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arXiv 2024年

作者： Zhang, Wenxiao Kong, Xiangrui Braunl, Thomas Hong, Jin B. Dept. of Computer Science and Software Engineering The University of Western Australia Perth Australia Dept. of Electrical Electronic and Computer Engineering The University of Western Australia Perth Australia

Embodied AI systems, including AI-powered robots that autonomously interact with the physical world, stand to be significantly advanced by Large Language Models (LLMs), which enable robots to better understand complex language commands and perform advanced tasks with enhanced comprehension and adaptability, highlighting their potential to improve embodied AI capabilities. However, this advancement also introduces safety challenges, particularly in robotic navigation tasks. Improper safety management can lead to failures in complex environments and make the system vulnerable to malicious command injections, resulting in unsafe behaviours such as detours or collisions. To address these issues, we propose SafeEmbodAI, a safety framework for integrating mobile robots into embodied AI systems. SafeEmbodAI incorporates secure prompting, state management, and safety validation mechanisms to secure and assist LLMs in reasoning through multi-modal data and validating responses. We designed a metric to evaluate mission-oriented exploration, and evaluations in simulated environments demonstrate that our framework effectively mitigates threats from malicious commands and improves performance in various environment settings, ensuring the safety of embodied AI systems. Notably, In complex environments with mixed obstacles, our method demonstrates a significant performance increase of 267% compared to the baseline in attack scenarios, highlighting its robustness in challenging conditions. © 2024, CC BY.

关键词： Mobile robots

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Explainable Email Spam Detection: A Transformer-based Language Modeling Approach

Explainable Email Spam Detection: A Transformer-based Langua...

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International Conference on computer and Information Technology (ICCIT)

作者： Mohammad Amaz Uddin Md. Mahiuddin Al Akram Chowdhury Dept. of Computer Science and Engineering International Islamic University Chittagong Chattogram Bangladesh Dept. of Software Engineering Metropolitan University Sylhet Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Email spam, also known as junk email, is any unwanted or irrelevant message that is delivered to a large number of recipients without the recipient's consent. Email communication is undoubtedly efficient and convenient, but there are serious risks associated with its rise in unwanted spam emails. These risks include resource waste and potential cybersecurity breaches. It also serves as a backdoor for malware infections, phishing attempts, and other cyber threats. Transformer-based models have a positive effect on the cybersecurity sector in order to address those cybersecurity concerns. In this research, we use a Bidirectional Encoder Representations from Transformers (BERT) variant, the DistilBERT model, to detect spam emails. For this purpose, we utilize the benchmark Enron spam email dataset, applying several preprocessing techniques to address the noisy data. Our model demonstrates high accuracy, achieving 99.18%. Additionally, to interpret the model's decision-making process, we used Transformers Interpret. This explainability tool provides insights into how our proposed model works and makes its decisions throughout the experiment.

关键词： Unsolicited e-mail Large language models Phishing Decision making Benchmark testing Transformers Robustness Malware Noise measurement Information technology

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A Machine Learning-Enhanced IoT Wearable Device for Monitoring and Providing Feedback on Stuttering 3

A Machine Learning-Enhanced IoT Wearable Device for Monitori...

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3rd International Conference on Computational Modelling, Simulation and Optimization, ICCMSO 2024

作者： Suttarwala, Hussain Kumar, Ritesh Deorankar, Madhavi L. Daulat Sarpate, Dikshendra Shingade, Pallavi Kumar Sharma, Surender More, Vrushali Patkar, Uday Chandrakant Sharma, Deepak K. American Express Senior Software Engineer PhoenixAZ85054 United States Dr. Akhilesh Das Gupta Institute of Professional Studies Dept. of AI and Data Science Delhi New Delhi110053 India Marathwada Mitramandal College of Engineering Dept. of Electronics and Telecommunications Maharashtra Pune411052 India Zeal College of Engineering and Research Dept. of AI and Data Science MaharashtraNarhe Pune411041 India D Y Patil College of Engineering Dept. of AI and Data Science Akurdi Pune411041 India Poornima University Dept. of Electrical Engineering Jaipur India Genba Sopanrao Moze College of Engineering Dept. of AI and Data Science Balewadi Maharashtra Pune411045 India Bharati Vidyapeeth's College of Engineering Department of Computer Engineering Lavale Pune412115 India Savitribai Phule Pune University Dept. of AI and Data Science Maharashtra Pune India

