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arXiv 2021年

作者： Hong, Fa-Ting Feng, Jia-Chang Xu, Dan Shan, Ying Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Peng Cheng Laboratory Shenzhen China Tencent PCG Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Department of Computer Science and Engineering HKUST Hong Kong Pazhou Lab Guangzhou China Guangdong Key Laboratory of Information Security Technology Sun Yat-sen University Guangzhou China

Weakly supervised temporal action localization (WS-TAL) is a challenging task that aims to localize action instances in the given video with video-level categorical supervision. Previous works use the appearance and motion features extracted from pre-trained feature encoder directly, e.g., feature concatenation or score-level fusion. In this work, we argue that the features extracted from the pre-trained extractors, e.g., I3D, which are trained for trimmed video action classification, but not specific for WS-TAL task, leading to inevitable redundancy and sub-optimization. Therefore, the feature re-calibration is needed for reducing the task-irrelevant information redundancy. Here, we propose a cross-modal consensus network (CO2-Net) to tackle this problem. In CO2-Net, we mainly introduce two identical proposed cross-modal consensus modules (CCM) that design a cross-modal attention mechanism to filter out the task-irrelevant information redundancy using the global information from the main modality and the cross-modal local information from the auxiliary modality. Moreover, we further explore inter-modality consistency, where we treat the attention weights derived from each CCM as the pseudo targets of the attention weights derived from another CCM to maintain the consistency between the predictions derived from two CCMs, forming a mutual learning manner. Finally, we conduct extensive experiments on two commonly used temporal action localization datasets, THUMOS14 and ActivityNet1.2, to verify our method, which we achieve the state-of-the-art results. The experimental results show that our proposed cross-modal consensus module can produce more representative features for temporal action localization. Copyright © 2021, The Authors. All rights reserved.

关键词： Carbon dioxide

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Editorial: special issue on “The security, privacy, and digital forensics of mobile networks and mobile cloud”

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Digital Communications and Networks 2019年第4期5卷 203-204页

作者： Lei Chen Guofei Gu Wenjia Li Rami J.Haddad the Department of Information Technology Allen E. Paulson College of Engineering and Computing at Georgia Southern University USA the Department of Computer Science & Engineering at Texas A&M University Computer Science at New York Institute of Technology (NYIT) since the Department of Electrical and Computer Allen E. Paulson College of Engineering and Computing at Georgia Southern University the Optical Networks and Smart Grid Applications (ONSmart) Laboratory GSU

To many people, smartphones and other mobile devices have been an indispensable part of their daily lives. With personal financial and medical information and job-related data processed on mobile devices,systems and networks as well as the connected mobile cloud, it is critically important that secure mobile operating systems, secure mobile applications, secure smartphone devices, secure cellular networks and secure mobile cloud all keep up with the ever-growing mobile usage.

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SA-ConvONet: Sign-agnostic optimization of convolutional occupancy networks

arXiv

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arXiv 2021年

作者： Tang, Jiapeng Lei, Jiabao Xu, Dan Ma, Feiying Jia, Kui Zhang, Lei School of Electronic and Information Engineering South China University of Technology Department of Computer Science and Engineering HKUST Hong Kong Department of Computing The Hong Kong Polytechnic University Hong Kong DAMO Academy Alibaba Group Pazhou Lab Guangzhou China Peng Cheng Laboratory Shenzhen China

Surface reconstruction from point clouds is a fundamental problem in the computer vision and graphics community. Recent state-of-the-arts solve this problem by individually optimizing each local implicit field during inference. Without considering the geometric relationships between local fields, they typically require accurate normals to avoid the sign conflict problem in overlapped regions of local fields, which severely limits their applicability to raw scans where surface normals could be unavailable. Although SAL breaks this limitation via sign-agnostic learning, further works still need to explore how to extend this technique for local shape modeling. To this end, we propose to learn implicit surface reconstruction by sign-agnostic optimization of convolutional occupancy networks, to simultaneously achieve advanced scalability to large-scale scenes, generality to novel shapes, and applicability to raw scans in a unified framework. Concretely, we achieve this goal by a simple yet effective design, which further optimizes the pre-trained occupancy prediction networks with an unsigned cross-entropy loss during inference. The learning of occupancy fields is conditioned on convolutional features from an hourglass network architecture. Extensive experimental comparisons with previous state-of-the-arts on both object-level and scene-level datasets demonstrate the superior accuracy of our approach for surface reconstruction from un-orientated point clouds. The code is available at https://***/tangjiapeng/SA-ConvONet. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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AstroLLaMA-Chat: Scaling AstroLLaMA with Conversational and Diverse Datasets

