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6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Tao, Min Yang, Xinmin Gu, Gao Li, Bohan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China 28th Research Institute of China Electronics Technology Group Corporation Nanjing China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology Nanjing211106 China Collaborative Innovation Center of Novel Software Technology and Industrialization NanjingJiangsu China

ISBN: (纸本)9789811581007

The explosive growth of the number of papers leads to the over-expansion of the paper information, resulting in the problem of information overload, causing a lot of trouble for researchers to find the papers they need. This paper implements a paper recommendation system based on LDA and PageRank. In this system, we recommend papers in the same field to researchers by modeling and analyzing paper data. Therefore, we can provide researchers with a quick and effective reference for related papers in the research field. In this paper, the probability distribution calculation and keywords extraction of paper topics and words are carried out according to the LDA of papers in the paper pool. At the same time, word2dec is used to represent the topic vector, doc2vec is used to represent the paper vector, and by calculating the system similarity between documents and topics, we get the top N papers that are most similar to the topic input by users. We use the PageRank algorithm to reorder the options in the reference network to get the final recommendation results. Under the experimental data set, the accuracy of system recommendation is more than 60%, which has high reliability and achieves the corresponding recommendation effect. © Springer Nature Singapore Pte Ltd 2020.

关键词： Recommender systems

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Time-Aware Missing Traffic Flow Prediction for Sensors with Privacy-Preservation

Time-Aware Missing Traffic Flow Prediction for Sensors with ...

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作者： Lianyong Qi Fan Wang Xiaolong Xu Wanchun Dou Xuyun Zhang Mohammad R.Khosravi Xiaokang Zhou School of Computer Science Qufu Normal University School of Computer and Software Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Department of Computing Macquarie University Department of Computer Engineering Persian Gulf University Department of Electrical and Electronic Engineering Shiraz University of Technology Faculty of Data Science Shiga University

With the continuous development of Io T, a number of sensors establish on the roadside to monitor traffic conditions in real time. The continuously traffic data generated by these sensors makes traffic management feasible. However, loss of data may occur due to inevitable sensor failure, impeding traffic managers to understand traffic dynamics clearly. In this situation, it is becoming a necessity to predict missing traffic flow accurately for effective traffic management. Furthermore, the traffic sensor data are often distributed and stored by different agencies,which inhibits the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it has become a major obstacle to balance the tradeoff between data sharing and vehicle privacy. In light of these challenges, we propose a privacyaware and accurate missing traffic flow prediction approach based on time-aware Locality-Sensitive Hashing technique. At last, we deploy a set of experiments based on a real traffic dataset. Experimental reports demonstrate the feasibility of our proposal in terms of traffic flow prediction accuracy and efficiency while guaranteeing sensor data privacy.

关键词： Traffic flow prediction Multi-party sensors Privacy Locality-Sensitive Hashing

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A bi-level formulation for label noise learning with spectral cluster discovery 29

A bi-level formulation for label noise learning with spectra...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Luo, Yijing Han, Bo Gong, Chen PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China Jiangsu Key Lab of Image Video Understanding for Social Security China Department of Computer Science Hong Kong Baptist University Hong Kong Department of Computing Hong Kong Polytechnic University Hong Kong

ISBN: (纸本)9780999241165

Practically, we often face the dilemma that some of the examples for training a classifier are incorrectly labeled due to various subjective and objective factors. Although intensive efforts have been put to design classifiers that are robust to label noise, most of the previous methods have not fully utilized data distribution information. To address this issue, this paper introduces a bi-level learning paradigm termed "Spectral Cluster Discovery" (SCD) for combating with noisy labels. Namely, we simultaneously learn a robust classifier (Learning stage) by discovering the low-rank approximation to the ground-truth label matrix and learn an ideal affinity graph (Clustering stage). Specifically, we use the learned classifier to assign the examples with similar label to a mutual cluster. Based on the cluster membership, we use the learned affinity graph to explore the noisy examples based on the cluster membership. Both stages will reinforce each other iteratively. Experimental results on typical benchmark and real-world datasets verify the superiority of SCD to other label noise learning methods. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

关键词： Iterative methods

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IATS: information-age aware task scheduling for vehicle-road-cloud cooperative systems

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Journal of Systems Architecture 2025年 167卷

