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arXiv 2023年

作者： Tang, Qi Zhao, Yao Liu, Meiqin Jin, Jian Yao, Chao Institute of Information Science Beijing Jiaotong University Beijing China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China Alibaba-NTU Singapore Joint Research Institute Nanyang Technological University Singapore School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China

As a critical clue of video super-resolution (VSR), inter-frame alignment significantly impacts overall performance. However, accurate pixel-level alignment is a challenging task due to the intricate motion interweaving in the video. In response to this issue, we introduce a novel paradigm for VSR named Semantic Lens, predicated on semantic priors drawn from degraded videos. Specifically, video is modeled as instances, events, and scenes via a Semantic Extractor. Those semantics assist the Pixel Enhancer in understanding the recovered contents and generating more realistic visual results. The distilled global semantics embody the scene information of each frame, while the instance-specific semantics assemble the spatial-temporal contexts related to each instance. Furthermore, we devise a Semantics-Powered Attention Cross-Embedding (SPACE) block to bridge the pixel-level features with semantic knowledge, composed of a Global Perspective Shifter (GPS) and an Instance-Specific Semantic Embedding Encoder (ISEE). Concretely, the GPS module generates pairs of affine transformation parameters for pixel-level feature modulation conditioned on global semantics. After that, the ISEE module harnesses the attention mechanism to align the adjacent frames in the instance-centric semantic space. In addition, we incorporate a simple yet effective pre-alignment module to alleviate the difficulty of model training. Extensive experiments demonstrate the superiority of our model over existing state-of-the-art VSR methods. © 2023, CC BY-NC-SA.

关键词： Semantics

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Cross-View Fuzziness and Intra-view Uncertainty-Based Weight Reconstruction for Multi-view Feature Selection 11th

Cross-View Fuzziness and Intra-view Uncertainty-Based Weigh...

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11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Xiang, Biao Chen, Hongmei Mi, Yong Sang, Binbin Horng, Shi-Jinn Li, Tianrui Institute of Artificial Intelligence School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Southwest Jiaotong University Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China College of Computer and Information Science Chongqing Normal University Chongqing401331 China Department of Computer Science and Information Engineering Asia University Taichung41354 Taiwan

ISBN: (纸本)9783031927430

Weight assignment is a universal and indispensable process in multi-view feature selection. However, most existing methods overlook the fuzziness and uncertainty implied in multi-view data. This may result in improper weight assignment, degrading the quality of selected features. A cross-view fuzziness and intra-view uncertainty-based weight reconstruction method (CFIUWR) is proposed for multi-view feature selection. With the fuzzy rough set theory, fuzzy approximation operators recognize cross-view fuzziness and intra-view uncertainty, which are evaluated by classification-related measurement. Cross-view fuzziness and intra-view uncertainty are incorporated to develop a reconstruction mechanism for view and feature weights. An optimization algorithm is derived, guaranteeing rapid convergence. Comparisons with state-of-the-art methods on several real-world datasets demonstrate the superiority of the CFIUWR. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Rough set theory

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An Image Enhancement and Data augmentation of Alzheimer’s MRI Data using modified SRGAN

An Image Enhancement and Data augmentation of Alzheimer’s M...

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International Conference on computing and Networking technology (ICCNT)

作者： Uppin Rashmi Beena B M Preethi A Sateesh Ambesange Research Scholar CSE Department Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India Department of Computer Science & Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bangalore India Department of ECE CMR Institute of Technology Bengaluru India Co-Founder PragyanAI Pragyan SmartAI Technology LLP Bengaluru India

Machine learning (ML) models, Deep Learning (DL) Models rely on labeled data for classification and prediction. Automated or computer-aided medical diagnosis requires a large dataset to maintain clinical accuracy and precision. High-resolution images of different clinical and symptomatic stages are essential for implementing ML /DL algorithms for medical diagnosis. High resolution MRI image aids in better performance of DL Models. Here we propose a model based on Super Resolution Generative Adversarial Networks (SRGAN) with Transfer learning to enhance the MR images for Alzheimer’s disease (AD). These images can also be used as data augmentation and increase dataset size for better model performance. AD, an irreversible cognitive impairment disorder, is one of the significant causes of death in the elderly. No cure is yet developed, and diagnosis at an early stage will enable the elderly to make informed decisions. The model enhance low resolution image of lower size to high resolution higher size. The model trained for 5000 epochs resulted in a final G loss of 0.001 and a D loss of 1.006, which is a significantly good result. The high-resolution images generated by the proposed model are validated by the pixel-wise loss function, with a comparable average MSE of 0.009.

