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International Conference on Computers, Communications, and systems (ICCCS)

作者： Xiaohan Niu Ling Huang Xialu Zhang Zhangqin Huang Jianhua Huang Jian Li Beijing Engineering Research Center for IoT Software and Systems Beijing University of Technology Beijing China Beijing Ruixingu Technology Co. Ltd. Beijing China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

The transportation pipeline of oil and gas buried in the ground will be affected by non-human factors such as geographic natural disasters and some human factors with the accumulation of time, which will lead to settlement and displacement. Using BeiDou satellite navigation system (BDS) positioning technology, radio frequency identification (RFID) technology, combined with data visualization and analysis technology, to analyze the current location status information of underground oil and gas pipelines, and to provide decision-making support for the monitoring of oil and gas pipeline settlement and displacement has become an important research content in the field of oil and gas pipelines. This paper is based on BeiDou high-precision coordinate information dataset and RFID tag information dataset, comprehensive use of geospatial data visualization method and multi-dimensional feature data visualization method, all-round, real-time display of the current pipeline monitoring point location status information, for oil and gas pipeline settlement displacement monitoring and early warning to provide a scientific basis for the improvement of the staff's efficiency of the daily pipeline inspection, timely reporting of the Risks, greatly reducing the frequency of pipeline accidents, effectively protect the safety of oil and gas pipelines.

关键词： Oils Disasters Current measurement Pipelines Data visualization Human factors Displacement measurement

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LinNet: Linear Network for Efficient Point Cloud Representation Learning 38

LinNet: Linear Network for Efficient Point Cloud Representat...

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Deng, Hao Jing, Kunlei Cheng, Shengmei Liu, Cheng Ru, Jiawei Bo, Jiang Wang, Lin State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Northwest University China Shaanxi Key Laboratory of Higher Education Institution of Generative Artificial Intelligence and Mixed Reality China School of Software Engineering Xi'an Jiaotong University China Department of Computing The Hong Kong Polytechnic University Hong Kong

Point-based methods have made significant progress, but improving their scalability in large-scale 3D scenes is still a challenging problem. In this paper, we delve into the point-based method and develop a simpler, faster, stronger variant model, dubbed as LinNet. In particular, we first propose the disassembled set abstraction (DSA) module, which is more effective than the previous version of set abstraction. It achieves more efficient local aggregation by leveraging spatial anisotropy and channel anisotropy separately. Additionally, by mapping 3D point clouds onto 1D space-filling curves, we enable parallelization of downsampling and neighborhood queries on GPUs with linear complexity. LinNet, as a purely point-based method, outperforms most previous methods in both indoor and outdoor scenes without any extra attention, and sparse convolution but merely relying on a simple MLP. It achieves the mIoU of 73.7%, 81.4%, and 69.1% on the S3DIS Area5, NuScenes, and SemanticKITTI validation benchmarks, respectively, while speeding up almost 10x times over PointNeXt. Our work further reveals both the efficacy and efficiency potential of the vanilla point-based models in large-scale representation learning. Our code will be available at https://***/DengH293/LinNet. © 2024 Neural information processing systems foundation. All rights reserved.

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Deep Learning Based Intrusion Detection for IoT Networks

Deep Learning Based Intrusion Detection for IoT Networks

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Ubiquitous Computing and Intelligent Information systems (ICUIS), International Conference on

作者： P. Ananthi K. Nirmaladevi Mothika G Department of Computer Technology-PG Kongu Engineering College Erode Tamilnadu Department of Computer Applications Madanapalle Institute of Technology &Science MSc Software Systems Kongu Engineering College Erode India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

