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The development of public relations for school of computer and information technology chiangrai rajabhat university using virtual reality technology 22

The development of public relations for school of computer a...

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22nd International computer Science and Engineering Conference, ICSEC 2018

作者： Chaikhamwang, Sumran Montri, Wijitra Anantasomboon, Konlachan Department of Computer Science Chiangrai Rajabhat University School of Computer and Information Technology Chiangrai Thailand

ISBN: (纸本)9781538681640

This research aims to 1) to study in the Development of public relations for school of computer and information technology Chiangrai Rajabhat University Using Virtual Reality technology 2) to design the Development of public relations for school of computer and information technology Chiangrai Rajabhat University Using Virtual Reality technology and 3) to Development of public relations for school of computer and information technology Chiangrai Rajabhat University Using Virtual Reality technology. This system provided continuously to developed school of computer and information technology environment 3D model, then created applications to promote information and activities with 3D glasses. © 2018 IEEE.

关键词： Public relations

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Federated local causal structure learning

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Science China(information Sciences) 2025年第3期68卷 110-124页

作者： Kui YU Chen RONG Hao WANG Fuyuan CAO Jiye LIANG School of Computer and Information Hefei University of Technology School of Computer and Information Technology Shanxi University

Local causal structure learning(LCS) efficiently identifies a set of direct neighbors of a specified variable from observational data. Additionally, it distinguishes direct causes and direct effects of this variable without learning the entire causal structure. While many LCS algorithms have been proposed, they do not consider the data privacy-preserving problem,which has attracted extensive attention from academia and industry. To address this issue, we propose a federated local causal structure learning(FedLCS) algorithm to learn local causal structures in privacy-preserving data in a federated ***, FedLCS introduces a layer-wise federated local skeleton learning algorithm to construct the local skeleton. Based on this skeleton, it introduces a federated local skeleton orientation algorithm and an extension-and-backtracking orientation algorithm to orient the edges. Finally, FedLCS uses a federated local extension-and-backtracking orientation algorithm to orient the remaining edges. Extensive experiments on benchmark, synthetic, and real datasets demonstrate that FedLCS can learn the local causal structure of a given variable in a federated setting.

关键词： local causal structure learning federated learning directed acyclic graph privacy-preserving data federated layer-wise strategy

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Witness encryption with updatable ciphertexts

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Science China(information Sciences) 2025年第3期68卷 181-196页

作者： Yuzhu WANG Mingwu ZHANG School of Computer Science and Information Security Guilin University of Electronic Technology School of Computer Science Hubei University of Technology

Witness encryption(WE) is a novel type of cryptographic primitive that enables a message to be encrypted via an NP instance. Anyone who possesses a solution to this instance(i.e., a witness) can then recover the message from the *** introduce a variant of WE that allows ciphertext updates, referred to as ciphertext updateable WE(CUWE). With CUWE,a user can encrypt a message using an instance x and a tag t, and those who possess a valid witness w for x and match the access policy defined by tag t can decrypt the message. Furthermore, CUWE allows for the use of an update token to change the tag t of ciphertext to a different tag. This feature enables fine-grained access control, even after the ciphertext has been created, thereby significantly increasing the usefulness of the WE scheme. We demonstrate that such a WE framework with an updatable ciphertext scheme can be constructed using our puncturable instance-based deterministic encryption(PIDE) and indistinguishability obfuscation(iO). We also propose an instantiation of PIDE utilizing puncturable pseudorandom functions(PRFs) that provide(selectively) indistinguishable security. Finally, we expand our CUWE to ciphertext-updatable functional WE(CUFWE), which offers enhanced data access control.

关键词： witness encryption NP relation updatable ciphertext access control indistinguishable obfuscation

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CM-CaFE: A Clustering Method with Causality-based Feature Embedding

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ACM Transactions on Knowledge Discovery from Data 2025年第3期19卷 1-23页

作者： Jing, Xuechun Cao, Fuyuan Yu, Kui Liang, Jiye The School of Computer and Information Technology Shanxi University Taiyuan China The School of Computer and Information Hefei University of Technology Hefei China

Clustering is a fundamental technique widely used for exploring the inherent data structure. Many studies indicate that an appropriate feature representation can effectively improve clustering performance. However, the existing feature representation methods are based on correlation to select or extract features, which makes it hard to deal with spurious correlations. The spurious correlations mislead the correlation-based methods to consider features that have no causal relationship as being correlative, which limits the clustering performance and feature interpretability. To tackle this issue, inspired by causal learning, we propose a new joint optimization Clustering Method with Causal Feature Embedding (CM-CaFE), which utilizes the causality of features to learn more discriminative representation for clustering. Specifically, to eliminate spurious correlations among features, we first employ any state-of-the-art Markov blanket learning method to learn an undirected causal graph. Next, we extract the maximal fully connected causal subgraphs from the learned undirected causal graph and propose an approach to merge them to generate the causal matrix. Based on the causal matrix, we present an objective function that consists of a clustering loss term and a causal matrix fitting term to learn a causal transformation matrix. The causal transformation matrix is utilized to map the original data into a new space for clustering. Finally, we comprehensively compare the proposed method with some state-of-the-art clustering approaches on several datasets to demonstrate the effectiveness and interpretability of the proposed method. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Embeddings

