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Feature Noise Resilient for QoS Prediction with Probabilistic Deep Supervision

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arXiv 2023年

作者： Wang, Ziliang Zhang, Xiaohong Li, Ze Shi Huang, Sheng Yan, Meng Ministry of Education School of Computer Science Peking University Beijing China The Department of Computer Science The University of Victoria Canada Ministry of Education China School of Big Data and Software Engineering Chongqing University Chongqing401331 China

Accurate Quality of Service (QoS) prediction is essential for enhancing user satisfaction in web recommendation systems, yet existing prediction models often overlook feature noise, focusing predominantly on label noise. In this paper, we present the Probabilistic Deep Supervision Network (PDS-Net), a robust framework designed to effectively identify and mitigate feature noise, thereby improving QoS prediction accuracy. PDS-Net operates with a dual-branch architecture: the main branch utilizes a decoder network to learn a Gaussian-based prior distribution from known features, while the second branch derives a posterior distribution based on true labels. A key innovation of PDS-Net is its condition-based noise recognition loss function, which enables precise identification of noisy features in objects (users or services). Once noisy features are identified, PDS-Net refines the feature's prior distribution, aligning it with the posterior distribution, and propagates this adjusted distribution to intermediate layers, effectively reducing noise interference. Extensive experiments conducted on two real-world QoS datasets demonstrate that PDS-Net consistently outperforms existing models, achieving an average improvement of 8.91% in MAE on dataset D1 and 8.32% on dataset D2 compared to the ate-of-the-art. These results highlight PDS-Net's ability to accurately capture complex user-service relationships and handle feature noise, underscoring its robustness and versatility across diverse QoS prediction environments. © 2023, CC0.

关键词： Prediction models

Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques

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arXiv 2023年

作者： Qazanfari, Hamed Alyan Nezhadi, Mohammad M. Khoshdaregi, Zohreh Nozari Image Processing and Data Mining Lab Shahrood University of Technology Shahrood Iran Faculty of Science University of Science and Technology of Mazandaran Behshahr Iran Department of Computer Engineering University of Bojnord Iran

Content-based image retrieval (CBIR) systems have emerged as crucial tools in the field of computer vision, allowing for image search based on visual content rather than relying solely on metadata. This survey paper presents a comprehensive overview of CBIR, emphasizing its role in object detection and its potential to identify and retrieve visually similar images based on content features. Challenges faced by CBIR systems, including the semantic gap and scalability, are discussed, along with potential solutions. It elaborates on the semantic gap, which arises from the disparity between low-level features and high-level semantic concepts, and explores approaches to bridge this gap. One notable solution is the integration of relevance feedback (RF), empowering users to provide feedback on retrieved images and refine search results iteratively. The survey encompasses long-term and short-term learning approaches that leverage RF for enhanced CBIR accuracy and relevance. These methods focus on weight optimization and the utilization of active learning algorithms to select samples for training classifiers. Furthermore, the paper investigates machine learning techniques and the utilization of deep learning and convolutional neural networks to enhance CBIR performance. This survey paper plays a significant role in advancing the understanding of CBIR and RF techniques. It guides researchers and practitioners in comprehending existing methodologies, challenges, and potential solutions while fostering knowledge dissemination and identifying research gaps. By addressing future research directions, it sets the stage for advancements in CBIR that will enhance retrieval accuracy, usability, and effectiveness in various application domains. © 2023, CC BY.

关键词： Object detection

Vaccinating Federated Learning for Robust Modulation Classification in Distributed Wireless Networks

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arXiv 2024年

作者： Lee, Hunmin Seong, Hongju Kim, Wonbin Kwon, Hyeokchan Seo, Daehee Department of Computer Science and Engineering University of Minnesota Minneapolis55455 United States Department of Computer Education Sunchon National University Suncheon57922 Korea Republic of Department of Artificial Intelligence and Data Engineering Sangmyung University Seoul03016 Korea Republic of Daejeon34129 Korea Republic of

