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arXiv 2023年

作者： Mahon, Louis Lukasiewicz, Thomas School of Informatics University of Edinburgh United Kingdom Institute of Logic and Computation Vienna University of Technology Austria Department of Computer Science University of Oxford United Kingdom

We present a new image complexity metric. Existing complexity metrics cannot distinguish meaningful content from noise, and give a high score to white noise images, which contain no meaningful information. We use the minimum description length principle to determine the number of clusters and designate certain points as outliers and, hence, correctly assign white noise a low score. The presented method is a step towards humans’ ability to detect when data contain a meaningful pattern. It also has similarities to theoretical ideas for measuring meaningful complexity. We conduct experiments on seven different sets of images, which show that our method assigns the most accurate scores to all images considered. Additionally, comparing the different levels of the hierarchy of clusters can reveal how complexity manifests at different scales, from local detail to global structure. We then present ablation studies showing the contribution of the components of our method, and that it continues to assign reasonable scores when the inputs are modified in certain ways, including the addition of Gaussian noise and the lowering of the resolution. Code is available at https://***/Lou1sM/assemblies. © 2023, CC BY.

关键词： Gaussian noise (electronic)

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Quantum Meets the Minimum Circuit Size Problem 13

Quantum Meets the Minimum Circuit Size Problem

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13th Innovations in Theoretical computer Science Conference, ITCS 2022

作者： Chia, Nai-Hui Chou, Chi-Ning Zhang, Jiayu Zhang, Ruizhe Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States School of Engineering and Applied Sciences Harvard University BostonMA United States Department of Computer Science Boston University MA United States California Institute and Technology PasadenaCA United States Department of Computer Science The University of Texas AustinTX United States

ISBN: (纸本)9783959772174

In this work, we initiate the study of the Minimum Circuit Size Problem (MCSP) in the quantum setting. MCSP is a problem to compute the circuit complexity of Boolean functions. It is a fascinating problem in complexity theory - its hardness is mysterious, and a better understanding of its hardness can have surprising implications to many fields in computer science. We first define and investigate the basic complexity-theoretic properties of minimum quantum circuit size problems for three natural objects: Boolean functions, unitaries, and quantum states. We show that these problems are not trivially in NP but in QCMA (or have QCMA protocols). Next, we explore the relations between the three quantum MCSPs and their variants. We discover that some reductions that are not known for classical MCSP exist for quantum MCSPs for unitaries and states, e.g., search-to-decision reductions and self-reductions. Finally, we systematically generalize results known for classical MCSP to the quantum setting (including quantum cryptography, quantum learning theory, quantum circuit lower bounds, and quantum fine-grained complexity) and also find new connections to tomography and quantum gravity. Due to the fundamental differences between classical and quantum circuits, most of our results require extra care and reveal properties and phenomena unique to the quantum setting. Our findings could be of interest for future studies, and we post several open problems for further exploration along this direction. © Nai-Hui Chia, Chi-Ning Chou, Jiayu Zhang, and Ruizhe Zhang;licensed under Creative Commons License CC-BY 4.0

关键词： Boolean functions

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Federated learning: Applications, challenges and future directions

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International Journal of Hybrid Intelligent Systems 2022年第1-2期18卷 19-35页

作者： Bharati, Subrato Mondal, M. Rubaiyat Hossain Podder, Prajoy Prasath, V.B. Surya Institute of Information and Communication Technology Bangladesh University of Engineering and Technology Dhaka Bangladesh Division of Biomedical Informatics Cincinnati Children's Hospital Medical Center Cincinnati OH United States Department of Pediatrics University of Cincinnati College of Medicine Cincinnati OH United States Department of Biomedical Informatics College of Medicine University of Cincinnati Cincinnati OH United States Department of Electrical Engineering and Computer Science University of Cincinnati Cincinnati OH United States

Federated learning (FL) refers to a system in which a central aggregator coordinates the efforts of several clients to solve the issues of machine learning. This setting allows the training data to be dispersed in order to protect the privacy of each device. This paper provides an overview of federated learning systems, with a focus on healthcare. FL is reviewed in terms of its frameworks, architectures and applications. It is shown here that FL solves the preceding issues with a shared global deep learning (DL) model via a central aggregator server. Inspired by the rapid growth of FL research, this paper examines recent developments and provides a comprehensive list of unresolved issues. Several privacy methods including secure multiparty computation, homomorphic encryption, differential privacy and stochastic gradient descent are described in the context of FL. Moreover, a review is provided for different classes of FL such as horizontal and vertical FL and federated transfer learning. FL has applications in wireless communication, service recommendation, intelligent medical diagnosis system and healthcare, which we review in this paper. We also present a comprehensive review of existing FL challenges for example privacy protection, communication cost, systems heterogeneity, unreliable model upload, followed by future research directions. © 2022 - IOS Press. All rights reserved.

