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The dynamics simulation of knowledge potentials of agents including the feedback 12

The dynamics simulation of knowledge potentials of agents in...

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12th International Conference on Mathematics, Science and Technology Education, ICon-MaSTEd 2020

作者： Pasichnyk, V.V. Bomba, A.Y. Nazaruk, M.V. Kunanets, N.E. Information Systems and Networks Department Lviv Polytechnic National University 12 Bandera Str. Lviv79000 Ukraine Computer Science and Applied Mathematics Department National University of Water and Environmental Engineering 11 Soborna Str. Rivne33028 Ukraine Informatics and Applied Mathematics Department Rivne State Humanitarian University 31 Plastova Str. Rivne33000 Ukraine

The conceptual and mathematical models of the agents' knowledge potential redistribution considering their constituent components are constructed in the paper. A nonlinear model describing the dynamics of agents' knowledge potentials is developed taking into account the feedback effects (agent-student) on the source of knowledge (teacher / lecturer). In particular, the procedure of constructing a multicomponent two-dimensional array of discrete values of the knowledge potential constituent components for generating procedures to enhance (improve) the professional competences of knowledge sources, is generalized and specified. © 2021 Published under licence by IOP Publishing Ltd.

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Evaluating the Efficacy of AI Techniques in Textual Anonymization: A Comparative Study

Evaluating the Efficacy of AI Techniques in Textual Anonymiz...

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Communications and Networking (BalkanCom), International Balkan Conference on

作者： Dimitris Asimopoulos Ilias Siniosoglou Vasileios Argyriou Sotirios K. Goudos Konstantinos E. Psannis Nikoleta Karditsioti Theocharis Saoulidis Panagiotis Sarigiannidis R&D Department MetaMind Innovations P.C Kozani Greece Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece Department of Networks and Digital Media Kingston University Kingston upon Thames United Kingdom Physics Department Aristotle University of Thessaloniki Thessaloniki Greece Department of Applied Informatics School of Information Sciences University of Macedonia Thessaloniki Greece Sidroco Holdings Ltd Nicosia Cyprus

ISBN: (数字)9798350365955

ISBN: (纸本)9798350365962

In the digital era, with escalating privacy concerns, it’s imperative to devise robust strategies that protect private data while maintaining the intrinsic value of textual information. This research embarks on a comprehensive examination of text anonymisation methods, focusing on Conditional Random Fields (CRF), Long Short-Term Memory (LSTM), Embeddings from Language Models (ELMo), and the transformative capabilities of the Transformers architecture. Each model presents unique strengths since LSTM is modeling long-term dependencies, CRF captures dependencies among word sequences, ELMo delivers contextual word representations using deep bidirectional language models and Transformers introduce self-attention mechanisms that provide enhanced scalability. Our study is positioned as a comparative analysis of these models, emphasising their synergistic potential in addressing text anonymisation challenges. Preliminary results indicate that CRF, LSTM, and ELMo individually outperform traditional methods. The inclusion of Transformers, when compared alongside with the other models, offers a broader perspective on achieving optimal text anonymisation in contemporary settings.

关键词： Data privacy Analytical models Scalability Transformers Conditional random fields Data models Information filtering

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ResNet-Attention model for human authentication using ECG signals

ResNet-Attention model for human authentication using ECG si...

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作者： Hammad, Mohamed Plawiak, Pawel Wang, Kuanquan Acharya, Udyavara Rajendra School of Computer Science and Technology Harbin Institute of Technology Harbin China Faculty of Computers and Information Menoufia University Menoufia Egypt Department of Information and Communications Technology Faculty of Computer Science and Telecommunications Cracow University of Technology Krakow Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland Department of Electronics and Computer Engineering Ngee Ann Polytechnic Singapore Singapore Department of Biomedical Engineering School of Science and Technology Singapore School of Social Sciences Singapore Singapore Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan

