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IEEE Transactions on Technology and Society 2023年第4期4卷 291-301页

作者： London, Alex John Razin, Yosef S. Borenstein, Jason Eslami, Motahhare Perkins, Russell Robinette, Paul Carnegie Mellon University Department of Philosophy PittsburghPA15213 United States Georgia Institute of Technology School of Aerospace Engineering AtlantaGA30332 United States Georgia Institute of Technology School of Public Policy and Office of Graduate Education AtlantaGA30332 United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA15213 United States University of Massachusetts Lowell Department of Electrical and Computer Engineering LowellMA01854 United States

This paper considers novel ethical issues pertaining to near-future artificial intelligence (AI) systems that seek to support, maintain, or enhance the capabilities of older adults as they age and experience cognitive decline. In particular, we focus on smart assistants (SAs) that would seek to provide proactive assistance and mediate social interactions between users and other members of their social or support networks. Such systems would potentially have significant utility for users and their caregivers if they could reduce the cognitive load for tasks that help older adults maintain their autonomy and independence. However, proactively supporting even simple tasks, such as providing the user with a summary of a meeting or a conversation, would require a future SA to engage with ethical aspects of human interactions which computational systems currently have difficulty identifying, tracking, and navigating. If SAs fail to perceive ethically relevant aspects of social interactions, the resulting deficit in moral discernment would threaten important aspects of user autonomy and well-being. After describing the dynamic that generates these ethical challenges, we note how simple strategies for prompting user oversight of such systems might also undermine their utility. We conclude by considering how near-future SAs could exacerbate current worries about privacy, commodification of users, trust calibration and injustice. © 2020 IEEE.

关键词： Cognitive systems

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Strengthening Capacity in Industrial Revolution 4.0 (IR 4.0): A Case Study on Training Product-service Systems Between the US and Vietnam

Strengthening Capacity in Industrial Revolution 4.0 (IR 4.0)...

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2024 Portland International Conference on Management of engineering and Technology, PICMET 2024

作者： Yip, Man Hang Santarnaria, Ana Sofia Castellanos Babcock, Julia University of Cambridge Centre for Technology Management Department of Engineering United Kingdom National Policy Consensus Center College of Urban and Public Affairs Portland State University Portland United States Maseeh College of Engineering and Computer Science Portland State University Portland United States

ISBN: (纸本)9781890843458

This case study analyzes how value is created in the development process of a collaborative training product-service system (PSS) designed for provincial government officials in Vietnam. This initiative, part of the USAID Strengthening Provincial Capacity (USAID SPC) project, encapsulates a series of training solutions designed to empower officials with key skills in engineering and technology management, including the knowledge to improve global value chains, upgrade technology infrastructure and sustainable production models. A primary driver for the 'four-day course delivery', the PSS analyzed in this study, was to bolster economic competitiveness at the provincial level in the context of Industrial Revolution 4.0 (IR 4.0). This case study offers lessons on stakeholder-value mapping and recommendations for future approaches to training courses development that enhances capacity development. These trainings aligned with the demands of balancing organizational development and public value creation in the context of developing countries transitioning to a knowledge economy, such as Vietnam. The learnings from this case study may provide a blueprint for similar endeavors in other countries navigating the complexities of digital transformation of public services. © 2024 PICMET.

关键词： Product development

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Towards an Automatic Extracting UML Class Diagram from System's Textual Specification 24

Towards an Automatic Extracting UML Class Diagram from Syste...

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7th International Conference on Networking, Intelligent Systems and Security, NISS 2024

作者： Babaalla, Zakaria Jakimi, Abdeslam Oualla, Mohamed Saadane, Rachid Chehri, Abdellah GL-ISI Team Department of Informatics Faculty of Sciences and Techniques Errachidia Morocco Electrical Engineering Department Hassania School of Public Works Casablanca Morocco Department of Mathematics and Computer Science Royal Military College of Canada Kingston Canada

ISBN: (纸本)9798400709296

Developing a software system from natural language requirements is a complex and delicate task that requires a high level of design and programming expertise. Increasing the level of abstraction used to describe these requirements is the most natural solution. Model-Driven engineering (MDE) also takes this route, using abstract models as primary entities to generate source code automatically or semi-automatically. Among these models, the UML class diagram occupies a privileged place in object-oriented systems because it not only serves as a basis for communication between developers but also provides a closely aligned static representation of the system implementation. However, creating a UML class diagram from a textual system specification poses a significant challenge due to the inherent imprecision and ambiguity commonly found in natural language expressions. In this paper, we propose a model-centric approach based on deep learning for the automatic extraction of UML class diagrams from textual requirements. © 2024 ACM.

