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Triplets-Based Steganographic Scheme to Improve the Image Quality in Spatial Domain 3

Triplets-Based Steganographic Scheme to Improve the Image Qu...

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3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Meftah Mafazy, Muhammad Terri Agustinus, Jeems Ikhwanul Haq, Thoha Jean De La Croix, Ntivuguruzwa Ahmad, Tohari Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia African Centre of Excellence in Internet of Things University of Rwanda Kigali Rwanda

ISBN: (纸本)9798350317022

Advances in information and communications technology have changed the way various sectors, from finance to the military, operate. However, this evolution has also increased cybersecurity threats, posing potential risks to user privacy and security. In response to these challenges, data masking methods have emerged as noteworthy solutions. These methods involve hiding information within other data, such as audio and video files, to add an extra layer of security. Retaining the quality of the stego image proves to be a primary challenge in steganography. In addressing the limitations of previous digital image steganography techniques, this research introduces a new algorithm centered on triplets. Our experiments using images from the SIPI dataset, our results showcase a notable improvement in performance, with signal-to-noise ratio (PSNR) results that vary from 43.080 dB to 65.564 dB. These experimental outcomes underscore a substantial enhancement compared to existing methods, affirming the efficacy of the proposed algorithm in bolstering cybersecurity defenses. © 2023 IEEE.

关键词： data hiding National Security network infrastructure spatial domain steganography

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Diffusion Models-based data Augmentation for the Cell Cycle Phase Classification 3

Diffusion Models-based Data Augmentation for the Cell Cycle ...

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3rd international conference on signal processing and Machine Learning, CONF-SPML 2023

作者： Chen, Zirui Department of Computer Science City University of Hong Kong Hong Kong999077 Hong Kong

For biological research, sample size imbalance is common due to the nature of the research subjects. For example, in the study of the cell cycle phase, the sample size of dividing cells is also much smaller due to the extremely short duration of the mitotic phase compared to the interphase. data augmentation using image generative models is an excellent way to address insufficient sample size and imbalanced distribution. In addition to the GAN-like models that have been extensively applied, the diffusion model, as an emerging model, has shown extraordinary performance in the field of image generation. This experiment uses the diffusion model as a means of image data enhancement. The experimental results expose that the performance of the classifier with data augmentation is significantly improved compared with the original dataset, and the positive predictive value is increased from about 0.7 to more than 0.9. The results reveal that the diffusion model has a good application prospect in the area of data enhancement and can effectively solve the problem of insufficient data or unbalanced sample size. © Published under licence by IOP Publishing Ltd.

关键词： Image classification

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Neural Architectures for Secure data Transmission in Next-Gen Networks 3

Neural Architectures for Secure Data Transmission in Next-Ge...

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3rd IEEE international conference on ICT in Business Industry and Government, ICTBIG 2023

作者： Aggarwal, Prateek Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350343274

As data transmission speeds and degrees of complexity are projected to reach previously unfathomable levels soon, it is now more necessary than ever to ensure data security while it is being transported across Next-Generation Networks (NGNs). This article provides an in-depth examination of the topic 'Neural Architectures for Secure data Transmission in Next-Generation Networks.' Our research investigates the convergence of cutting-edge neural network models with the sensitive subject of network security to meet the unexpected problems provided by NGNs. The initial stages in this research are to identify and characterize the particular security concerns that NGNs confront. These problems include, for example, fast data transfer, device networking, and the proliferation of Internet of Things (IoT) devices. The proposed strategy uses the capabilities of neural architectures to improve the overall security of NGNs. This is a description of our methodology, which is divided into three sections. This algorithm class uses autoencoders to identify even little changes in network data that may signal the presence of abnormalities. As society becomes more digitally linked, the safe transmission of such data through NGNs is steadily transforming from a technological challenge to a societal imperative. This is since NGNs are being used in an increasing variety of applications. Throughout this entire effort, neural architectures are supplied as critical enabling components for the main purpose. © 2023 IEEE.

