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International Conference on Electronics, Materials Engineering and Nano-Technology (IEMENTech)

作者： Daniel Anando Wangean Derwin Suhartono Master of Computer Science Binus Graduate Program Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331520762

ISBN: (纸本)9798331520779

Low resolution image-face recognition system is one of the challenging aspects of face recognition models' development. From machine learning, deep learning, and into ensemble learning are implemented to develop face recognition models. Including mpdCNN that published by Mishra in a journal article. This mpdCNN already achieved 88.6% accuracy score on SCFace dataset while published. Based on literature review, ensemble learning model can be improved by modifying parallel layers. The purpose of this research is to improve mpdCNN's performance by implementing some modifications include adding parallel layers, alternating the fusion layers, expanding the layers, and adding residual connections. Some modifications were successfully improved mpdCNN's accuracy score. By adding parallel layers and residual connections into mpdCNN's architecture, the modified mpdCNN that proposed in this research achieved 92.33% accuracy score, measured by Rank-k evaluation metric.

关键词： Measurement Deep learning Accuracy Image resolution Image recognition Face recognition Instruments Robustness Ensemble learning Systematic literature review

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Critical Factors of Disruptive Innovation: A Review

Critical Factors of Disruptive Innovation: A Review

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International Conference on computer Applications and Industrial Electronics (ICCAIE)

作者： Yoga Prihastomo Harjanto Prabowo Agung Trisetyarso Haryono Soeparno Computer Science Department - BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department - Department of Mathematics Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350348798

ISBN: (纸本)9798350348804

People acknowledge innovation as a critical and central factor in increasing economic output and productivity. The phrase disruptive innovation was first familiarized in 1995 and has evolved into a new phenomenon. It exemplifies how a new entrant in a field can eventually compete with the incumbent. Furthermore, many of them may replace them. The study strives to identify the critical factors underlying disruptive innovations. This study employs a systematic literature review strategy and PRISMA meta-analysis. It refers to various reputable data sources from 2013 to 2022. The study included 5,305 articles, analyzed 183 articles that encountered the criteria, and employed 45 appropriate articles. As a result, it encounters six critical factors to disruptive innovation: demand, technology readiness, regulation, labor, external technology, and capital. The proposed formula encapsulates a Success Score for assessing disruptive innovations, integrating various essential factors contributing to success.

关键词： Productivity Industrial electronics Economics Technological innovation Disruptive innovation Systematics Reviews

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Mental Status Identification for Infertile Patients Using Artificial Intelligence Models 14th

Mental Status Identification for Infertile Patients Using Ar...

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14th International Conference on Frontier Computing, FC 2024

作者： Hsu, Hao-Siang Wang, Jong-Yi Chiou, Hai-Chi Wu, Fu-Hsing Lin, Chuen-Horng Chan, Yung‑Kuan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taichung404336 Taiwan Department of Health Services Administration China Medical University Taichung406040 Taiwan Bachelor Degree Program of Artificial Intelligence National Taichung University of Science and Technology Taichung404336 Taiwan Department of Management Information Systems National Chung-Hsing University Taichung402202 Taiwan

ISBN: (纸本)9789819623945

Infertile patients may be a high-risk group of mental disorder. The precise identification of the mental status of infertile patients can provide decision support to healthcare professionals and may be helpful in providing preventive measures accordingly. Rare studies focused on the mental status identification using artificial intelligence (AI) models for infertile people. This study aimed to precisely identify the mental status (anxiety and depression) of infertile patients using three AI models, the XGBoost, decision tree, and stacking models. The dataset, retrieved from a structured questionnaire survey in 2011, which included 400 infertility outpatients seeking reproductive treatments at three teaching hospitals in Taiwan was adopted for training and testing AI models. Aforementioned AI models were designed and reached identification performances of accuracy, precision, recall and area under ROC curve (AUC) equaling 0.75–0.82, 0.70–0.80, 0.64–0.72, and 0.64–0.72, respectively. The XGBoost model (accuracy = 0.82, precision = 0.80, recall = 0.72, and AUC = 0.72) outperformed or equaled the decision tree and stacking models in terms of accuracy, precision, recall, and AUC values. Designed AI models for mental status identification can be integrated into the clinical decision support system (CDSS) to provide the anxiety and depression group automatically. Accordingly, the worsening of infertility-related anxiety and depression can be prevented. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： artificial intelligence infertile patients Mental status identification stacking model XGBoost

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OPEN LEARNING ANALYTICS: A SYSTEMATIC LITERATURE REVIEW AND FUTURE PERSPECTIVES

arXiv

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arXiv 2023年

作者： Muslim, Arham Chatti, Mohamed Amine Guesmi, Mouadh Social Computing Department of Computer Science and Applied Cognitive Science University of Duisburg-Essen Germany

