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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Federico Galbiati Ranier X. Gran Brendan D. Jacques Sullivan J. Mulhern Chun-Kit Ngan Department of Computer Science Worcester Polytechnic Institute Worcester USA Data Science Program Worcester Polytechnic Institute Worcester USA

This research work aims to develop an analytical approach for optimizing team formation and predicting team performance in a competitive environment based on data on the competitors' skills prior to the team formation. There are several approaches in scientific literature to optimize and predict a team's performance. However, most studies employ fine-grained skill statistics of the individual members or constraints such as teams with a set group of members. Currently, no research tackles the highly constrained domain of the FIRST Robotics Competition. This research effort aims to fill this gap by providing an analytical method for optimizing and predicting team performance in a competitive environment while allowing these constraints and only using metrics on previous team performance, not on each individual member's performance. We apply our method to the drafting process of the FIRST Robotics competition, a domain in which the skills change year-over-year, team members change throughout the season, each match only has a superficial set of statistics, and alliance formation is key to competitive success. First, we develop a method that could extrapolate individual members' performance based on overall team performance. An alliance optimization algorithm is developed to optimize team formation and a deep neural network model is trained to predict the winning team, both using highly post-processed real-world data. Our method is able to successfully extract individual members' metrics from overall team statistics, form competitive teams, and predict the winning team with 84.08 % accuracy.

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Deciphering Water Quality and Algal Dynamics in Clear Lake Through Hyperspectral Analysis Using Emit data

Deciphering Water Quality and Algal Dynamics in Clear Lake T...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Wenzhao Li Shahryar Fazli Surendra Maharjan Hesham El-Askary Earth Systems Science and Data Solutions Lab Schmid College of Science and Technology Chapman University Orange CA USA Computational and Data Sciences Graduate Program Schmid College of Science and Technology Chapman University Orange CA USA Department of Environmental Sciences Faculty of Science Alexandria University Alexandria Egypt

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

This study evaluates the potential application of hyperspectral Earth Surface Mineral Dust Source Investigation (EMIT) remote sensing for monitoring harmful algal blooms (HABs) and water quality in Clear Lake, California. The research focuses on correlating the chlorophyll-a (Chl-a) concentrations with EMIT spectral signatures, using waterbody-wide statistical analysis of Chl-a and EMIT data sampling at various lake locations. Results demonstrate distinct spectral signatures associated with varying Chl-a levels, highlighting the potential of hyperspectral imaging in differentiating algae levels and assessing water quality variables. It also indicates the EMIT’s utility in filling data gaps and offering high-resolution monitoring. This study underscores the need for further research in hyperspectral imaging for aquatic ecosystems, especially under challenging atmospheric conditions, enhancing our understanding of water quality dynamics.

关键词： Satellites Statistical analysis Algae Water quality Lakes Filling Minerals

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AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting

arXiv

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arXiv 2025年

作者： Benechehab, Abdelhakim Feofanov, Vasilii Paolo, Giuseppe Thomas, Albert Filippone, Maurizio Kégl, Balázs Huawei Noah’s Ark Lab Paris France Department of Data Science EURECOM Statistics Program KAUST Saudi Arabia

Pre-trained foundation models (FMs) have shown exceptional performance in univariate time series forecasting tasks. However, several practical challenges persist, including managing intricate dependencies among features and quantifying uncertainty in predictions. This study aims to tackle these critical limitations by introducing adapters—feature-space transformations that facilitate the effective use of pre-trained univariate time series FMs for multivariate tasks. Adapters operate by projecting multivariate inputs into a suitable latent space and applying the FM independently to each dimension. Inspired by the literature on representation learning and partially stochastic Bayesian neural networks, we present a range of adapters and optimization/inference strategies. Experiments conducted on both synthetic and real-world datasets confirm the efficacy of adapters, demonstrating substantial enhancements in forecasting accuracy and uncertainty quantification compared to baseline methods. Our framework, AdaPTS, positions adapters as a modular, scalable, and effective solution for leveraging time series FMs in multivariate contexts, thereby promoting their wider adoption in real-world applications. We release the code at https://***/abenechehab/AdaPTS. © 2025, CC BY.

