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International Conference on computer and Information Technology (ICCIT)

作者： Abir Ahmed Prapti Roy Durjoy Chandra Paul M. Shahidur Rahman Department of Software Engineering Shahjalal University of Science & Technology Sylhet Bangladesh Department of Computer Science & Engineering Shahjalal University of Science & Technology Sylhet Bangladesh Department of Computer Science & Engineering Sylhet Engineering College Sylhet Bangladesh

ISBN: (数字)9798331519094

ISBN: (纸本)9798331519100

Voice cloning has numerous useful applications, including assisting individuals who have lost their ability to speak, movie dubbing, and translating voices into different languages. However, voice cloning in Bangla is still in its early stages. In this work, we explore two standard approaches for zero-shot Bengali voice cloning, which can generate cloned audio from short samples, even for Bengali speakers not included in the training data. Despite using limited datasets, our models achieve cloning quality comparable to state-of-the-art models trained on much larger datasets, maximizing potential benefits for the Bengali community. We use a neural network technique called the speaker encoder (SE) voice cloning method, which achieved a Mean Opinion Score (MOS) of 3.8 and 83% cosine similarity between the original and cloned voice. This model was trained on a custom multi-speaker dataset. Additionally, we employ a speaker converter (SC) approach trained on a single-speaker dataset, which achieved a MOS of 4.0 and 82.64% cosine similarity. By combining the two models in an ensemble, we further improved performance, reaching an MOS of 4.4 and a cosine similarity of 85.21%. Our proposed approach can be an important step toward developing voice cloning technology for the Bengali language, potentially positively impacting the community. The demos can be accessed here.

关键词： Training Translation Neural networks Cloning Training data Motion pictures Ensemble learning Speech synthesis Information technology Standards

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QUIC meets ICN: A Versatile Wireless Transport Strategy in Multi-access Edge Environments

QUIC meets ICN: A Versatile Wireless Transport Strategy in M...

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International Workshop on Quality of Service

作者： Yaodong Huang Changkang Mo Tianhang Liu Biying Kong Lei Zhang Yukun Yuan Laizhong Cui College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science and Engineering University of Tennessee at Chattanooga Chattanooga TN USA

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

Information-centric Networking in edge computing environments exhibits the potential to significantly enhance the efficiency, reliability, and security of data transmission, making it a promising technology for future network deployments. However, the differences from traditional networks require applications to actively redevelop and redeploy onto edge devices, incurring additional costs for the proliferation of ICN applications. In this paper, we propose a system to adapt QUIC protocol over ICN networks in multi-access edge networks. The system aims to expand the application repertoire for ICN by providing a smooth transition of applications using QUIC to run on ICN networks. We design an ICN-QUIC conversion layer to manage the transmission of data from QUIC-based applications. We implement and evaluate the designed system. The experiment results show that, compared to existing networks, our system can enhance the transmission efficiency, i.e., up to 20 times better goodput in multicast situations, and achieves comparable results in unicast scenarios. We test the scalability of our system in real edge and wireless environments. We also deploy the real applications over the proposed system to demonstrate its compatibility in ICN and MEC environments.

关键词： Wireless communication Protocols Unicast Scalability Quality of service Data communication Communication system security

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Visualizing Student Attention in Smart Classrooms: A Preliminary Study 15

Visualizing Student Attention in Smart Classrooms: A Prelimi...

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15th International Conference on Innovations in Information Technology, IIT 2023

作者： Parambil, Medha Mohan Ambali Alhammadi, Amna Mohammed Abdulla Alnajjar, Fady Trabelsi, Zouheir Ali, Luqman Swavaf, Muhammed United Arab Emirate University Department of Information Systems and Security College of IT Al Ain United Arab Emirates United Arab Emirate University Department of Computer Science and Software Engineering College of IT Al Ain United Arab Emirates