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Coarse-to-fine lightweight meta-embedding for ID-based recommendations

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Science China(information Sciences) 2025年第4期68卷 82-97页

作者： Yang WANG Haipeng LIU Zeqian YI Biao QIAN Meng WANG School of Computer Science and Information Engineering Hefei University of Technology College of Information and Intelligence Hunan Agricultural University

State-of-the-art recommender systems are increasingly focused on optimizing implementation efficiency, such as enabling on-device recommendations under memory constraints. Current methods commonly use lightweight embeddings for users and items or employ compact embeddings to enhance reusability and reduce memory usage. However, these approaches consider only the coarse-grained aspects of embeddings, overlooking subtle semantic nuances. This limitation results in an adversarial degradation of meta-embedding performance, impeding the system's ability to capture intricate relationships between users and items, leading to suboptimal recommendations. To address this, we propose a novel approach to efficiently learn meta-embeddings with varying grained and apply fine-grained meta-embeddings to strengthen the representation of their coarse-grained counterparts. Specifically, we introduce a recommender system based on a graph neural network, where each user and item is represented as a node. These nodes are directly connected to coarse-grained virtual nodes and indirectly linked to fine-grained virtual nodes, facilitating learning of multi-grained semantics. Fine-grained semantics are captured through sparse meta-embeddings, which dynamically balance embedding uniqueness and memory constraints. To ensure their sparseness, we rely on initialization methods such as sparse principal component analysis combined with a soft thresholding activation function. Moreover, we propose a weight-bridging update strategy that aligns coarse-grained meta-embedding with several fine-grained meta-embeddings based on the underlying semantic properties of users and items. Comprehensive experiments demonstrate that our method outperforms existing baselines. The code of our proposal is available at https://***/htyjers/C2F-MetaEmbed.

关键词： lightweight meta-embedding coarse-to-fine learning ID-based recommendations

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Intelligent integrated sensing and communication:a survey

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Science China(information Sciences) 2025年第3期68卷 5-46页

作者： Jifa ZHANG Weidang LU Chengwen XING Nan ZHAO Naofal AL-DHAHIR George K.KARAGIANNIDIS Xiaoniu YANG School of Information and Communication Engineering Dalian University of Technology College of Information Engineering Zhejiang University of Technology School of Information and Electronics Beijing Institute of Technology Department of Electrical and Computer Engineering The University of Texas at Dallas Department of Electrical and Computer Engineering Aristotle University of Thessaloniki

Integrated sensing and communication (ISAC) is a promising technique to increase spectral efficiency and support various emerging applications by sharing the spectrum and hardware between these functionalities. However, the traditional ISAC schemes are highly dependent on the accurate mathematical model and suffer from the challenges of high complexity and poor performance in practical scenarios. Recently, artificial intelligence (AI) has emerged as a viable technique to address these issues due to its powerful learning capabilities, satisfactory generalization capability, fast inference speed, and high adaptability for dynamic environments, facilitating a system design shift from model-driven to data-driven. Intelligent ISAC, which integrates AI into ISAC, has been a hot topic that has attracted many researchers to investigate. In this paper, we provide a comprehensive overview of intelligent ISAC, including its motivation, typical applications, recent trends, and challenges. In particular, we first introduce the basic principle of ISAC, followed by its key techniques. Then, an overview of AI and a comparison between model-based and AI-based methods for ISAC are provided. Furthermore, the typical applications of AI in ISAC and the recent trends for AI-enabled ISAC are reviewed. Finally, the future research issues and challenges of intelligent ISAC are discussed.

关键词： artificial intelligence deep learning deep reinforcement learning federated learning generative artificial intelligence integrated sensing and communication machine learning transfer learning

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Privacy-preserving explainable AI: a survey

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Science China(information Sciences) 2025年第1期68卷 23-56页

作者： Thanh Tam NGUYEN Thanh Trung HUYNH Zhao REN Thanh Toan NGUYEN Phi Le NGUYEN Hongzhi YIN Quoc Viet Hung NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Mathematics and Computer Science University of Bremen Faculty of Information Technology HUTECH University Department of Computer Science Hanoi University of Science and Technology School of Electrical Engineering and Computer Science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

关键词： privacy-preserving explainable AI privacy attacks privacy defences PrivEx PPXAI

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Measuring discrete sensing capability for ISAC via task mutual information

