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Quality Metric Guided Portrait Line Drawing Generation from Unpaired Training Data

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arXiv 2022年

作者： Yi, Ran Liu, Yong-Jin Lai, Yu-Kun Rosin, Paul L. BNRist MOE-Key Laboratory of Pervasive Computing The Department of Computer Science and Technology Tsinghua University Beijing China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Computer Science and Informatics Cardiff University United Kingdom

Face portrait line drawing is a unique style of art which is highly abstract and expressive. However, due to its high semantic constraints, many existing methods learn to generate portrait drawings using paired training data, which is costly and time-consuming to obtain. In this paper, we propose a novel method to automatically transform face photos to portrait drawings using unpaired training data with two new features;i.e., our method can (1) learn to generate high quality portrait drawings in multiple styles using a single network and (2) generate portrait drawings in a "new style" unseen in the training data. To achieve these benefits, we (1) propose a novel quality metric for portrait drawings which is learned from human perception, and (2) introduce a quality loss to guide the network toward generating better looking portrait drawings. We observe that existing unpaired translation methods such as CycleGAN tend to embed invisible reconstruction information indiscriminately in the whole drawings due to significant information imbalance between the photo and portrait drawing domains, which leads to important facial features missing. To address this problem, we propose a novel asymmetric cycle mapping that enforces the reconstruction information to be visible and only embedded in the selected facial regions. Along with localized discriminators for important facial regions, our method well preserves all important facial features in the generated drawings. Generator dissection further explains that our model learns to incorporate face semantic information during drawing generation. Extensive experiments including a user study show that our model outperforms state-of-the-art methods. Copyright © 2022, The Authors. All rights reserved.

关键词： Generative adversarial networks

Impact of predation in the spread of an infectious disease with time fractional derivative and social behavior

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International Journal of Modeling, Simulation, and Scientific Computing 2021年第4期12卷 92-119页

作者： Soufiane Bentout Behzad Ghanbari Salih Djilali Lakshmi Narayan Guin Department of Mathematics and Informatics Belhadj Bouchaib University Center of Ain Temouchent BP 284 RP 46000Algeria Laboratoire d’Analyse Non Linaire et Mathmatiques Appliques Universit de Tlemcen TlemcenAlgeria Department of Engineering Science Kermanshah University of Technology KermanshahIran Department of Biomedical Engineering Faculty of Engineering and Naturel Sciences Bah¸ce¸sehir University 34349 IstanbulTurkey Faculty of Exact and Computer Sciences Mathematic Department Hassiba Benbouali University ChlefAlgeria Department of Mathematics Visva-Bharati Santiniketan-731235 West BengalIndia

The main purpose of this paper is to explore the influence of predation on the spread of a disease developed in the prey population where we assume that the prey has a social *** memory of the prey and the predator measured by the time fractional derivative plays a crucial role in modeling the dynamical response in a predator–prey *** memory can be modeled to articulate the involvement of interacting species by the presence of the time fractional derivative in the considered *** the purpose of studying the complex dynamics generated by the presence of infection and the time-fractional-derivative we split our study into two cases. The first one is devotedto study the effect of a non-selective hunting on the spread of the disease, where the localstability of the equilibria is investigated. Further the backward bifurcation is obtainedconcerning basic reproduction rate of the infection. The second case is for explaining theimpact of selecting the weakest infected prey on the edge of the herd by a predator onthe prevalence of the infection, where the local behavior is scrutinized. Moreover, for thegraphical representation part, a numerical simulation scheme has been achieved usingthe Caputo fractional derivative operator.

