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4th International Conference on Emerging Trends in Networks and computer Communications, ETNCC 2024

作者： Pal, Souvik Jhanjhi, N.Z. Akila, D. Abdullah, Monir Humayun, Memona Kumar, Sandeep Kumar, Rahul Saveetha Institute of Medical & Tech. Sciences Dept. of Management IS Saveetha College of Liberal Arts & Sciences Chennai602105 India Sister Nivedita University Dept. of CS and Engineering Kolkata700156 India School of Computer Science SCS Taylor's University Subang Jaya47500 Malaysia SIMATS Deemed University Saveetha College of Liberal Arts & Sciences Dept. of Computer Applications Chennai602105 India University of Bisha Department of CS and AI College of Computing & IT Bisha67714 Saudi Arabia School of Arts Humanities & Social Sciences University of Roehampton Department of Computing LondonSW15 5PJ United Kingdom India KKR and KSR Institute of Technology and Sciences Division of Research and Development Lovely Professional University Phagwara Andhra Pradesh Guntur144411 India

ISBN: (纸本)9798350353266

An innovative method for data optimization in the Internet of Things (IoT) assister sensing devices is presented in this research. The many elements of the cloud platform with IoT assistance are addressed. A fresh design for IoT-aided sensor networks is introduced to monitor the environment. A Data Optimisation in Cloud Internet of Things (DO-CIoT) is proposed in this paper to optimize the data stored in the cloud and presents methods to access the data in end devices. The suggested technique uses a membership function to improve data allocation while considering all node data. The suggested model is assessed regarding performance ratios, reliability percentage, coverage ratios, and sensing error and contrasted with two-stage efficiency enhancement. A mean performance ratio of 89%, reliability of 91%, coverage of 89 © 2024 IEEE.

关键词： Cloud platforms

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Sensor data fusion for optimal robotic navigation using regression based on an IOT system

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Measurement: Sensors 2022年 24卷

作者： Aroulanandam, Vijay Vasanth Satyam Sherubha, P. Lalitha, K. Hymavathi, J. Thiagarajan, R. Networking and Communications Department School of Computing SRM Institute of Science & Technology Kattankulathur India Sree Vidyanikethan Engineering College Tirupati India Department of Information Technology Karpagam College of Engineering Tamilnadu Coimbatore India Department of Computer Science and Engineering Kongunadu College of Engineering and Technology Tamilnadu Trichy India Department of Computer Science and Engineering K L University India Department of IT Prathyusha Engineering College Chennai India

Engineers can now build compact gadgets with potent processing power and network capabilities thanks to the semiconductor industry's rapid growth. The Internet of Things is one of the greatest subjects in the communication world right now, both professionally and academically. The technical idea behind the Internet of Things is to provide these various physical items the ability to use sensors to perceive information and transfer that data to a server. The goal of the industrial Internet of Things (IIoT) is to integrate multiple technologies to enhance business services across various industries. It alludes to how machine-to-machine connections operate. Every industrial Internet of Things (IIoT) service domain has unique communication needs that are assessed differently in terms of efficiency, dependability, and quality of service (QoS). For example, there are many surveillance applications for an Internet of Things-based robot with AI. To get around the problems with current robotic navigation, we suggest an Internet of Things (IoT) platform with an intelligent navigational robot that uses machine learning. It was designed with its Internet of Things (IoT) server, and it can do line tracing, gather contextual data from the sensors, and identify the aberrant condition in an environment before navigating appropriately. To allow contextually aware IoT platform services and to validate the learning accuracy of the provided CNN model using a dataset of images collected from the robot, we developed the Support-Vector Regression with Improved Adaptive Lion Optimization (IALOSVR) Algorithm model. According to experimental findings, learning accuracy exceeded 0.98, indicating that we had improved learning in picture context identification. The real-time evaluation of this paper's method demonstrates its ability to estimate navigation accurately, robustly, and with the estimation error, high estimation accuracy is steady within two percentage. © 2022 The Authors

关键词： Internet of things

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On the Representativeness Metric of Benchmark Problems in Numerical Optimization

SSRN

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SSRN 2024年

作者： Chen, Caifeng Liu, Qunfeng Jing, Yunpeng Zhang, Mingming Cheng, Shi Li, Yun School of Modern Information Industry Guangzhou College of Commerce Guangzhou511363 China School of Computer Science and Technology Dongguan University of Technology Dongguan523808 China Department of Mechanical and Mechatronics Engineering University of Auckland New Zealand School of Computer Science Shaanxi Normal University Xi’an710062 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China i4AI Ltd LondonWCIN3AX United Kingdom

