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arXiv 2023年

作者： Peng, Duo Ke, Qiuhong Lei, Yinjie Liu, Jun The Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore487372 Singapore The Department of Data Science and AI Monash University ParkvilleVIC3010 Australia The College of Electronics and Information Engineering Sichuan University Chengdu610065 China

Unsupervised Domain Adaptation (UDA) is quite challenging due to the large distribution discrepancy between the source domain and the target domain. Inspired by diffusion models which have strong capability to gradually convert data distributions across a large gap, we consider to explore the diffusion technique to handle the challenging UDA task. However, using diffusion models to convert data distribution across different domains is a non-trivial problem as the standard diffusion models generally perform conversion from the Gaussian distribution instead of from a specific domain distribution. Besides, during the conversion, the semantics of the source-domain data needs to be preserved for classification in the target domain. To tackle these problems, we propose a novel Domain-Adaptive Diffusion (DAD) module accompanied by a Mutual Learning Strategy (MLS), which can gradually convert data distribution from the source domain to the target domain while enabling the classification model to learn along the domain transition process. Consequently, our method successfully eases the challenge of UDA by decomposing the large domain gap into small ones and gradually enhancing the capacity of classification model to finally adapt to the target domain. Our method outperforms the current state-of-the-arts by a large margin on three widely used UDA datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Image classification

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Unprecedented Smart Algorithm for Uninterrupted SDN Services During DDoS Attack

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Computers, Materials & Continua 2022年第1期70卷 875-894页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Rizaludin Kaspin Iram Haider Sana Nisar J.P.C.Rodrigues Bhawani Shankar Chowdhry Muhammad AslamUqaili Satya Prasad Majumder Danda B.Rawat Richard Etengu Rajkumar Buyya Faculty of Computing&Informatics Multimedia UniversityPersiaranMultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation Centre63000CyberjayaSelangorMalaysia Federal University of Piauí(UFPI) TeresinaPIBrazil Instituto de Telecomunica珲es 6201-001Covilh?Portugal National Center of Robotics and Automation-Condition Monitoring Systems Lab MUETJamshoroPakistan Department of Electrical and Electronic Engineering BUETDhaka1205Bangladesh Department of Electrical Engineering and Computer Science Data Science and Cybersecurity CenterHoward UniversityWashingtonDCUSA Cloud Computing and Distributed Systems(CLOUDS)Laboratory School of Computing and Information SystemsThe University of MelbourneMelbourneVIC 3053Australia

In the design and planning of next-generation Internet of Things(IoT),telecommunication,and satellite communication systems,controller placement is crucial in software-defined networking(SDN).The programmability of the SDN controller is sophisticated for the centralized control system of the entire ***,it creates a significant loophole for the manifestation of a distributed denial of service(DDoS)attack ***,recently a Distributed Reflected Denial of Service(DRDoS)attack,an unusual DDoS attack,has been ***,minimal deliberation has given to this forthcoming single point of SDN infrastructure failure ***,recently the high frequencies of DDoS attacks have increased *** this paper,a smart algorithm for planning SDN smart backup controllers under DDoS attack scenarios has *** proposed smart algorithm can recommend single or multiple smart backup controllers in the event of DDoS *** obtained simulated results demonstrate that the validation of the proposed algorithm and the performance analysis achieved 99.99%accuracy in placing the smart backup controller under DDoS attacks within 0.125 to 46508.7 s in SDN.

关键词： SDN smart algorithm RTZLK-DAASCP DDoS attack DRDoS

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A Review on Energy Efficient Cooperative Routing Algorithm for Wireless Sensor Networks 1

A Review on Energy Efficient Cooperative Routing Algorithm f...