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MUD-PQFed: Towards Malicious User Detection in Privacy-Preserving Quantized Federated Learning

arXiv

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arXiv 2022年

作者： Ma, Hua Li, Qun Zheng, Yifeng Zhang, Zhi Liu, Xiaoning Gao, Yansong Al-Sarawi, Said F. Abbott, Derek The School of Electrical and Electronic Engineering The University of Adelaide Australia Data61 CSIRO Australia The School of Computer Science and Engineering Nanjing University of Science and Technology China The School of Computer Science and Technology Harbin Institute of Technology Guangdong Shenzhen China The School of Computing Technologies RMIT University Australia Data61 CSIRO Australia

Federated learning (FL), as a distributed machine learning paradigm, has been adapted to mitigate privacy concerns of clients. Attributing to its advancement, institutions (i.e., hospitals) with sensitive data leverage FL to collaboratively train a global model without transmitting their raw data. Despite attractive, there exists various inference attacks that can exploit the shared plaintext model updates embedding traces of clients private information, causing severe privacy concerns. To alleviate such privacy concerns, cryptographic techniques such as secure multi-party computation and homomorphic encryption have been incorporated for privacy-preserving FL. However, this inevitably exacerbates security concerns once clients are malicious to launch attacks, in particular, corrupting the model to ruin the main impetus of benign clients in FL. Those benign clients aim to gain a better global model by contributing their computational and communicational resources and their local data for local model updates. Such security issues in privacy-preserving FL, however, lack elucidation and are under-explored. This work presents the first attempt towards elucidating the trivialness of performing model corruption attacks against lightweight secret sharing based privacy-preserving FL. We consider the scenario where the model updates are quantized to reduce the communication overhead in this case, the adversary can simply provide local parameters out of the small legitimate range to corrupt the model. We then propose MUD-PQFed, a protocol that can precisely detect malicious clients who performed the attack and enforce fair punishments. By deleting the contributions from the detected malicious clients, the global model utility is preserved as comparable to the baseline global model in absence of the attack. Extensive experiments validate the efficacy in terms of retaining the baseline accuracy and effectiveness in terms of detecting malicious clients in a fine-grained manner. C

关键词： Sensitive data

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Sign Language Translator for Dumb and Deaf

Sign Language Translator for Dumb and Deaf

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International Conference on Soft computing and Machine Intelligence (ISCMI)

作者： Viswanathan P Harapriya Kar Safal Gautam MS Mekala Masoumeh Rahimi Amir H. Gandomi Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computing Robert Gorden University Aberdeen Scotland Shahre-e-Rey Branch IslamicAzad University Tehran Iran University of Technology Sydney Australia ÓbudaUniversity Budapest Hungary

The detection of sign language for the deaf and dumb community is a challenging task due to the complex and variable nature of signing gestures. One problem that can arise in this task is overfitting, where the model tries to memorize the training data rather than generalize patterns from it. This can lead to underwhelming results on new and unseen information. One common way to increase model capacity is to add more hidden units to the neural network, which can exacerbate overfitting. To address this problem, We propose combining L2 regularisation with convolutional neural networks and long short-term memory (LSTM) models implementing relu activation functions. This approach facilitates the model to acquire knowledge of complex temporal patterns, while the final dense layers enable meaningful classification. The probabilities of the output layers are determined by the softmax activation function. This method penalises greater weights in the model in order to promote the determination of simplified patterns in the data. By using this technique, we can increase the capacity of the model without overfitting, leading to better generalization performance on new data. Our proposed method has the potential to increase the precision of sign language detection, allowing the deaf and dumb to better interact with the hearing world.

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Adoption and Acceptance of Telehealth Technologies in Mental Health Services during COVID-19

Adoption and Acceptance of Telehealth Technologies in Mental...