Numerical comparison on benchmark problems is often necessary in evaluating optimization algorithms with or without theoretical analysis. An implicit assumption is that the adopted set of benchmark problems is representative. However, to our knowledge, there are few results about how to evaluate the representativeness of a test suite, partly due to the difficulty of this issue. In this paper, we first define three different levels of representativeness, and open up a window for addressing step by step the issue of representativeness-measuring. Then we turn to address the type-III representativeness-measuring problem, and provide a methodology and metric for this problem. To illustrate how to use the proposed metric, the representativeness-measuring problem of benchmark problems for single-objective optimization is *** analysis covers as many as 1141 single-objective unconstrained continuous benchmark problems, primarily focusing on existing benchmark problems. Based on the defined the representativeness metric, some classical features and calculations are used to assess the representativeness of the benchmark problems. Assessment results show that most of benchmark problems of high representativeness are non-separable problems from the CEC and BBOB test suites. We select the top $5\%$ of most representative problems to build a new test suite, providing a more representative and rigorous reference in verifying the overall performance of the optimization algorithms. © 2024, The Authors. All rights reserved.

关键词： Optimization

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E2DAS: An Efficient Equivariant Dynamic Aggregation Saliency Model for Omnidirectional Images 27th

E2DAS: An Efficient Equivariant Dynamic Aggregation Saliency...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Nana Liu, Qian Zhu, Dandan Zhu, Kun Zhai, Guangtao Yang, Xiaokang School of Computer Science and Technology Donghua University No.2999 Renmin North Road Songjiang District Shanghai201620 China Institute of AI Education Shanghai East China Normal University No.3663 Zhongshan North Road Putuo District Shanghai200333 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University No.4800 Cao’an Highway Jiading District Shanghai201804 China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University No. 800 Dongchuan Road Minhang District Shanghai200240 China

ISBN: (纸本)9783031781216

Recent years have witnessed rapid progress of convolutional neural networks (CNNs) and their successful application in the task of saliency prediction for omnidirectional images (ODIs). Albeit achieving tremendous performance improvements, these CNNs-based saliency models are plagued by two major shortcomings: spatial content-agnostic and computationally intensive. Inspired by the effectiveness of equivariant network in the majority of computer vision tasks, we propose a novel efficient equivariant dynamic aggregation saliency (E2DAS) model to efficiently tackle the issue of human fixation prediction in ODIs. To be specific, our proposed model consists of an efficient equivariant module, a dynamic convolutional aggregation module, and an optimization computation module. Different from existing saliency models for ODIs, we are the first attempt to introduce an efficient equivariant dynamic convolutional aggregation operation into the saliency prediction task, which can fundamentally alleviate the projection distortion problem and can effectively learn spatial content-adaptive features. Moreover, we clearly observe a considerable decrease in the number of parameters resulting from the replacement of standard convolution with dynamic convolution aggregation. Extensive experiments on several benchmark datasets show the proposed model’s superiority over other state-of-the-art methods in terms of performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Equivariant dynamic aggregation light-weight model omnidirectional images saliency prediction spatial content-adaptive

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Vision-Based Robust Lane Detection and Tracking under Different Challenging Environmental Conditions

arXiv

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arXiv 2022年

作者： Sultana, Samia Ahmed, Boshir Paul, Manoranjan Islam, Muhammad Rafiqul Ahmad, Shamim School of IT & Engineering Melbourne Institute of Technology SydneyNSW2000 Australia Department of Computer Science and Engineering Rajshahi University of Engineering and Technology Rajshahi6204 Bangladesh School of Computing Mathematics and Engineering Charles Sturt University BathurstNSW Australia Department of Computer Science & Engineering University of Rajshahi Rajshahi6205 Bangladesh

