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arXiv 2022年

作者： Lauri, Mikko Hsu, David Pajarinen, Joni Department of Informatics Universität Hamburg Germany Department of Computer Science National University of Singapore Singapore Department of Electrical Engineering and Automation Aalto University Finland Intelligent Autonomous Systems Technische Universität Darmstadt Germany

Noisy sensing, imperfect control, and environment changes are defining characteristics of many real-world robot tasks. The partially observable Markov decision process (POMDP) provides a principled mathematical framework for modeling and solving robot decision and control tasks under uncertainty. Over the last decade, it has seen many successful applications, spanning localization and navigation, search and tracking, autonomous driving, multi-robot systems, manipulation, and human-robot interaction. This survey aims to bridge the gap between the development of POMDP models and algorithms at one end and application to diverse robot decision tasks at the other. It analyzes the characteristics of these tasks and connects them with the mathematical and algorithmic properties of the POMDP framework for effective modeling and solution. For practitioners, the survey provides some of the key task characteristics in deciding when and how to apply POMDPs to robot tasks successfully. For POMDP algorithm designers, the survey provides new insights into the unique challenges of applying POMDPs to robot systems and points to promising new directions for further research. Copyright © 2022, The Authors. All rights reserved.

关键词： Surveys

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Controlling the transmission bandwidth of anti-resonant hollow-core fibers

Controlling the transmission bandwidth of anti-resonant holl...

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International Conference on Transparent Optical Networks

作者： S. Yerolatsitis J. Keohane S. Vinta R. Mears K. Harrington R. J. A. Francis-Jones K. Kalli K. Rusimova Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus CREOL The College of Optics and Photonics University of Central Florida 4304 Scorpius St Orlando Florida USA Centre for Photonics and Photonic Materials University of Bath Claverton Down Bath U.K. ORCA Computing London 30 Eastbourne Terrace U.K.

ISBN: (数字)9798350377323

ISBN: (纸本)9798350377330

We have developed an innovative anti-resonant hollow-core fiber design with capillaries of alternating thicknesses. This unique arrangement, featuring six silica capillaries of two different thicknesses, effectively narrows the transmission bandwidth while maintaining low attenuation. Validated through simulations, our design marks a significant step in developing the first in-line hollow-core bandwidth filter fiber. We envision that our novel fiber design has a variety of applications, such as suppressing non-linear effects and developing wavelength-selective components for future conventional and quantum communication networks.

关键词： Band-pass filters Optical fibers Optical design Optical attenuators Bandwidth Optical fiber networks Attenuation

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A Novel Approach for Cancer Image Segmentation

A Novel Approach for Cancer Image Segmentation

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Panagiotis N. Smyrlis Konstantinos Vogklis Odysseas Tsakai Alexandros Tzallas Nikolaos Giannakeas Markos G. Tsipouras Electrical & Computer Engineering University of Western Macedonia Kozani Greece Department of Tourism Ionian University Corfu Greece Q Base R&D Science & Technology Park of Epirus Arta Greece Informatics & Telecommunications University of Ioannina Arta Greece

In this study, we present a novel approach to address the task of identifying nuclei in a diverse set of microscopy scan images containing slides from several distinct cancerous tissue types. Our approach is based on YOLOv5 algorithm. We thoroughly discuss the crucial training specifics that contributed to our outcomes. Additionally, we propose an intuitive strategy for combining multiple segmentation results to further improve the accuracy of nucleus identification. By employing this lightweight model, we successfully achieved results better or close to the state-of-the-art performance.

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Bridging MDE and AI: A Systematic Review of Domain-Specific Languages and Model-Driven Practices in AI Software Systems engineering*

arXiv

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arXiv 2023年

作者： Rädler, Simon Berardinelli, Luca Winter, Karolin Rahimi, Abbas Rinderle-Ma, Stefanie Department of Computer Science TUM School of Computation Information and Technology Technical University of Munich Boltzmannstr. 3 Garching85748 Germany Business Informatics Group Technical University of Vienna Favoritenstraße 9-11/194-3 Vienna1040 Austria Department of Business Informatics–Software Engineering Johannes Kepler University Altenberger Straße 69 Linz4040 Austria Department of Industrial Engineering and Innovation Sciences Eindhoven University of Technology Groene Loper 3 Eindhoven5612 AE Netherlands

