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Text Mining for Automated data Analysis and Information Retrieval

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Text Mining for Automated Data Analysis and Information Retr...

International Conference on Computing and Networking Technology (ICCNT)

作者： Vaishali Singh S Varalakshmi N.T. Velusudha Ashwini R. Nawadkar Manish Srivastava Hitesh Kalra Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Center for Management Studies Jain (Deemed to be University) Bangalore Karnataka India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune INDIA Department of Electrical Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Textual content Mining is the method of automatically analyzing vast amounts of natural language documents so that you can discover latent topics, extract vital understanding, and summarize relevant statistics. It consists of techniques. It presents critical insights into the content of information articles, emails, books, and other files, allowing us to gain insight into trends and styles that otherwise wouldn’t be seen. It can also stumble on sentiment, discover underlying entity relationships, extract key terms, and convey summaries. Textual content Mining is, for this reason, turning into an effective device for automatic information evaluation and records retrieval.

关键词： Text mining data analysis Natural languages Turning Market research Information retrieval Electronic mail Predictive analytics Robots

NTIRE 2023 Challenge on Image Super-Resolution (×4): Methods and Results

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NTIRE 2023 Challenge on Image Super-Resolution (×4): Method...

2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Zhang, Yulun Zhang, Kai Chen, Zheng Li, Yawei Timofte, Radu Zhang, Junpei Zhang, Kexin Peng, Rui Ma, Yanbiao Jiao, Licheng Huang, Huaibo Zhou, Xiaoqiang Ai, Yuang He, Ran Qiu, Yajun Zhu, Qiang Li, Pengfei Li, Qianhui Zhu, Shuyuan Zhang, Dafeng Li, Jia Wang, Fan Li, Chunmiao Kim, TaeHyung Kil, Jungkeong Kim, Eon Yu, Yeonseung Lee, Beomyeol Lee, Subin Lim, Seokjae Chae, Somi Choi, Heungjun Huang, ZhiKai Chen, YiChung Chiang, YuanChun Yang, HaoHsiang Chen, WeiTing Chang, HuaEn Chen, I-Hsiang Hsieh, ChiaHsuan Kuo, SyYen Choi, Ui-Jin Conde, Marcos V. Khowaja, Sunder Ali Yoon, Jiseok Lee, Ik Hyun Gendy, Garas Sabor, Nabil Hou, Jingchao He, Guanghui Zhang, Zhao Li, Baiang Zheng, Huan Zhao, Suiyi Gao, Yangcheng Wei, Yanyan Ren, Jiahuan Wei, Jiayu Li, Yanfeng Sun, Jia Cheng, Zhanyi Li, Zhiyuan Yao, Xu Wang, Xinyi Li, Danxu Cui, Xuan Cao, Jun Li, Cheng Zheng, Jianbin Sarvaiya, Anjali Prajapati, Kalpesh Patra, Ratnadeep Barik, Pragnesh Rathod, Chaitanya Upla, Kishor Raja, Kiran Ramachandra, Raghavendra Busch, Christoph Computer Vision Lab Eth Zurich Switzerland Shanghai Jiao Tong University China University of Würzburg Germany Xidian University China Mais&cripac Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China University of Science and Technology of China China Beijing Institute of Technology China School of Information Science and Technology ShanghaiTech University China School of Information and Communication Engineering University of Electronic Science and Technology of China China China Lotte Data Communication Company Seoul Korea Republic of Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States MegaStudyEdu Korea Republic of Computer Vision Lab Caidas University of Würzburg Germany University of Sindh Pakistan Iklab Inc. Tech University of Korea Siheung-Si Korea Republic of Micro-Nano Electronics Department Shanghai Jiao Tong University China Electrical Engineering Department Faculty of Engineering Assiut University Egypt Hefei University of Technology China Beijing Jiaotong University China South China University of Technology Guangdong Guangzhou China Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technology Norway

ISBN: (纸本)9798350302493

This paper reviews the NTIRE 2023 challenge on image super-resolution (×4), focusing on the proposed solutions and results. The task of image super-resolution (SR) is to generate a high-resolution (HR) output from a corresponding low-resolution (LR) input by leveraging prior information from paired LR-HR images. The aim of the challenge is to obtain a network design/solution capable to produce high-quality results with the best performance (e.g., PSNR). We want to explore how high performance we can achieve regardless of computational cost (e.g., model size and FLOPs) and data. The track of the challenge was to measure the restored HR images with the ground truth HR images on DIV2K testing dataset. The ranking of the teams is determined directly by the PSNR value. The challenge has attracted 192 registered participants, where 15 teams made valid submissions. They achieve state-of-the-art performance in single image super-resolution. © 2023 IEEE.