Lane marking detection is fundamental for both advanced driving assistance systems and traffic surveillance systems. However, detecting lane is highly challenging when the visibility of a road lane marking is low, obscured or often invisible due to real-life challenging environment and adverse weather. Most of the lane detection methods suffer from four types of challenges: (i) light effects i.e. shadow, glare of light, reflection etc. created by different light sources like streetlamp, tunnel-light, sun, wet road etc.;(ii) Obscured visibility of eroded, blurred, dashed, colored and cracked lane caused by natural disasters and adverse weather (rain, snow etc.);(iii) lane marking occlusion by different objects from surroundings (wiper, vehicles etc.);and (iv) presence of confusing lane like lines inside the lane view e.g., guardrails, pavement marking, road divider etc. In this paper, we proposed a simple, real-time, and robust lane detection and tracking method to detect lane marking considering the abovementioned challenging conditions. In this method, we introduced three key technologies. First, we introduce a comprehensive intensity threshold range (CITR) to improve the performance of the canny operator in detecting different types of lane edges e.g., clear, low intensity, cracked, colored, eroded, or blurred lane edges. Second, we propose a two-step lane verification technique, the angle-based geometric constraint (AGC) and length-based geometric constraint (LGC) followed by Hough Transform, to verify the characteristics of lane marking and to prevent incorrect lane detection. Finally, we propose a novel lane tracking technique, to predict the lane position of next frame by defining a range of horizontal lane position (RHLP) along the x axis which will be updating with respect to the lane position of previous frame. It can keep track of the lane position when either left or right or both lane markings are partially and fully invisible. To evaluate the performanc

关键词： Hough transforms

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Real-time 3D-aware Portrait Video Relighting

arXiv

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arXiv 2024年

作者： Cai, Ziqi Jiang, Kaiwen Chen, Shu-Yu Lai, Yu-Kun Fu, Hongbo Shi, Boxin Gao, Lin Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences China Beijing Jiaotong University China University of California San Diego United States Cardiff University United Kingdom City University of Hong Kong Hong Kong The Hong Kong University of Science and Technology Hong Kong University of Chinese Academy of Sciences China National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University China National Engineering Research Center of Visual Technology School of Computer Science Peking University China

Synthesizing realistic videos of talking faces under custom lighting conditions and viewing angles benefits various downstream applications like video conferencing. However, most existing relighting methods are either time-consuming or unable to adjust the viewpoints. In this paper, we present the first real-time 3D-aware method for relighting in-the-wild videos of talking faces based on Neural Radiance Fields (NeRF). Given an input portrait video, our method can synthesize talking faces under both novel views and novel lighting conditions with a photo-realistic and disentangled 3D representation. Specifically, we infer an albedo tri-plane, as well as a shading tri-plane based on a desired lighting condition for each video frame with fast dual-encoders. We also leverage a temporal consistency network to ensure smooth transitions and reduce flickering artifacts. Our method runs at 32.98 fps on consumer-level hardware and achieves state-of-the-art results in terms of reconstruction quality, lighting error, lighting instability, temporal consistency and inference speed. We demonstrate the effectiveness and interactivity of our method on various portrait videos with diverse lighting and viewing conditions. Copyright © 2024, The Authors. All rights reserved.

关键词： Video conferencing

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ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction

arXiv

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arXiv 2023年

作者： Hu, Zhilei Li, Zixuan Xu, Daozhu Bai, Long Jin, Cheng Jin, Xiaolong Guo, Jiafeng Cheng, Xueqi CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China State Key Laboratory of Geo-Information Engineering Xi’an710054 China Xi’an Research Institute of Surveying and Mapping Xi’an710054 China

Event Relation Extraction (ERE) aims to extract multiple kinds of relations among events in texts. However, existing methods singly categorize event relations as different classes, which are inadequately capturing the intrinsic semantics of these relations. To comprehensively understand their intrinsic semantics, in this paper, we obtain prototype representations for each type of event relation and propose a Prototype-Enhanced Matching (ProtoEM) framework for the joint extraction of multiple kinds of event relations. Specifically, ProtoEM extracts event relations in a two-step manner, i.e., prototype representing and prototype matching. In the first step, to capture the connotations of different event relations, ProtoEM utilizes examples to represent the prototypes corresponding to these relations. Subsequently, to capture the interdependence among event relations, it constructs a dependency graph for the prototypes corresponding to these relations and utilized a Graph Neural Network (GNN)-based module for modeling. In the second step, it obtains the representations of new event pairs and calculates their similarity with those prototypes obtained in the first step to evaluate which types of event relations they belong to. Experimental results on the MAVEN-ERE dataset demonstrate that the proposed ProtoEM framework can effectively represent the prototypes of event relations and further obtain a significant improvement over baseline models. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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An android malware detection approach using multi-feature fusion and TF-IDF algorithm 6

An android malware detection approach using multi-feature fu...

