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2024 IEEE Calcutta Conference, CALCON 2024

作者： Chatterjee, Tiyasa Sarkar, Sudipta Basak, Ambik Pal, Soumyadip Mondal, Ashoke RCC Institute of Information Technology Department of Electronics and Communication Engineering Kolkata India Indian Institute of Engineering Science and Technology Shibpur School of Advanced Materials Green Energy and Sensor Systems Howrah India

ISBN: (纸本)9798331516666

In the present day, the number of people travelling in flight is rapidly increasing. All the passengers travelling through these airlines carry luggage. Which is increasing luggage. At the airport, all this luggage goes through multiple processes like checking, tagging, shorting, etc. In this process, due to various reasons, they got lost. Paper-based barcodes are used for ragging the luggage. The barcode method of luggage sorting is time-consuming, and prone to technical errors as the tags can store only limited data and the date needs to be manually updated. Damaged tags result in loss of cargo during the loading process. This article focuses on analysing the real-life problem of luggage sorting and tracking at a busy airport by using two advanced technologies RFID and IoT. Among multiple literature on RFID Based Luggage Sorting there is a gap in implementing IoT-based tracking of luggage, along with RFID-based identification. Which brings novelty to this project. The paper aims to provide a simplified technique for automatic luggage sorting using RFID and tracking over IoT, along with design, implementation and testing data. © 2024 IEEE.

关键词： Radio frequency identification (RFID)

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Sentiment Analysis of Twitter Feeds Using Flask Environment: A Superior Application of Data Analysis

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Annals of Data science 2024年第1期11卷 159-180页

作者： Modi, Astha Shah, Khelan Shah, Shrey Patel, Samir Shah, Manan Department of Information and Communication Technology School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar382426 India Department of Computer Science School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar382426 India Department of Chemical Engineering School of Energy Technology Pandit Deendayal Energy University Gujarat Gandhinagar382426 India

In this challenging world, social media plays a vital role as it is at the pinnacle of data sharing. The advancement in technology has made a huge amount of information available for data analysis and it is on the hotlist nowadays. Opinions of the people are expressed and shared across various social media platforms like Twitter, Facebook, and Instagram. Twitter is a prodigious platform containing an ample amount of data and analyzing the data is of topmost priority. One of the most widely utilized approaches for classifying an individual’s emotions displayed in subjective data is sentiment analysis. Sentiment analysis is done using various algorithms of machine learning like Support Vector Machine, Naive Bayes, Long Short-Term Memory, Decision Tree Classifier, and many more, but this paper aims at the generalized way of performing Twitter sentiment analysis using flask environment. Flask environment provides various inbuilt functionalities to analyze the sentiments of text into three different categories: positive, negative, and neutral. Also, it makes API calls to the Twitter Developer account to fetch the Twitter data. After fetching and analyzing the data, the results get displayed on a webpage containing the percentage of positive, negative, and neutral tweets for a phrase in a pie chart. It displays the language analysis for the same phrase. Furthermore, the webpage calls attention to the tweets done on that phrase and reveals the details of the tweets. Considering the major industry runners of three different sectors namely Enterprises, Sports Apparel Industry, and Multimedia Industry, we have analyzed and compared sentiments of two different Multinational companies from each sector. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022.

关键词： Python

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Using Factor Analysis to Increase Accuracy of Neural Networks for Predicting Student Test Scores 54

Using Factor Analysis to Increase Accuracy of Neural Network...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Brown, Michael Scott Momtaz, Maliha Mai, Aaron Modi, Dhruvil University of Maryland Baltimore County Information Systems Department BaltimoreMD United States Hood College Computer Science and It Department FrederickMD United States Texas A&m College Station Computer Science and Engineering Department College StationTX United States University of Maryland Baltimore County Department of Computer Science and Electrical Engineering BaltimoreMD United States

