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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Gurusubramani, S. Rosy, N. Arockia Chitra, D. Anwar Basha, H. Durairaj Isravel, Yabesh Abraham Shaker Reddy, Pundru Chandra Sri Sairam Engineering College Computer Science and Engineering Tamilnadu Chennai India Rmd Engineering College Department of Information Technology Tamilnadu Thiruvallur India Panimalar Engineering College Tamilnadu Chennai India School of Computer Science and Engineering Reva University Karnataka Bengaluru India School of Management S. A. College of Arts & Science Chennai Tamilnadu India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara India

ISBN: (纸本)9798350330724

As the frequency of security breaches continues to climb, cyber-security remains a major concern across all industries operating online. Because of the proliferation of new protocols, most of which originate with the Internet-of-Things(IoT), thousands of new zero-day attacks appear every day. Cyberattacks on the IoT have skyrocketed due to the proliferation of connected devices and the inherent security flaws in many network infrastructures. The safety of those systems depends on the ability to recognize and categorize harmful communications. This recommends that even cutting-edge methods, like conventional machine learning (ML) systems, have trouble spotting these atypical, yet dangerous, mutations in attacks over time. However, deep learning's (DL) widespread success in big data applications has piqued the curiosity of many in the cybersecurity community. In order to address multiple potential entry points at once, the authors of this research employ a distributed-framework built on DL to do so. The feedforward neural-network(FFNN) and the long short-term memory (LSTM) are two DL strategies that are compared and contrasted. Both the NSL-KDD and the BoT-IoT datasets are used to test the models' abilities to detect and prevent various cyberattacks. Accuracy of up to 99.98% was achieved across all configurations, proving that the proposed distributed system is successful in detecting multiple classes of cyberattacks. © 2023 IEEE.

关键词： Internet of things

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Bamboo Forest Growth Optimization Algorithm for Night Image Enhancement 15th

Bamboo Forest Growth Optimization Algorithm for Night Image...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Shi, Hao-Jie Pan, Jeng-Shyang Chu, Shu-Chuan Kong, Lingping Snášel, Václav College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Innovative Information Industry Research Center Harbin Institute of Technology Shenzhen Graduate School Shenzhen China Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

ISBN: (纸本)9789819700677

The Bamboo Forest Growth Optimization(BFGO) algorithm is an algorithm proposed by the growth law of bamboo forest, which has the advantages of fast convergence and not easily falling into local optimum. In this paper we have experimented the BFGO with several other population intelligence optimization algorithms on 23 benchmark test functions and proved its superiority. Also we apply the bamboo forest optimization algorithm to nighttime image enhancement in this paper, and the algorithm achieves better results on this application. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image enhancement

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Statistical Language Model for information Retrieval System Implementation in Digital Library

Statistical Language Model for Information Retrieval System ...

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International Conference on computer Research and Development (ICCRD)

作者： Muhammad Fikri Hasani Septian Eka Ady Buananta Reynaldi Adidarma Ryu Orlando Tamin Hafizh Ash Shiddiqi Vanessa Santonso Amanda Sugito Computer Science Departement School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331531881

ISBN: (纸本)9798331531898

A digital library is an application of technology to traditional libraries, providing access to various resources like books, magazines, and newspapers in digital formats. This allows users to access information anytime and anywhere with an internet connection. However, current digital library systems still face challenges, particularly in performing contextual searches for documents. Additionally, the order of search results does not always indicate how relevant the documents are to the user's query. This research explores the use of Statistical Language Models (SLM) to enhance the information retrieval system (IRS) process in digital library applications. SLM uses statistical techniques to analyze the semantic relationships between documents and users, increasing the efficiency and relevance of data collection. This research aims to identify the strengths and weaknesses of SLM, apply it to the IRS application, and evaluate its effectiveness for the application to be developed. These findings contribute to the development of an effective IRS, increasing the efficiency and accessibility of digital library applications and increasing the integration of SLM in IRS in the future.

