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data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： K. Thinakaran M. Anand T.Rajesh Kumar Saira Banu Atham K.M. Subramanian Department of Computer Science Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai Saveetha School of Engineering Saveetha Institute of Medical and Technical Science Department of Computer Science and Engineering School of CSE & IS Presidency University Bangaluru Department of Computer Science Engineering Shadan College of Engineering and Technology Hyderabad

The proposed models can design the airfoil by Cuckoo search with Levenberg-Marquardt. The Neural Network framework has impediments due to over-fitting. This paper proposed a modified cuckoo search. here the aerodynamic coefficient as an input to produce output the airfoil coordinates. The generated airfoil is compared to know its performance metrics. The Cuckoo search with the Feedforward Neural Network model yields the lowest prediction error.

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Total Nitrogen and Total Phosphorus Pollution Reshaped the Relationship Between Water Supply and Demand in the Huaihe River Watershed, China

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Chinese Geographical science 2023年第3期33卷 512-530页

作者： LU Yan LIU Lyuyi QIN Fen WANG Jiayao LIU Jianzhong LI Yingjie WAN Luwen School of Geoscience and Technology Zhengzhou UniversityZhengzhou 450001China Henan Provincial Academy of Eco-Environmental Sciences Zhengzhou 450003China Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions Ministry of EducationHenan UniversityKaifeng 475000China Henan Industrial Technology Academy of Spatio-Temporal Big Data Henan UniversityKaifeng 475000China College of Geography and Environmental Science Henan UniversityKaifeng 475000China Environmental Science and Policy Program Michigan State UniversityEast LansingMI 48824USA

As total nitrogen(TN)and total phosphorus(TP)pollution is the main source of water pollution in the Huaihe River watershed in China,it is important to understand how TN and TP pollution affect the relationship between water supply and *** their impacts and describing the spatiotemporal distribution of these relationships are necessary for furtherly deepening the theory of TN and TP pollution on water bodies,and this information is also particularly essential for managing water resources *** this study,based on the potential water supply,the water demand and the Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST)water purification models,we estimated the TN and TP pollution from agricultural fertilizer,livestock and poultry breeding,and rural residents in the Huaihe River watershed and simulated TN and TP impacts on the relationship between water supply and *** found that if the impact of TN and TP pollution on water supply was not taken into account,on average,there was excess water supply in 79.20%of the watershed and excess demand in 20.80%of the rest during 1980–*** the TN concentration limit,Grade-Ⅱ(The water quality meets the secondary level of water body qualified in GB3838–2002,classified as Grade-II)water was the main watersupply type in 1980–2018,followed by Grade-Ⅰ and Grade-Ⅲ *** total water shortage showed an inverted V-shaped trend:first increasing and then decreasing at the same *** proportion of the water shortage of Grade-I water in the total water shortage was the largest,followed by Grade-Ⅱ and Grade-III *** with excess demand were located on the north bank of Wang-Beng,Yishuhe,and Huxi regions,although the water in these sub-watersheds met the water quality standards of Grade-Ⅰ *** the TP concentration limit,Grade-Ⅱ and Grade-Ⅰ water were the main water-supply *** overall water shortage trend first increased and then decreased,exhibiting an inverted V-shape from

关键词： watershed breeding poultry

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Self-training through Classifier Disagreement for Cross-Domain Opinion Target Extraction

arXiv

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arXiv 2023年

作者： Sun, Kai Zhang, Richong Mensah, Samuel Aletras, Nikolaos Mao, Yongyi Liu, Xudong Beijing Advanced Innovation Center for Big Data and Brain Computing School of Computer Science and Engineering Beihang University Beijing China Department of Computer Science University of Sheffield Sheffield United Kingdom School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

