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23rd International Conference on Enterprise Information systems, ICEIS 2021

作者： Mane, Babacar Magalhaes, Ana Patricia Quinteiro, Gustavo Maciel, Rita Suzana Pitangueira Claro, Daniela Barreiro Federal University of Bahia – DCC – IME Av. Adhemar de Barros Ondina 40170-110 Bahia Salvador Brazil Department of Exact Sciences and Earth State University of Bahia 2555 Silveira Martins st. Bahia Cabula Brazil Post Graduate Program in Computing and Systems Salvador University 251 Dr. Jose Peroba st. Bahia Brazil

ISBN: (纸本)9789897585098

Cloud computing (CC) is a paradigm that manages a pool of virtualized resources at infrastructure, platform, and software levels to deliver them as services over the Internet. Cloud Platforms are heterogeneous, and therefore cloud users may face interoperability and integration issues regarding consumption, provisioning, management, and supervision resources among distinct clouds. Due to the lack of standards in such a heterogeneous environment, an organization may face a lock-in situation. A middleware can minimize the effort to overcome lock-in problems. The MIDAS middleware ensures semantic interoperability between Software as a Service (SaaS) and Data as a Service (DaaS), and at the same times provides data integration between DaaS. Currently, MIDAS runtime implementations rely on Cloud Foundry, Amazon Web Services, OpenShift and, Heroku providers. To avoid ambiguity in MIDAS development and deployment an unambiguous definition of MIDAS architectural concepts must be provided. Thus, our work presents a Domain-Specific Modeling Language (DSML) comprising a metamodel of MIDAS semantic architecture and a Unified Modeling Language (UML) profile. To evaluate the DSML expressiveness, we instantiate several middleware models, and the findings demonstrate that our modeling language has an acceptable level of concepts to specify the middleware. Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Web services

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AVTENet: Audio-Visual Transformer-based Ensemble Network Exploiting Multiple Experts for Video Deepfake Detection

arXiv

引用

arXiv 2023年

作者： Hashmi, Ammarah Shahzad, Sahibzada Adil Lin, Chia-Wen Tsao, Yu Wang, Hsin-Min The Social Networks and Human-Centered Computing Program Taiwan International Graduate Program Academia Sinica Taipei11529 Taiwan The Institute of Information Systems and Applications National Tsing Hua University Hsinchu30013 Taiwan The Department of Computer Science National Chengchi University Taipei11605 Taiwan The Department of Electrical Engineering The Institute of Communications Engineering National Tsing Hua University Hsinchu300044 Taiwan The Research Center for Information Technology Innovation Academia Sinica Taipei11529 Taiwan The Institute of Information Science Academia Sinica Taipei11529 Taiwan

Forged content shared widely on social media platforms is a major social problem that requires increased regulation and poses new challenges to the research community. The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous work on detecting AI-generated fake videos only utilizes visual modality or audio modality. While there are some methods in the literature that exploit audio and visual modalities to detect forged videos, they have not been comprehensively evaluated on multi-modal datasets of deepfake videos involving acoustic and visual manipulations. Moreover, these existing methods are mostly based on CNN and suffer from low detection accuracy. Inspired by the recent success of Transformer in various fields, to address the challenges posed by deepfake technology, in this paper, we propose an Audio-Visual Transformer-based Ensemble Network (AVTENet) framework that considers both acoustic manipulation and visual manipulation to achieve effective video forgery detection. Specifically, the proposed model integrates several purely transformer-based variants that capture video, audio, and audio-visual salient cues to reach a consensus in prediction. For evaluation, we use the recently released benchmark multi-modal audio-video FakeAVCeleb dataset. For a detailed analysis, we evaluate AVTENet, its variants, and several existing methods on multiple test sets of the FakeAVCeleb dataset. Experimental results show that our best model outperforms all existing methods and achieves state-of-the-art performance on Testset-I and Testset-II of the FakeAVCeleb dataset. © 2023, CC BY.