关键词： Statistical analysis Semantics information retrieval Transformers Libraries User experience Usability Research and development System implementation Testing

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DPT‐tracker:Dual pooling transformer for efficient visual tracking

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 948-959页

作者： Yang Fang Bailian Xie Uswah Khairuddin Zijian Min Bingbing Jiang Weisheng Li Key Laboratory of Data Engineering and Visual Computing Chongqing University of Posts and TelecommunicationsChongqingChina Department of Mechanical Precision Engineering Malaysia‐Japan International Institute of TechnologyUniversity of Technology MalaysiaKuala LumpurMalaysia Department of Electrical and Computer Engineering Inha UniversityIncheonRepublic of Korea School of Information Science and Technology Hangzhou Normal UniversityHangzhouChina

Transformer tracking always takes paired template and search images as encoder input and conduct feature extraction and target‐search feature correlation by self and/or cross attention operations,thus the model complexity will grow quadratically with the number of input *** alleviate the burden of this tracking paradigm and facilitate practical deployment of Transformer‐based trackers,we propose a dual pooling transformer tracking framework,dubbed as DPT,which consists of three components:a simple yet efficient spatiotemporal attention model(SAM),a mutual correlation pooling Trans-former(MCPT)and a multiscale aggregation pooling Transformer(MAPT).SAM is designed to gracefully aggregates temporal dynamics and spatial appearance information of multi‐frame templates along space‐time *** aims to capture multi‐scale pooled and correlated contextual features,which is followed by MAPT that aggregates multi‐scale features into a unified feature representation for tracking *** tracker achieves AUC score of 69.5 on LaSOT and precision score of 82.8 on Track-ingNet while maintaining a shorter sequence length of attention tokens,fewer parameters and FLOPs compared to existing state‐of‐the‐art(SOTA)Transformer tracking *** experiments demonstrate that DPT tracker yields a strong real‐time tracking baseline with a good trade‐off between tracking performance and inference efficiency.

关键词： human‐computer interfacing image motion analysis pattern recognition signal processing tracking

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Stabilization of T-S Fuzzy Nonlinear systems by Intermittent Control 20

Stabilization of T-S Fuzzy Nonlinear Systems by Intermittent...

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20th IEEE International Conference on Mechatronics and Automation, ICMA 2023

作者： Li, Kun Feng, Yuming Chongqing Three Gorges University School of Electronic and Information Engineering Chongqing Wanzhou China Chongqing Three Gorges University School of Computer Science and Engineering Chongqing Wanzhou China

ISBN: (纸本)9798350320831

As an extremely significant class of system, the stability analysis problem of nonlinear systems has always been a research hotspot. This paper studies the intermittent control problem of nonlinear systems, and represents the system as a Takagi Sugeno (T-S) fuzzy model system. In order to stabilize the nonlinear systems, the intermittent control with variable switching rate control strategy is put out. In the proposed method, the sequence of switching rate is both variable and monotonically diminishing. In addition, this method can also be used to estimate the range of control gain K. Finally, numerical simulation outcomes validate the method's viability and efficiency. © 2023 IEEE.

关键词： Nonlinear systems

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Scene Classification of Remote Sensing Images Based on Improved HrNet 3

Scene Classification of Remote Sensing Images Based on Impro...

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3rd International Conference on Electronic information engineering and computer science, EIECS 2023

作者： Jin, Qianqian Luo, Jian Li, Yang Hou, Xiangjun China West Normal University School of Electronic Information Engineering Nanchong China China West Normal University School of Computer Science Nanchong China

ISBN: (纸本)9798350316674

Since remote sensing images are acquired by long-distance aerial photography, they contain complex and variable feature targets, and there will be occlusion, proximity, and scale inequality between different targets, so it is very important to choose suitable feature information to express the image when performing scene classification of remote sensing images. Therefore, we improved HrNet and proposed the ASFI-HrNet model. We added Acmix to enable the model to adaptively capture information from different regions;added IEBN to enable the model to reduce the effect of noise;added four horizontal convolutional branches to integrate information from different scales to a greater extent;and added SA-based feature fusion module to further enhance feature extraction. Through a series of comparison experiments and ablation experiments, it is found that the accuracy of the ASFI-HrNet model on the RSSCN dataset is 96.43%, which is 3.4% higher than that of the HrNet model, while ASFI-HrNet obtains higher accuracy and various evaluation metrics of image classification than the other classification models in the experiments. © 2023 IEEE.