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1st IEEE International Conference on Automation, Computing and Renewable systems, ICACRS 2022

作者： Immanuvel Arokia James, K. Manjula, P. Mohana, M. Arthi, S. Department of Information Technology VEL TECH MULTI TECH Dr.Rangarajan Dr.Sakunthala Engineering College Chennai India Department of Computer Science and Business Systems Panimalar Engineering College Chennai India Department of Artificial Intelligence and Data Science VEL TECH MULTI TECH Dr.Rangarajan Dr.Sakunthala Engineering College Chennai India Department of Computer Science and Engineering VEL TECH MULTI TECH Dr.Rangarajan Dr.Sakunthala Engineering College Chennai India

ISBN: (纸本)9781665460842

Wireless communications have experienced speedy and technical development in this modern era. Significant improvements are required in terms of communication speed link, energy level, size of the device, network lifetime and applications for different generations of wireless devices. Due to the advancement in technical development, the researchers have started to build WNArchitectures the conventional method of data communication using peer-to-peer basis with a centralized (BS) controller. In Wireless Sensor Networks (WSNs), the conventional method of data communication in the network is instituted and this allows the nodes to forward data through other nodes in the communication network and create new communication paths that involve several wireless hops in the communication network. Recently, there has been an upraise in cooperative communication. This promising methodology plays an important role in relieving wireless channel fading, as well as, recuperating steadfastness in networks which permit nodes that allow cooperation among them. The nodes share data in cooperative communication by exploiting the communication in their broadcasting nature. Simulations were run for various adjustments in order to approve the Cooperative Power and Energy-efficient routing protocol (COPE) [14] convention's implementation alongside the proposed Minimum Power Least Cost Routing (MPLCR) Algorithm conventions. © 2022 IEEE

关键词： Energy efficiency

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Hierarchical Micro-Segmentations for Zero-Trust Services via Large Language Model (LLM)-enhanced Graph Diffusion

arXiv

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arXiv 2024年

作者： Liu, Yinqiu Liu, Guangyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui In Kim, Dong Shen, Xuemin College of Computing and Data Science Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of Department of Electrical and Computer Engineering University of Waterloo Canada

—In the rapidly evolving Next-Generation Networking (NGN) era, the adoption of zero-trust architectures has become increasingly crucial to protect security. However, provisioning zero-trust services in NGNs poses significant challenges, primarily due to the environmental complexity and dynamics. Motivated by these challenges, this paper explores efficient zero-trust service provisioning using hierarchical micro-segmentations. Specifically, we model zero-trust networks via hierarchical graphs, thereby jointly considering the resource- and trust-level features to optimize service efficiency. We organize such zero-trust networks through micro-segmentations, which support granular zero-trust policies efficiently. To generate the optimal micro-segmentation, we present the Large Language Model-Enhanced Graph Diffusion (LEGD) algorithm, which leverages the diffusion process to realize a high-quality generation paradigm. Additionally, we utilize policy boosting and Large Language Models (LLM) to enable LEGD to optimize the generation policy and understand complicated graphical features. Moreover, realizing the unique trustworthiness updates or service upgrades in zero-trust NGN, we further present LEGD-Adaptive Maintenance (LEGD-AM), providing an adaptive way to perform task-oriented fine-tuning on LEGD. Extensive experiments demonstrate that the proposed LEGD achieves 90% higher efficiency in provisioning services compared with other baselines. Moreover, the LEGD-AM can reduce the service outage time by over 50%. © 2024, CC BY.

关键词： Diffusion

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Exposing Computer-Generated Images Via Amplified Texture Differences Learning

Exposing Computer-Generated Images Via Amplified Texture Dif...

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IEEE International Conference on systems, Man and Cybernetics

作者： Qiang Xu Zhe Wang Zhongjie Mi Hong Yan Department of Electrical Engineering Centre for Intelligent Multidimensional Data Analysis City University of Hong Kong Kowloon Hong Kong China Department of Advanced Design and Systems Engineering Centre for Intelligent Multidimensional Data Analysis City University of Hong Kong Hong Kong China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

Many Computer-Generated (CG) images are spreading widely on the Internet, which may deliberately misinform or deceive the public. Therefore, distinguishing CG images from natural photographic (PG) has become a frontier research topic in the field of image forensics. Although many algorithms have been proposed, it is still very challenging to detect CG images generated by the recent cutting-edge generative methods. Besides, most existing algorithms tend to generalize poorly when facing different unseen multimodal generative models. To address this issue, a novel method based on amplified texture differences learning is proposed to tackle this problem. We first design a deep texture enhancement module for discriminative texture amplification. Specifically, a semantic segmentation module is utilized to generate semantic segmentation map for the affine transformation operation guidance, which can be further used to recover the texture in different regions of the input image. Then, the combination of the original image and the high-frequency components of the original and enhanced images are fed into a hybrid neural network equipped with attention mechanisms, which refines intermediate features and facilitates trace exploration in spatial and channel dimensions respectively. By verifying on several commonly used benchmark datasets and a newly constructed dataset 1 1 The benchmark is available at https://***/191578010/DSGCG. with more realistic and diverse images, the experimental results demonstrate that the proposed approach outperforms some existing methods.