Authentication is the process of verifying the claimed identity of the user. Recently, traditional authentication methods such as passwords, tokens, and so on are no longer used for authentication as they are more prone to theft and different types of violations. Therefore, new authentication approaches based on biometric modalities such as heartbeat pattern obtained from electrocardiogram (ECG) signals are considered. Unlike other biometrics, ECG provides the assurance that the person is alive, and is considered as one of the most accurate recent methods for authentication. In this article, two end-to-end deep neural network models for ECG-based authentication are proposed. In the first model, a convolutional neural network (CNN) is developed and in the second model, a residual convolutional neural network (ResNet) with attention mechanism called ResNet-Attention is designed for human authentication. We have used 2-s duration ECG signals obtained from two ECG databases (Physikalisch-Technische Bundesanstalt [PTB] and Check Your Bio-signals Here initiative [CYBHi]) for authentication. Our proposed ResNet-Attention algorithm achieved an accuracy of 98.85 and 99.27% using PTB and CYBHi, respectively. The results obtained by our developed model show that the performance is better than existing algorithms and can be used in real-time authentication systems after the validation with more diverse ECG data. © 2020 John Wiley & Sons, Ltd

关键词： Electrocardiograms

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Intra-Datacenter Load Balancing in a Federated Cloud with Throttled Algorithm

Intra-Datacenter Load Balancing in a Federated Cloud with Th...

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Damola Gideon Akinola Emmanuel Adetiba Abdultaofeek Abayomi Uche Nnaji Surendra Thakur Sibusiso Moyo Department of Electrical and Information Engineering Covenant Applied Informatics and Communications African Center of Excellence Covenant University Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Department of Information and Communication Technology Mangosuthu University of Technology Durban South Africa Dept. of Computer and Information Science Covenant Applied Informatics and Communications African Center of Excellence Covenant University Ota Nigeria KZN e-Skills CoLab Durban University of Technology Durban South Africa School for Data Science and Computational Thinking and Department of Mathematical Sciences Stellenbosch University Stellenbosch South Africa

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

The emergence of cloud computing technology has motivated a very high number of users in different organizations to access its services in running and delivering their various operations and services. However, this surge of cloud users has led to the problem of uneven load sharing among the cloud computing infrastructures. As a result, federated cloud infrastructure was initiated to provide more cloud computing resources to accommodate more cloud users’ requests and also to provide equal requests mapping with the available cloud resources. Nevertheless, the issue of unequal allocation of requests within the datacenter(s) that makes up the federated cloud environment still persists. This research work presents an intra-datacenter load balancing algorithm in a federated cloud environment named; the intra-balancer adapted throttled algorithm to evenly distribute requests in each datacenter that formulate the federated cloud infrastructures. The simulation of the intra-balancer was carried out in CloudAnalyst, the results of the implementation showed that the intra-balancer outperformed the Round-Robin and ESCE with 98.93ms and 2.26ms for the response and processing time respectively. The results assert that the intra-balancer algorithm provides a better load balancing solution in a federated cloud environment.

关键词： computers Cloud computing Organizations Load management Resource management Time factors Task analysis

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On the Accuracy and Efficiency of Received Signal Strength Modelling for a Forest Environment

On the Accuracy and Efficiency of Received Signal Strength M...

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Machine Learning for Communication and Networking (ICMLCN), IEEE International Conference on

作者： Vasileios P. Rekkas Sotirios P. Sotiroudis Georgios P. Koudouridis Panagiotis Sarigiannidis Zaharias D. Zaharis George K. Karagiannidis Sotirios K. Goudos ELEDIA@AUTH School of Physics Aristotle University of Thessaloniki Thessaloniki Greece Systems Management Global AI Accelerator Ericsson Stockholm Sweden Department of Informatics and Telecommunications Engineering University of Western Macedonia Kozani Greece School of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Cyber Security Systems and Applied AI Research Center Lebanese American University (LAU) Lebanon Department of Electronics and Communication Engineering Bharath University Chennai Tamilnadu India

