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Faster algorithms for shortest path and network flow based on graph decomposition

Journal of Graph Algorithms and Applications 2019年第5期23卷 781-813页

作者： Kashyop, Manas Jyoti Nagayama, Tsunehiko Sadakane, Kunihiko Department of Computer Science and Engineering Indian Institute of Technology Madras Chennai India Department of Mathematical Informatics Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan

We propose faster algorithms for the maximum ow problem and shortest path problems based on graph decomposition. Our algorithms first construct indices (data structures) from a given graph, then use them for solving the problems. Time complexities of our algorithms depend on the size of the maxi- mum triconnected component in the graph, say r. Max ow indexing problem is a basic network ow problem, which consists of two phases. In a preprocessing phase we construct an index and in a query phase we process the query using the index. We can solve all pairs maximum ow problem and minimum cut problem using the indices. Our algorithms run faster than known algorithms if r is small. The maximum ow problem can be solved in O(nr) time, which is faster than the best known O(nm) algorithm [29] if r = o(m), where n and m are the numbers of vertices and edges in the given network, respectively. Distance oracle problem is a basic problem in shortest path, consisting of two phases. In preprocessing phase we construct index and in query phase we use the index to find shortest path between two vertices. We use these indices to solve single source shortest path and all pair shortest path problems. If the given graph is undirected and all the weights are non-negative integers, then our algorithm finds shortest path between two vertices in O(m) time. If the given graph is directed or the weights are non-negative real numbers then our algorithm finds shortest path between two vertices in O(m + n log r) time. If the edge weights are real numbers (i.e some of the weights are negative) then our algorithm finds shortest path between two vertices in O(m + nr) time. © 2019, Brown University. All rights reserved.

关键词： Flow graphs

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A Brief Review of Explainable Artificial Intelligence in Healthcare

SSRN

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SSRN 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalo, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Swinburne University of Technology HawthornVIC3122 Australia Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, The Authors. All rights reserved.

关键词： Decision making

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Initial Study on Implementation of Smoothing Filters for the Purpose of Bioimpedance Spectroscopy Parameters Classification

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IFAC-PapersOnLine 2022年第4期55卷 393-398页

作者： Aleksandra Kawala-Sterniuk Amir F. Al-Bakri Mariusz Pelc Katarzyna Cichoń Wojciech Chlewicki Stepan Ozana radek Martinek Jakub Możaryn Volodymyr Khoma Halyna Kenyo Edward Jacek Gorzelańczyk Michał Podpora Jarosław Zygarlicki Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics 45-758 Opole Poland Department of Biomedical Engineering College of Engineering University of Babylon 51001 Babylon Iraq University of Greenwich School of Computing and Mathematical Sciences SE10 9LS London UK West Pomeranian University of Technology in Szczecin Faculty of Electrical Engineering 70-313 Szczecin Poland Faculty of Mechatronics Warsaw University of Technology 02-525 Warsaw Poland Lviv Polytechnic National University Institute of Computer Technologies Automation and Metrology Lviv Ukraine Institute of Philosophy Kazimierz Wielki University 85-092 Bydgoszcz Poland Faculty of Mathematics and Computer Science Adam Mickiewicz University in Poznań 61-614 Poznań Poland

Bioimpedance is a commonly used method for various conditions monitoring. In this paper, the authors carried out some research where they implemented various smoothing filters to enable identification of the bioimpedance spectroscopy parameters. The proposed filtering methods may also be used for application on embedded systems, which have smaller computing power but have become recently very popular. The obtained results with the implementation of smoothing filters were promising, however, some of the obtained results were unsatisfactory. This work also contains a brief introduction to bioimpedance spectroscopy and smoothing filters.

关键词： Bioimpedance Spectroscopy smoothing-filtering Savitzky-Golay Filter Median Filtering Moving-Average Filters

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A Brief Review of Explainable Artificial Intelligence in Healthcare

arXiv

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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

关键词： Decision making

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Retraction notice to "Incorporating crumb rubber in slag-based geopolymer: Experimental work and predictive modelling" [Case Stud. Constr. Mater. 21 (2024) e03893] (Case Studies in Construction Materials (2024) 21, (S2214509524010441), (10.1016/***.2024.e03893))

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Case Studies in Construction Materials 2025年 22卷

作者： raut, Ashwin Narendra Alyaseen, Ahmad Khan, Afzal Husain Janga, Supriya Poddar, Arunava Sihag, Parveen Sumra, Vinisha Lee, Daeho Kumar, raj Singhi, Tej Andhra Pradesh 522502 India Civil Engineering Department Shoolini University Himachal Pradesh Solan173229 India Civil and Architectural Engineering Department College of Engineering and Computer Sciences Jazan University PO Box 706 Jazan45142 Saudi Arabia Department of Civil Engineering Vikas Group of Institutions Nunna Vijayawada Rural Andhra Pradesh India Civil Engineering Department Chandigarh University Punjab Mohali140413 India Mullana Haryana Ambala India Department of Mechanical Engineering Gachon University 1342 Seongnamdaero Sujeong-gu Seongnam13120 Korea Republic of Jindal School of Environment & Sustainability O.P. Jindal Global University Haryana Sonipat131001 India Savaria Institute of Technology Faculty of Informatics ELTE Eötvös Loránd University Budapest1117 Hungary

