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Simulation Winter Conference

作者： Kathleen Salazar-Serna Lynnette Hui Xian Ng Kathleen Carley Lorena Cadavid Carlos Jaime Franco Department of Civil and Industrial Engineering Pontificia Universidad Javeriana Cali Valle del Cauca Colombia Software and Societal Systems Carnegie Mellon University Pittsburgh PA USA Department of Computer and Decision Sciences Universidad Nacional de Colombia Medellín Antioquia Colombia

Agent-based simulations have been used in modeling transportation systems for traffic management and passenger flows. In this work, we hope to shed light on the complex factors that influence transportation mode decisions within developing countries, using Colombia as a case study. We model an ecosystem of human agents that decide at each time step on the mode of transportation they would take to work. Their decision is based on a combination of their personal satisfaction with the journey they had just taken, which is evaluated across a personal vector of needs, the information they crowdsource from their prevailing social network, and their personal uncertainty about the experience of trying a new transport solution. We simulate different network structures to analyze the social influence for different decision-makers. We find that in low/medium connected groups inquisitive people actively change modes cyclically over the years while imitators cluster rapidly and change less frequently.

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Data-Driven Building Occupancy Profile Identification and Application for Ventilation Operating Schedule Optimization

SSRN

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SSRN 2023年

作者： Vassiljeva, Kristina Matson, Margarita Ferrantelli, Andrea Petlenkov, Eduard Thalfeldt, Martin Belikov, Juri Tallinn University of Technology Tallinn19086 Estonia Department of Computer Systems Tallinn University of Technology Tallinn12618 Estonia Department of Software Science Tallinn University of Technology Tallinn12618 Estonia Department of Civil Engineering and Architecture Tallinn University of Technology Tallinn19086 Estonia Department of Civil Engineering Aalto University Aalto00076 Finland

Facing the current sustainability challenges requires reduction of the building stock energy consumption towards the European Green Deal targets by 2050. This can be accomplished by adopting techniques such as fault detection and diagnosis, and efficiency optimization. An occupancy-based algorithm for scheduling ventilation operations in buildings is here developed, aimed at optimizing the system’s operation according to occupancy profiles while maintaining a comfortable indoor climate. By relying only on electricity meters without using carbon dioxide or occupancy sensors, we use historical data of an Estonian school to develop a DBSCAN-based clustering algorithm that generates consumption profiles. A novel occupancy estimation algorithm, based on threshold and time series methods, then creates 12 occupancy schedules that are either based on classical detection with on-off method or on occupancy estimation for demand-controlled ventilation (DCV). We find that, compared to standard on-off scheduling, DCV-based occupancy estimation induces systematic savings in terms of both energy and costs from 18.1% up to 62.6%, still complying with current national regulations of indoor air quality. Remarkably, as our method relies only on occupancy schedules, thus eliminating reliance on weather and other external factors, it can immediately be extended to other countries. © 2023, The Authors. All rights reserved.

关键词： Energy efficiency

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科学研究的第五范式——以智能驱动的材料设计为例

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engineering 2023年第5期24卷 126-137,I0003,I0004页

作者： Can Leng Zhuo Tang Yi-Ge Zhou Zean Tian Wei-Qing Huang Jie Liu Keqin Li Kenli Li Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410073China Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China National Supercomputing Center in Changsha Changsha 410082China College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China Institute of Chemical Biology and Nanomedicine State Key Laboratory of Chemo/Biosensing and ChemometricsCollege of Chemistry and Chemical EngineeringHunan UniversityChangsha 410082China Department of Applied Physics School of Physics and ElectronicsHunan UniversityChangsha 410082China Department of Computer Science State University of New YorkNew PaltzNY 12561USA

科学正在进入一个新时代——第五范式——它被认为是知识整合到不同领域的主要特征,是基于无所不在的机器学习系统的计算社区中智能驱动的工作。在此,我们通过在天河一号超级计算机系统上构建的催化材料专门设计的典型平台案例,生动地... 详细信息

科学正在进入一个新时代——第五范式——它被认为是知识整合到不同领域的主要特征,是基于无所不在的机器学习系统的计算社区中智能驱动的工作。在此,我们通过在天河一号超级计算机系统上构建的催化材料专门设计的典型平台案例,生动地阐明了第五范式的本质,旨在促进第五范式在其他领域的培养。第五范式平台主要包括模型自动构建(原始数据提取)、指纹自动构建(神经网络特征选择)以及跨学科知识串联的重复迭代(“火山图”)。与分解一起进行的是对迭代中实现的体系结构的性能评估。通过讨论,第五范式的智能驱动平台可以极大地简化和改进研究中极其繁琐和具有挑战性的工作,并通过补偿机器学习中缺少样本和替代一些由于计算资源不足而导致的数值计算来实现数值计算与机器学习之间的相互反馈,从而加速探索过程。在数据驱动的学科中,跨学科专家的协同作用和对动态数据需求的急剧增长仍然是一个挑战。我们相信,对第五范式平台的一瞥可以为其在其他领域的应用铺平道路。

