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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Adilson Krischanski Yuri K. Lopes Andre B. Leal Ricardo F. Martins Roberto S.U. Rosso Department of Computer Science Santa Catarina State University (UDESC) Joinville Brazil Graduate Program in Applied Computing (PPGCAP) UDESC Brazil Department of Electrical Engineering Santa Catarina State University (UDESC) Joinville Brazil

Relying only on behaviors that emerge from simple responsive controllers; swarms of robots have been shown capable of autonomously aggregate themselves or objects into clusters without any form of communication. We push these controllers to the limit, requiring robots to sort themselves or objects into different clusters. Based on a responsive controller that maps the current reading of a line-of-sight sensor to a pair of speeds for the robots' differential wheels, we demonstrate how multiple tasks instances can be accomplished by a robotic swarm. Using the dividing rectangles approach and physics simulation, a training step optimizes the parameters of the controller guided by a fitness function. We conducted a series of systematic trials in physics-based simulation and evaluate the performance in terms of dispersion and the ratio of clustered robots/objects. Across 20 trials where 30 robots cluster themselves into 3 groups, an average of 99.83% of them were correctly clustered into their group after 300 s. Across 50 trials where 15 robots cluster 30 objects into 3 groups, an average of 61.20%, 82.87%, and 97.73% of objects were correctly clustered into their group after 600 s, 900 s, and 1800 s, respectively. The object cluster behavior scales well while the aggregation does not, the latter due to the requirement of control tuning based on the number of robots.

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Smart Dome 4.0: Low-Cost, Independent, Automated Energy System for Agricultural Purposes enabled by Machine Learning 2

Smart Dome 4.0: Low-Cost, Independent, Automated Energy Syst...

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2021 2nd International Symposium on Automation, Information and Computing, ISAIC 2021

作者： Budiman, Arief S Gunawan, Fergyanto Djuana, Endang Pardamean, Bens Romeli, Sugiarto Putri, Dianing Nn Aji, Daisman Pb Rahardjo, Kuat Stevanus Ilham Wibowo, M. Daffa, Nadiev Owen, Raymond Industrial Engineering Department BINUS Graduate Program - Master of Industrial Engineering Bina Nusantara University Jakarta Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia PT. Impack Pratama Industri Tbk Jakarta Indonesia Mechanical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta Indonesia Industrial Engineering Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

Many economically essential crops in Indonesia (such as coffee, tea, chocolate, or copra) require storage or drying under certain environmental conditions, especially temperature and humidity. The solar dryer dome, typically used for agricultural purposes in Indonesia, produces a sufficient amount of heat to increase the evaporation rate inside the dome and reduce the moisture content of the commodity. A hybrid solar dryer accompanied by a photovoltaic panel, fan, and ventilation system is generally suitable. The system can provide an optimum environment with minimum control. However, as the outdoor temperature and humidity change dramatically, such as at night time, more control is required. Based on Industry 4.0 technologies, we have developed a new kind of hybrid solar dryer that provides an optimum environment 24/7. The system, called Smart Dome 4.0, is an intelligent, low-cost, self-sufficient drying and storage system to support Indonesia Agriculture 4.0. The system has a local power generation unit to self-sustain the required energy and operate without connecting to the electricity grid. The system utilizes a machine-learning algorithm to predict the environmental condition and optimally uses self-generated electric power. The developed Smart Dome 4.0 technology is critical to producing a sustainable solar dome under drastic environmental dynamics. © Published under licence by IOP Publishing Ltd.

关键词： Learning algorithms

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Aggregating Structural Features for Computational Analysis of Mutations in BRCT Domains of BRCA1 Protein

Aggregating Structural Features for Computational Analysis o...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Alam Ahmad Hidayat Rudi Nirwantono Mahmud Isnan Joko Pebrianto Trinugroho Bens Pardamean Mathematics Department School of Computer Science Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department Bioinformatics and Data Science Research Center BINUS Graduate Program Jakarta Indonesia

Understanding the mechanistic interpretability of mutation effects in a protein can help predict the clinical implications of the genetic variants. Hence, computational variant effect predictions that involve protein structural features of the protein mutations might be suitable in this case. In this work, we focus on BRCT domains of BRCA1 gene that is widely studied in breast cancer studies. We retrieved 88 selected missense variants found in BRCT domains annotated in both ClinVar and gnomAD databases. To computationally characterize the pathogenic property of the mutations we used two types of features extracted from protein structures: a change in free Gibbs energy and a set of features derived from molecular dynamics simulations of each mutant. Using a dimensional reduction and Gaussian mixture model (GMM)-based clustering we demonstrate that the variants are segregated into two regions that may correspond to their pathogenic status. This method can be a potential computational pipeline for providing the preliminary mechanistic interpretation of mutation effects in terms of their thermodynamic and structural features.

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A Distributed Task Allocation Protocol for Cooperative Multi-UAV Search and Rescue Systems

A Distributed Task Allocation Protocol for Cooperative Multi...

