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1st International Conference on Intelligent Manufacturing and Cloud Computing, ICIMCC 2024

作者： Zhang, Tingshu Tu, Ming Wang, Gefan Chen, Bingyong Zhao, Yishi Shang, Jianga School of Computer Science China University of Geosciences Wuhang China Intelligent Construction System Research Institute China Construction Third Engineering Buerau Group Co.L Wuhang China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education School of Computer Science China University of Geosciences Wuhang China

ISBN: (纸本)9781643685779

In recent years, the country has released a large number of standard documents related to prefabricated concrete components. Due to the dispersion and complexity of these standards, it is difficult for industry managers to implement them. Therefore, it is imperative to establish a knowledge graph in the field of quality standards for precast concrete components to achieve knowledge management. The construction of knowledge graph first requires named entity recognition, but due to the specificity of the construction industry, entity recognition of the text in this field still suffers from the problems of low recognition accuracy and high resource consumption. Aiming at the low recognition accuracy and high resource consumption in named entity recognition due to the specificity of the industry, this paper proposes a named entity recognition model ALBERT-BiLSTM-SA-CRF for the field of precast concrete component quality standard, which can capture the full text and local feature information more effectively through the combination of BiLSTM and self-attention mechanism, and accurately recognize entities in the standard. Meanwhile, using ALBERT as the word embedding layer can play the role of reducing resource consumption and improving processing efficiency. © 2025 The Authors.

关键词： Knowledge graph

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FreqSense: Universal and Low-Latency Adversarial Example Detection for Speaker Recognition with Interpretability in Frequency Domain

FreqSense: Universal and Low-Latency Adversarial Example Det...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Huang, Yihuan Li, Yuanzhe Ren, Yanzhen Tu, Weiping Yang, Yuhong Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education China School of Cyber Science and Engineering Wuhan University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University China

ISBN: (纸本)9798350368741

Speaker recognition (SR) systems are particularly vulnerable to adversarial example (AE) attacks. To mitigate these attacks, AE detection systems are typically integrated into SR systems. To overcome the limitations of low detection accuracy, poor generalization, and high latency in existing schemes, this paper proposes FreqSense, an AE detection scheme based on frequency distribution features. FreqSense detects a variety of unknown AE attacks with low latency, and provides interpretability in its detection process. The basic idea of FreqSense is that AE typically introduce carefully designed noise in specific frequency bands that are associated with highly distinctive speaker identities. Therefore, leveraging the distributional variations in these frequency bands can effectively distinguish between AE and benign audio. FreqSense models frequency distribution features by integrating time-frequency transformation technology with a self-attention mechanism and employs a neural network-based classifier to distinguish between AE and benign audio. Experimental results show that FreqSense achieves an overall detection accuracy of 99.2%, surpassing state-of-the-art (SOTA) schemes by 27.2%. When confronting unknown AE attacks, FreqSense achieves a detection accuracy of 98.3% with a latency of just 0.0014 seconds. © 2025 IEEE.

关键词： Adversarial Attack Adversarial Example Detection Speaker Recognition

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Bridging Machine Learning and Formal Concept Analysis for Effective Crowd Detection 25

Bridging Machine Learning and Formal Concept Analysis for Ef...

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25th International Arab Conference on information Technology, ACIT 2024

作者： Al-Oraiqat, Anas M. Drieiev, Oleksandr Samara, Ghassan Almatarneh, Sattam Al-Oraiqat, Karim A. Alrawashdeh, Hazim Drieieva, Hanna Elrashidi, Ali Hasan, Yassin M. Y. Zarqa University Faculty of Information Technology Department of Computer Science Jordan Central Ukrainian National Technical University Department of Cybersecurity and Software Kropyvnytskyi Ukraine Zarqa University Faculty of Information Technology Department of Data Science and Artificial Intelligence Jordan Zarqa University Faculty of Information Technology Department of Software Engineering Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Assiut University Electrical Engineering Department Egypt

