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arXiv 2024年

作者： Afshinmehr, Mahyar Danaei, Alireza Kazemi, Mehrafarin Mehlhorn, Kurt Rathi, Nidhi Department of Computer Engineering Sharif University of Technology Iran Department of Computer Science Sharif University of Technology Iran Max Planck Institute for Informatics SIC Germany

We consider the fundamental problem of fairly allocating a set of indivisible items among agents having valuations that are represented by a multi-graph – here, agents appear as the vertices and items as the edges between them and each vertex (agent) only values the set of its incident edges (items). The goal is to find a fair, i.e., envy-free up to any item (EFX) allocation. This model has recently been introduced by Christodoulou et al. [2023] where they show that EFX allocations always exist on simple graphs for monotone valuations, i.e., where any two agents can share at most one edge (item). A natural question arises as to what happens when we go beyond simple graphs and study various classes of multi-graphs? We answer the above question affirmatively for the valuation class of bipartite multi-graphs and multi-cycles. Our main positive result is that EFX allocations on bipartite multi-graphs (and multi-cycles) always exist and can be computed in polynomial time for additive valuations. We, therefore, push the frontiers of our understanding of EFX allocations and expand the scenarios where they are known to exist for an arbitrary number of agents. Next, we study EFX orientations (i.e., allocations where every item is allocated to one of its two endpoint agents) and give a complete picture of when they exist for bipartite multi-graphs dependent on two parameters—the number of edges shared between any two agents and the diameter of the graph. Finally, we prove that it is NP-complete to determine whether a given fair division instance on a bipartite multi-graph admits an EFX orientation. © 2024, CC BY.

关键词： Graph theory

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A Proposed Methodology for Enhancing User Experience in Mobile Applications Design for Cultural Promotion 7

A Proposed Methodology for Enhancing User Experience in Mobi...

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7th South-East Europe Design Automation, Computer engineering, Computer Networks and Social Media Conference, SEEDA-CECNSM 2022

作者： Vonitsanos, Gerasimos Voutos, Yorghos Panagiotakopoulos, Theodor Kanavos, Andreas Mylonas, Phivos Computer Engineering and Informatics Department University of Patras Patras Greece Department of Informatics Ionian University Corfu Greece School of Technology and Science Hellenic Open University Patras Greece Department of Digital Media and Communication Ionian University Kefalonia Greece

ISBN: (数字)9798350398588

ISBN: (纸本)9798350398588

User Experience (UX), as a theoretical and practical field of research, is based on the theory of Human Computer Interaction (HCI), studies systems, products, and services in the field of (mainly) Information technology (IT), and aims at user satisfaction and better, more pleasant and more efficient interaction with systems. The popularity of Mobile Device Applications (MDAs) in the last decade has introduced new interaction environments and thus the need to formulate, develop and adapt to them, theories, and methods of UX. We present UX as a theoretical and practical framework and record its methodology - both the general one for various systems so far, and the specific one for Mobile Device Applications and more specifically for those that project cultural content in conventional ways and through augmentation. As part of the research, high-fidelity prototypes were designed and were tested during a user survey (usability test, questionnaire) for extracting conclusions to improve the design of the application. © 2022 IEEE.

关键词： Augmented reality

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Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients:A Multi-Center Retrospective Study in China

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engineering 2022年第1期8卷 116-121页

作者： Ye Yuan Chuan Sun Xiuchuan Tang Cheng Cheng Laurent Mombaerts Maolin Wang Tao Hu Chenyu Sun Yuqi Guo Xiuting Li Hui Xu Tongxin Ren Yang Xiao Yaru Xiao Hongling Zhu Honghan Wu Kezhi Li Chuming Chen Yingxia Liu Zhichao Liang Zhiguo Cao Hai-Tao Zhang Ioannis Ch.Paschaldis Quanying Liu Jorge Goncalves Qiang Zhong Li Yan School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Mechanical Science and Engineering Huazhong University of Science and TechnologyWuhan 430074China Luxembourg Centre for Systems Biomedicine University of LuxembourgBelval L-4367Luxembourg Department of Plant Sciences University of CambridgeCambridge CB21TNUK Department of Emergency Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China AMITA Health Saint Joseph Hospital Chicago ChicagoIL 60657USA Department of Anesthesiology Tongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Huazhong University of Science and Technology-Wuxi Research Institute Wuxi 214174China Division of Cardiology Department of Internal MedicineTongji HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430030China Institute of Health Informatics University College LondonLondon NW12DAUK Department of Biomedical Engineering Southern University of Science and TechnologyShenzhen 518055China Department of Infectious Diseases Shenzhen Key Laboratory of Pathogenic Microbiology and ImmunologyNational Clinical Research Center for Infectious DiseaseThe Third People’s Hospital of Shenzhen(Second Hospital Affiliated with the Southern University of Science and Technology)Shenzhen 518055China Department of Electrical and Computer Engineering&Division of Systems Engineering&Department of Biomedical Engineering Boston UniversityBostonMA 02215USA

