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International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)

作者： Oleksandr Kushnerov Pierre Murr Serhii Herasymov Stanislav Milevskyi Marharyta Melnyk Sergii Golovashych Department of Economic Cybernetics Sumy State University Sumy Ukraine Computer Engineering Department International University of Science and Technology in Kuwait Kuwait Kuwait Cybersecurity department National Technical University “Kharkiv polytechnic institute” Kharkiv Ukraine Department of Cyber Security and information protection SET University Kyiv Ukraine Department of Software Engineering and Management Intelligent Technologies National Technical University “Kharkiv polytechnic institute” Kharkiv Ukraine

ISBN: (数字)9798350354423

ISBN: (纸本)9798350354430

Monitoring and analyzing network traffic are critically important tasks in the context of increasing cyber threats. Traditional data processing methods often struggle to handle the large volumes and complexity of modern network traffic, necessitating the use of advanced technologies such as neural networks and machine learning. This article demonstrates the application of neural networks for monitoring and analyzing network traffic, highlighting their ability to learn from large datasets to detect complex patterns and anomalies. Using TensorFlow, we developed a neural network model based on an autoencoder that effectively identifies and classifies anomalies in network traffic. The model's effectiveness was validated using a synthetic dataset from Kaggle, showing high accuracy on both training and testing data. Our approach significantly improves the efficiency and accuracy of threat detection compared to traditional methods, making it a valuable tool for enhancing cybersecurity.

关键词： Training Accuracy Neural networks Telecommunication traffic Machine learning Data models Threat assessment computer security Monitoring Testing

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COMPUTATION OF THE LEAST PRIMITIVE ROOT

arXiv

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arXiv 2022年

作者： McGown, Kevin J. Sorenson, Jonathan P. Department of Mathematics and Statistics California State University Chico United States Computer Science and Software Engineering Department Butler University IndianapolisIN46208 United States

Let g(p) denote the least primitive root modulo p, and h(p) the least primitive root modulo p2. We computed g(p) and h(p) for all primes p ≤ 1016. As a consequence we are able to prove that g(p) 5/8 for all primes p > 3 and that h(p) 2/3 for all primes p. We also present some additional *** Codes 11A07, 11Y16 © 2022, CC BY-NC-ND.

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AFF-Dehazing: Attention-based feature fusion network for low-light image Dehazing

AFF-Dehazing: Attention-based feature fusion network for low...

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作者： Zhou, Yu Chen, Zhihua Sheng, Bin Li, Ping Kim, Jinman Wu, Enhua Department of Computer Science and Engineering East China University of Science and Technology Shanghai China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computing The Hong Kong Polytechnic University Kowloon Hong Kong School of Information Technologies The University of Sydney Sydney Australia State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Faculty of Science and Technology University of Macau China

Images captured in haze conditions, especially at nighttime with low light, often suffer from degraded visibility, contrasts, and vividness, which makes it difficult to carry out the following vision tasks. In this article, we propose an attention-based feature fusion network (AFF-Dehazing) for low-light image dehazing. Our method decomposes the low-light image dehazing into two task-independent streams containing four modules: image dehazing module, low-light feature extractor module, feature fusion module, and image restoration module. The basic block of these modules is the proposed attention-based residual dense block. Since the dual-branch are used, AFF-Dehazing can avoid learning the mixed degradation all-in-one and enhance the details of low-light haze images. Extensive experiments show that our method surpasses previous state-of-the-art image dehazing methods and low-light enhancement methods by a very large margin both quantitatively and qualitatively. © 2021 John Wiley & Sons, Ltd.

关键词： Image reconstruction

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MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficient Crowdsourcing for Video Analytics

MACRO: Incentivizing Multi-Leader Game-Based Pareto-Efficien...