arXiv

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arXiv 2024年

作者： Perkowski, Ernest Pan, Rui Nguyen, Tuan Dung Ting, Yuan-Sen Kruk, Sandor Zhang, Tong O’Neill, Charlie Jablonska, Maja Sun, Zechang Smith, Michael J. Liu, Huiling Schawinski, Kevin Iyer, Kartheik Ciucă, Ioana Camino Bajo del Castillo s/n Villanueva de la Cañada Madrid28692 Spain Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Department of Computer and Information Science University of Pennsylvania PhiladelphiaPA19104 United States Research School of Astronomy & Astrophysics Australian National University Cotter Rd. WestonACT2611 Australia School of Computing Australian National University ActonACT2601 Australia Department of Astronomy The Ohio State University ColumbusOH43210 United States The Ohio State University ColumbusOH43210 United States Department of Computer Science University of Illinois Urbana-Champaign United States Mathematical Science Institute Australian National University ActonACT2601 Australia Department of Astronomy MongManWai Building Tsinghua University Beijing100084 China Aspia Space Tremough Innovation Centre PenrynTR10 9TA United Kingdom Department of Modern Physics University of Science and Technology of China Anhui Hefei230026 China Modulos Technoparkstrasse 1 Zurich8005 Switzerland Columbia Astrophysics Laboratory Columbia University New YorkNY10027 United States

We explore the potential of enhancing LLM performance in astronomy-focused question-answering through targeted, continual pre-training. By employing a compact 7B-parameter LLaMA-2 model and focusing exclusively on a curated set of astronomy corpora—comprising abstracts, introductions, and conclusions—we achieve notable improvements in specialized topic comprehension. While general LLMs like GPT-4 excel in broader question-answering scenarios due to superior reasoning capabilities, our findings suggest that continual pre-training with limited resources can still enhance model performance on specialized topics. Additionally, we present an extension of AstroLLaMA: the fine-tuning of the 7B LLaMA model on a domain-specific conversational dataset, culminating in the release of the chat-enabled AstroLLaMA for community use. Comprehensive quantitative benchmarking is currently in progress and will be detailed in an upcoming full paper. The model, AstroLLaMA-Chat, is now available at https://***/universeTBD, providing the first open-source conversational AI tool tailored for the astronomy community. Copyright © 2024, The Authors. All rights reserved.

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Anomaly detection on attributed networks via contrastive self-supervised learning

arXiv

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arXiv 2021年

作者： Liu, Yixin Li, Zhao Pan, Shirui Gong, Chen Zhou, Chuan Karypis, George The Department of Data Science and AI Faculty of Information Technology Monash University ClaytonVIC3800 Australia The Alibaba Group Hangzhou310000 China The PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China The Department of Computing The Hong Kong Polytechnic University Hong Kong The Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100093 China The Department of Computer Science and Engineering University of Minnesota MinneapolisMN55455 United States

Anomaly detection on attributed networks attracts considerable research interests due to wide applications of attributed networks in modeling a wide range of complex systems. Recently, the deep learning-based anomaly detection methods have shown promising results over shallow approaches, especially on networks with high-dimensional attributes and complex structures. However, existing approaches, which employ graph autoencoder as their backbone, do not fully exploit the rich information of the network, resulting in suboptimal performance. Furthermore, these methods do not directly target anomaly detection in their learning objective and fail to scale to large networks due to the full graph training mechanism. To overcome these limitations, in this paper, we present a novel Contrastive self-supervised Learning framework for Anomaly detection on attributed networks (CoLA for abbreviation). Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair, which can capture the relationship between each node and its neighboring substructure in an unsupervised way. Meanwhile, a well-designed graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure and measure the agreement of each instance pairs with its outputted scores. The multi-round predicted scores by the contrastive learning model are further used to evaluate the abnormality of each node with statistical estimation. In this way, the learning model is trained by a specific anomaly detection-aware target. Furthermore, since the input of the graph neural network module is batches of instance pairs instead of the full network, our framework can adapt to large networks flexibly. Experimental results show that our proposed framework outperforms the state-of-the-art baseline methods on all seven benchmark datasets. © 2021, CC BY.

