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Involution symmetry quantification using recurrences

Physical Review E 2024年第2期110卷 024203-024203页

作者： Gabriel Marghoti Thiago de Lima Prado Sergio Roberto Lopes Yoshito Hirata Physics Department Degree Program in Computer Science Interdisciplinary Center for Science Technology and Innovation CICTI Departament of Physics - Telegraphenberg 14473 Potsdam Germany Institute of Systems and Information Engineering

Symmetries are ubiquitous in science, aiding theoretical comprehension by discerning patterns in mathematical models and natural phenomena. This work introduces a method for assessing the extent of symmetry within a time series. We explore both microscopic and macroscopic features extracted from a recurrence plot. By analyzing the statistics of small recurrence matrices, our approach delves into microscale dynamics, facilitating the identification of symmetric time series segments through diagonal macroscale structures on a recurrence plot. We validate our approach by successfully quantifying involution symmetries for three-dimensional dynamical models, specifically, order-2 rotational symmetry in the Lorenz '63 model, and inversion symmetry in the Chua circuit. Our quantifier also detects symmetry breaking in the modified Lorenz model for El Niño phenomenon. The method can be applied in a versatile manner, not only to three-dimensional trajectories but also to univariate time series. Symmetry quantification in time series is promising for enhancing dynamical system modeling and profiling.

关键词： Chaos Dynamical systems Time series analysis

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United We Pretrain, Divided We Fail! Representation Learning for Time Series by Pretraining on 75 Datasets at Once

arXiv

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arXiv 2024年

作者： Kraus, Maurice Divo, Felix Steinmann, David Dhami, Devendra Singh Kersting, Kristian Department of Computer Science TU Darmstadt Darmstadt Germany Darmstadt Germany Mathematics and Computer Science Departement TU Eindhoven Eindhoven Netherlands Darmstadt Germany Centre for Cognitive Science TU Darmstadt Darmstadt Germany

In natural language processing and vision, pretraining is utilized to learn effective representations. Unfortunately, the success of pretraining does not easily carry over to time series due to potential mismatch between sources and target. Actually, common belief is that multi-dataset pretraining does not work for time series! Au con-traire, we introduce a new self-supervised contrastive pretraining approach to learn one encoding from many unlabeled and diverse time series datasets, so that the single learned representation can then be reused in several target domains for, say, classification. Specifically, we propose the XD-MixUp interpolation method and the Soft Interpolation Contextual Contrasting (SICC) loss. Empirically, this outperforms both supervised training and other self-supervised pretraining methods when finetuning on low-data regimes. This disproves the common belief: We can actually learn from multiple time series datasets, even from 75 at once. © 2024, CC BY.

关键词： Time series

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Combining DistilBERT and LSTM to Enhanced Detection of Depression in Social Media Text

Combining DistilBERT and LSTM to Enhanced Detection of Depre...

引用

International Conference on Information Management and Technology (ICIMTech)

作者： Felix Indra Kurniadi Ni Luh Putu Satyaning Pradnya Paramita Erna Fransisca Angela Sihotang Maria Susan Anggreainy Rongfang Zhan Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Statistics Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Department of Rehabilitation and Health Service School of Computer Science University of North Texas United States of America

ISBN: (数字)9798350390025

ISBN: (纸本)9798350390032

Depression affects a significant number of people, and it has a significant impact not only on their lives but also on society as a whole. In light of this, we require more advanced methods that are capable of locating individuals who are depressed in a timely and accurate manner. This research investigates the effectiveness of Long Short-Term Mem- ory (LSTM) networks used in conjunction with DistilBERT, a small transformer-based model, in identifying indications of melancholy in posts made on social networking platforms. The text was cleaned up using a dataset that was obtained from Reddit, and then we used the mixed DistilBERT+LSTM model to evaluate how well it performed in comparison to the DistilBERT model by itself. The findings that we obtained indicate that both approaches produce results that are comparable but not flawless, with only slight variations in terms of accuracy, precision, recall, and F1 scores. The fact that this is the case demonstrates that there are issues with optimizing the model and displaying the data.

关键词： Micromechanical devices Accuracy Social networking (online) Mood Mental health Depression Transformers Data models Information management Long short term memory

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Property selling system with support for validation and verification process 7

Property selling system with support for validation and veri...