ISBN: (纸本)9798350382396

In recent years, there has been a swift progression in employing novel methods in classrooms to enhance students' academic achievements, especially in line with the growing digitization of education. Such methods often encompass systems like facial recognition to monitor various aspects of students, including attendance, emotional states, and attention. These tools are capable of evaluating students' presence and engagement in class, offering quantifiable metrics regarding their concentration and emotions. However, a prominent challenge has been the translation of this data into an accessible form that enables educators to assess and enhance their teaching techniques swiftly. Our suggested solution tackles this issue by offering a real-Time visual depiction of students' classroom status through different visualization techniques. These visual aids allow teachers to promptly recognize trends in student focus, thus aiding in the strategic alteration of teaching styles. Furthermore, these visual representations can be tailored to display various metrics and applied to tasks beyond monitoring attention, like overseeing attendance or assessing student progress. By integrating these advanced visualizations into the educational process, both teaching efficacy and the learning experience for students and teachers alike can be substantially elevated. © 2023 IEEE.

关键词： Students

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SemLog: A Semantics-based Approach for Anomaly Detection in Big Data System Logs 29

SemLog: A Semantics-based Approach for Anomaly Detection in ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Tan, Xiaoman Han, Ningning Lu, Siyang Chen, Wu Wang, Dongdong College of Computer and Information Science College of Software Southwest University Chongqing China School of Computer and Information Technology Beijing Jiaotong University Beijing China Environmental and Construction Engineering University of Central Florida Department of Civil OrlandoFL United States

ISBN: (纸本)9798350330717

Syslog-based anomaly detection is crucial for protecting the systems from malicious attacks or malfunctions. System logs are semi-structured text messages printed by logging statements to record the system's run-time status, involving rich semantic information. However, the existing BERT-based log anomaly detection method is based on the log key sequence, does not consider the semantics of the log data, and discards the variable part, resulting in a high rate of missed detection. In this paper, we propose SemLog, a self-supervised framework for log anomaly detection based on BERT. By incorporating log semantics and variables and employing multi-feature fusion, we mitigate the independent assumption issue in the Masked Language Modeling model. The experimental results on three benchmarks show that SemLog achieves high performance compared with the state-of-the-art approaches for anomaly detection. © 2023 IEEE.

关键词： BERT log anomaly detection log semantics log variables multi-feature fusion

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Investigation of Environmental Monitoring Based on Three IoT Wireless Sensors 2

Investigation of Environmental Monitoring Based on Three IoT...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Yamini, B. Arthy, M. Priya, S Geetha Kathiresan, S. Jeneetha Jebanazer, J. Uthayakumar, G.S. Srm Institute of Science and Technology Department of Networking and Communication Engineering Tamil Nadu Kattankulathur Chennai India Srm Institute of Science and Technology School of Computing Department of Data Science& Business Systems Tamil Nadu Kattankulathur Chennai India R. M. D Engineering College Department of Computer Science Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350325799

Environmental monitoring has become more crucial as a result of the recent climatic changes, making it a hot issue and a very active study field. This field uses wireless sensor networks and remote sensing to collect data about the environment. Recent developments, including the Internet of Things (IoT) concept, the cloud computing paradigm, and cyber-physical systems, assist the administration and transmission of enormous volumes of data about changes in environmental parameters. The current work presents three different IoT-based wireless sensors for monitoring the environment and the surrounding area: one uses Wi-Fi User Datagram Protocol (UDP)-based communication, one uses Wi-Fi and Hypertext Transfer Protocol (HTTP), and a third one uses Bluetooth Smart. The ability to capture data at distant places and see it from any device with an Internet connection is a feature shared by all of the systems that have been discussed. This makes it possible to monitor vast geographic regions. These systems' specific development details are discussed, along with their main distinctions and affinities. The three created systems were evaluated for their suitability for IoT-based solutions, taking into consideration their energy independence, usability, solution complexity, and Internet connectivity capability. © 2023 IEEE.