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International Conference on Data engineering

作者： Yu Chen Sheng Zhang Ziying Zhou Xiaokun Wang Yu Liang Ning Chen Yuting Yan Mingjun Xiao Jie Wu Zhuzhong Qian Harry Xu State Key Lab. for Novel Software Technology Nanjing University P.R. China School of Systems Science Beijing Normal University China School of Computer and Electronic Information / School of Artificial Intelligence Nanjing Normal University China School of Computer Science and Technology University of Science and Technology of China P.R. China Center for Networked Computing Temple University USA Computer Science Department Samueli School of Engineering University of California Los Angeles USA

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

In recent years, many crowdsourcing platforms have emerged, using the resources of recruited workers to perform diverse outsourcing tasks, where the video analytics attracts much attention due to its practical implications. For maximum profits, platforms carefully choose the workers and determine the video analytics configurations to ensure accuracy; meanwhile, workers possess the flexibility to tailor the configurations for their indivi-dual gains, which makes it hard for platforms to optimize their profits considering the platform-worker conflicts. In this paper, we design an incentive mechanism for Multi-leader game-based video Analytics upon CROwdsourcing, named MACRO, to over-come the above situation. Under that mechanism, we first formu-late the utility optimization problems for platforms and workers, respectively. We then propose a dual ascent-based method to op-timally determine the video analytics configurations for a multi-platform game, ensuring Pareto efficiency. Moreover, in the context of a multi-leader game involving platform-worker conflicts, we design an incentive function with its incentive factor update strategy and propose an ADMM-based approach for maximizing incentives that motivate workers to contribute to the platforms' profits. Rigorous proofs demonstrate the linear convergence of the MACRO to the multi-leader Stackelberg equilibrium. Trace-driven experiments show that MACRO improves the Pareto efficiency by 26.3%, outperforming other approaches.

关键词： Crowdsourcing Accuracy Visual analytics Games Data engineering Outsourcing Task analysis

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Goal-Conflict Identification Based on Local Search and Fast Boundary-Condition Verification Based on Incremental Satisfiability Filter

SSRN

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SSRN 2022年

作者： Luo, Weilin Wan, Hai Zhong, Hongzhen Cai, Shaowei Xiao, Zhanhao Chen, Polong School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Ministry of Education China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China School of Computer Science Guangdong Polytechnic Normal University Guangzhou China

Identifying boundary conditions (BCs) is of fundamental importance for goal-oriented requirements engineering. A BC can capture a divergence, a particular combination of circumstances in which the goals of the requirement cannot be satisfied as a whole. Existing approaches only handle small-scale cases and ignore the general BC, a higher quality BC, because of the complex definition of BC and high computational *** observe an interesting phenomenon that some pairs of BCs are similar in structural. It inspires us to develop a local search algorithm, named LOGION, to identify general BCs on large-scale specifications. Specifically, we utilize the structural similarity of BCs to construct the neighborhood relation of local search algorithm. Besides, we design strengthening and weakening operators of formula to capture the semantic of general BCs in local searching. In order to boost up the BC verification, we propose a lasso-based incremental satisfiability filter (LISF). LISF is to use trace checking as a light-weight pre-checking. We conduct extensive experiments to evaluate LOGION, comparing against state-of-the-art approaches. Experimental results show that LOGION produces one order of magnitude more general BCs and is the first one to handle industrial cases. It is also shown that LISF significantly boosts up the performance. © 2022, The Authors. All rights reserved.

关键词： Boundary conditions

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Verifiable and Privacy-Preserving Cooperative Federated Learning in UAV-Assisted Vehicular Networks

Verifiable and Privacy-Preserving Cooperative Federated Lear...

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IEEE International Conference on Communications (ICC)

作者： Qichao Xu Yulin Lan Zhou Su Dongfeng Fang Hongbing Zhang School of Mechatronic Engineering and Automation Shanghai University Shanghai China School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an China Department of Computer Science California Polytechnic State University San Luis Obispo USA Shanghai DaoCloud Network Technology Co. Ltd Shanghai China

