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SSRN 2024年

作者： Li, Zengyang Zhang, Xiaoyong Wang, Wenshuo Liang, Peng Mo, Ran Tan, Jie Liu, Hui School of Computer Science Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Central China Normal University Wuhan China School of Computer Science Wuhan University Wuhan China Intelligent Game and Decision Lab Beijing China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Context: Nowadays, most deep learning frameworks (DLFs) use multilingual programming of Python and C/C++, facilitating the flexibility and performance of the DLF. However, inappropriate interlanguage interaction may introduce design smells involving multiple programming languages (PLs), i.e., Inter-Language Design Smells (ILDS). Despite the negative impact of ILDS on multi-language DLFs, there is a lack of an automated approach for detecting ILDS in multi-language DLFs and a comprehensive understanding on ILDS in such ***: This work aims to automatically detect ILDS in multi-language DLFs written in the combination of Python and C/C++, and to obtain a comprehensive understanding on such ILDS in ***: We first developed an approach to automatically detecting ILDS in the multi-language DLFs written in the combination of Python and C/C++, including a number of ILDS and their detection rules defined based on inter-language communication mechanisms and code analysis. Then, we developed the CPSMELL tool that implements detection rules for automatically detecting such ILDS, and manually validated the accuracy of the tool. Finally, we performed an empirical study to evaluate the ILDS in multi-language ***: We proposed seven ILDS and achieved an accuracy of 98.17% in the manual validation of CPSMELL in 5 popular multi-language DLFs. The study results revealed that among the 5 DLFs, TensorFlow, PyTorch, and PaddlePaddle exhibit relatively high prevalence of ILDS;each smelly file contains around 5 ILDS instances on average, with ILDS Long Lambda Function For Inter-language Binding and Unused Native Entity being relatively prominent;throughout the evolution process of the 5 DLFs, some ILDS were resolved to a certain extent, but the overall count of ILDS instances shows an upward ***: The automated detection of the proposed ILDS achieved a high accuracy, and the empirical study provides a comprehensive understanding on ILDS in the multi-l

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Parallel Driving with Big Models and Foundation intelligence in Cyber-hysical-ocial Spaces

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Research 2024年第2期2024卷 1-17页

作者： Xiao Wang Jun Huang Yonglin Tian Chen Sun Lie Yang Shanhe Lou Chen Lv Changyin Sun Fei-Yue Wang School of Artificial Intelligence Anhui UniversityHefeiChina Macao University of Science and Technology MacaoChina State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina MVSLab Department of Mechanical and Mechatronics EngineeringUniversity of Waterloo200 University Ave WestWaterlooON N2L3G1Canada School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingaporeSingapore

Recent years have witnessed numerous technical breakthroughs in connected and autonomous vehicles (CAVs). On the one hand, these breakthroughs have significantly advanced the development of intelligent transportation systems (ITSs);on the other hand, these new traffic participants introduce more complex and uncertain elements to ITSs from the social space. Digital twins (DTs) provide real-time, data-driven, precise modeling for constructing the digital mapping of physical-world ITSs. Meanwhile, the metaverse integrates emerging technologies such as virtual reality/mixed reality, artificial intelligence, and DTs to model and explore how to realize improved sustainability, increased efficiency, and enhanced safety. More recently, as a leading effort toward general artificial intelligence, the concept of foundation model was proposed and has achieved significant success, showing great potential to lay the cornerstone for diverse artificial intelligence applications across different domains. In this article, we explore the big models embodied foundation intelligence for parallel driving in cyber-physical-social spaces, which integrate metaverse and DTs to construct a parallel training space for CAVs, and present a comprehensive elucidation of the crucial characteristics and operational mechanisms. Beyond providing the infrastructure and foundation intelligence of big models for parallel driving, this article also discusses future trends and potential research directions, and the ?S?goals of parallel driving.

关键词： breakthrough integrate corners

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Twitter Spam Detection Using Word Vector and Binary Classifier

Twitter Spam Detection Using Word Vector and Binary Classifi...