关键词： Artificial intelligence computing methodologies federated learning healthcare machine learning privacy protection

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Digital Twin Definition for Detecting Abnormal Perspiration Patterns in High-Stress Professions 23

Digital Twin Definition for Detecting Abnormal Perspiration ...

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23rd RoEduNet Conference: Networking in Education and Research, RoEduNet 2024

作者： Vizitiu, Cristian Turcin, Ioan Dominey, Kevin Alexander Softic, Selver Dinculescu, Adrian-Catalin Bocanet, Vlad I. Dugan, Cosmin Moraru, Sorin-Aurel The Space Applications and Technologies Laboratory Institute of Space Science-Subsidiary of INFLPR Magurele Romania Transilvania University of Brasov Faculty of Electrical Engineering and Computer Science Department of Automatics and Information Technology Brasov Romania CAMPUS 02 University of Applied Sciences Department Automation Technology Graz Austria CAMPUS 02 University of Applied Sciences Department IT and Business Informatics Graz Austria Robotics and Production Management Technical University of Cluj-Napoca Cluj-Napoca Faculty of Industrial Engineering Department of Manufacturing Engineering Romania Bucharest University Emergency Hospital Bucharest Romania

ISBN: (纸本)9798331540388

Perspiration is a physiological response in high-stress situations, that also plays a key role in thermoregulation and stress management. Understanding perspiration patterns is used for assessing physiological responses and optimizing human performance especially in professions such as emergency medical services and military operations, as well as for individuals exposed to Isolated Confined Extreme (ICE) environments such as astronauts and space analog mission participants. This paper defines a Digital Twin (DT) model, that will be integrated with an existing physical sweat simulator module (SSM) prototype. Its goal is to simulate human perspiration under high-stress conditions by integrating environmental, physiological, and psychological data. The system architecture is developed via Model-Based Systems Engineering (MBSE). The scope of the designed virtual model is to accurately reflect physical perspiration, detect abnormal perspiration patterns, and generate recommendations regarding stress countermeasures. The goal is to influence the behavioral and/or physiological state of the physical twin, thereby creating a dynamic, responsive system. Machine learning (ML) models will be used to identify patterns in the environmental, physiological, and psychological inputs and predict sweat volume, chemical composition, and regional sweat distributions across the body. ML will also be used to detect abnormal perspiration patterns based on input data. The application of generative AI for creating test data from simulation templates is anticipated to facilitate the comparative analysis of two to three distinct profiles, such as astronauts, soldiers, and paramedics or firefighters, using simulated values. The SSM, with its integrated sensors, will serve the dual purpose of validating the model and providing realistic physical simulation. © 2024 IEEE.

关键词： Physiological models

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A Machine Learning Approach for Analyzing Texture and Morphometry Markers of Glioblastoma TERT Promoter Mutations in DAPI Fluorescence Images

A Machine Learning Approach for Analyzing Texture and Morpho...

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Medical Imaging 2025: Digital and Computational Pathology

作者： Afsari, Fatemeh Naglah, Ahmed Gupta, Akshita Lohaan, Harshit Onubogu, Ugoma Janiszewska, Michalina Sarder, Pinaki Division of Nephrology Hypertension and Renal Transplantation-Quantitative Health Section Department of Medicine College of Medicine University of Florida GainesvilleFL32603 United States Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida GainesvilleFL32611 United States Department of Computer and Information Science and Engineering University of Florida GainesvilleFL32611 United States Department of Molecular Medicine The Herbert Wertheim UF Scripps Institute for Biomedical Innovation and Technology JupiterFL33458 United States

ISBN: (纸本)9781510686045

Advanced molecular and imaging techniques, such as STAR-FISH, are frequently required to identify mutations in tissue specimens to properly diagnose Glioblastoma (GBM), an aggressive type of brain tumor. Mutations to the telomerase reverse transcriptase promoter (TERTp) are common in cancer and present in approximately 80% of all GBM tumors. We hypothesize that machine learning can stratify cells by their TERTp mutation status using DAPI fluorescence images since the STAR-FISH technique can be costly and inaccessible for some healthcare facilities. In this study, we present a machine learning-based approach to recognize nuclei harboring TERTp mutations solely based on the DAPI fluorescence images of GBM tissues. The proposed pipeline integrates a convolutional neural network (CNN)-based deep learning model to segment cells, extract hand-engineered features, and feed them into fully connected (FC) layers within the neural network or other machine learning classifiers to identify TERTp mutant cells. This approach involves extracting DAPI features, including both morphological and textural image features, followed by an ensemble of machine learning modules to predict TERTp mutant cells. We evaluate our approach on a cohort of 18 patients (70 pairs of DAPI and STAR-FISH processed images). Our experimental results show that the FC layers within the neural network achieve the highest AUC, F1-Score, and recall of 0.77, 0.69, and 0.86, respectively, with the integrated CNN model achieving a Dice score of 0.83, accurately segmenting cells. Our work is one of the earliest attempts that we are aware of to use machine-learning models for TERTp mutation identification using DAPI fluorescence images. Our results indicate the possibility of improved GBM diagnosis accuracy. To improve patient diagnosis and treatment planning for GBM, future research aims will expand the cohort and enhance the models to provide more precise and diagnostically valuable models for the interpretation o

