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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Ying Luo Xudong Luo Guibin Chen Guangxi Key Lab of Multi-source Information Mining and Security Guangxi Key Lab of Education Blockchain and Intelligent Technology of Ministry of Education School of Computer Science and Engineering Guangxi Normal University Guilin China

ISBN: (数字)9798331527235

ISBN: (纸本)9798331527242

This paper presents a novel multi-choice question answering model tailored for the legal domain, which integrates a DeBERTa-based framework with bilinear attention mechanisms. The model is designed to enhance the interpretation of complex legal texts, utilising vector similarity searches to align questions with pertinent statutes and a neural network to analyse this data effectively. It aims to improve the accuracy of legal question answering by employing advanced techniques of natural language processing. A binary classifier is also incorporated to assess answer options and generate conclusive responses. Experimental results demonstrate the model's superiority over traditional methods, particularly in handling intricate legal questions. This study underscores the model's potential to advance legal AI technology significantly, offering substantial benefits for judicial examinations and legal professionals.

关键词： Analytical models Accuracy Attention mechanisms Law Transforms Question answering (information retrieval) Vectors Security Reliability Artificial intelligence

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BuildEnVR: An Immersive Analysis System for Environmental Field

BuildEnVR: An Immersive Analysis System for Environmental Fi...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Zhenghan Zhou Kebin Liu Yantong Xie Hangxu Jin Shi Liu Ruiqing Wang Haitian Zhao Borong Lin Xiaofang Mu Hui Qi Global Innovation Exchange Tsinghua University Fuzhou Fuyao Institute for Advanced Study School of Architecture Carnegie Mellon University School of Architecture Tsinghua University College of Computer Science and Technology Taiyuan Normal University Shanxi Key Laboratory of Intelligent Optimization Computing and Blockchain Technology

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Amidst global warming and escalating extreme weather events, indoor environmental quality’s impact on human health and public hygiene gains prominence. Environmental parameters exist essentially as fields, which are characterized by high dimensionality, density and complexity, and contain massive amounts of information in space. To facilitate visualization and analysis of indoor environmental field, we design and implement BuildEnVR, an immersive analysis system by virtual reality, enabling remote analysis of real-time and historical environmental field data. Grounded in user needs and cognitive psychology, three visualization modes emerge: the Virtual Sensor mode enables users to access perceptual data in real-time at any 3D coordinates in ambient space, the 4D Heatmap mode visualizes spatial variations and trends over time in environmental field data, and the Synaesthesia mode realizes the fusion display of multi-dimensional environmental field data, allowing users to quickly understand the overall condition of the indoor environment with a low cognitive load. Extensive user surveys validate BuildEnVR’s intuitiveness and precision, and it is suitable for both experts and general users.

关键词： Wireless communication Wireless sensor networks Three-dimensional displays Buildings Psychology Data visualization Cognitive load Real-time systems Space heating Meteorology Climate change Global warming Environmental factors Immersive learning

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Leveling Up Mental Well-Being: Harnessing the Power of Gamification Using Machine Learning

Leveling Up Mental Well-Being: Harnessing the Power of Gamif...

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Student Conference on Research and Development (SCOReD)

作者： Mohammad Hadi Ahmadi Milaghardan Hemalata Vasudavan WeiWei Goh Sumathi Balakrishnan School of Computer Science Digital Health and Innovation Impact Lab Taylor’s University Selangor Malaysia School of Technology Asia Pacific University of Technology & Innovation Kuala Lumpur Malaysia

ISBN: (数字)9798350318821

ISBN: (纸本)9798350318838

Disorders, stress-related conditions, and mental health problems are major issues in our societies, often difficult to handle by individuals. Meanwhile, there is a necessary need for solutions to related issues. Solutions that can facilitate the development of positive mental well-being habits and provide environments to users looking for help. This paper aims to explore and examine potential solutions by employing a deliberate framework known as “The Wheel of Sukr”. This framework serves as a guide for the developmental process, directing it toward the objectives outlined in this paper and the creation of a prototype application. The prototype leverages machine learning and gamification features to predict user needs in terms of mental health issues and create an immersive daily engagement environment. A prototype is developed with features such as mood tracking, avatar companions, and monthly reports to create awareness of mental health and well-being practices tailored to users’ personalities. Nevertheless, we recognize specific challenges and constraints, particularly the lack of adequate atomization. These obstacles can be systematically addressed and improved through subsequent comprehensive studies and ongoing research endeavors in the future.

