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2022 International Russian Automation Conference, RusAutoCon 2022

作者： Bogatyrev, Vladimir Anatolievich Le, Anh Tu Abramova, Evgenia Alexandrovna Saint-Petersburg State University of Aerospace Instrumentation Department Information Systems Security Saint Petersburg Russia University Itmo Faculty of Software Engineering and Computer Technology Saint Petersburg Russia

ISBN: (数字)9781665466554

ISBN: (纸本)9781665466554

The reliability of data transmission is one of the most important factors in building network models. The need for timely delivery of packets to the user leads to the search for new methods to improve the configuration of network structures. A study of the possibility to improve the structural reliability of multipath routing networks in the presence of request sources with full and non-full connection to a set of network paths has been conducted. Improving the reliability and fault tolerance of multipath routing networks is based on a configuration that allows new routes to be formed as a result of using segments of existing paths that have remained connected after failures. The investigated possibility of increasing the reliability of a multipath network is achieved by optimizing the placement of route switch nodes. © 2022 IEEE.

关键词： Network routing

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Enhancing Intrusion Detection systems Using RNN, LSTM, and Hybrid RNN-LSTM Models

Enhancing Intrusion Detection Systems Using RNN, LSTM, and H...

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Machine Learning and Autonomous systems (ICMLAS), International Conference on

作者： Sahar Ebadinezhad Nooshin Nooraei Nia Nasratullah Shirzad Nwabueze Kenneth Osemeha Department of Computer Information Systems Computer Information Systems Research and Technology Center (CISRTC) Near East University Nicosia Turkey Artificial Intelligence Near East University Nicosia North Cyprus Software Engineering Department Near East University Nicosia North Cyprus

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Due to The lack of comparison studies and practical applications of RNN, LSTM, and hybrid RNN-LSTM models for intrusion detection systems, especially when managing class imbalances in complex network datasets, represents a gap in the literature. This study investigates the application of deep learning techniques to improve the detection capabilities of IDS. For this study, we applied and assessed Recurrent Neural Network, Long Short-Term Memory, and Hybrid RNN-LSTM models on a UNSW-NB15 dataset, and to redress class imbalance we trained and validated our models using synthetic minority over-sampling. The RNN-LSTM model was shown to perform the best with a 9 4. 0 0 % accuracy in comparison to other models. These experimental results signify the possibility of enhancing IDS performance and a dependable method of detecting various network intrusions with the use of hybrid models comprised of RNN & LSTM.

关键词： Deep learning Recurrent neural networks Accuracy Computational modeling Intrusion detection computer architecture Network security Hyperparameter optimization Network intrusion Long short term memory

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Location Intelligence Based Smart E-Commerce Platform for Residential Real-Estate Industry 3

Location Intelligence Based Smart E-Commerce Platform for Re...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Balasooriya, Tharindu Kavishka, Lakindu Dananjana, Isini Yumna, Maryam Thelijjagoda, Samantha Attanayaka, Buddhima Marasinghe, Lanil Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Sliit Business School Malabe Sri Lanka Department of Computer Systems Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka Sysco Labs Colombo Sri Lanka

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Making the decision to purchase or invest in real estate can be a very crucial process due to its high financial risk. The purchasing decision of residential real estate properties can be even more decisive because, apart from the financial risk, the choice of a property can have a great impact on the future lifestyle of the buyer. When considering residential real estate, one major factor to be considered is the property location. This research sought to determine the applicability of modern technologies such as location intelligence and machine learning in the development of an e-commerce system that may assist users in making optimal residential real estate location decisions. Third-party web APIs were used to obtain location data, and as a result of the study, methodologies were defined to convert location data into meaningful insights by using statistical methods like weighted sum and the analytical hierarchy process. The functionalities in the proposed system have been designed, considering the roles of both buyers and sellers in the real estate business. In the proposed system, the location quality index framework provides overall insights on the location, the personalized insights and alternative locations are generated via the personal preference-based suitability analyzer and the price prediction system provides the current and future price fluctuations. The usefulness of image processing technologies and machine learning for making the sellers' journey easier on a real estate platform has also been assessed. © 2022 IEEE.

关键词： Decision making

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Control of an electric vehicle speed using RST controller 1

Control of an electric vehicle speed using RST controller

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1st International Conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Kraa, Okba Benbraika, Mohamed Kamel Telli, Khaled Belhadi, Yacine Himeur, Yassine Bourekkache, Samir Zabia, Djallal Eddine Energy Systems Modeling Laboratory Mohamed Khider University Biskra Algeria University of El-Oued El-Oued Algeria College of Engineering and Information University of Dubai Dubai United Arab Emirates Smart Computer Science Laboratory Mohamed Khider University Biskra Algeria LI3CUB Laboratory Mohamed Khider University Biskra Algeria

ISBN: (纸本)9798350348637

This paper introduces the concept of developing a polynomial RST controller to regulate the velocity of an electric *** computation of the RST polynomials assumes that the electric vehicle's speed control forms a closed-loop system characterized by a discrete second-order polynomial. This approach is adopted to attain the desired performance and ensure robust stability. This controller significantly improves the vehicle's overall performance by guaranteeing precise speed control and dynamic response. It also enables the motor and battery to provide sufficient power to reach the target speed. An extensive set of simulation tests and analysis of different driving cycle profiles are used to validate the effectiveness of the suggested controller. This strong RST controller contributes to better EV performance and economy by ensuring greater vehicle dynamics and facilitating optimised power. © 2024 IEEE.

