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arXiv 2024年

作者： Zi, Wei Wang, Siyi Kim, Hyunji Sun, Xiaoming Chattopadhyay, Anupam Rebentrost, Patrick State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore Hansung University Seoul Korea Republic of Centre for Quantum Technologies National University of Singapore Singapore School of Computing National University of Singapore Singapore

In recent years, Quantum Machine Learning (QML) has increasingly captured the interest of researchers. Among the components in this domain, activation functions hold a fundamental and indispensable role. Our research focuses on the development of activation functions quantum circuits for integration into fault-tolerant quantum computing architectures, with an emphasis on minimizing T-depth. Specifically, we present novel implementations of ReLU and leaky ReLU activation functions, achieving constant T-depths of 4 and 8, respectively. Leveraging quantum lookup tables, we extend our exploration to other activation functions such as the sigmoid. This approach enables us to customize precision and T-depth by adjusting the number of qubits, making our results more adaptable to various application scenarios. This study represents a significant advancement towards enhancing the practicality and application of quantum machine learning. Copyright © 2024, The Authors. All rights reserved.

关键词： Table lookup

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All Grains, One Scheme (AGOS): Learning Multi-grain Instance Representation for Aerial Scene Classification

arXiv

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arXiv 2022年

作者： Bi, Qi Zhou, Beichen Qin, Kun Ye, Qinghao Xia, Gui-Song School of Remote Sensing and Information Engineering Wuhan University Wuhan China University of California San Diego United States The National Engineering Research Center for Multimedia Software School of Computer Science Institute of Artificial Intelligence China The State Key Lab. LIESMARS Wuhan University Wuhan China

Aerial scene classification remains challenging as: 1) the size of key objects in determining the scene scheme varies greatly;2) many objects irrelevant to the scene scheme are often flooded in the image. Hence, how to effectively perceive the region of interests (RoIs) from a variety of sizes and build more discriminative representation from such complicated object distribution is vital to understand an aerial scene. In this paper, we propose a novel all grains, one scheme (AGOS) framework to tackle these challenges. To the best of our knowledge, it is the first work to extend the classic multiple instance learning into multi-grain formulation. Specially, it consists of a multi-grain perception module (MGP), a multi-branch multi-instance representation module (MBMIR) and a self-aligned semantic fusion (SSF) module. Firstly, our MGP preserves the differential dilated convolutional features from the backbone, which magnifies the discriminative information from multi-grains. Then, our MBMIR highlights the key instances in the multi-grain representation under the MIL formulation. Finally, our SSF allows our framework to learn the same scene scheme from multi-grain instance representations and fuses them, so that the entire framework is optimized as a whole. Notably, our AGOS is flexible and can be easily adapted to existing CNNs in a plug-and-play manner. Extensive experiments on UCM, AID and NWPU benchmarks demonstrate that our AGOS achieves a comparable performance against the state-of-the-art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Antennas

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Positive unlabeled learning with class-prior approximation 29

Positive unlabeled learning with class-prior approximation

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Chang, Shizhen Du, Bo Zhang, Liangpei School of Computer Science State Key Lab of Information Engineering on Survey Mapping and Remote Sensing Institute of Artificial Intelligence National Engineering Research Center for Multimedia Software Wuhan University China

ISBN: (纸本)9780999241165

The positive unlabeled (PU) learning aims to train a binary classifier from a set of positive labeled samples and other unlabeled samples. Much research has been done on this special branch of weakly supervised classification problems. Since only part of the positive class is labeled, the classical PU model trains the classifier assuming the class-prior is known. However, the true class prior is usually difficult to obtain and must be learned from the given data, and the traditional methods may not work. In this paper, we formulate a convex formulation to jointly solve the class-prior unknown problem and train an accurate classifier with no need of any class-prior assumptions or additional negative samples. The class prior is estimated by pursuing the optimal solution of gradient thresholding and the classifier is simultaneously trained by performing empirical unbiased risk. The detailed derivation and theoretical analysis of the proposed model are outlined, and a comparison of our experiments with other representative methods prove the superiority of our method. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Risk perception

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The diversity and ecological significance of microbial traits potentially involved in B_(12) biosynthesis in the global ocean