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://***/about/policies/article-withdrawal). This article has been retracted at the request of the Editors-in-Chief. Following concerns raised by a reader, an investigation of this paper was undertaken by the editors. The findings indicate that this paper [https://***/10.1016/***.2024.e03893] and another in Construction and Building Materials [https://***/10.1016/***.2024.138985] utilise the same dataset (see supplementary data provided for each paper) and the same methodology (identical Figures 5 and 12 in CSCM and Figures 5 and 18 in CBM paper). Four other figures (1, 2a, 3 and 4) and four tables (Tables 1, 3, 4 and 5) are also repeated in both papers. As part of this investigation, the lead author (raut) provided additional supporting data for review. The investigating editor found the mix design data did not appear to match that reported in the published supplementary material and the compression results were suspect for their minimal apparent variation over 108 geopolymer mixes. Additionally, mix design data and density was incorrectly calculated. The corresponding author stated that this CSCM paper was submitted on June 27, 2024, and published first and claims to have no knowledge of the CBM submission on 28 August 2024 despite five common coauthors. Both papers were in review at the same time and ultimately published within five days of each other. © 2025 The Authors

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A novel transfer learning-based model for diagnosing malaria from parasitized and uninfected red blood cell images

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Decision Analytics Journal 2023年 9卷

作者： Qadri, Azam Mehmood raza, Ali Eid, Fatma Abualigah, Laith Institute of Computer Science Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan 64200 Pakistan Technology Management Stony Brook University 11794 NY United States Department of Applied Artificial Intelligence College of Computing and Informatics Sungkyunkwan University Seoul 03063 South Korea Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos 13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied Science Research Center Applied science private university Amman 11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia

Malaria represents a potentially fatal communicable illness triggered by the Plasmodium parasite. This disease is transmitted to humans through the bites of Anopheles mosquitoes that carry the infection. This disease has significant and devastating consequences on the health systems of fragile countries, particularly in sub-Saharan Africa. Malaria affects red blood cells by invading and replicating within them, destroying them, and releasing toxic byproducts into the bloodstream. The parasite's ability to stick and modify the surface of red blood cells can cause them to become sticky, obstructing blood flow in vital organs such as the brain and spleen. Therefore, efficient approaches for the early detection of malaria are critical to saving patients’ lives. The main aim of this study is to develop an efficient model for early malaria diagnosis. We used malaria images based on parasitized and uninfected red blood cells for the study experiments. We applied neural network-based approaches such as Neural Search Architecture Network (NASNet) and compared its performance with machine learning techniques. Moreover, we proposed a novel NNR (NASNet random forest) method for feature engineering. The proposed NNR approach first extracts spatial features from input malaria images, then class prediction probability features are extracted from these spatial features. The feature set obtained from the data extraction trains machine learning models. Our comprehensive experiments show that the support vector machine outperformed state-of-the-art models, achieving a high-performance score of 99% and having an inference time near 0.025 s. We validated the performance using k-fold cross-validation and optimized the hyperparameters through tuning. Our proposed research has improved the early diagnosis of malaria and can assist medical specialists in reducing the mortality rate. © 2023 The Author(s)

关键词： Deep learning Machine learning Malaria diagnosis Neural search architecture network Red blood cells Transfer learning

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Quantum continual learning of quantum data realizing knowledge backward transfer

arXiv

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arXiv 2022年

作者： Situ, Haozhen Lu, Tianxiang Pan, Minghua Li, Lvzhou College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China Institute of Quantum Computing and Computer Theory School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin541004 China Department of Physics Tsinghua University Beijing100084 China

For the goal of strong artificial intelligence that can mimic human-level intelligence, AI systems would have the ability to adapt to ever-changing scenarios and learn new knowledge continuously without forgetting previously acquired knowledge. When a machine learning model is consecutively trained on multiple tasks that come in sequence, its performance on previously learned tasks may drop dramatically during the learning process of the newly seen task. To avoid this phenomenon termed catastrophic forgetting, continual learning, also known as lifelong learning, has been proposed and become one of the most up-to-date research areas of machine learning. As quantum machine learning blossoms in recent years, it is interesting to develop quantum continual learning. This paper focuses on the case of quantum models for quantum data where the computation model and the data to be processed are both quantum. The gradient episodic memory method is incorporated to design a quantum continual learning scheme that overcomes catastrophic forgetting and realizes knowledge backward transfer. Specifically, a sequence of quantum state classification tasks is continually learned by a variational quantum classifier whose parameters are optimized by a classical gradient-based optimizer. The gradient of the current task is projected to the closest gradient, avoiding the increase of the loss at previous tasks, but allowing the decrease. Numerical simulation results show that our scheme not only overcomes catastrophic forgetting, but also realize knowledge backward transfer, which means the classifier's performance on previous tasks is enhanced rather than compromised while learning a new task. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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On the undecidability of the panopticon detection problem

