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International Conference on information Management and Technology (ICIMTech)

作者： Bambang Dwi Wijanarko Dina Fitria Murad Yaya Heryadi Lukas Hapnes Toba Widodo Budiharto Computer Science Department BINUS Online Learning Bina Nusantara University Jakarta Indonesia Information Systems Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Faculty of Engineering Univeritas Katolik Indonesia Atma Jaya Cognitive Engineering Research Group (CERG) Jakarta Indonesia

The widespread interest toward online learning at higher education worldwide brings about necessity toward an smart Learning Management System (LMS), LMS with analytic functionalities, to increase learning outcome of the students. In an effort to develop a smart LMS which is able to both guiding students on learning process and giving automated feedback on online learning delivery to online learning management, this paper presents a preliminary and exploratory results toward textual documents from learning material repository and online discussion forum. The dataset for this study obtained from Bina Nusantara Online Learning repository. The study found interesting finding namely: there is a significant discrepancy between cognitive domain Taxonomy level between question raised in discussion forum and its respective level of learning outcomes. This findings were confirmed using Anova with P-value

关键词： Taxonomy computer science Discussion forums Medical services Support vector machines Machine learning Analysis of variance

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Exploring Macroscopic Fluctuation of Facial Expression for Mood Disorder Classification

Exploring Macroscopic Fluctuation of Facial Expression for M...

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Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia)

作者： Qian-Bei Hong Chung-Hsien Wu Ming-Hsiang Su Kun-Yi Huang Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Tainan Taiwan Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9781538653128

In clinical diagnosis of mood disorder, a large portion of bipolar disorder patients (BDs) are misdiagnosed as unipolar depression (UDs). Clinicians have confirmed that BDs generally show "reduced affect'' during clinical treatment. Thus, it is expected to build an objective and one-time diagnosis system for diagnosis assistance by using machine-learning techniques. In this study, facial expressions of BD, UD and control group (C) elicited by emotional video clips are collected for exploring temporal fluctuation characteristics of intensities of facial muscles expression among the three groups. The differences of facial expressions among mood disorders are investigated by observing macroscopic fluctuations. To deal with these problems, the corresponding methods for feature extraction and modeling are proposed. From the viewpoint of macroscopic facial expression, action unit (AU) is applied for describing the temporal transformation of muscles. Then, modulation spectrum is used for extracting short-term variation of AU. The multilayer perceptron (MLP)-based disorder prediction model is then applied to obtain the prediction results. For evaluation of the proposed method, 12 subjects for three group are included in the K-fold (K=12) cross validation experiments. The experiment results reached 61.1% classification accuracy, and outperformed the other baseline methods.

关键词： Mood Gold Fluctuations Databases Feature extraction Modulation Predictive models

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Syncgan: Synchronize the Latent Spaces of Cross-Modal Generative Adversarial Networks

Syncgan: Synchronize the Latent Spaces of Cross-Modal Genera...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Wen-Cheng Chen Chien-Wen Chen Min-Chun Hu Dept. of Computer Science and Information Engineering National Cheng Kung University Taiwan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Taiwan

Generative adversarial network (GAN) has achieved impressive success on cross-domain generation, but it faces difficulty in cross-modal generation due to the lack of a common distribution between heterogeneous data. Most existing methods of conditional based cross-modal GANs adopt the strategy of one-directional transfer and have achieved preliminary success on text-to-image transfer. Instead of learning the transfer between different modalities, we aim to learn a synchronous latent space representing the cross-modal common concept. A novel network component named synchronizer is proposed in this work to judge whether the paired data is synchronous/corresponding or not, which can constrain the latent space of generators in the GANs. Our GAN model, named as SyncGAN, can successfully generate synchronous data (e.g., a pair of image and sound) from identical random noise. For transforming data from one modality to another, we recover the latent code by inverting the mappings of a generator and use it to generate data of different modality. In addition, the proposed model can achieve semi-supervised learning, which makes our model more flexible for practical applications.

关键词： Synchronization Gallium nitride Generators Data models Training Generative adversarial networks Training data

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LSTM-based Text Emotion Recognition Using Semantic and Emotional Word Vectors

LSTM-based Text Emotion Recognition Using Semantic and Emoti...