Automatic modulation classification (AMC) serves a vital role in ensuring efficient and reliable communication services within distributed wireless networks. Recent developments have seen a surge in interest in deep neural network (DNN)based AMC models, with Federated Learning (FL) emerging as a promising framework. Despite these advancements, the presence of various noises within the signal exerts significant challenges while optimizing models to capture salient features. Furthermore, existing FL-based AMC models commonly rely on linear aggregation strategies, which face notable difficulties in integrating locally fine-tuned parameters within practical non-IID (Independent and Identically Distributed) environments, thereby hindering optimal learning convergence. To address these challenges, we propose FedVaccine, a novel FL model aimed at improving generalizability across signals with varying noise levels by deliberately introducing a balanced level of noise. This is accomplished through our proposed harmonic noise resilience approach, which identifies an optimal noise tolerance for DNN models, thereby regulating the training process and mitigating overfitting. Additionally, FedVaccine overcomes the limitations of existing FL-based AMC models’ linear aggregation by employing a split-learning strategy using structural clustering topology and local queue data structure, enabling adaptive and cumulative updates to local models. Our experimental results, including IID and non-IID datasets as well as ablation studies, confirm FedVaccine’s robust performance and superiority over existing FL-based AMC approaches across different noise levels. These findings highlight FedVaccine’s potential to enhance the reliability and performance of AMC systems in practical wireless network environments. © 2024, CC BY.

关键词： Signal to noise ratio

JC5A: Service Delay Minimization for Aerial MEC-assisted Industrial Cyber-Physical Systems

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arXiv 2024年

作者： Sun, Geng Wu, Jiaxu Sun, Zemin He, Long Wang, Jiacheng Niyato, Dusit Jamalipour, Abbas Mao, Shiwen College of Computer Science and Technology Jilin University Changchun130012 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China College of Computing and Data Science Nanyang Technological University Singapore639798 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Electrical and Computer Engineering The University of Sydney SydneyNSW2006 Australia Department of Electrical and Computer Engineering Auburn University Auburn United States

In the era of the sixth generation (6G) and industrial Internet of Things (IIoT), an industrial cyber-physical system (ICPS) drives the proliferation of sensor devices and computing-intensive tasks. To address the limited resources of IIoT sensor devices, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) has emerged as a promising solution, providing flexible and cost-effective services in close proximity of IIoT sensor devices (ISDs). However, leveraging aerial MEC to meet the delay-sensitive and computation-intensive requirements of the ISDs could face several challenges, including the limited communication, computation and caching (3C) resources, stringent offloading requirements for 3C services, and constrained on-board energy of UAVs. To address these issues, we first present a collaborative aerial MEC-assisted ICPS architecture by incorporating the computing capabilities of the macro base station (MBS) and UAVs. We then formulate a service delay minimization optimization problem (SDMOP). Since the SDMOP is proved to be an NP-hard problem, we propose a joint computation offloading, caching, communication resource allocation, computation resource allocation, and UAV trajectory control approach (JC5A). Specifically, JC5A consists of a block successive upper bound minimization method of multipliers (BSUMM) for computation offloading and service caching, a convex optimization-based method for communication and computation resource allocation, and a successive convex approximation (SCA)-based method for UAV trajectory control. Moreover, we theoretically prove the convergence and polynomial complexity of JC5A. Simulation results demonstrate that the proposed approach can achieve superior system performance compared to the benchmark approaches and algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

Impact of Fuzziness for Skin Lesion Classification with Transformer-Based Model

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Impact of Fuzziness for Skin Lesion Classification with Tran...

