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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haodi Zhang Yichi Wang Yifan Jian Jiahui Jiang Zhaohai Bai Lin Ma College of Computer Science and Software Engineering Shenzhen University Center for Agricultural Resources Research Institute of Genetic and Developmental Biology Chinese Academy of Sciences State Key Laboratory of Pollution Control and Resource Reuse School of the Environment Nanjing University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Climate downscaling is crucial for detailed small- scale analysis and for acquiring climate data in regions without weather stations. Operator learning has proven potential for this task. However, several challenges remain in operator learning, such as multimodal fusion, spatiotemporal fusion and input state and query adaptation. To address these challenges, we propose a Spatiotemporal Multimodal Fusion Operator with a State- Query Coupled Kernel (SMCK). This framework includes a latent space fusion encoder that encodes climate variables using position-wise multihead attention for multimodal fusion and integrates historical information to generate robust and precise representation. Additionally, we introduce a state-query coupled kernel that combines radial basis functions and discrete fourier encoding to enhance query location representation, while also adapting to the state to obtain the coupled kernel. Extensive experiments demonstrate that our method achieves state-of-the-art performance and provides strong support for climate downscaling and the planning of climate-related strategies.

关键词： Couplings Signal processing Encoding Robustness Spatiotemporal phenomena Planning Kernel Speech processing Forecasting Meteorology

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Directional Antenna Based Spatial and Energy Efficient Semi-Distributed Spectrum Sensing in Cognitive Internet-of-Things Networks

SSRN

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SSRN 2023年

作者： Lee, Chunghyun Oh, Junsuk Na, Woongsoo Yoon, Jongha Noh, Wonjong Cho, Sungrae School of Software ChungAng University Seoul Korea Republic of Air and Missile Defense Team in Hanwha Systems Seongnam Korea Republic of Division of Computer Science and Engineering Kongju National University Cheonan Korea Republic of School of Software Hallym University Chuncheon Korea Republic of

Recently, cognitive networks and mmWave-based massive antenna transmission are becoming promising approaches for high performance in dense, complicated 6G networks. However, accurate and efficient spectrum sensing that can provide primary user and secondary user (SU) with satisfactory performance is still difficult. In this paper, we propose a new semi-distributed and cooperative passive spectrum sensing method for directional antenna based cognitive radio networks, which can be considered as a key technology of industrial-internet-of-things systems. It finds optimal directional sensing-beams and spectrum detection energy threshold to maximizes a system utilization that consists of accurate spectrum sensing probability and spatial efficiency. To find the optimal controls in a semi-distributed manner, we employ a modified elimination method and a low-complex coordination algorithm among localized user groups with proof of concavity. The simulation results reveal that the proposed control provides enhanced spectrum sensing in terms of sensing accuracy, spatial efficiency, and energy consumption compared to legacy omnidirectional antenna-based sensing approach and directional antenna-based fully distributed sensing approach. In fact, the performance gain becomes larger as the number of SUs increases. We also confirm that the proposed scheme achieves a near-optimal performance within around 5% of a directional antenna-based centralized sensing approach. © 2023, The Authors. All rights reserved.

关键词： Cognitive radio

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Self-Supervised Representation Learning for Motion Control of Autonomous Vehicles

Self-Supervised Representation Learning for Motion Control o...

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International Conference on Wavelet Active Media Technology and Information Processing (ICWAMTIP)

作者： Melese Ayalew Shijie Zhou Maregu Assefa Kumie Gedamu Getinet Yilma School of Information and Software Engineering University of Electronic Science and Technology of China School of Computer Science and Engineering University of Electronic Science and Technology of China China Sichuan Artificial Intelligence Research Institute Yibin China

ISBN: (纸本)9781665493901

To address the generalization problem of imitation learning-based end-to-end driving methods in unseen environments, we propose a self-supervised representation learning framework for motion control of autonomous vehicles (SRLMC). SRLMC first efficiently learns spatiotemporal representations invariant across different driving views using attentive Siamese 3D-CNNs and contrastive loss and thereby improving the generalization capability of multi-task leaning networks for vehicle motion control. SRLMC is trained on a large-scale driving dataset and evaluated with discrete and continuous control command prediction tasks. The experimental results and comparison to prior works confirm that the proposed SRLMC is effective and generalizable to unseen environments.

关键词： Representation learning Self-supervised learning Information processing Media Multitasking Spatiotemporal phenomena Motion control

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CSP-RM: Reputation Management Decision Support for Crowdsourcing Service Providers

CSP-RM: Reputation Management Decision Support for Crowdsour...

