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Acceptance Analysis of IWB Systems in Education using UTAUT2 Model: The Moderating Role of Perceived Technological Innovations

Acceptance Analysis of IWB Systems in Education using UTAUT2...

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2023 IEEE International Conference on Automatic Control and Intelligent Systems, I2CACIS 2023

作者： De Jesus, Noelyn Castro, Eltimar T. Buyan, Cory Antonio Batangas State University College of Informatics & Computing Sciences Batangas Philippines Technological Institute of the Philippines Graduate Program Department Manila Philippines Ballard Power Systems Business Technology Burnaby Canada

ISBN: (纸本)9798350321302

Interactive Whiteboard (IWB) Systems are increasingly being used to replace traditional whiteboards in educational activities, making them more efficient and interesting for both teachers and students. However, no studies have been conducted to investigate how age, educational level, and employment status can impact the adoption and usage of IWB systems in the educational setting. The study aims to analyze the acceptance and the influencing factors on faculty members' intention and usage behavior towards IWB systems in teaching, utilizing the Modified-Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. 135 faculty members from various colleges and departments across Batangas State University ARASOF-Nasugbu Campus were surveyed and data was analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) through SmartPLS software for hypotheses testing. The results of the study indicate that faculty members' behavioral intention to use interactive whiteboard systems in teaching is positively influenced by performance expectancy and social influence. Moreover, facilitating conditions and habit positively impact usage behavior. This study, therefore, concludes that there is significant roles of performance expectancy and social influence as well as the moderating effect of age on the behavioral intention to use interactive whiteboard (IWB) systems in education. Furthermore, the study confirms that effort expectancy, hedonic motivation, and status as moderating variables significantly influence faculty members' usage behavior when using IWB systems in teaching. © 2023 IEEE.

关键词： Economic and social effects

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Collective Intelligence for the design of emergency response

Collective Intelligence for the design of emergency response

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International Conference on Computer Supported Cooperative Work in Design

作者： Vivacqua, Adriana S. Borges, Marcos R.S. PPGI/DCC-IM - Graduate Program in Informatics Department of Computer Science Institute of Mathematics UFRJ - Federal University of Rio de Janeiro Rio de Janeiro Brazil PPGI/DCC-IM - Graduate Program in Informatics Department of Computer Science Institute of Mathematics NCE - Electronic Computing Institute UFRJ - Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9781424467631

Collective Intelligence has received much attention in recent years, as organizations and businesses discover the power of crowds. With new technologies, such as blogs, twitter, wikis, photo sharing, collaborative tagging and social networking sites, people may create and disseminate content as never before. Many organizations are looking for ways to harness this power, which is being called collective intelligence. Additionally, many open-source initiatives have shown that it is possible to obtain high quality results from collectively produced work. In this paper, we consider the domain of emergency response. We have been working in this domain for a few years now, mostly focusing on small disaster relief groups and field command. One of the command problems is lack of information about the location where the disaster occurred. We believe some of this information could be crowdsourced, changing current practices and enabling more effective response to the situation. In this paper, we discuss the possibilities for crowdsourcing and present an initial proposal for harnessing collective intelligence for the fire department. © 2010 IEEE.

关键词： Emergency services

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Stroke Lesion Detection Using Convolutional Neural Networks

Stroke Lesion Detection Using Convolutional Neural Networks

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2018 International Joint Conference on Neural Networks, IJCNN 2018

作者： Pereira, Danillo Roberto Filho, Pedro P. Rebouças De Rosa, Gustavo Henrique Papa, João Paulo De Albuquerque, Victor Hugo C. Department of Computing São Paulo State University Bauru SP Brazil Federal Institute of Education Science and Technology of Ceará CE Brazil Graduate Program in Applied Informatics University of Fortaleza Fortaleza CE Brazil

ISBN: (纸本)9781509060146

Stroke is an injury that affects the brain tissue, mainly caused by changes in the blood supply to a particular region of the brain. As consequence, some specific functions related to that affected region can be reduced, decreasing the quality of life of the patient. In this work, we deal with the problem of stroke detection in Computed Tomography (CT) images using Convolutional Neural Networks (CNN) optimized by Particle Swarm optimization (PSO). We considered two different kinds of strokes, ischemic and hemorrhagic, as well as making available a public dataset to foster the research related to stroke detection in the human brain. The dataset comprises three different types of images for each case, i.e., the original CT image, one with the segmented cranium and an additional one with the radiological density's map. The results evidenced that CNN's are suitable to deal with stroke detection, obtaining promising results. © 2018 IEEE.

关键词： Convolution

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Fine Tuning Swin Transformer Based Pretrained Model for Microscopic Fungi Images Classification 11

Fine Tuning Swin Transformer Based Pretrained Model for Micr...