关键词： Object oriented programming

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From Lab to Field: Real-World Evaluation of an Ai-Driven Smart Video Solution to Enhance Community Safety

SSRN

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SSRN 2024年

作者： Yao, Shanle Ardabili, Babak Rahimi Pazho, Armin Danesh Noghre, Ghazal Alinezhad Neff, Christopher Bourque, Lauren Tabkhi, Hamed University of North Carolina at Charlotte Department of Electrical and Computer Engineering United States University of North Carolina at Charlotte Department of Public Policy United States

This article adopts and evaluates an AI-enabled Smart Video Solution (SVS) designed to enhance safety in the real world. The system integrates with existing infrastructure camera networks, leveraging recent advancements in AI for easy adoption. Prioritizing privacy and ethical standards, pose based data is used for downstream AI tasks such as anomaly detection. Cloud-based infrastructure and mobile app are deployed, enabling real-time alerts within communities. The SVS employs innovative data representation and visualization techniques, such as the Occupancy Indicator, Statistical Anomaly Detection, Bird's Eye View, and Heatmaps, to understand pedestrian behaviors and enhance public safety. Evaluation of the SVS demonstrates its capacity to convert complex computer vision outputs into actionable insights for stakeholders, community partners, law enforcement, urban planners, and social scientists. This article presents a comprehensive real-world deployment and evaluation of the SVS, implemented in a community college environment across 16 cameras. The system integrates AI-driven visual processing, supported by statistical analysis, database management, cloud communication, and user notifications. Additionally, the article evaluates the end-to-end latency from the moment an AI algorithm detects anomalous behavior in real-time at the camera level to the time stakeholders receive a notification. The results demonstrate the system's robustness, effectively managing 16 CCTV cameras with a consistent throughput of 16.5 frames per second (FPS) over a 21-hour period and an average end-to-end latency of 26.76 seconds between anomaly detection and alert issuance. © 2024, The Authors. All rights reserved.

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Robust Frequency Estimation Under Additive Mixture Noise

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computers, Materials & Continua 2022年第7期72卷 1671-1684页

作者： Yuan Chen Yulu Tian Dingfan Zhang Longting Huang Jingxin Xu School of Computer and Communication Engineering University of Science&Technology BeijingBeijing100083China School of Automation Science and Electrical Engineering Beihang UniversityBeijing100191China School of Information Engineering Wuhan University of TechnologyWuhan430070China Department of Energy and Public Works Queensland4702Australia

In many applications such as multiuser radar communications and astrophysical imaging processing,the encountered noise is usually described by the finite sum ofα-stable(1≤α<2)*** this paper,a new parameter estimator is developed,in the presence of this new heavy-tailed *** the closed-formPDF of theα-stable variable does not exist exceptα=1 andα=2,we take the sum of the Cauchy(α=1)and Gaussian(α=2)noise as an example,namely,additive Cauchy-Gaussian(ACG)*** probability density function(PDF)of the mixed random variable,can be calculated by the convolution of the Cauchy’s PDF and Gaussian’s *** of the complicated integral in the PDF expression of the ACG noise,traditional estimators,e.g.,maximum likelihood,are analytically not *** obtain the optimal estimates,a new robust frequency estimator is devised by employing the Metropolis-Hastings(M-H)***,to guarantee the fast convergence of the M-H chain,a new proposal covariance criterion is also devised,where the batch of previous samples are utilized to iteratively update the proposal covariance in each sampling *** simulations are carried out to indicate the superiority of the developed scheme,when compared with several conventional estimators and the Cramér-Rao lower bound.