Open Learning Analytics (OLA) is an emerging research area that aims at improving learning efficiency and effectiveness in lifelong learning environments. OLA employs multiple methods to draw value from a wide range of educational data coming from various learning environments and contexts in order to gain insight into the learning processes of different stakeholders. As the research field is still relatively young, only a few technical platforms are available and a common understanding of requirements is lacking. This paper provides a systematic literature review of tools available in the learning analytics literature from 2011-2019 with an eye on their support for openness. 137 tools from nine academic databases are collected to form the base for this review. The analysis of selected tools is performed based on four dimensions, namely ‘Data, Environments, Context (What?)’, ‘Stakeholders (Who?)’, ‘Objectives (Why?)’, and ‘Methods (How?)’. Moreover, five well-known OLA frameworks available in the community are systematically compared. The review concludes by eliciting the main requirements for an effective OLA platform and by identifying key challenges and future lines of work in this emerging field. © 2023, CC BY-NC-ND.

关键词： computer aided instruction

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Multi-Object RANSAC: Efficient Plane Clustering Method in a Clutter

Multi-Object RANSAC: Efficient Plane Clustering Method in a ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Seunghyeon Lim Youngjae Yoo Jun Ki Lee Byoung-Tak Zhang Interdisciplinary Program in Cognitive Science Seoul National University Seoul Korea Department of Computer Science and Engineering Seoul National University Seoul Korea AIIS Seoul National University Seoul Korea

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this paper, we propose a novel method for plane clustering specialized in cluttered scenes using an RGB-D camera and validate its effectiveness through robot grasping experiments. Unlike existing methods, which focus on large- scale indoor structures, our approach—Multi-Object RANSAC emphasizes cluttered environments that contain a wide range of objects with different scales. It enhances plane segmentation by generating subplanes in Deep Plane Clustering (DPC) module, which are then merged with the final planes by postprocessing. DPC rearranges the point cloud by voting layers to make subplane clusters, trained in a self-supervised manner using pseudo-labels generated from RANSAC. Multi-Object RANSAC demonstrates superior plane instance segmentation performances over other recent RANSAC applications. We conducted an experiment on robot suction-based grasping, comparing our method with vision-based grasping network and RANSAC applications. The results from this real-world scenario showed its remarkable performance surpassing the baseline methods, highlighting its potential for advanced scene understanding and manipulation.

关键词： Point cloud compression Instance segmentation Clustering methods Robot vision systems Grasping Cameras Clutter

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Interpretable Concept Bottlenecks to Align Reinforcement Learning Agents

arXiv

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arXiv 2024年

作者： Delfosse, Quentin Sztwiertnia, Sebastian Rothermel, Mark Stammer, Wolfgang Kersting, Kristian Computer Science Department TU Darmstadt Germany Darmstadt Germany Centre for Cognitive Science TU Darmstadt Germany Darmstadt Germany

Goal misalignment, reward sparsity and difficult credit assignment are only a few of the many issues that make it difficult for deep reinforcement learning (RL) agents to learn optimal policies. Unfortunately, the black-box nature of deep neural networks impedes the inclusion of domain experts for inspecting the model and revising suboptimal policies. To this end, we introduce Successive Concept Bottleneck Agents (SCoBots), that integrate consecutive concept bottleneck (CB) layers. In contrast to current CB models, SCoBots do not just represent concepts as properties of individual objects, but also as relations between objects which is crucial for many RL tasks. Our experimental results2 provide evidence of SCoBots' competitive performances, but also of their potential for domain experts to understand and regularize their behavior. Among other things, SCoBots enabled us to identify a previously unknown misalignment problem in the iconic video game, Pong, and resolve it. Overall, SCoBots thus result in more human-aligned RL agents. © 2024, CC BY.

关键词： Deep reinforcement learning

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Adopting Augmented Reality (AR) Technology in Retail Sector: Systematic Review of Prisma Methods 7

Adopting Augmented Reality (AR) Technology in Retail Sector:...