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Science China(information Sciences) 2025年第5期68卷 287-288页

作者： Fei SHANG Haohua DU Panlong YANG Xin HE Jingjing WANG Xiang-Yang LI School of Computer Science and Technology University of Science and Technology of China School of Cyber Science and Technology Beihang University School of Computer Science Nanjing University of Information Science and Technology School of Computer and Information Anhui Normal University

Thanks to its ubiquity,using radio frequency (RF) signals for sensing has found widespread *** traditional integrated sensing and communication systems,such as joint radar-communication systems,common sensing tasks include target localization and ***,increasingly intelligent systems,such as smart agriculture,lowaltitude economy,and smart healthcare,have demanded more comprehensive and continuous information sensing capabilities to support higher-level *** sensing has the potential to offer both spatial and temporal continuity,meeting the multi-dimensional sensing needs of these intelligent ***,numerous advanced systems have been proposed,expanding the application scope of RF sensing to be more pervasive,including discrete state ubiquitous sensing tasks (such as material identification [1]),and continuous state ubiquitous sensing tasks (such as health monitoring [2]).With the advent of the 6G era,it is anticipated that the sensing potential of RF systems will be further unleashed.

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Multi-label, Classification-based Prediction of Breast Cancer Metastasis Directions

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2025年第1期52卷 1-10页

作者： Wang, Tingting Fan, Qi Tan, Liang Zhang, Beier School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China School of Computer and Software Engineering Anhui Institute of Information Technology China School of Computer Science and Technology Huaibei Normal University China

Predicting the metastatic direction of primary breast cancer (BC), thus assisting physicians in precise treatment, strict follow-up, and effectively improving the prognosis. The clinical data of 293,946 patients with primary BC diagnosed between 2010 and 2015 were collected from the Surveillance, Epidemiology, and End Results database. Multiple interpolations and Multi-label Synthetic Minority Over-sampling Technique methods were used for data analysis, and machine learning model was established for multi-label classification. Finally, Surgical information, lymph node status, distant metastasis, tumor size, chemotherapy, histological type, and radiotherapy had significant influence as inputs. Compared with the k-nearest neighbor model, average accuracies of the decision tree and random forest (RF) models increased from 88.84% to 93.59% and 94.14%, respectively. Their average precision, recall rate, F1 score, area under the receiver operating characteristic curve and weighted-F1 increased from 87.24% to 95.85% and 94.74%, 87.73% to 90.40% and 91.76%, 87.07% to 92.16% and 93.45%, 97.11% to 99.53% and 99.95%, 82.13% to 89.44% and 90.48%, respectively. In conclusion, the RF model, which showed the best performance, can be used in multi-label prediction of BC metastasis directions, and can assist physicians in diagnosing and treating patients with primary BC. © (2025), (International Association of Engineers). All rights reserved.

关键词： Lung cancer

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On-device diagnostic recommendation with heterogeneous federated BlockNets

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Science China(information Sciences) 2025年第4期68卷 33-49页

作者： Minh Hieu NGUYEN Thanh Trung HUYNH Thanh Toan NGUYEN Phi Le NGUYEN Hien Thu PHAM Jun JO Thanh Tam NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Information Technology HUTECH University School of Information and Communication Technology Hanoi University of Science and Technology Commonwealth Scientific and Industrial Research Organization (CSIRO)

The evolution of edge computing has advanced the accessibility of E-health recommendation services, encompassing areas such as medical consultations, prescription guidance, and diagnostic assessments. Traditional methodologies predominantly utilize centralized recommendations, relying on servers to store client data and dispatch advice to ***, these conventional approaches raise significant concerns regarding data privacy and often result in computational inefficiencies. E-health recommendation services, distinct from other recommendation domains, demand not only precise and swift analyses but also a stringent adherence to privacy safeguards, given the users' reluctance to disclose their identities or health information. In response to these challenges, we explore a new paradigm called on-device recommendation tailored to E-health diagnostics, where diagnostic support(such as biomedical image diagnostics), is computed at the client *** leverage the advances of federated learning to deploy deep learning models capable of delivering expert-level diagnostic suggestions on clients. However, existing federated learning frameworks often deploy a singular model across all edge devices, overlooking their heterogeneous computational capabilities. In this work, we propose an adaptive federated learning framework utilizing BlockNets, a modular design rooted in the layers of deep neural networks, for diagnostic recommendation across heterogeneous devices. Our framework offers the flexibility for users to adjust local model configurations according to their device's computational power. To further handle the capacity skewness of edge devices, we develop a data-free knowledge distillation mechanism to ensure synchronized parameters of local models with the global model, enhancing the overall accuracy. Through comprehensive experiments across five real-world datasets, against six baseline models, within six experimental setups, and various data distribution scenario

关键词： intelligent recommendation federated learning heterogeneous devices E-health diagnostics

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