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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Little femilin Jana D Prema S. Kadam Neeraj Sharma Avni Garg Kalyan Acharjya Venkadeshwaran K Department of Science and Humanities 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 Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Mechanical Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka India

Photograph processing is a discipline of laptop science that concentrates on manipulating digital photos and using algorithms to recognize and beautify the photo's quality. Contour coding is a powerful approach used in virtual picture processing that allows one to increase, shop, and examine virtual snapshots from a combination of strategies like aspect detection, morphological processing, and segmentation. Contour coding aims to reduce complex virtual pix to a fixed of fundamental symbolic features that may be created, stored, and manipulated without loss in quality or element. Applications of contour coding are specially used for item reputation, segmentation, spatial pattern reputation, and scientific photograph processing. This newsletter offers the theory and fundamentals of contour coding and explores its software in actual-world virtual picture processing scenarios. We speak about the significance of contour coding and present a short evaluation of existing algorithms and improvement techniques used to achieve virtual photograph processing desires. We additionally gift the advances and challenges of contour coding and how it could form the future of digital picture processing.

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MetaGCD: Learning to Continually Learn in Generalized Category Discovery

arXiv

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arXiv 2023年

作者： Wu, Yanan Chi, Zhixiang Wang, Yang Feng, Songhe Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing100044 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Department of Electrical and Computer Engineering University of Toronto TorontoM5G1V7 Canada Department of Computer Science and Software Engineering Concordia University MontrealH3G2J1 Canada

In this paper, we consider a real-world scenario where a model that is trained on pre-defined classes continually encounters unlabeled data that contains both known and novel classes. The goal is to continually discover novel classes while maintaining the performance in known classes. We name the setting Continual Generalized Category Discovery (C-GCD). Existing methods for novel class discovery cannot directly handle the C-GCD setting due to some unrealistic assumptions, such as the unlabeled data only containing novel classes. Furthermore, they fail to discover novel classes in a continual fashion. In this work, we lift all these assumptions and propose an approach, called MetaGCD, to learn how to incrementally discover with less forgetting. Our proposed method uses a meta-learning framework and leverages the offline labeled data to simulate the testing incremental learning process. A meta-objective is defined to revolve around two conflicting learning objectives to achieve novel class discovery without forgetting. Furthermore, a soft neighborhood-based contrastive network is proposed to discriminate uncorrelated images while attracting correlated images. We build strong baselines and conduct extensive experiments on three widely used benchmarks to demonstrate the superiority of our method. Our code is available at https://***/ynanwu/MetaGCD. © 2023, CC0.

关键词： Learning systems

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Enhancing Cloud Security with AI: Developing Robust, Scalable Solutions for Threat Mitigation and data Protection in Cloud Platforms

Enhancing Cloud Security with AI: Developing Robust, Scalabl...

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

作者： AbuFaizur Rahman Abusalih Rahumath Ali K Saravanan B.Bala Abirami V.Samuthira Pandi Vijay Vasanth Aroulanandam S. Parthiban Enterprise Solution Architect Tekskills Inc. Princeton NJ USA Department of Information Technology R.M.K Engineering College Kavaraipettai Tamil Nadu India Department of Artificial Intelligence and Data Science Panimalar Engineering College Centre for Advanced Wireless Integrated Technology Chennai Institute of Technology Chennai Tamil Nadu India Networking and Communications Department School of Computing SRM Institute of Science & Technology Kattankulathur Tamil Nadu India Department of CSE SIMATS Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Thandalam Chennai

ISBN: (数字)9798331530013

ISBN: (纸本)9798331530020

As cloud computing continues to gain popularity, guaranteeing the security of cloud environments has become an issue of the utmost importance for businesses all over the world. With a particular emphasis on the development of solutions that are both robust and scalable for the purpose of threat mitigation and data protection, this study investigates the application of artificial intelligence (AI) in the process of improving cloud security. Security frameworks that are driven by artificial intelligence are able to proactively discover, evaluate, and respond to new threats in real time. This is accomplished by employing machine learning algorithms and advanced analytics. With the purpose of demonstrating the efficacy of various artificial intelligence approaches in tackling common vulnerabilities such as data breaches, unauthorized access, and distributed denial of service attacks, we investigate a variety of AI methodologies, such as anomaly detection, behavior analysis, and automated incident response. In addition, we analyze the significance of a multilayered security approach, which involves integrating artificial intelligence tools with conventional security measures in order to develop an all-encompassing defense plan. The purpose of this article is to present case studies that illustrate successful implementations of artificial intelligence-enhanced security solutions on cloud platforms. These case studies demonstrate the influence that these solutions have on increasing security posture and operational efficiency. Ultimately, the findings of this study highlight the significant role that artificial intelligence plays in the transformation of cloud security practices. This paves the way for cloud infrastructures that are more robust and secure in the face of changing cyber threats.