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7th International Conference on Information Management and Technology, ICIMTech 2022

作者： Maryani Prabowo, Harjanto Gaol, Ford Lumban Hidayanto, Ahmad Nizar School of Information Systems BINUS Graduate Program-Doctor of Computer Science Bina Nusantara University Information Systems Department Computer Science Department Jakarta11480 Indonesia BINUS Business School Doctor of Research in Management Bina Nusantara University Management Department Jakarta11480 Indonesia BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Faculty of Computer Science Universitas Indonesia Depok16424 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

One of the most popular technologies nowadays is augmented reality (AR). popular technologies in various industries. Many industries have adopted this AR technology, one of which is with the aim of marketing the products offered to their customers. With more and more adopting this technology, retailers are also using it. The goal of this research is to determine out what important variables are often encountered in the application of AR technology. A qualitative technique is used in this study, as well as a Systematic Literature Review Prism approach. The result are 56 papers, 35 candidate papers and 26 papers were selected. We examine where and how in retail, augmented reality (AR) is used, the aspects of Augmented reality technology that are frequently examined, and the psychological and behavioral effects that AR can have. © 2022 IEEE.

关键词： Augmented reality

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A Bayesian-based approach for extracting the pion charge radius from electron-electron scattering data

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Chinese Physics C 2021年第8期45卷 12-27页

作者： Alam A Hidayat Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara UniversityJakarta 11480Indonesia Computer Science Department BINUS Graduate Program-Master of Computer ScienceBina Nusantara UniversityJakarta 11480Indonesia

In this study,we utilize a potentially versatile Bayesian parameter approach to compute the value of the pion charge radius and quantify its uncertainty from several experimental e^(+)e^(-) datasets for the pion vector form *** employ dispersion relations to model the pion vector form factor to extract the *** model selection is used to determine the order of polynomial appearing in the form factor formulation that can be supported by the data,adapting the computation of Bayes evidence and Bayesian effective complexity based on Occam's *** findings indicate that five out of six used datasets favor the nine-parameter model for radius extraction,and accordingly,we average the radii from the *** some inconsistencies with the most updated radius values,our approach may serve as a more intuitive method of addressing parameter estimations in dispersion theory.

关键词： Bayesian complexity dispersion hadron pion

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The Utilization of DistilBERT Features Space for Topics Suggestion in the Context of Learning Content

The Utilization of DistilBERT Features Space for Topics Sugg...

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Artificial Intelligence and Image Processing (IWAIIP), International Workshop on

作者： Kenny Jingga Hidayaturrahman Hidayaturrahman Computer Science Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Recommender system is one of the popular topics in artificial intelligence fields as it can widely be used in the *** service provider, e-commerce, e-learning, and many other fields can utilize recommender system to give the personalization for the users. This research will try to use recommender system to provide the recommended topics that are suitable to each learning content. As part of developing the most suitable recommender system, this research will also focus on the data pre-processing,as the data is still raw and contains too much unused information yet. Text vectorization or the embedding process was conducted to the dataset using DistilBERT, pre-trained BERT model. After the vectorization, the recommender system used cosine similarity from the result to discover the largest cosine similarity, which was used to determine the recommendation. Based on the experiment, using cosine similarity could do the recommendation well enough by giving the appropriate topics recommendation based on the content. For example, given the content: “Chapter 2: Comments Chapter 2 of the book on C programming”, the top 5 recommended topics were: “Objective-C”, “C++”, “MATLAB”,”VBA”, and “Perl”. Based on the results, it can be considered that the recommender system had performed as expected. However,it still had a lot of areas that could be improved for the future research, especially in the data preprocessing. Other text vectorization models can be considered to be used, such as: BERT Multilingual, RoBERTa, and SpanBERT. Other consideration is the content preparation that will be used as the input for the system. Combination of DistilBERT and cosine similarity as the recommender system can be considered to be implemented for other areas.

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Exploring the Racial Bias in Pain Detection with a computer Vision Model 44

Exploring the Racial Bias in Pain Detection with a Computer ...

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44th Annual Meeting of the cognitive science Society: cognitive Diversity, CogSci 2022

作者： Fabi, Sarah Xu, Xiaojing de Sa, Virginia R. Department of Cognitive Science Halicioglu Data Science Institute University of California San Diego United States Neuro-Cognitive Modeling Group University of Tübingen Germany Department of Electrical and Computer Engineering University of California San Diego United States

People detect painful expressions more easily in members of their racial ingroup than outgroup. Here, we wanted to investigate this racial bias with a machine learning model trained to detect activations of different action units of painful facial expressions. We examined whether the model detected higher action unit activation for European than African faces when trained on datasets with mostly White faces. To control for confounding variables, pictures of faces were generated with the FaceGen Modeller. Results revealed that there exist differences in the visual detectability of some facial muscle activations due to skin color or other race-dependent facial features. Despite the bias towards European looking faces in the training data, some activations were more easily detectable in African faces. Thus, neither the perceptual detectability, nor the larger exposure to own-race faces seems to solely explain the racial bias in pain detection. © 2022 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY)

关键词： Chemical activation

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