关键词： Mood Avatars Prototypes Wheels Virtual environments Mental health Machine learning

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ScanFormer: Referring Expression Comprehension by Iteratively Scanning

arXiv

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arXiv 2024年

作者： Su, Wei Miao, Peihan Dou, Huanzhang Li, Xi College of Computer Science and Technology Zhejiang University China School of Software Technology Zhejiang University China Zhejiang-Singapore Innovation and Al Joint Research Lab China

Referring Expression Comprehension (REC) aims to localize the target objects specified by free-form natural language descriptions in images. While state-of-the-art methods achieve impressive performance, they perform a dense perception of images, which incorporates redundant visual regions unrelated to linguistic queries, leading to additional computational overhead. This inspires us to explore a question: can we eliminate linguistic-irrelevant redundant visual regions to improve the efficiency of the model? Existing relevant methods primarily focus on fundamental visual tasks, with limited exploration in vision-language fields. To address this, we propose a coarse-to-fine iterative perception framework, called ScanFormer. It can iteratively exploit the image scale pyramid to extract linguistic-relevant visual patches from top to bottom. In each iteration, irrelevant patches are discarded by our designed informativeness prediction. Furthermore, we propose a patch selection strategy for discarded patches to accelerate inference. Experiments on widely used datasets, namely RefCOCO, RefCOCO+, RefCOCOg, and ReferItGame, verify the effectiveness of our method, which can strike a balance between accuracy and efficiency. © 2024, CC BY.

关键词： Iterative methods

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Hyperbolic geometric latent diffusion model for graph generation 24

Hyperbolic geometric latent diffusion model for graph genera...

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Proceedings of the 41st International Conference on Machine Learning

作者： Xingcheng Fu Yisen Gao Yuecen Wei Qingyun Sun Hao Peng Jianxin Li Xianxian Li Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Institute of Artificial Intelligence Beihang University Beijing China and Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China School of Software Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China

Diffusion models have made significant contributions to computer vision, sparking a growing interest in the community recently regarding the application of them to graph generation. Existing discrete graph diffusion models exhibit heightened computational complexity and diminished training efficiency. A preferable and natural way is to directly diffuse the graph within the latent space. However, due to the non-Euclidean structure of graphs is not isotropic in the latent space, the existing latent diffusion models effectively make it difficult to capture and preserve the topological information of graphs. To address the above challenges, we propose a novel geometrically latent diffusion framework HypDiff. Specifically, we first establish a geometrically latent space with interpretability measures based on hyperbolic geometry, to define anisotropic latent diffusion processes for graphs. Then, we propose a geometrically latent diffusion process that is constrained by both radial and angular geometric properties, thereby ensuring the preservation of the original topological properties in the generative graphs. Extensive experimental results demonstrate the superior effectiveness of HypDiff for graph generation with various topologies.

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Fusing Local and Global Mobility Patterns for Trajectory Recovery 28th

Fusing Local and Global Mobility Patterns for Trajectory R...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Deng, Liwei Zhao, Yan Sun, Hao Yang, Changjie Xie, Jiandong Zheng, Kai University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Chengdu China Aalborg University Aalborg Denmark Peking University Beijing China Cloud Database Innovation Lab of Cloud BU Huawei Technologies Co. Ltd. Chengdu China

ISBN: (纸本)9783031306365

The prevalence of various mobile devices translates into a proliferation of trajectory data that enables a broad range of applications such as POI recommendation, tour recommendation, urban transportation planning, and the next location prediction. However, trajectory data in real-world is often sparse, noisy and incomplete. To make the subsequent analysis more reliable, trajectory recovery is introduced as a pre-processing step and attracts increasing attention recently. In this paper, we propose a neural attention model based on graph convolutional networks, called TRILL, to enhance the accuracy of trajectory recovery. In particular, to capture global mobility patterns that reflect inherent spatio-temporal regularity in human mobilities, we construct a directed global location transition graph and model the mobility patterns of all the trajectories at point level using graph convolutional networks. Then, a self-attention layer and a window-based cross-attention layer are sequentially adopted to refine the representations of missing locations by considering intra-trajectory and inter-trajectory information, respectively. Meanwhile, an information aggregation layer is designed to leverage all the historical information. We conduct extensive experiments using real trajectory data, which verifies the superior performance of the proposed model. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Trajectories

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Scalable Reinforcement Learning for Virtual Machine Scheduling

arXiv

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arXiv 2025年

作者： Sheng, Junjie Wu, Jiehao Cui, Haochuan Hu, Yiqiu Zhou, Wenli Zhu, Lei Peng, Qian Li, Wenhao Wang, Xiangfeng Algorithm Innovation Lab Huawei Cloud Huawei Technologies Co. Ltd China School of Computer Science and Technology East China Normal University China Alkaid Lab Huawei Cloud Huawei Technologies Co. Ltd China School of Computer Science and Technology Tongji University China

Recent advancements in reinforcement learning (RL) have shown promise for optimizing virtual machine scheduling (VMS) in small-scale clusters. The utilization of RL to large-scale cloud computing scenarios remains notably constrained. This paper introduces a scalable RL framework, called Cluster Value Decomposition Reinforcement Learning (CVD-RL), to surmount the scalability hurdles inherent in large-scale VMS. The CVD-RL framework innovatively combines a decomposition operator with a look-ahead operator to adeptly manage representation complexities, while complemented by a Top-k filter operator that refines exploration efficiency. Different from existing approaches limited to clusters of 10 or fewer physical machines (PMs), CVD-RL extends its applicability to environments encompassing up to 50 PMs. Furthermore, the CVD-RL framework demonstrates generalization capabilities that surpass contemporary SOTA methodologies across a variety of scenarios in empirical studies. This breakthrough not only showcases the framework’s exceptional scalability and performance but also represents a significant leap in the application of RL for VMS within complex, large-scale cloud infrastructures. The code is available at https://***/r/marl4sche-D0FE. © 2025, CC BY.