关键词： Statistical tests

FedMQ+: Towards efficient heterogeneous federated learning with multi-grained quantization

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Journal of Systems Architecture 2025年 167卷

作者： Mei Cao Huiyu Wang Yuan Yuan Jianbo Lu Xiaojun Cai Dongxiao Yu Mengying Zhao School of Computer Science and Technology Shandong University China School of Control Science and Engineering Shandong University China School of Software & Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) Shandong University China

Federated Learning (FL) is a distributed machine learning paradigm that enables collaborative model training while preserving client data privacy. Although this approach significantly enhances data privacy, it introduces substantial communication overhead. Quantization techniques mitigate this challenge by compressing model parameters into fewer bits. However, traditional quantization methods are primarily implemented at the client level, often overlooking the heterogeneous importance of distinct model parameters. In our previous work, FedMQ, we explored quantization mechanisms at both inter-client and intra-client levels to improve communication efficiency. Nevertheless, this approach compromises model accuracy due to the aggregation of models with limited expressive capability. Additionally, it lacks comprehensive theoretical analysis and mathematical verification. In this paper, we propose FedMQ+, an improved framework for heterogeneous federated learning with enhanced dequantization, to optimize global model performance. First, we design a precise dequantization strategy based on normal functions to accurately reconstruct full-precision weights from the given low-precision weights. Next, we conduct a rigorous theoretical analysis of the FedMQ+, establish an upper bound for its convergence, and mathematically demonstrate its O ( 1 / T ) convergence rate. Finally, we perform extensive experiments across diverse datasets and models. Experimental results demonstrate that FedMQ+ achieves significant improvements in convergence speed from 3.1% to 85.8%, while maintaining comparable model accuracy and achieving superior communication efficiency compared with state-of-the-art baselines.

关键词： Communication Efficiency Federated learning non-IID data Quantization and Dequantization

Prior-Guided Adversarial Initialization for Fast Adversarial Training

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arXiv 2022年

作者： Jia, Xiaojun Zhang, Yong Wei, Xingxing Wu, Baoyuan Ma, Ke Wang, Jue Cao, Xiaochun SKLOIS Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Tencent AI Lab Shenzhen China Institute of Artificial Intelligence Beihang University Beijing China School of Data Science Secure Computing Lab of Big Data Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China School of Computer Science and Technology UCAS Beijing China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Shenzhen518107 China

Fast adversarial training (FAT) effectively improves the efficiency of standard adversarial training (SAT). However, initial FAT encounters catastrophic overfitting, i.e., the robust accuracy against adversarial attacks suddenly and dramatically decreases. Though several FAT variants spare no effort to prevent overfitting, they sacrifice much calculation cost. In this paper, we explore the difference between the training processes of SAT and FAT and observe that the attack success rate of adversarial examples (AEs) of FAT gets worse gradually in the late training stage, resulting in overfitting. The AEs are generated by the fast gradient sign method (FGSM) with a zero or random initialization. Based on the observation, we propose a prior-guided FGSM initialization method to avoid overfitting after investigating several initialization strategies, improving the quality of the AEs during the whole training process. The initialization is formed by leveraging historically generated AEs without additional calculation cost. We further provide a theoretical analysis for the proposed initialization method. We also propose a simple yet effective regularizer based on the prior-guided initialization, i.e., the currently generated perturbation should not deviate too much from the prior-guided initialization. The regularizer adopts both historical and current adversarial perturbations to guide the model learning. Evaluations on four datasets demonstrate that the proposed method can prevent catastrophic overfitting and outperform state-of-the-art FAT methods. The code is released at https://***/jiaxiaojunQAQ/FGSM-PGI. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning

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arXiv 2024年

作者： Zhang, Yaning Yu, Zitong Wang, Tianyi Huang, Xiaobin Shen, Linlin Gao, Zan Ren, Jianfeng Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University China Jinan250014 China Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China School of Computer Science University of Nottingham Ningbo China