关键词： Statistical tests

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Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records

arXiv

引用

arXiv 2023年

作者： Sivarajkumar, Sonish Huang, Yufei Wang, Yanshan Intelligent Systems Program School of Computing and Information University of Pittsburgh PittsburghPA United States Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States Department of Health Information Management University of Pittsburgh PittsburghPA United States Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Clinical and Translational Science Institute University of Pittsburgh PittsburghPA United States

Objective: To pre-train fair and unbiased patient representations from Electronic Health Records (EHRs) using a novel weighted loss function that reduces bias and improves fairness in deep representation learning models. Methods: We defined a new loss function, called weighted loss function, in the deep representation learning model to balance the importance of different groups of patients and features. We applied the proposed model, called Fair Patient Model (FPM), to a sample of 34,739 patients from the MIMIC-III dataset and learned patient representations for four clinical outcome prediction tasks. Results: FPM outperformed the baseline models in terms of three fairness metrics: demographic parity, equality of opportunity difference, and equalized odds ratio. FPM also achieved comparable predictive performance with the baselines, with an average accuracy of 0.7912. Feature analysis revealed that FPM captured more information from clinical features than the baselines. Conclusion: FPM is a novel method to pre-train fair and unbiased patient representations from EHR data using a weighted loss function. The learned representations can be used for various downstream tasks in healthcare and can be extended to other domains where bias and fairness are important. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

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User Preference Modelling from Movie Database 7

User Preference Modelling from Movie Database

引用

7th International Conference on ICT for Smart Society, ICISS 2020

作者： Qomariyah, Nunung Nurul Kazakov, Dimitar Fajar, Ahmad Nurul Bina Nusantara University Faculty of Computing and Media Computer Science Department Jakarta11480 Indonesia University of York Computer Science Department York United Kingdom Bina Nusantara University BINUS Graduate Program-Master of Information Systems Management Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781665404228

Film industry becomes one of the largest amongst the other economic sectors worldwide nowadays. This industry also has a significant impact in the global economic market. Many movies have been produced each year. However, some movies still fail to reach break-even points every year. Players in this industry need to think carefully when spending most of their money to make a great and successful movie in the market. Several attempts also need to be made to estimate the success performance before a movie can be released. On the audience side, there is an indicator that we can use to decide that a movie is good by looking to its ratings. The industry can use this rating systems as a factor to be considered when evaluating the past movie performances to make better movie for the next production. This study aims to predict the movie ratings and find the most important features which correlate to the ratings. We performed an experiment on IMDb dataset which contains 5,043 movie metadata on four models of machine learning, Linear Regression (LR), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbor (KNN). From the experiment, Random Forest achieves the best performance amongst the four models. © 2020 IEEE.

关键词： Decision trees

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Predicting User Preferences with XGBoost Learning to Rank Method 3

Predicting User Preferences with XGBoost Learning to Rank Me...

引用

3rd International Seminar on Research of Information Technology and Intelligent systems, ISRITI 2020

作者： Qomariyah, Nunung Nurul Kazakov, Dimitar Fajar, Ahmad Nurul Bina Nusantara University Faculty of Computing and Media Computer Science Department Jakarta11480 Indonesia University of York Computer Science Department York United Kingdom Binus Graduate Program-Master of Information Systems Management Bina Nusantara University Information Systems Management Department Jakarta11480 Indonesia

ISBN: (纸本)9781728184067

Learning user preferences become very important as the personalization systems grow rapidly in this current era. Offering special and personal services can be an added value for the companies to maintain their customer loyalty. Building a personalized recommendation requires a good machine learning model to understand the individual preferences. Every user can be presented with a list of items sorted by its score learned from the individual preferences. So the first couple items shown will be the most liked items by the user. We can borrow the Learning to Rank algorithm from Information Retrieval to solve this problem. In this paper, we present the implementation of user preferences learning by using XGBoost Learning to Rank method in movie domain. We show the evaluation of three different approaches in Learning to Rank according to their Normalized Discounted Cumulative Gain (NDCG) score. We can conclude that in our case study, the pairwise approach appears to be the best solution to produce a personalized list of recommendation. © 2020 IEEE.