关键词：机器学习自动构建天河一号数据驱动神经网络动态数据知识整合科学研究

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EC-KitY: Evolutionary Computation Tool Kit in Python with Seamless Machine Learning Integration

arXiv

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arXiv 2022年

作者： Sipper, Moshe Halperin, Tomer Tzruia, Itai Elyasaf, Achiya Department of Computer Science Ben-Gurion University of the Negev Beer-Sheva8410501 Israel Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva8410501 Israel

EC-KitY is a comprehensive Python library for doing evolutionary computation (EC), licensed under the BSD 3-Clause License, and compatible with scikit-learn. Designed with modern software engineering and machine learning integration in mind, EC-KitY can support all popular EC paradigms, including genetic algorithms, genetic programming, coevolution, evolutionary multi-objective optimization, and more. This paper provides an overview of the package, including the ease of setting up an EC experiment, the architecture, the main features, and a comparison with other libraries. © 2022, CC BY.

关键词： Machine learning

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Information Technology for Contact Tracking in the COVID-19 Pandemic 8

Information Technology for Contact Tracking in the COVID-19 ...

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8th IEEE International Conference on Problems of Infocommunications, Science and Technology, PIC S and T 2021

作者： Kuznetsov, Alexandr Gorbenko, Yuriy Kiian, Anastasiia Kuznetsova, Tetiana Ulianovska, Yuliia Rudyanova, Tatyana V. N. Karazin Kharkiv National University Department of Information Systems And Technologies Security Kharkiv Ukraine Jsc 'Institute of Information Technologies' Kharkiv Ukraine University of Customs And Finance Department of Computer Science And Software Engineering Dnipro Ukraine

ISBN: (纸本)9781665406826

The development and implementation of the latest information technologies for contact tracking in the context of the COVID-19 pandemic is an extremely important and urgent task, which is directly related to the possible adjustment and mitigation of quarantine restrictions without increasing the risks and threats of infection. In this article, we analyze, research, and substantiate various information technologies (such as Bluetooth, Wi-Fi, GPS, etc.) in contact tracking tools to prevent the spread of infectious diseases. We explore various aspects such as centralization;the need to use a central server (data warehouse);used cryptographic protection algorithms;acceptable actions when establishing contact, etc. The research results make it possible to formulate practical recommendations on the choice of information technologies for building global contact tracing systems. © 2021 IEEE.

关键词： Data warehouses

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System to Improve the Quality of Water Resources in Sri Lanka Using Machine Learning and Image Processing

System to Improve the Quality of Water Resources in Sri Lank...

引用

Advancements in Computing (ICAC), International Conference on

作者： M.H. Samindi Liyanage G.M.B. Gajanayake S Oshini Wijewickrama S.D.S. Fernando A Dinuka Wijendra Aruna Ishara Gamage Department of Information Technology Sri Lanka Institute of Information Technology Malabe Sri Lanka Department Computer Systems & Software Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398106

Water covers approximately 71% of the earth’s surface, but only 1.2% of it can be used for drinking. However, due to the amount of waste water released into water resources, the presence of harmful microorganisms, and natural occurrences such as eutrophication, even that water cannot be used directly for drinking purposes without purification. One method of purifying water is chlorination. However, if the chlorine level exceeds the standard, it can cause both long-term and short-term illnesses. As a result, a system is imposed to solve four problems: predicting the pH value of chlorinated drinking water, determining the quantification value of active sludge in a wastewater plant, detecting microorganisms in drinking water, and predicting the percentage of eutrophication in a water resource.

关键词： Microorganisms Purification Image processing Optical character recognition Machine learning Turning Wastewater

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A Comprehensive Study of Real-World Bugs in Machine Learning Model Optimization

A Comprehensive Study of Real-World Bugs in Machine Learning...

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International Conference on software engineering (ICSE)

作者： Hao Guan Ying Xiao Jiaying Li Yepang Liu Guangdong Bai The University of Queensland Brisbane Australia Southern University of Science and Technology Shenzhen China UQ-SUSTech Joint Program Microsoft Software Technology Center Asia Beijing China Department of Computer Science and Engineering Research Institute of Trustworthy Autonomous Systems

Due to the great advance in machine learning (ML) techniques, numerous ML models are expanding their application domains in recent years. To adapt for resource-constrained platforms such as mobile and Internet of Things (IoT) devices, pre-trained models are often processed to enhance their efficiency and compactness, using optimization techniques such as pruning and quantization. Similar to the optimization process in other complex systems, e.g., program compilers and databases, optimizations for ML models can contain bugs, leading to severe consequences such as system crashes and financial loss. While bugs in training, compiling and deployment stages have been extensively studied, there is still a lack of systematic understanding and characterization of model optimization bugs (MOBs). In this work, we conduct the first empirical study to identify and characterize MOBs. We collect a comprehensive dataset containing 371 MOBs from TensorFlow and PyTorch, the most extensively used open-source ML frameworks, covering the entire development time span of their optimizers (May 2019 to August 2022). We then investigate the collected bugs from various perspectives, including their symptoms, root causes, life cycles, detection and fixes. Our work unveils the status quo of MOBs in the wild, and reveals their features on which future detection techniques can be based. Our findings also serve as a warning to the developers and the users of ML frameworks, and an appeal to our research community to enact dedicated countermeasures.

