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4th International Conference on High Performance Big Data and Intelligent systems, HDIS 2022

作者： Wang, Fei Liu, Kai Ding, Zhaoyun Liu, Bin Zhao, Yu National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (数字)9781665491440

ISBN: (纸本)9781665491440

Cyberspace security has risen to the height of national strategies, represented by APT attacks, threatening important industries in various countries. In order to ensure cyberspace security, it is urgent to develop situational awareness of APT attacks. In view of the multi-step attack characteristics of APT, this paper proposes a series of questions that need to be answered in cyberspace threat situational awareness, and constructs the APT attack ontology. And through the representation and reasoning of a case, the consistency and effectiveness of the APT attack ontology are verified. It is proved that the ontology can realize the automatic correlation and reasoning of APT attack multi-source intelligence, find the weaknesses and loopholes exploited by each step of the attack, and deduce the corresponding mitigation measures. At the same time, we will find the causal relationship between various weaknesses and mitigation measures, and provide support for the auxiliary decision-making of country's cyberspace asset protection. © 2022 IEEE.

关键词： Ontology

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Anomalous heat transfer in nonequilibrium quantum systems

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Physical Review Research 2025年第1期7卷 L012044-L012044页

作者： Teng Ma Jing-Ning Zhang Yuan-Sheng Wang Hong-Yi Xie Man-Hong Yung Beijing Academy of Quantum Information Sciences Beijing 100193 China BGI Research Shenzhen 518083 China BGI Research Wuhan 430074 China School of Systems Science Beijing Normal University Beijing 100875 China Department of Physics and Astronomy Center for Quantum Research and Technology University of Oklahoma Norman Oklahoma 73069 USA Shenzhen Institute for Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China International Quantum Academy Shenzhen 518048 China Guangdong Provincial Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China Shenzhen Key Laboratory of Quantum Science and Engineering Southern University of Science and Technology Shenzhen 518055 China

Anomalous heat transfer (AHT), a process by which heat spontaneously flows from a cold system into a hot one, superficially contradicts the Clausius statement of the second law of thermodynamics. Here we provide a full classification of mechanisms of AHT in nonequilibrium quantum systems from a quantum-information perspective. For initial states in local equilibrium, we find that AHT can arise from three resources: initial correlation, intrasystem interaction, and intrasystem temperature inhomogeneity. In particular, for qubit systems, we prove that initial quantum coherence is necessary for AHT if the intersystem interactions are limited to the two-body type. We explicitly show AHT dominated by each of the mechanisms in a three-qubit system. Our classification scheme may offer a guideline for developing high-efficiency quantum autonomous thermal machines.

关键词： Qubits

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A Diffusion Model for Traffic Data Imputation

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 606-617页

作者： Bo Lu Qinghai Miao Yahui Liu Tariku Sinshaw Tamir Hongxia Zhao Xiqiao Zhang Yisheng Lv Fei-Yue Wang the School of Artificial Intelligence University of Chinese Academy of Sciences Baidu Inc. IEEE the State Key Laboratory for Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Meituan Guangdong University of Technology the Department of Transportation Engineering School of Transportation Science and Technology Harbin Institute of Technology

Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS) in the real world. As a state-of-the-art generative model, the diffusion model has proven highly successful in image generation, speech generation, time series modelling *** now opens a new avenue for traffic data imputation. In this paper, we propose a conditional diffusion model, called the implicit-explicit diffusion model, for traffic data imputation. This model exploits both the implicit and explicit feature of the data simultaneously. More specifically, we design two types of feature extraction modules, one to capture the implicit dependencies hidden in the raw data at multiple time scales and the other to obtain the long-term temporal dependencies of the time series. This approach not only inherits the advantages of the diffusion model for estimating missing data, but also takes into account the multiscale correlation inherent in traffic data. To illustrate the performance of the model, extensive experiments are conducted on three real-world time series datasets using different missing *** experimental results demonstrate that the model improves imputation accuracy and generalization capability.

关键词： Data imputation diffusion model implicit feature time series traffic data

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A Study on Resource Allocation for Mobile Edge Computing-Assisted Industrial Iot

A Study on Resource Allocation for Mobile Edge Computing-Ass...

