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NPRportrait 1.0:A three-level benchmark for non-photorealistic rendering of portraits

Computational Visual Media 2022年第3期8卷 445-465页

作者： Paul L.Rosin Yu-Kun Lai David Mould Ran Yi Itamar Berger Lars Doyle Seungyong Lee Chuan Li Yong-Jin Liu Amir Semmo Ariel Shamir Minjung Son Holger Winnemöller School of Computer Science and Informatics Cardiff UniversityCardiffUK School of Computer Science Carleton UniversityOttawaCanada Department of Computer Science and Technology Tsinghua UniversityBeijingChina Reichman University(the Interdisciplinary Center) HerzliyaIsrael Department of Computer Science and Engineering Pohang University of Science and TechnologyPohangRepublic of Korea Lambda Labs Inc.San FranciscoUSA Hasso Plattner Institute University of PotsdamPotsdamGermany Multimedia Processing Laboratory Samsung Advanced Institute of TechnologySuwonRepublic of Korea Adobe Systems Inc.San JoseUSA

Recently,there has been an upsurge of activity in image-based non-photorealistic rendering(NPR),and in particular portrait image stylisation,due to the advent of neural style transfer(NST).However,the state of performance evaluation in this field is poor,especially compared to the norms in the computer vision and machine learning ***,the task of evaluating image stylisation is thus far not well defined,since it involves subjective,perceptual,and aesthetic *** make progress towards a solution,this paper proposes a new structured,threelevel,benchmark dataset for the evaluation of stylised portrait *** criteria were used for its construction,and its consistency was validated by user ***,a new methodology has been developed for evaluating portrait stylisation algorithms,which makes use of the different benchmark levels as well as annotations provided by user studies regarding the characteristics of the *** perform evaluation for a wide variety of image stylisation methods(both portrait-specific and general purpose,and also both traditional NPR approaches and NST)using the new benchmark dataset.

关键词： non-photorealistic rendering(NPR) image stylization style transfer portrait evaluation benchmark

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Trust Establishment for the Role-Based Collaborative Multi-Robot systems

Trust Establishment for the Role-Based Collaborative Multi-R...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Akbari, Behzad Zhu, Haibin Pan, Ya-Jun Dalhousie University Advanced Control and Mechatronics Laboratory Department of Mechanical Engineering HalifaxNS Canada Nipissing University Collaborative Systems Laboratory Department of Computer Science and Mathematics North BayON Canada

ISBN: (纸本)9798350337020

Trust evaluation and trust establishment play crucial roles in the management of trust within a multi-agent system. When it comes to collaboration systems, trust becomes directly linked to the specific roles performed by agents. The Role-Based Collaboration (RBC) methodology serves as a framework for assigning roles that facilitate agent collaboration. Within this context, the behavior of an agent with respect to a role is referred to as a process role. This research paper introduces a role engine that incorporates a trust establishment algorithm aimed at identifying optimal and reliable process roles. In our study, we define trust as a continuous value ranging from 0 to 1. To optimize trustworthy process roles, we have developed a consensus-based Gaussian Process Factor Graph (GPFG) tool. Our simulations and experiments validate the feasibility and efficiency of our proposed approach with autonomous robots in unsignalized intersections and narrow hallways. © 2023 IEEE.

关键词： Gaussian process factor graph Multi-Agent systems (MASs) Role-based collaboration Trust management systems

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Evaluating Non-Expert Stakeholder Interaction with Artificial Intelligence on Energy Urban Domain Using VIRTSI: The Case of ChatGPT 15

Evaluating Non-Expert Stakeholder Interaction with Artificia...