关键词： Closed loop control systems

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Learning Transferable Negative Prompts for Out-of-Distribution Detection

arXiv

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arXiv 2024年

作者： Li, Tianqi Pang, Guansong Bai, Xiao Miao, Wenjun Zheng, Jin School of Computer Science and Engineering State Key Laboratory of Complex & Critical Software Environment Jiangxi Research Institute Beihang University China School of Computing and Information Systems Singapore Management University Singapore

Existing prompt learning methods have shown certain capabilities in Out-of-Distribution (OOD) detection, but the lack of OOD images in the target dataset in their training can lead to mismatches between OOD images and In-Distribution (ID) categories, resulting in a high false positive rate. To address this issue, we introduce a novel OOD detection method, named ‘NegPrompt’, to learn a set of negative prompts, each representing a negative connotation of a given class label, for delineating the boundaries between ID and OOD images. It learns such negative prompts with ID data only, without any reliance on external outlier data. Further, current methods assume the availability of samples of all ID classes, rendering them ineffective in open-vocabulary learning scenarios where the inference stage can contain novel ID classes not present during training. In contrast, our learned negative prompts are transferable to novel class labels. Experiments on various ImageNet benchmarks show that NegPrompt surpasses state-of-the-art prompt-learning-based OOD detection methods and maintains a consistent lead in hard OOD detection in closed- and open-vocabulary classification scenarios. Code is available at https://***/mala-lab/negprompt. © 2024, CC BY.

关键词： Chemical detection

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Exploiting Mode-Switching between Aerial-RIS and Active Radio in UAV-Assisted Wireless Networks 25

Exploiting Mode-Switching between Aerial-RIS and Active Radi...

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25th IEEE Wireless Communications and Networking Conference, WCNC 2024

作者： Zhao, Songhan Long, Yusi Gu, Bo Luong, Nguyen Cong Lyu, Bin Gong, Shimin School Of Intelligent Systems Engineering Sun Yat-sen University Shenzhen Campus China Guangdong Provincial Key Laboratory Of Fire Science And Intelligent Emergency Technology China Phenikaa University Faculty Of Computer Science Viet Nam School Of Communications And Information Engineering Nanjing University Of Posts And Telecommunications China

ISBN: (纸本)9798350303582

In this paper, we employ dual-mode unmanned aerial vehicles (UAVs) equipped with both the active radio frequency (RF) module and aerial reconfigurable intelligent surface (ARIS) to assist ground users (GUs) for both the downlink energy transfer and uplink data transmission in a wireless-powered network. To maximize the GUs' minimum throughput, we propose a collaborative mode switching scheme for the dual-mode UAVs to dynamically switch between the active RF and passive ARIS modes according to the time-varying channel conditions. Besides, we jointly optimize the GUs' transmission control, the UAVs' beamforming, and the trajectory planning strategies. This optimization problem is intractable directly due to the non-convexity in both the objective and constraints. We design an iterative algorithm to first decompose the original problem into several subproblems, and then solve each subproblem individually by approximate optimization methods. Numerical results verify that the UAVs' collaborative mode switching along with their trajectories efficiently improves the transmission performance compared to the benchmarks in which both UAVs are operating in one fixed mode. © 2024 IEEE.

关键词： Antennas

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HairDiffusion: vivid multi-colored hair editing via latent diffusion 24

HairDiffusion: vivid multi-colored hair editing via latent d...

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Proceedings of the 38th International Conference on Neural information Processing systems

作者： Yu Zeng Yang Zhang Jiachen Liu Linlin Shen Kaijun Deng Weizhao He Jinbao Wang Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University and Shenzhen Institute of Artificial Intelligence and Robotics for Society and National Engineering Laboratory for Big Data System Computing Technology Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University and Guangdong Provincial Key Laboratory of Intelligent Information Processing

ISBN: (纸本)9798331314385

Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using text descriptions or reference images, while preserving irrelevant attributes (e.g., identity, background, cloth). Many existing methods are based on StyleGAN to address this task. However, due to the limited spatial distribution of StyleGAN, it struggles with multiple hair color editing and facial preservation. Considering the advancements in diffusion models, we utilize Latent Diffusion Models (LDMs) for hairstyle editing. Our approach introduces Multi-stage Hairstyle Blend (MHB), effectively separating control of hair color and hairstyle in diffusion latent space. Additionally, we train a warping module to align the hair color with the target region. To further enhance multi-color hairstyle editing, we fine-tuned a CLIP model using a multi-color hairstyle dataset. Our method not only tackles the complexity of multi-color hairstyles but also addresses the challenge of preserving original colors during diffusion editing. Extensive experiments showcase the superiority of our method in editing multi-color hairstyles while preserving facial attributes given textual descriptions and reference images.