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2021 International Conference on Unmanned Aircraft Systems, ICUAS 2021

作者： De Freitas, Edison Pignaton Basso, Maik Da Silva, Antonio Arlis Santos Vizzotto, Marcos Rodrigues Correa, Mateus Schein Cavalheiro Federal University of Rio Grande Do sul Graduate Program in Electrical Engineering Porto Alegre Brazil Federal University of Rio Grande Do sul Graduate Program in Computer Science Porto Alegre91.501-970 Brazil Federal University of Rio Grande Do sul Department of Electric Automation and Energy Porto Alegre Brazil

ISBN: (纸本)9780738131153

Nowadays, applications involving multiple robotic systems connected through a wireless network - to perform tasks together - have an increased research interest. Unmanned Aerial Vehicles (UAVs) are excellent tools for evaluating and surveying distributed robotic systems applications due to their flexibility and three-dimensional mobility capability. Cooperative systems composed of multiples UAVs may have a centralized coordination which assists the members of a team to make their decisions in a distributed manner regarding the tasks to be performed. This paper proposes a system composed of embedded hardware and a high-level communication protocol that serves as a basis for identifying, distributing, and allocating tasks in a simple and distributed way to be used by groups of UAVs. The proposal is evaluated in a search and rescue scenario. The setup is configured with two types of UAVs - namely, searchers (search for new tasks) and workers (execution of tasks). Also, a set of nine simulation experiments covering three different setups were performed. Then the proposal is evaluated by two sets of field experiments with real vehicles. The obtained results in the proposed experiments demonstrate that the system can be successfully used in the proposed scenario for task distribution and allocation. © 2021 IEEE.

关键词： Antennas

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Cloud Quantum Computing Concept and Development: A Systematic Literature Review

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Procedia computer science 2021年 179卷 944-954页

作者： Haryono Soeparno Anzaludin Samsinga Perbangsa Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480

A cloud quantum computer is a quantum computer that can be accessed in a cloud environment through a network. Today, there are numbers of cloud quantum computing services that can be accessed by users. They are used to solve complex problems that require powerful computing. Different cloud quantum computing services deliver different architecture and performances. In our study, we conducted a research on some services to test and evaluate the performances of different cloud quantum computing services and make a comparison out of it. The test will be conducted using two different methods such as visual programming and qiskit. From the result, we can see that the amount of qubit per backend and shots per run pretty much affect the execution time of a cloud quantum computing. This test will give the users some insight and enables them to decide which cloud quantum computing services deliver better performance or faster execution time based on the specification each cloud quantum computer offers.

关键词： Cloud Quantum Computing Benchmarking Comparison

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Investigating Cases and Clusters-Based Reuse Policies for Card-Playing Agents

Investigating Cases and Clusters-Based Reuse Policies for Ca...

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Gustavo B. Paulus Daniel P. Vargas Joaquim V. C. Assunção Luis A. L. Silva Federal Institute of Education Science and Technology of Rio Grande do Sul Ibirubá Brazil Graduate Program in Computer Science Federal University of Santa Maria Santa Maria Brazil Applied Computing Department Federal University of Santa Maria Santa Maria Brazil

computer games continue to present challenging experimental fields for developing Artificial Intelligence (AI) models. With Case-Based Reasoning and Clustering, this work proposes novel cases and clusters-based reuse criteria for implementing card-playing agents with diversified playing skills. Using the game of Truco, a common game in South America, we detail how game actions are reused from past cases selected as query answers for given game problems. In doing so, the majority rule, the probability-based lottery, the probability of victory, and the number of points won reuse policies are used to select a cluster of cases. Then these policies are also used to select game actions from the cases within the selected cluster. Investigating the combined exploration of reuse policies, experiments of different natures evaluate the performance of implemented Truco bots disputing matches against each other. Players in these tournaments are equipped with varied policies and use case bases constructed differently.

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Dynamic Healthcare Embeddings for Improving Patient Care

arXiv

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arXiv 2023年

作者： Jang, Hankyu Lee, Sulyun Hasan, D.M. Hasibul Polgreen, Philip M. Pemmaraju, Sriram V. Adhikari, Bijaya Department of Computer Science University of Iowa Iowa CityIA United States Interdisciplinary Graduate Program in Informatics University of Iowa Iowa CityIA United States CDC MInD Healthcare Network United States