Intrusion detection is crucial for securing IoT networks amid the rapid proliferation of devices, posing significant security challenges. This study offers a unique intrusion detection model for IoT deep learning methods used in networks include Convolutional Neural Networks (CNN), TabNet, and the Transient Search Optimization (TSO) method. The project starts by developing and evaluating distinct intrusion detection components. CNN, specializing in feature extraction, analyzes network traffic for potential intrusions. TabNet, known for interpretability and effectiveness with structured data, captures intricate relationships. The novel TSO algorithm improves the system's ability to detect temporary anomalies. Each component's performance is assessed individually, highlighting unique strengths. TabNet excels in structured IoT data pattern identification, surpassing CNN. The TSO algorithm strengthens temporary anomaly recognition, enhancing IoT network security. An ensemble technique combines CNN and TabNet predictions, reducing false positives and negatives to elevate overall detection accuracy. The ensemble, using both CNN and TabNet, achieves an impressive accuracy rate exceeding 99%, demonstrating significant gains in intrusion detection performance.

关键词： Deep learning Accuracy Intrusion detection Telecommunication traffic Prediction algorithms Ubiquitous computing Convolutional neural networks Internet of Things Transient analysis Optimization

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Age of Multi-Source Information Minimization for UAV-Assisted Wireless Sensor Networks

Age of Multi-Source Information Minimization for UAV-Assiste...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yiming Xiao Mingxiong Zhao Jianping Yao Jemin Lee Tony Q. S. Quek National Pilot School of Software Yunnan University School of Information Engineering Guangdong University of Technology School of Electrical and Electronic Engineering Yonsei University Information Systems Technology and Design Singapore University of Technology and Design

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

This paper explores an unmanned aerial vehicle (UAV)-assisted wireless sensor network (WSN), where one UAV-enabled mobile data collector periodically collects data from a set of ground sensor nodes (SNs) to the data center (DC), intending to fulfill the users’ diverse needs. To accurately evaluate information freshness, we introduce the Age of Multi-Source Information (AomsI) metric. Under this framework, we formulate the optimization problem aiming to minimize the average AomsI for all users. To tackle this non-convex problem, we decompose it into two sub-problems: the SN-side optimization problem and the UAV-side optimization problem. For the first subproblem, we propose the parallel optimization method to obtain the optimal solution. For the second subproblem, we first determine the optimal UAV transmission power, and then propose the task-associated genetic algorithm (TAGA) to design the UAV’s visiting order. Simulation results demonstrate that the UAV tends to prioritize the collection of all SN data required by the same user during data collection.

关键词： Measurement Wireless sensor networks Simulation Optimization methods Data collection Autonomous aerial vehicles Minimization Global communication Optimization Genetic algorithms

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Cyberbullying Classification Using a Natural Language Processing and Machine Learning Techniques

Cyberbullying Classification Using a Natural Language Proces...

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2024 International Conference on Advances in Computing Research on Science engineering and technology, ACROSET 2024

作者： Mankar, Nikhilesh Pramod Mahure, Tushar Joshi, Anushka A. Pande, Sagar Dhanraj Khamparia, Aditya Nathe, Ankita Shrikrushna Turjman, Fadi Al Pune India Independent Researcher Amravati India Babasaheb Bhimrao Ambedkar University Department of Computer Science Amethi India Kavikulguru Institute of Technology and Science Department of Information Technology Nagpur India Ai and Robotics Institute Near East University Artificial Intelligence Software Information Systems Engineering Departments Nicosia Cyprus University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey

ISBN: (纸本)9798350388800

The usage of online entertainment has increased dramatically after some time with the enhancement of the Internet and has turned into the most compelling systems administration stage in this century. Notwithstanding, the upgrade of a social network regularly makes adverse consequences on society that add to two or three terrible peculiarities, for example, online maltreatment, badgering cyberbullying, cybercrime, and internet savaging. Cyberbullying habitually prompts genuine mental and actual misery, especially for ladies and kids, and even occasionally make them to use self-destruction methods. Multiple number of episodes have as of late happened worldwide because of online badgering, like sharing private talks, bits of hearsay, and sexual comments. In this way, the distinguishing proof of harassing messages or messages via online entertainment has gained a significant measure for development for consideration with in scientists. The ground or motive for this exploration is to create a full-fledged way or a method to differentiate online harmful and tormenting drafts by combining various methods and techniques in the domain of natural language processing and AI © 2024 IEEE.

