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Topic Modeling Approaches—A Comparative Analysis 22nd

Topic Modeling Approaches—A Comparative Analysis

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22nd international conference on Hybrid intelligent Systems, HIS 2022 and the 18th international conference on Information Assurance and Security, IAS 2022

作者： Lakshminarayana Reddy, D. Shoba Bindu, C. Research Scholar Department of Computer Science and Engineering JNTUA Andhra Pradesh Anantapuramu India Department of Computer Science and Engineering JNTUACEA Andhra Pradesh Anantapuramu India

ISBN: (纸本)9783031274084

Valuable information from a corpus for a specific purpose can be obtained by finding, extracting, and processing the text through text mining. A corpus is a group of documents and the documents could be anything from newspaper articles, tweets, or any kind of data that needs to study. For processing and understanding the structure of a corpus, a technique in text mining is Natural Language Processing (NLP). The study of a corpus in different fields like bioinformatics, software engineering, sentiment analysis, Education, and Linguistics (scientific research) is a challenging task as it contains a vast amount of data. Thus for latent data identification, establishing connections between data and text documents needs topic modeling. Evolutions of topic models are analyzed in this paper from 1990 to present. To better understand the topic modeling concept, a detailed evaluation of techniques is discussed in detail. In this study, we looked into highly scientific articles from 2010 to 2022 based on methods used in different areas to discover current trends, research development, and the intellectual structure of topic modeling. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sentiment analysis

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The Shifting Landscape of Cybersecurity: The Impact of Remote Work and COVID-19 on data Breach Trends 22nd

The Shifting Landscape of Cybersecurity: The Impact of Rem...

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22nd international conference on Software engineering Research and Practice, SERP 2024, and 23rd international conference on e-learning, e-Business, Enterprise Information Systems, and e-Government, EEE 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

作者： Ozer, Murat Kose, Yasin Bastug, Mehmet Kucukkaya, Goksel Varlioglu, Eva Ruhsar School of Information Technology University of Cincinnati CincinnatiOH United States Cybercrime and Forensic Computing Friedrich-Alexander-Universität Erlangen Germany CJ and Criminology Department University of Scranton ScrantonPA United States

ISBN: (纸本)9783031866432

This study examines the impact of the COVID-19 pandemic on cybersecurity and data breaches, with a specific focus on the shift toward remote work. The study identifies trends and offers insights into cybersecurity incidents by analyzing data breaches two years before and two years after the start of remote work. data was collected from the Montana Department of Justice data Breach database and consisted of data breaches that occurred between April 2018 and April 2022. The findings inform best practices for cybersecurity preparedness in remote work environments, aiding organizations to enhance their defenses. Although the study’s data is limited to Montana, it offers valuable insights for cybersecurity professionals worldwide. As remote work continues to evolve, organizations must remain adaptable and vigilant in their cybersecurity strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cyber attacks

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Deep learning Based Real-Time Detection of False data Injection Attacks in Power Grids 22

Deep Learning Based Real-Time Detection of False Data Inject...

引用

22nd international Middle East Power Systems conference, MEPCON 2021

作者： Mukherjee, Debottam Chakraborty, Samrat Banerjee, Ramashis Bhunia, Joydeep Guchhait, Pabitra Kumar Dept. of Electrical Engineering Varanasi India National Institute of Technology Dept. of Electrical Engineering Arunachal Pradesh India National Institute of Technology Silchar Dept. of Electrical Engineering Assam India Punjab Engineering College Dept. of Electrical Engineering Chandigarh India Bengal College of Engineering and Technology Dept. of Electrical Engineering Durgapur India

ISBN: (纸本)9781665419987

False data injection attack is an advanced class of modern cyber-attacks against the state estimation algorithm of the smart grid. Such attacks can inherently delude the bad data detectors at the control center and develop critical scenarios by corrupting the set of estimated states. This work furnishes an effective detection of such class of attacks with predefined bounds. The detection policy involves a robust, nonlinear deep learning approach that is capable of not only forecasting the operating states of the grid, but also can be effectively deployed by the operator to determine any attacks within the raw measurements. It is seen that such scalable models working in real-time promote a robust performance under measurement noise as well. The proposed model with its set of optimal hyper-parameters showcases a better state forecasting scheme with minimum error margin than most of the state of the art forecasting strategies. A diligent analysis on the IEEE 14 bus test system effectively promotes the aforementioned propositions. © 2021 IEEE.

