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作者： Zhou, Chengcheng Xu, Haitao Tang, Runze Lin, Ruijie Guo, Chao Han, Zhu University of Science and Technology Beijing School of Computer and Communication Engineering Beijing China Beijing Institute of Electronic System Engineering Beijing China China Academy of Information and Communications Technology Beijing China Beijing Electronics Science and Technology Institute Department of Electronic and Communication Engineering Beijing China University of Houston HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

In this article, the power control and interference pricing problems between the macro base station (MBS) and small cell base stations (SBSs) in Heterogeneous networks (HetNets) are investigated. To optimally allocate power resources and effectively manage the power resources to reduce interference in HetNets, an innovative Stackelberg differential game approach is proposed. In the proposed Stackelberg game, the SBSs control their transmit power to achieve cost minimization based on the interference price offered by the MBS. Given the optimal power control strategies of SBSs, the MBS can adjust the interference price to realize the objective optimization. Renewable energy sources are introduced into the proposed HetNets, and differential equations formulate the SBSs' dynamic energy. Meanwhile, the MBS's state is given by the dynamic spectrum resources. Then the differential game is imported to achieve optimal power control and interference pricing. The open-loop Nash equilibrium solutions are first analyzed for the MBS and SBSs. Then the feedback Nash equilibrium solutions are given. Under the feedback pattern, the proposed model's performances in the non-cooperative and cooperative situations are discussed. Numerical simulations are given to show the convergence and effectiveness of the proposed game approach. © 1967-2012 IEEE.

关键词： Power control

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Advances in Noble Metal-Decorated Metal Oxide Nanomaterials for Chemiresistive Gas Sensors:Overview

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Nano-Micro Letters 2023年第6期15卷 353-427页

作者： Li‑Yuan Zhu Lang‑Xi Ou Li‑Wen Mao Xue‑Yan Wu Yi‑Ping Liu Hong‑Liang Lu State Key Laboratory of ASIC and System Shanghai Institute of Intelligent Electronics and SystemsSchool of MicroelectronicsFudan UniversityShanghai 200433People’s Republic of China School of Opto‑Electronic Information and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093People’s Republic of China State Key Laboratory of Metal Matrix Composites School of Material Science and EngineeringShanghai Jiao Tong UniversityShanghai 200240People’s Republic of China

Highly sensitive gas sensors with remarkably low detection limits are attractive for diverse practical application fields including real-time environmental monitoring,exhaled breath diagnosis,and food freshness *** various chemiresistive sensing materials,noble metal-decorated semiconducting metal oxides(SMOs)have currently aroused extensive attention by virtue of the unique electronic and catalytic properties of noble *** review highlights the research progress on the designs and applications of different noble metal-decorated SMOs with diverse nanostructures(e.g.,nanoparticles,nanowires,nanorods,nanosheets,nanoflowers,and microspheres)for high-performance gas sensors with higher response,faster response/recovery speed,lower operating temperature,and ultra-low detection *** key topics include Pt,Pd,Au,other noble metals(e.g.,Ag,Ru,and Rh.),and bimetals-decorated SMOs containing ZnO,SnO_(2),WO_(3),other SMOs(e.g.,In_(2)O_(3),Fe_(2)O_(3),and CuO),and heterostructured *** addition to conventional devices,the innovative applications like photo-assisted room temperature gas sensors and mechanically flexible smart wearable devices are also ***,the relevant mechanisms for the sensing performance improvement caused by noble metal decoration,including the electronic sensitization effect and the chemical sensitization effect,have also been summarized in ***,major challenges and future perspectives towards noble metal-decorated SMOs-based chemiresistive gas sensors are proposed.

关键词： Noble metal Bimetal Semiconducting metal oxide Chemiresistive gas sensor Electronic sensitization Chemical sensitization

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VulPin: Finer-Grained Slicing for Pinpointing Vulnerability in Binary Programs 23

VulPin: Finer-Grained Slicing for Pinpointing Vulnerability ...