关键词： Stochastic systems

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Deep Learning for Multivariate Prediction of Building Energy Performance of Residential Buildings

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Computers, Materials & Continua 2023年第6期75卷 5947-5964页

作者： Ibrahim Aliyu Tai-Won Um Sang-Joon Lee Chang Gyoon Lim Jinsul Kim Department of ICT Convergence System Engineering Chonnam National UniversityGwangju61186Korea Graduate School of Data Science Chonnam National UniversityGwangju61186Korea Interdisciplinary Program of Digital Future Convergence Service Chonnam National UniversityGwangju61186Korea Department of Computer Engineering Chonnam National UniversityYeosu59626Korea

In the quest to minimize energy waste,the energy performance of buildings(EPB)has been a focus because building appliances,such as heating,ventilation,and air conditioning,consume the highest ***,effective design and planning for estimating heating load(HL)and cooling load(CL)for energy saving have become *** this vein,efforts have been made to predict the HL and CL using a univariate ***,this approach necessitates two models for learning HL and CL,requiring more computational ***,the one-dimensional(1D)convolutional neural network(CNN)has gained popularity due to its nominal computa-tional complexity,high performance,and low-cost hardware *** this paper,we formulate the prediction as a multivariate regression problem in which the HL and CL are simultaneously predicted using the 1D *** the building shape characteristics,one kernel size is adopted to create the receptive fields of the 1D CNN to extract the feature maps,a dense layer to interpret the maps,and an output layer with two neurons to predict the two real-valued responses,HL and *** the 1D data are not affected by excessive parameters,the pooling layer is not applied in this ***,the use of pooling has been questioned by recent *** performance of the proposed model displays a comparative advantage over existing models in terms of the mean squared error(MSE).Thus,the proposed model is effective for EPB prediction because it reduces computational time and significantly lowers the MSE.

关键词： Artificial intelligence(AI) convolutional neural network(CNN) cooling load deep learning energy energy load energy building performance heating load prediction

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Conjugacy properties of multivariate unified skew-elliptical distributions

arXiv

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arXiv 2024年

作者： Karling, Maicon J. Durante, Daniele Genton, Marc G. Statistics Program King Abdullah University of Science and Technology Thuwal Saudi Arabia Department of Decision Sciences Institute for Data Science and Analytics Bocconi University Milan Italy

The broad class of multivariate unified skew-normal (SUN) distributions has been recently shown to possess important conjugacy properties. When used as priors for the coefficients vector in probit, tobit, and multinomial probit models, these distributions yield posteriors that still belong to the SUN family. Although this result has led to advancements in Bayesian inference and computation, its applicability beyond likelihoods associated with fully-observed, discretized, or censored realizations from multivariate Gaussian models remains yet unexplored. This article covers such a gap by proving that the wider family of multivariate unified skew-elliptical (SUE) distributions, which extends SUNs to more general perturbations of elliptical densities, guarantees conjugacy for broader classes of models, beyond those relying on fully-observed, discretized or censored Gaussians. Such a result leverages the closure under linear combinations, conditioning and marginalization of SUE to prove that this family is conjugate to the likelihood induced by multivariate regression models for fully-observed, censored or dichotomized realizations from skew-elliptical distributions. This advancement enlarges the set of models that enable conjugate Bayesian inference to general formulations arising from elliptical and skew-elliptical families, including the multivariate Student’s t and skew-t, among others. Copyright © 2024, The Authors. All rights reserved.

关键词： Normal distribution

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Web Development of Direct-to-consumer Genetics Testing 4

Web Development of Direct-to-consumer Genetics Testing

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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Azizatikarna, Kians Parung, Deby Erina Amirullah, Dian Hidayat, Alam Ahmad Cenggoro, Tjeng Wawan Budiarto, Arif Simon Pardamean, Bens Genetics Indonesia Jakarta12940 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665453950

As the cost of genetic testing becomes more affordable each year, direct-to-customer (DTC) genetic testing services witness rapid market growth. This has encouraged the development of an easy-to-use website application to optimize potential customers to obtain informed choice regarding the offered DTC genetic testing as well as purchasing information. We present a wireframing process as a part of the agile software development process to build a web portal prototype for an Indonesia-based genetic testing service called DNAku. The approach in building the prototype used in this case is a part of the Agile Software Development Method. In brief, the wireframing stage resulted in this work consists of three main important outcomes. The first one is the use case diagram as a blueprint of this web-based system. A site map is then proposed as an extended version of the use case diagram which visualizes the whole pages with the links. Finally, based on this site map a series of page mock-ups are designed using Hyper Text Marking Language (HTML) and Cascading Style Sheets (CSS). The result of the prototype can be used as the basis for the next development stage, which is the coding stage based on the collected feedback from users. Considering the continuous application development via the wireframing method by taking into account the cycle of feedbacks from involved parties, this approach can strengthen the infrastructure to sustain DTC genetic business model, which has a niche market in Indonesia. © 2022 IEEE.