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15th International Conference on Information, Intelligence, systems and Applications, IISA 2024

作者： Tsihrintzis, George A. Sarmas, Elissaios Marinakis, Vangelis Panagoulias, Dimitrios Tsihrintzi, Evangelia-Aikaterini Virvou, Maria University of Piraeus Department of Informatics Piraeus Greece School of Electrical & Computer Engineering National Technical University of Athens Decision Support Systems Laboratory Greece National and Kapodistrian University of Athens Department of Informatics and Telecommunicatios Athens Greece

ISBN: (纸本)9798350368833

This paper investigates the interaction of non-expert stakeholders with Artificial Intelligence (AI) in the energy urban domain, using the VIRTSI model and focusing on the capabilities of ChatGPT. VIRTSI (Variability and Impact of Reciprocal Trust States towards Intelligent systems), is a rigorous computational model for human-AI Interaction that simulates human trust states, spanning from overtrust to distrust, through user modelling and quantifies the efficiency of the interaction in VIRTSI-adapted confusion matrices. The research employed an 16-question survey, evaluating the accuracy and usefulness of ChatGPT's responses regarding energy consumption, cost-effective solutions, and renewable energy production for residential buildings in Greece. Each answer was assessed by human stakeholders who were non-expert in the energy Urban Domain (e.g house owners, building managers, etc.), who either accepted or rejected the responses based on validation processes. The analysis highlighted key aspects such as repetition, specification, and objections in the interaction with ChatGPT, offering insights into the effectiveness of AI in supporting energy-related decisions. The findings reveal that while AI can provide valuable information, user validation and expert consultation are critical for practical implementation. This highlights both the potential and limitations of integrating AI tools like ChatGPT in enhancing non-expert stakeholder engagement and decision-making in urban energy management, emphasizing the ongoing need for human expert involvement. © 2024 IEEE.

关键词： Decision making

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How to Make Money From Fresh Data: Subscription Strategies in Age-Based systems

How to Make Money From Fresh Data: Subscription Strategies i...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Kaswan, Priyanka Bastopcu, Melih Ulukus, Sennur Etesami, S. Rasoul Basar, Tamer University of Maryland Department of Electrical and Computer Engineering College ParkMD20742 United States University of Illinois Urbana Champaign Coordinated Science Laboratory UrbanaIL61801 United States

ISBN: (纸本)9798350351255

We consider a communication system consisting of a server that tracks and publishes updates about a time-varying data source or event, and a gossip network of users interested in closely tracking the event. The timeliness of the information is measured through the version age of information. The users wish to have their expected version ages remain below a threshold, and have the option to either rely on gossip from their neighbors or subscribe to the server directly to follow updates about the event if the former option does not meet the timeliness requirements. The server wishes to maximize its profit by increasing the number of subscribers and reducing costs associated with the frequent sampling of the event. We model the problem setup as a Stackelberg game between the server and the users, where the server commits to a frequency of sampling the event, and the users make decisions on whether to subscribe or not. As an initial work, we focus on directed networks with unidirectional flow of information and obtain the optimal equilibrium strategies for all the players. We provide simulation results to confirm the theoretical findings and provide additional insights. © 2024 IEEE.

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Eye-tracking in Mixed Reality for Diagnosis of Neurodegenerative Diseases 46

Eye-tracking in Mixed Reality for Diagnosis of Neurodegenera...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Daniol, Mateusz Hemmerling, Daria Sikora, Jakub Jemiolo, Pawel Wodzinski, Marek Wojcik-Pedziwiatr, Magdalena Agh University of Kraków Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Krakow Poland Information Systems Institute Sierre Switzerland Andrzej Frycz Modrzewski Krakow University Department of Neurology Krakow Poland

ISBN: (纸本)9798350371499

Parkinson's disease ranks as the second most prevalent neurodegenerative disorder globally. This research aims to develop a system leveraging Mixed Reality capabilities for tracking and assessing eye movements. In this paper, we present a medical scenario and outline the development of an application designed to capture eye-tracking signals through Mixed Reality technology for the evaluation of neurodegenerative diseases. Additionally, we introduce a pipeline for extracting clinically relevant features from eye-gaze analysis, describing the capabilities of the proposed system from a medical perspective. The study involved a cohort of healthy control individuals and patients suffering from Parkinson's disease, showcasing the feasibility and potential of the proposed technology for non-intrusive monitoring of eye movement patterns for the diagnosis of neurodegenerative *** relevance - Developing a non-invasive biomarker for Parkinson' s disease is urgently needed to accurately detect the disease's onset. This would allow for the timely introduction of neuroprotective treatment at the earliest stage and enable the continuous monitoring of intervention outcomes. The ability to detect subtle changes in eye movements allows for early diagnosis, offering a critical window for intervention before more pronounced symptoms emerge. Eye tracking provides objective and quantifiable biomarkers, ensuring reliable assessments of disease progression and cognitive function. The eye gaze analysis using Mixed Reality glasses is wireless, facilitating convenient assessments in both home and hospital settings. The approach offers the advantage of utilizing hardware that requires no additional specialized attachments, enabling examinations through personal eyewear © 2024 IEEE.