关键词： Smart power grids

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Jamming-Resilient Over-the-Air Computation for Federated learning in Edge Intelligence 19

Jamming-Resilient Over-the-Air Computation for Federated Lea...

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2022 IEEE SmartWorld, 19th IEEE international conference on Ubiquitous Intelligence and Computing, 2022 IEEE international conference on Autonomous and Trusted Vehicles conference, 22nd IEEE international conference on Scalable Computing and Communications, 2022 IEEE international conference on Digital Twin, 8th IEEE international conference on Privacy Computing and 2022 IEEE international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Zou, Yifei Xu, Minghui Li, Huiqun Hu, Qin Deng, Xianjun Yu, Dongxiao Cheng, Xiuzhen Shandong University School of Computer Science and Technology Qingdao266237 China Shandong University School of Mathematics Jinan250100 China Indiana University-Purdue University Department of Computer and Information Science Indianapolis United States Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China

ISBN: (纸本)9798350346558

By making full use of the accumulative feature of wireless signals, the over-the-air computation makes it possible for the federated learning to aggregate all the parameters simultaneously from the distributed learning agents, which lessens the delay of updating the parameters caused by the collisions of wireless signals. However, the accumulative feature in the over-the-air computation also opens the door to the jamming attackers. Specifically, an adversary can easily tamper the aggregated result in the over-the-air computation by launching a simple jamming signal. Thus, it is significant to improve the jamming resilience of the over-the-air computation so that it can be adopted in a wider range of wireless scenarios. In this paper, we propose the first jamming-resilient over-the-air computation algorithm for the federated learning via the multiple channel technique. Specifically, in our jamming-resilient algorithm, the learning agents randomly and uniformly choose one of the multi-channels to update their parameters, and the server drop a fraction of results according to a greedy screening rule. A theoretical analysis with some rigorous proofs is presented to show that the expected aggregated result by our algorithm is asymptotically close to the authentic value when the data on the learning agents has a uniform distribution. An extensive simulation is also conducted to show the correctness of our algorithm. © 2022 IEEE.

关键词： Parameter estimation

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Enhancing AdultSize Humanoid Localization Accuracy: A Vision-based aMCL Leveraging Object Detection Model and Hungarian Algorithm

Enhancing AdultSize Humanoid Localization Accuracy: A Vision...

引用

IEEE-RAS international conference on Humanoid Robots

作者： Jun Young Kim Min Sung Ahn Jeakweon Han Department of Interdisciplinary Robot Engineering Systems Hanyang University ERICA Ansan Republic of Korea Department of Mechanical and Aerospace Engineering University of California Los Angeles CA USA Department of Robotics Hanyang University ERICA Ansan Republic of Korea

The robot's decision-making is an essential component of the autonomous robot. To fulfill this component, the accurate position estimation of the robot is a fundamental prerequisite. Localization determines the robot's relative position within the map environment, and existing mobile robots have been extensively studied. However, localization is still challenging for humanoids because the bipeds' movement is not as stable as mobile robots, and camera view frames oscillate from side to side. Developing localization with high accuracy under the above-limited constraints is necessary. This paper proposes an estimation of a 1.3m tall humanoid robot's position with an adaptation of vision-based Augmented Monte-Carlo localization (aMCL) in the soccer field. Several approaches were also applied to enhance the localization performance. First, a deep learning-based object detection model was selected to identify predefined landmarks. In addition, the data association process was improved from the nearest neighbor matching algorithm to the Hungarian algorithm. This method enhanced data association performance, and the robot's position was successfully estimated in real-time.