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Proceedings of the 2023 7th International Conference on computer Science and Artificial Intelligence

作者： Abdulrahman Hamman Adama Chukkol Senlin Luo Haruna Yunusa Abdulganiyu Abdu Yusuf Abulfatahi Mohammed Information and Electronics Department of Information and Electronics. Beijing Institute of Technology Beijing China China Information and Electronics Information System and Security & Countermeasures Experimental Center Beijing Institute of Technology Beijing China China Automation Science and Electrical Engineering School of Automation Science and Electrical Engineering Beihang University Beijing China China Computer Science School of Computer Science and Technology Beijing Institute of Technology Beijing China China Information Technology School of Management and Information Technology Modibbo Adama University Yola Nigeria Nigeria

ISBN: (纸本)9798400708688

Identifying vulnerabilities in binary code with precision is a critical area of study in the field of software security. Most existing techniques fail to pinpoint the vulnerability's exact location, due to the extensive labelled data requirement and lack of targeted approach to improve on the problematic code. In this paper, we introduce VulPin, a system specifically designed to pinpoint vulnerabilities in binary code. This framework uses a hierarchical attention network that analyze program slices along with a vulnerability position matrix. Experimental outcomes indicate that our framework not only outperforms existing approaches but also pinpoints vulnerabilities more rapidly with a high Accuracy and a very low rate of false positives.

关键词： Binary codes KEYWORDS Program slice Neural networks Vulnerability detection Vulnerability pinpoint

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Spectral characteristics analysis of typical crops in different growth periods based on spectral domain interpolation 9

Spectral characteristics analysis of typical crops in differ...

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9th Symposium on Novel Photoelectronic Detection technology and Applications

作者： Li, Xusheng Shao, Yakui Jiang, Chenchen Zhao, Yingjun Qin, Kai Zhang, Donghui Liu, Yufeng National Key Laboratory of Remote Sensing Information and Imagery Analyzing Technology Beijing Research Institute of Uranium Geology Beijing100029 China Precision Forestry Key Laboratory of Beijing Beijing Forestry University Beijing100083 China Institute of Remote Sensing and Geographic Information System School of Earth and Space Sciences Peking University Beijing100871 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Computer and Information Engineering Chuzhou University Anhui 239000 China

ISBN: (纸本)9781510664432

Under the influence of the physicochemical characteristics of the crops and the space-time environmental factors, even the same crop will show some oscillation in the spectrum. Previous studies mostly used arithmetic mean value to reduce the uncertainty caused by spectral oscillation, but the characterization ability of mean value is susceptible to the degree of numerical difference. To solve these problems and to explore the relationship between the accuracy of typical crop identification and the growth period and spectral characteristics, we proposed a spectral construction algorithm based on spectral domain interpolation (SDI). Using SDI and traditional arithmetic mean (AM) method, the characteristic spectra of typical crops (winter wheat, oilseed grape) and the main background vegetation (trees, grasses, rice stubble) were constructed at different growth stages (March, April and May). Then, feature parameters were extracted based on the constructed characteristic spectrum. The importance evaluation and linear discriminant analysis of the extracted feature parameters were carried out at last. The optimal identifying growth period and identifying feature parameters of typical crops were obtained through comparative analysis, at the same time, the availability and superiority of SDI were verified. The following conclusions were drawn: (1) SDI has a good resistance to extremes, and can retain the spectral characteristics of crops well, and construct a more characterizing characteristic spectrum. (2) The best identifying growth periods of oilseed grape and winter wheat are early flowering period in March and heading period in April, respectively. The best identifying characteristics of winter wheat and oilseed grape are yellow edge position and red edge amplitude, respectively. (3) Winter wheat and oilseed grape can be well identified by using the position of the yellow edge in March and the blue edge area and the red edge amplitude in April. © 2023 SPIE.

关键词： Crops

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Design of Dual Polarized Filtering Dipole Antenna

Design of Dual Polarized Filtering Dipole Antenna

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2021 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2021

作者： Liang, Yu-Zhong Xiang, Kai-Ran Dong, Yong Chen, Fu-Chang Wang, Yun South China University of Technology School of Electronic and Information Engineering Guangzhou510641 China Beijing Institute of Spacecraft System Engineer Beijing100094 China

ISBN: (纸本)9781665418287

A printed filtering dipole antenna (PFDA) is investigated. The proposed PFDA consists of a pair of identical arms separately placed on the both sides of the substrate plate. Two half-wavelength resonators are used for feeding one arm of the dipole. Cross coupling is introduced in this design to achieve one radiation null at the upper stopband, which makes the proposed antenna have a good out-of-band suppression. The impedance bandwidth of the PFDA is 10%, and its ports isolation is higher than 19 dB. © 2021 IEEE.