Federated learning (FL) is a promising distributed learning paradigm, which enables devices to collaboratively train an AI model without exposing participants' private data. However, FL is vulnerable to various attacks and thus remains exposed to privacy issues. For example, malicious parties can launch attacks to recover sensitive and private training data from the shared parameters. Leakage of privacy data can cause serious damage to data providers. Furthermore, user anonymity and data verification in FL also need to be considered. To tackle these problems, in this paper, a verifiable and privacy-preserving cooperative FL (VPPFL) scheme is proposed in UAV-assisted vehicular networks (UVNs). Specifically, to preserve the identity privacy of vehicles, elliptic curve cryptosystem (ECC) is used to generate pseudonyms for vehicles. To preserve the data privacy, Paillier homomorphic encryption algorithm is utilized to encrypt the updates of vehicles, whereby UAVs directly perform global aggregations on encrypted updates instead of raw ones. Additionally, pseudonym-based signature mechanism is presented for vehicles to generate verifiable signatures, so as to ensure the authenticity and validity of uploaded local model updates. Besides, to sufficiently use the multi-source data, multiple UAVs share the local updates packets with each other to execute global aggregation. Finally, simulations are carried out to demonstrate that the proposed scheme can achieve high accuracy and verification with providing strict privacy protection.

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An Empathetic AI Coach for Self-Attachment Therapy

An Empathetic AI Coach for Self-Attachment Therapy

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IEEE International Conference on Cognitive Machine Intelligence (CogMI)

作者： Lisa Alazraki Ali Ghachem Neophytos Polydorou Foaad Khosmood Abbas Edalat Imperial College London United Kingdom Computer Science and Software Engineering California Polytechnic State University San Luis Obispo USA

ISBN: (纸本)9781665416221

In this work, we present a new dataset and a computational strategy for a digital coach that aims to guide users in practicing the protocols of self-attachment therapy. Our framework augments a rule-based conversational agent with a deep-learning classifier for identifying the underlying emotion in a user's text response, as well as a deep-learning assisted retrieval method for producing novel, fluent and empathetic utterances. We also craft a set of human-like personas that users can choose to interact with. Our goal is to achieve a high level of engagement during virtual therapy sessions. We evaluate the effectiveness of our framework in a non-clinical trial with N=16 participants, all of whom have had at least four interactions with the agent over the course of five days. We find that our platform is consistently rated higher for empathy, user engagement and usefulness than the simple rule-based framework. Finally, we provide guidelines to further improve the design and performance of the application, in accordance with the feedback received.

关键词： Protocols Conferences Medical treatment Artificial intelligence Machine intelligence Guidelines

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Powerformer: A Section-Adaptive Transformer for Power Flow Adjustment

SSRN

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SSRN 2024年

作者： Chen, Kaixuan Luo, Wei Liu, Shunyu Wei, Yaoquan Zhou, Yihe Qing, Yungpeng Zhang, Quan Wang, Yong Song, Jie Song, Mingli College of Computer Science and Technology Zhejiang University Hangzhou310027 China Polytechnic Institute Zhejiang University Hangzhou310027 China College of Software Zhejiang University Ningbo315000 China College of Electrical Engineering Zhejiang University Hangzhou310027 China State Grid Shandong Electric Power Company Jinan250013 China

In this paper, we present a novel transformer architecture tailored for learning robust power system state representations, which strives to optimize power dispatch for the power flow adjustment across different transmission sections. Specifically, our proposed approach, named Powerformer, develops a dedicated section-adaptive attention mechanism, separating itself from the self-attention used in conventional transformers. This mechanism effectively integrates power system states with transmission section information, which facilitates the development of robust state representations. Furthermore, by considering the graph topology of power system and the electrical attributes of bus nodes, we introduce two customized strategies to further enhance the expressiveness: graph neural network propagation and multi-factor attention mechanism. Extensive evaluations are conducted on three power system scenarios, including the IEEE 118-bus system, a realistic 300-bus system in China, and a large-scale European system with 9241 buses, where Powerformer demonstrates its superior performance over several baseline methods. © 2024, The Authors. All rights reserved.

关键词： Electric load dispatching

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Analyzing and Comparing Results for Multiple Machine Learning Models

Analyzing and Comparing Results for Multiple Machine Learnin...

