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arXiv 2024年

作者： Wang, Jintao Zhou, Binggui Ma, Chengzhi Gong, Shiqi Yang, Guanghua Ma, Shaodan The State Key Laboratory of Internet of Things for Smart City The Department of Electrical and Computer Engineering University of Macau 999078 China The School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China The School of Intelligent Systems Science and Engineering Jinan University Zhuhai519070 China

In this paper, we propose a dynamic hybrid active-passive reconfigurable intelligent surface (HRIS) to enhance multiple-input-single-output (MISO) communications, leveraging the property of dynamically placing active elements. Specifically, considering the impact of hardware impairments (HWIs), we investigate channel-aware configurations of the receive antennas at the base station (BS) and the active/passive elements at the HRIS to improve transmission reliability. To this end, we address the average mean-square-error (MSE) minimization problem for the HRIS-aided MISO system by jointly optimizing the BS receive antenna selection matrix, the reflection phase coefficients, the reflection amplitude matrix, and the mode selection matrix of the HRIS. To overcome the non-convexity and intractability of this problem, we first transform the binary and discrete variables into continuous ones, and then propose a penalty-based exact block coordinate descent (PEBCD) algorithm to alternately solve these subproblems. Numerical simulations demonstrate the significant superiority of our proposed scheme over conventional benchmark schemes. Copyright © 2024, The Authors. All rights reserved.

关键词： Mean square error

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Oblivious Encrypted Keyword Search with Fine-Grained Access Control for Cloud Storage

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5521-5533页

作者： Tong, Qiuyun Deng, Junyi Li, Xinghua Miao, Yinbin Wang, Yunwei Deng, Robert H. Xidian University State Key Laboratory of Integrated Services Networks and the School of Cyber Engineering Xi’an710071 China Hefei University of Technology School of Computer Science and Information Engineering Hefei230601 China Xidian University State Key Laboratory of Integrated Service Networks School of Cyber Engineering Xi’an710071 China Ministry of Education Engineering Research Center of Big data Security Xi’an710071 China Singapore Management University School of Information Systems Singapore

With the rapid expansion of data volumes in cloud computing, more data owners are opting to outsource their data to cloud service providers to reduce local storage and management costs. However, data outsourcing deprives data owners of direct physical control over their data, increasing the risk of unauthorized access and exposure of sensitive information. To mitigate these risks, various privacy-preserving keyword search schemes with access control have been developed, but many are vulnerable to leakage-abuse attacks due to the exposure of access, search or volume patterns, which can lead to privacy breaches in outsourced data and queries. To solve this problem, we propose an oblivious encrypted keyword search scheme with fine-grained access control, called OEKA. It enables efficient oblivious keyword search over encrypted multi-maps by using the adapted XOR filter and distributed point function, ensuring protection of access, search and volume patterns. Moreover, OEKA enforces role-based access control by using polynomial-based access strategy and keyword-based private information retrieval, allowing access policies of retrieved objects to be detecting without revealing the objects themselves. A formal security analysis verifies the scheme’s robustness, and experimental results demonstrate its practical efficiency. 2005-2012 IEEE.

关键词： Authorization

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Spatio-temporal load shifting for truly clean computing

arXiv

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arXiv 2024年

作者： Riepin, Iegor Brown, Tom Zavala, Victor M. Department of Digital Transformation in Energy Systems TU Berlin Germany Department of Chemical and Biological Engineering University of Wisconsin-Madison United States Mathematics and Computer Science Division Argonne National Laboratory United States