关键词： Sales

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review 2

Trends, Opportunities, and Challenges in Detecting Depressiv...

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2nd International Conference on Computer System, Information Technology, and Electrical Engineering, COSITE 2023

作者： Elwirehardja, Gregorius Natanael Isnan, Mahmud Perbangsa, Anzaludin Samsinga Muchtar, Kahlil Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Mahmud Isnan Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9798350343069

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches. © 2023 IEEE.

关键词： Machine learning

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Spatio-Temporal Analysis of Rates Derived from Count data Using Generalized Fused Lasso Poisson Model 15th

Spatio-Temporal Analysis of Rates Derived from Count Data ...

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15th International KES Conference on Intelligent Decision Technologies, KESIDT 2023

作者： Yamamura, Mariko Ohishi, Mineaki Yanagihara, Hirokazu Department of Statistics Radiation Effects Research Foundation Hiroshima732-0815 Japan Center for Data-driven Science and Artificial Intelligence Tohoku University Sendai980-8576 Japan Mathematics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Higashi-Hiroshima739-8526 Japan

ISBN: (纸本)9789819929689

We propose a method to statistically analyze rates obtained from count data in spatio-temporal terms, allowing for regional and temporal comparisons. Generalized fused Lasso Poisson model is used to estimate the spatio-temporal effects of the rates;the coordinate descent algorithm is used for estimation. The results of an analysis using data on crime rates in Japan’s Osaka Prefecture from 1995 to 2008 confirm the validity of the approach. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Adaptive fused lasso Coordinate descent algorithm Spatio-temporal proportion data

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Compressing VAE-Based Out-of-Distribution Detectors for Embedded Deployment

arXiv

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arXiv 2024年

作者： Bansal, Aditya Yuhas, Michael Easwaran, Arvind College of Computing and Data Science Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore

Out-of-distribution (OOD) detectors can act as safety monitors in embedded cyber-physical systems by identifying samples outside a machine learning model’s training distribution to prevent potentially unsafe actions. However, OOD detectors are often implemented using deep neural networks, which makes it difficult to meet real-time deadlines on embedded systems with memory and power constraints. We consider the class of variational autoencoder (VAE) based OOD detectors where OOD detection is performed in latent space, and apply quantization, pruning, and knowledge distillation. These techniques have been explored for other deep models, but no work has considered their combined effect on latent space OOD detection. While these techniques increase the VAE’s test loss, this does not correspond to a proportional decrease in OOD detection performance and we leverage this to develop lean OOD detectors capable of real-time inference on embedded CPUs and GPUs. We propose a design methodology that combines all three compression techniques and yields a significant decrease in memory and execution time while maintaining AUROC for a given OOD detector. We demonstrate this methodology with two existing OOD detectors on a Jetson Nano and reduce GPU and CPU inference time by 20% and 28% respectively while keeping AUROC within 5% of the baseline. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Deep learning for oil palm fruit ripeness classification with densenet 6

Deep learning for oil palm fruit ripeness classification wit...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Herman, Herman Cenggoro, Tjeng Wawan Susanto, Albert Pardamean, Bens Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Program Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665449373

Oil palm fruit farming is one of the most leading agriculture industries in the South East Asia region. Unfortunately, most of the harvesting method is still done through manual labor. Multiple research has been conducted to help farmers automatically detect the ripeness level of the oil palm fruit. We conducted an experiment of detecting the ripeness level of oil palm fruit by using one of the state-of-the-art computer vision approaches, which was deep learning with a Convolutional Neural Network (CNN). The dataset used consists of 7 levels of ripeness with 400 images of oil palm fruits. The models used in this test scenario were both AlexNet and DenseNet. The result of this study showed that DenseNet has been proven to outperform AlexNet by 8.5% in terms of accuracy and 8% in F1 score. © 2021 IEEE.

关键词： Computer vision

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