关键词： Reinforcement learning

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Scloud+: An Efficient LWE-Based KEM Without Ring/Module Structure 9th

Scloud+: An Efficient LWE-Based KEM Without Ring/Module Stru...

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9th International Conference on Security Standardisation Research, SSR 2024

作者： Wang, Anyu Zheng, Zhongxiang Zhao, Chunhuan Qiu, Zhiyuan Zeng, Guang Yuan, Ye Mu, Changchun Wang, Xiaoyun Institute for Advanced Study BNRist Tsinghua University Beijing China School of Computer and Cyber Sciences Communication University of China Beijing China Shield Lab Huawei Technologies Beijing China Shandong Institute of Blockchain Jinan China Digital Currency Institute the People’s Bank of China China Zhongguancun Laboratory Beijing China National Financial Cryptography Research Center Beijing China Key Laboratory of Cryptologic Technology and Information Security School of Cyber Science and Technology Shandong University Qingdao China

ISBN: (纸本)9783031875403

We present Scloud+, an LWE-based key encapsulation mechanism (KEM). The key feature of Scloud+ is its use of the unstructured-LWE problem (i.e., without algebraic structures such as rings or modules) and its incorporation of ternary secrets and lattice coding to enhance performance. A notable advantage of the unstructured-LWE problem is its resistance to potential attacks exploiting algebraic structures, making it a conservative choice for constructing high-security schemes. However, a key disadvantage of such schemes is their limited computational and communication efficiency. Scloud+ utilizes ternary secrets and BW32 lattice codes to enhance noise control and ensure robust error correction during decryption, enabling smaller parameters while maintaining low decryption failure probabilities. Equipped with these techniques, Scloud+ exhibits a significant improvement in efficiency. When compared with FrodoKEM for parameter sets targeting 128, 192, and 256 bits of security respectively, Scloud+ achieves practical performance with a public key size approximately 0.71∼0.87x and a ciphertext size approximately 0.56∼0.78x that of FrodoKEM. The encapsulation plus decapsulation time is approximately 0.74∼0.84x that of FrodoKEM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Quantum cryptography

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Alzheimer Disease Detection Empowered with Transfer Learning

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computers, Materials & Continua 2022年第3期70卷 5005-5019页

作者： Taher M.Ghazal Sagheer Abbas Sundus Munir M.A.Khan Munir Ahmad Ghassan F.Issa Syeda Binish Zahra Muhammad Adnan Khan Mohammad Kamrul Hasan Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan Malaysia(UKM)43600 BangiSelangorMalaysia School of Information Technology Skyline University CollegeUniversity City Sharjah1797SharjahUAE School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

Alzheimer’s disease is a severe neuron disease that damages brain cells which leads to permanent loss of memory also called *** people die due to this disease every year because this is not curable but early detection of this disease can help restrain the ***’s ismost common in elderly people in the age bracket of 65 and *** automated system is required for early detection of disease that can detect and classify the disease into multiple Alzheimer *** learning and machine learning techniques are used to solvemanymedical problems like *** proposed system Alzheimer Disease detection utilizes transfer learning on Multi-class classification using brain Medical resonance imagining(MRI)working to classify the images in four stages,Mild demented(MD),Moderate demented(MOD),Non-demented(ND),Very mild demented(VMD).Simulation results have shown that the proposed systemmodel gives 91.70%*** also observed that the proposed system gives more accurate results as compared to previous approaches.

关键词： Convolutional neural network(CNN) alzheimer’s disease(AD) medical resonance imagining mild demented

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blockchain-based Private Set Intersection Protocol with Attribute-based Access Control

Blockchain-based Private Set Intersection Protocol with Attr...

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International Conference on computer Supported Cooperative Work in Design

作者： Song Wei Limin Ma Zeyu He Wei Zhang Computer School Beijing Information Science and Technology University Beijing China Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing China Beijing Laboratory of National Economic Security Early-Warning Engineering Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the rise of cloud computing, many users outsource their data to be stored in cloud servers, enjoying the convenience while facing many security issues. In the field of secure multiparty computing, traditional privacy set intersection protocol data users use cloud servers for their data outsourcing to operate for calculating the intersection, the data is susceptible to tampering by the cloud servers, and the data users do not formulate fine-grained access control policies. To solve the above problems, this paper combines blockchain with privacy set intersection for the first time, and proposes a blockchain-based privacy set intersection protocol with attribute access control. This scheme utilizes blockchain instead of a cloud server as a trusted third party, data users utilize smart contracts to compute intersection via OPRF, and attribute encryption is used to provide access control for privacy set intersection protocol. Thus, it solves the risk of data vulnerability to tampering. This paper analyzes the security of the scheme and conducts simulation experiments.

关键词： Access control Cloud computing Data privacy Privacy Protocols Smart contracts blockchains

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