arXiv

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arXiv 2021年

作者： Liagkou, V. Nastou, P.E. Spirakis, P. Stamatiou, Y.C. Computer Technology Institute and Press - "Diophantus" University of Patras Campus 26504 Greece Department of Computer Science University of Liverpool UK and Computer Engineering and Informatics Department University of Patras 26504 Greece Department of Business Administration University of Patras 26504 Greece University of Ioannina Department of Informatics and Telecommunications Koatakioi Arta47100 Greece Department of Mathematics University of the Aegean Applied Mathematics and Mathematical Modeling Laboratory Samos Greece

The Panopticon (which means "watcher of everything") is a well-known structure of continuous surveillance and discipline proposed by Bentham in 1785. This device was, later, used by Foucault and other philosophers as a paradigm and metaphor for the study of constitutional power and knowledge as well as a model of individuals’ deprivation of freedom. Nowadays, technological achievements have given rise to new, non-physical (unlike prisons), means of constant surveillance that transcend physical boundaries. This, combined with the confession of some governmental institutions that they actually collaborate with these Internet giants to collect or deduce information about people, creates a worrisome situation of several co-existing Panopticons that can act separately or in close collaboration. Thus, they can only be detected and identified through the expense of (perhaps considerable) effort. In this paper we provide a theoretical framework for studying the detectability status of Panopticons that fall under two theoretical, but not unrealistic, definitions. We show, using Oracle Turing Machines, that detecting modern day, ICT-based, Panopticons is an undecidable problem. Furthermore, we show that for each sufficiently expressive formal system, we can effectively construct a Turing Machine for which it is impossible to prove, within the formal system, either that it is a Panopticon or it is not a Panopticon. © 2021, CC BY.

关键词： Turing machines

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A Review of 5G technology: Architecture and Challenges

A Review of 5G Technology: Architecture and Challenges

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International Conference on Computing, engineering, and Design (ICCED)

作者： Zainab S. Attarbashi Abdulazeez E. Altaleb Azana Hafizah Mohd Aman Aisha Hassan Abdalla Hashim Sebahattin Eker Information & Communication Technology International Islamic University Malaysia Kuala Lumpur Malaysia Communication Systems Department Informatics Institute Istanbul Technical University Istanbul Turkey Faculty of Information Science & Technology University Kebangsaan Malaysia Bangi Malaysia Computer & Information Engineering International Islamic University Malaysia Kuala Lumpur Malaysia

ISBN: (纸本)9781665453905

5G Networks are considered as a new evolution since it aims to increase the wireless capacity 1000 times the previous generations. By 2025, it is expected that 5G will be connecting around 3 billion people and 41.6 billion devices enabling the Internet of Things (IoT) and the Internet of Everything (IoE) while increasing energy efficiency 90% more than 4G and reducing 20% of carbon emissions. In this paper, the deployment requirements and challenges of 5G Networks are reviewed. Starting with the overall network architecture and network slicing framework including the service-type slicing and the infrastructure slicing are investigated. Later, the spectrum and multiple access techniques are explained. Finally, the challenges and the available solutions have been discussed.

关键词： Wireless communication Energy consumption 5G mobile communication Network slicing computer architecture Carbon dioxide Energy efficiency

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Modeling Prototypical Preference Behavior and Diversity using rank Score Characteristic Functions

Modeling Prototypical Preference Behavior and Diversity usin...

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IEEE International Conference on Cognitive informatics

作者： Christina Schweikert Shinsuke Shimojo Hannah Glasser Rebecca Hendsey rami Alsaber D. Frank Hsu St. John's University Queens NY USA Division of Biology & Biological Engineering / Computation & Neural Systems California Institute of Technology Pasadena CA USA Department of Computer and Information Science Laboratory of Informatics and Data Mining Fordham University New York NY USA

When given two human face images to choose, a subject's decision process is recorded as a sequence of eye movement gaze points. This sequence is then analyzed to detect and predict the preference made by the subject. In an experiment with twelve subjects, each with sixty trials, we have analyzed the 720 sequences using five attributes and combinatorial fusion. Results are promising with good accuracy and efficiency. In this paper, we characterize the decision-making behavior of each subject and measure the cognitive diversity between each of these twelve subjects and a prototypical subject. Our study contributes to improving the data and predictive quality of the experiment and the computational modeling.

关键词： Computational modeling Decision making Behavioral sciences Cognitive systems Faces

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