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Asian Conference on Affective Computing and Intelligent Interaction (ACII Asia)

作者： Ming-Hsiang Su Chung-Hsien Wu Kun-Yi Huang Qian-Bei Hong Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan Graduate Program of Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Tainan Taiwan

ISBN: (纸本)9781538653128

This study proposes a long-short term memory (LSTM)-based approach to text emotion recognition based on semantic word vector and emotional word vector of the input text. For each word in an input text, the semantic word vector is extracted from the word 2vec model. Besides, each lexical word is projected to all the emotional words defined in an affective lexicon to derive an emotional word vector. An autoencoder is then adopted to obtain the bottleneck features from the emotional word vector for dimensionality reduction. The autoencoder bottleneck features are then concatenated with the features in the semantic word vector to form the final textual features for emotion recognition. Finally, given the textual feature sequence of the entire sentence, the LSTM is used for emotion recognition by modeling the contextual emotion evolution of the input text. For evaluation, the NLPCC-MHMC-TE database containing seven emotion categories: anger, boredom, disgust, anxiety, happiness, sadness, and surprise was constructed and used. Five-fold cross-validation was employed to evaluate the performance of the proposed method. Experimental results show that the proposed LSTM-based method achieved a recognition accuracy of 70.66%, improving 5.33% compared with the CNN-based method. Besides, the proposed method based on integration of the semantic word vector and emotional word vector of the input text outperformed that using the individual feature vector.

关键词： Emotion recognition Databases Logic gates Feature extraction Semantics Speech recognition computer science

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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The Form of High-Performance Computing: A Survey

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IOP Conference Series: Materials Science and engineering 2019年第5期662卷

作者： Priati Assiroj H L H S Warnars R Kosala B Ranti S Supangat A I Kistijantoro E Abdurrachman Binus Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Information System Dept. Buana Perjuangan University Karawang Computer Science Department BINUS Graduate Program Doctor of Computer Science Bina Nusantara University Jakarta Information System Department Bina Nusantara University Jakarta Information System Technology Department Institut Teknologi Bandung School of Electronics Engineering and Informatics Institut Teknologi Bandung

Computational technology is an important thing that needed in the whole of human life. Along with the development of computer technology, there are a significant data created and wait to be processed; we will get more advantages if it well-processed, if not we will see the data explosion, the data is just a useless stack. Research mitigates the other research paper and makes a conclusion and a report by PRISMA method. The development of computer technology will support the development of an organization. Considering any factors in choosing the right and suitable high-performance computer resource due to our organization is a must. The existing high-performance computing forms are Supercomputer, Grid Computing, Cluster Computing, and Cloud Computing. Based on this work, the last form of high-performance computing, Cloud Computing is one of the most used forms thus we mitigate its advantages and disadvantages. Recently, to process this vast data, we must have a high-performance computer and to provide it, this is not a cheap resource.

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Data mining technique with cluster anaysis use K-means algorithm for LQ45 index on Indonesia stock exchange 1

Data mining technique with cluster anaysis use K-means algor...

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1st International Conference on information and Communications Technology, ICOIACT 2018

作者： Condrobimo, A. Raharto Abbas, Bahtiar Saleh Trisetyarso, Agung Suparta, Wayan Kang, Chul-Ho Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information System Bina Nusantara University Jakarta11480 Indonesia Civil Engineering Department University of Technology Yogyakarta Yogyakarta Indonesia Department of Electronic and Communication Engineering Kwangwoon University Korea Republic of

ISBN: (纸本)9781538609545

This study aims to apply data mining techniques with cluster analysis on stock data registered in LQ45 in Indonesia Stock Exchange. The cluster analysis used in this method is k-means algorithm, the data in this research is taken from Indonesia Stock Exchange. The cluster analysis in this study analyzed the characteristics of data volumes and stock values, while the results in this study were presented in the form of cluster members visually. Therefore, this cluster analysis in this research can be used for quick and efficient identifier for each member of LQ45 index cluster based on share value for each cluster and its volume. The identification results can be used by beginner-level investors that begun to be interested in stock investments to help make informed decisions about stock trading on desired cluster groups. © 2018 IEEE.

关键词： Cluster analysis

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A Blockchain Approach for the Future Renewable Energy Transaction

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Journal of Physics: Conference Series 2019年第1期1175卷

作者： Sandi Rahmadika Diena Rauda Ramdania Maisevli Harika Interdisciplinary Program of Information Security Graduate School PKNU Pukyong National University Busan South Korea Informatics Engineering UIN Sunan Gunung Djati Bandung Indonesia Computer Engineering and Informatics Politeknik Negeri Bandung Indonesia

Blockchain technology holds great promise to rewire the current financial system which relies on the third party. Every transaction is recorded in a secure and transparent for the parties in the blockchain network. Blockchain technology in the energy sector becomes an interesting topic among researchers at this moment. The use of blockchain allows producers and consumers to trade energy transactions through smart grids because of a decentralized energy trading system. The trading activities without a third party involved would reduce the cost of a transaction thus it brings to a new level of quality of service in the trading system. In this paper, we propose an architectural model for decentralized energy trading system among the neighbors that allows the producer who has the surplus energy to conduct a trading activity with his/her neighbors. The transactions manage by the miners in the same blockchain network. Moreover, we analyze the security issues from various attacks and presenting the performance of the selected attack that might occur in the model.

