Analysis for Diabetic Disease Using Machine Learn...

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Information Technology (OCIT), OITS International Conference on

作者： N. Senthamarai Jafar Ali Ibrahim Syed Masood M. Sathya N. S. Kalyan Chakravarthy Bharati Rathore M Mohamed Kalith Oli Department of Network and Communications SRM Institute of Science and Technology Chennai India Department of IoT School of Computer Science and Engineering Vellore Institute of Technology Vellore India Department of IT Nadar Saraswathi College of Engineering and Technology Theni India Center for Data Science AI&ML QIS College of Engineering and Technology Ongole India Faculty of Business and Creative Industries University of South Wales Pontypridd UK School of Bio Sciences and Technology Vellore Institute of Technology Vellore India

ISBN: (数字)9798331510404

ISBN: (纸本)9798331510411

The Diabetes Disease Prediction System is based on predictive modeling, which forecasts the user's disease based on the information that the user inputs to the system, including the number of pregnancies, age, weight, blood pressure, insulin, glucose, and other factors contributing to diabetes. The system evaluates the data supplied by the user as input and outputs the likelihood of the prediction. Nowadays, better production results are key for machine learning activities. Research has been conducted on a dataset from the Kaggle data set where single machine learning algorithms have been applied, and recorded results have been compared with results of multialgorithms of machine learning using neural networks. Some major classification algorithms, Random Forest, SVM, and Hybrid model of SVM and Random Forest, have been broadly utilized to anticipate the disease, where different accuracies were obtained. data preprocessing and feature selection were performed before building the models to increase the model's accuracy. Evaluation of the models in the proposed paper is based on the model's accuracy. The hybrid model performed best with 83% accuracy.

关键词： Support vector machines Accuracy Machine learning algorithms Linear regression Predictive models Prediction algorithms Diabetes Classification algorithms Random forests Diseases

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QoE Maximization for Multiple-UAV-Assisted Multi-Access Edge Computing: An Online Joint Optimization Approach

arXiv

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arXiv 2024年

作者： He, Long Sun, Geng Sun, Zemin Wu, Qingqing Kang, Jiawen Niyato, Dusit Han, Zhu Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China The College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China The School of Automation Guangdong University of Technology Guangzhou510006 China The University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446701 Korea Republic of The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

In disaster scenarios, conventional terrestrial multi-access edge computing (MEC) paradigms, which rely on fixed infrastructure, may become unavailable due to infrastructure damage. With high-probability line-of-sight (LoS) communication, flexible mobility, and low cost, unmanned aerial vehicle (UAV)-assisted MEC is emerging as a new promising paradigm to provide edge computing services for ground user devices (UDs) in disaster-stricken areas. However, the limited battery capacity, computing resources, and spectrum resources also pose serious challenges for UAV-assisted MEC, which can potentially shorten the service time of UAVs and degrade the quality of experience (QoE) of UDs without an effective control approach. To this end, in this work, we first present a hierarchical architecture of multiple-UAV-assisted MEC networks that enables the coordinated provision of edge computing services by multiple UAVs. Then, we formulate a joint task offloading, resource allocation, and UAV trajectory planning optimization problem (JTRTOP) to maximize the QoE of UDs while considering the energy consumption constraints of UAVs. Since the problem is proven to be a future-dependent and NP-hard problem, we propose a novel online joint task offloading, resource allocation, and UAV trajectory planning approach (OJTRTA) to solve the problem. Specifically, the JTRTOP is first transformed into a per-slot real-time optimization problem (PROP) using the Lyapunov optimization framework. Then, a two-stage optimization method based on game theory and convex optimization is proposed to solve the PROP. Simulation results provide empirical evidence supporting the superior system performance of the proposed OJTRTA in comparison to alternative approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Game theory

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Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review

arXiv

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arXiv 2024年

作者： Hassani, Hossein Razavi-Far, Roozbeh Saif, Mehrdad Lin, Liang Department of Electrical and Computer Engineering Western University LondonONN6A 3K7 Canada Faculty of Computer Science Canadian Institute for Cybersecurity University of New Brunswick FrederictonNBE3B 5A3 Canada Department of Electrical and Computer Engineering University of Windsor WindsorONN9B 3P4 Canada School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China