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6th IEEE International Conference on Data science in Cyberspace, DSC 2021

作者： Zhang, Haipeng Li, Shudong Wu, Xiaobo Han, Weihong Wang, Laifu Cyberspace Institute Of Advanced Technology Guangzhou University Guangzhou China School Of Computer Science And Cyber Engineering Guangzhou University Guangzhou China Research Institute Of China Telecom Co. Ltd. Guangzhou China

ISBN: (纸本)9781665418157

With the popularity of smartphones, android quickly occupied the market, and at the same time, millions of android malware increase every year, which caused great losses to users. so it's crucial to detect unknown android malware efficiently before install it. Machine learning has achieved outstanding performance in many fields, and so does android malware detection. Nowadays, many methods of detecting android malware based on machine learning have been proposed. These methods can be classified as dynamic analysis, static analysis, and hybrid analysis according to what kind of features they use. the dynamic analysis will waste a lot of time because it requires running software. What's more, it's hard to get all malicious behaviors because of Anti-Sandbox technology, which means malware might change its behavior to avoid detecting when malware is running in Sandbox. In this paper, we proposed a new android malware detection approach using multi-features and TF-IDF algorithm. The static features we use cover permissions and API calls of each android application and the size of the android application package. We used BOW(bag of words) to process permission feature, standardization to process packages size feature, and TF-IDF algorithm to handle API calls features. Machine-learning algorithms including AdaBoost, LinearSVC, GaussianNB are used for Classification. The experimental results on public datasets demonstrated our system could detect android malware efficiently. © 2021 IEEE.

关键词： Android malware

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The altered network complexity of resting-state functional brain activity in schizophrenia and bipolar disorder patients

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Brain science Advances 2023年第2期9卷 78-94页

作者： Yan Niu Nan Zhang Mengni Zhou Lan Yang Jie Sun Xueting Cheng Yanan Li Lefan Guo Jie Xiang Bin Wang College of Information and Computer Taiyuan University of TechnologyTaiyuan 030024ShanxiChina Graduate School of Interdisciplinary Science and Engineering in Health Systems Okayama UniversityOkayamaJapan Research Center for Medical Artificial Intelligence Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055GuangzhouChina

Schizophrenia(SZ)and bipolar disorder(BD)are two of themost frequent mental *** disorders exhibit similarpsychotic symptoms,making diagnosis challenging and leadingto ***,the network complexity changes drivingspontaneous brain activity in SZ and BD patients are still *** entropy(FE)is a novel way of measuring the dispersion(or spread)of functional connectivities inside the *** FE wasutilized in this study to examine the network complexity of the resting-state fMRI data of SZ and BD patients at three levels,including global,modules,and *** three levels,the FE of SZand BD patients was considerably lower than that of normal control(NC).At the intra-module level,the FE of SZ was substantially higher than that of BD in the cingulo-opercular ***,a strong negative association between FE and clinical measureswas discovered in patient ***,we classified using theFE features and attained an accuracy of 66.7%(BD *** ***)and an accuracy of 75.0%(SZ ***).These findings proposed that network connectivity’s complexity analyses using FE can provideimportant insights for the diagnosis of mental illness.

关键词： functional entropy network complexity schizophrenia bipolar disorder resting-state fMRI

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An adaptive transformer model for anomaly detection in wireless sensor networks in real-time

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Measurement: Sensors 2023年 25卷

作者： Siva Kumar, A. Raja, S. Pritha, N. Raviraj, Havaldar Babitha Lincy, R. Jency Rubia, J. Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Tamilnadu Kattankulathur India School of Electrical & Communication Department of Electronics & Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamilnadu India Department of ECE Panimalar Engineering College Tamilnadu India Department of BME KLE Dr. M S Sheshgiri College of Engineering and Technology Belgaum India Computer and Communication Engineering Department Sri Eshwar College of Engineering Tamilnadu India Department of ECE K. Ramakrishnan College of Engineering Tamilnadu India

Defense, environmental monitoring, healthcare, home automation, and other fields are just a few of the many that make use of wireless sensor networks. There are sensor nodes in these networks that are sent out into the field to collect data and relay it back to the network's hub. The data streams may be infused with anomalous data due to interferences, malfunctioning nodes, etc. The security of the network depends on the prompt identification of any irregularities. Due to noise, missing data, and the dynamic nature of the network, identifying and detecting abnormal data is *** study introduces a dynamic context-capturing deep learning model for sensor data anomaly detection, constructed as a Transformer with a spatio-temporal attention mechanism. The proposed model is trained and tested with a public dataset built from real-time sensor data captured in a water treatment plant. With an accuracy of 0.09142, F1 of 0.9109, and precision of 0.9191, the STA-Tran model outperforms the best existing methods. © 2022

关键词： Anomaly detection

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