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IEEE International Conference on Web Services (ICWS)

作者： Shipeng Wang Qingzhong Li Lizhen Cui Yali Jiang Zhiqi Shen Han Yu School of Software Shandong University Jinan China Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) Shandong University China School of Computer Science and Engineering Nanyang Technological University Singapore

The increasing popularity of crowdsourcing has resulted in the emergence of multiple crowdsourcing service providers (CSPs), such as Mechnical Turk and Crowdflower, which compete to attract crowd workers (CWs). CWs can share their experience working for various CSPs, which forms the basis of CSP reputation score. This information can be used for trust building and facilitating future CWs’ decisions on which CSP to work for. Existing reputation management research in crowdsourcing has mainly focused on controlling task quality and improving revenue from the perspective of CSPs. Little attention has been paid to helping CSPs manage their reputation to attract and retain CWs. In this paper, we propose the Crowdsourcing Service Provider Reputation Management (CSP-RM) framework to bridge this important gap. Based on the current reputation of CSPs, it dynamically balances the trade-off between the reputation maintenance cost and the long-term profit for a given CSP. It performs dynamic commission allocation for a CSP based on Lyapunov optimization to guide the recruitment of CWs, while considering the revenue and the changes in the number of CWs. Extensive experiments based on highly competitive crowdsourcing market demonstrate that CSP-RM makes the most advantageous cost-benefit trade-off compared to existing approaches, outperforming the best baseline by 23.83%, 39.21% and 3.36% in terms of average cumulative revenue, average number of CWs and public reputation, respectively. To the best of our knowledge, it is the first decision support framework for enabling CSPs to recruit more CWs in a highly competitive market, while maintaining their reputation and ensuring long-term benefit.

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Structure-Preserving Graph Representation Learning

Structure-Preserving Graph Representation Learning

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IEEE International Conference on Data Mining (ICDM)

作者： Ruiyi Fang Liangjian Wen Zhao Kang Jianzhuang Liu School of Information and Software Engineering University of Electronic Science and Technology of China The Noah’s Ark Lab Huawei Technologies Company Limited School of Computer Science and Engineering University of Electronic Science and Technology of China

Though graph representation learning (GRL) has made significant progress, it is still a challenge to extract and embed the rich topological structure and feature information in an adequate way. Most existing methods focus on local structure and fail to fully incorporate the global topological structure. To this end, we propose a novel Structure-Preserving Graph Representation Learning (SPGRL) method, to fully capture the structure information of graphs. Specifically, to reduce the uncertainty and misinformation of the original graph, we construct a feature graph as a complementary view via k-Nearest Neighbor method. The feature graph can be used to contrast at node-level to capture the local relation. Besides, we retain the global topological structure information by maximizing the mutual information (MI) of the whole graph and feature embeddings, which is theoretically reduced to exchanging the feature embeddings of the feature and the original graphs to reconstruct themselves. Extensive experiments show that our method has quite superior performance on semi-supervised node classification task and excellent robustness under noise perturbation on graph structure or node features. The source code is available at https://***/uestc-lese/SPGRL.

关键词： Representation learning Uncertainty Source coding Perturbation methods Feature extraction Robustness Data mining

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Dynamic Random Route Mutation Mechanism for Moving Target Defense in SDN 6

Dynamic Random Route Mutation Mechanism for Moving Target De...

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6th International Symposium on computer and Information Processing Technology, ISCIPT 2021

作者： Zhang, Bofeng Han, Li Sun, Shimin Tianjin University of Technology School of Computer Science and Engineering Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China Tiangong University Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Computer Science and Technology Tianjin China

ISBN: (纸本)9781665441377

Moving Target Defense (MTD) is commonly used in changing the asymmetric situation between attack and defense in cyberspace. Route mutation is a vital branch of MTD. Current route mutation methods still have some limitations on unpredictability of mutating routing paths and time overhead. In this paper, we propose a Dynamic Random Route Mutation (DRRM) mechanism in order to maximize the unpredictability of mutation and reduce the time overhead. First, we adopt Jaccard distance matrix and temporal constraint to improve mutation space. Mutation space can be dynamically shifted to enhance the unpredictability. Then, we weaken attackers' capabilities of launching effective eavesdropping at a certain node by using route weight. Finally, we construct a pre-distribution strategy to reduce the transmission latency caused by route mutation. Simulation results show that DRRM not only raises the difficulty of network eavesdropping, but also ensures low mutation time overhead. © 2021 IEEE.

关键词： Dynamics

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An End-to-End Conformer-Based Speech Recognition Model for Mandarin Radiotelephony Communications in Civil Aviation 16th

An End-to-End Conformer-Based Speech Recognition Model for M...

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16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Shi, Yihua Ma, Guanglin Ren, Jin Zhang, Haigang Yang, Jinfeng Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic Shenzhen518055 China School of Computer Science and Software Engineering University of Science and Technology Liaoning Anshan114051 China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China

ISBN: (纸本)9783031202322

In civil aviation radiotelephony communications, misunderstandings between air traffic controllers and flight crews can result in serious aviation accidents. Automatic semantic verification is a promising assistant solution to decrease miscommunication, thanks to advancements in speech and language processing. Unfortunately, existing general speech recognition models are ineffective when it comes to capturing contextual long-distance dependent local similarity features in radiotelephony communications. To address these problems, this paper proposes an end-to-end Conformer-based multi-task learning speech recognition model for Mandarin radiotelephony communications in civil aviation. The Conformer model improves local information capture while retaining the global information modeling capabilities of contextual long-distance dependencies, owing to the introduction of the convolution module to the Transformer model. Meanwhile, multi-task learning is used to further improve performance by combining connectionist temporal classification (CTC) and attention-based encoder-decoder (AED) models. The experimental results show that the proposed model can perform global and local acoustic modeling effectively, making it particularly suitable for extracting acoustic features of Mandarin civil aviation radiotelephony communications. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Radio telephone