ISBN: (纸本)9798331540012

Crowd detection and prevention systems have become essential for managing densely populated areas. Modern systems leverage the combined power of machine learning, data mining, and image processing to extract and analyse features from crowded zones, enabling the identification of behavioural patterns and anomalies. However, most current solutions primarily focus on detection and lack robust decision-making and recommendation mechanisms for selecting appropriate crowd-prevention strategies. To address this gap, we integrate the predictive capabilities of machine learning models with the analytical and clustering strengths of Fuzzy Formal Concept Analysis (fuzzy FCA). Machine learning is employed to extract valuable insights from images of the area, with a neural network used to identify human figures, track individuals' positions, and predict crowd levels. This data is fed into an FFCA-based decision system, where crowd information is structured and clustered using lattice theory. This latter helped exclude low-crowd zones and cluster the rest of the monitored areas based on their crowd features. Additionally, we define bottom-up parsing algorithms to recommend suitable crowd-prevention plans based on crowd density levels within fuzzy formal concepts. Experiments confirmed not only the ability of fuzzy FCA to "exclude"low-crowd zones thanks to a used crowd threshold but also the efficient "feature-based clustering"of crowded zones into hierarchical formal concepts and these latter's "bottom-up parsing"to finally identify the dense zones. © 2024 IEEE.

关键词： Adversarial machine learning

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Analysis of the Spatio-Temporal Characteristics of Winter Surface Urban Heat Island:A Case Study in Beijing,China

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Journal of Earth science 2024年第5期35卷 1640-1653页

作者： Shanshan Lu Fujiang Liu Yunshuang Ye Jiayu Tang Peng Li Weihua Lin Yan Guo Ruqiang Ma Jun Wang School of Geography and Information Engineering China University of GeosciencesWuhan 430074China AutoNavi Software Co. Ltd.Beijing 100102China School of Computer Science China University of GeosciencesWuhan 430078China Yantai Geological Survey of Coastal Resources Center Yantai 264000China

This study reveals the temporal and spatial evolution characteristics of the winter nighttime urban heat island(UHI)effect in the case of Beijing,*** land surface temperature(LST)is retrieved by radiative transfer equation by using the remote sensing data from Landsat ETM+/OLI_TIRS from 2007 to 2017 for the winter nighttime period,and LST is then divided by the mean-standard deviation method into different levels of thermal landscapes.A combination of the migration calculation of gravity center and multi-directional profile analysis is used to study the directional differentiation characteristics of LST and the migratory characteristics of the gravity center of ***,the overall temporal and spatial evolution characteristics of winter nighttime surface urban heat island(SUHI)in Beijing are studied,and the possible reasons for the changes are *** show that Beijing's UHI effect first increased and subsequently decreased from 2007 to *** winter heat island in the urban area developed from low-density agglomeration to high-density agglomeration to lowdensity ***,the high-level thermal landscapes migrated to the southwest along with the city center of gravity,and the expansion rate is fastest in the southwest,which is directly linked to the changes in the urban construction ***,the overall spatial distribution of winter nighttime LST is high in the east and south and low in the west and north,and is influenced by topography,land cover,urbanization,anthropogenic heat,and other factors as well.

关键词： land surface temperature urban heat island spatio-temporal characteristics winter environmentgeology

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OpenCML: An Open Customizable Modeling Language for Directed Acyclic Graphs 22

OpenCML: An Open Customizable Modeling Language for Directed...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Wei, Zhenjie Xing, Weiwei Liu, Weibin Zou, Zhiyuan Chen, Genxiang Beijing Jiaotong University School of Software Engineering Beijing China Beijing Jiaotong University Institute of Information Science Beijing China Beijing Information Science and Technology University Computer School Beijing China Minzu University of China College of Science Beijing China

ISBN: (纸本)9798331509712

Directed Acyclic Graphs (DAGs) are widely utilized across various domains for tasks such as graph algorithms, data flow analysis, program optimization, and machine learning. Representing DAGs using General-purpose Programming Languages (GPLs) or Data Serialization Formats (DSFs) can lead to complex and obscure expressions, making it challenging to comprehend and manage the codebase. Domain-Specific Languages (DSLs) offer a more tailored approach, but come with limitations and development overhead. This paper introduces the Open Customizable Modeling Language (OpenCML), a universal DAG modeling language specification that aims to provide a standardized and customizable framework for modeling and scripting DAGs. Evaluations demonstrate that OpenCML offers expressive power, customizability, and interoperability, simplifying the learning process and providing a powerful solution for DAG modeling and scripting. © 2024 IEEE.