Opinion target extraction (OTE) or aspect extraction (AE) is a fundamental task in opinion mining that aims to extract the targets (or aspects) on which opinions have been expressed. Recent work focus on cross-domain OTE, which is typically encountered in real-world scenarios, where the testing and training distributions differ. Most methods use domain adversarial neural networks that aim to reduce the domain gap between the labelled source and unlabelled target domains to improve target domain performance. However, this approach only aligns feature distributions and does not account for class-wise feature alignment, leading to suboptimal results. Semi-supervised learning (SSL) has been explored as a solution, but is limited by the quality of pseudo-labels generated by the model. Inspired by the theoretical foundations in domain adaptation [2], we propose a new SSL approach that opts for selecting target samples whose model output from a domain-specific teacher and student network disagree on the unlabelled target data, in an effort to boost the target domain performance. Extensive experiments on benchmark cross-domain OTE datasets show that this approach is effective and performs consistently well in settings with large domain shifts. © 2023, CC BY.

关键词： Sentiment analysis

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Link/Switch Fault-Tolerant Hamiltonian Path Embedding in BCube Networks for Deadlock-Free Routing

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Lin, Wanling Li, Xiao-Yan Chang, Jou-Ming Xiang, Dong Jia, Xiaohua Fuzhou University College of Computer and Data Science Fuzhou350108 China National Taipei University of Business Institute of Information and Decision Sciences Taipei10051 Taiwan Tsinghua University School of Software Beijing100084 China City University of Hong Kong Department of Computer Science Hong Kong

BCube stands as a renowned server-centric data center network (DCN), boasting numerous advantages, such as low diameter, high aggregate throughput, and abundant parallel paths. As DCNs expand rapidly, followed by the daily increasing likelihood of failures, fault tolerance has become an important issue in DCNs. Hamiltonian paths constitute a pivotal network topology for parallel and distributed computing, suitable for designing deadlock-free routing algorithms, fault-tolerant routing algorithms, and congestion avoidance. The partitioned edge fault (PEF) model is a recently proposed fault model that exploits the properties of networks to achieve fault tolerance with an exponential scale. In this paper, we explore the existence of Hamiltonian paths in BCube under the PEF model. Since one switch failure will result in multiple faulty links, we also extend the conclusions related to Hamiltonian paths to analyze the fault tolerance of BCube under the PEF model when switch failures occur. Moreover, we provide algorithms to embed a Hamiltonian path between arbitrary two distinct servers into BCube under the PEF model. Experimental analysis and comparisons demonstrate that our approach exhibits exponential enhancements over the other known results, and BCube DCNs possess remarkable fault tolerance in response to both link and switch failures under the PEF model. As a by-product, we obtain a deadlock-free routing based on the constructed Hamiltonian path and assess the routing performance compared to the benchmark routing algorithms. © 2004-2012 IEEE.

关键词： Hamiltonians

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Pseudo Triplet Guided Few-shot Composed Image Retrieval

arXiv

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arXiv 2024年

作者： Hou, Bohan Lin, Haoqiang Wen, Haokun Liu, Meng Xu, Mingzhu Song, Xuemeng The Department of Computer Science and Technology Shandong University Qingdao China Shenzhen China The Department of Data Science City University of Hong Kong Hong Kong The Department of Computer Science Shandong Jianzhu University Jinan China The School of Software Shandong University Jinan China

Composed Image Retrieval (CIR) is a challenging task that aims to retrieve the target image with a multimodal query, i.e., a reference image, and its complementary modification text. As previous supervised or zero-shot learning paradigms all fail to strike a good trade-off between the model’s generalization ability and retrieval performance, recent researchers have introduced the task of few-shot CIR (FS-CIR) and proposed a textual inversion-based network based on pretrained CLIP model to realize it. Despite its promising performance, the approach encounters two key limitations: simply relying on the few annotated samples for CIR model training and indiscriminately selecting training triplets for CIR model fine-tuning. To address these two limitations, we propose a novel two-stage pseudo triplet guided few-shot CIR scheme, dubbed PTG-FSCIR. In the first stage, we propose an attentive masking and captioning-based pseudo triplet generation method, to construct pseudo triplets from pure image data and use them to fulfill the CIR-task specific pretraining. In the second stage, we propose a challenging triplet-based CIR fine-tuning method, where we design a pseudo modification text-based sample challenging score estimation strategy and a robust top range-based random sampling strategy for sampling robust challenging triplets to promote the model fine-tuning. Notably, our scheme is plug-and-play and compatible with any existing supervised CIR models. We test our scheme across two backbones on three public datasets (i.e., FashionIQ, CIRR, and Birds-to-Words), achieving maximum improvements of 13.3%, 22.2%, and 17.4% respectively, demonstrating our scheme’s efficacy. © 2024, CC BY.