ISBN: (纸本)9798350361391

This study introduces an innovative IoT-enabled wearable device tailored for monitoring and providing feedback on stuttering, leveraging advanced machine learning algorithms to enhance precision and real-time responsiveness. The device integrates an array of hardware and software elements, including sensors, microcontrollers, and communication protocols, to accurately capture speech patterns and movements. Utilizing machine learning techniques such as Support Vector Machines (SVM) and Convolutional Neural Networks (CNN), the device performs real-time analysis to identify stuttering instances. Detailed discussions cover the criteria for selecting machine learning models, the training and testing procedures, and the metrics used for performance evaluation. The prototype underwent extensive testing and validation, showcasing high accuracy, sensitivity, and specificity in differentiating stuttered from fluent speech. User feedback emphasized the ergonomic design, user-friendly interface, and real-time feedback features, highlighting its potential to significantly enhance stuttering therapy outcomes. Comparative analysis with existing solutions revealed the device's superior performance, indicating strong potential for clinical application. The study discusses the implications for stuttering therapy, acknowledges the limitations, outlines future research directions, and underscores the device's contributions to personalized and effective stuttering interventions. This research advances the field of assistive technologies in speech therapy and demonstrates the promise of IoT-based wearable devices integrated with machine learning for improving the quality of life for individuals with stuttering disorders. © 2024 IEEE.

关键词： Convolutional neural networks

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Course Recommendation System for Company Job Placement Using Collaborative Filtering and Hybrid Model 13

Course Recommendation System for Company Job Placement Using...

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13th International Conference on Data science, Technology and Applications, DATA 2024

作者： Park, Jaeheon Lee, Suan Lee, Woncheol Kim, Jinho Kangwon Joint Program of Software Convergence Course Kangwon National University Chuncheon Korea Republic of School of Computer Science Semyung University Jecheon Korea Republic of SeedsSoft Co. Ltd. Chuncheon Korea Republic of Dept. of Computer Science and Engineering Kangwon National University Chuncheon Korea Republic of

ISBN: (纸本)9789897587078

This study introduces a novel recommendation system aimed at enhancing university career counseling by adapting it to more accurately align with students' interests and career trajectories. Recognizing the challenges students face in selecting courses that complement their career goals, our research explores the efficacy of employing both collaborative filtering and a hybrid model approach in the development of this system. Uniquely, this system utilizes a company-course recommendation method, diverging from the traditional student-course paradigm, to generalize company-course relationships, thereby enhancing the system's recommendation precision. Through meticulous feature engineering, we improved the performance of the NeuMF model. Our experiments demonstrate that the proposed method outperforms other models by 10% to 79% based on the mAP metric, suggesting that the proposed model can effectively recommend courses for employment. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Collaborative learning

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Max-Min Fairness Power Allocation in Uplink RIS-Aided Cell-Free Massive MIMO

Max-Min Fairness Power Allocation in Uplink RIS-Aided Cell-F...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Tran, Thong-Nhat Interdonato, Giovanni Da Costa, Daniel Benevides An, Beongku Hongik University Dept. of Electronics and Computer Engineering Graduate School Korea Republic of Italy Digital Science Research Center Technology Innovation Institute Abu Dhabi Masdar City United Arab Emirates Hongik University Dept. of Software and Communications Engineering Korea Republic of

ISBN: (纸本)9798350370218

This paper evaluates the uplink spectral efficiency (SE) achieved by a reconfigurable intelligent surface (RIS)-aided cell-free massive MIMO system with max-min fairness (MMF) power allocation. We provide an exact closed-form expression for an achievable SE expression which assumes maximum ratio receive combining at the access points (APs) and centralized large-scale fading decoding at the central processing unit. The derived SE expression also accounts for imperfect channel estimation and spatially correlated Ricean fading channels for both direct (APs-users) and cascaded (APs-RISs-users) links. Alongside this, we provide a generalized SE expression and evaluate the performance achieved by local partial minimum mean-squared error combining. Finally, we optimally solve the RIS phase-shift selection problem in order to maximize the aggregate channel gains, and derive a closed-form solution to the MMF power allocation problem. Simulation results validate the effectiveness of the proposed framework in enhancing the unlink SE with resnect to various benchmarks. © 2023 IEEE.