关键词： Acmix HrNet IEBN Remote sensing images SA Scene Classification

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Document-level Event Extraction combined with Writing Style and Argument Role RelationA Document-level Event Extraction model DEEWR 4

Document-level Event Extraction combined with Writing Style ...

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4th International Conference on Artificial Intelligence and computer engineering, ICAICE 2023

作者： Zhao, Jingsheng Yan, Shuai Ni, Qihui Wang, Yongzheng Zhu, Qiaoming School of Information and Control Engineering Qingdao University of Technology Qingdao266520 China School of Computer Science and Technology Soochow University Suzhou215006 China

ISBN: (纸本)9798400708831

Document-level event extraction is an important task in natural language processing, which faces the challenges of arguments-scattering and multi-events. The current mainstream method is to decompose document-level event extraction into three subtasks: entity extraction, event type recognition, and argument-filling. However, existing methods flew consider the impact of writing style on event extraction, and have complex models with high training costs. To address these problems, this paper proposes a document-level event extraction model that incorporates both writing style and argument role relation. First, Roberta is used to encode the document sentences to obtain semantic features, and BiLSTM-CRF is used to identify candidate entities. Second, the writing style is modeled as the relative distribution of argument roles in the document and is used to supervise the event type recognition results. Third, argument-filling is simplified to the relation judgment between the argument roles. This method effectively addresses the problem of scattered arguments by constructing event clusters based on the relation of argument roles. It also deals with the multi-event problem through event cluster division. Compared with previous path expansion-based methods, experimental results show that the model has lower training costs and performs better in low training epochs. © 2023 ACM.

关键词： Filling

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Conformal Graph-level Out-of-distribution Detection with Adaptive Data Augmentation 25

Conformal Graph-level Out-of-distribution Detection with Ada...

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34th ACM Web Conference, WWW 2025

作者： Lin, Xixun Cao, Yanan Sun, Nan Zou, Lixin Zhou, Chuan Zhang, Peng Zhang, Shuai Zhang, Ge Wu, Jia Institute of Information Engineering Chinese Academy of Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Beijing China School of Cyber Science and Engineering Wuhan University Wuhan China Academy of Mathematics and Systems Science CAS School of Cyber Security University of Chinese Academy of Sciences Beijing China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China Academy of Mathematics and Systems Science CAS Beijing China School of Computer Science and Technology Donghua University Shanghai China Department of Computing Macquarie University Sydney Australia

ISBN: (纸本)9798400712746

Graph-level out-of-distribution (OOD) detection, which attempts to identify OOD graphs originated from an unknown distribution, is a vital building block for safety-critical applications in Web and society. Current approaches concentrate on how to learn better graph representations, but fail to provide any statistically guarantee on detection results, therefore impeding their deployments in the scenario where detection errors would result in serious consequences. To overcome this critical issue, we propose the Conformal Graph-level Out-of-distribution Detection (CGOD), extending the theory of conformal prediction to graph-level OOD detection with a rigorous control over the false positive rate. In CGOD, we develop a new aggregated non-conformity score function based on the proposed adaptive data augmentation. Through the guidance from two designed metrics, i.e., score consistency and representation diversity, our augmentation strategy can generate multiple non-conformity scores, and aggregating these generated non-conformity scores together is robust to the misleading information. Meanwhile, our score function can perceive the subsequent process of conformal inference, enabling the aggregated non-conformity score to be adaptive to different input graphs and deriving a more accurate score estimation. We conduct experiments on multiple real-world datasets with different empirical settings. Extensive results and model analyses demonstrate the superior performance of our approach over several competitive baselines. © 2025 Copyright held by the owner/author(s).