关键词： Zero-shot learning

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Apache Kafka on Big data Event Streaming for Enhanced data Flows 8

Apache Kafka on Big Data Event Streaming for Enhanced Data F...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Padmanaban, K. Babu, T.R. Ganesh Karthika, K. Pattanaik, Balachandra Dhanabhavithra, K. Srinivasan, C. Department of Electronics and Communication Engineering Tamil Nadu Namakkal India Kcg College of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Wollega University Department of Electrical and Computer Engineering Nekemte Ethiopia Agni College of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376425

Apache Kafka's distributed architecture and message queuing capabilities offer significant improvements in real-time and batch data processing efficiency and reliability. This research aims to optimize Kafka setups, data partitioning, and Kafka Connect integration to create a robust and scalable data streaming infrastructure. The study focuses on enhancing data input, processing, and dissemination across systems and applications. By optimizing Kafka configurations and leveraging its capabilities, the research seeks to achieve significant improvements in data processing speed, real-time analytics, and scalable data pipelines. The evaluation of Kafka Event Stream Throughput Over Time and Latency Distribution Across Brokers demonstrates the system's performance and efficiency. The results indicate a throughput of 750-1340 events per hour and a latency distribution of 6-15 milliseconds. Additionally, Consumer Lag Over Time analysis reveals consistent performance with values ranging from 70-140. This research contributes to the advancement of big data processing by demonstrating the effectiveness of Apache Kafka in creating a robust and efficient data streaming infrastructure. The findings provide valuable insights for organizations seeking to optimize their data pipelines and leverage the power of real-time analytics. © 2024 IEEE.

关键词： data streams

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Recognition of data Breach Method from Story Using Advanced Deep Learning

Recognition of Data Breach Method from Story Using Advanced ...

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Innovation in Technology (INOCON), IEEE International Conference for

作者： Samiha Maisha Jeba Tanjim Taharat Aurpa Md Samiul Alom Md. Wadud Mahmud Joy Asm Shakil Ahamed Satya Ranjan Dash Md Shoaib Ahmed Dept. of Computer Science and Engineering International University of Business Agriculture and Technology Bangladesh Department of Data Science Bangabandhu Sheikh Mujibur Rahman Digital University Bangladesh School of Computer Applications KIIT University Odisha India Department of Computer Science Boise State University Boise Idaho USA

We often open our eyes in the morning with the appalling news of data breaches of different popular companies. This is a significant threat not only to giant companies but also to the general people’s privacy. Various methods are maneuvered for data breaches, and identifying these methods in a short can help understand the loss and take proper steps against these incidents. Researchers nowadays focus on the data breaches issue to reduce its threats to the tech industry. In this research, we have used advanced deep learning methods(e.g., BERT, XLNet, and Electra) to predict data breach methods using the data breach story. The proposed classifier has provided an accuracy of 92.86%.