引用

computer engineering and Intelligent Control (ICCEIC) International Conference on

作者： Jefferson Ng Yijian Li Xuxiao Ji Can Yang Jiaqiao Wan Xingyu Lu Department of Mechanical Engineering University of California Irvine USA Courant Institute of Mathematical Sciences New York University NY USA School of Computer Science University of Waterloo Waterloo CA Department of Electrical Engineering and Computer Science Oregon State University Corvallis USA Software College Zhengzhou University Zhengzhou China United World College Changshu China Jiangsu China

In the contemporary landscape of atmospheric sciences, the ability to recognize specific weather phenomena accurately and efficiently has become paramount, largely due to the exponential growth in sensor-generated data. This paper undertakes an in-depth analysis of advanced algorithms, namely YOLOV8, ResNet50, and Convolutional Neural Networks (CNN), for the purpose of weather pattern identification and classification. Building on the foundation of deep learning, the YOLOV8's real-time object detection capabilities are leveraged to discern intricate weather patterns in diverse datasets, from meteorological stations to satellite imagery. On the other hand, Resnet 50 which is another CNN based model was also tested along with CNN for comparison. An exhaustive evaluation of these algorithms covers various metrics, including accuracy, precision, computational efficiency, and real-world applicability. Special emphasis is placed on the crucial stages of data preprocessing, feature extraction, and model tuning, highlighting their impact on the algorithms' overall performance. Our findings suggest that, when appropriately optimized, both YOLOV8 and CNN exhibit exceptional capabilities in discerning and classifying intricate weather patterns, whereas ResNet50 exhibits comparatively less performance.

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Methods for Monitoring the Photovoltaic Panel: A Review

Methods for Monitoring the Photovoltaic Panel: A Review

引用

International Conference on Agro-Geoinformatics

作者： Yongheng Shang Haiyu He Zilun Zhang Xiaoyu Zhang Zhengwei Shen Jianwei Yin Xiaoye Miao JunHan Institute of Advanced Technology Zhejiang University Hangzhou China School of Software Engineering Zhejiang University Ningbo China School of Computer Science and Technology Zhejiang University Hangzhou China Research Institute of Hainan Zhejiang University Sanya China Polytechnic Institute Zhejiang University Hangzhou China State Grid Jiangsu Economic Research Institute Najing China

ISBN: (数字)9798350380606

ISBN: (纸本)9798350380613

With the rapid development of Photovoltaic (PV) solar energy technology, a vast array of PV systems have been installed globally. According to the latest reports from the International Energy Agency (IEA), an astonishing 420GW of solar power has been installed, representing a doubling of solar energy capacity from 2022 to 2023, equivalent to the entire world’s output in 2022. PV systems primarily fall into two categories: centralized large-scale PV power stations and distributed rooftop PV systems. Centralized PV power stations transmit their generated energy efficiently into the power grid through dedicated transmission lines. Conversely, distributed rooftop PV systems feed energy back into the grid via inverters positioned close to the roof-mounted panels. However, the increasing installation of photovoltaic panels poses significant challenges to the resilience and carrying capacity of the power grid, potentially compromising its safety, especially during peak daylight hours. Therefore, diligent monitoring of photovoltaic panel installations and precise estimation of the peak power fed into the national grid are of utmost importance. Currently, two primary methodologies are utilized for this purpose: hardware-based monitoring and remote sensing techniques. The hardware approach offers unparalleled accuracy by directly reading parameters from photovoltaic systems, but it comes with a substantial cost. In contrast, remote sensing methods, which employ satellites, video, or infrared imagery, provide a cost-effective, convenient, and standardized approach to estimating power generation across vast areas. This comprehensive review examines the various methodologies used for photovoltaic monitoring, aiming to provide a robust foundation for the future development of solar photovoltaic power generation. By doing so, it significantly contributes to the advancement and widespread adoption of clean energy solutions, paving the way for a greener and more sustainable future f

关键词： Photovoltaic systems Green energy Wireless sensor networks Image resolution Solar energy Planning Safety

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