关键词： Image segmentation

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Joint Packet Scheduling and UAV Trajectory Design in Air-Ground Integrated Network

Joint Packet Scheduling and UAV Trajectory Design in Air-Gro...

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IEEE Globecom Workshops

作者： Shichao Li Zhiqiang Yu Hongbin Chen Ning Zhang Mianxiong Dong School of Information and Communication Guilin University of Electronic Technology Guilin China Department of Electrical and Computer Engineering University of Windsor Windsor ON Canada Department of Sciences and Informatics Muroran Institute of Technology Muroran Japan

Compared with the terrestrial network, the air-ground integrated network consisting of unmanned aerial ve-hicles (UAV s) and high altitude platforms (HAPs) offers the advantages of large coverage, high capacity, and seamless connection, which can provide effective communication services for the Internet of remote things (IoRT). In this paper, we investigate the joint packet scheduling and UAV trajectory design problem, with the objective of minimizing the average packet queue delay of IoRT in the air-ground integrated network. Since the problem is a non-convex in nature, we reformulate it into a Markov decision process (MDP) firstly. Then, based on the multi-agent deep deterministic policy gradient (MADDPG) method, we propose a joint packet scheduling and UAV trajectory design (JPSTD) algorithm. Simulation results show that the proposed JPSTD algorithm outperforms the benchmark algorithms in terms of reducing the average packet queue delay.

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Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN

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computers, Materials & Continua 2021年第9期68卷 4087-4108页

作者： Sehar Shahzad Farooq Mustansar Fiaz Irfan Mehmood Ali Kashif Bashir Raheel Nawaz KyungJoong Kim Soon Ki Jung School of Computer Science and Engineering Kyungpook National UniversityDaeguKorea Faculty of Engineering&Informatics School of MediaDesign and TechnologyUniversity of BradfordBradfordBD71DPUK Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Department of Operations TechnologyEvents and Hospitality ManagementManchester Metropolitan UniversityUK Institute of Integrated Technology Gwangju Institute of Science and TechnologyKorea

Game player modeling is a paradigm of computational models to exploit players’behavior and experience using game and player *** modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player’s behavior within the game as well as the player’s experience with the *** behavior focuses on dynamic and static information gathered at the time of *** experience concerns the association of the human player during gameplay,which is based on cognitive and affective physiological measurements collected from sensors mounted on the player’s body or in the player’s *** this paper,player experience modeling is studied based on the board puzzle game“Candy Crush Saga”using cognitive data of players accessed by physiological and peripheral *** Short-Term Memory-based Deep Neural Network(LSTM-DNN)is used to predict players’effective states in terms of valence,arousal,dominance,and liking by employing the concept of transfer *** learning focuses on gaining knowledge while solving one problem and using the same knowledge to solve different but related *** homogeneous transfer learning approach has not been implemented in the game domain before,and this novel study opens a new research area for the game industry where the main challenge is predicting the significance of innovative games for entertainment and players’*** not only from a player’s point of view,it is also a benchmark study for game developers who have been facing problems of“cold start”for innovative games that strengthen the game industrial economy.

关键词： Game player modeling experience modeling player analytics deep learning LSTM game play data Candy Crush Saga

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The Smart Analysis of Performing Scalable Inference for Big Data Analytics

The Smart Analysis of Performing Scalable Inference for Big ...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Bhandarkar, Vishakha D. Rongali, Aruna Sri Shamout, Mohamed Dawood Pund, Sachin Nama, Gigih Forda Patil, Shrishailappa Yeshwantrao Chavan College of Engineering Applied Mathematics and Humanities India University of the Cumberlands Graduate Doctoral Information Technology Information Technology Emphasis/Information Technology United States College of Business Administration University of Sharjah Department of Management Sharjah United Arab Emirates Shri Ramdeobaba College of Engineering and Management Department of Industrial Engineering Maharashtra Nagpur India Doctoral Program of Environtmental Science University of Lampung Department of Informatics Bandar Lampung Indonesia Vishwakarma Institute of Technology Computer Engineering Pune India