关键词： Intrusion detection

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Leveraging Infrastructure Knowledge Graph for Cloud 3

Leveraging Infrastructure Knowledge Graph for Cloud

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3rd international conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Jolly, Aman Jaglan, Gaurav Pandey, Vikas Shashikant Sharma, Meenakshi Shrivastava, Vineet Kiet Group of Institutions Cse Dept. UP India Pvt. Ltd. Semantics & Ai Dept. Delhi India Babu Banarasi das University Electrical Engineering Dept. UP India Inderprastha Engineering College Electronics & Communication Engineering Dept. UP India Raj Kumar Goel Institute of Technology Cse Dept. UP India

ISBN: (纸本)9798350330724

Semantic Web and Cloud Computing are two prominent areas within the field of Information Technology that offer distinct solutions to various challenges. The Semantic Web focuses on representing and organizing data in a structured and meaningful way, enabling effective data integration, interoperability, and knowledge discovery. On the other hand, Cloud Computing offers scalable and on-demand access to computing resources, enabling cost-effective storage, processing, and delivery of data and applications. This research article explores the integration of Infrastructure Knowledge Graph in cloud environments. It covers key topics in knowledge graph and cloud technologies, application of semantic web technologies, requirements, performance metrics, and key features of deploying a cloud infrastructure knowledge graph. The paper emphasizes the potential benefits of this integration, including enhanced resource management, improved scalability and performance, streamlined provisioning, data integration, and compliance. By leveraging these technologies, organizations can optimize their cloud infrastructure, drive innovation, and meet the demands of modern computing environment. © 2023 IEEE.

关键词： data integration

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Findings of the Shared Task on Multimodal Abusive Language Detection and Sentiment Analysis in Tamil and Malayalam 3

Findings of the Shared Task on Multimodal Abusive Language D...

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3rd Workshop on Speech and Language Technologies for Dravidian Languages, DravidianLangTech 2023

作者： Premjith, B. Jyothish Lal, G. Sowmya, V. Chakravarthi, Bharathi Raja Natarajan, Rajeswari Nandhini, K. Murugappan, Abirami Bharathi, B. Kaushik, M. Prasanth, S.N. Raj, R. Aswin Vijai Simmon, S. Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham Coimbatore India Insight SFI Research Centre for Data Analytics School of Computer Science University of Galway Ireland SASTRA Deemed to be University SRC Campus Tamilnadu Kumbakonam India School of Mathematics and Computer Sciences Central University of Tamil Nadu India Department of Information Science and Technology Anna University India SSN College of Engineering Tamilnadu India

ISBN: (纸本)9789544520854

This paper summarizes the shared task on multimodal abusive language detection and sentiment analysis in Dravidian languages as part of the third Workshop on Speech and Language Technologies for Dravidian Languages at RANLP 2023. This shared task provides a platform for researchers worldwide to submit their models on two crucial social media data analysis problems in Dravidian languages - abusive language detection and sentiment analysis. Abusive language detection identifies social media content with abusive information, whereas sentiment analysis refers to the problem of determining the sentiments expressed in a text. This task aims to build models for detecting abusive content and analyzing fine-grained sentiment from multimodal data in Tamil and Malayalam. The multimodal data consists of three modalities - video, audio and text. The datasets for both tasks were prepared by collecting videos from YouTube. Sixty teams participated in both tasks. However, only two teams submitted their results. The submissions were evaluated using macro F1-score. © DravidianLangTech 2023 - 3rd Workshop on Speech and Language Technologies for Dravidian Languages, associated with 14th international conference on Recent Advances in Natural Language processing, RANLP 2023 - Proceedings.

关键词： Sentiment analysis

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Evaluation Methods of Machine Translation Based on Big data Algorithms 3

Evaluation Methods of Machine Translation Based on Big Data ...