关键词： Network theory (graphs)

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Tunable Multicolor Fluorescence of Perovskite-Based Composites for Optical Steganography and Light-Emitting Devices

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Research 2023年第1期2022卷 185-195页

作者： Kewei Ma Qingfeng Gui Cihui Liu Yunyi Yang Fangjian Xing Yunsong Di Xiaoming Wen Baohua Jia Zhixing Gan Center for Future Optoelectronic Functional Materials School of Computer and Electronic Information/School of Artificial IntelligenceNanjing Normal UniversityNanjing 210023China College of Naval Architecture and Ocean Engineering Jiangsu Maritime InstituteNanjing 211170China Centrefor Translational Atomaterials Schoolof ScienceSwinburne University of TechnologyJohn Street Hawthon VIC3122Australia School of Science RMIT UniversityMelbourne3000 VICAustralia College of Materials Science and Engineering Qingdao University of Science and TechnologyQingdao 266042China

Multicolor fluorescence of mixed halide perovskites enormously enables their applications in photonics and ***,it remains an arduous task to obtain multicolor emissions from perovskites containing single halogen to avoid phase ***,a fluorescent composite containing Eu-based metal-organic frameworks(MOFs),OD Cs PbBre,and 3D CsPbBrs is *** excitations at 365 nm and 254nm,the pristine composite emits blue(B)and red(R)fluorescence,which are ascribed to radiative defects within CsPbBr and 5Do→7F,transitions of Eu^(3+),***,after light soaking in the ambient environment,the blue fluorescence gradually converts into green(G)emission due to the defect repairing and 0D-3D phase *** permanent and unique photochromic effect enables anticounterfeiting and microsteganography with increased security through a micropatterning ***,the RGB luminescence is highly stable after encapsulation by a transparent polymer ***,trichromatic light-emitting modules are fabricated by using the fluorescent composites as color-converting layers,which almost fully cover the standard color ***,this work innovates a strategy for construction of tunable multicolor luminescence by manipulating the radiative defects and structural dimensionality.

关键词： composite composites perovskite

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A Machine Learning Edged Self-Adaptable Vehicle Slowdown Earliest Warning information System Using IOT Based Traffic Prediction Model

A Machine Learning Edged Self-Adaptable Vehicle Slowdown Ear...

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2023 International Conference on Artificial Intelligence and Knowledge Discovery in Concurrent engineering, ICECONF 2023

作者： Murali, N. David, D. Beulah Saveetha School of Engineering Simats Department of Computer Science and Engineering Tamilnadu Chennai India Institute of Information Technology Saveetha School of Engineering Simats Department of Data Analytics Tamilnadu Chennai India

ISBN: (纸本)9798350334357

Development of vehicle safety is the work's primary goal, according to its abstract vehicle in traffic congestion using the Internet of Things (IoT). The intense growth of the city's vehicle population needs intelligent traffic systems to be considered resourcefully and sustainably by enhancing the full advantage of recent technology. The unpredictable traffic flow is a substantial issue that carries a colossal traffic movement in real-time scenarios. The random traffic control systems during peak and non-peak hours with unproductive human possessions, which leads to increased traffic and road offenses due to ineffective traffic monitoring systems. To sort down this dynamic scenario, the self-adaptable machine learning approach has been adopted to sequence the intelligent traffic flow by using IOT based early warning system in vehicles. The proposed work focused on the traffic congestion prediction operationalized using the unsupervised algorithm to train the gathered data sets using a neural network. It also aims to deliver a clarification that will upturn the comfort level of travelers to make intelligent and better transference choices. A neural network is a reasonable approach to finding traffic circumstances in sequence with this, the machine learning algorithms and their accuracy, which practices an outline in the collected data sets and then produces crucial decisions in evidence about traffic flow and congestion levels. The most focused part of the research is to enhance the intelligence in the IoT docking system, which prevents the vehicle congestion and returns a flawless, innovative design. © 2023 IEEE.

关键词： Machine learning

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