ISBN: (纸本)9798350357769

This paper examines the reproducibility of massive information analytics under particular factors. The paper proposes the 'performing Scalable Inference' technique to cope with scalability troubles and to exploit current big statistics platforms for efficient computing and statistics garage of the statistics. In particular, the paper describes how to perform leak-free, parallelizable visible analytics over massive datasets using present extensive records analytics frameworks such as Apache Flink. This method presents an automated manner to execute analytics that preserves reproducibility and the ability to make adjustments without re-running the entire technique. The paper also demonstrates how these analytics may help several real-world use instances, explore affected person cohorts for studies, and develop stratified patient cohorts for hospital therapy. In the end, the paper observes how the proposed method may be exercised within the real world. Actively scalable inference for massive information analytics is pivotal in optimizing decision-making and allocation of assets. Typically, such inferences are made based on information accumulated from numerous sources, databases, unstructured data, and different digital sources. So one can ensure scalability, a complete cloud-primarily based platform has to be hired. This solution will permit the ***, deploying the essential records series and evaluation algorithms are prime here. It could permit the platform to recognize the styles inside the statistics and discover any ability correlations or traits. Additionally, predictive analytics and system mastering strategies may be incorporated to provide insights into the results of the information. In the long run, by leveraging those techniques, the platform can draw efficient inferences and appropriately compare situations in an agile and green way.. © 2023 IEEE.

关键词： adjustments an a lytics automated preserves statistics

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Implementing a Modified Low-complexity U-Net Model for Segmenting Brain Tumors

Implementing a Modified Low-complexity U-Net Model for Segme...

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Communication and Information technology (AICCIT), Al-Sadiq International Conference on

作者： Mawj Abdul-Ameer Al-Murshidawy Omran Al-Shamma Department of computer science Information Institute for Postgraduate Studies Iraqi Commission for Computers and Informatics Baghdad Iraq Scientific Affairs Department University of Information Technology and Communications Baghdad Iraq

Accurate segmenting of brain tumors has become a challenge recently. Manual segmentation needs experts with high skills and takes a long time. This challenge motivates several researchers to introduce various models to solve the problem with different degrees of success. The main issue with these models is that the model accuracy increased proportionally with model complexity, which may develop further difficulties in the improvement stage. Thus, this paper aims to implement a low-complexity model with several layers less than the standard U-Net, achieving higher accuracy than the state-of-the-art models. The proposed model has obtained more than a 40% reduction in layers and better performance than the available models. The dataset BRAST 2018 is employed with a total image of 7750. The proposed model attained a 0.9222 dice score, 98.99% accuracy, and 0.1889 loss value. The findings indicate that this model has promising applications and can be used in clinical settings to segment different brain tumor sub-regions. Such models facilitate the tumor localization and classification stages.

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Blockchain-Based Secure and Efficient Secret Image Sharing With Outsourcing Computation in Wireless Networks

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IEEE Transactions on Wireless Communications 2024年第1期23卷 423-435页

作者： Zhou, Zhili Wan, Yao Cui, Qi Yu, Keping Mumtaz, Shahid Yang, Ching-Nung Guizani, Mohsen Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou510006 China Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China Graduate School of Science and Engineering Hosei University Tokyo184-8584 Japan RIKEN Center for Advanced Intelligence Project RIKEN Tokyo103-0027 Japan Department of Applied Informatics Silesian University of Technology Gliwice44-100 Poland Department of Engineering Nottingham Trent University NottinghamNG1 4FQ United Kingdom Department of Computer Science and Information Engineering National Dong Hwa University Hualien97401 Taiwan Abu Dhabi United Arab Emirates

Secret Image Sharing (SIS) is the technology that shares any given secret image by generating and distributing n shadow images in the way that any subset of k shadow images can restore the secret image. However, in the existing SIS schemes, the shadow images will be easily tampered and corrupted during the communication, which will pose serious security issues. Recently, blockchain has emerged as a promising paradigm in the field of data communication and information security. To securely communicate and effectively protect the secret image data in wireless networks, we propose a Blockchain-based Secure and Efficient Secret Image Sharing (BC-SESIS) scheme with outsourcing computation in wireless networks. In the proposed BC-SESIS scheme, the shadow images are encrypted and stored in the blockchain to prevent them from being tampered and corrupted. The identity authentication-enabled smart contract is deployed to achieve the (k,n) threshold for secret image restoring. Furthermore, to reduce the computational burden of smart contract and users, an efficient outsourcing computation method is designed to outsource the restoring task, which is securely implemented by agent miners in the encryption domain. Theoretical analysis and extensive experiments demonstrate that the BC-SESIS scheme can achieve desirable communication security and high computational efficiency in the wireless networks. © 2023 IEEE.

关键词： Smart contract

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