ISBN: (数字)9798350343199

ISBN: (纸本)9798350343205

Within telecommunications, the accuracy and efficiency of machine learning models (ML) define their utility, and consequently, the choice of ML mechanisms assumes paramount importance. This study focuses on the exploration and comparison of diverse ML and ensemble learning techniques, with a specific emphasis on their significance in crafting precise and extensive models. To this end, the quality of the received signal and the optimization and functioning of wireless communication networks rely heavily on accurately predicting the received signal strength indicator (RSSI) and path loss (PL). The studied environment, which is highly complex, spans 2000 km2 of the intricate landscapes of the American River Hydrologic Observatory (ARHO) networks and is characterized by a diverse blend of terrain features and vegetation distributions. Notable independent variables under consideration include path distance, canopy coverage, terrain variability, and path angle. The proposed ensemble ML approaches demonstrate remarkable accuracy and efficiency when it comes to modeling and predicting the RSSI values in forested environments.

关键词： Accuracy Wireless networks Vegetation mapping Forestry Predictive models Telecommunications Rivers

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Evaluating the Effect of Volatile Federated Timeseries on Modern DNNs: Attention over Long/Short Memory

Evaluating the Effect of Volatile Federated Timeseries on Mo...

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International Conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Ilias Siniosoglou Konstantinos Xouveroudis Vasileios Argyriou Thomas Lagkas Sotirios K. Goudos Konstantinos E. Psannis Panagiotis Sarigiannidis Department of Electrical and Computer Engineering University of Western Macedonia Kozani Greece R&D Department MetaMind Innovations P.C. Kozani Greece Department of Networks and Digital Media Kingston University Kingston Upon Thames United Kingdom Department of Computer Science International Hellenic University Greece Physics Department Aritostle University of Thessaloniki Thessaloniki Greece Department of Applied Informatics School of Information Sciences University of Macedonia Thessaloniki Greece

In regards to the field of trend forecasting in time series, many popular Deep Learning (DL) methods such as Long Short Term Memory (LSTM) models have been the gold standard for a long time. However, depending on the domain and application, it has been shown that a new approach can be implemented and possibly be more beneficial, the Transformer deep neural networks. Moreover, one can incorporate Federated Learning (FL) in order to further enhance the prospective utility of the models, enabling multiple data providers to jointly train on a common model, while maintaining the privacy of their data. In this paper, we use an experimental Federated Learning System that employs both Transformer and LSTM models on a variety of datasets. The sytem receives data from multiple clients and uses federation to create an optimized global model. The potential of Federated Learning in real-time forecasting is explored by comparing the federated approach with conventional local training. Furthermore, a comparison is made between the performance of the Transformer and its equivalent LSTM in order to determine which one is more effective in each given domain, which shows that the Transformer model can produce better results, especially when optimised by the FL process.

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MassSpecGym: a benchmark for the discovery and identification of molecules 24

MassSpecGym: a benchmark for the discovery and identificatio...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Roman Bushuiev Anton Bushuiev Niek F. de Jonge Adamo Young Fleming Kretschmer Raman Samusevich Janne Heirman Fei Wang Luke Zhang Kai Dührkop Marcus Ludwig Nils A. Haupt Apurva Kalia Corinna Brungs Robin Schmid Russell Greiner Bo Wang David S. Wishart Li-Ping Liu Juho Rousu Wout Bittremieux Hannes Rost Tytus D. Mak Soha Hassoun Florian Huber Justin J.J. van der Hooft Michael A. Stravs Sebastian Böcker Josef Sivic Tomáš Pluskal Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Czech Institute of Informatics Robotics and Cybernetics Czech Technical University Bioinformatics Group Wageningen University & Research Department of Computer Science University of Toronto Chair for Bioinformatics Institute for Computer Science Friedrich Schiller University Jena Department of Computer Science University of Antwerp Department of computing science University of Alberta and Alberta Machine Intelligence Institute Department of Molecular Genetics University of Toronto Bright Giant GmbH Department of Computer Science Tufts University Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences Department of computing science and Department of Biological Sciences University of Alberta Department of Computer Science Aalto University Mass Spectrometry Data Center National Institute of Standards and Technology Department of Computer Science and Department of Chemical and Biological Engineering Tufts University Centre for Digitalisation and Digitality University of Applied Sciences Düsseldorf Bioinformatics Group Wageningen University & Research and Department of Biochemistry University of Johannesburg Eawag: Swiss Federal Institute of Aquatic Science and Technology