International Conference on Computing, Electronics & Communications engineering (iCCECE)

作者： Israt Yasmin Suriya Sultana Syeda Jobaida Begum Muhammed J. A. Patwary Tarik Adnan Almohamad Iftekhar Salam Department of Computer Science and Engineering International Islamic University Chittagong Chattogram Bangladesh Electrical-Electronics Engineering Karabuk University Karabuk Türkiye School of Computing and Data Science Xiamen University Malaysia Sepang Malaysia

Skin lesion is one of the most commonly encountered illnesses that need to be detected and treated at an early stage. Numerous Convolutional Neural Network (CNN) classifiers were utilized to identify skin conditions using visual features. When it comes to computational effectiveness and accuracy, the recently released Vision Transformers (ViT) have been shown to outperform CNNs. Since the ViTs perform well in classifying images, we use ViT models in our approach. This research proposes a fuzziness-based transformer model to classify images. The divide-and-conquer technique is employed to examine the effectiveness of fuzzy logic and classifying images more accurately. To increase the effectiveness of image classification, the testing dataset has been divided into three groups, including low, medium, and high-fuzzy instances, based on the degree of fuzziness for each sample. For better classification of images, we propose a straightforward mechanism that consists of a fuzzy divide-and-conquer strategy with ViT. Experiments are carried out to compare the model’s performance with and without using this proposed mechanism. We noticed that retraining classifiers with low fuzzy samples can enhance image classifier performance. The classification performance on the PAD-UFES-20 dataset using this strategy revealed substantial improvements in the classifier’s performance.

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DeepfakeBench: a comprehensive benchmark of deepfake detection 23

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DeepfakeBench: a comprehensive benchmark of deepfake detecti...

Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Zhiyuan Yan Yong Zhang Xinhang Yuan Siwei Lyu Baoyuan Wu School of Data Science The Chinese University of Hong Kong Shenzhen (CUHK-Shenzhen) China Tencent AI Lab Department of Computer Science and Engineering University at Buffalo State University of New York

A critical yet frequently overlooked challenge in the field of deepfake detection is the lack of a standardized, unified, comprehensive benchmark. This issue leads to unfair performance comparisons and potentially misleading results. Specifically, there is a lack of uniformity in data processing pipelines, resulting in inconsistent data inputs for detection models. Additionally, there are noticeable differences in experimental settings, and evaluation strategies and metrics lack standardization. To fill this gap, we present the first comprehensive benchmark for deepfake detection, called DeepfakeBench, which offers three key contributions: 1) a unified data management system to ensure consistent input across all detectors, 2) an integrated framework for state-of-the-art methods implementation, and 3) standardized evaluation metrics and protocols to promote transparency and reproducibility. Featuring an extensible, modular-based codebase, DeepfakeBench contains 15 state-of-the-art detection methods, 9 deepfake datasets, a series of deepfake detection evaluation protocols and analysis tools, as well as comprehensive evaluations. Moreover, we provide new insights based on extensive analysis of these evaluations from various perspectives (e.g., data augmentations, backbones). We hope that our efforts could facilitate future research and foster innovation in this increasingly critical domain. All codes, evaluations, and analyses of our benchmark are publicly available at https://***/SCLBD/DeepfakeBench.

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Graph Prompt Learning: A Comprehensive Survey and Beyond

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arXiv 2023年

作者： Sun, Xiangguo Zhang, Jiawen Wu, Xixi Cheng, Hong Xiong, Yun Li, Jia Department of Systems Engineering and Engineering Management Shun Hing Institute of Advanced Engineering The Chinese University of Hong Kong Hong Kong Hong Kong University of Science and Technology Guangzhou China Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China

Artificial General Intelligence (AGI) has revolutionized numerous fields, yet its integration with graph data, a cornerstone in our interconnected world, remains nascent. This paper presents a pioneering survey on the emerging domain of graph prompts in AGI, addressing key challenges and opportunities in harnessing graph data for AGI applications. Despite substantial advancements in AGI across natural language processing and computer vision, the application to graph data is relatively underexplored. This survey critically evaluates the current landscape of AGI in handling graph data, highlighting the distinct challenges in cross-modality, cross-domain, and cross-task applications specific to graphs. Our work is the first to propose a unified framework for understanding graph prompt learning, offering clarity on prompt tokens, token structures, and insertion patterns in the graph domain. We delve into the intrinsic properties of graph prompts, exploring their flexibility, expressiveness, and interplay with existing graph models. A comprehensive taxonomy categorizes over 100 works in this field, aligning them with pre-training tasks across node-level, edge-level, and graph-level objectives. Additionally, we present, ProG, a Python library, and an accompanying website, to support and advance research in graph prompting. The survey culminates in a discussion of current challenges and future directions, offering a roadmap for research in graph prompting within AGI. Through this comprehensive analysis, we aim to catalyze further exploration and practical applications of AGI in graph data, underlining its potential to reshape AGI fields and beyond. ProG and the website can be accessed by https: //***/WxxShirley/Awesome-Graph-Prompt, and https://***/sheldonresearch/ProG, respectively. Copyright © 2023, The Authors. All rights reserved.