Background: Technical systems are becoming increasingly complex due to the increasing number of components, functions, and involvement of different disciplines. In this regard, Model-Driven engineering techniques and practices tame complexity during the development process by using models as primary artifacts. Modeling can be carried out through Domain-Specific Languages whose implementation is supported by model-driven techniques. Today, the amount of data generated during product development is rapidly growing, leading to an increased need to leverage Artificial Intelligence algorithms. However, using these algorithms in practice can be difficult and time-consuming. Therefore, leveraging domain-specific languages and model-driven techniques for formulating AI algorithms or parts of them can reduce these complexities and be advantageous. Objective: This study aims to investigate the existing model-driven approaches relying on domain-specific languages in support of the engineering of AI software systems to sharpen future research further and define the current state of the art. Method: We conducted a Systemic Literature Review (SLR), collecting papers from five major databases resulting in 1335 candidate studies, eventually retaining 18 primary studies. Each primary study will be evaluated and discussed with respect to the adoption of (1) MDE principles and practices and (2) the phases of AI development support aligned with the stages of the CRISP-DM methodology. Results: The study’s findings show that language workbenches are of paramount importance in dealing with all aspects of modeling language development (metamodel, concrete syntax, and model transformation) and are leveraged to define domain-specific languages (DSL) explicitly addressing AI concerns. The most prominent AI-related concerns are training and modeling of the AI algorithm, while minor emphasis is given to the time-consuming preparation of the data sets. Early project phases that support interdisc

关键词： Machine learning

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MLCardiovascularPrediction: Machine Learning-Based Cardiovascular Disease Prediction

MLCardiovascularPrediction: Machine Learning-Based Cardiovas...

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Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC), International

作者： Diaa Salama AbdElminaam Magda M. Madbouly Magdy Abd-elghany Zeid Ibrahim Abd Elatif Gomaa Laith Abualigah Faculty of Computer Science Misr International University Cairo Egypt Faculty of Computers and Artificial Intelligence Benha University Benha Egypt Faculty of Computer and Data Science Alexandria University Alexandria Egypt Computer Science Department Alobour high institute for computer and informatics Cairo Egypt Computer Science Department Al al-Bayt University Mafraq Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan MEU Research Unit Middle East University Amman Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya Malaysia

Like most diseases that affect a human's life, car-diovascular disease is dangerous and unwanted for anyone who has heard of it as it brings a high risk of death. In today's world, most deaths are caused by heart disease as it could come from age, heritage, smoking, and many factors that could seem normal, and it doesn't matter what part of the world you are in. There must be a way to know or predict if this person is at high risk of cardiovascular disease. This paper and project talk about six algorithms: K-Nearest-Neighbour (KNN), Decision Tree (DT), Naive Bayes, Neural Network, Logistic Regression, and Support Vector Machine (SVM). The results came between 60% to 70% in one dataset and from 80 % to 90 % in the second. Still, they mostly favor the Neural Network algorithm as it can learn and model around complex relationships. The SVM showed different outputs between the two datasets as one is significantly larger than the other, possibly due to the outliers in both datasets being different and more complex in the larger one. Cardiovascular disease can be very dangerous, but finding and discovering it soon can be a lifesaver. These algorithms also can help find and predict it, as it was shown with not just decent, but good and respectable success rates for each one.

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3D Beamforming Based on Deep Learning for Secure Communication in 5G and beyond Wireless Networks

3D Beamforming Based on Deep Learning for Secure Communicati...