作者： Sijie Lin Liying Li Jining Chen Peijin Cong Tian Wang Junlong Zhou School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 China Guangxi Key Laboratory of Digital Infrastructure Guangxi Zhuang Autonomous Region Information Center Nanning 530000 China Jiangsu College of Finance and Accounting Lianyungang 222061 China

As the Internet of Things (IoT) continues to evolve at an unprecedented pace, smart vehicles can now support various real-time applications like object detection that require timely situational awareness. The freshness of state information is critical for these time-sensitive applications, as it directly affects the timeliness and accuracy of situational awareness. Fresh state information enables smart vehicles to make correct decisions in dynamic environments. However, this aspect is often ignored in prior work. Besides, these applications are typically computation-intensive, posing a challenge to resource-limited smart vehicles. Considering that the state information’s freshness can be characterized using the age of information (AoI) and the vehicle-road-cloud computing architecture is effective in integrating the resources of roadside units and the cloud to assist with processing tasks for vehicles, to minimize the system’s long-term average AoI without violating delay and energy constraints, this paper explores the AoI-aware task scheduling problem in a vehicle-road-cloud cooperative system. To achieve this goal, we first develop an AoI model tailored for smart vehicles within the cooperative system and formulate an AoI optimization problem. In order to tackle the proposed issue, we design a multi-agent reinforcement learning-based task scheduling method that can perform task scheduling in complex, dynamic, and decentralized decision-making environments. The algorithm iteratively trains the network continuously such that all agents obtain the optimal scheduling strategy. Finally, we implement extensive simulations and testbed-based experiments to validate our method. The results indicate that our method reduces the average AoI by 81.91% on average and 95.23% at the highest compared to benchmarking approaches.

关键词： Multi agent systems

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Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution

arXiv

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arXiv 2021年

作者： Feng, Chun-Mei Yan, Yunlu Yu, Kai Xu, Yong Yang, Jian Shao, Ling Fu, Huazhu The Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China The Institute of High Performance Computing A*STAR Singapore138632 Singapore The PCA Laboratory Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing210094 China The Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Terminus Group China

Super-resolving the Magnetic Resonance (MR) image of a target contrast under the guidance of the corresponding auxiliary contrast, which provides additional anatomical information, is a new and effective solution for fast MR imaging. However, current multi-contrast super-resolution (SR) methods tend to concatenate different contrasts directly, ignoring their relationships in different clues, e.g., in the high-intensity and low-intensity regions. In this study, we propose a separable attention network (comprising high-intensity priority attention and low-intensity separation attention), named SANet. Our SANet could explore the areas of high-intensity and low-intensity regions in the "forward" and "reverse" directions with the help of the auxiliary contrast, while learning clearer anatomical structure and edge information for the SR of a target-contrast MR image. SANet provides three appealing benefits: (1) It is the first model to explore a separable attention mechanism that uses the auxiliary contrast to predict the high-intensity and low-intensity regions regions, diverting more attention to refining any uncertain details between these regions and correcting the fine areas in the reconstructed results. (2) A multi-stage integration module is proposed to learn the response of multi-contrast fusion at multiple stages, get the dependency between the fused representations, and boost their representation ability. (3) Extensive experiments with various state-of-the-art multi-contrast SR methods on fastMRI and clinical in vivo datasets demonstrate the superiority of our model. © 2021, CC BY.

关键词： Magnetic resonance imaging

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Toward a wearable RFID system for real-time activity recognition using radio patterns

arXiv

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arXiv 2020年

作者： Wang, Liang Gu, Tao Tao, Xianping Lu, Jian State Key Laboratory of Novel Computer Software Nanjing University Nanjing Jiangsu China School of Computer Science and Information Technology RMIT University MelbourneVIC Australia