ISBN: (纸本)9798331314385

The discovery and identification of molecules in biological and environmental samples is crucial for advancing biomedical and chemical sciences. Tandem mass spectrometry (MS/MS) is the leading technique for high-throughput elucidation of molecular structures. However, decoding a molecular structure from its mass spectrum is exceptionally challenging, even when performed by human experts. As a result, the vast majority of acquired MS/MS spectra remain uninterpreted, thereby limiting our understanding of the underlying (bio)chemical processes. Despite decades of progress in machine learning applications for predicting molecular structures from MS/MS spectra, the development of new methods is severely hindered by the lack of standard datasets and evaluation protocols. To address this problem, we propose MassSpecGym - the first comprehensive benchmark for the discovery and identification of molecules from MS/MS data. Our benchmark comprises the largest publicly available collection of high-quality labeled MS/MS spectra and defines three MS/MS annotation challenges: de novo molecular structure generation, molecule retrieval, and spectrum simulation. It includes new evaluation metrics and a generalization-demanding data split, therefore standardizing the MS/MS annotation tasks and rendering the problem accessible to the broad machine learning community. MassSpecGym is publicly available at https://***/pluskal-lab/MassSpecGym.

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Latent fingerprint enhancement and segmentation technique based on hybrid edge adaptive DTV model 2

Latent fingerprint enhancement and segmentation technique ba...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Hilles, Shadi M S Liban, Abdilahi Miaikil, Othman A.M. Mahmoud Altrad, Abdullah Baker El-Ebiary, Yousef A. Hilles, Mohanad M Contreras, Jennifer Istanbul OKAN University Software Engineering Department Istanbul Turkey Hargeisa University Computer Science Department Hargeisa Somalia Al-Madinah International University Computer Science Department Kuala Lumpur Malaysia Universiti Sultan Zainal Abidin Faculty of Informatics and Computing Trengganu Malaysia University College of Applied Science Information Technology Department Gaza Palestine FEU Institute of Technology Department of Information Technology Manila Philippines

ISBN: (纸本)9781665432221

Image enhancement and segmentation is widely used for fingerprint identification and authorization in biometrics devices, criminal scene is most challenges due to low quality of fingerprint, the most significant efforts is to develop algorithm for latent fingerprint enhancement which become challenging problem due to the complex and existing problem for instance, developing algorithms of latent fingerprint is able to extract features of image blocks and removing overlapping and isolate the poor and noisy background. however, it's still challenging and interested problem specifically latent fingerprint enhancement and segmentation. The aim study of this paper is to propose latent fingerprint enhancement and segmentation based on hybrid model and Chan-Vese method for segmentation, in order to reduce low image quality and increase the accuracy of fingerprint. The desired characteristics of intended technique are adaptive, effective and accurate, hybrid model of edge adaptive direction achieves accurate latent fingerprint enhancement and segmentation, the target needs to improve feature detection and performance, this research has proposed system architecture of research method in fingerprint enhancement and segmentation where is the method content two stages, the first is normalization and second is reconstruction, using EDTV model is required for adaptive noise, in addition Chan-vase technique contributed for identification of fingerprint image features, the result and testing using RMSE with three categories of fingerprint images good, bad and ugly show better performance for all three categories, as well RMSE shows the average of good latent fingerprint before and after enhancement. Latent Fingerprint Enhancement and Segmentation Technique Based on Hybrid Model Edge Adaptive Directional Total Variation © 2021 IEEE.

关键词： Image enhancement

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Adaptive latent fingerprint image segmentation and matching using Chan-vese technique based on EDTV model 2

Adaptive latent fingerprint image segmentation and matching ...