As hospitals move towards automating and integrating their computing systems, more fine-grained hospital operations data are becoming available. These data include hospital architectural drawings, logs of interactions between patients and healthcare professionals, prescription data, procedures data, and data on patient admission, discharge, and transfers. This has opened up many fascinating avenues for healthcare-related prediction tasks for improving patient care. However, in order to leverage off-the-shelf machine learning software for these tasks, one needs to learn structured representations of entities involved from heterogeneous, dynamic data streams. Here, we propose DECENT, an auto-encoding heterogeneous co-evolving dynamic neural network, for learning heterogeneous dynamic embeddings of patients, doctors, rooms, and medications from diverse data streams. These embeddings capture similarities among doctors, rooms, patients, and medications based on static attributes and dynamic interactions. DECENT enables several applications in healthcare prediction, such as predicting mortality risk and case severity of patients, adverse events (e.g., transfer back into an intensive care unit), and future healthcare-associated infections. The results of using the learned patient embeddings in predictive modeling show that DECENT has a gain of up to 48.1% on the mortality risk prediction task, 12.6% on the case severity prediction task, 6.4% on the medical intensive care unit transfer task, and 3.8% on the Clostridioides difficile (***) Infection (CDI) prediction task over the state-of-the-art baselines. In addition, case studies on the learned doctor, medication, and room embeddings show that our approach learns meaningful and interpretable embeddings. Copyright © 2023, The Authors. All rights reserved.

关键词： Embeddings

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Predicting Relegation Clubs in Italian Serie A with Method based C4.5 Decision Tree Algorithm 1

Predicting Relegation Clubs in Italian Serie A with Method b...

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1st Bukittinggi International Conference on Education, BICED 2019

作者： Syukmana, F. Wahyudi, E. Gata, W. Wahono, H. Febianto, N.I. Kuntoro, A.Y. Nitra, R.O. Effendi, L. Saputra, D.D. Sulaeman, O.R. Computer Science Postgraduate Program STMIK Nusa Mandiri Indonesia Graduate School Master's Degree Computer Science STMIK Nusa Mandiri Indonesia

The purpose of this study is to help small clubs from Italian Serie A in finding the minimum targets to avoid relegation into Serie B competition (below Serie A league). Relegation will reduce the club's income from TV revenues and the decline of enthusiastic supporters. Based on the data from the final standings (seasons 2006 until 2018), this can be explained by the Decision Tree method using the C4.5 algorithm. The methods used in this study are data collection, data pre-processing, model proposal, model testing, and model validation. In this study, it is expected that the value of accuracy exceeds 85% to achieve a proper classification. © Published under licence by IOP Publishing Ltd.

关键词： Decision trees

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Design and Implementation of Virtual-Single-Length Turbo Decoder for Multi-User Parallel Decoding

SSRN

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SSRN 2023年

作者： Middya, Amitava Hsu, Terng-Yin National Yang Ming Chiao Tung University Department of EECS International Graduate Program Hsinchu300 Taiwan National Yang Ming Chiao Tung University Department of Department of Computer Science Hsinchu300 Taiwan

Dealing with multiple users in cellular basestation with multiple codewords of variable length is a challenging task in parallel decoding. Thus, a novel turbo decoder with variable codeword-based parallel decoding is proposed for the 3GPP LTE (4G) system. To make multi-user decoding more efficient, all users are aggregated into a single block with zero insertions where all users can be decoded in the same cycle. Different users are separated by additional zeros to decode in parallel and improve the hardware utilization. Moreover, a stationary boundary enhancement is utilized to exchange and update the decoded messages between different sub-blocks of codeword for the next decoding interaction. This scheme can balance the performance loss and decoding latency of variable codeword-based parallel decoding. The proposed turbo decoder has a maximum throughput of 280.5 Mbps (VC707) and 212 Mbps (ZC706), where the design has been integrated and tested with OpenAirInterface (OAI) 4G eNB in campus trials. © 2023, The Authors. All rights reserved.

关键词： Decoding

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Graph Laplacian Learning with Exponential Family Noise

arXiv

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arXiv 2023年

作者： Shi, Changhao Mishne, Gal The Electrical and Computer Engineering Department UC San Diego CA92093 United States The Halıcıoglu Data Science Institute The Neurosciences Graduate Program UC San Diego CA92093 United States

Graph signal processing (GSP) is a prominent framework for analyzing signals on non-Euclidean domains. The graph Fourier transform (GFT) uses the combinatorial graph Laplacian matrix to reveal the spectral decomposition of signals in the graph frequency domain. However, a common challenge in applying GSP methods is that in many scenarios the underlying graph of a system is unknown. A solution in such cases is to construct the unobserved graph from available data, which is commonly referred to as graph or network inference. Although different graph inference methods exist, these are restricted to learning from either smooth graph signals or simple additive Gaussian noise. Other types of noisy data, such as discrete counts or binary digits, are rather common in real-world applications, yet are underexplored in graph inference. In this paper, we propose a versatile graph inference framework for learning from graph signals corrupted by exponential family noise. Our framework generalizes previous methods from continuous smooth graph signals to various data types. We propose an alternating algorithm that jointly estimates the graph Laplacian and the unobserved smooth representation from the noisy signals. We also extend our approach to a variational form to account for the inherent stochasticity of the latent smooth representation. Finally, since real-world graph signals are frequently non-independent and temporally correlated, we further adapt our original setting to a time-vertex formulation. We demonstrate on synthetic and real-world data that our new algorithms outperform competing Laplacian estimation methods that suffer from noise model mismatch. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal processing

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