关键词： Data flow analysis

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Empowering Deaf Children with Sinhala Sign Language, Emotion Detection, and Sound Recognition 5

Empowering Deaf Children with Sinhala Sign Language, Emotion...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Fernando, W.H.K. Perera, P.L.D.N. Fernando, M.I.N. Gunasekara, P.T.E.W. Fernando, Harinda De Zoysa, Rivoni Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer System Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Science Software Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This study describes a broad endeavor to use cutting-edge technologies to empower deaf primary school students in Sri Lanka. Three key elements make up the study: a sound recognition and classification system, an Android software for translating gestures in Sinhala Sign Language (SSL), and a mobile app for emotion recognition and text-tospeech in Sinhala. By promptly alerting caregivers to potential dangers, the sound recognition technology secures the security of deaf children at home, providing an invaluable layer of security. The Android software bridges the communication gap and improves family interactions by translating SSL gestures captured by the device's camera into textual representations. This promotes successful communication between deaf children and their parents. Convolutional Neural Networks (CNNs) are used in the mobile application to detect and understand emotions based on facial expressions. This enables non-verbal youngsters to communicate successfully through Sinhala textto-speech output with adaptive tone modulation. This research aims to build a caring and inclusive atmosphere, enabling increased independence and enhanced communication skills in deaf children by combining these advances. The suggested solutions could significantly improve the lives of deaf kids, providing them with opportunities for better learning, social interaction, and emotional health. © 2023 IEEE.

关键词： Emotion Recognition

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Evaluating LoRaWAN performance in intentional and unintentional DoS attacks by legacy 900MHz network devices 6

Evaluating LoRaWAN performance in intentional and unintentio...

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6th International Conference on Advanced Communication Technologies and Networking, CommNet 2023

作者： Bang, Seokhyeon Jang, Junyoung Ro, Minju Choi, Yuseon Kwon, Doyong Lee, Kangyeon Smith, Anthony Chung-Ang University School of Computer Science and Engineering Seoul Korea Republic of Chonnam National University Department of Software Engineering Gwangju Korea Republic of Kyungpook National University School of Computer Science and Engineering Daegu Korea Republic of Purdue University Department of Computer and Information Technology IN United States

ISBN: (纸本)9798350329391

The deployment of LoRaWAN on the Internet of Things (IoT) has increased since its advent and LoRaWAN now predominates the IoT market over other Low Powered Wide Area Networks (LPWAN). However, since LoRaWAN uses Chirp Spread Spectrum (CSS), it is susceptible to wideband jamming attacks. In this paper, we demonstrate with experiments and concrete numerical results that jamming US915 LoRaWAN frequency is possible by the usual data transmission and reception process of 900MHz Canopy, one of the legacy 900MHz network device. Intentional attack is possible in the same manner. The experiments emulate the real-world environment operated in medical and agriculture industries, in outdoor and indoor conditions, respectively. In addition, this paper introduces and utilizes the novel metric, Jamming Effect (JE), that indicates the network performance of wireless networks that spread the data on air. © 2023 IEEE.