关键词： Anomaly detection

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Quantum Reinforcement Learning for QoS-Aware Real-Time Job Scheduling in Cloud Systems

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IEEE Systems Journal 2025年

作者： Dai, Shuhong Saurabh, Nishant Wang, Qingle Nian, Jiawei Kan, Shuwen Mao, Ying Cheng, Long North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources the School of Control and Computer Engineering Beijing102206 China Utrecht University Department of Information and Computing Sciences CS Utrecht 3584 Netherlands Fordham University Department of Computer and Information Science New YorkNY10458 United States

Effective cloud job scheduling is essential for enhancing the performance and operational efficiency of cloud-based services, directly impacting their quality of service (QoS). Among existing methodologies, deep reinforcement learning (DRL) has proven effective in addressing complex, multidimensional optimization challenges in real-time scheduling. With advancements in quantum computing, quantum neural networks (QNNs) are showing unique advantages in information representation and processing. This study is the first to explore quantum reinforcement learning (QRL) for real-time job scheduling in cloud systems. Specifically, we propose a QRL framework that utilizes variational and encoding layers to convert state information into quantum data, repeatedly embedded into a QNN to compute optimal value returns. This approach aims to enhance QoS by improving job execution success rates and reducing average response times with unpredictable job arrivals. We present the detailed design of our approach, and our simulation results demonstrate that the QRL method significantly exceeds established baselines, including those based on DRL, across a range of workload intensities and computational resource configurations. This is particularly evident under high-load conditions, where our approach can achieve 55.2% higher success rates, underscoring its significant potential in cloud job scheduling optimization. © 2007-2012 IEEE.

关键词： Qubits

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Achieving Privacy-Preserving Iris Identification Via El Gamal

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computers, Materials & Continua 2019年第8期61卷 727-738页

作者： Yong Ding Lei Tian Bo Han Huiyong Wang Yujue Wang James Xi Zheng Guangxi Key Laboratory of Cryptography and Information Security School of Computer and Information SecurityGuilin University of Electronic TechnologyGuilin541004China School of Mathematics and Computer Science Guilin University of Electronic TechnologyGuilin541004China Software Engineering Department of Computing Macquarie UniversitySydneyNSW 2109 Australia

Currently,many biometric systems maintain the user’s biometrics and templates in plaintext format,which brings great privacy risk to uses’biometric *** are unique and almost unchangeable,which means it is a great concern for users on whether their biometric information would be *** address this issue,this paper proposes a confidential comparison algorithm for iris feature vectors with masks,and develops a privacy-preserving iris verification scheme based on the El Gamal encryption *** our scheme,the multiplicative homomorphism of encrypted features is used to compare of iris features and their mask ***,this paper improves the Hamming distance of iris features,which makes the similarity matching work better than existing *** results confirm the practicality of our proposed schemes in real world applications,that is,for the iris feature vectors and masks of 2048 bits,nearly 12 comparisons can be performed per second.

关键词： Homomorphic encryption template matching Hamming distance

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Quantum generative training using rényi divergences

arXiv

引用

arXiv 2021年

作者： Kieferová, Mária Marrero, Carlos Ortiz Wiebe, Nathan Centre for Quantum Computation and Communication Technology Centre for Quantum Software and Information University of Technology Sydney Data Sciences & Analytics Group Department of Electrical & Computer Engineering Pacific Northwest National Laboratory North Carolina State University Department of Computer Science Department of Physics High Performance Computing Group University of Toronto University of Washington Pacific Northwest National Laboratory