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3rd IEEE international conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Zhang, Bing School Of Foreign Languages Guangdong University Of Science And Technology Dongguan China

ISBN: (纸本)9798350318609

In order to solve the challenges of machine translation evaluation methods, this study proposes an innovative machine translation evaluation method based on big data algorithms in view of the shortcomings of existing dynamic programming algorithms. This new scheme uses the principle of overlapping sub-problem theory to accurately identify and locate the key influencing factors, and accordingly carries out a wise classification of indicators to reduce possible interference. At the same time, by using the unique mechanism of big data algorithms, this scheme cleverly constructs the design strategy of translation evaluation methods. The empirical results show that the proposed scheme shows a significant improvement over the traditional dynamic programming algorithm in terms of the accuracy of the machine translation evaluation method and the processing efficiency of key factors, showing its obvious strong advantages. Machine translation evaluation methods play a crucial role in translation systems, which can accurately predict and optimize the growth trend and output results of machine translation evaluation methods. However, in the face of complex simulation tasks, traditional dynamic programming algorithms show some inherent shortcomings, especially when dealing with multi-level challenges, their performance is often unsatisfactory. To overcome this, this study introduces a new idea of machine translation evaluation method optimized by big data algorithms, and accurately controls the influencing parameters through the overlapping sub-problem theory, and uses this as a road map for index allocation, and then uses big data algorithms to innovate and construct a system scheme. © 2024 IEEE.

关键词： Big data

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ECG and sEMG Conditioning and Wireless Transmission with a Biosignal Acquisition Board 3rd

ECG and sEMG Conditioning and Wireless Transmission with a B...

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3rd international conference on Optimization, Learning Algorithms and Applications (OL2A)

作者： Luiz, Luiz E. Coutinho, Fabio R. Teixeira, Joao P. Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Campus Santa Apolonia P-5300253 Braganca Portugal Univ Tecnol Fed Parana Campus Toledo BR-85902490 Curitiba Parana Brazil Inst Politecn Braganca Lab Sustentabilidade & Tecnol Reg Montanha SusTEC Campus Santa Apolonia P-5300253 Braganca Portugal

ISBN: (纸本)9783031530357;9783031530364

The market for Wearable Health Monitoring Systems (WHMS) has grown together with the demand for devices that offer greater medical reliability and lower cost. This study introduces a wearable system comprising conditioning blocks for electrocardiogram and surface electromyogram signals, an analog-to-digital converter, and wireless data transmission capabilities. These features have been implemented reliably in accordance with the specific requirements of these signals, as well as complying with patient safety directives and ensuring the quality of the resulting signal, allowing it to be used as a data collector for subsequent software implementation. To evaluate its performance, this system is compared against commercially available wearable devices, and the expected outcomes are examined. The obtained results are then presented, showcasing the system's capabilities and leading to a positive conclusion. As future work, there is a focus on enhancing the user interface and implement digital processing in the result for use in pathology recognition software with greater accuracy.

关键词： Electrocardiogram ECG Electromyogram EMG Surface Electromyogram sEMG signal Conditioning Wearable Health Monitoring Systems WHMS

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Overview of Shared-task on Abusive Comment Detection in Tamil and Telugu 3

Overview of Shared-task on Abusive Comment Detection in Tami...

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3rd Workshop on Speech and Language Technologies for Dravidian Languages, DravidianLangTech 2023

作者： Priyadharshini, Ruba Chakravarthi, Bharathi Raja Subramanian, Malliga Navaneethakrishnan, Subalalitha Chinnaudayar Shanmugavadivel, Kogilavani Premjith, B. Murugappan, Abirami Kumaresan, Prasanna Kumar Prashanth, Karnati Sai Reddy, Mangamuru Sai Rishith Chandu, Janakiram Gandhigram Rural Institute-Deemed to be University India Insight SFI Research Centre for Data Analytics School of Computer Science University of Galway Ireland Kongu Engineering College Tamil Nadu India SRM Institute of Science and Technology Kattankulathur Chennai India Amrita School of Artificial Intelligence Amrita Vishwa Vidyapeetham India Department of Information Science and Technology Anna University India