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An Approach to Optimize Symbolic Execution in Ethereum Smart Contracts

An Approach to Optimize Symbolic Execution in Ethereum Smart...

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IEEE International Conference on software Quality, Reliability and Security Companion (QRS-C)

作者： Yi Liu Lizhi Cai Chenchen Chai Yuan Ma School of Information Science and Engineering East China University of Science and Technology Shanghai China Shanghai Key Laboratory of Computer Software Testing & Evaluating Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

As the application of smart contracts in blockchain technology becomes increasingly widespread, their security issues have emerged as a focal point of both research and practice. Although symbolic execution technology has been extensively applied for the detection of security vulnerabilities in smart contracts, its limitations in terms of execution efficiency and code coverage remain significant. In response to these challenges, this study introduces an innovative method named HoneyToken-DetectorRev, which optimizes traditional symbolic execution through program slicing technology, aiming to enhance the efficiency and effectiveness of smart contract security detection. By selecting ten smart contracts from the Ethereum platform, which vary in business logic and complexity, we conducted an empirical analysis of HoneyToken-DetectorRev. The experimental results demonstrate that this method significantly improves the efficiency of smart contract detection without sacrificing precision. This research not only provides an effective technical approach for the security detection of smart contracts but also offers theoretical and practical guidance for further enhancing the security of blockchain applications.

关键词： Codes Smart contracts software quality Inspection Boosting Complexity theory software reliability Security Logic Business

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Stereo Image Super-Resolution via Disparity Estimation and Domain Diffusion 9th

Stereo Image Super-Resolution via Disparity Estimation and D...

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9th International Symposium on Artificial Intelligence and Robotics, ISAIR 2024

作者： Wang, Wanjun Ma, Chunyan Zhu, Hongjun Xu, Kai Han, Huihui School of Computer and Software Engineering Anhui Institute of Information Technology WuHu241199 China School of Artificial Intelligence Department of Information Hohai University NanJing210024 China Platforms of Automotive Intelligent Software and Systems Anhui Institute of Information Technology WuHu241199 China

ISBN: (纸本)9789819629107

Image super-resolution (SR) and disparity estimation are closely related in stereo images, with effective utilization of disparity maps enhancing SR performance. This paper proposes a stereo image super-resolution reconstruction network utilizing disparity estimation and a domain diffusion model. The network integrates disparity estimation with diffusion models for feature summation and domain space construction, enabling interaction between forward and backward diffusion memory units. To further improve reconstructed image quality, we introduce a novel memory unit inspired by long short-term memory concepts. Disparity loss and structural similarity are included in the loss function to achieve more accurate results. Experiments show that our method outperforms existing techniques. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Stereo image processing

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Activity Diagram Synthesis Using Labelled Graphs and the Genetic Algorithm

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Journal of computer Science & Technology 2021年第6期36卷 1388-1406页

作者： Chun-Hui Wang Zhi Jin Wei Zhang Didar Zowghi Hai-Yan Zhao Wen-Pin Jiao Key Laboratory of High Confidence Software Technology(Ministry of Education) Peking University Beijing 100871China Institute of Software School of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China School of Computer Science Inner Mongolia Normal UniversityHohhot 010022China Faculty of Engineering and Information Technology University of TechnologySydney 2007Australia

Many applications need to meet diverse requirements of a large-scale distributed user *** challenges the current requirements engineering ***-based requirements engineering was proposed as an umbrella term for dealing with the requirements development in the context of the large-scale user ***,there are still many *** others,a key issue is how to merge these requirements to produce the synthesized requirements description when a set of requirements descriptions from different participants are *** techniques are needed for supporting the requirements *** are widely used in industry to represent *** paper chooses the activity diagrams and proposes a novel approach for the activity diagram synthesis which adopts the genetic algorithm to repeatedly modify a population of individual solutions toward an optimal *** a result,it can automatically generate a resulting diagram which combines the commonalities as many as possible while leveraging the variabilities of a set of input *** approach is featured by:1)the labelled graph proposed as the representation of the candidate solutions during the iterative evolution;2)the generalized entropy proposed and defined as the measurement of the solutions;3)the genetic algorithm designed for sorting out the high-quality *** cases of different scales are used to evaluate the effectiveness of the *** experimental results show that not only the approach gets high precision and recall but also the resulting diagram satisfies the properties of minimization and information preservation and can support the requirements traceability.

关键词： crowd-based requirements engineering requirements synthesis activity diagram genetic algorithm

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