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arXiv 2023年

作者： Azad, Reza Kazerouni, Amirhossein Heidari, Moein Aghdam, Ehsan Khodapanah Molaei, Amirali Jia, Yiwei Jose, Abin Roy, Rijo Merhof, Dorit Faculty of Electrical Engineering and Information Technology RWTH Aachen University Aachen Germany School of Electrical Engineering Iran University of Science and Technology Tehran Iran Department of Electrical Engineering Shahid Beheshti University Tehran Iran School of Computer Engineering Iran University of Science and Technology Tehran Iran Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

The remarkable performance of the Transformer architecture in natural language processing has recently also triggered broad interest in computer Vision. Among other merits, Transformers are witnessed as capable of learning long-range dependencies and spatial correlations, which is a clear advantage over convolutional neural networks (CNNs), which have been the de facto standard in computer Vision problems so far. Thus, Transformers have become an integral part of modern medical image analysis. In this review, we provide an encyclopedic review of the applications of Transformers in medical imaging. Specifically, we present a systematic and thorough review of relevant recent Transformer literature for different medical image analysis tasks, including classification, segmentation, detection, registration, synthesis, and clinical report generation. For each of these applications, we investigate the novelty, strengths and weaknesses of the different proposed strategies and develop taxonomies highlighting key properties and contributions. Further, if applicable, we outline current benchmarks on different datasets. Finally, we summarize key challenges and discuss different future research directions. In addition, we have provided cited papers with their corresponding implementations in https://***/mindflow-institue/Awesome-Transformer. © 2023, CC BY.

关键词： Deep neural networks

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Automatic identification of metastasis in histopathological images using deep learning 22

Automatic identification of metastasis in histopathological ...

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22nd IEEE International Conference on E-Health Networking, Application and Services, HEALTHCOM 2020

作者： Luz, Daniel S. Costa, Renesio J.O. Rabelo, Ricardo De Andrade L. Rodrigues, Joel J. P. C. Araujo, Flavio H.D. Department of Electrical Engineering Federal University of Piauí Teresina - PI Brazil Department of Informatics Federal Institute of Piauí Picos - PI Brazil Departament of Information Systems Federal University of Piauí Picos - PI Brazil Department of Computer Science Federal University of Piauí Teresina - PI Brazil Instituto de Telecomunicações Portugal

ISBN: (纸本)9781728162676

Metastatic tumor is one that spreads from its place of origin to other parts of the body. A tumor formed by metastatic cancer cells is called a metastatic tumor or metastasis. The early identification of these tumors is essential to increase the chances of success in treating the disease. However, for this identification it is necessary to analyze extensive tissues of the affected organs, which is a tiring and error-prone task. In this paper, it is present three deep learning strategies for automatic identification of metastasis in histopathological images. For the development and evaluation of these strategies it was used the PCam database, which is composed of 327, 680 color images extracted from histopathological exams of sections of lymph nodes. The obtained results using the fine tuning technique are promising, showing that deep learning models can be used for metastasis identification. © 2021 IEEE.

关键词： Image processing

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YAQQ: Yet Another Quantum Quantizer - Design Space Exploration of Quantum Gate Sets using Novelty Search

arXiv

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arXiv 2024年

作者： Sarkar, Aritra Kundu, Akash Steinberg, Matthew Mishra, Sibasish Fauquenot, Sebastiaan Acharya, Tamal Miszczak, Jaroslaw A. Feld, Sebastian Quantum Intelligence research team Quantum Machine Learning research group Quantum Computing division QuTech Netherlands Department of Quantum & Computer Engineering Delft University of Technology Netherlands Joint Doctoral School Silesian University of Technology Gliwice Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland QTF Centre of Excellence Department of Physics University of Helsinki Finland