关键词： Frequency estimation additive cauchy-gaussian noise voigt profile metropolis-hastings algorithm cramér-rao lower bound

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Algorithmic Trading Model for Stock Price Forecasting Integrating Forester with Golden Ratio Strategy 12

Algorithmic Trading Model for Stock Price Forecasting Integr...

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12th IEEE Region 10 Humanitarian Technology Conference, R10-HTC 2024

作者： Govindan, Niranjana Baskaran, Bharathi Azizan, Azizul Yakub, Fitri Dhamanti, Inge Dhaya, C. SRMIST Faculty of Engineering & Technology Department of Computing Technologies Kattankulathur Chennai India Faculty of Intelligence Kuala Lumpur Malaysia Malaysia-Japan International Institute of Technology UTM Electronic System Engineering Department Kuala Lumpur Malaysia Universitas Airlangga Faculty of Public Health Department of Health Policy and Administration Surabaya Indonesia Adhiparasakthi Engineering College Department of Computer Science and Technology Melmaruvathur India

ISBN: (纸本)9798350340204

Algorithmic trading has emerged as a powerful tool for making data-driven trading decisions in financial markets. In this paper, we present an innovative algorithmic trading model for stock price forecasting that integrates forest-based machine learning algorithms with the Golden Ratio strategy. Our proposed approach aims to leverage the predictive power of machine learning while incorporating insights from technical analysis to enhance trading performance and profitability. The methodology involves data collection and preprocessing, feature engineering, model development, and performance evaluation. Historical stock price data, financial indicators, and sentiment analysis data are gathered and pre-processed to ensure data quality. Feature engineering techniques are applied to extract relevant features, and forest-based machine learning algorithms are trained on the data to forecast stock prices. The Golden Ratio strategy is integrated into the trading model to identify the entry point and exit point. Empirical validation and performance evaluation demonstrate the effectiveness and robustness of the proposed approach. Through back testing and simulation-based testing, we evaluate the model's accuracy, profitability, and risk-adjusted returns. Our findings contribute to the advancement of algorithmic trading research and practice, offering insights into the potential benefits of combining machine learning algorithms with established trading strategies. The proposed hybrid model incorporates Golden Ratio Strategy, integrating Random Forest (RF). The results of the proposed random forest with Golden Ratio Strategy are compared with the results of individual model. This comparison helps in proving the efficiency of the proposed model. © 2024 IEEE.

关键词： Adversarial machine learning

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Operational Modeling of the Nexus Between Water-Distribution and Electricity Systems

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Current Sustainable/Renewable Energy Reports 2025年第1期12卷 1-16页

作者： Gu, Weiying Sioshansi, Ramteen Department of Integrated Systems Engineering The Ohio State University 1971 Neil Avenue ColumbusOH43210-1271 United States Department of Engineering and Public Policy Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213-3815 United States Carnegie Mellon Electricity Industry Center Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213-3815 United States Department of Electrical and Computer Engineering Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213-3815 United States Wilton E. Scott Institute for Energy Innovation Carnegie Mellon University 5000 Forbes Avenue PittsburghPA15213-3815 United States

Purpose of Review: We survey operational models of water-distribution systems. Although such modeling is important in its own right, our focus is motivated by the growing desire to examine and manage the nexus between water-distribution and electricity systems. As such, our survey discusses computational challenges in modeling water-distribution systems, co-ordination dynamics between water-distribution and electricity systems, and gaps in the literature. Recent Findings: Modeling water-distribution systems is made difficult by their highly non-linear and non-convex physical properties. Co-ordinating water-distribution and electricity systems, especially with the growing supply and demand uncertainties of the latter, requires fast optimization techniques for real-time system management. Although many works suggest means of co-ordinating the two systems, practical applications are limited, due to the systems having separate and autonomous management and ownership. Nonetheless, recent works are navigating this challenge, by seeking methods to foster improved co-ordination of the two systems while respecting their autonomy. Additionally, with the backdrop of increased security threats, there is a growing need to bolster infrastructure protection, which is complicated by the intertwined nature of the two systems. Summary: By providing a steady supply of potable water to satisfy residential, commercial, agricultural, and industrial demands, water-distribution systems are pivotal components of modern society and infrastructure. The extant literature presents many models and optimization strategies that are tailored for operating water-distribution systems. Yet, there remain unexplored problems, particularly related to simplifying model computation, capturing the flexibility of water-distribution systems, and capturing interdependencies between water-distribution and other systems and infrastructures. Future research that addresses these gaps will allow greater operational