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Yang, Helin Lam, Kwok-Yan Nie, Jiangtian Zhao, Jun Garg, Sahil Xiao, Liang Xiong, Zehui Guizani, Mohsen Nanyang Technological University Strategic Centre for Research in Privacy-Preserving Technologies and Systems Singapore Nanyang Technological University School of Computer Science and Engineering Singapore Ecole de Technologie Superieure Universite du Quebec Electrical Engineering Department Montreal Canada Xiamen University School of Informatics Xiamen China Singapore University of Technology and Design Pillar of Information System Technology and Design Singapore Qatar University Department of Computer Science and Engineering Doha Qatar

ISBN: (纸本)9781665423908

Three-dimensional (3D) beamforming is a potential technique to enhance communication security of new generation networks such as 5G and beyond. However, it is difficult to achieve optimal beamforming due to the challenges of nonconvex optimization problem and imperfect channel state information (CSI). To tackle this problem, this paper proposes a novel deep learning-based 3D beamforming scheme, where a deep neural network (DNN) is trained to optimize the beamforming design for wireless signals in order to guard against eavesdropper under the imperfect CSI. With our approach, the system is capable of training the DNN model offline, and the trained model can then be adopted to instantaneously select the 3D secure beamforming matrix for achieving the maximum secrecy rate of the system, which is measured by the signal received by eavesdroppers outside the path of the beam. Simulation results demonstrate that the proposed solution outperforms the classical deep learning algorithm and 2D beamforming solution in terms of the secrecy rate and robust performance. © 2021 IEEE.

关键词： Beamforming

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Minimum Separator Reconfiguration

arXiv

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arXiv 2023年

作者： Gomes, Guilherme C.M. Legrand-Duchesne, Clément Mahmoud, Reem Mouawad, Amer E. Okamoto, Yoshio dos Santos, Vinicius F. van der Zanden, Tom C. Department of Computer Science Federal University of Minas Gerais Belo Horizonte Brazil LaBRI CNRS Université de Bordeaux Bordeaux France Virginia Commonwealth University RichmondVA United States Department of Computer Science American University of Beirut Beirut Lebanon Graduate School of Informatics and Engineering The University of Electro-Communications Chofu Japan Department of Data Analytics and Digitalisation Maastricht University Maastricht Netherlands

We study the problem of reconfiguring one minimum s-t-separator A into another minimum s-t-separator B in some n-vertex graph G containing two non-adjacent vertices s and t. We consider several variants of the problem as we focus on both the token sliding and token jumping models. Our first contribution is a polynomial-time algorithm that computes (if one exists) a minimum-length sequence of slides transforming A into B. We additionally establish that the existence of a sequence of jumps (which need not be of minimum length) can be decided in polynomial time (by an algorithm that also outputs a witnessing sequence when one exists). In contrast, and somewhat surprisingly, we show that deciding if a sequence of at most jumps can transform A into B is an NP-complete problem. To complement this negative result, we investigate the parameterized complexity of what we believe to be the two most natural parameterized counterparts of the latter problem;in particular, we study the problem of computing a minimum-length sequence of jumps when parameterized by the size k of the minimum s-t-separators and when parameterized by the number of jumps . For the first parameterization, we show that the problem is fixed-parameter tractable, but does not admit a polynomial kernel unless NP ⊆ coNP/poly. We complete the picture by designing a kernel with O(2) vertices and edges for the length of the sequence as a parameter. © 2023, CC0.

关键词： Separators

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A machine learning method for predicting disease-associated microRNA connections using network internal topology data

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Healthcare Analytics 2023年 4卷 100215-100215页

作者： Jindal, Latika Sharma, Aditi Prasad, K.D.V. Irshad, Azeem Rivera, Richard Karimovna, Abdurakhimova Dilora Department of Computer Science and Engineering Medi-Caps university Indore India Department of computer science institute of engineering & technology Lucknow India Symbiosis Institute of Business Management Hyderabad India Symbiosis International (Deemed University) Pune India Asghar Mall College Rawalpindi Higher Education Department (HED) Govt. of the Punjab Pakistan Department of Informatics and Computer Science Escuela Politécnica Nacional Quito Ecuador Department of Corporate Finance and Securities Tashkent Institute of Finance Tashkent Uzbekistan

Recent studies have shown that aberrations or mutations of microRNAs (miRNA) can cause various diseases. Therefore, identifying disease-associated microRNAs can aid in diagnosing and treating related disorders. However, getting precise correlations through biological research is expensive and time-consuming. This research suggests a machine learning method (HNDLM) based on network internal topology data to anticipate disease-miRNA connections. HNDLM applies the recently described network embedding technique to biological networks rather than creating similar ones. According to the experimental findings, HNDLM performs better in terms of accuracy and Area Under the ROC Curve (AUC) value when compared to the traditional algorithms MIDPE, MIDP, WBSMDA, RLSMDA, Collective Prediction based on Transductive Learning (CPTL), and Human Disease-related MiRNA Prediction (HDMP). The top 30 potential miRNAs suggested by HNDLM can also be verified by earlier research in the case study. HNDLM can identify probable disease-miRNA correlations, which will aid in the investigation of illness path physiology and advance the field of bioinformatics. © 2023 The Authors

