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9th International Conference on Signal Processing and Intelligent Systems, ICSPIS 2023

作者： Afriliana, Nunik Meyliana Gaol, Ford Lumban Soeparno, Haryono Bina Nusantara University Binus Graduate Program Computer Science Program Computer Science Department Jakarta Indonesia Bina Nusantara University School of Information System Information System Department Jakarta Indonesia

ISBN: (纸本)9798350369922

To ensure the sustainability of their operations, higher education institutions have to establish a robust strategic plan. Higher education institutions have an obligation to continuously assess the quality of their educational offerings in every program to comply with the standards established by the government and other educational entities. The implementation of intelligent decision support systems is a feasible approach among other techniques for addressing the aforementioned challenges. An intelligent decision support system has the potential to enable the business to uphold its adherence to regulations, enhance operational effectiveness, and optimize overall performance. This study aimed to determine the current state of intelligent decision support systems through the implementation of a comprehensive literature review. The second goal is to evaluate the best practices, methods, and implementation of intelligent decision support systems within higher education institutions. The findings indicate that intelligent decision support systems have the capacity to serve as a viable option for higher education, particularly in terms of enhancing the accuracy and efficiency of decision-making processes. Intelligent decision support systems exhibit a strong correlation with the fields of data mining and computational intelligence techniques. Nevertheless, the amount of research conducted on this subject is limited. © 2023 IEEE.

关键词： Data mining

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A self-stabilizing distributed algorithm for the 1-MIS problem under the distance-3 model

A self-stabilizing distributed algorithm for the 1-MIS probl...

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作者： Kakugawa, Hirotsugu Kamei, Sayaka Shibata, Masahiro Ooshita, Fukuhito Faculty of Advanced Science and Technology Ryukoku University Shiga Otsu Japan Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Higashihiroshima Japan Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Iizuka Japan Faculty of Engineering Fukui University of Technology Fukui Fukui Japan

Fault-tolerance and self-organization are critical properties in modern distributed systems. Self-stabilization is a class of fault-tolerant distributed algorithms which has the ability to recover from any kind and any finite number of transient faults and topology changes. In this article, we propose a self-stabilizing distributed algorithm for the 1-MIS problem under the unfair central daemon assuming the distance-3 model. Here, in the distance-3 model, each process can refer to the values of local variables of processes within three hops. Intuitively speaking, the 1-MIS problem is a variant of the maximal independent set (MIS) problem with improved local optimizations. The time complexity (convergence time) of our algorithm is (Formula presented.) steps and the space complexity is (Formula presented.) bits, where (Formula presented.) is the number of processes. Finally, we extend the notion of 1-MIS to (Formula presented.) -MIS for each nonnegative integer (Formula presented.), and compare the set sizes of (Formula presented.) -MIS ((Formula presented.)) and the maximum independent set. © 2024 John Wiley & Sons Ltd.

关键词： Consensus algorithm

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Integrating Local and Global Frequency Attention for Multiteacher Knowledge Distillation 1

Integrating Local and Global Frequency Attention for Multite...

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1st Asian Conference on Communication and Networks, ASIANComNet 2024

作者： Yao, Zhidi Cheng, Xin Zhang, Zhiqiang Du, Mengxin Yu, Wenxin Hosei University Graduate School of Science and Engineering Tokyo Japan Southwest University of Science and Technology School of Computer Science and Technology Mianyang China Instrumentation Technology and Economy Institute Beijing China Sichuan Civil-military Integration Institute Fujiang Laboratory Mianyang China

ISBN: (纸本)9798350367003

Knowledge distillation (KD), particularly in multiteacher settings, presents significant challenges in effectively transferring knowledge from multiple complex models to a more compact student model. Traditional approaches often fall short in capturing the full spectrum of useful information. In this paper, we propose a novel method that integrates local and global frequency attention mechanisms to enhance the multiteacher KD process. By simultaneously addressing both fine-grained local details and broad global patterns, our approach improves the student model's ability to assimilate and generalize from the diverse knowledge provided by multiple teachers. Experimental evaluations on standard benchmarks demonstrate that our method consistently outperforms existing multiteacher distillation techniques, achieving superior accuracy and robustness. Our results suggest that incorporating frequency-based attention mechanisms can significantly advance the effectiveness of KD in multiteacher scenarios, offering new insights and techniques for model compression and transfer learning. © 2024 IEEE.

关键词： Deep learning

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Faster Algorithms for a Lexicographic Bi-Criteria Line Balancing Problem with Totally Ordered Precedence Constraints 13

Faster Algorithms for a Lexicographic Bi-Criteria Line Balan...