关键词： Cell-free massive MIMO max-min fairness power control reconfigurable intelligent surface spectral efficiency

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Blockchain-based Reputation System For IoT Data Ecosystem: A Utility Maximization Approach 19

Blockchain-based Reputation System For IoT Data Ecosystem: A...

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19th IEEE International Conference on Smart Communities: Improving Quality of Life Using ICT, IoT and AI, HONET 2022

作者： Khezr, Seyednima Yassine, Abdulsalam Benlamri, Rachid Lakehead University Dept. of Electrical and Computer Engineering Thunder BayON Canada Lakehead University Dept. of Software Engineering Thunder BayON Canada University of Doha for Science and Technology Dept. of Information Technology Doha Qatar

ISBN: (纸本)9781665461979

Recently, blockchain technology has been used to build private and secure internet of things (IoT) data markets and trading systems. Reputation of the trading parties is an important attribute that affect their profitability and trading prosperity. However, current reputation systems are prone to malicious manipulation. Such an issue can be avoided by registering all reputation scores in the blockchain which guarantees trans-parency, encryption, privacy, and security. To this end, this paper proposes a blockchain-based reputation system where market players can maximize their utility while engaged in the IoT data ecosystem. We also propose an adaptive learning mechanism that allows the data providers and consumers to enhance their reputation and review credibility scores. We provide extensive experiments to ensure our system satisfies the feasibility, usability, and availability properties. These properties are essential for functional deployment in real IoT data markets. © 2022 IEEE.

关键词： Blockchain

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Multi-granularity Coal Mine Risk Conceptual Prediction Based on Time-varying Cloud Model

Multi-granularity Coal Mine Risk Conceptual Prediction Based...

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2023 International Conference on Cyber Security, Artificial Intelligence, and Digital Economy, CSAIDE 2023

作者： Dai, Jin Hu, Sunling Zhang, Lei Dept. of Software Engineering Chongqing University of Posts and Telecommunications Chongqing China Dept. of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (纸本)9781510666597

The big data of coal mine was characterized by large scale, many influencing factors and weak correlation. The existing big data mining based on quantitative data analysis usually adopts fixed framework processing, which is easy to fall into the dilemma of overfitting and cannot acquire elastic knowledge across layers. From the perspective of coal mine monitoring and management, a multi-granularity time-varying cloud model is proposed based on a multi-granularity semantic transformation model and a grey prediction model to achieve the conceptual prediction of coal mine gas concentration risk. The new model first converts the gas concentration data into semantic concepts using a semantic transformation model, then processes the sequence of semantic concept sets for randomness and oscillation, and builds a grey prediction model for them, which is used to project future semantic concepts. The new model is compared with the existing model, and the experimental results show that the new method can not only predict coal mine risk concepts at different granularities, but also has high prediction accuracy. © 2023 SPIE.

关键词： Coal mines

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Impacts of Earthing Variations on Overvoltage in Meshed Multi-terminal DC Grid

Impacts of Earthing Variations on Overvoltage in Meshed Mult...

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Annual Conference of Industrial Electronics Society

作者： Amir Ar-Rahman Bader Abhisek Ukil Dept. of Engineering Science The University of Auckland Auckland New Zealand Dept. of Electrical Computer and Software Engineering The University of Auckland Auckland New Zealand

ISBN: (数字)9781665464543

ISBN: (纸本)9781665464550

This paper investigates the impact of different earthing methods on overvoltage within modular multilevel Voltage Source Converter (MMC VSC) based Direct Current (DC) transmission and distribution grids, particularly in meshed multi-terminal (MTDC) configurations. A bipolar MTDC grid is modelled and simulated in PSCAD/EMTDC to investigate the influence of earthing on midpoint overvoltages. The transient response of the grid under DC fault scenarios is analysed through various case-studies, including solid earthing placement, variations in earthing impedance, and the application of surge arresters. The findings show the critical role of earthing in mitigating overvoltage, with low-resistive earthing options proving to be the most effective trade-off.