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Results of analysis of multimedia traffic of elements of the Internet of Things

Results of analysis of multimedia traffic of elements of the...

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2021 International Conference on information Science and Communications Technologies, ICISCT 2021

作者： Nishanbayev, Tuygun Muradova, Alevtina Davletova, Kholisakhon Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Data Communication Networks and Systems Tashkent100084 Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Telecommunication Engineering Tashkent100084 Uzbekistan

ISBN: (纸本)9781665432580

The article offers options for reference models for the construction and interaction of Internet of Things (IoT) systems. The results of solving the problem are presented, namely, data acquisition and traffic analysis of devices used in smart home systems - smart sockets. Sockets manufactured by TP-Link and Redmond are considered. For more accurate traffic data, both the data generated and received by the outlets are considered. The obtained results of information about the operation of the presented devices can be used for a deeper study in order to predict jumps in the volume of traffic and the behavior of the system as a whole, as well as for directly computer models of networks of intelligent devices. © 2021 IEEE.

关键词： Internet of things

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Perceptual Video Summarization Using Keyframes Extraction Technique

Perceptual Video Summarization Using Keyframes Extraction Te...

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Innovative Practices in Technology and Management (ICIPTM), International Conference on

作者： Rajitha Jasmine R Padmaja Nimmagadda K. Sudhakar Benitha Christinal J P. Rajasekar Syed Musthafa A Department of Information Technology R. M. K. Engineering College Kavaraipettai India Department of Electronics and Communication Mohan Babu University Tirupati India Department of Computer Science Madanapalle Institute of Technology Madanapalle India Department of Information Technology Panimalar Engineering College Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Chengalpattu India Department of Information Technology M. Kumarasamy College of Engineering Karur India

The growth of video content in recent years is a challenging problem due to increased memory storage and time consuming for analyzing content of the video. Therefore, there is a need to reduce the content for human usage. This paper presents a keyframes technique that uses co-occurrence matrix and permutation computation for perceptual video summarization (PVS). The Human Visual System (HVS) needs to incorporate the PVS. It helps to allow for the importance of perceptually significant contents. The proposed method uses different kinds of videos to evaluate the effectiveness of the work. The subjective evaluation scores have evaluated the proposed work.

关键词： Color Visual systems Feature extraction

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Digital Innovations Development: Causal factors and relations - A Case Study of Montenegro 28

Digital Innovations Development: Causal factors and relation...

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2024 28th International Conference on information Technology, IT 2024

作者： Ognjanovic, Ivana Lakovic, Luka Sendelj, Ramo Bokor, Laszlo Kara, Peter A. Reich, Christoph Madan, Manav Roganovic, Milovan Zouilas, Emmanouil Mantas, John Erakovic, Jevto Radusinovic, Tanja Rakocevic, Nada University of Donja Gorica Faculty for Information Systems and Technologies Podgorica81000 Montenegro University of Donja Gorica Humanistic Studies Podgorica81000 Montenegro Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services Budapest1111 Hungary Furtwangen University Institute of Data Science Cloud Computing and It Security Furtwangen78120 Germany Clinic for Neurology Clinical Centre of Montenegro Podgorica81000 Montenegro National and Kapodistrian University of Athens Faculty of Nursing School of Health Sciences Goudi11527 Greece Chamber of Economy of Montenegro Podgorica81000 Montenegro

ISBN: (纸本)9798350369618

In order for economies to expand and develop globally, innovation is essential. A popular statistic for evaluating innovation between countries is the Global Innovation Index (GII), which provides a thorough analysis of the state of innovation worldwide. In this context, this paper aims to contribute to the literature by examining the multi-factoral effects of the innovation ecosystem on innovation development (internal causal effects), and the impact of global trends and crises on the innovation ecosystem (external causal effects) in Montenegro. The resources provided in this paper offer valuable insights into the innovation landscape in Montenegro and the Western Balkans, as well as the impact of globalization, global trends, and crises on innovation in the region. The paper also discusses the function of innovation hubs such as the DigNest project, in promoting cooperation between academic institutions, government agencies and corporations, and supporting the exchange of knowledge and fresh concepts. © 2024 IEEE.