关键词： Dipole antennas

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Bird Swarm Algorithm with Fuzzy Min-Max Neural Network for Financial Crisis Prediction

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computers, Materials & Continua 2022年第10期73卷 1541-1555页

作者： K.Pradeep Mohan Kumar S.Dhanasekaran I.S.Hephzi Punithavathi P.Duraipandy Ashit Kumar Dutta Irina V.Pustokhina Denis A.Pustokhin Department of Computing Technologies Associate ProfessorSRM Institute of Science and TechnologyKattankulathur603203India Department of Information Technology Kalasalingam Academy of Research and Education626126India Department of Computer Science and Engineering Sphoorthy Engineering CollegeHyderabadTelangana501510India Department of Electrical and Electronics Engineering J B Institute of Engineering and TechnologyHyderabadTelangana500075India Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of Management109542MoscowRussia

Financial crisis prediction(FCP)models are used for predicting or forecasting the financial status of a company or financial *** is considered a challenging issue in the financial *** and machine learning(ML)models can be employed for the design of accurate FCP *** numerous works have existed in the literature,it is needed to design effective FCP models adaptable to different *** study designs a new bird swarm algorithm(BSA)with fuzzy min-max neural network(FMM-NN)model,named BSA-FMMNN for *** major intention of the BSA-FMMNN model is to determine the financial status of a firm or *** presented BSA-FMMNN model primarily undergoes minmax normalization to transform the data into uniformity ***,k-medoid clustering approach is employed for the outlier removal ***,the classification process is carried out using the FMMNN model,and the parameters involved in it are tuned by the use of *** utilization of proficient parameter selection process using BSA demonstrate the novelty of the *** experimental result analysis of the BSA-FMMNN model is validated using benchmark dataset and the comparative outcomes highlighted the supremacy of the BSA-FMMNN model over the recent approaches.

关键词： Financial crisis predictive model machine learning outlier removal clustering metaheuristics

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On the Discriminability of Self-Supervised Representation Learning

arXiv

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arXiv 2024年

作者： Song, Zeen Qiang, Wenwen Zheng, Changwen Sun, Fuchun Xiong, Hui The University of Chinese Academy of Sciences Beijing China The Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China The Science & Technology on Integrated Information System Laboratory Department of Computer Science and Technology Tsinghua University Beijing China The Hong Kong University of Science and Technology China

Self-supervised learning (SSL) has recently achieved significant success in downstream visual tasks. However, a notable gap still exists between SSL and supervised learning (SL), especially in complex downstream tasks. In this paper, we show that the features learned by SSL methods suffer from the crowding problem, where features of different classes are not distinctly separated, and features within the same class exhibit large intra-class variance. In contrast, SL ensures a clear separation between classes. We analyze this phenomenon and conclude that SSL objectives do not constrain the relationships between different samples and their augmentations. Our theoretical analysis delves into how SSL objectives fail to enforce the necessary constraints between samples and their augmentations, leading to poor performance in complex tasks. We provide a theoretical framework showing that the performance gap between SSL and SL mainly stems from the inability of SSL methods to capture the aggregation of similar augmentations and the separation of dissimilar augmentations. To address this issue, we propose a learnable regulator called Dynamic Semantic Adjuster (DSA). DSA aggregates and separates samples in the feature space while being robust to outliers. Through extensive empirical evaluations on multiple benchmark datasets, we demonstrate the superiority of DSA in enhancing feature aggregation and separation, ultimately closing the performance gap between SSL and SL. © 2024, CC BY.

关键词： Supervised learning

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Deep Learning Enabled Object Detection and Tracking Model for Big Data Environment

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computers, Materials & Continua 2022年第11期73卷 2541-2554页

作者： K.Vijaya Kumar E.Laxmi Lydia Ashit Kumar Dutta Velmurugan Subbiah Parvathy Gobi Ramasamy Irina V.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering Vignan’s Institute of Engineering for WomenVisakhapatnam530049India Department of Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science and Information System College of Applied SciencesAlMaarefa UniversityRiyadh11597Kingdom of Saudi Arabia Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126TamilnaduIndia Department of Computer Science Christ UniversityBangalore560029India Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of ManagementMoscow109542Russia

Recently,big data becomes evitable due to massive increase in the generation of data in real time ***,object detection and tracking applications becomes popular among research communities and finds useful in different applications namely vehicle navigation,augmented reality,surveillance,*** paper introduces an effective deep learning based object tracker using Automated Image Annotation with Inception v2 based Faster RCNN(AIA-IFRCNN)model in big data *** AIA-IFRCNN model annotates the images by Discriminative Correlation Filter(DCF)with Channel and Spatial Reliability tracker(CSR),named DCF-CSRT *** AIA-IFRCNN technique employs Faster RCNN for object detection and tracking,which comprises region proposal network(RPN)and Fast *** addition,inception v2 model is applied as a shared convolution neural network(CNN)to generate the feature ***,softmax layer is applied to perform classification *** effectiveness of the AIA-IFRCNN method undergoes experimentation against a benchmark dataset and the results are assessed under diverse aspects with maximum detection accuracy of 97.77%.