关键词： Adversarial machine learning

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Parameter Optimization via Reinforcement Learning for the Regulation of Swarms 9

Parameter Optimization via Reinforcement Learning for the Re...

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9th International Conference on Information, Cybernetics, and Computational Social systems, ICCSS 2023

作者： Wu, Qizhen Liu, Gaoxiang Liu, Kexin Chen, Lei School of Automation Science and Electrical Engineering Beihang University Beijing China Environment School of Automation Science and Electrical Engineering Beihang University The State Key Laboratory of Software Development Beijing China Advanced Research Institute of Multidisciplinary Sciences Beijing Institute of Technology Beijing China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing China

ISBN: (纸本)9798350342239

The bird-oid object (Boids) model proposes a control algorithm to make the positions between agents achieve cooperative stability. By changing the parameters of cohesion and repulsion in the algorithm, the agents in the swarm can be made to converge to different positions, causing expansion and contraction of the formation. But it is often more difficult to select the appropriate parameters to form the ideal formation. Therefore, this paper proposes a method to improve the cohesive and repulsive parameters in the Boids model based on Q-learning network to achieve a simulation scenario with continuous obstacle avoidance and maximum coverage of space. © 2023 IEEE.

关键词： Reinforcement learning

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Prediction of New Functional Fluorooxoborates with Expected Anionic Framework:A Case of CaB_(3)O_(5)F

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Chinese Physics Letters 2025年第3期42卷 85-92页

作者： Ke-Wang Zhang Wen-Qi Jin Ke Li Abudukadi Tudi Lin-Lin Liu Cong-Wei Xie Yu Xie Research Center for Crystal Materials CAS Key Laboratory of Functional Materials and Devices for Special Environmental ConditionsXinjiang Key Laboratory of Functional Crystal MaterialsXinjiang Technical Institute of Physics&ChemistryCASUrumqi 830011China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Materials Simulation Methods&Software of Ministry of Education International Center of Future ScienceKey Laboratory of Physics and Technology for Advanced Batteries(Ministry of Education)College of PhysicsJilin UniversityChangchun 130012China

The prediction of new fluorooxoborates as ultraviolet(UV)/deep ultraviolet(DUV)opto-electronic functional materials from a largely unexplored chemical space is a challenging *** has been suggested that the anionic frameworks formed by B–O and B–O–F units significantly determine the physical properties of ***,the rational design of anionic frameworks could facilitate the materials discovery ***,we propose that a candidate anionic framework can be efficiently derived from an existing one by slightly altering its oxygen *** this idea,we hypothesized the existence of a 1D[B_(3)O_(5)F]_(∞)chain from the wellknown 2D[B_(6)O_(9)F_(2_)]_(∞)***,seven CaB_(3)O_(5)F structures with the expected anionic framework were successfully ***-principles calculations show that all these structures have potential in the UV/DUV birefringent or nonlinear optical(NLO)material field,indicating that the 1D[B_(3)O_(5)F]_(∞)chain is indeed a promising anionic framework for achieving UV/DUV birefringent and NLO performance.

关键词： process borate anionic

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Curriculum Dataset Distillation

arXiv

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arXiv 2024年

作者： Ma, Zhiheng Cao, Anjia Yang, Funing Wei, Xing Shenzhen University of Advanced Technology China School of Software Engineering Xi'an Jiaotong University China Guangdong Provincial Key Laboratory of Computility Microelectronics China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China

Most dataset distillation methods struggle to accommodate large-scale datasets due to their substantial computational and memory requirements. In this paper, we present a curriculum-based dataset distillation framework designed to harmonize scalability with efficiency. This framework strategically distills synthetic images, adhering to a curriculum that transitions from simple to complex. By incorporating curriculum evaluation, we address the issue of previous methods generating images that tend to be homogeneous and simplistic, doing so at a manageable computational cost. Furthermore, we introduce adversarial optimization towards synthetic images to further improve their representativeness and safeguard against their overfitting to the neural network involved in distilling. This enhances the generalization capability of the distilled images across various neural network architectures and also increases their robustness to noise. Extensive experiments demonstrate that our framework sets new benchmarks in large-scale dataset distillation, achieving substantial improvements of 11.1% on Tiny-ImageNet, 9.0% on ImageNet-1K, and 7.3% on ImageNet-21K. The source code will be released to the community. Copyright © 2024, The Authors. All rights reserved.