Coronavirus disease 2019(COVID-19)has become a worldwide *** patients of COVID-19 suffer from a high mortality rate,motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk ***,we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital,Wuhan,China(development cohort)and externally validated with data from two other centers:141 inpatients from Jinyintan Hospital,Wuhan,China(validation cohort 1)and 432 inpatients from The Third People’s Hospital of Shenzhen,Shenzhen,China(validation cohort 2).The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of *** risk score can predict the mortality of individual patients more than 12 d in advance with more than 90%accuracy across all ***,the Kaplan-Meier score shows that patients can be clearly differentiated upon admission as low,intermediate,or high risk,with an area under the curve(AUC)score of *** summary,a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2(SARS-CoV-2);it has also been validated in independent cohorts.

关键词： COVID-19 Risk score Mortality risk prediction

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A novel neuroprotective method against ischemic stroke by accelerating the drainage of brain interstitial fluid

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science China(Life sciences) 2024年第10期67卷 2213-2223页

作者： Jingge Lian Liu Yang Hanbo Tan Shaoyi Su Leonor Serrano Lopes Fangxiao Cheng Junhao Yan Yu Fu Wanyi Fu Zhaoheng Xie Jianfei Sun Jicong Zhang Zhiqian Tong Yajuan Gao Hongbin Han Department of Radiology Peking University Third HospitalBeijing 100191China Beijing Key Laboratory of Magnetic Resonance Imaging Technology Beijing 100191China Institute of Medical Technology Peking University Health Science CenterBeijing 100191China Department of Informatics Technical University of MunichGarching 80539Germany Department of Anatomy and Histology School of Basic Medical SciencesPeking UniversityBeijing 100191China Department of Neurology Peking University Third HospitalBeijing 100191China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China State Key Laboratory of Bioelectronics School of Biological Science and Medical EngineeringSoutheast UniversityNanjing 210096China School of Biological Science and Medical Engineering Beihang UniversityBeijing 100191China Key Laboratory of Alzheimer’s Disease of Zhejiang Province Institute of AgingSchool of Mental HealthWenzhou Medical UniversityWenzhou 325035China NMPA Key Laboratory for Evaluation of Medical Imaging Equipment and Technique Beijing 100191China

Ischemic stroke is a leading cause of death and disability *** response after stroke determines the outcome of ischemic injury.A recent study has reported an efficient method,epidural arterial implantation(EAI),for accelerating interstitial fluid(ISF)drainage,which provides a promising strategy to clear pro-inflammatory cytokines in the brain extracellular space(ECS).In this study,the method of EAI was modified(m-EAI)to control its function of accelerating the ISF drainage at different time points following ischemic *** neuroprotective effect of m-EAI on ischemic stroke was evaluated with the transient middle cerebral artery occlusion(tMCAO)rat *** results demonstrated the accumulation of IL-1β,IL-6,and TNF-αwas significantly decreased by activating m-EAI at 7 d before and immediately after ischemic attack in tMCAO rats,accompanied with decreased infarct volume and improved neurological *** study consolidates the hypothesis of exacerbated ischemic damage by inflammatory response and provides a new perspective to treat encephalopathy via brain *** research is essential to investigate whether m-EAI combined with neuroprotective drugs could enhance the therapeutic effect on ischemic stroke.

关键词： ischemic stroke brain extracellular space interstitial fluid modified epidural arterial implantation

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Comparative Evaluation of Resampling Techniques on Machine Learning for Predicting Customer Purchasing Behavior

Comparative Evaluation of Resampling Techniques on Machine L...

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International Conference on Information technology (INCIT)

作者： Saucha Diwandari Adhistya Erna Permanasari Indriana Hidayah Department of Electrical and Information Engineering Universitas Gadjah Mada Indonesia Informatics Department Faculty of Science and Technology Universitas Teknologi Yogyakarta Indonesia

ISBN: (数字)9798350366303

ISBN: (纸本)9798350366310

In the era of subscription-based services, customer purchasing behavior presents a significant challenge for organizations because customer retention is essential to long-term success. Imbalanced datasets, where the majority class significantly outweighs the minority class, often cause models to favor the dominant class, leading to biased predictions. This study investigates customer purchasing behavior, focusing specifically on the issue of class imbalance. It offers a comparative analysis of various classification techniques and includes widely used resampling methods such as the Synthetic Minority Over-sampling Technique (SMOTE), Edited Nearest Neighbors, Tomek Links, and several hybrid sampling approaches. Our research findings demonstrate the competitive effectiveness of using the Random Forest algorithm with undersampling methods such as TomekLinks and Edited Nearest Neighbors (ENN), which have shown the highest F1 scores. However, the hybridization of oversampling and undersampling yields unsatisfactory results, below our expectations. This suggests that the dataset used in this study exhibits a high degree of overlap between the two classes. Consequently, undersampling proves to be an effective method for mitigating the negative impact of class overlap. This research makes a substantial contribution to the field of predicting customer purchasing behavior.