关键词： Bioinformatics Correlations Link prediction Machine learning MicroRNAs

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Application of Kronecker convolutions in deep learning technique for automated detection of kidney stones with coronal CT images

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Information Sciences 2023年 640卷

作者： Patro, Kiran Kumar Allam, Jaya Prakash Neelapu, Bala Chakravarthy Tadeusiewicz, Ryszard Acharya, U Rajendra Hammad, Mohamed Yildirim, Ozal Plawiak, Pawel Department of Electronics and Communication Engineering Aditya Institute of Technology and Management AP Tekkali532201 India Department of Electronics and Communication Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India Department of Biotechnology and Medical Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India AGH University of Science and Technology Department of Biocybernetics and Biomedical Engineering Krakow Poland School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology Faculty of Computers and Information Menoufia University Shibin El Kom32511 Egypt Department of Software Engineering Firat University Elazig Turkey Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Warszawska 24 Krakow31-155 Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Baltycka 5 Gliwice44-100 Poland

Kidney stone disease is a serious public health concern that is getting worse with changes in diet, obesity, medical conditions, certain supplements etc. A kidney stone also called a renal calculus, is a hard buildup of urine minerals that form in the kidneys. Computed tomography (CT) is one of the imaging models used to identify kidney stones by clinical experts. Due to the low resolution of these images, sometimes detecting kidney stones is tedious with the naked eye, which may lead to false alarms. In this work, a computer-based diagnosis system with a deep learning technique has been developed as a practical solution to aid clinicians in their diagnosis. The traditional convolutional neural network (CNN)-based deep learning technology can detect stones in the kidney. Still, it suffers from the performance and standard implementation of the convolution operations in convolution layers. A Kronecker product-based convolution technique is incorporated in the proposed deep learning architecture to reduce the redundancy in feature maps without convolution overlapping. Our proposed method helps to make the network more effective by extracting abstract and in-depth features from the input images. The publicly available GitHub kidney stone CT scans are utilized to develop the proposed architecture. Our automated model detected kidney stones with an accuracy of 98.56% utilizing CT images. Our system is more effective than the most recent and cutting-edge techniques developed for identifying kidney stones of any size, including the smallest ones. © 2023 The Author(s)

关键词： Convolution

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Development of a Relative Capacitance Sensor for Oil Palm Fruit Maturity Classification: A Systems engineering Approach

Development of a Relative Capacitance Sensor for Oil Palm Fr...

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Agrosystem engineering, Technology & Applications (AGRETA), IEEE International Conference on

作者： Luis M. Aristizabal Isis Bonet Juan Sebastian Valencia Mario Gongora Ivan Ochoa Department of Mechanical Engineering Universidad Pontificia Bolivariana Medellín Colombia Artificial Intelligence and Robotics Group Universidad EIA Envigado Colombia De Montfort University Institute of Artificial Intelligence School of Computer Science and Informatics Leicester UK UNIPALMA de Los Llanos S.A. Meta Colombia

ISBN: (数字)9798350373660

ISBN: (纸本)9798350373677

This paper presents the development of a relative capacitance sensor for the classification of oil palm fruit maturity, using a systems engineering approach. This sensor is based on capacitive sensing technologies that require integration to productive processes as data sources. Systems engineering is suited for the development of complex systems based primarily on novel sensing technology that requires integration at the component, subsystem, and system levels. The goal is to provide a reliable and efficient method for assessing the maturity of fruit bunches, both in the harvest phase and in the production phase, which can ultimately contribute to improving the quality and yield of palm oil production. This study seeks to advance the frontier of precision agriculture and increase the efficacy of contemporary agricultural practices.

关键词： Precision agriculture Oils Soft sensors Production Capacitance Manufacturing Stakeholders Reliability Complex systems System validation

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