引用

7th International Conference on computer science and Computational Intelligence, CSCI 2022

作者： Iskandar, Karto Christianto Herlina, Maria Grace Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia

Due to the COVID-19 pandemic, today's property trade transactions through the website have become common. This phenomenon requires the availability of a trustable property commerce website. This research aims to produce a website trusted for sellers and buyers of property in conducting trade. So, we need to know what information is required by the property validation process so that sellers and buyers feel safe committing the transactions. This website will help increase public trust because it is supported by a verification and validation process by third parties regarding property information. The research method begins with distributing questionnaires to discover the needs of property sellers and buyers, then developing a website using the waterfall methodology of the Laravel application framework. The result of this research is to establish a set of information needed for the property validation process that needs to be verified before conducting transactions. Finally, this web application can increase trust by 59.5%, whereas 37.6% gave the maximum value and 8.7% of respondents gave only moderate weight after being tested on 173 respondents. © 2022 Elsevier B.V.. All rights reserved.

关键词： Websites

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EVALUATING REPRESENTATIONAL SIMILARITY MEASURES FROM THE LENS OF FUNCTIONAL CORRESPONDENCE

arXiv

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arXiv 2024年

作者： Bo, Yiqing Soni, Ansh Srivastava, Sudhanshu Khosla, Meenakshi Department of Cognitive Science UC San Diego United States Department of Psychology University of Pennsylvania United States Department of Computer Science UC San Diego United States

Neuroscience and artificial intelligence (AI) both face the challenge of interpreting high-dimensional neural data, where the comparative analysis of such data is crucial for revealing shared mechanisms and differences between these complex systems. Despite the widespread use of representational comparisons and the abundance classes of comparison methods, a critical question remains: which metrics are most suitable for these comparisons? While some studies evaluate metrics based on their ability to differentiate models of different origins or constructions (e.g., various architectures), another approach is to assess how well they distinguish models that exhibit distinct behaviors. To investigate this, we examine the degree of alignment between various representational similarity measures and behavioral outcomes, employing group statistics and a comprehensive suite of behavioral metrics for comparison. In our evaluation of eight commonly used representational similarity metrics in the visual domain—spanning alignment-based, Canonical Correlation Analysis (CCA)-based, inner product kernel-based, and nearest-neighbor methods—we found that metrics like linear Centered Kernel Alignment (CKA) and Procrustes distance, which emphasize the overall geometric structure or shape of representations, excelled in differentiating trained from untrained models and aligning with behavioral measures, whereas metrics such as linear predictivity, commonly used in neuroscience, demonstrated only moderate alignment with behavior. These insights are crucial for selecting metrics that emphasize behaviorally meaningful comparisons in NeuroAI research. © 2024, CC BY-NC-ND.

关键词： Nearest neighbor search

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Speech Separation for Low-Resource Languages

Speech Separation for Low-Resource Languages

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Borsdorf, Marvin Pan, Zexu Himmelmann, Pascal Li, Haizhou Schultz, Tanja Machine Listening Lab University of Bremen Bremen Germany Alibaba Group Singapore Department of Mathematics and Computer Science FernUniversität in Hagen Hagen Germany School of Data Science Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Department of Electrical and Computer Engineering National University of Singapore Singapore Cognitive Systems Lab University of Bremen Bremen Germany

ISBN: (纸本)9798350368741

Speech separation aims to equip machines with the human ability of selective listening, i.e. to focus attention on specific information in spoken communication. Studies have shown that the language spoken in a cocktail party scenario matters. While the development of speech separation models can leverage extensive databases, for the majority of languages only very limited data is available. This work presents the very first study on speech separation for low-resource languages. We choose blind source separation as the task to be studied and analyze three strategies to overcome the data scarcity of two low-resource languages from the GlobalPhoneMS2 database. We show that data from other languages can be used to develop models that work for low-resource languages. Finetuning additionally boosts the performance, and training on multiple languages increases both performance and robustness. We show that dynamic mixing in the development helps to find a trade-off between performance and development time. © 2025 IEEE.

关键词： cocktail party problem GlobalPhone low-resource language multilingual speech separation

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

arXiv

引用

arXiv 2024年

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

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 post-processing. 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. Copyright © 2024, The Authors. All rights reserved.