关键词： Predator–prey model herd behavior fractional derivative numerical schema backward bifurcation eco-epidemiological model

BioDrone: A Bionic Drone-based Single Object Tracking Benchmark for Robust Vision

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arXiv 2024年

作者： Zhao, Xin Hu, Shiyu Wang, Yipei Zhang, Jing Hu, Yimin Liu, Rongshuai Ling, Haibin Li, Yin Li, Renshu Liu, Kun Li, Jiadong School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China Institute of Automation Chinese Academy of Sciences Beijing China School of Instrument Science and Engineering Southeast University Nanjing China Suzhou Institute of Nano-tech and Nano-bionics Chinese Academy of Sciences Suzhou China Department of Computer Science Stony Brook University Stony Brook United States Biostatistics & Medical Informatics Computer Sciences University of Wisconsin-Madison Madison United States

Single object tracking (SOT) is a fundamental problem in computer vision, with a wide range of applications, including autonomous driving, augmented reality, and robot navigation. The robustness of SOT faces two main challenges: tiny target and fast motion. These challenges are especially manifested in videos captured by unmanned aerial vehicles (UAV), where the target is usually far away from the camera and often with significant motion relative to the camera. To evaluate the robustness of SOT methods, we propose BioDrone – the first bionic drone-based visual benchmark for SOT. Unlike existing UAV datasets, BioDrone features videos captured from a flapping-wing UAV system with a major camera shake due to its aerodynamics. BioDrone hence highlights the tracking of tiny targets with drastic changes between consecutive frames, providing a new robust vision benchmark for SOT. To date, BioDrone offers the largest UAV-based SOT benchmark with high-quality fine-grained manual annotations and automatically generates frame-level labels, designed for robust vision analyses. Leveraging our proposed BioDrone, we conduct a systematic evaluation of existing SOT methods, comparing the performance of 20 representative models and studying novel means of optimizing a SOTA method (KeepTrack [1]) for robust SOT. Our evaluation leads to new baselines and insights for robust SOT. Moving forward, we hope that BioDrone will not only serve as a high-quality benchmark for robust SOT, but also invite future research into robust computer vision. The database, toolkits, evaluation server, and baseline results are available at http://***. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

Human Behavior-based Personalized Meal Recommendation and Menu Planning Social System

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arXiv 2023年

作者： Islam, Tanvir Joyita, Anika Rahman Alam, Md. Golam Rabiul Hassan, Mohammad Mehedi Hassan, Md. Rafiul Gravina, Raffaele The Department of Computer Science and Engineering BRAC University Dhaka1212 Bangladesh Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia The College of Arts & Sciences University of Maine at Presque IsleME04769 United States The Department of Informatics Modeling Electronics and Systems University of Calabria Italy

The traditional dietary recommendation systems are basically nutrition or health-aware where the human feelings on food are ignored. Human affects vary when it comes to food cravings, and not all foods are appealing in all moods. It takes a lot of effort to learn people’s food preferences and make recommendations based on their affects and nutrition. A questionnaire-based and preference-aware meal recommendation system can be a solution. However, automated recognition of social affects on different foods and planning the menu considering nutritional demand and social-affect has some significant benefits over the questionnaire-based and preference-aware meal recommendations. A patient with severe illness, a person in a coma, or patients with locked-in syndrome and amyotrophic lateral sclerosis (ALS) cannot express their meal preferences. Therefore, the proposed framework includes a social-affective computing module to recognize the affects of different meals where the person’s affect is detected using electroencephalography signals. EEG allows to capture the brain signals and analyze them to anticipate affective state toward a food. In this study, we have used a 14-channel wireless Emotiv Epoc+ to measure affectivity for different food items. A hierarchical ensemble method is applied to predict affectivity upon multiple feature extraction methods and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is used to generate a food list based on the predicted affectivity. In addition to the meal recommendation, an automated menu planning approach is also proposed considering a person’s energy intake requirement, affectivity, and nutritional values of the different menus. The bin-packing algorithm is used for the personalized menu planning of breakfast, lunch, dinner, and snacks. The experimental findings reveal that the suggested affective computing, meal recommendation, and menu planning algorithms perform well across a variety of assessment param

关键词： Electroencephalography

Automated Controller Placement for Software-Defined Networks to Resist DDoS Attacks

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computers, Materials & Continua 2021年第9期68卷 3147-3165页