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2023 International Conference on Self Sustainable artificial intelligence Systems, ICSSAS 2023

作者： Marimuthu, Ramaprabha Muthulingam, Gurusigaamani Ayyanar Vinoth, N.A.S. Ganesan, Sivasathiya Ponnuchamy, Kardeepa Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Virudhunagar India Srm Institute of Science and Technology Department of Computing Technologies Chennai Kattankulathur India Easwari Engineering College Department of Artificial Intelligence and Data Science Chennai Ramapuram India

ISBN: (纸本)9798350300857

In recent years, social media communication plays an important role in sharing messages, thoughts, comments and ideas around the world. Among them, Twitter is one of the most popular communication mediums. Twitter provides free services for sharing information's that are limited to 140 characters. Each month nearly 40 million new twitter accounts were created. Since usage of twitter increases rapidly, fake accounts were created for posting the spam messages and links for redirecting to phishing websites. Researchers proposed various machine learning methodologies for filtering the spam to handle and maintain the social network security problems. But still detecting spam tweets, malicious link in real world scenario is still a complex task. In the proposed work, a novel deep learning technique is introduced to address the above mention issues. Word Vector model is used for learning the syntax of each tweet. Later by using proceeding representation data set, binary classifier is developed. For experimental purpose 1-month twitter datasets are collected. Proposed work outperforms when compared with various existing methodologies and also to detect the non-text based as well. The ratio of spam is determined using the precision, recall, and F-measure metrics. © 2023 IEEE.

关键词： Classification (of information)

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SigStyle: Signature Style Transfer via Personalized Text-to-Image Models 39

SigStyle: Signature Style Transfer via Personalized Text-to-...

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39th Annual AAAI Conference on artificial intelligence, AAAI 2025

作者： Wang, Ye Bai, Tongyuan Xie, Xuping Yi, Zili Wang, Yilin Ma, Rui School of Artificial Intelligence Jilin University China College of Computer Science and Technology Jilin University China School of Intelligence Science and Technology Nanjing University China Adobe United States Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE China

ISBN: (纸本)157735897X

Style transfer enables the seamless integration of artistic styles from a style image into a content image, resulting in visually striking and aesthetically enriched outputs. Despite numerous advances in this field, existing methods did not explicitly focus on the signature style, which represents the distinct and recognizable visual traits of the image such as geometric and structural patterns, color palettes and brush strokes etc. In this paper, we introduce SigStyle, a framework that leverages the semantic priors that embedded in a personalized text-to-image diffusion model to capture the signature style representation. This style capture process is powered by a hypernetwork that efficiently fine-tunes the diffusion model for any given single style image. Style transfer then is conceptualized as the reconstruction process of content image through learned style tokens from the personalized diffusion model. Additionally, to ensure the content consistency throughout the style transfer process, we introduce a time-aware attention swapping technique that incorporates content information from the original image into the early denoising steps of target image generation. Beyond enabling high-quality signature style transfer across a wide range of styles, SigStyle supports multiple interesting applications, such as local style transfer, texture transfer, style fusion and style-guided text-to-image generation. Quantitative and qualitative evaluations demonstrate our approach outperforms existing style transfer methods for recognizing and transferring the signature styles. Copyright © 2025, Association for the Advancement of artificial intelligence (***). All rights reserved.

关键词： Hypertext systems

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SigStyle: Signature Style Transfer via Personalized Text-to-Image Models

arXiv

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arXiv 2025年

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Identification of High Emission Mobile Sources Based on Self-supervised Representation Network 21st

Identification of High Emission Mobile Sources Based on Se...