Reinforcement learning (RL) is a sub-domain of machine learning, mainly concerned with solving sequential decision-making problems by a learning agent that interacts with the decision environment to improve its behavior through the reward it receives from the environment. This learning paradigm is, however, well-known for being time-consuming due to the necessity of collecting a large amount of data, making RL suffer from sample inefficiency and difficult generalization. Furthermore, the construction of an explicit reward function that accounts for the trade-off between multiple desiderata of a decision problem is often a laborious task. These challenges have been recently addressed utilizing transfer and inverse reinforcement learning (T-IRL). In this regard, this paper is devoted to a comprehensive review of realizing the sample efficiency and generalization of RL algorithms through T-IRL. Following a brief introduction to RL, the fundamental T-IRL methods are presented and the most recent advancements in each research field have been extensively reviewed. Our findings denote that a majority of recent research works have dealt with the aforementioned challenges by utilizing human-in-the-loop and sim-to-real strategies for the efficient transfer of knowledge from source domains to the target domain under the transfer learning scheme. Under the IRL structure, training schemes that require a low number of experience transitions and extension of such frameworks to multi-agent and multi-intention problems have been the priority of researchers in recent years. © 2024, CC BY.

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Graph Neural Networks for Wireless Networks: Graph Representation, Architecture and Evaluation

arXiv

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arXiv 2024年

作者： Lu, Yang Li, Yuhang Zhang, Ruichen Chen, Wei Ai, Bo Niyato, Dusit School of Computer and Technology China Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China

Graph neural networks (GNNs) have been regarded as the basic model to facilitate deep learning (DL) to revolutionize resource allocation in wireless networks. GNN-based models are shown to be able to learn the structural information about graphs representing the wireless networks to adapt to the time-varying channel state information and dynamics of network topology. This article aims to provide a comprehensive overview of applying GNNs to optimize wireless networks via answering three fundamental questions, i.e., how to input the system parameters of wireless networks into GNNs, how to improve the expressive performance of GNNs, and how to evaluate GNNs. Particularly, two graph representations are given to transform wireless network parameters into graph-structured data. Then, we focus on the architecture design of the GNN-based models via introducing the basic message passing as well as model improvement methods including multi-head attention mechanism and residual structure. At last, we give task-oriented evaluation metrics for DL-enabled wireless resource allocation schemes. We also highlight certain challenges and potential research directions for the application of GNNs in wireless networks. © 2024, CC BY.

关键词： Graph neural networks

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Deepfakes: A Review on Technologies, Applications and Strategies

Deepfakes: A Review on Technologies, Applications and Strate...

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International Conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Kuldeep Singh Kaswan Kiran Malik Jagjit Singh Dhatterwal Mahaveer Singh Naruka D Govardhan School of Computing Science and Engineering Galgotias University Greater Noida UP India Department of Computer Science and Engineering MRIEM Rohtak India Department of Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Andhra Pradesh India G L Bajaj Institute of Technology and Management Greater Noida India Department of Aeronautical Engineering Institute of Aeronautical Engineering Hyderabad

Deepfakes are artificial intelligence-generated multimedia, primarily videos or audio, that effectively edit or invent information by superimposing one person's likeness or voice onto that of another. They use deep learning techniques such as GAN s to generate hyper-realistic simulations that blur the line between reality and fiction. Deepfakes encompass everything from political satire to possible concerns in the fields of disinformation, privacy invasion, and cybersecurity. Understanding deepfakes is critical in an age when manipulated media may influence public opinion and undermine confidence. The primary objective of this paper is to provide a holistic overview of deepfake technology, shedding light on both its creative potential and inherent risks. Through a systematic exploration of the evolution, applications, techniques, and detection strategies, we aim to equip readers with an informed perspective on this ever-evolving field. By synthesizing existing knowledge and insights, this paper intends to contribute to the ongoing discourse surrounding deepfakes, enabling stakeholders to make informed decisions, develop effective countermeasures, and anticipate the future trajectory of this technology. Ultimately, our work strives to foster awareness and preparedness in an age where discerning the truth has never been more vital.

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