Elderly care is one of the many applications supported by real-time activity recognition systems. Traditional approaches use cameras, body sensor networks, or radio patterns from various sources for activity recognition. However, these approaches are limited due to ease-of-use, coverage, or privacy preserving issues. In this paper, we present a novel wearable Radio Frequency Identification (RFID) system aims at providing an easy-to-use solution with high detection coverage. Our system uses passive tags which are maintenance-free and can be embedded into the clothes to reduce the wearing and maintenance efforts. A small RFID reader is also worn on the user's body to extend the detection coverage as the user moves. We exploit RFID radio patterns and extract both spatial and temporal features to characterize various activities. We also address the issues of false negative of tag readings and tag/antenna calibration, and design a fast online recognition system. Antenna and tag selection is done automatically to explore the minimum number of devices required to achieve target accuracy. We develop a prototype system which consists of a wearable RFID system and a smartphone to demonstrate the working principles, and conduct experimental studies with four subjects over two weeks. The results show that our system achieves a high recognition accuracy of 93.6 percent with a latency of 5 seconds. Additionally, we show that the system only requires two antennas and four tagged body parts to achieve a high recognition accuracy of 85 percent. Copyright © 2020, The Authors. All rights reserved.

关键词： Radio frequency identification (RFID)

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Efficient reinforcement learning in continuous state and action spaces with Dyna and policy approximation

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Frontiers of computer Science 2019年第1期13卷 106-126页

作者： Shan ZHONG Quan LIU Zongzhang ZHANG Qiming FU School of Computer Science and Technology Soochow UniversitySuzhou 215000China School of Computer Science and Engineering Changshu Institute of TechnologyChangshu 215500China Jiangsu Province Key Laboratory of Intelligent Building Energy Efficiency Suzhou University of Science and TechnologySuzhou 215006China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun 130012China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing 210000China College of Electronic &Information Engineering Suzhou University of Science and TechnologySuzhou 215000China

Dyna is an effective reinforcement learning (RL) approach that combines value function evaluation with model ***,existing works on Dyna mostly discuss only its efficiency in RL problems with discrete action *** paper proposes a novel Dyna variant,called Dyna-LSTD-PA,aiming to handle problems with continuous action ***-LSTD-PA stands for Dyna based on least-squares temporal difference (LSTD)and policy ***-LSTD-PA consists of two simultaneous,interacting *** learning process determines the probability distribution over action spaces using the Gaussian distribution;estimates the underlying value function,policy,and model by linear representation;and updates their parameter vectors online by LSTD(,t).The planning process updates the parameter vector of the value function again by using ofttine LSTD(2).Dyna-LSTD-PA also uses the Sherman-Morrison formula to improve the efficiency of LSTD(,t),and weights the parameter vector of the value function to bring the two processes ***,the global error bound is derived by considering approximation,estimation,and model ***,Dyna-LSTD-PA outperforms two representative methods in terms of convergence rate,success rate,and stability performance on four benchmark RL problems.

关键词： problem solving control methods heuristic search methods dynamic programming

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Application of Lightweight Multi-pooling Convolutional Neural Network in Satellite Cloud Image Recognition

Application of Lightweight Multi-pooling Convolutional Neura...

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IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE)

作者： Xiang Wei Feng Wang Xi Chen College of Information and Computer Science Taiyuan University of Technology State Key Laboratory of Air Traffic Management System and Technology Taiyuan Shanxi Province China College of Information and Computer Science Taiyuan University of Technology Taiyuan Shanxi Province China State Key Laboratory of Air Traffic Management System and Technology Nanjing Jiangsu Province

ISBN: (数字)9781728198279

ISBN: (纸本)9781728181585

Thunderstorm weather and other small-scale meteorology will have a very serious impact on people's personal and property safety. At this stage, early warning of thunderstorm weather in satellite cloud images still requires very rich professional knowledge, and it is also difficult to meet the real-time requirements of thunderstorm warning. In view of the above problems, this paper proposes a network structure, which uses deep separable convolution and residual blocks to combine with each other, and uses a multi-pooling structure for global information extraction, while ensuring accuracy and reducing the number of network parameters. Meet the real-time requirements. Experiments show that the use of the proposed lightweight multi-pooling network structure has a certain improvement in the accuracy of segmentation of thunderstorm cloud clusters, and can have a shorter response time in a satellite cloud image, compared with commonly used image segmentation, the network has also improved.