The standard model of quantum computation is based on quantum circuits, where the number and quality of the quantum gates composing the circuit influence the runtime and fidelity of the computation. The fidelity of the decomposition of quantum algorithms, represented as unitary matrices, to bounded depth quantum circuits depends strongly on the set of gates available for the decomposition routine. To investigate this dependence, we explore the design space of discrete quantum gate sets and present a software tool for comparative analysis of quantum processing units and control protocols based on their native gates. The evaluation is conditioned on a set of unitary transformations representing target use cases on the quantum processors. The cost function considers three key factors: (i) the statistical distribution of the decomposed circuits’ depth, (ii) the statistical distribution of process fidelities for the approximate decomposition, and (iii) the relative novelty of a gate set compared to other gate sets in terms of the aforementioned properties. The developed software, called YAQQ (Yet Another Quantum Quantizer), enables the discovery of an optimized set of quantum gates through this tunable joint cost function. To identify these gate sets, we use the novelty search algorithm, circuit decomposition techniques (like Solovay-Kitaev, Cartan, and quantum Shannon decomposition), and stochastic optimization to implement YAQQ within the Qiskit quantum simulator environment. YAQQ exploits reachability tradeoffs conceptually derived from quantum algorithmic information theory. Our results demonstrate the pragmatic application of identifying gate sets that are advantageous to popularly used quantum gate sets in representing quantum algorithms. Consequently, we demonstrate pragmatic use cases of YAQQ in comparing transversal logical gate sets in quantum error correction codes, designing optimal quantum instruction sets, and compiling to specific quantum processors. © 202

关键词： Cost functions

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Jointly learned symbol detection and signal reflection in RIS-aided multi-user MIMO systems

arXiv

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arXiv 2022年

作者： Wang, Liuhang Shlezinger, Nir Alexandropoulos, George C. Zhang, Haiyang Wang, Baoyun Eldar, Yonina C. School of Communication and Information Engineering Nanjing University of Posts and Telecommunications Nanjing China School of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva Israel Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Faculty of Math and Computer Science Weizmann Institute of Science Rehovot Israel

Reconfigurable Intelligent Surfaces (RISs) are regarded as a key technology for future wireless communications, enabling programmable radio propagation environments. However, the passive reflecting feature of RISs induces notable challenges on channel estimation, making coherent symbol detection a challenging task. In this paper, we consider the uplink of RIS-aided multi-user Multiple-Input Multiple-Output (MIMO) systems and propose a Machine Learning (ML) approach to jointly design the multi-antenna receiver and configure the RIS reflection coefficients, which does not require explicit full knowledge of the channel input-output relationship. Our approach devises a ML-based receiver, while the configurations of the RIS reflection patterns affecting the underlying propagation channel are treated as hyperparameters. Based on this system design formulation, we propose a Bayesian ML framework for optimizing the RIS hyperparameters, according to which the transmitted pilots are directly used to jointly tune the RIS and the multi-antenna receiver. Our simulation results demonstrate the capability of the proposed approach to provide reliable communications in non-linear channel conditions corrupted by Gaussian noise. Copyright © 2022, The Authors. All rights reserved.

关键词： MIMO systems

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The roots of digital aggression: Exploring cyber-violence through a systematic literature review

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International Journal of Information Management Data Insights 2024年第2期4卷

作者： Mukred, Muaadh Mokhtar, Umi Asma Moafa, Fahad Abdullah Gumaei, Abdu Sadiq, Ali Safaa Al-Othmani, Abdulaleem Department of Business Analytics Sunway Business School Sunway University 5 Jalan University 47500 Petaling Jaya Selangor Bandar Sunway Malaysia Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Selangor Bangi 43600 Malaysia Department of Computers King Fahd Naval Academy Jubail 34724 Saudi Arabia Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Ab-dulaziz University Al-Kharj 11942 Saudi Arabia Department of Computer Science Nottingham Trent University Clifton Lane Nottingham NG11 8NS United Kingdom School of Computer Science and Informatics Faculty of Computing Engineering and Media De Montfort University United Kingdom

In both the developed and developing world, cyber-violence presents a problem that affects most families and societies. However, there is a lack of diagnostic studies dedicated to this phenomenon, especially its factors and the relevant models. Consequently, the aim of this study was to extract the factors that bear upon cyber-violence by conducting a systematic literature review. This was anticipated to lead to a better understanding of those factors with their models and effects, as explored in previous research. This systematic literature review resulted in a comprehensive and rigorous examination of the relevant literature, which included numerous articles of interest. The findings indicate that most of the past research on cyber-violence has primarily looked at the factors affecting the behavioral intention to engage in the phenomenon, while very few studies have looked at its effects, especially in developing countries. The period from which the primary studies were collected was 2010-2024, using the ScienceDirect, IEEE Explorer, Springer, SAGE, Taylor and Francis, MDPI and Emerald Insight databases. Inclusion and exclusion criteria were applied, resulting in 91 studies to be systematically reviewed. It emerged from this review that engagement in cyber-violence was predominantly due to a lack of cyber-violence models and a failure to consider the factors that potentially contribute to engagement in cyber-violence. Hence, these factors are identified in this paper, looking at the academic, economic and social impact of cyber-violence. The outcomes of this review could assist future researchers by providing a roadmap of institutional repositories and guidelines for recognizing the models and factors of cyber-violence, together with its effects. © 2024 The Authors