关键词： Recommender systems

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A Framework for Moving from Traditional to Smart University

A Framework for Moving from Traditional to Smart University

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International Conference on computing for Sustainable Global Development (INDIACom)

作者： Adnan Ahmed Abi Sen Yaser Mohammed Aljohani Abdulelah Alhussain Hassan Hakami Nour Mahmoud Bahbouh Ahmad Alkhodre Mohammed S. Alghamdi Computing and Information Technology Islamic University Al-Madinah Saudi Arabia YasRef Program Yanbu Saudi Arabia Saudi Electronic University Al-Madinah Saudi Arabia Department of Information and Communication Sciences Granada University Granada Spain Faculty of Computer and Information Systems Islamic University Al-Madinah Saudi Arabia Faculty of the Holy Quran and Islamic Studies Islamic University Al-Madinah Saudi Arabia

The revolution of technologies and communications pushed for smarter applications and services. A smart university has become a target for educational institutions in many countries in the last few years. The smart-university provides many advantages in all aspects: academic, administrative, and research. Transformation to a smart university will save time, effort, and cost, in addition to increasing the quality of services, users' satisfaction, and sustainability. This research provided reviewing study for the most important standards, services, applications, and tools that must be present in smart universities. Then, the research proposed a framework to transform universities into smart ones. In recent years, the Kingdom of Saudi Arabia (KSA) is considered one of the leading countries in the field of digital transformation. So, in the next phase, we will try to apply the proposed framework in one of the KSA universities.

关键词： Heuristic algorithms Digital transformation Urban areas Education Transforms Quality of service Internet of Things

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An efficient multi-objective evolutionary approach for solving the operation of multi-reservoir system scheduling in hydro-power plants

arXiv

引用

arXiv 2021年

作者： Marcelino, Carolina Gil Leite, G.M.C. Delgado, C.A.D.M. De Oliveira, L.B. Wanner, E.F. Jiménez-Fernández, S. Salcedo-Sanz, S. Department of Signal Processing and Communications Universidad de Alcalá Spain Institute of Computing Federal University of Rio de Janeiro Brazil Post-Graduate Program in Systems Engineering and Computer Science Federal University of Rio de Janeiro Brazil Computing Department Federal Center of Technology Education of Minas Gerais Brazil

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system - a cascade-based operation scenario. For this, we propose a new mathematical modelling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multiobjective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out. © 2021, CC BY-NC-ND.

关键词： Multiobjective optimization

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Automated Fidelity Assessment for Strategy Training in Inpatient Rehabilitation using Natural Language Processing

arXiv

引用

arXiv 2022年

作者： Osterhoudt, Hunter Schneider, Courtney E. Mohammad, Haneef A. Shih, Minmei Harper, Alexandra E. Zhou, Leming Skidmore, Elizabeth R. Wang, Yanshan Department of Computer Science School of Computing and Information University of Pittsburgh United States Department of Occupational Therapy School of Health and Rehabilitation Sciences University of Pittsburgh United States Department of Information Science School of Computing and Information University of Pittsburgh United States Department of Health Information Management School of Health and Rehabilitation Sciences University of Pittsburgh United States Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Intelligent Systems Program University of Pittsburgh PittsburghPA United States