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11th International Conference on Computer, Control, informatics and its Applications, IC3INA 2024

作者： Anggara, Muchamad Galih Hindasyah, Ahmad Amda, Julfa Muhammad Waskita, A.A. Graduate Program of Informatics Engineering Universitas Pamulang Pamulang Indonesia Research Center for Computing National Research and Innovation Agency Cibinong Indonesia Research Center for Data and Information Sciences National Research and Innovation Agency Bandung Indonesia

ISBN: (纸本)9798331542313

Accurate image classification of microscopic pathogenic fungi is a challenge in the field of mycology. This study proposes a Swin Transformer-based approach to automate the identification process. The Swin Transformer model, known for its ability to capture complex spatial interactions, is adapted from ImageNet’s pre-trained model. The adaptation process is focused on fine-tuning the final classification layer to match the 5 classes of fungi present in the dataset. This classification layer is replaced by a new fully-connected layer that has the number of output according to the number of fungal classes. The weights on these layers are randomly initialized and trained in conjunction with several final convolutional layers. In addition, regularization techniques such as dropouts are applied to prevent overfitting. Adaptation using 5-fold cross-validation was carried out to find the best fold with hyperparameters including AdamW optimizer with a learning rate of 10−3, StepLR scheduler, batch size of 128 images, and a total of 9 epochs / iterations. After that, fine-tuning is carried out on the best fold with hyperparameters including AdamW optimizer with a learning rate of 10−5, StepLR scheduler, batch size of 32 images, and a total of 100 epochs / iterations. The final result showed excellent performance, with accuracy, precision, recall and an average F1-score of 95%. These results show the potential of the Swin Transformer in becoming an effective tool for mycologists. © 2024 IEEE.

关键词： Deep learning

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Prototype Selection Using Self-Organizing-Maps and Entropy for Overlapped Classes and Imbalanced Data

Prototype Selection Using Self-Organizing-Maps and Entropy f...

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International Joint Conference on Neural Networks

作者： Marcio Rubbo Leandro A. Silva Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University Sao Paulo SP Brazil Computing and Informatics Faculty & Graduate Program in Electrical Engineering and Computing Mackenzie Presbyterian University Sao Paulo SP Brazil

The k nearest neighbor (kNN) is a traditional supervised classifier used in data mining tasks. However, when used in real applications, mainly in a dataset with class imbalance or class overlap, kNN suffers with problems in accuracy performance. In this paper, we propose three prototype selection methods using self-organizing maps (SOM) and information entropy to increase the effectiveness of the kNN classifier in datasets with these conditions. The methods, named SOMEntropyKnn, were able to increase the effectiveness of the kNN classifier in all the 14 datasets used in the experiment, increasing the accuracy performance from datasets with imbalance or overlap problems.

关键词： Prototypes Entropy Self-organizing feature maps Neurons Training Information entropy Electrical engineering

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On self-organizing feature map (SOFM) formation by direct optimization through a genetic algorithm

On self-organizing feature map (SOFM) formation by direct op...

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8th International Conference on Hybrid Intelligent Systems, HIS 2008

作者： Maia, José Everardo B. Barreto, Guilherme A. Coelho, André L. V. State Univ. of Ceará Department of Statistics and Computing Fortaleza Ceará Brazil Federal Univ. of Ceará Department of Teleinformatics Engineering Fortaleza Ceará Brazil University of Fortaleza Graduate Program in Applied Informatics Fortaleza Ceará Brazil

ISBN: (纸本)9780769533261

This paper examines the formation of self-organizing feature maps (SOFM) by the direct optimization of a cost function through a genetic algorithm (GA). The resulting SOFM is expected to produce simultaneously a topologically correct mapping between input and output spaces and a low quantization error. The proposed approach adopts a cost (fitness) function which is a weighted combination of indices that measure these two aspects of the map quality, specifically, the quantization error and the Pearson correlation coefficient between the corresponding distances in input and output spaces. The resulting maps are compared with those generated by the Kohonen's self-organizing map (SOM) algorithm in terms of the Quantization Error (QE), the Weighted Topological Error (WTE) and the Pearson correlation coefficient (PCC) indices. The experiments show the proposed approach produces better values of the quality indices as well as is more robust to outliers. © 2008 IEEE.

关键词： Self organizing maps

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Multi-task Learning Graph Neural Networks for Cancer Prognosis Prediction with Genomic Data 46

Multi-task Learning Graph Neural Networks for Cancer Prognos...

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

作者： Lin, Tsung-Wei Arriagada, Sofia Ormazabal Lin, Che Taiwan Taiwan International Graduate Program AIoT Ntu Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan Department of Electrical Engineering Ntu Taiwan Center for Advanced Computing and Imaging in Biomedicine Ntu Taiwan Smart Medicine and Health Informatics Program Ntu Taiwan

ISBN: (纸本)9798350371499

Providing robust prognosis predictions for cancers with limited data samples remains a challenge for precision oncology. In this study, we propose a novel approach that combines multi-task learning (MTL) and graph neural networks (GNNs) to address this issue. By representing gene-gene interactions as a graph network, our approach leverages multi-task learning to effectively capture the relationships of genes relevant to the oncogenesis and progression of breast, lung, and colon cancer. We demonstrate that our approach improves the cancer prognosis prediction for cancers with fewer samples, such as colon adenocarcinoma, by leveraging the shared gene-gene interactions across different cancer types, obtaining increases in the area under the precision-recall curve (AUPRC) of 24%. Our work contributes to the field of smart healthcare by demonstrating the potential of MTL and GNNs for enhancing cancer prognosis prediction, even with limited data samples. © 2024 IEEE.