关键词： Clouds Convolution Satellites Feature extraction Meteorology Neural networks Training

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Topological spin-torque diode effect in skyrmion-based magnetic tunnel junctions

arXiv

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arXiv 2024年

作者： Liu, Shuhui Tomasello, Riccardo Wu, Yuxuan Fang, Bin Chen, Aitian Zheng, Dongxing Zhang, Baoshun Darwin, Emily Hug, Hans J. Carpentieri, Mario Jiang, Wanjun Zhang, Xixiang Finocchio, Giovanni Zeng, Zhongming Nanofabrication facility Suzhou Institute of Nano-Tech and Nano-Bionics Chinese Academy of Sciences Jiangsu Suzhou215123 China School of Nano Technology and Nano Bionics University of Science and Technology of China Anhui Hefei230026 China Department of Mathematical and Computer Sciences Physical Sciences and Earth Sciences University of Messina MessinaI-98166 Italy Department of Electrical and Information Engineering Politecnico di Bari BariI-70125 Italy Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955–6900 Saudi Arabia Empa Swiss Federal Laboratories for Materials Science and Technology Ueberlandstrasse 129 Dübendorf8600 Switzerland Department of Physics University of Basel Klingelbergstrasse 82 Basel4056 Switzerland State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China

The growing market and massive use of Internet of Things nodes is placing unprecedented demands of energy efficient hardware for edge computing and microwave devices. In particular, magnetic tunnel junctions (MTJs), as main building blocks of spintronic microwave technology, can offer a path for the development of compact and high-performance microwave detectors. On the other hand, the fascinating field of skyrmionics is bridging together concepts from topology and spintronics. Here, we show the proof-of-concept of the direct electrical excitation and detection of the dynamics of a topological protected magnetic texture, i.e. skyrmion at room temperature and for a wide region of applied fields, including the zero-field case. This topological spin-torque diode is realized with an MTJ on top of a skyrmionic material. Quantitative Magnetic Force Microscopy measurements are employed to confirm the existence of a single skyrmion in the MTJ free layer. Spin-torque diode electrical measurements show the electrical excitation via spin-transfer torque (STT) of a skyrmion resonant mode with frequencies near 4 GHz and a selectivity one order of magnitude smaller than the uniform modes excited in the same device. Micromagnetic simulations identify these dynamics with the excitation of the breathing mode and point out the role of thickness dependent magnetic parameters (magnetic anisotropy field and Dzyaloshinkii–Moriya interaction) in both stabilizing and exciting the magnetic skyrmions. This work marks a milestone for the development of topological spin-torque diodes. © 2024, CC BY.

关键词： Magnetic anisotropy

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Automated Authentication of Large-Scale IoT Devices with Hybrid Feature Selection 7th

Automated Authentication of Large-Scale IoT Devices with Hyb...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Chen, Qi Song, Yubo Hu, Aiqun Wang, Junbo Key Laboratory of Computer Network Technology of Jiangsu Province School of Cyber Science and Engineering Southeast University Nanjing211189 China Purple Mountain Laboratories Nanjing211189 China National Mobile Communications Research Laboratory Nanjing211189 China School of Information Science and Engineering Southeast University Nanjing211189 China

ISBN: (纸本)9783030786205

Nowadays, large-scale IoT (Internet of Things) devices are connected to the network. However, due to the simplicity, limited memory and poor computing power of most IoT devices, it is difficult to apply complex security authentication protocols to these devices. Many attackers use this loophole, impersonating legitimate devices to launch malicious attacks on the network, threatening the security of other legitimate devices. Therefore, in order to strengthen the identification and management of devices, the existing device fingerprint identification technology based on machine learning is to extract device traffic at the access gateway and generate device fingerprinting to realize the identification of IoT devices. However, to achieve high recognition accuracy, existing device fingerprint algorithms need to extract a large number of fingerprint features from network traffic for machine learning, which increases the complexity of implementation and delay of device identification. Also, it is not suitable for the current large-scale access environment. Therefore, this paper proposes a hybrid feature selection method to preprocess features for the optimal feature subset, which can not only reduce the feature dimension, but also improve the accuracy of device recognition. Firstly, the method filters the irrelevant features according to the characteristic fluctuation and the entropy of correlation information between the feature and the target. Then, based on the classification accuracy of the learning algorithm, a random search method—forest optimization algorithm is used to further optimize the filtered feature subsets under the evaluation criteria. Experiments show that this method can reduce feature dimension by 81.0% and improve classification accuracy from 86.4% to 93.1%. © 2021, Springer Nature Switzerland AG.

关键词： Internet of things

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