关键词： Neurodegenerative diseases

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Cerberus: The Power of Choices in Datacenter Topology Design

Cerberus: The Power of Choices in Datacenter Topology Design

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作者： Griner, Chen Zerwas, Johannes Blenk, Andreas Ghobadi, Manya Schmid, Stefan Avin, Chen School of Electrical and Computer Engineering Ben-Gurion University of the Negev Beer-Sheva Israel Department of Electrical and Computer Engineering Technical University of Munich Munich Germany Department of Electrical and Computer Engineering Technical University of Munich Faculty of Computer Science University of Vienna Munich Germany Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Boston United States Faculty of Electrical Engineering and Computer Science TU Berlin University of Vienna Berlin Germany

The bandwidth and latency requirements of modern datacenter applications have led researchers to propose various topology designs using static, dynamic demand-oblivious (rotor), and/or dynamic demand-aware switches. However, given the diverse nature of datacenter traffic, there is little consensus about how these designs would fare against each other. In this work, we analyze the throughput of existing topology designs under different traffic patterns and study their unique advantages and potential costs in terms of bandwidth and latency "tax". To overcome the identified inefficiencies, we propose Cerberus, a unified, two-layer leaf-spine optical datacenter design with three topology types. Cerberus systematically matches different traffic patterns with their most suitable topology type: E.g., latency-sensitive flows are transmitted via a static topology, all-to-all traffic via a rotor topology, and elephant flows via a demand-aware topology. We show analytically and in simulations that Cerberus can improve throughput significantly compared to alternative approaches and operate datacenters at higher loads while being throughput-proportional. © 2022 Owner/Author.

关键词： Topology

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Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture

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computers, Materials & Continua 2023年第11期77卷 2545-2564页

作者： Medhat A.Tawfeek Nacim Yanes Leila Jamel Ghadah Aldehim Mahmood A.Mahmood Department of Computer Science College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia Department of Computer Science Faculty of Computers and InformationMenoufia UniversityP.O.Box 6121890MenoufiaEgypt Department of Software Engineering College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia RIADI Laboratory La Manouba UniversityP.O.Box2010ManoubaTunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems and Technology Faculty of Graduate Studies and ResearchCairo UniversityP.O.Box 3753450GizaEgypt

The trend towards smart greenhouses stems from various factors,including a lack of agricultural land area owing to population concentration and housing construction on agricultural land,as well as water *** study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research *** proposed model uses a one-dimensional convolutional neural network(CNN)deep learning model to control the growth of strategic crops,including cucumber,pepper,tomato,and *** proposed model uses the Internet of Things(IoT)to collect data on agricultural operations and then uses this data to control and monitor these operations in real *** helps to ensure that crops are getting the right amount of fertilizer,water,light,and temperature,which can lead to improved yields and a reduced risk of crop *** dataset is based on data collected from expert farmers,the photovoltaic construction process,agricultural engineers,and research *** experimental results showed that the precision,recall,F1-measures,and accuracy of the one-dimensional CNN for the tested dataset were approximately 97.3%,98.2%,97.25%,and 97.56%,*** new smart greenhouse automation system was also evaluated on four crops with a high turnover *** system has been found to be highly effective in terms of crop productivity,temperature management and water conservation.

关键词： Greenhouse wireless sensor network deep learning Internet of Things strategic crops monitoring smart irrigation

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Convergence of Monte Carlo Exploring Starts with TD-Learning 63

Convergence of Monte Carlo Exploring Starts with TD-Learning

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Winnicki, Anna Srikant, R. University of Illinois at Urbana-Champaign Coordinated Science Laboratory Department of Electrical and Computer Engineering United States Uiuc C3.ai Digital Transformation Institute United States

ISBN: (纸本)9798350316339

The use of TD-learning has been widely employed in reinforcement learning algorithms due to its efficiency and practicality. Herein, we study the convergence of a variant of Monte Carlo Exploring Starts when operatornameTD(λ) is used in policy evaluation and policy improvement, and lookahead is used in the policy improvement step. Our results provide a threshold for the amount of lookahead that ensures convergence of Monte Carlo Exploring Starts with T D(λ) as a function of λ in[0,1]. © 2024 IEEE.