关键词： Lung cancer

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Evaluation of process variability on current for nanotechnologies devices

Evaluation of process variability on current for nanotechnol...

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2012 IEEE 3rd Latin American Symposium on Circuits and Systems, LASCAS 2012

作者： Meinhardt, Cristina Reis, Ricardo Computer Science Center - C3 Federal University of Rio Grande - FURG Rio Grande Brazil Graduate Program in Computing Institute of Informatics Federal University of Rio Grande Do Sul - UFRGS Porto Alegre Brazil

ISBN: (纸本)9781467312080

The continuous shrinking of devices has introduced new challenges to integrated circuit design, mainly to deal with the parametric variations in process parameters. This paper presents an evaluation of the process variability on the current Ids of nanotechnologies devices, individually and simultaneously, taking into account the correlation among them. The results show that the deviation from mean value is quite significant ≈ 16% for high performance models. The variation of L has the dominant effect on the overall variation of the device in high performance models while the dominant effect on the overall variation of the device in low power models still being due to Vth variations. Lastly, the effect of process parameter variations deteriorates with technology scaling, with a considerable increase in the deviation from the 22nm to 16nm technology. © 2012 IEEE.

关键词： Nanotechnology

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SIMROAA Multi-Agent Recommendation System for Recommending Accessible Learning Objects

SIMROAA Multi-Agent Recommendation System for Recommending A...

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Frontiers in Education (FIE) Conference

作者： Andreza Bastos Mourão José Francisco Magalhães Netto Post-Graduate Program in Informatics (PPGI) Institute of Computing of the Federal University of Amazonas (ICOMP-UFAM) State University of Amazonas (UEA) Manaus Brasil Post-Graduate Program in Informatics (PPGI) Institute of Computing of the Federal University of Amazonas (ICOMP-UFAM) Manaus Brasil

ISBN: (数字)9781728117461

ISBN: (纸本)9781728117478

This full paper presents the Multi-Agent Recommendation System for Recommending Accessible Learning Objects developed to attend students with disabilities and professors' computation area. The system was proposed based on the qualitative analysis of the questionnaires applied and answered by the professors of the area, to classify, organize and provide the most appropriate Learning Objects according to professors' preferences. In this way, the professor informs the type of impairment (visual, physical, hearing and/or cognitive), discipline and content, based on these requirements, receives the best recommendations of the intelligent agent. The methodology that we use is Multiagent Systems Engineering. The web page was developed using the Laravel Framework PHP, the data repository was created using the MySQL database, and to perform the implementation we used the Java Agent Development Framework. As a result of the research, we present a recommendation system of accessible learning objects using intelligent agents and communities of practice through the recommendation mechanism based on trust. The results obtained through the implementation of the system provide a new motivational practice trough accessible educational content, which contributes significantly to the teaching-learning process of the computation course and to inclusive education.

关键词： Metadata Education Intelligent agents Multi-agent systems Web pages Tools Standards

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Rapid improvement of students' soft-skills based on an agile-process approach

Rapid improvement of students' soft-skills based on an agile...

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Frontiers in Education (FIE) Conference

作者： Eduardo Valentin José Reginaldo Hughes Carvalho Raimundo Barreto Post-graduate Program in Informatics (PPGI) Federal University of Amazonas (UFAM) Coroado Manaus Brazil Post-graduate Program in Informatics (PPGI) Institute of Computing (ICOMP) Universidade Federal do Amazonas Manaus AM BR

ISBN: (纸本)9781479984558

Manaus, the capital city of the state of Amazonas and the sixth Brazilian largest gross domestic product, demands highly qualified hardware and software developers for consumer electronics and telecommunication sectors of its free trading zone. However, what can be viewed as an opportunity for the youth alumni of local institutions of higher education contrasts with the low level of qualification provided by such institutions. As a consequence, most positions either remain opened or are occupied by professionals from other Brazilian regions. Moreover, the issue relies more on soft skills than on hard skills, as the local institutions are aligned with International standard academic curriculum and cover modern technologies. Soft skill is the enter barrier to the job market. English proficiency, communication skill, writing (in both Portuguese and English) skill, among others, are top listed failure reasons in the hiring processes. As one project manager highlighted: “it is much cheaper and quicker for the company to fill in some specific technological gaps, than to leverage a poor English proficiency”. This paper presents a proposal of an involving activity aiming to address several soft skills at the same time, with minimal disturbance in the student daily routine. The concept is based on the impact of immersing the student in a systematic project development experience. Clear communicated project goal, where the students have specific roles, with assigned accountable peers, everything under the umbrella of an Agile-like software development process (derived from SCRUM) are in the core of the activity. This paper details the incremental implementation methodology of the program among four research areas, and 23 projects or project activities, involving 34 students. The results of the two years running program will also be described, including the increase of progress visibility by advisors, the improvement of students communication and writing skills and the qualifi

关键词： Software Writing Cities and towns Companies Scrum (Software development) Context Education

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