Companies with datacenters are procuring significant amounts of renewable energy to reduce their carbon footprint. There is increasing interest in achieving 24/7 Carbon-Free Energy (CFE) matching in electricity usage, aiming to eliminate all carbon footprints associated with electricity consumption on an hourly basis. However, the variability of renewable energy resources poses significant challenges for achieving this goal. We explore the impact of shifting computing jobs and associated power loads both in time and between datacenter locations. We develop an optimization model to simulate a network of geographically distributed datacenters managed by a company leveraging spatio-temporal load flexibility to achieve 24/7 CFE matching. We isolate three signals relevant for informed use of load flexiblity: varying average quality of renewable energy resources, low correlation between wind power generation over long distances due to different weather conditions, and lags in solar radiation peak due to Earth's rotation. We illustrate that the location of datacenters and the time of year affect which signal drives an effective load-shaping strategy. The energy procurement and load-shifting decisions based on informed use of these signals facilitate the resource-efficiency and cost-effectiveness of clean computing-the costs of 24/7 CFE are reduced by 1.29±0.07 e/MWh for every additional percentage of flexible load. We provide practical guidelines on how companies with datacenters can leverage spatio-temporal load flexibility for truly clean computing. Our results and the open-source optimization model can also be useful for a broader variety of companies with flexible loads and an interest in eliminating their carbon *** Codes 90-10, 91-10 © 2024, CC BY.

关键词： Carbon footprint

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Deep Learning-Based Prediction of Subsurface Oil Reservoir Pressure Using Spatio-Temporal Data 49

Deep Learning-Based Prediction of Subsurface Oil Reservoir P...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Cheng, Haibo He, Yunpeng Zeng, Peng Li, Shichao Vyatkin, Valeriy Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350331820

Prediction of subsurface oil reservoir pressure are critical to hydrocarbon production. However, the accurate pressure estimation faces great challenges due to the complexity and uncertainty of reservoir. The underground seepage flow and petrophysical parameters (permeability and porosity) are important but difficult to measure in oilfield. Deep learning methods have been successfully used in reservoir engineering and oil & gas production process. In this study, the effective but inaccessible subsurface seepage fields are not used, only the spatial coordinates and temporal information are selected as model input to predict reservoir pressure. A stacked GRU-based deep learning model is proposed to map the relationship between spatio-temporal data and reservoir pressure. The proposed deep learning method is verified by using a three-dimensional reservoir model, and compared with commonly-used methods. The results show that the stacked GRU model has a better performance and higher accuracy than other deep learning or machine learning methods in pressure prediction. © 2023 IEEE.

关键词： Forecasting

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Prophet: Prompting Large Language Models with Complementary Answer Heuristics for Knowledge-based Visual Question Answering

arXiv

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arXiv 2023年

作者： Yu, Zhou Ouyang, Xuecheng Shao, Zhenwei Wang, Meng Yu, Jun The Key Laboratory of Complex Systems Modeling and Simulation the School of Computer Science Hangzhou Dianzi University China The HDU-ITMO Joint Institute Hangzhou Dianzi University China The School of Computer Science and Information Engineering Hefei University of Technology China

Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. Early studies retrieve required knowledge from explicit knowledge bases (KBs), which often introduces irrelevant information to the question, hence restricting the performance of their models. Recent works have resorted to using a powerful large language model (LLM) as an implicit knowledge engine to acquire the necessary knowledge for answering. Despite the encouraging results achieved by these methods, we argue that they have not fully activated the capacity of the blind LLM as the provided textual input is insufficient to depict the required visual information to answer the question. In this paper, we present Prophet—a conceptually simple, flexible, and general framework designed to prompt LLM with answer heuristics for knowledge-based VQA. Specifically, we first train a vanilla VQA model on a specific knowledge-based VQA dataset without external knowledge. After that, we extract two types of complementary answer heuristics from the VQA model: answer candidates and answer-aware examples. Finally, the two types of answer heuristics are jointly encoded into a formatted prompt to facilitate the LLM’s understanding of both the image and question, thus generating a more accurate answer. By incorporating the state-of-the-art LLM GPT-3 [1], Prophet significantly outperforms existing state-of-the-art methods on four challenging knowledge-based VQA datasets. To demonstrate the generality of our approach, we instantiate Prophet with the combinations of different VQA models (i.e., both discriminative and generative ones) and different LLMs (i.e., both commercial and open-source ones). © 2023, CC BY.