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2nd International Conference on Smart Computing and Electronic Enterprise, ICSCEE 2021

作者： Hilles, Shadi M S Liban, Abdilahi Altrad, Abdullah Mahmoud Miaikil, Othman A.M. El-Ebiary, Yousef A. Baker Contreras, Jennifer Hilles, Mohanad M Istanbul OKAN University Software Engineering Department Istanbul Turkey Hargeisa University Computer Science Department Hargeisa Somalia Al-Madinah International University Computer Science Department Kuala Lumpur Malaysia Universiti Sultan Zainal Abidin Faculty of Informatics and Computing Trengganu Malaysia FEU Institute of Technology Department of Information Technology Manila Philippines University College of Applied Science Information Technology Department Gaza Palestine

ISBN: (纸本)9781665432221

Biometrics such as face, fingerprint, iris, voice and palm prints are the most widely used, and as well the fingerprints are one of the most frequently used biometrics to identify individuals and authenticate their identity. commonly categorized into three different categories which are rolled, plain and latent fingerprints. The reliability of image segmentation for latent fingerprint which is used in criminal issues still challenges, The difficulty of latent fingerprint image segmentation mainly lies in the poor quality of fingerprint patterns and the presence of the noise in the background, This research has investigated the fingerprint segmentation and matching based on EDTV and presented Chan-vese active contour segmentation technique, in addition, presented NIST SD27 for grayscale dataset of latent fingerprint which is standard by National Institute of Standard and Technology, where is dataset have varieties of fingerprint image samples, a total about 258 of latent fingerprint, those samples collected from crime scenes and matching fingerprint and shown the performance of matching accuracy ROC and CMC curves, To evaluate the performance of the matching ROC and CMC curves has been deployed, The area under curve (AUC) of the ROC of the good images performance is 72% with CMC rank1-idnetification of 42% and rank-20 identification of 79%. the result shows that the latent fingerprint method performance is better for good latent fingerprint images compare to bad and ugly images, while there is no much difference for bad and ugly image. © 2021 IEEE.

关键词： Biometrics

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Comparative Analysis of Various Image Splicing Algorithms 8th

Comparative Analysis of Various Image Splicing Algorithms

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8th International Workshop on Soft Computing Applications, SOFA 2018

作者： ur Rhhman, Hafiz Arif, Muhammad Ullah, Anwar Al-Azani, Sadam Balas, Valentina Emilia Geman, Oana Khan, Muhammad Jalal Islam, Umar School of Computer Science and Educational Software Guangzhou University Guangzhou510006 China Wollongong Joint Institute Central China Normal University WuhanHubei China Department of Information and Computer Science King Fahd University of Petroleum and Minerals Dhahran Saudi Arabia Department of Automatics and Applied Informatics Aurel Vlaicu University of Arad Arad Romania Faculty of Electrical Engineering and Computer Science University Suceava Suceava Romania Department of Computer Science COMSATS University Abbottabad Abbottabad Pakistan

ISBN: (纸本)9783030521899

Daily millions of images are uploaded and download to the web, as a result the data is available in the paperless form in the computer system for organization. Nowadays, with the help of powerful computer software such as Photoshop and Corel Draw, it is very easy to alter the contents of the authenticated image without leaving any clues. This led to a big problem due to the negative impact of image splicing. It is highly recommended to develop image tampering detection technique to recognize the authentic and temper images. In this paper, we propose an enhanced technique for blind images splicing by combing Discrete Cosine Transform Domain (DTC) and Markov feature in the spatial domain. Moreover, Principal Component Analysis (PCA) is used to select the most significant features. Finally, Support Vector Machine (SVM) is applied to classify the image as being tempered or genuine on the publicly available dataset using ten-fold cross-validation. By applying different statistical techniques, the results showed that the proposed technique performs better than other available detection techniques in the literature. © 2021, Springer Nature Switzerland AG.

关键词： Principal component analysis

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