关键词： Cloud computing security Prevention and mitigation data protection data breach Real-time systems Vectors Security artificial intelligence Faces Anomaly detection

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LMVD: A Large-Scale Multimodal Vlog dataset for Depression Detection in the Wild

arXiv

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arXiv 2024年

作者： He, Lang Chen, Kai Zhao, Junnan Wang, Yimeng Pei, Ercheng Chen, Haifeng Jiang, Jiewei Zhang, Shiqing Zhang, Jie Wang, Zhongmin He, Tao Tiwari, Prayag The School of Computer Science and Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China The Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi’an710121 China The Xi’an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi’an710121 China The School of Electronic Engineering Xi’an University of Posts and Telecommunications Xi’an710121 China The College of Electronic Information and Artificial Intelligence Shaanxi University of Science and Technology Xi’an710016 China The Institute of Intelligent Information Processing Taizhou University Zhejiang Taizhou318000 China The School of Education Shenzhen University Shenzhen518060 China The School of Information Technology Halmstad University Halmstad Sweden

Depression can significantly impact many aspects of an individual’s life, including their personal and social functioning, academic and work performance, and overall quality of life. Many researchers within the field of affective computing are adopting deep learning technology to explore potential patterns related to the detection of depression. However, because of subjects’ privacy protection concerns, that data in this area is still scarce, presenting a challenge for the deep discriminative models used in detecting depression. To navigate these obstacles, a large-scale multimodal vlog dataset (LMVD), for depression recognition in the wild is built. In LMVD, which has 1823 samples with 214 hours of the 1475 participants captured from four multimedia platforms (Sina Weibo, Bilibili, Tiktok, and YouTube). A novel architecture termed MDDformer to learn the non-verbal behaviors of individuals is proposed. Extensive validations are performed on the LMVD dataset, demonstrating superior performance for depression detection. We anticipate that the LMVD will contribute a valuable function to the depression detection community. The data and code will released at the link: https://***/helang818/LMVD/. © 2024, CC BY-NC-ND.

关键词： Deep learning

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Fast Quaternion Product Units for Learning Disentangled Representations in SO(3)

TechRxiv

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TechRxiv 2022年

作者： Qin, Shaofei Zhang, Xuan Xu, Hongteng Xu, Yi MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Department of Computer Science & Engineering Texas A&M University College StationTX77843 United States Gaoling School of Artificial Intelligence Renmin University of China Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China

Real-world 3D structured data like point clouds and skeletons often can be represented as data in a 3D rotation group (denoted as SO(3)). However, most existing neural networks are tailored for the data in the Euclidean space, which makes the 3D rotation data not closed under their algebraic operations and leads to sub-optimal performance in 3D-related learning tasks. To resolve the issues caused by the above mismatching between data and model, we propose a novel non-real neuron model called quaternion product unit (QPU) to represent data on 3D rotation groups. The proposed QPU leverages quaternion algebra and the law of the 3D rotation group, representing 3D rotation data as quaternions and merging them via a weighted chain of Hamilton products. We demonstrate that the QPU mathematically maintains the SO(3) structure of the 3D rotation data during the inference process and disentangles the 3D representations into "rotation-invariant" features and "rotation-equivariant" features, respectively. Moreover, we design a fast QPU to accelerate the computation of QPU. The fast QPU applies a tree-structured data indexing process, and accordingly, leverages the power of parallel computing, which reduces the computational complexity of QPU in a single thread from O(N) to O(log N). Taking the fast QPU as a basic module, we develop a series of quaternion neural networks (QNNs), including quaternion multi-layer perceptron (QMLP), quaternion message passing (QMP), and so on. In addition, we make the QNNs compatible with conventional real-valued neural networks and applicable for both skeletons and point clouds. Experiments on synthetic and real-world 3D tasks show that the QNNs based on our fast QPUs are superior to state-of-the-art real-valued models, especially in the scenarios requiring the robustness to random rotations. © 2022, CC BY.