关键词： Clutter (information theory)

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Toward Digital Transformation Adoption: A Conceptual Framework from Transformational Leadership Perspective

引用

Procedia computer science 2024年 234卷 1175-1182页

作者： Marcel Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Computer Science Department BINUS Graduate Program – Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Information System Department Universitas Kristen Krida Wacana Jakarta Indonesia Smart Cities and Communities Innovation Center Faculty of Digital Communication and Hotel & Tourism Institute of Technology Bandung Bandung Indonesia Computer Science Department Faculty of Computer Science University of Indonesia Depok Indonesia

Digital transformation is about transforming processes, business models, domains, and culture. Studies show that the failure rate of digital transformation is quite high up to 90%. Studies show that the transformational leadership model has a significant impact on digital transformation adoption. This paper identifies the positive and negative attributes of transformational leadership including the components that support and are affected for successful adoption of digital transformation. Furthermore, the paper combines several findings related to the attributes and components in the form of a conceptual framework. The conceptual framework can serve as a guide for organizations for their digital transformation journey.

关键词： transformational leadership digital transformation digital adoption conceptual framework

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Proposed IT Governance Model for Smart Tourism Destinations based on COBIT 2019 Framework 7

Proposed IT Governance Model for Smart Tourism Destinations ...

引用

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

作者： Hiererra, Siti Elda Gaol, Ford Lumban Ranti, Benny Supangkat, Suhono Harso BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia University of Indonesia Faculty of Computer Science Computer Science Department Depok16424 Indonesia Institute of Technology Bandung Smart Cities and Communities Innovation Centre Faculty of Digital Communication and Hotel & Tourism Bandung40116 Indonesia

ISBN: (数字)9781665450904

ISBN: (纸本)9781665450904

Tourism is one of Indonesia's main economic drivers because it can absorb many workers and bring in foreign exchange through tourism activities. Research related to Smart Tourism Destinations (STDs) and the technology that supports them has gained popularity and has received special attention in the last ten years. STDs show that various technologies are applied to a tourist destination, one of which aims to increase added value for the surrounding community and tourists. This study employs the systematic literature review (SLR) method to investigate smart tourism destinations and governance topics. This research aims to identify and analyze the main components of Smart Tourism Destinations Governance. Two research questions were developed and incorporated into the search term. This research resulted in 24 research papers on smart tourism destinations and governance. The result of this study is a proposed IT Governance Model, which can be a guideline for tourism's DMOs (Destination Management Organizations), tourism enterprises, IT companies that support smart tourism concepts, and policymakers (government) to improve their service to society/community. The model can be used as a guide to governing the IT implementation in smart tourism destinations projects. Limitations of this study regarding the scalability of database and keyword usage and their methodologies. Theoretical implication for scholars in the next research is that research can be conducted on how to implement this model and whether this model can be easily adopted. This future work also regarding the IT Governance Maturity Model or the measurement of IT Gov readiness in smart tourism destinations. © 2022 IEEE.

关键词： Tourism

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Energy Sector Stock Price Prediction Using The CNN, GRU & LSTM Hybrid Algorithm

Energy Sector Stock Price Prediction Using The CNN, GRU & LS...

引用

computer science, Information Technology and Engineering (ICCoSITE), International Conference on

作者： Bambang Sulistio Harco Leslie Hendric Spits Warnars Ford Lumban Gaol Benfano Soewito Computer Science Department Binus Graduate Program – Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

Nowadays, many people are starting to care about early investment. One of the most popular investments lately, especially for millennials, is a stock investment. In investing, there are advantages and risks of loss. One way to reduce the risk of loss is by using price predictions before investing in stocks. This paper proposes the use of deep learning in making stock predictions. We conducted research by calculating the performance of six deep-learning algorithms to predict stock closing prices. The application of the CNN-LSTM-GRU hybrid algorithm combination produces the best performance compared to other methods, based on the value: Root Mean Squared Error (RMSE) decreased by 1.100 by 14%, Mean Absolute Error (MAE) was successfully reduced by 0.798 by 13.4%, and R Square increased by 0.957 by 3.9%. In predicting stock prices on the Indonesian Stock Exchange, especially in the energy sector, CNN-LSTM-GRU is more appropriate for investors than using a single algorithm to make decisions in investing in stocks..

关键词： Deep learning computer science Recurrent neural networks Sociology Prediction methods Prediction algorithms Convolutional neural networks

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