Strategy training is a multidisciplinary rehabilitation approach that teaches skills to reduce disability among those with cognitive impairments following a stroke. Strategy training has been shown in randomized, controlled clinical trials to be a more feasible and efficacious intervention for promoting independence than traditional rehabilitation approaches. A standardized fidelity assessment is used to measure adherence to treatment principles by examining guided and directed verbal cues in video recordings of rehabilitation sessions. Although the fidelity assessment for detecting guided and directed verbal cues is valid and feasible for single-site studies, it can become labor intensive, time consuming, and expensive in large, multi-site pragmatic trials. To address this challenge to widespread strategy training implementation, we leveraged natural language processing (NLP) techniques to automate the strategy training fidelity assessment, i.e., to automatically identify guided and directed verbal cues from video recordings of rehabilitation sessions. We developed a rule-based NLP algorithm, a long-short term memory (LSTM) model, and a bidirectional encoder representation from transformers (BERT) model for this task. The best performance was achieved by the BERT model with a 0.8075 F1-score. This BERT model was verified on an external validation dataset collected from a separate major regional health system and achieved an F1 score of 0.8259, which shows that the BERT model generalizes well. The findings from this study hold widespread promise in psychology and rehabilitation intervention research and practice. Copyright © 2022, The Authors. All rights reserved.

关键词： Video recording

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PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network for PM2.5 Forecasting 20

PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network ...

引用

28th ACM SIGSPATIAL International Conference on Advances in Geographic Information systems, SIGSPATIAL GIS 2020

作者： Wang, Shuo Li, Yanran Zhang, Jiang Meng, Qingye Meng, Lingwei Gao, Fei School of Systems Science Normal University Beijing China Department of Computing Hong Kong Polytechnic University Hong Kong ColorfulClouds Pacific Technology Co. Ltd. Beijing China Atmospheric and Oceanic Sciences Program Princeton University Princeton United States

ISBN: (纸本)9781450380195

When predicting PM2.5 concentrations, it is necessary to consider complex information sources since the concentrations are influenced by various factors within a long period. In this paper, we identify a set of critical domain knowledge for PM2.5 forecasting and develop a novel graph based model, PM2.5-GNN, being capable of capturing long-term dependencies. On a real-world dataset, we validate the effectiveness of the proposed model and examine its abilities of capturing both fine-grained and long-term influences in PM2.5 process. The proposed PM2.5-GNN has also been deployed online to provide free forecasting service. © 2020 Owner/Author.

关键词： Domain Knowledge

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Interactome of SARS-CoV-2 / nCoV19 modulated host proteins with computationally predicted PPIs

Research Square

引用

Research Square 2020年 rs3rs-28592页

作者： Karunakaran, Kalyani B. Balakrishnan, N. Ganapathiraju, Madhavi K. Supercomputer Education and Research Centre Indian Institute of Science Bangalore560 012 India Department of Biomedical Informatics School of Medicine and Intelligent Systems Program School of Computing and Information University of Pittsburgh Pittsburgh United States Intelligent Systems Program School of Computing and Information University of Pittsburgh Pittsburgh United States

World over, people are looking for solutions to tackle the pandemic coronavirus disease (COVID-19) caused by the virus SARS-CoV-2/nCoV-19. Notable contributions in biomedical field have been characterizing viral genomes, host transcriptomes and proteomes, repurposable drugs and vaccines. In one such study, 332 human proteins targeted by nCoV19 were identified. We expanded this set of host proteins by constructing their protein interactome, including in it not only the known protein-protein interactions (PPIs) but also novel, hitherto unknown PPIs predicted with our High-precision Protein-Protein Interaction Prediction (HiPPIP) model that was shown to be highly accurate. In fact, one of the earliest discoveries made possible by HiPPIP is related to activation of immunity upon viral infection. We found that several interactors of the host proteins are differentially expressed upon viral infection, are related to highly relevant pathways, and that the novel interaction of NUP98 with CHMP5 may activate an antiviral mechanism leading to disruption of viral budding. We are making the interactions available as downloadable files to facilitate future systems biology studies and also on a web-server at http://***/corona that allows not only keyword search but also queries such as "PPIs where one protein is associated with ‘virus’ and the interactors with 'pulmonary'". © 2020, CC BY.

关键词： Coronavirus

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