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Machine learning and Real-Time Signal Features Integration for Strength Modelling in Friction Stir Welding Process 1

引用

2nd international conference on Recent Advances in Manufacturing, RAM 2021

作者： Das, B. Ramon, Jasper Department of Mechanical Engineering National Institute of Technology Silchar Assam Silchar788010 India

ISBN: (数字)9789811699528

ISBN: (纸本)9789811699511

The current research focuses on integrating real-time signal features and artificial intelligent technique to model the tensile strength of the welded joints produced through friction stir welding. Vertical force signals are acquired in real time during the experiments and processed in the time domain to compute the statistical features from the signals. The useful feature domain is generated and integrated with influencing process parameters in the welding process. The data pool created is used for developing the machine learning model to predict joint strength. In the investigation, it is revealed that the integration of signal features in the strength modelling results in better prediction accuracy compared to the result obtained without integration of the signal features in the strength modelling. Strength prediction increases to 99%, with signal features. The proposed method can be effective in an automated platform for effective process monitoring and control in the friction stir welding process. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Friction stir welding Signal features Statistical features Strength modelling Support vector machine time domain features

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TiKEM: Knowledge Enhanced Tibetan Pre-trained Language Model 22

TiKEM: Knowledge Enhanced Tibetan Pre-trained Language Model

引用

22nd Chinese National conference on Computational Linguistics, CCL 2023

作者： Deng, Junjie Chen, Long Zhang, Ting Sun, Yuan Zhao, Xiaobing School of Information Engineering Minzu University of China Beijing100081 China School of Chinese Ethnic Minority Languages and Literature Minzu University of China China National Language Resources Monitoring Research Center for Minority Languages China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE China

ISBN: (纸本)9781713876229

The pre-trained language model has excellent performance in the Chinese and English fields. But the research of pre-trained language models in low-resource languages such as Tibetan have just made initial progress. The main reason is that it's difficult to obtain low-resource language data. The existing Tibetan pre-trained language model uses a large-scale unstructured text corpus for self-supervised learning, which lacking external knowledge guidance. So their knowledge memory and knowledge reasoning abilities are limited. To solve the above problems, this paper build a Tibetan knowledge enhancement pre-trained dataset containing 500, 000 triples of knowledge. Then, this paper combine structured knowledge representation and unstructured text representation to train the knowledge enhanced Tibetan pre-trained language model TiKEM. This method can improve the knowledge memory and reasoning abilities of the model. Finally, this paper verifies the effectiveness of the model in Text Classification, Tibetan relationship classification and Tibetan machine reading comprehension. © 2023 China National conference on Computational Linguistics.

关键词： Self-supervised learning

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A Service-Enhanced Task Offloading Method in MEC-Enabled IoV Networks 19

A Service-Enhanced Task Offloading Method in MEC-Enabled IoV...

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作者： Zhao, Bohai Peng, Kai Leung, Victor C.M. Xia, Yunni Huaqiao University College of Engineering Quanzhou China Nanjing University State Key Lab. for Novel Software Technology Nanjing China Shenzhen University School of Computer Science and Software Engineering Shenzhen China The University of British Columbia Department of Electrical and Computer Engineering Vancouver Canada Chongqing University School of Computers Chongqing China