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Extended Kalman Filter Algorithm to Detect False data Injection in Wireless Sensor Network 2

Extended Kalman Filter Algorithm to Detect False Data Inject...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Babuji, R. Ganesan, J. Jeya Lekha, D. Karthikeyan, M. Kalai Selvi, D.M. Rmk Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Sri Sairam Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350325799

Wireless Sensor Networks (WSN) contains spatially distributed sensor nodes that collaborate with each other. However, the WSN is susceptible since the wireless medium is unpredictable. Several conventional approaches are used to distinguish the abnormal in the WSN, and all have difficulties. An Extended Kalman Filter Algorithm (EKFA) to detect false inject data in WSN is proposed in this paper. It addresses two issues such as abnormality recognition and obstacle detection. It also distinguishes false insert data. Particularly, it observes neighbors' behaviors and then applying EKFA to forecast their future states. The node behavior is observed by incorporating abnormal node examination and system examination elements. The Path Optimization Method (POM) is a shortest route that is used to discover the obstacle in the WSN. The EKFA mechanism minimizes the forwarder count and delay by applying POM. Simulation results demonstrate that the EKFA approach enhances abnormal node detection and minimizes the false negative ratio. © 2023 IEEE.

关键词： Extended Kalman filters

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Exploring Reinforcement Learning in Large-Scale data Processing 3

Exploring Reinforcement Learning in Large-Scale Data Process...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Bhambu, Pawan Kumar, Ritesh Sharmila, P. Patil, Vivek D. Khurana, Shivam Vivek, V. Vivekananda Global University Department of Computer Science & Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Prince Shri Venkateshwara Padmavathy Engineering College Department of Science and Humanities Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India

ISBN: (纸本)9798350319125

Reinforcement mastering (RL) is vital for big-scale fast processing. RL utilizes the availability of large datasets and dealers skilled in this information to remedy a wide variety of obligations, from automated diagnostics and self-reliant using structures to clinical information analysis. Using exploiting the traits of the modern-day significant information era, RL allows efficient and correct decision-making in massive-scale information processing and answers complicated troubles. This paper explores the use of reinforcement learning (RL) in large-scale data processing. RL is a subfield of machine learning that focuses on decision making in dynamic environments. With the increasing demand for processing large amounts of data, traditional techniques such as rule-based systems and batch processing are becoming inadequate. This has led to the emergence of RL as a potential solution due to its ability to learn and adapt to changing environments. Through a comprehensive review of existing literature, this paper discusses the applications of RL in large-scale data processing and its potential benefits. Furthermore, it examines various challenges and their potential solutions in implementing RL for data processing tasks. The paper concludes by highlighting the future research directions in this domain and the potential impact of RL on improving data processing efficiency and accuracy. It similarly examines some of the demanding situations and possibilities related to using RL for huge-scale records processing and the capability programs of RL in the future. © 2023 IEEE.

关键词： applications challenges opportunities potential processing

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Privacy-Enhanced data Deduplication Computational Intelligence Technique for Secure Healthcare Applications

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computers, Materials & Continua 2022年第2期70卷 4169-4184页

作者： Jinsu Kim Sungwook Ryu Namje Park Department of Convergence Information Security Graduate SchoolJeju National University63294Korea Master’s Program in Future Strategy Korea Advanced Institute of Science and Technology34141Korea Department of Computer Education Teachers CollegeJeju National University63294Korea

A significant number of cloud storage environments are already implementing deduplication *** to the nature of the cloud environment,a storage server capable of accommodating large-capacity storage is *** storage capacity increases,additional storage solutions are *** leveraging deduplication,you can fundamentally solve the cost ***,deduplication poses privacy concerns due to the structure *** this paper,we point out the privacy infringement problemand propose a new deduplication technique to solve *** the proposed technique,since the user’s map structure and files are not stored on the server,the file uploader list cannot be obtained through the server’s meta-information analysis,so the user’s privacy is *** addition,the personal identification number(PIN)can be used to solve the file ownership problemand provides advantages such as safety against insider breaches and sniffing *** proposed mechanism required an additional time of approximately 100 ms to add a IDRef to distinguish user-file during typical deduplication,and for smaller file sizes,the time required for additional operations is similar to the operation time,but relatively less time as the file’s capacity grows.

关键词： Computational intelligence cloud multimedia data deduplication

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