关键词： Cyber-bullying Cyber-crime Cyber-harassment Cyber-stalking Cyber—violence Theory of Planned Behavior

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Need for Computational and Psycho-linguistics Models in Natural Language Processing for Web Documents 4

Need for Computational and Psycho-linguistics Models in Natu...

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4th International Symposium on Multidisciplinary Studies and Innovative Technologies, ISMSIT 2020

作者： Naqvi, Muhammad Raza Shahzad, Syed Khuram Iqbal, Muhammad Waseem Ahmed, Muhammad Tahir, Muhammad Usman Khan, Babar Ayub Superior College Department of Computer Science Lahore Pakistan University of Management Technology Department of Informatics and Systems Lahore Pakistan Superior College Department of Software Engineering Lahore Pakistan

ISBN: (纸本)9781728190907

Natural Language Processing (NLP) is an argument that provokes territory of figural manner for motorized scrutiny and depiction of humane language. Research in it unfolded from the term of punch cards till the age of intelligent examination engines which takes less than a second to Figure out millions of web pages and digital documents. In the web, pieces of evidence and end-users produced content to have formerly stimulated text. The obligation of advantageous and astute content driving is decisive forthwith. Consequently, the web is being treated as a foremost source of evidence brought about the extensive extent of a probe to perceive the compass of the machine. Considering the democratization of online content formulation, it has skeptically influenced the information resurgence and eradication. This paper pulls contemporary developments in the NLP probe to perceive the scope of machine learning in a modern era to avoid ramification of adverse instructions. © 2020 IEEE.

关键词： Computational and psycholinguistics models Cortana Google Assistant ALO Natural language processing Siri Web documents

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Compositional construction of abstractions for infinite networks of switched systems

arXiv

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arXiv 2021年

作者： Sharifi, Maryam Swikir, Abdalla Noroozi, Navid Zamani, Majid The School of Electrical and Computer Engineering University of Tehran Iran Department of Electrical and Computer Engineering Technical University of Munich Germany The Institute of Informatics LMU Munich Germany Computer Science Department University of Colorado BoulderCO80309 United States

We construct compositional continuous approximations for an interconnection of infinitely many discrete-time switched systems. An approximation (known as abstraction) is itself a continuous-space system, which can be used as a replacement of the original (known as concrete) system in a controller design process. Having synthesized a controller for the abstract system, the controller is refined to a more detailed controller for the concrete system. To quantify the mismatch between the output trajectory of the approximation and of that the original system, we use the notion of so-called simulation functions. In particular, each subsystem in the concrete network and its corresponding one in the abstract network is related through a local simulation function. We show that if the local simulation functions satisfy a certain small-gain type condition developed for a network of infinitely many subsystems, then the aggregation of the individual simulation functions provides an overall simulation function between the overall abstraction and the concrete network. For a network of linear switched systems, we systematically construct local abstractions and local simulation functions, where the required conditions are expressed in terms of linear matrix inequalities and can be efficiently computed. We illustrate the effectiveness of our approach through an application to frequency control in a power gird with a switched (i.e. time-varying) topology. © 2021, CC BY.

关键词： Controllers

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Experimental Analysis for Position Estimation using Trilateration and RSSI in Industry 4.0 11

Experimental Analysis for Position Estimation using Trilater...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Chouhan, Robin Singh Rajawat, Anand Singh Goyal, S.B. Bedi, Pradeep Constantin, Neagu Bogdan Raboaca, Maria Simona Verma, Chaman Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India School of Computer Science and Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Galgotais University Greater Noida India Gheorghe Asachi Technical University of Iasi Department of Power Engineering Iasi Romania National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea Romania University of Eötvös Loránd Faculty of Informatics Budapest1053 Hungary

ISBN: (纸本)9781665487320

With the introduction of industry 4.0, the role of robots especially autonomous bots has increased exponentially. The next thing in order to improve the navigation for these bots is the need for a proper indoor navigation system. The paper proposes experimental analysis for estimating the position using trilateration. The experiment includes three wifi nodes operating at 2.4GHz working as wifi beacons. The paper provides an efficient and cost-effective solution to estimate the approximate position of the node in terms of coordinates. The paper also includes a working mobile application to showcase the node's position in real-time in the form of a 2d map. © 2022 IEEE.