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Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2024

作者： Imahori, Shinji Karuno, Yoshiyuki Ueda, Yuma Chuo University Faculty of Science and Engineering Tokyo Japan Kyoto Institute of Technology Faculty of Mechanical Engineering Kyoto Japan Graduate School of Science and Technology Kyoto Institute of Technology Mechanodesign Program Kyoto Japan

ISBN: (纸本)9798350373332

The line balancing has been an important production strategy which aims at redressing the balance of workloads on machines in an assembly type of production system. In this paper, a lexicographic bi-criteria optimization model of the line balancing is revisited for a mass-production of one homogeneous product. A sequence of machines and a totally ordered set of tasks are given. The machines are considered to be situated along with a production line, according to their sequence. Precedence relations between tasks are specified by the total order of tasks. The task set also consists of a given number of disjoint subsets, namely categories such that tasks in a category are considered to constitute a certain kind of family of technical skills. Each task has a positive processing time and a category index. A solution to the problem is expressed by an assignment of tasks to machines, and for the feasibility of a solution, the assignment must obey the totally ordered precedence constraint on tasks. The sum of processing times of tasks assigned to a machine in a feasible solution is the load of the machine, and the number of category indexes of tasks assigned to a machine is referred to as the degree of diverseness of the task subset. The primary objective of the problem is to minimize the maximum machine load, and the second objective is to minimize the maximum degree of diverseness of tasks assigned to a machine. The lexicographic bi-criteria line balancing problem with totally ordered precedence constraints has been known to admit a polynomial time exact algorithm, whose time complexity is linear in the number of machines and squared in the number of tasks. In this paper, a faster polynomial time exact algorithm is proposed, which improves the time complexity to be squared in the number of tasks. © 2024 IEEE.

关键词： Polynomial approximation

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Leveraging Attention to Achieve Generalization for Image Forgery Detection 1

Leveraging Attention to Achieve Generalization for Image For...

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1st International Conference of Intelligent Methods, Systems and Applications, IMSA 2023

作者： Atta, Mohamed Nile University Artificial Intelligence Program School of Information Technology and Computer Science Giza Egypt

ISBN: (纸本)9798350335569

Recently, image forgery has become an alarming trend with the growth of available easy-to-use editing and generation tools. Modern DeepFake methods have achieved extraordinary progress in realistic face manipulation, thus raising concerns among the public about the misuse of such technologies. Unfortunately, with the obnoxiously wide range of possible manipulation and artifact-covering methods, most existing state-of-the-art detection methods lack the generalization capability to handle the output variations. To address this issue, a noticeable shift towards using attention mechanisms has emerged using balanced portions of the latest challenging datasets to detect intra-and inter-spatial relations. Our paper provides a comprehensive analysis of modern deep learning-based methods, showing the benefits of the shift. In addition, we make propositions for future research directions and dataset-building methodology. © 2023 IEEE.

关键词： Generative adversarial networks

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Into 3D-Reconstructed Apparel over Virtual Try-On with Virtual Reality

Into 3D-Reconstructed Apparel over Virtual Try-On with Virtu...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Doungtap, Surasachai Petchhan, Jirayu Phanichraksaphong, Varinya Wang, Jenq-Haur International Graduate Program of Electrical Engineering and Computer Science Taiwan National Taiwan University of Science & Technology Dept. of Electrical Engineering Taiwan National Taipei University of Technology Department of Computer Science and Information Engineering Taiwan

ISBN: (纸本)9798350324174

Virtualization technologies are still growing bigger and faster. Despite the greatness of its advancement, the costume industry is still very accessible when it comes to real trials. Off-the-shelf stuff are inadequate details for the desired individual to assess its in-depth utility for each garment trying on for a second, including custom stuff are much harder to try out right away. To this end, 2D image-based 3D reconstruction inclusive of touchable-virtualized space is accessible easier to stuff details for mans' decision making in purchasing. We establish the overall end-to-end pipeline from reconstruction until visualization for one instance to be triable on its stuff for a moment. As an expectation, our proposed approach can bring objects into the experimental area and use them immediately without any obstacle. © 2023 IEEE.