ISBN: (纸本)9798350351507

This work is a full research-to-practice paper that describes a predictive method to improve the prediction of student test scores. Predicting student test scores is difficult. However, doing so can improve education greatly by improving advising, scheduling, tutoring assignment and other educational processes. This research extends previous research by using a domain space reduction technique to improve accuracy. Factor Analysis is used to reduce the number of domain attributes for improving the accuracy of a Neural Network to predict student test scores. In this research datasets for Mathematics and Language of high school student test scores were used. Test scores were predicted using a Neural Network computing the Mean Absolute Error as a measurement of accuracy. The datasets have 30 domain attributes each. Factor Analysis was used to reduce the domain size from between 1 to 29, each time using it to train the Neural Network. Because the Mean Absolute Error may vary depending upon which records in the dataset are used for training versus testing, 50 trials of each dataset size were executed producing an Average Mean Absolute Error for each domain size. A statistical test was used to show statistical significance between the Neural Network without Factor Analysis and the Neural Network with varying domain sizes using Factor Analysis. Results were very promising and correspond to previous research that used Principal Component Analysis. Numerous domain sizes had significantly better Average Mean Absolute Errors than the accuracy of the Neural Network without Factor Analysis. This research shows that reducing the domain size using Factor Analysis can greatly improve the accuracy of Neural Networks when predicting student test scores. The best improvements occurred when domain sizes were very small ranging from 2 to 6. Domain reduction techniques, such as Factor Analysis, have been shown to improve predictive models for student test score prediction. Future research

关键词： Support vector machines

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Byzantine-Resilient Over-the-Air Federated Learning under Zero-Trust Architecture

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IEEE Journal on Selected Areas in Communications 2025年第6期43卷 1954-1969页

作者： Yao, Jiacheng Shi, Wei Xu, Wei Yang, Zhaohui Lee Swindlehurst, A. Niyato, Dusit Southeast University National Mobile Communications Research Laboratory Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Zhejiang Laboratory Hangzhou311121 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Hangzhou310007 China University of California at Irvine Center for Pervasive Communications and Computing Henry Samueli School of Engineering IrvineCA92697 United States Nanyang Technological University School of Computer Science and Engineering 308232 Singapore

Over-the-air computation (AirComp) has emerged as an essential approach for enabling communication-efficient federated learning (FL) over wireless networks. Nonetheless, the inherent analog transmission mechanism in AirComp-based FL (AirFL) intensifies challenges posed by potential Byzantine attacks. In this paper, we propose a novel Byzantine-robust FL paradigm for over-the-air transmissions, referred to as federated learning with secure adaptive clustering (FedSAC). FedSAC aims to protect a portion of the devices from attacks through zero trust architecture (ZTA) based Byzantine identification and adaptive device clustering. By conducting a one-step convergence analysis, we theoretically characterize the convergence behavior with different device clustering mechanisms and uneven aggregation weighting factors for each device. Building upon our analytical results, we formulate a joint optimization problem for the clustering and weighting factors in each communication round. To facilitate the targeted optimization, we propose a dynamic Byzantine identification method using historical reputation based on ZTA. Furthermore, we introduce a sequential clustering method, transforming the joint optimization into a weighting optimization problem without sacrificing the optimality. To optimize the weighting, we capitalize on the penalty convex-concave procedure (P-CCP) to obtain a stationary solution. Numerical results substantiate the superiority of the proposed FedSAC over existing methods in terms of both test accuracy and convergence rate. © 1983-2012 IEEE.

关键词： Federated learning

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3D Physical Layer Secure Transmission for UAV-Assisted Mobile Communications Without Locations of Eavesdroppers 18th

3D Physical Layer Secure Transmission for UAV-Assisted Mobi...

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18th International Conference on Wireless Artificial Intelligent Computing systems and Applications, WASA 2024

作者： Yu, Wenlu Li, Jianqiang Fan, Xin Wang, Guopeng Li, Guangkai Luo, Chuanwen Hong, Yi Chen, Ting School of Information Science and Technology Beijing Forestry University Beijing100083 China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing100029 China Zhejiang Ocean University Zhoushan316022 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9783031714665

The flexibility and cost-effectiveness of unmanned aerial vehicles (UAVs) in a wide range of scenarios have made them indispensable tools for mobile communications. However, UAV communications are also facing increasing security threats, especially in open spaces where they are vulnerable to potential eavesdroppers. In this paper, we employ an extra UAV as a cooperative jammer to enhance UAV-to-ground communication security by sending artificial noise to effectively resist passive eavesdroppers. Unlike previous work, we focus on the dynamic scenario where the source UAV and the ground user move simultaneously. In this scenario, we propose a joint optimization algorithm of trajectory and power for the jamming UAV to obtain the optimal jamming strategy. Due to the absence of eavesdroppers’ locations, the goal of the optimization is to maximize a secure region. Numerical results demonstrate that our optimization algorithm can significantly enlarge the secure region compared to randomly selecting a position of the jamming UAV. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Jamming

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A Robust Trajectory Azimuth Control Strategy for Mitigating Weight-on-bit Uncertainty

A Robust Trajectory Azimuth Control Strategy for Mitigating ...