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21st Asia Simulation Conference, AsiaSim 2022

作者： Wang, Renjun Xia, Xiushan Xu, Zhenyi School of Computer Science and Technology Anhui University Hefei230039 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230088 China Institute of Advanced Technology University of Science and Technology of China Hefei230088 China

ISBN: (纸本)9789811991943

High-emission mobile sources are the main contributors to road traffic emission pollution, and how to accurately identify high-emission road mobile sources is of great significance to urban traffic pollution control and environmental protection. On-Board Diagnostics (OBD) is a device that records the operating conditions of a vehicle in real time. Usually, for vehicles with excessive tailpipe emissions, OBD monitoring value is compared with the set threshold to identify high emission vehicles. However, it often leads to misjudgment if a vehicle is judged to be high emission only by its excessive emission value. Because this excessive value may originate from external objective factors (such as vehicle idling, uneven road), resulting in pseudo-high emission states. Faced with this challenge, we propose a self-supervised representation network (SRN) for identifying high-emission mobile sources. A self-supervised learning module is integrated to learn general emission representations. Meanwhile, a representation memory module is introduced to make the module retain key emission representations through iterative learning. By reconstructing the time-series characterization of mobile sources, it is achieved for the classification identification of high and normal emissions. Experiments on a real diesel vehicle OBD emission monitoring sequence dataset show that the present method obtains a higher performance for emission source classification compared to other methods, demonstrating the effectiveness of the proposed method. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Vehicles

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Predicting Fish Habitat in the Persian Gulf Using artificial intelligence 1

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International Conference on Advances in Communication technology and computer Engineering, ICACTCE 2023

作者： Hai, Tao Zhou, Jincheng Ahmadi, Hoorieh Ekiye, Ayibatonbo Ebiare Wei, Yangping Iwendi, Celestine Boulouard, Zakaria School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun558000 China School of Creative Technologies University of Bolton Bolton United Kingdom LIM Hassan II University of Casablanca Casablanca Morocco School of Information and Artificial Intelligence Nanchang Institute of Science and Technology Nanchang China

ISBN: (数字)9783031371646

ISBN: (纸本)9783031371639

The Persian Gulf is one of the most important habitats in the Middle East. It can be extremely beneficial to aquatic species’ survival and environmental preservation to continuous monitoring and collect data about aquatic animals, their habitats, and behaviours. Finding a novel and suitable method to carry out accurate and automatic monitoring with low timing and low cost for monitoring aquatic species’ behaviour in this high potential area is helpful. To predict fish habitat in Persian Gulf Convolutional Neural Network method and Naïve Bayes algorithm are used. Deep learning convolutional neural network technology is mostly used for data science classification and recognition because of its exceptional accuracy and to solve search and optimization issues, the Naïve Bayes algorithm is employed. Results indicate for predicting fish habitat in the Persian Gulf, the accuracy of the Convolutional Neural Network algorithm and the Naïve Bayes algorithms is 97.32% and 95.47%, respectively. With p = 0.025 (p0.05), there is a substantial difference between the Naïve Bayes method and the Convolutional Neural Network algorithm. Therefore, The Convolutional Neural Network method seems to be more accurate than the Naïve Bayes method at predicting fish habitat in the Persian Gulf. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Ecosystems

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Research on IPv6 Address Security Application based on DUID and SNMP

Research on IPv6 Address Security Application based on DUID ...

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computer Applications and Information technology (ISCAIT), International Symposium on

作者： Siqi Jia Dan Ran Ji Chen Dan Shen Ning Chen Wenqian Qu Zhong Li Peng Ye Cong Han Tong Xiao Chongqing Gas Mine of PetroChina Southwest Oil & Gas Field Company China School of Computer Science and Engineering (School of Artificial Intelligence) Chongqing University of Science and Technology Chongqing China School of Petroleum Engineering Chongqing University of Science and Technology Chongqing China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

With the wide application of IPv6, the problem of IPv4 address resource exhaustion has been solved, but it also brings new challenges. Aiming at the address security management problem in IPv6 network, this paper proposes an IPv6 address management and alarm method based on DUID (DHCPv6 Unique Identifier), DHCPv6 and SNMP. In view of the difficulties in IPv6 address management and operation, and the inability to effectively read terminal equipment information in the application scenario of gas mine office network, a management system based on C/S mode was designed. The client collected local equipment network configuration information through the network card, and uploaded it to the server for centralized management. After the server checks the validity, once the illegal access is detected, the system will automatically trigger an alarm and notify the administrator. At the same time, by using SNMP protocol in the server to collect network equipment information, to ensure the effective management of network equipment. The actual deployment and application results show that this method improves the management efficiency in Ipv6 environment, and ensures the convenience and efficiency of IPv6 address management and application security.