关键词： Focusing Organizations Predictive models Behavioral sciences Classification algorithms Information technology Random forests

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Grasshopper Optimization Algorithm Based Spam Detection System Using Multi-Objective Wrapper Feature Selection and Neural Network Classification 2nd

Grasshopper Optimization Algorithm Based Spam Detection Syst...

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2nd International Conference on Emerging Technologies and Intelligent Systems, ICETIS 2022

作者： Ghaleb, Sanaa A. A. Mohamad, Mumtazimah Ghanem, Waheed A. H. M. Abdullahi, Akibu Mahmoud Nasser, Abdullah B. Saleh, Sami Abdulla Mohsen Arshad, Humaira Omolara, Abiodun Esther Abiodun, Oludare Isaac Ghetas, Mohamed Faculty of Informatics and Computing Universiti Sultan Zainal Abidin Terengganu Malaysia Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Malaysia Faculty of Education Saber University of Lahej Lahej Yemen Faculty of Engineering University of Aden Aden Yemen Faculty of Education Aden University of Aden Aden Yemen Faculty of Computing and Informatics Albukhary International University Kedah Malaysia School of Technology and Innovation University of Vaasa Vaasa Finland School of Electrical and Electronic Engineering Universiti Sains Malaysia Nilai Malaysia Department of Computer Science Islamia University of Bahawalpur Bahawalpur Pakistan Department of Computer Science University of Abuja Abuja Nigeria Department of Computer Science Bingham University Nasarawa Nigeria Faculty of Computer Science Nahda University New Beni Seuf City Egypt

ISBN: (纸本)9783031252730

Email classification is essential to the trouble of email and pattern recognition. Nowadays, a number of unsolicited messages are circulated over the internet. While plenty of machine learning techniques are a success in detecting textual primarily based on totally unsolicited mail, this isn’t the case for messages spams, which can without difficulty avoid those textual-unsolicited mail detection systems. This paper proposes the introduction of MOBGOA constructed on the binary version of the GOA algorithm, for multi-objective selection of features purposes and the wrapper approach of selection of features and purpose of the EGOAMLP algorithm utilized this algorithm as the wrapper classifier. Spam assassin dataset was used to validate the performance of the MOB-EGOAMLP. The result uncovered that the new technique outflanks the wide selection of different methods in previously published works. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Feature Selection

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Approximation Polynomial-Time Algorithms for Consistency-Aware Multi-Server Network Design in Delay-Sensitive Applications

IEEE Networking Letters

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IEEE Networking Letters 2025年

作者： Oda, Masaki Kawabata, Akio Oki, Eiji Kyoto University Graduate School of Informatics Kyoto606-8501 Japan Toyohashi University of Technology Department of Computer Science and Engineering Aichi 441-8580 Japan

This paper proposes two polynomial-time approximation algorithms for allocating servers to design a consistency-aware multi-server network for delay-sensitive applications. Each algorithm selects servers and determines the main-secondary server pairs to minimize the total delay. Previous work has not provided any polynomial-time algorithm. The proposed algorithms are theoretically guaranteed to output an approximate value within three times the optimal value. Numerical results show that the more computationally efficient of the two algorithms is 46.4 to 5.26 × 104 times faster than an integer linear programming technique, while the maximum delay is, on average, merely 1.0196 times the optimal value. © 2019 IEEE.

关键词： Integer linear programming

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Modulating surface oxygen species via facet engineering for efficient conversion of nitrate to ammonia

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Journal of Energy Chemistry 2023年第3期78卷 211-221,I0007页

作者： Wenye Zhong Zhiheng Gong Zuyun He Nian Zhang Xiongwu Kang Xianwen Mao Yan Chen School of Environment and Energy State Key Laboratory of Pulp and Paper EngineeringSouth China University of TechnologyGuangzhou 510006GuangdongChina State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information TechnologyChinese Academy of SciencesShanghai 200050China Department of Materials Science and Engineering National University of SingaporeSingapore 119077Singapore Institute of Functional Intelligent Materials National University of SingaporeSingapore 119077Singapore Centre for Advanced 2D Materials National University of SingaporeSingapore 119077Singapore