Quantum neural networks (QNNs) are a framework for creating quantum algorithms that promises to combine the speedups of quantum computation with the widespread successes of machine learning. A major challenge in QNN development is a concentration of measure phenomenon known as a barren plateau that leads to exponentially small gradients for a range of QNNs models. In this work, we examine the assumptions that give rise to barren plateaus and show that an unbounded loss function can circumvent the existing no-go results. We propose a training algorithm that minimizes the maximal Rényi divergence of order two and present techniques for gradient computation. We compute the closed form of the gradients for Unitary QNNs and Quantum Boltzmann Machines and provide sufficient conditions for the absence of barren plateaus in these models. We demonstrate our approach in two use cases: thermal state learning and Hamiltonian learning. In our numerical experiments, we observed rapid convergence of our training loss function and frequently archived a 99% average fidelity in fewer than 100 epochs. © 2021, CC BY.

关键词： Quantum computers

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Outage Performance of Uplink Rate Splitting Multiple Access with Randomly Deployed Users

arXiv

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arXiv 2022年

作者： Lu, Huabing Xie, Xianzhong Shi, Zhaoyuan Lei, Hongjiang Zhao, Nan Cai, Jun The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian116024 China The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Key Laboratory of Intelligent Perception and Computing of Anhui Province Anqing Normal University Anqing246011 China The School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Electrical and Computer Engineering Concordia University MontrealQCH3G 1M8 Canada

With the rapid proliferation of smart devices in wireless networks, more powerful technologies are expected to fulfill the network requirements of high throughput, massive connectivity, and diversify quality of service. To this end, rate splitting multiple access (RSMA) is proposed as a promising solution to improve spectral efficiency and provide better fairness for the next-generation mobile networks. In this paper, the outage performance of uplink RSMA transmission with randomly deployed users is investigated, taking both user scheduling schemes and power allocation strategies into consideration. Specifically, the greedy user scheduling (GUS) and cumulative distribution function (CDF) based user scheduling (CUS) schemes are considered, which could maximize the rate performance and guarantee scheduling fairness, respectively. Meanwhile, we re-investigate cognitive power allocation (CPA) strategy, and propose a new rate fairness-oriented power allocation (FPA) strategy to enhance the scheduled users' rate fairness. By employing order statistics and stochastic geometry, an analytical expression of the outage probability for each scheduling scheme combining power allocation is derived to characterize the performance. To get more insights, the achieved diversity order of each scheme is also derived. Theoretical results demonstrate that both GUS and CUS schemes applying CPA or FPA strategy can achieve full diversity orders, and the application of CPA strategy in RSMA can effectively eliminate the secondary user's diversity order constraint from the primary user. Simulation results corroborate the accuracy of the analytical expressions, and show that the proposed FPA strategy can achieve excellent rate fairness performance in high signal-to-noise ratio region. Copyright © 2022, The Authors. All rights reserved.

关键词： Wireless networks

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Cramér-rao bound optimization for joint radar-communication design

arXiv

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arXiv 2021年

作者： Liu, Fan Liu, Ya-Feng Li, Ang Masouros, Christos Eldar, Yonina C. The Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen518055 China The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematicsand Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The School of Information and Communications Engineering Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’anShaanxi710049 China The Department of Electronic and Electrical Engineering University College London LondonWC1E 7JE United Kingdom The Faculty of Mathematics and Computer Science Weizmann Institute of Science Rehovot Israel

In this paper, we propose multi-input multi-output (MIMO) beamforming designs towards joint radar sensing and multi-user communications. We employ the Cramér-Rao bound (CRB) as a performance metric of target estimation, under both point and extended target scenarios. We then propose minimizing the CRB of radar sensing while guaranteeing a pre-defined level of signal-to-interference-plus-noise ratio (SINR) for each communication user. For the single-user scenario, we derive a closed form for the optimal solution for both cases of point and extended targets. For the multi-user scenario, we show that both problems can be relaxed into semidefinite programming by using the semidefinite relaxation approach, and prove that the global optimum can always be obtained. Finally, we demonstrate numerically that the globally optimal solutions are reachable via the proposed methods, which provide significant gains in target estimation performance over state-of-the-art benchmarks. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal to noise ratio

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