关键词： Knowledge based systems

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Multiple Redundant K-Means Clustered Scheme Based on Weighted Cluster Head Selection for Damaged S-UAV

Multiple Redundant K-Means Clustered Scheme Based on Weighte...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Grace Khayat Constandinos X Mavromoustakis Andreas Pitsillides Jordi Mongay Batalla Evangelos K. Markakis Department of Computer Science Mobile Systems Laboratory (MoSys Lab) University of Nicosia Nicosia Cyprus Department of Computer Science Cyprus and Department of Electrical & Electronic Engineering Science University of Cyprus University of Johannesburg (Visiting Professor) Warsaw University of Technology Warsaw Poland Department of Electrical and Computer Engineering Hellenic Mediteranean University Estavromenos Heraklion Greece

A swarm of unmanned aerial vehicles (S-UAVs) consists of UAVs flying together with the target of accomplishing a certain task in a faster and more reliable way as compared to a single UAV. In a crisis scenario, UAVs have been widely used in rescue missions. Clustering is one of the most reliable routing schemes for S-UAVs. The UAVs are grouped into clusters with a cluster-head (CH) and cluster-members (CM). The CH plays a major role in clustering schemes as it handles all inter-cluster communication. In a crisis case, any UAV is at risk of getting non-functional, thus resulting in a disconnected cluster. This paper proposes a new clustering scheme based on K-means and weighted formulas. The K-means protocol is applied to generate pilot phase clusters. Afterward, whenever the metrics of the networks are established, the weighted formula is applied for cluster formation and CH selection. The weighted formula is based on the performance index, the relative movement, and the remaining energy. To ensure end-to-end communication despite CH non-functionality, our proposed protocol selects a redundant CH for every CH. This protocol had been simulated using MATLAB. The results obtained and analyzed towards the end of this paper demonstrate that the proposed scheme is very promising.

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A Toolkit to Generate Social Navigation Datasets 21st

A Toolkit to Generate Social Navigation Datasets

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21st International Workshop of Physical Agents, WAF 2020

作者： Baghel, Rishabh Kapoor, Aditya Bachiller, Pilar Jorvekar, Ronit R. Rodriguez-Criado, Daniel Manso, Luis J. Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Guwahati India Department of Computer Science and Information Systems Birla Institute of Technology and Science Goa Pilani India Robotics and Artificial Vision Laboratory University of Extremadura Badajoz Spain Department of Computer Engineering Pune Institute of Computer Technology Pune India Department of Computer Science College of Engineering and Physical Sciences Aston University Birmingham United Kingdom

ISBN: (纸本)9783030625788

Social navigation datasets are necessary to assess social navigation algorithms and train machine learning algorithms. Most of the currently available datasets target pedestrians’ movements as a pattern to be replicated by robots. It can be argued that one of the main reasons for this to happen is that compiling datasets where real robots are manually controlled, as they would be expected to behave when moving, is a very resource-intensive task. Another aspect that is often missing in datasets is symbolic information that could be relevant, such as human activities, relationships or interactions. Unfortunately, the available datasets targeting robots and supporting symbolic information are restricted to static scenes. This paper argues that simulation can be used to gather social navigation data in an effective and cost-efficient way and presents a toolkit for this purpose. A use case studying the application of graph neural networks to create learned control policies using supervised learning is presented as an example of how it can be used. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Navigation

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GCFormer: Granger Causality based Attention Mechanism for Multivariate Time Series Anomaly Detection

GCFormer: Granger Causality based Attention Mechanism for Mu...

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IEEE International Conference on Data Mining (ICDM)

作者： Shiwang Xing Jianwei Niu Tao Ren State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Zhongguancun Laboratory Beijing China Laboratory for Internet Software Technologies Institute of Software Chinese Academy of Sciences China