关键词： Ecosystems

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Enhanced Multi-Tuple Extraction for Alloys: Integrating Pointer Networks and Augmented Attention

arXiv

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arXiv 2025年

作者： Hei, Mengzhe Zhang, Zhouran Liu, Qingbao Pan, Yan Zhao, Xiang Peng, Yongqian Ye, Yicong Zhang, Xin Bai, Shuxin National Key Laboratory of Information Systems Engineering National University of Defense Technology Hunan Changsha410072 China Department of Materials Science and Engineering National University of Defense Technology Hunan Changsha410072 China Laboratory for Big Data and Decision National University of Defense Technology Hunan Changsha410072 China

Extracting high-quality structured information from scientific literature is crucial for advancing material design through data-driven methods. Despite the considerable research in natural language processing for dataset extraction, effective approaches for multi-tuple extraction in scientific literature remain scarce due to the complex interrelations of tuples and contextual ambiguities. In the study, we illustrate the multi-tuple extraction of mechanical properties from multi-principal-element alloys and presents a novel framework that combines an entity extraction model based on MatSciBERT with pointer networks and an allocation model utilizing inter- and intra-entity attention. Our rigorous experiments on tuple extraction demonstrate impressive F1 scores of 0.963, 0.947, 0.848, and 0.753 across datasets with 1, 2, 3, and 4 tuples, confirming the effectiveness of the model. Furthermore, an F1 score of 0.854 was achieved on a randomly curated dataset. These results highlight the model’s capacity to deliver precise and structured information, offering a robust alternative to large language models and equipping researchers with essential data for fostering data-driven innovations. © 2025, CC BY.

关键词： Natural language processing systems

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A General Framework for Clustering and Distribution Matching with Bandit Feedback

arXiv

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arXiv 2024年

作者： Yavas, Recep Can Huang, Yuqi Tan, Vincent Y.F. Scarlett, Jonathan Descartes Program CNRS@CREATE 138602 Singapore Department of Information Systems and Analytics School of Computing National University of Singapore 119077 Singapore Department of Mathematics Department of Electrical and Computer Engineering National University of Singapore 119077 Singapore Department of Computer Science Department of Mathematics Institute of Data Science National University of Singapore 119077 Singapore

We develop a general framework for clustering and distribution matching problems with bandit feedback. We consider a K-armed bandit model where some subset of K arms is partitioned into M groups. Within each group, the random variable associated to each arm follows the same distribution on a finite alphabet. At each time step, the decision maker pulls an arm and observes its outcome from the random variable associated to that arm. Subsequent arm pulls depend on the history of arm pulls and their outcomes. The decision maker has no knowledge of the distributions of the arms or the underlying partitions. The task is to devise an online algorithm to learn the underlying partition of arms with the least number of arm pulls on average and with an error probability not exceeding a pre-determined value δ. Several existing problems fall under our general framework, including finding M pairs of arms, odd arm identification, and N-ary clustering of K arms belong to our general framework. We derive a non-asymptotic lower bound on the average number of arm pulls for any online algorithm with an error probability not exceeding δ. Furthermore, we develop a computationally-efficient online algorithm based on the Track-and-Stop method and Frank–Wolfe algorithm, and show that the average number of arm pulls of our algorithm asymptotically matches that of the lower bound. Our refined analysis also uncovers a novel bound on the speed at which the average number of arm pulls of our algorithm converges to the fundamental limit as δ *** Codes 68T05 Copyright © 2024, The Authors. All rights reserved.

关键词： Clustering algorithms

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