关键词： Object detection tracking convolutional neural network inception v2 image annotation

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An Empirical Analysis of Various Techniques of Solving Obstacles through Artificial Intelligence 4

An Empirical Analysis of Various Techniques of Solving Obsta...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Vaidya, Himadri Dhabliya, Dharmesh Jweeg, Muhseen Almusawi, Muntather Naser, Zamen Latef Hashem, Amjad Jawad, Ahmed Qusay Graphic Era Hill University Department of Cse India Graphic Era Deemed to Be University Uttarakhand Dehradun248007 India Information Technology Vishwakarma Institute of Information Technology Pune India Al-Farahidi University Baghdad Iraq The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq National University of Science and Technology College of Technical Engineering Dhi Qar64001 Iraq Alkunooze University College Computer Engineer Department Basra Iraq Kut University College Department of Electrical Engineering Technologies Wasit Iraq

ISBN: (纸本)9798350360165

In the space sector, artificial intelligence (AI), and especially machine learning (ML), is becoming more and more popular. These days, ML algorithms are extensively used in many different contexts, including operational control of satellite constellations, autonomous navigation, and spacecraft health monitoring. There are two main types of surveys that are now available on AI applications in space missions, each with certain drawbacks. Generally speaking, the first category is out of date since it does not include more recent advancements like Deep Learning (DL) and bioinspired AI algorithms. On the other hand, the second group tends to be too detailed and concentrates only on certain AI approaches, which can put off those who are looking for a more comprehensive knowledge, especially those with technical expertise in other subjects. In order to rectify these deficiencies, we provide a succinct summary that provides a thorough understanding of the uses and difficulties of AI in the space sector. Our study covers classical AI approaches used in several space mission disciplines, emphasizing their usefulness in real-world applications rather than going into formal methodologies. We also investigate bioinspired AI algorithms and point out possible uses for them. Beyond technical obstacles, we recognize and talk about significant legal ramifications of using AI in space projects. We expand our evaluation to include developments in Blockchain and Internet of Things (IoT) technology in the consideration of future directions. Specifically, we highlight how AI and Blockchain are convergent and pinpoint three major application fields in the space community that stand to gain a great deal from this convergence. These areas include satellite communications, spacecraft data security, and the Internet of Things in Space (IoST). Our review strives to be a useful tool for anyone interested in learning more about the complex world of artificial intelligence applications in space b

关键词： Satellite communication systems

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An improvised CNN model for fake image detection

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International Journal of information technology (Singapore) 2023年第1期15卷 5-15页

作者： Hamid, Yasir Elyassami, Sanaa Gulzar, Yonis Balasaraswathi, Veeran Ranganathan Habuza, Tetiana Wani, Sharyar Information Security and Engineering Technology Abu Dhabi Polytechnic Abu Dhabi United Arab Emirates Department of Management Information System College of Business Administration King Faisal University Riyadh Saudi Arabia Department of Networking and Communications School of Computing SRM Institute of Science and Technology Chengalpattu India Department of Computer Science and SWE College of Info. Tech UAEU Al Ain United Arab Emirates Department of Computer Science International Islamic University Malaysia Kuala Lumpur Malaysia

The last decade has witnessed a multifold growth of image data courtesy of the emergence of social networking services like Facebook, Instagram, LinkedIn etc. The major menace faced by today’s world is the issue of doctored images, where-in the photographs are altered using a rich set of ways like splicing, copy-move, removal to change their meaning and hence demands serious mitigation mechanisms to be thought of. The problem when seen from the prism of Artificial intelligence is a binary classification one, where-in the characterization must be drawn between the original and the manipulated images. This research work proposes a computer vision model based on Convolution Neural Networks for fake image detection. A comparative analysis of 6 popular traditional machine learning models and 6 different CNN architectures to select the best possible model for further experimentation. The proposed model based on ResNet50 employed with powerful preprocessing techniques results in a perfect fake image detector having a total accuracy of 0.99 having an improvement of around 18% performance with other models. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Artificial Intelligence Classification Convolution neural networks Doctored images Splicing

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