作者： Muhammad Reazul Haque Saw Chin Tan Zulfadzli Yusoff Kashif Nisar Lee Ching Kwang Rizaludin Kaspin Bhawani Shankar Chowdhry Rajkumar Buyya Satya Prasad Majumder Manoj Gupta Shuaib Memon Faculty of Computing&Informatics Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Engineering Multimedia UniversityPersiaran MultimediaCyberjaya63100SelangorMalaysia Faculty of Computing and Informatics University Malaysia SabahJalan UMSKota Kinabalu Sabah88400Malaysia Telekom Malaysia Research&Development TM Innovation CentreCyberjaya63000SelangorMalaysia National Center of Robotics and Automation Mehran University of Engineering&TechnologyJamshoroPakistan Department of Computer Science and Engineering Hanyang UniversitySeoul04763South Korea School of Electrical and Electronic Engineering Nanyang Technological University639798Singapore Cloud Computing and Distributed Systems Laboratory The University of MelbourneMelbourneVIC 3053Australia Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology(BUET)Dhaka1205Bangladesh Department of Electronics and Communication Engineering JECRC UniversityVidhaniJaipur303905India Auckland Institute of Studies Mt AlbertAucklandNew Zealand

In software-defined networks(SDNs),controller placement is a critical factor in the design and planning for the future Internet of Things(IoT),telecommunication,and satellite communication *** research has concentrated largely on factors such as reliability,latency,controller capacity,propagation delay,and energy ***,SDNs are vulnerable to distributed denial of service(DDoS)attacks that interfere with legitimate use of the *** ever-increasing frequency of DDoS attacks has made it necessary to consider them in network design,especially in critical applications such as military,health care,and financial services networks requiring high *** propose a mathematical model for planning the deployment of SDN smart backup controllers(SBCs)to preserve service in the presence of DDoS *** a number of input parameters,our model has two distinct ***,it determines the optimal number of primary controllers to place at specific locations or nodes under normal operating ***,it recommends an optimal number of smart backup controllers for use with different levels of DDoS *** goal of the model is to improve resistance to DDoS attacks while optimizing the overall cost based on the *** simulated results demonstrate that the model is useful in planning for SDN reliability in the presence of DDoS attacks while managing the overall cost.

关键词： SDN automated controller placement SBC ILP DDoS attack

Photonic Neuromorphic Accelerators for Event-Based Imaging Flow Cytometry

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arXiv 2024年

作者： Tsilikas, Ioannis Tsirigotis, Aris Sarantoglou, George Deligiannidis, Stavros Bogris, Adonis Posch, Christoph Van den Branden, Gerd Mesaritakis, Charis Department of Information and Communication Systems Engineering University of the Aegean Palama 2 Samos83100 Greece Department of Physics School of Applied Mathematical and Physical Sciences Zografou Campus Athens157 80 Greece Department of Informatics and Computer Engineering University of West Attica Ag. Spyridonos Egaleo Greece Prophesee Metavision rue du faubourg Saint-Antoine 74 Paris75012 France

In this work, we present experimental results of a high-speed label-free imaging cytometry system that seamlessly merges the high-capturing rate and data sparsity of an event-based CMOS camera with lightweight photonic neuromorphic processing. This combination offers high classification accuracy and a massive reduction in the number of trainable parameters of the digital machine-learning back-end. The event-based camera is capable of capturing 1 Gevents/sec, where events correspond to pixel contrast changes, similar to the retina’s ganglion cell function. The photonic neuromorphic accelerator is based on a hardware-friendly passive optical spectrum slicing technique that is able to extract meaningful features from the generated spike-trains using a purely analogue version of the convolutional operation. The experimental scenario comprises the discrimination of artificial polymethyl methacrylate calibrated beads, having different diameters, flowing at a mean speed of 0.01m/sec. Classification accuracy, using only lightweight, FPGA-compatible digital machine-learning schemes has topped at 98.2%. On the other hand, by experimentally pre-processing the raw spike data through the proposed photonic neuromorphic spectrum slicer at a rate of 3×106 images per second, we achieved an accuracy of 98.6%. This performance was accompanied by a reduction in the number of trainable parameters at the classification back-end by a factor ranging from 8 to 22, depending on the configuration of the digital neural network. These results confirm that neuromorphic sensing and neuromorphic computing can be efficiently merged to a unified bio-inspired system, offering a holistic enhancement in emerging bio-imaging applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Cameras

Prediction of COVID-19 Patients’ Emergency Room Revisit using Multi-Source Transfer Learning

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Prediction of COVID-19 Patients’ Emergency Room Revisit usi...