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第43届中国控制会议

作者： She Wang Jing Xiao Zhen Cai Hai Huang School of Computer and Information Engineering Wuhan City Polytechnic University School of Information Science and Engineering Wuhan University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

This study concentrates on trajectory azimuth control amidst the uncertainty of weight-on-bit in the directional drilling *** trajectory azimuth model intricately considers the variable weight-on-bit and integrates the uncertainty factor within its framework. A sophisticated two-layer observer-based control scheme has been crafted to address the challenges posed by the lack of direct azimuth measurement, the weight-on-bit uncertainty, and external interferences. The stability of the system is meticulously appraised using the Lyapunov function, which is subsequently transformed into linear matrix inequality(LMI) to derive the control parameter gains efficiently. Ultimately, simulations demonstrate that our control system adeptly negates the impact of weight-on-bit uncertainty and extrinsic disruptions, showcasing its effectiveness.

关键词： Trajectory Azimuth Attitude Control Weight-on-bit Uncertainty

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Cyber security skills development in South Africa: Addressing the Gender Gap in the Industry 8

Cyber security skills development in South Africa: Addressin...

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8th Conference on information Communication Technology and Society, ICTAS 2024

作者： Ramonyai, Tebogo Motlatso Mpekoa, Noluntu Tom, Sheethal University of Johannesburg Academy of Computer Science & Software Engineering Cape Town South Africa IIE Varsity College School of Information Technology Cape Town South Africa

ISBN: (纸本)9798350314892

This paper investigates the gender gap in South Africa's cybersecurity sector and its effects on both the sector and the nation's economic growth. There is a significant shortage of skilled workers in the sector, and women are notably underrepresented. The main research question examines how the gender gap affects the industry's development, innovation, and national security posture, emphasizing the importance of utilizing the nation's talent pool to its fullest capacity to combat the evolving cyber threats. Insights and perspectives on the gender disparity in the cybersecurity sector were gathered through a questionnaire circulated amongst cybersecurity professionals and academics. These participants were chosen due to their knowledge and direct engagement in the subject, which offers valuable insights into the obstacles that women encounter in their workplace. The study highlights a number of problems, including gender stereotypes, a lack of access to education and training, and a non-inclusive workplace culture, that contribute to the gender gap. The paper makes the case that in order to solve these problems and advance diversity and inclusivity in the workplace, all stakeholders must work together to close the gender gap. In order to overcome the gender gap in cybersecurity in South Africa, the study suggests special programs aimed at expanding access to education and training opportunities, promoting gender diversity and inclusivity, and creating a more hospitable and equal work environment. This will boost the country's economic growth and strengthen security against cyberattacks. © 2024 IEEE.

关键词： Cybersecurity

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A novel shilling attack on black-box recommendation systems for multiple targets

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Neural Computing and Applications 2025年第5期37卷 3399-3417页

作者： Liu, Shuangyu Yu, Siyang Li, Huan Yang, Zhibang Duan, Mingxing Liao, Xiangke School of Information Science and Engineering Hunan University Hunan Changsha410082 China College of Information Technology and Management Hunan University of Finance and Economics Hunan Changsha410000 China Hunan Province Key Laboratory of Industrial Internet Technology and Security Changsha University Hunan Changsha410022 China Shenzhen Institute Hunan University Guangdong Shenzhen518063 China School of Computer Science National University of Defense Technology Hunan Changsha410073 China