关键词： Indoor positioning systems

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A Location-Independent Flood Prediction Model for Bangladesh's Rivers

A Location-Independent Flood Prediction Model for Bangladesh...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Md Abid Hasan Nirjhor Tahmidur Rouf Md Sajid Hossain Lamia Binta Latif Anika Tasnim Marcin Grzegorzek Institute of Medical Informatics University of Lübeck Lübeck Germany Dept. Of ECE North Carolina State University USA department of EEE American International University Bangladesh Electrical and Electronic Engineering American International University Bangladesh Computer Science and Engineering Jahangirnagar University Bangladesh Silicon Engineering Ulkasemi Private Limited Bangladesh Fraunhofer IMTE Lübeck Germany

The involvement of a multitude of parameters adds to the complexity of modeling a flood. However, floods are among the most destructive of natural disasters and therefore, flood forecasting is one of the key priorities of hydrology. Flood forecasting goes a long way to minimize the loss of lives as well as economic losses. Furthermore, proper modeling of floods can contribute immensely towards future risk reduction and the introduction of necessary policies. At present, the application of machine learning in river and flood analysis has dramatically increased among hydrologists. In this research, we propose a location (District) independent flood prediction model (Random Forest-RF) of commercially significant rivers in Bangladesh. The data Imbalance problem is solved by synthetic minority oversampling of numerical and categorical (SMOTENC) data augmentation techniques. Our results show that the proposed framework outperforms the previously reported results by up to 9%. To the best of our knowledge, our proposed flood prediction framework achieved the best performance in terms of all evaluation matrices on the specific dataset.

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An Optimizing Method for Performance and ResourceUtilization in Quantum Machine Learning Circuits

Research Square

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Research Square 2022年

作者： Salehi, Tahereh Zomorodi, Mariam Plawiak, Pawel Abbaszade, Mina Salari, Vahid Department of Computer Engineering Ferdowsi University of Mashhad Mashhad Iran Department of Computer Science Faculty of Computer Science and Telecommunications Cracow University of Technology Krakow Poland Theoretical and Applied Informatics Polish Academy of Sciences Gliwice Poland Department of Physics Isfahan University of Technology Isfahan84156-83111 Iran Bilbao Spain Department of Physics and Astronomy Howard University WashingtonDC20059 United States

Quantum computing is a new and advanced topic that refers to calculations based on the principles of quantum mechanics. It makes certain kinds of problems be solved easier compared to classical computers. This advantage of quantum computing can be used to implement many existing problems in different fields incredibly effectively. One important field that quantum computing has shown great results in machine learning. Until now, many different quantum algorithms have been presented to perform different machine learning approaches. In some special cases, the execution time of these quantum algorithms will be reduced exponentially compared to the classical ones. But at the same time, with increasing data volume and computation time, taking care of systems to prevent unwanted interactions with the environment can be a daunting task and since these algorithms work on machine learning problems, which usually includes big data, their implementation is very costly in terms of quantum resources. Here, in this paper, we have proposed an approach to reduce the cost of quantum circuits and to optimize quantum machine learning circuits in particular. To reduce the number of resources used, in this paper an approach including different optimization algorithms is considered. Our approach is used to optimize quantum machine learning algorithms for big data. In this case, the optimized circuits run quantum machine learning algorithms in less time than the original ones and by preserving the original functionality. Our approach improves the number of quantum gates by 10.7% and 14.9% in different circuits and the number of time steps is reduced by three and 15 units, respectively. This is the amount of reduction for one iteration of a given sub-circuit U in the main circuit. For cases where this sub-circuit is repeated more times in the main circuit, the optimization rate is increased. Therefore, by applying the proposed method to circuits with big data, both cost and performance are

关键词： Big data

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