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International Conference on Computational and Characterization Techniques in engineering & sciences (CCTES)

作者： Thakur Prashant Sunil R.Senthamil Selvan Wilson Lukose K.Soumya Basi Reddy. A Department of Social Work Pune India Department of Electronics and Communication Engineering Annamacharya Institute of Technology and Sciences Tirupati India Bharati Vidyapeeth College of Hotel & Tourism Management Studies Navi Mumbai India Vignana Bharathi Institute of Technology Lovely Professional University Hyderabad India Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

A quick shift to mental health (MH) care delivery via telephone and video was provoked by COVID-19 social distancing principles. This research used data collected from 327 mental health organizations across 22 states between May 2020 and August 2020 to look at three things: (a) how these technologies were adopted throughout the continuum of mental health services; (b) how providers felt about various electronic tools; and (c) how they planned to utilize these technologies after this sudden spread of this virus. More than 69% of organizations said they used phone or video for the majority of their services, indicating significant usage of technology. Organizations indicated a much higher probability of wanting to employ technology to supply services post-COVID-19 about every video service and minimally three phones. Many people prefer video calls over phone calls. A positive view for the adoption of video-based services and some telephone after-COVID-19 services epidemic is shown by their generally recognized ease of use and usefulness.

关键词： COVID-19 Pandemics computer viruses Telemedicine Mental health Organizations Telephone sets Social factors Diseases

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Using Pattern Analysis and Machine Learning to Categorise users of Online Directories based on their Surfing Habits

Using Pattern Analysis and Machine Learning to Categorise us...

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Sustainable Communication Networks and Application (ICSCNA), International Conference on

作者： S. Athinarayanan M. Robinson Joel Shaik Jumlesha K. Susmitha Limgamgunta Charitha Department of CSE School of Computing Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Avadi Chennai Tamil Nadu India Department of Computer Science and Engineering AITS Tirupati Andra Pradesh Department of Information Technology Kings Engineering College Chennai India

The major goal is to group users together, including in student organizations. Assume that the notion is required to cluster consumers in educational institutions. A cluster of websites or web pages organized by user interest makes up an online use directory. Web mining is the process of using strategies to extract websites from the web proxy log file. We are employing the Apriori Algorithm and the Fuzzy Clustering technique for it. Typically, data cleaning, a preprocessing step, is followed by aggregation of the data after cleanliness in web use mining. We do a pattern analysis following the data's classification. The outcomes of the studies are contrasted with another intriguing technique that is made public. In comparison to other efforts, the outcomes produced by the suggested system exhibit better performance. The technique we utilised, called WDUM or web directories user mining, produces better results than earlier techniques. We compare user growth and penetration performance to the threshold values. Thus, after preprocessing, the log files are retrieved, and the Apriori Algorithm and fuzzy logic classification are applied. These outcomes demonstrate the greatest methods for capturing user interest. Finally, the projected user clicked the prediction findings using machine learning techniques.

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A Convolution-Based System for Malicious URLs Detection

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computers, Materials & Continua 2020年第1期62卷 399-411页

作者： Chaochao Luo Shen Su Yanbin Sun Qingji Tan Meng Han Zhihong Tian Institute of Computer Application China Academy of Engineering PhysicsMianyang621054China Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhou510006China School of Mechanical and Electrical Engineering Heilongjiang State Farm Science Technology Vocational CollegeHarbin150431China Department of Computing and Software Engineering Kennesaw State UniversityKennesawGA 30144USA

Since the web service is essential in daily lives,cyber security becomes more and more important in this digital *** Uniform Resource Locator(URL)is a common and serious threat to *** hosts unsolicited content and lure unsuspecting users to become victim of scams,such as theft of private information,monetary loss,and malware ***,it is imperative to detect such ***,traditional approaches for malicious URLs detection that based on the blacklists are easy to be bypassed and lack the ability to detect newly generated malicious *** this paper,we propose a novel malicious URL detection method based on deep learning model to protect against web ***,we firstly use auto-encoder to represent ***,the represented URLs will be input into a proposed composite neural network for *** order to evaluate the proposed system,we made extensive experiments on HTTP CSIC2010 dataset and a dataset we collected,and the experimental results show the effectiveness of the proposed approach.