In the digital era, e-platforms ubiquitously deploy recommendation systems, utilizing machine learning paradigms to tailor content according to user preferences and needs. Yet, the integrity of these systems is often compromised by shilling attacks, where malicious entities inject fake user profiles to skew product exposure and sales. This vulnerability has catalyzed research efforts to bolster the robustness and security of recommendation systems, primarily through controlled attacks and reinforcement training. This paper introduces an innovative approach to combat the inefficiency of traditional single-target shilling attacks. We propose a novel multi-target shilling strategy for black-box recommendation systems, capable of generating convincing and aggressive fake user profiles. Given the complexity of the recommendation system algorithms, we employ surrogate models to replicate and understand the target system’s behavior. The surrogate model is then subjected to attacks using our proposed methodology. An attack is deemed successful when it achieves an 80% or higher success rate against the surrogate, preluding an attack on the actual recommendation system. Our model, RWA-GAN, stands out by integrating two components: a generative adversarial network (GAN) for crafting realistic fake user profiles, and a mechanism to enhance attack success rates. This dual approach not only expedites the attack process but also ensures the generation of more authentic and well-distributed fake profiles, making the model easier to migrate. Additionally, the paper delineates several defense strategies against such attacks, contributing significantly to the discourse on enhancing the robustness of recommender systems. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Generative adversarial networks

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Vehicles Detection, Tracking, And Classification Using 35GHz FMCW Radar

Vehicles Detection, Tracking, And Classification Using 35GHz...

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2023 IEEE International Radar Conference, RADAR 2023

作者： Zhu, Nannan Qu, Zhenhua Fang, Xueqing Niu, Guo Xu, Shiyou School of Systems Science and Engineering Sun Yat-sen University Guangzhou China Research Institute China Telecom Corporation Limited Guangzhou China Department of Computer Science Jinan University Guangzhou China School of Electronics and Information Engineering Foshan University Foshan China School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen China

This paper introduces a new vehicle detection, tracking, and classification system that utilizes a 35 GHz FMCW radar. The system incorporates a monolithic microwave integrated circuit to replace traditional transmitting and receiving components, which reduces the size, power consumption, and overall cost of the radar. Additionally, a phase-locked loop frequency synthesis scheme is used to further optimize the system. The result is a cost-effective radar system that costs less than ten dollars. This paper not only explores a wide range of vehicle detection, tracking, and classification algorithms, but also optimizes them to run seamlessly on micro-controllers with low power consumption, low computational complexity, and low cost. This radar is capable of measuring the speed, distance, and traffic volume of multiple vehicles simultaneously - making it a versatile and valuable addition to any traffic monitoring system. © 2023 IEEE.

关键词： Cost effectiveness

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MUSC: ZERO-SHOT INDUSTRIAL ANOMALY CLASSIFICATION AND SEGMENTATION WITH MUTUAL SCORING OF THE UNLABELED IMAGES 12

MUSC: ZERO-SHOT INDUSTRIAL ANOMALY CLASSIFICATION AND SEGMEN...

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12th International Conference on Learning Representations, ICLR 2024

作者： Li, Xurui Huang, Ziming Xue, Feng Zhou, Yu School of Electronic Information and Communications Huazhong University of Science and Technology China Artificial Intelligence Research Institute Wuhan JingCe Electronic Group Co. Ltd. China Department of Information Engineering and Computer Science University of Trento Italy

This paper studies zero-shot anomaly classification (AC) and segmentation (AS) in industrial vision. We reveal that the abundant normal and abnormal cues implicit in unlabeled test images can be exploited for anomaly determination, which is ignored by prior methods. Our key observation is that for the industrial product images, the normal image patches could find a relatively large number of similar patches in other unlabeled images, while the abnormal ones only have a few similar patches. We leverage such a discriminative characteristic to design a novel zero-shot AC/AS method by Mutual Scoring (MuSc) of the unlabeled images, which does not need any training or prompts. Specifically, we perform Local Neighborhood Aggregation with Multiple Degrees (LNAMD) to obtain the patch features that are capable of representing anomalies in varying sizes. Then we propose the Mutual Scoring Mechanism (MSM) to leverage the unlabeled test images to assign the anomaly score to each other. Furthermore, we present an optimization approach named Re-scoring with Constrained Image-level Neighborhood (RsCIN) for image-level anomaly classification to suppress the false positives caused by noises in normal images. The superior performance on the challenging MVTec AD and VisA datasets demonstrates the effectiveness of our approach. Compared with the state-of-the-art zero-shot approaches, MuSc achieves a 21.1% PRO absolute gain (from 72.7% to 93.8%) on MVTec AD, a 19.4% pixel-AP gain and a 14.7% pixel-AUROC gain on VisA. In addition, our zero-shot approach outperforms most of the few-shot approaches and is comparable to some one-class methods. Code is available at https://***/xrli-U/MuSc. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Pixels

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