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mLife 2023年第4期2卷 416-427页

作者： Jiayin Zhou Wei Qin Xinda Lu Yunfeng Yang David Stahl Nianzhi Jiao Jizhong Zhou Jihua Liu Qichao Tu Institute of Marine Science and Technology Shandong UniversityQingdaoChina Joint Lab for Ocean Research and Education at Dalhousie University Shandong University and Xiamen UniversityQingdaoChina School of Biological Sciences University of OklahomaNormanOklahomaUSA Department of Civil and Environmental Engineering Massachusetts Institute of TechnologyCambridgeMassachusettsUSA State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentTsinghua UniversityBeijingChina Department of Civil and Environmental Engineering University of WashingtonSeattleWashingtonUSA Institute of Marine Microbes and Ecospheres Xiamen UniversityXiamenChina Earth and Environmental Sciences Lawrence Berkeley NationalLaboratoryBerkeley CaliforniaUSA Institutefr Environmental Genomics University of OklahomaNormanOklahomaUSA School of Civil Engineering and Environmental Sciences University of OklahomaNormanOklahomaUSA School of Computer Sciences University of OklahomaNormanOklahomaUSA DermBiont Inc. BostonMassachusettsUSA

Cobalamin(B_(12)),an essential nutrient and growth cofactor for many living organisms on Earth,can be fully synthesized only by selected prokaryotes in ***,microbial communities related to B_(12) biosynthesis could serve as an example subsystem to disentangle the underlying ecological mechanisms balancing the function and taxonomic make-up of complex functional *** anchoring microbial traits potentially involved in B_(12) biosynthesis,we depict the biogeographic patterns of B_(12) biosynthesis genes and the taxa harboring them in the global ocean,despite the limitations of detecting de novo B_(12) synthesizers via metagenomes *** the taxonomic and functional composition of B_(12) biosynthesis genes were strongly shaped by depth,differentiating the epipelagic zones from the mesopelagic *** genes related to B_(12) biosynthesis were relatively stably distributed across different oceans,but the taxa harboring them varied considerably,showing clear functional redundancy among microbial *** taxa carrying B_(12) biosynthesis genes in the surface water were influenced by environmental factors such as temperature,oxygen,and ***,the composition of functional genes was only weakly associated with these environmental *** model analyses demonstrated that determinism governed the variations in B_(12) biosynthesis genes,whereas a higher degree of stochasticity was associated with taxonomic *** associations were observed between the chlorophyll a concentration and B_(12) biosynthesis,confirming its importance in primary production in the global *** results of this study reveal an essential ecological mechanism governing the assembly of microbes in nature:the environment selects for function rather than taxonomy;functional redundancy underlies stochastic community assembly.

关键词： B_(12)biosynthesis community assembly functional genes functional redundancy ocean primary production

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Multi-Temporal Relationship Inference in Urban Areas

arXiv

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arXiv 2023年

作者： Li, Shuangli Zhou, Jingbo Liu, Ji Xu, Tong Chen, Enhong Xiong, Hui School of Computer Science and Technology University of Science and Technology of China Baidu Research China Business Intelligence Lab Baidu Research China Big Data Lab Baidu Research China School of Computer Science and Technology University of Science and Technology of China State Key Laboratory of Cognitive Intelligence China Anhui Province Key Laboratory of Big Data Analysis and Application University of Science and Technology of China State Key Laboratory of Cognitive Intelligence China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology China

Finding multiple temporal relationships among locations can benefit a bunch of urban applications, such as dynamic offline advertising and smart public transport planning. While some efforts have been made on finding static relationships among locations, little attention is focused on studying time-aware location relationships. Indeed, abundant location-based human activities are time-varying and the availability of these data enables a new paradigm for understanding the dynamic relationships in a period among connective locations. To this end, we propose to study a new problem, namely multi-Temporal relationship inference among locations (Trial for short), where the major challenge is how to integrate dynamic and geographical influence under the relationship sparsity constraint. Specifically, we propose a solution to Trial with a graph learning scheme, which includes a spatially evolving graph neural network (SEENet) with two collaborative components: spatially evolving graph convolution module (SEConv) and spatially evolving self-supervised learning strategy (SE-SSL). SEConv performs the intra-time aggregation and inter-time propagation to capture the multifaceted spatially evolving contexts from the view of location message passing. In addition, SE-SSL designs time-aware self-supervised learning tasks in a global-local manner with additional evolving constraint to enhance the location representation © 2023, CC BY.