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On the Design of Communication-Efficient Federated Learning for Health Monitoring

arXiv

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arXiv 2022年

作者： Chu, Dong Jaafar, Wael Yanikomeroglu, Halim School of Information and Communication Engineering The University of Electronic Science and Technology of China Chengdu China Software and Information Technology Engineering Department of École de Technologie Supérieure QC Canada Systems and Computer Engineering Department Carleton University ON Canada

With the booming deployment of Internet of Things, health monitoring applications have gradually prospered. Within the recent COVID-19 pandemic situation, interest in permanent remote health monitoring solutions has raised, targeting to reduce contact and preserve the limited medical resources. Among the technological methods to realize efficient remote health monitoring, federated learning (FL) has drawn particular attention due to its robustness in preserving data privacy. However, FL can yield to high communication costs, due to frequent transmissions between the FL server and clients. To tackle this problem, we propose in this paper a communication-efficient federated learning (CEFL) framework that involves clients clustering and transfer learning. First, we propose to group clients through the calculation of similarity factors, based on the neural networks characteristics. Then, a representative client in each cluster is selected to be the leader of the cluster. Differently from the conventional FL, our method performs FL training only among the cluster leaders. Subsequently, transfer learning is adopted by the leader to update its cluster members with the trained FL model. Finally, each member fine-tunes the received model with its own data. To further reduce the communication costs, we opt for a partial-layer FL aggregation approach. This method suggests partially updating the neural network model rather than fully. Through experiments, we show that CEFL can save up to to 98.45% in communication costs while conceding less than 3% in accuracy loss, when compared to the conventional FL. Finally, CEFL demonstrates a high accuracy for clients with small or unbalanced datasets. © 2022, CC BY.

关键词： Privacy-preserving techniques

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On the Design of Communication-Efficient Federated Learning for Health Monitoring

On the Design of Communication-Efficient Federated Learning ...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Dong Chu Wael Jaafar Halim Yanikomeroglu School of Information and Communication Engineering of the University of Electronic Science and Technology of China Chengdu China department of École de Technologie Software and Information Technology Engineering Supérieure QC Canada Systems and Computer Engineering department Carleton University ON Canada

ISBN: (纸本)9781665435413

关键词： Training Costs Federated learning Pandemics Transfer learning Neural networks Optimized production technology

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deepNIR: Datasets for generating synthetic NIR images and improved fruit detection system using deep learning techniques

arXiv

引用

arXiv 2022年

作者： Sa, Inkyu Lim, JongYoon Ahn, Ho Seok MacDonald, Bruce CSIRO Data61 Robotics and Autonomous Systems Group Robot Perception Team Switzerland CARES Department of Electrical Computer and Software Engineering University of Auckland New Zealand

This paper presents datasets utilised for synthetic near-infrared (NIR) image generation and bounding-box level fruit detection systems. A high-quality dataset is one of the essential building blocks that can lead to success in model generalisation and the deployment of data-driven deep neural networks. In particular, synthetic data generation tasks often require more training samples than other supervised approaches. Therefore, in this paper, we share the NIR+RGB datasets that are re-processed from two public datasets (i.e., nirscene and SEN12MS), expanded our previous study, deepFruits, and our novel NIR+RGB sweet pepper(capsicum) dataset. We oversampled from the original nirscene dataset at 10, 100, 200, and 400 ratios that yielded a total of 127 k pairs of images. From the SEN12MS satellite multispectral dataset, we selected Summer (45 k) and All seasons (180 k) subsets and applied a simple yet important conversion;digital number (DN) to pixel value conversion followed by image standardisation. Our sweet pepper dataset consists of 1615 pairs of NIR+RGB images that were collected from commercial farms. We quantitatively and qualitatively demonstrate that these NIR+RGB datasets are sufficient to be used for synthetic NIR image generation. We achieved Frechet Inception Distance (FID) of 11.36, 26.53, and 40.15 for nirscene1, SEN12MS, and sweet pepper datasets respectively. In addition, we release manual annotations of 11 fruit bounding boxes that can be exported as various formats using cloud service. Four newly added fruits [blueberry, cherry, kiwi, and wheat] compound 11 novel bounding box datasets on top of our previous work presented in the deepFruits project [apple, avocado, capsicum, mango, orange, rockmelon, strawberry]. The total number of bounding box instances of the dataset is 162 k and it is ready to use from cloud service. For the evaluation of the dataset, Yolov5 single stage detector is exploited and reported impressive mean-average-precision, mAP[0.

关键词： Generative adversarial networks

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