Multivariate time series anomaly detection, crucial for ensuring the safety of real-world systems, primarily focuses on extracting characteristics from time series under normal condition, and identifying potential anomalies throughout the evaluation process. Recent studies have achieved fruitful progress through mining the spatio-temporal relationships from multivariate time series, however, these approaches mostly neglect the latency among series which could lead to higher false alarm. Granger causality presents a promising solution to extract these inherent time-lagged relationships. Nonetheless, the intricate and dynamic relationships among numerous time series in real-world systems surpass the ability of linear Granger causality. To address this, we extend the linear Granger causality and propose the Granger Causal Former (GCFormer), a novel approach that leverages attention mechanisms to learn the inherent causal spatio-temporal relationships between historical and current timestamps across multiple time series. Specifically, GCFormer develops a Spatio-Mask (SM) to select the top-k most relevant series and a Temporal-Mask (TM) to concentrate attention on more recent historical timestamps. Moreover, to mitigate overfitting and ensure a smooth training process, GCFormer introduces an adjust top-k method and a TM penalty term. We evaluated GCFormer on four real-world benchmark datasets, demonstrating its superior performance over state-of-the-art approaches. Further analysis and a case study highlight the model’s novelty and interpretability.

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Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection

arXiv

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arXiv 2024年

作者： Liu, Xinyue Wang, Jianyuan Leng, Biao Zhang, Shuo School of Computer Science and Engineering Beihang University Beijing China Key Laboratory of Intelligent Bionic Unmanned Systems Ministry of Education School of Intelligence Science and Technology University of Science and Technology Beijing Beijing China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer & Technology Beijing Jiaotong University Beijing China

Knowledge distillation based on student-teacher network is one of the mainstream solution paradigms for the challenging unsupervised Anomaly Detection task, utilizing the difference in representation capabilities of the teacher and student networks to implement anomaly localization. However, over-generalization of the student network to the teacher network may lead to negligible differences in representation capabilities of anomaly, thus affecting the detection effectiveness. Existing methods address the possible over-generalization by using differentiated students and teachers from the structural perspective or explicitly expanding distilled information from the content perspective, which inevitably results in an increased likelihood of underfitting of the student network and poor anomaly detection capabilities in anomaly center or edge. In this paper, we propose Dual-Modeling Decouple Distillation (DMDD) for the unsupervised Anomaly Detection. In DMDD, a Decouple Student-Teacher Network is proposed to decouple the initial student features into normality and abnormality features. We further introduce Dual-Modeling Distillation based on normal-anomalous image pairs, fitting normality features of anomalous image and the teacher features of the corresponding normal image, widening the distance between abnormality features and the teacher features in anomalous regions. Synthesizing these two distillation ideas, we achieve anomaly detection which focuses on both edge and center of anomaly. Finally, a Multi-perception Segmentation Network is proposed to achieve focused anomaly map fusion based on multiple attention. Experimental results on MVTec AD show that DMDD surpasses SOTA localization performance of previous knowledge distillation-based methods, reaching 98.85% on pixel-level AUC and 96.13% on PRO. Copyright © 2024, The Authors. All rights reserved.

关键词： Students

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DMINet: A lightweight dual-mixed channel-independent network for cataract recognition

DMINet: A lightweight dual-mixed channel-independent network...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xiao Wu Yu Chen Qiuyang Yan Yuhang Zhao Jilu Zhao Xiaoqing Zhang Risa Higashita Jiang Liu Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Tomey Corporation Nagoya Japan Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

Cataracts are the leading cause of visual impairment and blindness globally attracting abroad attention from society. Over the years researchers have developed many state-of-the-art convolutional neural networks (CNNs) to recognize cataract severity levels based on different ophthalmic images. However most current works focus on improving cataract recognition performance by designing complex CNNs often ignoring resource-constrained medical device limitations. To this problem this paper proposes a novel dual-mixed channel-independent convolution (DMIConv) method which takes advantage of the multiscale convolution kernels by combining a depthwise convolution with a depthwise dilated convolution sequentially. Moreover we build a lightweight dual-mixed channel-independent network (DMINet) to recognize cataracts. To verify the effectiveness and efficiency of DMINet we conduct extensive experiments on a clinical anterior segment optical coherence tomography (AS-OCT) dataset of nuclear cataract (NC) and a publicly available OCT dataset. The results show that our proposed DMINet keeps a better tradeoff between the model complexity and the classification performance than efficient CNNs e.g DMINet outperforms MixNet by 3.34% of accuracy by using 4.58 % fewer parameters

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