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Cryptocurrencies for Smart Territories: an exploratory study

Cryptocurrencies for Smart Territories: an exploratory study

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International Joint Conference on Neural Networks (IJCNN)

作者： Alexandre C. Barbosa Thays A. Oliveira Vitor N. Coelho Graduate Program in Sustainable Territorial Development Universitá Degli Studi di Padova Italy and Katholieke Universiteit Leuven Belgium Universitè Paris 1 Pantheon-Sorbonne France Graduate Program in Engineering and Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain Institute of Computer Science Universidade Federal Fluminense Niterói RJ Brazil

The smart city debate will still be the scope of business, policy-making and territorial planning in the following years. Based on that, the paper aims to propose an interdisciplinary conceptualization of smart territories. The goal is to promote the contrary movement of megacities creation and also consolidate the geographical aspect of smartness. The concept was formulated based on the elements of sustainable territorial development, strategic spatial planning and smart governance. In addition, taking hand of a parallel literature review, the study explores the potential of creating complementary cryptocurrencies as a strategy of territorial planning. This term was built based on already existing complementary currency systems in distributed ledger technologies scope. The idea provided presents a great potential for poverty alleviation by bringing together financial and digital inclusion.

关键词： Planning Smart cities Collaboration Decision making Government Cryptography Cultural differences

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Probing the universality of topological defect formation in a quantum annealer: Kibble-Zurek mechanism and beyond

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Physical Review Research 2020年第3期2卷 033369-033369页

作者： Yuki Bando Yuki Susa Hiroki Oshiyama Naokazu Shibata Masayuki Ohzeki Fernando Javier Gómez-Ruiz Daniel A. Lidar Sei Suzuki Adolfo del Campo Hidetoshi Nishimori Institute of Innovative Research Tokyo Institute of Technology Nagatsuta-cho Midori-ku Yokohama 226-8503 Japan Institute of Innovative Research Tokyo Institute of Technology Oh-okayama Meguro-ku Tokyo 152-8550 Japan Department of Physics Tohoku University Sendai 980-8578 Japan Graduate School of Information Sciences Tohoku University Sendai 980-8579 Japan Sigma-i Co. Ltd. Konan Minato-ku Tokyo 108-0075 Japan Donostia International Physics Center E-20018 San Sebastián Spain Departments of Electrical and Computer Engineering Chemistry and Physics University of Southern California Los Angeles California 90089 USA Center for Quantum Information Science & Technology University of Southern California Los Angeles California 90089 USA Department of Liberal Arts Saitama Medical University Moroyama Saitama 350-0495 Japan IKERBASQUE Basque Foundation for Science E-48013 Bilbao Spain Department of Physics University of Massachusetts Boston 100 Morrissey Boulevard Boston Massachusetts 02125 USA RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program (iTHEMS) Wako Saitama 351-0198 Japan

The number of topological defects created in a system driven through a quantum phase transition exhibits a power-law scaling with the driving time. This universal scaling law is the key prediction of the Kibble-Zurek mechanism (KZM), and testing it using a hardware-based quantum simulator is a coveted goal of quantum information science. Here we provide such a test using quantum annealing. Specifically, we report on extensive experimental tests of topological defect formation via the one-dimensional transverse-field Ising model on two different D-Wave quantum annealing devices. We find that the quantum simulator results can indeed be explained by the KZM for open-system quantum dynamics with phase-flip errors, with certain quantitative deviations from the theory likely caused by factors such as random control errors and transient effects. In addition, we probe physics beyond the KZM by identifying signatures of universality in the distribution and cumulants of the number of kinks and their decay, and again find agreement with the quantum simulator results. This implies that the theoretical predictions of the generalized KZM theory, which assumes isolation from the environment, applies beyond its original scope to an open system. We support this result by extensive numerical computations. To check whether an alternative, classical interpretation of these results is possible, we used the spin-vector Monte Carlo model, a candidate classical description of the D-Wave device. We find that the degree of agreement with the experimental data from the D-Wave annealing devices is better for the KZM, a quantum theory, than for the classical spin-vector Monte Carlo model, thus favoring a quantum description of the device. Our work provides an experimental test of quantum critical dynamics in an open quantum system, and paves the way to new directions in quantum simulation experiments.

关键词： Critical exponents Nonequilibrium statistical mechanics Open quantum systems Quantum computation Quantum master equation Quantum simulation Quantum statistical mechanics Quantum spin models

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