关键词： Resistance Industrial electronics Arresters Transient response Grounding Voltage Solids Voltage control Surges Voltage-source converters

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Evaluating Datagram Congestion Control Protocol (DCCP) for Real-Time Applications: A Comparative Study with TCP in Multi-Node Networks 10

Evaluating Datagram Congestion Control Protocol (DCCP) for R...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Hassan, Aqib Memon, Vengas Shaikh, Farhan Bashir Alahmari, Saad Iqbal, Mansoor Azam, Farooq School of Computing and Mathematical Sciences University of Greenwich London United Kingdom Universiti Tunku Faculty of Information and Communication Tech. Abdul Rahman Perak Malaysia Northern Border University Department of Computer Science Arar Saudi Arabia University of Wah Department of Computer Science Wah Cantt Pakistan Nust Dept. of Computer Software Engineering Islamabad Pakistan

ISBN: (纸本)9798331532895

The rise in demand for real-time applications, such as live streaming, online gaming, and Internet telephony, has highlighted the necessity for transport protocols that offer low latency and network stability. Traditional protocols, namely Transmission Control Protocol (TCP) and User Datagram Protocol (UDP), have inherent limitations. TCP ensures reliable data delivery but suffers from latency, making it unsuitable for real-time needs, while UDP provides fast data transmission but lacks congestion control, which can lead to network instability. This gap underscores the need for a protocol that combines high-speed data transfer with effective congestion management. To address this issue, the Internet engineering Task Force (IETF) introduced the Datagram Congestion Control Protocol (DCCP), designed to optimize data delivery speed while managing congestion. This study evaluates DCCP's performance against TCP in network scenarios of 8 and 16 nodes using the NS2 simulation environment. By comparing throughput and delay across various bandwidth and latency settings, the study finds that DCCP offers up to 35% higher throughput in 8-node setups and reduces packet loss by 20% in high-bandwidth scenarios. These results suggest that DCCP is well-suited for real-time applications, achieving a crucial balance between fast data transmission and network stability. © 2024 IEEE.

关键词： Congestion control (communication)

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Interpretable Pulmonary Disease Diagnosis with Graph Neural Network and Counterfactual Explanations 2

Interpretable Pulmonary Disease Diagnosis with Graph Neural ...

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2nd International Conference on Sensing, Measurement, Communication and Internet of Things Technologies, SMC-IoT 2023

作者： Li, Jiahong Chen, Yiyuan Wang, Yichi Ye, Yiqiang Sun, Min Ren, Hao Cheng, Weibin Zhang, Haodi College of Computer and Software Engineering Guangzhou China Cornell University Dept. Computer Science New York United States Shenzhen University College of Computer and Software Engineering Shenzhen China China Mobile Information Technology Co. Limited Shenzhen China Guangdong Second Provincial General Hospital Institute for Healthcare Artificial Intelligence Guangzhou China

ISBN: (纸本)9798350373257

With recent fast development of artificial intelligence and related techniques, computer-aided diagnosis is increasingly emerging. During the process of automated diagnosis, the importance of explainable decision making has grown significantly. Traditional black-box models have often hindered the practical implementation of automated diagnosis algorithms. To address these challenges, we propose a novel framework that seamlessly integrates prototype learning with Graph Neural Networks (GNN) while also providing post-hoc explanations. On one hand, our model achieves intrinsic interpretability by reasoning based on the similarity calculations with prototypes for each disease. On the other hand, it employs counterfactual reasoning on graphs to pinpoint the most significant features for post-hoc explanations, supporting the diagnosis process. Building on these strengths, we integrate vision-language models to effectively harness multimodal patient information, thereby capturing a more detailed understanding of their medical condition. Our experiments on real Chinese EMRs of Pulmonary diseases demonstrate that our method not only delivers precise diagnoses but also accurately identifies medical findings substantiating the diagnoses. © 2023 IEEE.

关键词： Pulmonary diseases

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