关键词： Bluetooth

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Unsupervised Rail Surface Defect Detection Method 9

Unsupervised Rail Surface Defect Detection Method

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Zhao, ChenYu Han, Yi Ouyang, Ningkang Zhao, YiFan School of Artificial Intelligence Zhengzhou Software Vocational and Technical College Zhengzhou China Anyang Institute of Technology Engineering Department of Computer Science and Information Anyang China North China University of Water Resources and Electric Power School of Information Engineering Zhengzhou China

ISBN: (纸本)9798350363043

Railroad travel has become an essential part of modern life, therefore, rail surface defect detection has become a problem that cannot be ignored. However, most of the currently existing methods often require a large number of defect samples for learning, which are labor-intensive and very difficult to obtain. In order to alleviate the data dependence of the detection model, this paper proposes an unsupervised method. Specifically, we use Transformer's attention mechanism to construct the encoder-decoder structure and train it only on normal samples with the purpose of reconstruction, so that those regions that cannot be reconstructed will be regarded as defects at the time of detection. To prevent the model from generalizing its generalization ability to those unknown defective samples, i.e., to avoid overgeneralization, we introduce a noise perturbation in the training so that the model focuses on the reconstruction of normal features. Experiments show that the proposed unsupervised approach outperforms a host of supervised models with low data requirements and can effectively address the current challenges of track defect detection. © 2024 IEEE.

关键词： Unsupervised learning

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AI in Education: Improving Quality for Both Centralized and Decentralized Frameworks 14

AI in Education: Improving Quality for Both Centralized and ...

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14th IEEE Global engineering Education Conference, EDUCON 2023

作者： Madathil, Nisha Thorakkattu Alrabaee, Saed Al-Kfairy, Mousa Damseh, Rafat Belkacem, Abdelkader N College of IT United Arab Emirates University Department of Information Systems and Security United Arab Emirates College of Technological Innovation Zayed University Dubai United Arab Emirates College of IT Department of Computer Science and Software Engineering United Arab Emirates University United Arab Emirates College of IT Department of Computer Engineering and Networks United Arab Emirates University United Arab Emirates

ISBN: (纸本)9798350399431

Education is essential for achieving many Sustainable Development Goals (SDGs). Therefore, the education system focuses on empowering more educated people and improving the quality of the education system. One of the latest technologies to enhance the quality of education is Artificial Intelligence (AI)-based Machine Learning (ML). As a result, ML has a significant influence on the education system. ML is currently widely applied in the education system for various tasks, such as creating models by monitoring student performance and activities that accurately predict student outcomes, their engagement in learning activities, decision-making, problem-solving capabilities, etc. In this research, we provide a survey of machine learning frameworks for both distributed (clusters of schools and universities) and centralized (university or school) educational institutions to predict the quality of students' learning outcomes and find solutions to improve the quality of their education system. Additionally, this work explores the application of ML in teaching and learning for further improvements in the learning environment for centralized and distributed education systems. © 2023 IEEE.

关键词： Sustainable development

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Securing Agricultural Communications: Blockchain Integration in UAV Networks for Smart Farming

Securing Agricultural Communications: Blockchain Integration...

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IEEE International Conference on Communications Workshops, ICC

作者： Ushasri Peddibhotla Randhir Kumar C.C. Sobin Prabhat Kumar Danish Javeed Najmul Islam Department of Computer Science and Engineering SRM University AP Department of Software Engineering LUT School of Engineering Science LUT University Lappeenranta Finland Software College Northeastern University Shenyang China

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

The integration of Unmanned Aerial Vehicles (UAVs) in smart agriculture has significantly enhanced precision farming practices, enabling real-time monitoring and data collection for improved crop management. However, the reliance on wireless communication in UAV networks poses security challenges that can compromise the integrity and confidentiality of sensitive agricultural data. This paper proposes a novel approach to address these concerns through the incorporation of blockchain technology for secure communication in UAV networks deployed for smart agriculture. The proposed system leverages the decentralized and tamper-resistant nature of blockchain to establish a trust-based communication framework. Each UAV node in the network is equipped with a blockchain-enabled communication protocol, ensuring that data exchanges are securely recorded in an immutable ledger. This not only enhances data integrity but also mitigates the risk of unauthorized access and manipulation. To facilitate secure communication, smart contracts are employed to automate and enforce predefined rules governing data transactions within the UAV network. This ensures that only authenticated and authorized entities can access and modify agricultural data, fostering a transparent and accountable ecosystem. Additionally, cryptographic techniques such as public-key encryption enhance the confidentiality of transmitted data, safeguarding sensitive information from eavesdropping and unauthorized interception. The proposed blockchain-enabled secure communication system is further enhanced by incorporating consensus mechanisms that validate and confirm the integrity of data across the network. By doing so, the trustworthiness of the entire UAV network is strengthened, reducing the likelihood of malicious activities and enhancing overall system resilience.