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2023 International Conference on Networks, Communications and Intelligent Computing, NCIC 2023

作者： Lu, Jie Zhang, Yuexia Li, Junjie Ministry of Information Industry Beijing Information Science and Technology University Key Laboratory of Information and Communication Systems Beijing China Control Technology Ministry of Education Beijing Information Science and Technology University Key Laboratory of Modern Measurement Beijing China

ISBN: (纸本)9798350371949

Since the advancement of mobile communication technology, traditional industries are gradually transforming and upgrading, which leads to problems such as insufficient computing resources and excessive energy consumption of Internet of Things (IoT) devices. To address this issue, a resource allocation algorithm for the mobile edge computing-assisted industrial IoT (IIoT) is proposed. First, a mobile edge computing-assisted IIoT network model was constructed. Using the collaboration between IoT devices, mobile edge computing (MEC) servers, and cloud servers, the issue of minimizing all IIoT devices energy cost was proposed. Subsequently, in order to address the optimization issue, obtain its optimal solution, the alternating direction method of multipliers (ADMM) algorithm is introduced. Finally, the ADMM algorithm's convergence was verified through an experimental simulation. In contrast to baseline algorithm 1 and algorithm 2, the ADMM algorithm can significantly diminish energy cost, proving the effectiveness of the proposed scheme. © 2023 IEEE.

关键词： Resource allocation

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Giant reversible barocaloric effects with high thermal cycle stability in epoxybonded(MnCoGe)_(0.96)(CuCoSn)_(0.04) composite

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International Journal of Minerals,Metallurgy and Materials 2024年第11期31卷 2528-2534页

作者： Yafei Kuang Kun Tao Bo Yang Peng Tong Yan Zhang Zhigang Sun Kewei Zhang Dunhui Wang Jifan Hu Liang Zuo College of Materials Science and Engineering Taiyuan University of Science and TechnologyTaiyuan 030024China Shanxi Key Laboratory of Magnetic and Electric Functional Materials and Applications Taiyuan University of Science and TechnologyTaiyuan 030024China Key Laboratory of Materials Physics Institute of Solid State PhysicsChinese Academy of SciencesHefei 230031China Key Laboratory for Anisotropy and Texture of Materials(Ministry of Education) School of Materials Science and EngineeringNortheastern UniversityShenyang 110819China School of Electronics and Information Hangzhou Dianzi UniversityHangzhou 310018China

Hexagonal MnMX-based(M=Co or Ni,X=Si or Ge)alloys exhibit giant reversible barocaloric ***,giant volume expansion would result in the as-cast MnMX ingots fragmenting into powders,and inevitably bring the deterioration of mechanical properties and *** fragmentation can bring degradation of structural transformation entropy change during cyclic application and removal of *** this paper,giant reversible barocaloric effects with high thermal cycle stability can be achieved in the epoxy bonded(MnCoGe)0.96(CuCoSn)0.04 *** reversible isothermal entropy change of 43.0 J·kg^(−1)·K^(−1) and adiabatic temperature change from barocaloric effects(ΔT_(BCE))of 15.6 K can be obtained within a wide temperature span of 30 K at 360 MPa,which is mainly attributed to the integration of the change in the transition temperature driven by pressure of−101 K·GPa^(−1) and suitable thermal hysteresis of 11.1 ***,the variation of reversibleΔ_(TBCE) against the applied hydrostatic pressure reaches up to 43 K·GPa^(−1),which is at the highest level among the other reported giant barocaloric *** importantly,after 60 thermal cycles,the composite does not break and the calorimetric curves coincide well,demonstrating good thermal cycle stability.

关键词： barocaloric effects cycle stability reversible isothermal entropy change reversible adiabatic temperature change MnCoGe alloys

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Securing STAR-FC-RIS empowered integrated sensing and multiuser communications against target eavesdropping

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science China(information sciences) 2024年第11期67卷 335-346页

作者： Shuying LIN Yulong ZOU Fu XIAO Bin LI School of Telecommunications and Information Engineering Nanjing University of Posts and Telecommunications School of Computer Science Nanjing University of Posts and Telecommunications Key Laboratory of Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications

This paper investigates a simultaneous-transmitting-and-reflecting fully-connected reconfigurable intelligent surface(STAR-FC-RIS) empowered integrated sensing and multiuser communications(ISAMC) network, where a dual-functional radar-communication base station detects a malicious radar target nearby and communicates with multiple legitimate users on the other side of the STAR-RIS. We utilize an integrated architecture that combines the fully-connected(FC)-RIS, an emerging type of beyond-diagonal(BD)-RIS, with the time-switching(TS)-STAR-RIS to enhance both the sensing and communications at the cost of possible target intercepting and propose the simultaneous-transmitting-and-reflecting fully-connected RIS(STAR-FCR) schemes to strike a balance between sensing and communications performance. Thereafter, observing the security-reliability performance tradeoff of the downlink ISAMC, we conduct closed-form analyses to compare COPs of round-robin scheduling(RS) and multiuser scheduling(MS) with the aid of a TS-based STAR-FC-RIS. Furthermore, we derive closed-form expressions of the sensing outage probability, communications outage probability(COP), and communications intercept probability, where an average of the three probabilities is exploited to obtain an optimized time allocation(OTA) of the *** results verify that the STAR-FCR-MS scheme outperforms the STAR-FCR-RS scheme in terms of sensing reliability and communications security. Moreover, an OTA remarkably enhances the overall performance of the STAR-FCR schemes of ISAMC systems.

关键词： simultaneous-transmitting-and-reflecting reconfigurable intelligent surface (RIS) fully-connected beyond-diagonal RIS integrated sensing and communications multiuser scheduling security

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An Anomaly Detection Algorithm for Estimating Interannual Variation in Crop Yields 5

An Anomaly Detection Algorithm for Estimating Interannual Va...

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5th IEEE International Conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Vinoth Kumar, R. Malathy Manjula, M. Veerandeswari, J. Indumathy, M. Raghul, B.S. Rajiv Gandhi College of Engineering & Technology Information Technology Department Puduherry Kriumambakkam India Data Science and Business Systems Srm Institute of Science and Technology Kattankulathur India

ISBN: (纸本)9798350383522

Crop production is a vital aspect of human survival. Reducing global poverty heavily relies on increasing the robustness in crop yields. The quantity of crops harvested from a given agricultural land, also known as crop yields of that land is the important parameter that helps to increase the smartness in adopting novel agricultural plans. Measuring this parameter can be done through supervised machine learning techniques which helps with accurate predictions given the historical data of crop yield. This paper proposes an ideation, that uses Support Vector Machines (SVMs) because of its strength being accuracy, robust and flexible. SVM can predict reliable crop yield estimates that help to allocate resources and smart planning for agricultural activities. Also, SVMs are good enough to handle the imperfections in historical crop yield datasets, outliers, and inconsistencies. Furthermore, SVMs can bring out the relationships and any nonlinear patterns in the crop yields which are influenced by uncertain environmental factors over various seasons. This helps to identify the anomalies in the historical data. The anomalies could be exceptionally high yield or the failures which happen to be pinpointed as abnormal years. The identified outliers are excluded by the SVM, thus bringing out a prediction model that focusses on 'normal years'. By this, it is possible to represent more accurate inter-annual trends of crop yields avoiding the anomalies aiding the farmer to make better decisions for much more productivity in the crop yield. Based on the ideation, simulations are performed that showcase the success of the SVM in predicting good for the crop yields in inter-annual period. © 2024 IEEE.

关键词： Anomaly detection crop yield inter-annual variability SVM

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Three-Branch BERT-Based Text Classification Network for Gastroscopy Diagnosis Text

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International Journal of Crowd science 2024年第1期8卷 56-63页

作者： Wang, Zhichao Zheng, Xiangwei Zhang, Jinsong Zhang, Mingzhe School of Information Science and Engineering Shandong Normal University Jinan250358 China State Key Laboratory of High-end Server & Storage Technology Jinan250013 China

During a hospital visit, a significant volume of Gastroscopy Diagnostic Text (GDT) data are produced, representing the unstructured gastric medical records of patients undergoing gastroscopy. As such, GDTs play a crucial role in evaluating the patient’s health, shaping treatment plans, and scheduling follow-up visits. However, given the free-text nature of GDTs, which lack a formal structure, physicians often find it challenging to extract meaningful insights from them. Furthermore, while deep learning has made significant strides in the medical domain, to our knowledge, there are not any readily available text-based pre-trained models tailored for GDT classification and analysis. To address this gap, we introduce a Bidirectional Encoder Representations from Transformers (BERT) based three-branch classification network tailored for GDTs. We leverage the robust representation capabilities of the BERT pre-trained model to deeply encode the texts. A unique three-branch decoder structure is employed to pinpoint lesion sites and determine cancer stages. Experimental outcomes validate the efficacy of our approach in GDT classification, with a precision of 0.993 and a recall of 0.784 in the early cancer category. In pinpointing cancer lesion sites, the weighted F1 score achieved was 0.849. © The author(s) 2024.