关键词： Protocols Security management System performance computer architecture Network security Real-time systems Fuel processing industries Servers IP networks Information technology

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Cloud Computing Based POC Diagnostic Device: Rapid Infectious Disease Testing by data Analytics 1

Cloud Computing Based POC Diagnostic Device: Rapid Infectiou...

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1st International Conference on artificial intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Basha, H. Anwer Ramalingam, L. Hasan, Md Ashfaqul Ananda, M.H. Velmurugan, S. Maniraj, S.P. Saveetha College of Liberal Arts & Sciences Simats University Department of Computer Science Tamil Nadu Chennai India Loyola Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Malla Reddy Institute of Engineering and Technology Department of Computer Science and Engineering Telangana Hyderabad India Reva University School of Electrical and Electronics Engineering Karnataka Bangalore India Tjs Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350330915

To meet the growing need for affordable, efficient, and accurate diagnostic tools, the system has developed a Raspberry Pi-based Point-of-Care (POC) gadget for speedy and reliable detection of infectious illnesses. This groundbreaking gadget, built on the flexible and powerful Raspberry Pi computer, ushers in a new era of state-of-the-art POC diagnostics by combining molecular and image analysis methods. Complex testing for pathogens like COVID-19 and malaria may be carried out using this state-of-the-art system, which integrates a camera module and custom biosensors. The device's software parses sensor data, improves diagnostic precision using machine learning techniques, and presents a straightforward interface for clinicians and patients alike. This study takes a holistic approach by considering all relevant factors, including adherence to regulations, user education, upkeep, and data management. Our POC diagnostic gadget driven by a Raspberry Pi provides a low-cost, high-throughput method for diagnosing illness, which may have far-reaching implications for the future of healthcare delivery in both under-resourced and well-endowed regions. © 2023 IEEE.

关键词： Healthcare Infectious Disease Internet of Things, Cloud Computing Point-of-Care Diagnostics

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FineParser: A Fine-Grained Spatio-Temporal Action Parser for Human-Centric Action Quality Assessment

FineParser: A Fine-Grained Spatio-Temporal Action Parser for...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Jinglin Xu Sibo Yin Guohao Zhao Zishuo Wang Yuxin Peng School of Intelligence Science and Technology University of Science and Technology Beijing Wangxuan Institute of Computer Technology Peking University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Existing action quality assessment (AQA) methods mainly learn deep representations at the video level for scoring diverse actions. Due to the lack of a fine-grained understanding of actions in videos, they harshly suffer from low credibility and interpretability, thus insufficient for stringent applications, such as Olympic diving events. We argue that a fine-grained understanding of actions requires the model to perceive and parse actions in both time and space, which is also the key to the credibility and inter-pretability of the AQA technique. Based on this insight, we propose a new fine-grained spatial-temporal action parser named FineParser. It learns human-centric foreground action representations by focusing on target action regions within each frame and exploiting their fine-grained alignments in time and space to minimize the impact of in-valid backgrounds during the assessment. In addition, we construct fine-grained annotations of human-centric fore-ground action masks for the FineDiving dataset, called FineDiving-HM. With refined annotations on diverse target action procedures, FineDiving-HM can promote the development of real-world AQA systems. Through extensive experiments, we demonstrate the effectiveness of FineParser, which outperforms state-of-the-art methods while supporting more tasks of fine-grained action understanding. data and code are available at https://***/PKU-ICST-MIPL/FineParser_CVPR2024.

关键词： computer vision Codes Annotations Focusing Quality assessment Pattern recognition

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