关键词： Location

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Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency

arXiv

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arXiv 2023年

作者： Liu, Xiaogeng Li, Minghui Wang, Haoyu Hu, Shengshan Ye, Dengpan Jin, Hai Wu, Libing Xiao, Chaowei School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab China School of Cyber Science and Engineering Wuhan University China Arizona State University United States

Deep neural networks are proven to be vulnerable to backdoor attacks. Detecting the trigger samples during the inference stage, i.e., the test-time trigger sample detection, can prevent the backdoor from being triggered. However, existing detection methods often require the defenders to have high accessibility to victim models, extra clean data, or knowledge about the appearance of backdoor triggers, limiting their practicality. In this paper, we propose the test-time corruption robustness consistency evaluation (TeCo)1, a novel test-time trigger sample detection method that only needs the hard-label outputs of the victim models without any extra information. Our journey begins with the intriguing observation that the backdoor-infected models have similar performance across different image corruptions for the clean images, but perform discrepantly for the trigger samples. Based on this phenomenon, we design TeCo to evaluate test-time robustness consistency by calculating the deviation of severity that leads to predictions' transition across different corruptions. Extensive experiments demonstrate that compared with state-of-the-art defenses, which even require either certain information about the trigger types or accessibility of clean data, TeCo outperforms them on different backdoor attacks, datasets, and model architectures, enjoying a higher AUROC by 10% and 5 times of stability. Copyright © 2023, The Authors. All rights reserved.

关键词： Crime

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AIDE: Attack Inference Based on Heterogeneous Dependency Graphs with MITRE ATT&CK

AIDE: Attack Inference Based on Heterogeneous Dependency Gra...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weidong Zhou Chunhe Xia Nan Feng Xinyi Pan Tianbo Wang Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Software & Microelectronics Peking University Beijing China School of Cyber Science and Technology Beihang University Beijing China School of Computer Science and Engineering Guangxi Normal University Guilin China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

The MITRE ATT&CK Framework provides a rich and actionable repository of adversarial tactics, techniques, and procedures (TTPs). By leveraging the associations among these techniques, we can infer unobserved techniques based on those have been observed. Current machine learning inference methods based on small-scale data face the following problems: "logical dependencies missing" and "complex associations missing." The former means that current association methods rely solely on the statistical dependencies between attack techniques while ignoring the logical dependencies involved in executing those techniques. The latter indicates that current inference methods simplify the complex associations between technologies based on statistical assumptions, failing to adequately capture the long-range and intricate real-world relationships between techniques. To address these problems, we propose an Attack Inference model based on heterogeneous DEpendency graphs (AIDE). First, we extract three types of logical dependencies: CVE, Pattern, and Conditional, which allows us to construct a heterogeneous dependency graph among the techniques, thus addressing the "logical dependencies missing" problem. Second, we employ Relation Graph Convolution Neural Networks (RGCN) to aggregate heterogeneous dependencies into representation vectors of the techniques. Finally, we utilize the sequence inference model Decoder, which excels at capturing implicit contextual associations, to infer unknown attacks, thus addressing the "complex associations missing" problem. Experiments conducted on real datasets demonstrate that our model significantly outperforms state-of-the-art baseline models in both hits@k and map@k metrics.

关键词： Adaptation models Aggregates Refining Machine learning Vectors Threat assessment Real-time systems Decoding Security Context modeling

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A Many-Objective Evolutionary Algorithm Assisted by Ideal Hyperplane

SSRN

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SSRN 2022年

作者： Zhang, Zhixia Wen, Jie Wang, Hui Cai, Xingjuan Cui, Zhihua Zhang, Wensheng School of Computer Science and Technology Taiyuan University of Science and Technology China School of Information Engineering Nanchang Institute of Technology Nanchang330099 China State Key Laboratory of Intelligent Control and Management of Complex System Institute of Automation Chinese Academy of Sciences China State Key Lab. for Novel Software Technology Nanjing University China

In many-objective optimization problems (MaOPs), it is a difficult task for evolutionary algorithms to balance convergence and diversity while rapidly converging to the Pareto front. As the number of objectives increases, the difficulty of evolutionary algorithms increases with individual selection pressure. To address above problems, we propose a many-objective evolution algorithm assisted by an ideal hyperplane (MaOEA-IH). Firstly, the ideal hyperplane as a reference surface to guide the individual to search the Pareto front, which is established from the extremum of each dimension of the objective space. Secondly, a parallel p -norm mating selection strategy is proposed to improve convergence. In addition, two factors are considered in this strategy: 1) different p -norm are used to measure different spatial scales;2) individual selection uncertainty is defined by incorporating a probabilistic perturbation mechanism. Thirdly, the improved shift-based density estimation strategy is used as the evaluation criterion for environmental selection to improve the convergence and diversity of solution sets and enhance selection pressure. To verify the effectiveness of the proposed algorithm, MAOEA-IH is compared with seven excellent algorithms in three benchmark sets (DTLZ, WFG and MaF). The results show that the proposed MAOEA-IH is highly competitive in solving MaOPs. © 2022, The Authors. All rights reserved.