ISBN: (纸本)9789544520854

This paper discusses the submissions to the shared task on abusive comment detection in Tamil and Telugu codemixed social media text conducted as part of the third Workshop on Speech and Language Technologies for Dravidian Languages at RANLP 2023. The task encourages researchers to develop models to detect the contents containing abusive information in Tamil and Telugu codemixed social media text. The task has three subtasks - abusive comment detection in Tamil, Tamil-English and Telugu-English. The dataset for all the tasks was developed by collecting comments from YouTube. The submitted models were evaluated using macro F1-score, and prepared the rank list accordingly. © DravidianLangTech 2023 - 3rd Workshop on Speech and Language Technologies for Dravidian Languages, associated with 14th international conference on Recent Advances in Natural Language processing, RANLP 2023 - Proceedings.

关键词： Social networking (online)

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Impact of EMG signal Filters on Machine Learning Model Training: A Comparison with Clustering on Raw signal 3rd

Impact of EMG Signal Filters on Machine Learning Model Train...

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3rd international conference on Optimization, Learning Algorithms and Applications (OL2A)

作者： Barbosa, Ana Ferreira, Edilson Grilo, Vinicius Mattos, Laercio Lima, Jose Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI P-5300253 Braganca Portugal Ctr Fed Educ Tecnol Minas Gerais CEFET MG Belo Horizonte MG Brazil Inst Politecn Braganca Lab Associado Sustentabilidade & Tecnol Reg Monta P-5300253 Braganca Portugal

ISBN: (纸本)9783031530357;9783031530364

Our current society faces challenges in integrating individuals with disabilities, making this process difficult and painful. People with disabilities (PwD) are often mistakenly considered incapable due to the difficulties they face in daily tasks due to the lack of adapted means and tools. In this context, assistive technologies play a crucial role in improving the quality of life for these individuals. However, assistive technologies still have various limitations, making research in this area essential to enhance existing solutions and develop new approaches that meet individual needs, aiming to promote inclusion and equal opportunities. This paper presents a research project that focuses on the study of electromyography (EMG) signal processing generated by individuals who have undergone amputations. These signals are essential in assistive technologies, such as myoelectric prostheses. The study focuses on the impact of different filters and machine learning training methods on this processing. The results of this study have the potential to provide relevant findings for the development of more efficient assistive technologies. By understanding the processing of EMG signals and applying machine learning techniques, it is possible to improve the accuracy and response speed of prosthetics, increasing the functionality and naturalness of movements performed by users, as well as paving the way for the emergence of new technologies.

关键词： Assistive technologies Electromyography (EMG) signal processing Machine learning

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Road Traffic Status Discrimination Method Based on data Fusion of Multiple Floating Vehicles 3

Road Traffic Status Discrimination Method Based on Data Fusi...

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3rd international conference on Digital signal and Computer Communications, DSCC 2023

作者： Huang, Yanguo Zuo, Kefei Yang, Renzheng Jin, Chao School of electrical engineering and automation Jiangxi University of Science and Technology Ganzhou China

ISBN: (数字)9781510666566

ISBN: (纸本)9781510666559

In view of the fact that a single type of floating vehicle data can not fully reflect the traffic state of the road, this paper proposes a method to judge the urban traffic state based on multiple floating vehicle data. According to the operating characteristics of different vehicles, integrate the advantages and disadvantages of three common floating vehicles, fuse the data, and then use clustering algorithm to divide the traffic state, so as to more accurately reflect the real-time traffic state of urban roads. The experiment shows that this method has a good discrimination effect on the urban road traffic state. It can provide reference for urban road status discrimination and traffic management. © 2023 SPIE.

关键词： data fusion

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