Electrochemical reduction of nitrate,a common pollutant in aquatic environment,to valuable ammonia(NO3-RR) using renewably-sourced electricity has attracted widespread interests,with past efforts mainly focused on designing electrocatalysts with high activity and *** detailed correlation between catalyst properties and NO3-RR kinetics,nevertheless,is still not fully *** this work,we modulate the surface oxygen species of Cu_(2)O via facet engineering,and systematically study the impact of these oxygen species on the NO_(3)^(-)RR *** advanced spectroscopic techniques,densi ty fu n ctional theory calculations and molecular dynamics simulations,we find that while oxygen vacancies on Cu_(2)O(111) surface promote the adsorption of reactants and reaction intermediates,hydroxyl groups effectively inhibit the side reaction of hydrogen evolution and facilitate the hydrogenation process of *** two effects work in concert to render Cu_(2)O(111) facet the highest NO3-RR activity relative to those from other *** study provides critical insights into the synergistic effect of exposed facets and surface oxygen species on heterogeneous catalysis,and offers a generalizable,facet engineeringbased strategy for improving the performance of a variety of electrocatalysts important for renewable energy conversion.

关键词： Facet engineering Oxygen vacancy Hydroxyl group Electrochemical nitrate reduction Ammonia

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Predicting Student Attrition: A Case Study of the University of Namibia Bachelor of Accounting (Chartered Accountancy) Programme

Predicting Student Attrition: A Case Study of the University...

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International Conference on Emerging Trends in Networks and Computer Communications (ETNCC)

作者： Samuel Nghidengwa Nakale Lameck Mbangula Amugongo Department of Informatics Journalism & Media Technology Namibia University of Science and Technology Windhoek Namibia Department of Software Engineering Namibia University of Science and Technology Windhoek Namibia

Understanding the reasons behind student attrition is essential to developing retention strategies. In this study, we developed models to predict student attrition for the Chartered Accountancy programme at the University of Namibia (UNAM). Three hundred and eighty-nine records of accounting students were collected from the UNAM student database. We applied five classification machine learning algorithms: logistic regression (LR), support vector machines (SVM), k-nearest neighbors (k-NN), decision trees (DT) and Gaussian Naive Bayes (GNB) to identify patterns in student attrition. After, accuracy, specificity, sensitivity, F-measure and Area Under Curve were computed to measure the performance of each model. Finally, Shapley additive explanations (SHAP) was used to determine how variables influenced model prediction. The LR, k-NN, DT, SVM and GNB achieved an accuracy of 80.3%, 73.8%, 82%, 80.3% and 77%, respectively. Overall, the DT classifier performed the best at predicting student attrition. We found that academic performance in year 1 core modules: financial accounting A and financial accounting B were the most important features, on average contributing the most to the predicted probability of dropping out. We have demonstrated that it is feasible to predict students who are likely to abandon their studies while in their first year. The major difference between our study and previous studies is that we do not focus on traditional studied areas (science, technology, engineering and mathematics). In this study, we developed predictive models to identify student attrition in the bachelor of accounting program in a Namibian university.

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Intelligent Control for Automated Fish Feeding System in an Aquaponic Environment 7

Intelligent Control for Automated Fish Feeding System in an ...

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7th International Conference on informatics and Computational sciences, ICICoS 2024

作者： De Ocampo, Anton Louise P. Alon, Alvin Sarraga Montalbo, Francis Jesmar P. MacAlisang, Jonel R Aggari, John Carlo V. Batangas State University Digital Transformation Center Batangas City Philippines Nrcp Department of Science and Technology Taguig City Philippines Batangas State University College of Informatics and Computing Sciences Batangas City Philippines Technological University of the Philippines College of Industrial Technology Manila Philippines Batangas State University Department of Electronics Engineering Batangas City Philippines

ISBN: (纸本)9798350375886

Fish feeding in aquaponics presents challenges in accurately estimating and distributing feed, risking the health of both fish and plants due to potential water contamination. To address these issues, our study employs computer vision and deep learning to automate feed dispensing in aquaponics farms, ensuring precise feed quantities. We utilize the Mask R-CNN algorithm to identify fish measurements in tanks, aiding in feed quantity determination and minimizing over or underfeeding. By calculating a fish-to-feed ratio, we regulate feed amounts to enhance system performance. The remaining fish segment's length and width are converted into weight, guiding feed calculations for each session. We conducted experiments with 432 Nile Tilapia, using their measurements to train the Mask R-CNN model. Results show a significant mean error of only 0.98% and 4.56% for length and weight predictions, respectively, with a high correlation coefficient of 0.97. © 2024 IEEE.

关键词： Image segmentation

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