关键词： Virtual reality

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Predictive Analytics for Future Air Pollution Levels Based on Population Growth

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Procedia computer science 2024年 245卷 834-843页

作者： Darel Yeshe Irma Kartika Wairooy Brilly Andro Makalew Computer Science Department School of Computer Science Jakarta Indonesia 11480 Mobile Application & Technology Program Computer Science Department School of Computer Science Jakarta Indonesia 11480

Air is very beneficial and crucial for every living creature on earth, hence it is very important to protect the air quality in order to avoid diseases. However, due to the increase in population, some human activities have been causing detrimental damage to the air quality. Some people have realized the potential impact of this problem and have done studies on predicting future air pollution. Some implemented machine learning models such as random forest regression, support vector machine, etc, while others have utilized deep learning models in their research.. This study will implement five different models, specifically linear regression, ridge regression, random forest regression, and multilayer perceptron regression. There will be two datasets used in this paper which will be merged and processed. There will be three key evaluation metrics being used in this paper namely root mean squared error, mean absolute error, and r-squared. The results from all of the models have concluded that more optimization and factors are needed in order to boost the final result of this study.

关键词： Machine Learning Predicting Regression Algorithm technology Education Artificial Intelligence

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A Visual Interpretation-Based Self-improved Classification System Using Virtual Adversarial Training 9th

A Visual Interpretation-Based Self-improved Classification S...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Jiang, Shuai Kamei, Sayaka Li, Chen Hou, Shengzhe Morimoto, Yasuhiko Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan Graduate School of Informatics Nagoya University Nagoya Japan College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9783031466731

The successful application of large pre-trained models such as BERT in natural language processing has attracted more attention from researchers. Since the BERT typically acts as an end-to-end black box, classification systems based on it usually have difficulty in interpretation and low robustness. This paper proposes a visual interpretation-based self-improving classification model with a combination of virtual adversarial training (VAT) and BERT models to address the above problems. Specifically, a fine-tuned BERT model is used as a classifier to classify the sentiment of the text. Then, the predicted sentiment classification labels are used as part of the input of another BERT for spam classification via a semi-supervised training manner using VAT. Additionally, visualization techniques, including visualizing the importance of words and normalizing the attention head matrix, are employed to analyze the relevance of each component to classification accuracy. Moreover, brand-new features will be found in the visual analysis, and classification performance will be improved. Experimental results on Twitter’s tweet dataset demonstrate the effectiveness of the proposed model on the classification task. Furthermore, the ablation study results illustrate the effect of different components of the proposed model on the classification results. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Classification (of information)

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Long Short-Term Memory-based Models for Sleep Quality Prediction from Wearable Device Time Series Data 8

Long Short-Term Memory-based Models for Sleep Quality Predic...

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8th International Conference on computer science and Computational Intelligence, ICCSCI 2023

作者： Hidayat, Alam Ahmad Budiarto, Arif Pardamean, Bens Mathematics Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

Several studies suggest that sleep quality is associated with physical activities. Moreover, deep sleep time can be used to determine the sleep quality of an individual. In this work, we aim to find the association between physical activities and deep sleep time by modeling the time series data such as heart rate and a number of steps captured from a commercial wearable device. Our previous study demonstrates that deep learning-based time series modeling is well suited for our problem since the temporal patterns in the two physical parameters need to be captured to obtain more accurate results. We first preprocess our series data to have a time-step size of 10 minutes. To improve our previous effort in this modeling, we compare four different variants of Long Short-Term Memory (LSTM)-based models, ranging from single input to dual input models. Our result shows that the simple stacked LSTM model performs better for our data because the remaining models suffer from overfitting due to a larger number of the trained parameters. © 2023 The Authors. Published by Elsevier B.V.

关键词： deep learning forecasting sleep quality time series wearable devices

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Investigation of player emotion and instinct on racing video game using 6-11 framework (Case study: split second (2010) single player modes)

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Procedia computer science 2024年 245卷 87-99页

作者： Galih Dea Pratama Andien Dwi Novika Elshad Ryan Ardiyanto Game Application & Technology Program Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Master of Computer Science Binus Graduate Program Bina Nusantara University Jakarta 11480 Indonesia

Racing is one of the most prominent genres in the modern video game industry, where the games within the genre enable players to use any vehicle of their choice and win through doing series of action within gameplay. Thus, it made the genre popular over the years, spawning many famous titles such as Split Second back in 2010. Like every other video game title, Split Second also invokes the player emotion and instinct during its gameplay session. To understand the emotion and instinct aspect present in players of the game, we will use 6-11 framework which focuses on the two aspects. As part of the conclusion, Split Second game involves instincts such as Competition, Collecting, and Survival, with emotions including Fear, Pride, Joy, and Excitement, which can be found from its vast single player game modes.

关键词： video games racing video games game analysis 6-11 framework

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