The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable. Thus, benchmarking and advancing techniques detecting digital manipulation become an urgent issue. Although there have been a number of publicly available face forgery datasets, the forgery faces are mostly generated using GAN-based synthesis technology, which does not involve the most recent technologies like diffusion. The diversity and quality of images generated by diffusion models have been significantly improved and thus a much more challenging face forgery dataset shall be used to evaluate SOTA forgery detection literature. In this paper, we propose a large-scale, diverse, and fine-grained high-fidelity dataset, namely GenFace, to facilitate the advancement of deepfake detection, which contains a large number of forgery faces generated by advanced generators such as the diffusion-based model and more detailed labels about the manipulation approaches and adopted generators. In addition to evaluating SOTA approaches on our benchmark, we design an innovative Cross Appearance-Edge Learning (CAEL) detector to capture multi-grained appearance and edge global representations, and detect discriminative and general forgery traces. Moreover, we devise an Appearance-Edge Cross-Attention (AECA) module to explore the various integrations across two domains. Extensive experiment results and visualizations show that our detection model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Code and datasets will be available at https://***/Jenine-321/GenFace. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

Maximizing Cloud Security: Empirical Evaluation of an Efficient Identity oriented Signature Verification Scheme for Wireless Networks

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Maximizing Cloud Security: Empirical Evaluation of an Effici...

Communication & Computing (WCONF), World Conference on

作者： C. Anitha V Praveena. V Samuthira Pandi Srivardhan Kumar B. Shiva A. Muniyappan Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Artificial Intelligence and Machine Learning S.A. Engineering College Chennai Tamil Nadu India Centre for Advanaced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering CMR College of Engineering and Technology Medchal Hyderabad India Centre for Computational Modeling Chennai Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798350395327

ISBN: (纸本)9798350395334

A reliable and effective way to aggregate large amounts of data is very desirable in the area of wireless networks that are helped by cloud computing. Target tracking and environmental management are only two examples of the many real-world uses of wireless sensor networks. The cloud does, however, make data vulnerable to a variety of threats, including data eavesdropping and data contamination. Elevated Identity based Signature Verification (EISV) is an identity-based aggregate signature verification scheme that is cross-validated with the conventional Attribute based Encryption (ABE) security scheme to evaluate the efficiency of the proposed scheme for data integrity security in wireless sensor networks. This technique will reduce storage and bandwidth costs in wireless sensor networks while keeping data private, according to the benefits of aggregate signatures. With online signature methods, the signer can generate an electronic signature using a pre-calculated digital signature when a document is submitted. Applications involving wireless sensor networks with limited resources benefit greatly from such approaches. Our suggested technique, Elevated Identity based Signature Verification, has been implemented, and the results show that it is practical and feasible to utilize in a cloud based wireless sensor network configuration.

关键词： Cloud computing Wireless sensor networks Handwriting recognition Target tracking Wireless networks Aggregates data integrity Computational modeling Secure storage Testing

Accurate Leukocyte Detection Based on Deformable-DETR and Multi-Level Feature Fusion for Aiding Diagnosis of Blood Diseases

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arXiv 2024年

作者： Chen, Yifei Zhang, Chenyan Chen, Ben Huang, Yiyu Sun, Yifei Wang, Changmiao Fu, Xianjun Dai, Yuxing Qin, Feiwei Peng, Yong Gao, Yu HDU-ITMO Joint Institute Hangzhou Dianzi University Hangzhou310018 China Medical Big Data Lab Shenzhen Research Institute of Big Data Shenzhen518172 China School of Artificial Intelligence Zhejiang College of Security Technology Wenzhou325016 China National-local Joint Engineering Research Center for Digitalized Electrical Design Technology Wenzhou University Wenzhou325035 China School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Department of Hematology Zhejiang Hospital Hangzhou310018 China