关键词： Geometry

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DISTDET: a cost-effective distributed cyber threat detection system 23

DISTDET: a cost-effective distributed cyber threat detection...

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Proceedings of the 32nd USENIX Conference on Security Symposium

作者： Feng Dong Liu Wang Xu Nie Fei Shao Haoyu Wang Ding Li Xiapu Luo Xusheng Xiao School of Cyber Science and Engineering Huazhong University of Science and Technology and Sangfor Technologies Inc Beijing University of Posts and Telecommunications Case Western Reserve University School of Cyber Science and Engineering Huazhong University of Science and Technology Key Laboratory of High-Confidence Software Technologies (MOE) School of Computer Science Peking University The Hong Kong Polytechnic University Arizona State University

ISBN: (纸本)9781939133373

Building provenance graph that considers causal relationships among software behaviors can better provide contextual information of cyber attacks, especially for advanced attacks such as Advanced Persistent Threat (APT) attacks. Despite its promises in assisting attack investigation, existing approaches that use provenance graphs to perform attack detection suffer from two fundamental limitations. First, existing approaches adopt a centralized detection architecture that sends all system auditing logs to the server for processing, incurring intolerable costs of data transmission, data storage, and computation. Second, they adopt either rule-based techniques that cannot detect unknown threats, or anomaly-detection techniques that produce numerous false alarms, failing to achieve a balance of precision and recall in APT detection. To address these fundamental challenges, we propose DISTDET, a distributed detection system that detects APT attacks by (1) performing light weight detection based on the host model built in the client side, (2) filtering false alarms based on the semantics of the alarm proprieties, and (3) deriving global models to complement the local bias of the host models. Our experiments on a large-scale industrial environment (1,130 hosts, 14 days, ∼1.6 billion events) and the DARPA TC dataset show that DISTDET is as effective as sate-of-the-art techniques in detecting attacks, while dramatically reducing network bandwidth from 11.28Mb/s to 17.08Kb/S (676.5× reduction), memory usages from 364MB to 5.523MB (66× reduction), and storage from 1.47GB to 130.34MB (11.6× reduction). By the time of this writing, DISTDET has been deployed to 50+ industry customers with 22,000+ hosts for more than 6 months, and identified over 900 real-world attacks.

关键词：

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Deep Reinforcement Learning for Energy-Efficient Fresh Data Collection in Rechargeable UAV-assisted IoT Networks

Deep Reinforcement Learning for Energy-Efficient Fresh Data ...

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IEEE Conference on Wireless Communications and Networking

作者： Mengjie Yi Xijun Wang Juan Liu Yan Zhang Ronghui Hou School of Cyber Engineering Xidian University Xi’an China School of Electronics and Information Technology Sun Yat-sen University Guangzhou China School of Electrical Engineering and Computer Science Ningbo University Zhejiang China State Key Lab of Integrated Service Networks Information Science Institute Xidian University Xi’an China

The unmanned aerial vehicle (UAV) can act as the edge server in delay-sensitive monitoring for data collection and processing in the Internet of things (IoT) networks due to its flexibility and low operational cost. One of its major disadvantages is the limited battery level. This paper focuses on a problem with the rechargeable UAV-assisted energy-efficient and fresh data collection in the IoT networks. In particular, the UAV takes off from the initial position to collect data packets from sensor nodes (SNs) in the IoT networks and needs to reach the final position at a given time. Some charging stations (CSs) are in the IoT networks, which can recharge the UAV by the wireless power transfer technique to keep the UAV’s energy level from falling below the threshold energy. To minimize the weighted sum of the average age of information (AoI) and the average recharging price, we design a Markov Decision Process (MDP) to determine the UAV’s flight trajectory, the scheduling of SNs, and energy recharging. The MDP is then solved using a rechargeable UAV-assisted data collection algorithm based on dueling double deep Q-networks (D3QN). Numerous simulations show that the proposed D3QN algorithm can reduce the weighted sum of the average AoI and the average recharging price more effectively than the baseline algorithms.

关键词： Simulation Wireless power transfer Data collection Autonomous aerial vehicles Energy efficiency Scheduling Trajectory

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