关键词： 3D modeling

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Multi-rater Prism: Learning self-calibrated medical image segmentation from multiple raters

arXiv

引用

arXiv 2022年

作者： Wu, Junde Fang, Huihui Yang, Yehui Liu, Yuanpei Gao, Jing Duan, Lixin Yang, Weihua Xu, Yanwu Baidu Inc. Beijing China Department of Statistics and Actuarial Science The University of Hong Kong Hong Kong Electrical and Computer Engineering Purdue University West LafayetteIN United States School of Computer Science and Technology University of Electronic Science and Technology of China Sichuan Chengdu China Big Data and Artificial Intelligence Institute Shenzhen Eye Hospital Jinan University Guangdong Shenzhen China

In medical image segmentation, it is often necessary to collect opinions from multiple experts to make the final decision. This clinical routine helps to mitigate individual bias. But when data is multiply annotated, standard deep learning models are often not applicable. In this paper, we propose a novel neural network framework, called Multi-Rater Prism (MrPrism) to learn the medical image segmentation from multiple labels. Inspired by the iterative half-quadratic optimization, the proposed MrPrism will combine the multi-rater confidences assignment task and calibrated segmentation task in a recurrent manner. In this recurrent process, MrPrism can learn inter-observer variability taking into account the image semantic properties, and finally converges to a self-calibrated segmentation result reflecting the inter-observer agreement. Specifically, we propose Converging Prism (ConP) and Diverging Prism (DivP) to process the two tasks iteratively. ConP learns calibrated segmentation based on the multi-rater confidence maps estimated by DivP. DivP generates multi-rater confidence maps based on the segmentation masks estimated by ConP. The experimental results show that by recurrently running ConP and DivP, the two tasks can achieve mutual improvement. The final converged segmentation result of MrPrism outperforms state-of-the-art (SOTA) strategies on a wide range of medical image segmentation tasks. © 2022, CC BY.

关键词： Medical imaging

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A DNN Operation Scheme Based on IPSO for UAV-Assisted MEC Networks

A DNN Operation Scheme Based on IPSO for UAV-Assisted MEC Ne...

引用

International Conference on Communication Technology (ICCT)

作者： Zhenqian Wang Wei Yang Bo Zhang Linlin Zhuo Huigui Rong College of Computer Science and Electronic Engineering Hunan University Changsha China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Guangdong Southern Planning & Designing Institute of Telecom Consultation Co. Ltd. Shenzhen China School of Mechanical Engineering Shanghai Jiao Tong University Shanghai China School of Data Science and Artificial Intelligence Wenzhou University of Technology Wenzhou China

Applications' demands on computing resources have increased in recent years. One potential option is to offload tasks with high computational demands to edge servers. Therefore, Mobile Edge Computing (MEC) deployed on Unmanned Aerial Vehicles (UAVs) emerges as the need arises to offer task offloading for ground User Devices (UDs). However, due to the changing environment, how to offer quick decision-making solutions is still a popular research topic. In this paper, we provide a Deep Neural Network (DNN) operation scheme based on Improved Particle Swarm Optimization (IPSO), which combines a conventional heuristic approach with neural networks to give an effective operation scheme. We take into account the requirement to decrease latency and conserve energy while maintaining equity in offloading, which is formulated as a Mixed Integer Non-Linear Programming (MINLP) issue. We first use the IPSO approach to generate labeled data and then train the neural network. The well-trained neural network is utilized to generate quick decisions. Finally, simulation results reveal our scheme's advantage in terms of fast environment adaptation.

关键词： Deep learning Training Simulation Neural networks Supervised learning Reinforcement learning Autonomous aerial vehicles

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TradeFL: A Trading Mechanism for Cross-Silo Federated Learning

TradeFL: A Trading Mechanism for Cross-Silo Federated Learni...

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International Conference on Distributed Computing Systems

作者： Shijing Yuan Hongtao Lv Hongze Liu Chentao Wu Song Guo Zhi Liu Hongyang Chen Jie Li Department of Computer Science and Engineering Shanghai Jiao Tong University China School of Software & Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) Shandong University Department of Computing The Hong Kong Polytechnic University Hong Kong The University of Electro-Communications Tokyo Japan Zhejiang Lab Research Center for Graph Computing China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Yancheng Blockchain Research Institute China