关键词： Smart agriculture Wireless communication Fault tolerance Conferences Smart contracts Public key Autonomous aerial vehicles

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Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture

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computers, Materials & Continua 2023年第11期77卷 2545-2564页

作者： Medhat A.Tawfeek Nacim Yanes Leila Jamel Ghadah Aldehim Mahmood A.Mahmood Department of Computer Science College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia Department of Computer Science Faculty of Computers and InformationMenoufia UniversityP.O.Box 6121890MenoufiaEgypt Department of Software Engineering College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia RIADI Laboratory La Manouba UniversityP.O.Box2010ManoubaTunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems and Technology Faculty of Graduate Studies and ResearchCairo UniversityP.O.Box 3753450GizaEgypt

The trend towards smart greenhouses stems from various factors,including a lack of agricultural land area owing to population concentration and housing construction on agricultural land,as well as water *** study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research *** proposed model uses a one-dimensional convolutional neural network(CNN)deep learning model to control the growth of strategic crops,including cucumber,pepper,tomato,and *** proposed model uses the Internet of Things(IoT)to collect data on agricultural operations and then uses this data to control and monitor these operations in real *** helps to ensure that crops are getting the right amount of fertilizer,water,light,and temperature,which can lead to improved yields and a reduced risk of crop *** dataset is based on data collected from expert farmers,the photovoltaic construction process,agricultural engineers,and research *** experimental results showed that the precision,recall,F1-measures,and accuracy of the one-dimensional CNN for the tested dataset were approximately 97.3%,98.2%,97.25%,and 97.56%,*** new smart greenhouse automation system was also evaluated on four crops with a high turnover *** system has been found to be highly effective in terms of crop productivity,temperature management and water conservation.

关键词： Greenhouse wireless sensor network deep learning Internet of Things strategic crops monitoring smart irrigation

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Achieving Efficient and Accurate Segmentation for Autonomous Driving 9

Achieving Efficient and Accurate Segmentation for Autonomous...

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9th International Conference on Intelligent Informatics and BioMedical sciences, ICIIBMS 2024

作者： Ouyang, Ningkang Dong, Jie Han, Yi Zhao, ChenYu North China University of Water Resources and Electric Power School of Information Engineering Zhengzhou China Anyang Institute of Technology Engineering Department of Computer Science and Information Anyang China Zhengzhou Software Vocational and Technical College School of Artificial Intelligence Zhengzhou China

ISBN: (纸本)9798350363043

Autonomous driving technology, as a mainstream trend in today's technological development, holds significant commercial value. Semantic segmentation, a core technology in this field, faces challenges with current mainstream models due to their complex structures, large numbers of parameters, and the need for high-performance hardware. These issues hinder their application in mobile devices. Therefore, this paper proposes a road scene semantic segmentation algorithm based on dynamic asymmetric convolution and capsule networks, named Asymmetric Capsule Convolutional Networks (ACCN). Specifically, the algorithm uses dynamic asymmetric convolution to construct a baseline network that combines dynamic convolution and capsule dynamic routing algorithms to enhance the model's representational capabilities. Additionally, a dispersed attention module is built to strengthen the dependency between features to achieve more adaptable network weights. Experiments demonstrate that ACCN achieves a good balance between segmentation accuracy and detection speed. On the CamVid and Cityscapes test datasets, the proposed method in this paper achieved an average Intersection over Union (mIoU) of 80.8% at 55.2 fps, showcasing better trade-offs in segmentation accuracy and efficiency. The experimental results indicate the effectiveness of our method. © 2024 IEEE.

关键词： Semantic Segmentation

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