关键词： CNN anomaly detection web security auto-encoder deep learning

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CL-MIL: Multi-Instance Learning on Whole-Slide Images with Self-Supervised Contrastive Learning 10

CL-MIL: Multi-Instance Learning on Whole-Slide Images with S...

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10th International Conference on computer and Communications, ICCC 2024

作者： Xiu, Pengfei Liu, Dongying Wang, Sheng Wang, Hongliang Wang, Wei Li, Shun Shenyang Institute of Computing Technology Chinese Academy of Science Shenyang China University of Chinese Academy of Sciences Beijing China Liaoning Province Human-Computer Interaction System Engineering Research Center Based on Digital Twin Shenyang China Shengjing Hospital of China Medical University Department of Gastroenterology Shenyang China School of Computer Science and Engineering Northeastern University Shenyang China

Histopathological examination of tissue slides is considered the gold standard for clinical cancer diagnosis. However, it heavily relies on pathologists' subjective judgment and involves substantial workload. Existing convolutional neural network (CNN)-based whole slide image (WSI) diagnostic methods suffer from high annotation costs and complex data acquisition processes. Therefore, this study proposes a weakly supervised multiple instance learning method based on self-supervised contrastive learning (CL-MIL) for WSI classification. By integrating self-supervised contrastive learning strategies, leveraging data augmentation through H&E stain separation, and introducing causal intervention via backdoor adjustment to mitigate dataset bias, our approach significantly enhances feature representation and classification performance in pathological images. Experimental results on the Camelyon16 and TCGA lung cancer datasets demonstrate that our method outperforms other comparable WSI classification models across various evaluation metrics, achieving higher diagnostic efficiency and accuracy. Additionally, our method exhibits good interpretability, enabling precise localization of pathological regions and providing robust technical support for clinical applications. © 2024 IEEE.

关键词： Self-supervised learning

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Enhancing IoT-Edge Computation with Data Forwarding based Decentralized Deep Neural Networks

Enhancing IoT-Edge Computation with Data Forwarding based De...

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International Conference on Image Information Processing

作者： Bmg Prasad Rachappa Jopate Pankaj Savita A Basi Reddy B Prabu Shankar M S Arunkumar Department of CSE Ravindra College of Engineering for Women(RCEW Autonomous) Kurnool Andhra Pradesh India Department of Information Technology University of Technology And Applied Sciences - Al Mussanah Al Muladha Muscat Sultanate of Oman Department of CSE Sagar Institute of Science & Technology Bhopal Organization-Sagar Institute of Science & Technology Bhopal Madhya Pradesh India Department of Computer Science and Engineering School of Computing Mohan Babu University Tirupati Andhra Pradesh India Department of Computer Science and Engineering Alliance College of Engineering and Design Alliance University Banglore Karnataka India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India

ISBN: (数字)9798350371406

ISBN: (纸本)9798350371413

The incorporation of edge computing has become essential in the big data and Internet of Things age to tackle issues related to latency, real-time processing, and data scale. This paper delves into the imperative need for edge computing within the context of big data environments. Distributed deep learning methodologies are explored, encompassing a diverse array of deep learning models. Particularly, a novel paradigm named Data Forwarding based Decentralized Deep Neural Network (DF-DDNN) is introduced to achieve low latency IoT-Edge computation. In order to reduce latency in Io T ecosystems, the DF-DDNN model makes use of edge devices’ processing capabilities. By strategically distributing data processing and computation at the edge, this model seeks to overcome inherent limitations of conventional Io T architectures, with a keen focus on network, processing, and overall latencies. The research provides a thorough performance evaluation that compares the DF-DDNN model’s performance versus conventional IoT models in a number of areas. The outcomes demonstrate how much better the suggested DF-DDNN model is than traditional IoT systems. Notably, improvements are evident in terms of reduced network latency, accelerated processing speeds, and overall latency mitigation. This research underscores the critical importance of embracing edge computing in the context of big data and deep learning applications. The DF-DDNN model emerges as a promising avenue for advancing IoT-Edge computation, effectively addressing latency concerns and augmenting the performance of IoT-enabled systems.

关键词： Deep learning Performance evaluation Computational modeling Biological system modeling Artificial neural networks Big Data Data models

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