IEEE International Conference on Healthcare informatics (ICHI)

作者： Yuelyu Ji Yuhe Gao Runxue Bao Qi Li Disheng Liu Yiming Sun Ye Ye Department of Information Science School of Computing and Information University of Pittsburgh Pittsburgh USA Department of Biomedical Informatics School of Medicine University of Pittsburgh Pittsburgh USA Department of Electrical and Computer Engineering Swanson School of Engineering University of Pittsburgh Pittsburgh USA School of Business State University of New York at New Paltz New Paltz USA

The coronavirus disease 2019 (COVID-19) has led to a global pandemic of significant severity. In addition to its high level of contagiousness, COVID-19 can have a heterogeneous clinical course, ranging from asymptomatic carriers to severe and potentially life-threatening health complications. Many patients have to revisit the emergency room (ER) within a short time after discharge, which significantly increases the workload for medical staff. Early identification of such patients is crucial for helping physicians focus on treating life-threatening cases. In this study, we obtained Electronic Health Records (EHRs) of 3,210 encounters from 13 affiliated ERs within the University of Pittsburgh Medical Center between March 2020 and January 2021. We leveraged a Natural Language Processing technique, ScispaCy, to extract clinical concepts and used the 1001 most frequent concepts to develop 7-day revisit models for COVID-19 patients in ERs. The research data we collected were obtained from 13 ERs, which may have distributional differences that could affect the model development. To address this issue, we employed a classic deep transfer learning method called the Domain Adversarial Neural Network (DANN) and evaluated different modeling strategies, including the Multi-DANN algorithm (which considers the source differences), the Single-DANN algorithm (which doesn’t consider the source differences), and three baseline methods: using only source data, using only target data, and using a mixture of source and target data. Results showed that the Multi-DANN models outperformed the Single-DANN models and baseline models in predicting revisits of COVID-19 patients to the ER within 7 days after discharge (median AUROC = 0.8 vs. 0.5). Notably, the Multi-DANN strategy effectively addressed the heterogeneity among multiple source domains and improved the adaptation of source data to the target domain. Moreover, the high performance of Multi-DANN models indicates that EHRs are informat

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Energy approximation for some double phase functionals

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arXiv 2025年

作者： Carozza, Menita De Filippis, Filomena Giova, Raffaella Leonetti, Francesco Department of Engineering University of Sannio Corso Garibaldi 107 Benevento82100 Italy Department of Mathematics Physics and Informatics University of Parma Parco Area delle Scienze 53/a Parma43124 Italy Department of Economic and Legal Studies University Parthenope of Napoli Via Generale Parisi 13 Napoli80132 Italy Department of Information Engineering Computer Science and Mathematics University of l’Aquila Via Vetoio snc L’Aquila67100 Italy

In the present manuscript we give a structure condition that allows for energy approximation and we provide two examples, enjoying such a condition, that cannot be dealt with the results in the *** Codes 49N99, 35J99,...

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Digital Twins for Energy-Efficient Manufacturing

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Digital Twins for Energy-Efficient Manufacturing

Annual IEEE Systems Conference

作者： Nader Mohamed Sanja Lazarova-Molnar Jameela Al-Jaroodi Department of Computer Science and Information Systems Pennsylvania Western University California PA USA Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Karlsruhe Germany Faculty of Engineering University of Southern Denmark Odense Denmark Department of Engineering Robert Morris University Pittsburgh PA USA

Some manufacturing industries consume massive energy for their manufacturing and logistics processes. The costs of manufacturing can increase significantly as the energy cost increases. Such increase amplifies the final cost of the created products on the consumers. This makes finding different approaches that utilize innovative technologies to improve energy efficiency in manufacturing extremely important. This paper investigates how digital twins can be utilized to enable improved energy efficiency in manufacturing. Different applications of digital twins for energy-efficient manufacturing are investigated. In addition, the paper discusses some challenges of utilizing digital twins for this purpose. A framework for utilizing digital twins for energy-efficient manufacturing is also discussed.

关键词： Manufacturing industries Costs Manufacturing processes Buildings Energy efficiency Raw materials Manufacturing