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Digital Twin and DRL-Driven Semantic Dissemination for 6G Autonomous Driving Service

Digital Twin and DRL-Driven Semantic Dissemination for 6G Au...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yihang Tao Jun Wu Xi Lin Shahid Mumtaz Soumaya Cherkaoui School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Faculty of Science and Engineering Waseda University Japan Department of Engineering Nottingham Trent University UK Department of Computer and Software Engineering Polytechnique Montreal Canada

Data dissemination is critical for 6G autonomous driving (AD) service because of the extensive demand for real-time traffic information. However, the heavier data transmission burden and more stringent requirements of AD service bring challenges for current data dissemination methods. In this paper, we first propose a novel digital twin (DT)-based semantic dissemination architecture to better support 6G AD service. Under this architecture, an energy-efficient semantic communication mechanism is developed to reduce the data dissemination burden while keeping low semantic model update costs. Meanwhile, the DT network is leveraged to disseminate semantic data in parallel with the physical vehicular networks, which alleviates the physical transmission contention and improves the dissemination efficiency. Second, we design a deep-reinforcement-learning (DRL)-driven semantic data dissemination scheme for the proposed architecture, named Proximal-policy-optimization for Digital-twin-aided Data Dissemination (PD3), which seeks the optimal DT transfer and semantic transmission scheduling strategy. Finally, experimental results show that our approach surpasses the state-of-the-art methods by 18.36% lower dissemination delay and 4.51% higher dissemination ratio on average.

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A new lightweight data security system for data security in the cloud computing

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Measurement: Sensors 2023年 29卷

作者： Mohammed, Shameer Nanthini, S. Bala Krishna, N. Srinivas, Inumarthi V. Rajagopal, Manikandan Ashok Kumar, M. University of Technology and Applied Sciences Sur Oman Department of AI & DS Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Thandalam Chennai India Department of CSE Mohan Babu University &SreeVidyanikethan Engineering College Andhrapradesh Tirupati517102 India Mumbai400077 India School of Business and Management Christ University Bangalore560029 India Faculty of Computer Science Department of Computer Science and Software Engineering Skyline University Nigeria Kano Nigeria

In recent decades, data has proved indispensable to all facets of human existence. The development of several applications has resulted in the exponential expansion of data. This information can be encrypted and stored in secure areas. Cloud computing is the technology that can be used to store these massive data sets. The article suggests a Cloud-based Data Security System (C-DSS) that employs a five-tiered trust model for cloud-edge data-sharing architectures. The data owner can select an appropriate trust level and Cyber Threat Information (CTI) sanitization procedure before releasing CTI for analytic strategy. In addition, this cleansing method is conducted either by an end device or by the cloud service supplier, based on the organization's degree of confidence in latter. Research presents the trust architecture, cloud architecture, and installation methodology, all of which are intended to meet widest variety of needs for exchanging secret CTI information. The testing findings high degree of data security and an evident improvement in terms of cypher processing time and security services when compared to the encryption systems that are most often employed in cloud technology. In conclusion, research briefly outlines development and evaluation performed so far by pilot applications confirming the architecture. © 2023 The Authors

关键词： Cryptography

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CNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set Scenario

arXiv

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arXiv 2023年

作者： Guo, Jingcai Guo, Song Ma, Shiheng Sun, Yuxia Xu, Yuanyuan The Department of Computing The Hong Kong Polytechnic University Hong Kong The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200030 China The Department of Computer Science Jinan University Guangzhou510632 China The School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu610054 China

We study the challenging task of malware recognition on both known and novel unknown malware families, called malware open-set recognition (MOSR). Previous works usually assume the malware families are known to the classifier in a close-set scenario, i.e., testing families are the subset or at most identical to training families. However, novel unknown malware families frequently emerge in real-world applications, and as such, require to recognize malware instances in an open-set scenario, i.e., some unknown families are also included in the test-set, which has been rarely and non-thoroughly investigated in the cyber-security domain. One practical solution for MOSR may consider jointly classifying known and detecting unknown malware families by a single classifier (e.g., neural network) from the variance of the predicted probability distribution on known families. However, conventional well-trained classifiers usually tend to obtain overly high recognition probabilities in the outputs, especially when the instance feature distributions are similar to each other, e.g., unknown v.s. known malware families, and thus dramatically degrades the recognition on novel unknown malware families. To address the problem and construct an applicable MOSR system, we propose a novel model that can conservatively synthesize malware instances to mimic unknown malware families and support a more robust training of the classifier. More specifically, we build upon the generative adversarial networks (GANs) to explore and obtain marginal malware instances that are close to known families while falling into mimical unknown ones to guide the classifier to lower and flatten the recognition probabilities of unknown families and relatively raise that of known ones to rectify the performance of classification and detection. A cooperative training scheme involving the classification, synthesizing and rectification are further constructed to facilitate the training and jointly improve the model p

关键词： Malware

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