关键词： Adversarial machine learning

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Towards No Penalty Control Hazard Handling 66

Towards No Penalty Control Hazard Handling

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2023 IEEE 66th International Midwest Symposium on Circuits and systems, MWSCAS 2023

作者： Surya Balasubramanian, Linknath Rizkalla, Maher Lee, John J. Ytterdal, Trond Kumar, Mukesh Indiana University Purdue University Indianapolis Department of Electrical and Computer Engineering Indianapolis United States Norwegian University of Science and Technology Department of Electronic Systems Trondheim Norway Indian Institute of Technology Indore Department of Electrical Engineering Indore India

ISBN: (纸本)9798350302103

Achieving higher throughput is one of the most important requirements of a modern microcontroller. It is therefore not affordable for it to waste a considerable number of clock cycles in branch mispredictions. This paper proposes a novel mechanism that makes microcontrollers forgo branch predictors and thereby eliminate branch mispredictions. Further, we synthesized the proposed design with 7nm FinFET process and compared its latency with other designs to make sure that the microcontroller's operating frequency is not degraded by using this design. The proposed technique is implemented as five different modules that work together to forward required operands, resolve branches without prediction, and calculate the next instruction's address in the first stage of an in-order five stage pipelined micro-architecture. Since the address of successive instruction to a control transfer instruction is calculated in the first stage of pipeline, branch prediction is no longer necessary, thereby eliminating the clock cycle penalties occurred when using a branch predictor. The designed system was tested using test-bench with modifications to make the test-code compatible with the designed architecture. The designed architecture was able to successfully calculate the address of next correct instruction and fetch it without any wastage of clock cycles. Moreover, the latency of instruction fetch stage of pipeline integrated with the proposed architecture is 330 ps (3 GHz approx.) which is more than the operating frequencies of microcontrollers. Therefore, the proposed design can be integrated in high performance microcontrollers. © 2023 IEEE.

关键词： branch predictor control transfer instruction in-order five stage pipeline microcontroller mispredictions

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Breast Cancer Detection and Classification from Mammogram Images Using Improved Convolutional Neural Network Model 2nd

Breast Cancer Detection and Classification from Mammogram Im...

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2nd International Conference on Connected Objects and Artificial Intelligence, COCIA 2024

作者： Olanloye, Odunayo Dauda Adeniyi, Abidemi Emmanuel Aworinde, Halleluyah Oluwatobi Awotunde, Joseph Bamidele Imoize, Agbotiname Lucky Mejdoub, Youssef Department of Computer Science Bowen University Osun Iwo Nigeria Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin240003 Nigeria Department of Electrical and Electronics Engineering University of Lagos Akoka Lagos Nigeria RITM Laboratory Higher School of Technology ESTC Hassan II University Casablanca Morocco

ISBN: (纸本)9783031704109

A prominent application in healthcare is the classification and detection of breast cancer using convolutional neural networks (CNNs). It entails creating a system that can precisely pinpoint malignant spots or gauge the tendency of breast cancer through the examination of diagnostic pictures like mammograms. This study focuses on the use of Convolutional Neural Networks for the detection and classification of breast cancer. The proposed model analyzes mammogram images to distinguish cancerous and non-cancerous instances automatically. Preparing the dataset developing the CNNs model’s architecture, training the model, assessing its performance, and producing predictions based on hypothetical data are all the steps in the process. The CNN model learns to extract relevant features and patterns from a large dataset, increasing classification accuracy. Tests show that the CNN model is effective at correctly identifying breast cancer. This method will help medical professionals diagnose patients quickly and correctly, improving patient outcomes in the fight against breast cancer. The prediction can be improved using post-processing techniques and the trained model can be used in practical applications. The model accuracy was 0.98 and the model loss was 0.14. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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