关键词： Websites

Vision Transformers and YoloV5 based Driver Drowsiness Detection Framework

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arXiv 2022年

作者： Krishna, Ghanta Sai Supriya, Kundrapu Vardhan, Jai Mallikharjuna Rao, K. Data Science and Artificial Intelligence Department IIIT Naya Raipur India Computer science and Engineering IIIT Naya Raipur India

Human drivers have distinct driving techniques, knowledge, and sentiments due to unique driving traits. Driver drowsiness has been a serious issue endangering road safety;therefore, it is essential to design an effective drowsiness detection algorithm to bypass road accidents. Miscellaneous research efforts have been approached the problem of detecting anomalous human driver behaviour to examine the frontal face of the driver and automobile dynamics via computer vision techniques. Still, the conventional methods cannot capture complicated driver behaviour features. However, with the origin of deep learning architectures, a substantial amount of research has also been executed to analyze and recognize driver's drowsiness using neural network algorithms. This paper introduces a novel framework based on vision transformers and YoloV5 architectures for driver drowsiness recognition. A custom YoloV5 pre-trained architecture is proposed for face extraction with the aim of extracting Region of Interest (ROI). Owing to the limitations of previous architectures, this paper introduces vision transformers for binary image classification which is trained and validated on a public dataset UTA-RLDD. The model had achieved 96.2% and 97.4% as it's training and validation accuracies respectively. For the further evaluation, proposed framework is tested on a custom dataset of 39 participants in various light circumstances and achieved 95.5% accuracy. The conducted experimentations revealed the significant potential of our framework for practical applications in smart transportation systems. © 2022, CC BY.

关键词： Face recognition

Algorithmic Techniques for Necessary and Possible Winners

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ACM/IMS Transactions on data science 2021年第3期2卷 1–23页

作者： Chakraborty, Vishal Delemazure, Theo Kimelfeld, Benny Kolaitis, Phokion G. Relia, Kunal Stoyanovich, Julia Computer Science and Engineering Department Jack Baskin School of Engineering University of California Santa Cruz Santa CruzCA95064 United States Ecole Normale Superieure 45 Rue d'Ulm Paris75005 France Henry and Marilyn Taub Faculty of Computer Science Technion Haifa3200003 Israel Computer Science and Engineering Department Jack Baskin School of Engineering University of California Santa Cruz Santa CruzCA95064 United States Computer Science and Engineering Department Tandon School of Engineering New York University 370 Jay Street BrooklynNY11201 United States Center for Data Science New York University 60 5th Avenue New YorkNY10011 United States

We investigate the practical aspects of computing the necessary and possible winners in elections over incomplete voter preferences. In the case of the necessary winners, we show how to implement and accelerate the polynomial-time algorithm of Xia and Conitzer. In the case of the possible winners, where the problem is NP-hard, we give a natural reduction to Integer Linear Programming (ILP) for all positional scoring rules and implement it in a leading commercial optimization solver. Further, we devise optimization techniques to minimize the number of ILP executions and, oftentimes, avoid them altogether. We conduct a thorough experimental study that includes the construction of a rich benchmark of election data based on real and synthetic data. Our findings suggest that, the worst-case intractability of the possible winners notwithstanding, the algorithmic techniques presented here scale well and can be used to compute the possible winners in realistic scenarios. © 2021 Association for Computing Machinery.

关键词： Integer programming