关键词： Diseases

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Multi-Granularity Perceptual Ensemble Learning Model with An Application

Multi-Granularity Perceptual Ensemble Learning Model with An...

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2022 International Conference on Machine Learning, Cloud Computing and Intelligent Mining, MLCCIM 2022

作者： Huang, Xueqin Zhu, Xianqiang Xu, Xiang Zhu, Mengting Nian, Aixin Guo, Yuanyuan National University of Defense Technology Science and Technology on Information Systems Engineering Laboratory Changsha China

ISBN: (数字)9781665498586

ISBN: (纸本)9781665498586

For the multi-granularity perceptual problem of structured data and its machine learning tasks, proposing a new ensemble learning framework, multi-granularity perceptual ensemble learning model (GEL), which introduced the concepts of granularity, granule and granular layer. GEL mainly includes multi-granularity perceptual data derivation algorithm (MPDDA), selection and training of base learners, and granule weighted ensemble strategy (GWES). GEL is a supervised learning model, in which the MPDDA provides it with multi-level and perspective on data and to solve problems, this enhanced the interpretability of the model. In GEL, it mainly adopts algorithm of decision tree type as the base to study, GWES will improve the ensemble effect. Finally, a user online behavior abnormal detection task is taken as the experimental application object to prove the effectiveness of GEL model. The experimental results showed that GEL model can improve the prediction effect to a certain extent, and its interpretability is significantly better than that of traditional ensemble learning methods. It can provide decision-makers with a reference of multi-granularity perspective and reduce the cognitive bias and prediction error caused by machine learning model viewing things from a single perspective. © 2022 IEEE.

关键词： Decision trees

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Lignin derived absorbent for efficient and sustainable CO_(2) capture

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Chinese Journal of Chemical engineering 2023年第2期54卷 89-97页

作者： Yuandong Cui Bin He Yu Lei Yu Liang Wanting Zhao Jian Sun Xiaomin Liu School of Chemistry and Chemical Engineering Qingdao UniversityQingdao 266071China School of Life Science Beijing Institute of TechnologyBeijing 100081China Key Laboratory of Medical Molecule Science and Pharmaceutics Engineering Ministry of Industry and Information TechnologyBeijing Engineering Research Center of Cellulose and Its DerivativesAdvanced Research Institute of Multidisciplinary ScienceBeijing Institute of TechnologyBeijing 100081China

High and cost-efficient capture of CO_(2) is a prerequisite and an inevitable path of carbon emission reduction. To address the challenges(high cost, low efficiency, less sustainability, etc.) of existing petroleum-based CO_(2) absorbents, herein, a class of efficient and sustainable lignin-based absorbents were resoundingly prepared by grafting the active amine group on a lignin derived compound vanillin and alkali lignin. The results demonstrated that vanillin modified by acrylamide achieved the excellent absorption capacity among the three absorbents, whose ability was 0.114 g CO_(2) per gram of absorbent under 25 ℃ and 100 kPa. In addition, the absorbent retained stable absorbability of CO_(2) after 6 *** absorbing capacity of the absorbent formed by the coupling of vanillin and acrylamide to CO_(2) was much greater than their own(i.e. 0 g CO_(2) ·g^(-1)vanillin, 0.01 g CO_(2) ·g^(-1) acrylamide, respectively).Detailed information revealed the multi-site synergistic absorption mechanism, in which CO_(2) has C and O double interactions with the amide group of the absorbent, and single interaction with the hydroxyl oxygen on the benzene ring of the absorbent. The absorption capacity of modified lignin for CO_(2) is as high as 0.12 g CO_(2) per gram of absorbent, which is comparable with that of model compound *** work not only provides a new idea for the design of bio-absorbents for CO_(2) capture, but explores the application potential of lignin-based materials.

关键词： Lignin(model compound) CO_(2)absorption Amide group Multi-site Acid-base Hydrogen bonding

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