关键词： Curricula

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An Extensive Study of the Structure Features in Transformer-based Code Semantic Summarization

An Extensive Study of the Structure Features in Transformer-...

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IEEE/ACM International Conference on Program Comprehension (ICPC)

作者： Kang Yang Xinjun Mao Shangwen Wang Yihao Qin Tanghaoran Zhang Yao Lu Kamal Al-Sabahi Key Laboratory of Software Engineering for Complex Systems National University of Defense Technology China University of Technology and Applied Sciences-Ibra Oman

Transformers are now widely utilized in code intelligence tasks. To better fit highly structured source code, various structure information is passed into Transformer, such as positional encoding and abstract syntax tree (AST) based structures. However, it is still not clear how these structural features affect code intelligence tasks, such as code summarization. Addressing this problem is of vital importance for designing Transformer-based code models. Existing works are keen to introduce various structural information into Transformers while lacking persuasive analysis to reveal their contributions and interaction effects. In this paper, we conduct an empirical study of frequently-used code structure features for code representation, including two types of position encoding features and AST-based structure features. We propose a couple of probing tasks to detect how these structure features perform in Transformer and conduct comprehensive ablation studies to investigate how these structural features affect code semantic summarization tasks. To further validate the effectiveness of code structure features in code summarization tasks, we assess Transformer models equipped with these code structure features on a structural dependent summarization dataset. Our experimental results reveal several findings that may inspire future study: (1) there is a conflict between the influence of the absolute positional embeddings and relative positional embeddings in Transformer; (2) AST-based code structure features and relative position encoding features show a strong correlation and much contribution overlap for code semantic summarization tasks indeed exists between them; (3) Transformer models still have space for further improvement in explicitly understanding code structure information.

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FLAME: Frozen Large Language Models Enable Data-Efficient Language-Image Pre-training

arXiv

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arXiv 2024年

作者： Cao, Anjia Wei, Xing Ma, Zhiheng School of Software Engineering Xi’an Jiaotong University China Shenzhen University of Advanced Technology China Guangdong Provincial Key Laboratory of Computility Microelectronics China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China

Language-image pre-training faces significant challenges due to limited data in specific formats and the constrained capacities of text encoders. While prevailing methods attempt to address these issues through data augmentation and architecture modifications, they continue to struggle with processing long-form text inputs, and the inherent limitations of traditional CLIP text encoders lead to suboptimal downstream generalization. In this paper, we propose FLAME (Frozen Large lAnguage Models Enable data-efficient language-image pre-training) that leverages frozen large language models as text encoders, naturally processing long text inputs and demonstrating impressive multilingual generalization. FLAME comprises two key components: 1) a multifaceted prompt distillation technique for extracting diverse semantic representations from long captions, which better aligns with the multifaceted nature of images, and 2) a facet-decoupled attention mechanism, complemented by an offline embedding strategy, to ensure efficient computation. Extensive empirical evaluations demonstrate FLAME’s superior performance. When trained on CC3M, FLAME surpasses the previous state-of-the-art by 4.9% in ImageNet top-1 accuracy. On YFCC15M, FLAME surpasses the WIT-400M-trained CLIP by 44.4% in average image-to-text recall@1 across 36 languages, and by 34.6% in text-to-image recall@1 for long-context retrieval on Urban-1k. Code is available at https://***/MIV-XJTU/FLAME. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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