Cross-silo federated learning (CFL) is a distributed learning paradigm that allows organizations (e.g., financial or medical entities) to train a global model on siloed data. Recent studies on mechanisms designed for CFL, however, rarely jointly consider the potential inter-organizational competition and the lack of credibility between organizations, which may discourage organizational participation. In this paper, we investigate the problem of inter-organizational competition and credibility assurance. We propose a distributed trading mechanism, called $TradeFL$ , to incentivize organizations to contribute data and computational resources through mutual trading among organizations. Technically, TradeFL characterizes the competition among organizations and compensates for their damage incurred by competition. TradeFL runs on distributed organizations and provides credibility guarantees for compensation through a customized smart contract 1 1 Illustration of the prototype: https://***/user10963.. We prove that the interaction among organizations that contribute resources to maximize personal payoffs is a weighted potential game. Then, we propose a centralized algorithm and a distributed algorithm to determine the optimal resource contribution. Simulation results and evaluations based on real-world datasets demonstrate that our scheme achieves higher social welfare, increases the amount of contributed data by up to 64%, and improves the accuracy of the global model by at most 23.2%.

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Progressive LiDAR Adaptation for Road Detection

引用

IEEE/CAA Journal of Automatica Sinica 2019年第3期6卷 693-702页

作者： Zhe Chen Jing Zhang Dacheng Tao IEEE the UBTECH Sydney Artificial Intelligence Centre and the School of Computer Science Faculty of Engineering and Information Technologies University of Sydney the School of Automation Hangzhou Dianzi University the University of Technology Sydney

Despite rapid developments in visual image-based road detection, robustly identifying road areas in visual images remains challenging due to issues like illumination changes and blurry images. To this end, LiDAR sensor data can be incorporated to improve the visual image-based road detection,because LiDAR data is less susceptible to visual noises. However,the main difficulty in introducing LiDAR information into visual image-based road detection is that LiDAR data and its extracted features do not share the same space with the visual data and visual features. Such gaps in spaces may limit the benefits of LiDAR information for road detection. To overcome this issue, we introduce a novel Progressive LiDAR adaptation-aided road detection(PLARD) approach to adapt LiDAR information into visual image-based road detection and improve detection performance. In PLARD, progressive LiDAR adaptation consists of two subsequent modules: 1) data space adaptation, which transforms the LiDAR data to the visual data space to align with the perspective view by applying altitude difference-based transformation; and 2) feature space adaptation, which adapts LiDAR features to visual features through a cascaded fusion structure. Comprehensive empirical studies on the well-known KITTI road detection benchmark demonstrate that PLARD takes advantage of both the visual and LiDAR information, achieving much more robust road detection even in challenging urban scenes. In particular, PLARD outperforms other state-of-theart road detection models and is currently top of the publicly accessible benchmark leader-board.

关键词： Autonomous driving computer vision deep learning LiDAR processing road detection

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Uncertainty Quantification of the Virial Black Hole Mass with Conformal Prediction

arXiv

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arXiv 2023年

作者： Yong, Suk Yee Ong, Cheng Soon Australia Astrophysics and Space Technologies Research Centre Macquarie University SydneyNSW2109 Australia School of Mathematical and Physical Sciences Macquarie University SydneyNSW2109 Australia Faculty of Science and Engineering Macquarie University SydneyNSW2109 Australia Machine Learning and Artificial Intelligence Future Science Platform CSIRO Australia Data61 CSIRO CanberraACT2601 Australia School of Computing The Australian National University CanberraACT2601 Australia

Precise measurements of the black hole mass are essential to gain insight on the black hole and host galaxy co-evolution. A direct measure of the black hole mass is often restricted to nearest galaxies and instead, an indirect method using the single-epoch virial black hole mass estimation is used for objects at high redshifts. However, this method is subjected to biases and uncertainties as it is reliant on the scaling relation from a small sample of local active galactic nuclei. In this study, we propose the application of conformalised quantile regression (CQR) to quantify the uncertainties of the black hole predictions in a machine learning setting. We compare CQR with various prediction interval techniques and demonstrated that CQR can provide a more useful prediction interval indicator. In contrast to baseline approaches for prediction interval estimation, we show that the CQR method provides prediction intervals that adjust to the black hole mass and its related properties. That is it yields a tighter constraint on the prediction interval (hence more certain) for a larger black hole mass, and accordingly, bright and broad spectral line width source. Using a combination of neural network model and CQR framework, the recovered virial black hole mass predictions and uncertainties are comparable to those measured from the Sloan Digital Sky Survey. The code is publicly available at https://***/yongsukyee/uncertain_blackholemass. Copyright © 2023, The Authors. All rights reserved.

关键词： Forecasting

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