关键词： Risk assessment

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K-Banhatti Sombor Invariants of Certain computer Networks

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computers, Materials & Continua 2022年第10期73卷 15-31页

作者： Khalid Hamid Muhammad Waseem Iqbal Abaid Ur Rehman Virk Muhammad Usman Ashraf Ahmed Mohammed Alghamdi Adel A.Bahaddad Khalid Ali Almarhabi Department of Computer Science Superior UniversityLahore54000Pakistan Department of Software Engineering Superior UniversityLahore54000Pakistan Department of Mathematics University of SialkotSialkot51013Pakistan Department of Computer Science GC Women UniversitySialkot53310Pakistan Department of Software Engineering College of Computer Science and EngineeringUniversity of Jeddah21493Saudi Arabia Department of Information System Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computing in Al-QunfudahUmm Al-Qura UniversityMakkah24381Saudi Arabia

Any number that can be uniquely determined by a graph is called a graph *** the last twenty years’countless mathematical graph invariants have been characterized and utilized for correlation ***,no reliable examination has been embraced to decide,how much these invariants are related with a network graph or molecular *** this paper,it will discuss three different variants of bridge networks with good potential of prediction in the field of computer science,mathematics,chemistry,pharmacy,informatics and biology in context with physical and chemical structures and networks,because k-banhatti sombor invariants are freshly presented and have numerous prediction qualities for different variants of bridge graphs or *** study solved the topology of a bridge graph/networks of three different types with two invariants KBanhatti Sombor Indices and its reduced *** deduced results can be used for the modeling of computer networks like Local area network(LAN),Metropolitan area network(MAN),and Wide area network(WAN),backbone of internet and other networks/structures of computers,power generation,bio-informatics and chemical compounds synthesis.

关键词： Bridge networks invariants k-banhatti sombor indices maple network graph molecular graph

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PLAME: Piecewise-Linear Approximate Measure for Additive Kernel SVM (Extended abstract) 40

PLAME: Piecewise-Linear Approximate Measure for Additive Ker...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Chan, Tsz Nam Li, Zhe Leong Hou, U. Cheng, Reynold Shenzhen University Department of Computer Science and Software Engineering China Alibaba Cloud University of Macau State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science China The University of Hong Kong Department of Computer Science and Musketeers Foundation Institute of Data Science Hong Kong

ISBN: (纸本)9798350317152

Additive Kernel SVM has been extensively used in many applications, including human activity detection and pedestrian detection. Since training an additive kernel SVM model is very time-consuming, which is not scalable to largescale datasets, many efficient solutions have been developed in the past few years. However, most of the existing methods normally fail to achieve one of these three important conditions which are (1) low classification error, (2) low memory space, and (3) low training time. In order to simultaneously fulfill these three conditions, we develop the new piecewise-linear approximate measure (PLAME) for training additive kernel SVM models. Experimental results verify that this approach can achieve the best trade-off between accuracy, memory space, and training time compared with different types of state-of-the-art methods. © 2024 IEEE.

关键词： Additives

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Expert System for Kubernetes Cluster Autoscaling and Resource Management 4

Expert System for Kubernetes Cluster Autoscaling and Resourc...

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4th International Conference on Advancements in Computing, ICAC 2022

作者： Hettiarachchi, Lasal Sandeepa Jayadeva, Senura Vihan Bandara, Rusiru Abhisheak Vikum Palliyaguruge, Dilmi Samaratunge Arachchillage, Udara Srimath S. Kasthurirathna, Dharshana Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (纸本)9798350398090

The importance of orchestration tools such as Kubernetes has become paramount with the popularity of software architectural styles such as microservices. Furthermore, advancements in containerization technologies such as Docker has also played a vital role when it comes to advancements in the field of DevOps, enabling developers and system engineers to deploy are manage applications much more effectively. However, infrastructure configuration and management of resources are still challenging due to the disjointed nature of the infrastructure and resource management tools' failure to comprehend the deployed applications and create a holistic view of the services. This is partly due to the extensive knowledge required to operate these tools or due to the inability to perform specific tasks. As a result, multiple tools and platforms need to configure together to automate the deployment, monitoring and management processes to provide the optimal deployment strategy for the applications. In response to this issue, this research proposes an expert system that creates a centralized approach to cluster autoscaling and resource management, which also provides an automated low-latency container management system and resiliency evaluation for dynamic systems. Furthermore, the time series load prediction is done using a BiLSTM and periodically creates an optimized autoscaling policy for cluster performance, thus creating a seamless pipeline from deployment, monitoring scaling, and troubleshooting of distributed applications based on Kubernetes. © 2022 IEEE.

关键词： Expert systems

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