In standard hospital blood tests, the traditional process requires doctors to manually isolate leukocytes from microscopic images of patients’ blood using microscopes. These isolated leukocytes are then categorized via automatic leukocyte classifiers to determine the proportion and volume of different types of leukocytes present in the blood samples, aiding disease diagnosis. This methodology is not only time-consuming and labor-intensive, but it also has a high propensity for errors due to factors such as image quality and environmental conditions, which could potentially lead to incorrect subsequent classifications and misdiagnosis. Contemporary leukocyte detection methods exhibit limitations in dealing with images with fewer leukocyte features and the disparity in scale among different leukocytes, leading to unsatisfactory results in most instances. To address these issues, this paper proposes an innovative method of leukocyte detection: the Multi-level Feature Fusion and Deformable Self-attention DETR (MFDS-DETR). To tackle the issue of leukocyte scale disparity, we designed the High-level Screening-feature Fusion Pyramid (HS-FPN), enabling multi-level fusion. This model uses high-level features as weights to filter low-level feature information via a channel attention module and then merges the screened information with the high-level features, thus enhancing the model’s feature expression capability. Further, we address the issue of leukocyte feature scarcity by incorporating a multi-scale deformable self-attention module in the encoder and using the self-attention and cross-deformable attention mechanisms in the decoder, which aids in the extraction of the global features of the leukocyte feature maps. The effectiveness, superiority, and generalizability of the proposed MFDS-DETR method are confirmed through comparisons with other cutting-edge leukocyte detection models using the private WBCDD, public LISC and BCCD datasets. Our source code and private WBCCD

关键词： Object detection

Investigating the Role of Deep Learning in data science: Promises and Prospects

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Investigating the Role of Deep Learning in Data Science: Pro...

International Conference on Computing and Networking Technology (ICCNT)

作者： R Pramod G Vijayalakshmi Nakul S. Sharma Kunal Sharma Ritesh Kumar Madhur Grover Department of Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar District Karnataka India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Technology Pune INDIA Department of Mechanical Engineering Vivekananda Global University Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

data science has revolutionized how we create products and offerings and solve problems. With access to vast shops of virtual statistics and the application of state-of-the-art system learning algorithms to this information, data scientists can spot styles, cluster facts, and make predictions leading to the method of actionable insights. Deep mastering, a subset of machine learning, has lately received significant interest in records science due to its capacity to address complicated issues. This technology allows computer systems to research underlying institutions and traits in datasets with minimum human intervention. Deep mastering has enabled the improvement of state-of-the-art stop-to-cease structures that routinely wear out duties that had been historically very difficult for computers due to the full-size complexity of parameters and datasets. In this article, we discover the guarantees and potentialities of deep gaining knowledge of in statistics technological know-how, how it is being used to attain groundbreaking effects, and what the destiny ability might be. We additionally speak about a number of the challenges that should be addressed to recognize this generation’s ability genuinely. ultimately, deep gaining knowledge can enormously increase the modern kingdom of facts and technological knowhow, provided that the proper equipment and techniques are used to extract its total price.

关键词： Deep learning Knowledge engineering Information science Machine learning algorithms Neurons Neural networks data science Prediction algorithms Natural language processing Information technology

Incorporating simulated spatial context information improves the effectiveness of contrastive learning models

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arXiv 2024年

作者： Zhu, Lizhen Wang, James Z. Lee, Wonseuk Wyble, Brad Data Science and Artificial Intelligence Area College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Human-Computer Interaction Area College of Information Sciences and Technology The Pennsylvania State University University ParkPA United States Department of Communication and Media School of Social Sciences and Humanities Loughborough University Leicestershire Loughborough United Kingdom Department of Computer Science and Engineering The Pennsylvania State University University ParkPA United States Department of Psychology The Pennsylvania State University University ParkPA United States

Visual learning often occurs in a specific context, where an agent acquires skills through exploration and tracking of its location in a consistent environment. The historical spatial context of the agent provides a similarity signal for self-supervised contrastive learning. We present a unique approach, termed Environmental Spatial Similarity (ESS), that complements existing contrastive learning methods. Using images from simulated, photorealistic environments as an experimental setting, we demonstrate that ESS outperforms traditional instance discrimination approaches. Moreover, sampling additional data from the same environment substantially improves accuracy and provides new augmentations. ESS allows remarkable proficiency in room classification and spatial prediction tasks, especially in unfamiliar environments. This learning paradigm has the potential to enable rapid visual learning in agents operating in new environments with unique visual characteristics. Potentially transformative applications span from robotics to space exploration. Our proof of concept demonstrates improved efficiency over methods that rely on extensive, disconnected datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： computer vision