ISBN: (纸本)9798350346558

To facilitate the evolution from independent transportation means to vehicle-to-infrastructure communications, traditional vehicle-to-vehicle (V2V) technologies are growing to a more advanced and generalized paradigm, i.e., the Internet of Vehicles (IoV). This paradigm has stimulated ever-increasing calling for more capabilities and intelligence for vehicles to foster the potential of supporting a plethora of emerging applications and ubiquitous automotive network access. Combining caching-aided communication and edge computing has been expected to be an advantageous framework to cope with the surging requirements for timely data processing and storage from intelligent IoV applications. In light of the delay restriction and the heterogeneity of edge service nodes (ESNs), we study the service-enhanced problem in edge-enabled IoV networks by configuring proactive service caching. Specifically, based on task scale and relational restrictions, we design an adaptive non-linear caching probability mapping function and further propose a proactive service-enhanced task offloading method in edge-enabled IoV networks named SEOEV. Performance evaluation demonstrates that SEOEV efficiently reduces task processing overhead in comparison to other comparative computing migration approaches. © 2022 IEEE.

关键词： Multiobjective optimization

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Social engineering:Role of Teachers in Cohabitation of AI with Education

Social Engineering:Role of Teachers in Cohabitation of AI wi...

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Cyber Resilience (ICCR), international conference on

作者： Swamynathan Ramakrishnan Malini Mittal Bishnoi Shanmugan Joghee S Jijitha Anand Kumar Amity Business School Amity University Dubai Dubai UAE School of HAAS Amity University Dubai Dubai UAE School of Business Skyline University College Sharjah Sharjah UAE Deparmetnt of Compter Applications Bharathiar University Coimbatore TN India Department of Electrical Engineering Amity University Dubai Dubai UAE

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

The role of teachers in utilizing artificial intelligence (AI) in educational services is a vital component of social engineering in modern education. AI technologies have the potential to transform education and learning by delivering tailored training, intelligent tutoring systems, and automated grading. However, effective AI integration in education is primarily reliant on instructors' direction, knowledge, and adaptability. This literature review study examines the various roles that teachers play in harnessing the power of AI in educational services. To begin, teachers act as guides for students as they traverse AI-powered tools and platforms. They offer guidance on how to leverage AI-powered resources to improve the outcomes of learning. Teachers are also in charge of creating and curating AI-powered learning material that corresponds with curricular objectives and engages students in meaningful ways. These are the pathways to do social engineering. However, challenges and problems may occur with the integration of AI in education, such as potential teacher displacement and ethical concerns about data privacy and algorithmic biases. As a result, instructors must actively participate in professional development in order to improve their grasp of AI, its consequences, and how to successfully integrate it into their teaching practice.

关键词： Training Technological innovation Virtual assistants Education Transforms Market research Problem-solving

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SEGA: Semantic Guided Attention on Visual Prototype for Few-Shot learning 22

SEGA: Semantic Guided Attention on Visual Prototype for Few-...

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22nd IEEE/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Yang, Fengyuan Wang, Ruiping Chen, Xilin Chinese Acad Sci Key Lab Intelligent Informat Proc Inst Comp Technol Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Beijing Acad Artificial Intelligence Beijing 100084 Peoples R China

ISBN: (纸本)9781665409155

Teaching machines to recognize a new category based on few training samples especially only one remains challenging owing to the incomprehensive understanding of the novel category caused by the lack of data. However, human can learn new classes quickly even given few samples since human can tell what discriminative features should be focused on about each category based on both the visual and semantic prior knowledge. To better utilize those prior knowledge, we propose the SEmantic Guided Attention (SEGA) mechanism where the semantic knowledge is used to guide the visual perception in a top-down manner about what visual features should be paid attention to when distinguishing a category from the others. As a result, the embedding of the novel class even with few samples can be more discriminative. Concretely, a feature extractor is trained to embed few images of each novel class into a visual prototype with the help of transferring visual prior knowledge from base classes. Then we learn a network that maps semantic knowledge to category-specific attention vectors which will be used to perform feature selection to enhance the visual prototypes. Extensive experiments on minilmageNet, tieredImageNet, CIFAR-FS, and CUB indicate that our semantic guided attention realizes anticipated function and outperforms state-of-the-art results.

关键词： Training Knowledge engineering Visualization Computer vision Correlation Semantics Prototypes

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