关键词： Water distribution systems

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Analysis of Maintenance Events in Medical Equipment in the Largest Trauma Hospital in Natal/RN 9th

Analysis of Maintenance Events in Medical Equipment in th...

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9th Latin American Congress on Biomedical engineering, CLAIB 2022 and 28th Brazilian Congress on Biomedical engineering, CBEB 2022

作者： Paz, Lyssandra Moura, Lucas Barreto, Tiago Lima, Marcelo Suassuna, Alice Federal Institute of Education Science and Technology of Rio Grande do Norte RN Natal Brazil Department of Electrical and Computer Engineering Federal University of Rio Grande do Norte RN Natal Brazil Secretary of State for Public Health RN Natal Brazil Department of Electrical Engineering Federal University of Uberlândia MG Uberlândia Brazil

ISBN: (纸本)9783031494093

The Advancements in biomedical technologies have led to the need for a sector responsible for management of all medical equipment in health establishments, thus coming to clinical engineering. There are three basic types of causes of failures that occur in these equipment: human error, technology failure, and external phenomena. Monitoring maintenance events is essential to understand what leads to these failures and, thus, seek solutions to mitigate them. Thus, the present work aims to analyze the requests for maintenance in the largest trauma hospital in Natal, Rio Grande do Norte, categorizing them in an attempt to understand the maintenance occurrences and carelessness with the equipment. For this, a survey of maintenance calls for biomedical equipment was performed, in the period of 3 months (February to May 2022), at the Monsenhor Walfredo Gurgel Hospital in Natal, Rio Grande do Norte. After data collection, it was found that 51.18% of the calls for maintenance corresponded to human error, and that a large part of the occurrences due to human error were related to problems with dirt and the others to false calls arising from the lack of information about correct operation of this equipment. From the classification, it is understood in a process of listening and observation of these professionals, that errors are caused, above all, by the lack of periodic training of the hospital’s medical team. The implementation of a training routine would be a solution to alleviate the occurrences of maintenance in the hospital in question. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Hospitals

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Capturing Physiological Correlates of Stress-Induced Blood Pressure Elevation Using a Multimodal Wearable Sensing Patch in Patients with a History of Myocardial Infarction 46

Capturing Physiological Correlates of Stress-Induced Blood P...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Nawar, Afra Sanchez-Perez, Jesus Antonio Rahman, Farhan N. Gazi, Asim H. Chan, Michael Ziegler, Carrie Daaboul, Obada Haddad, George Al-Abboud, Omar A. Ahmed, Hashir Murrah, Nancy Vaccarino, Viola Shah, Amit J. Inan, Omer T. Emory University Rollins School of Public Health Department of Epidemiology AtlantaGA United States Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA United States Georgia Institute of Technology School of Biomedical Engineering AtlantaGA United States

ISBN: (纸本)9798350371499

Persons who have experienced a prior myocardial infarction (MI) are at a greater risk of experiencing a secondary event. External factors, such as stress, can cause transient hypertension, further increasing cardiovascular disease risk. However, challenges to monitoring stress-induced hypertension include obtrusiveness of monitoring devices, unknown validity in high-risk populations, and unsubstantiated sensitivity in stressful environments. In this pilot study, we investigated data from a validated, multimodal, wearable patch to examine physiological correlates of laboratory-based hypertensive stress responses. The device collected electrocardiogram, seismocardiogram, and photoplethysmogram signals from 35 participants (26 post-MI and 9 healthy participants) during a protocol involving a public speaking stressor. We calculated the change from rest to stress in 10 features extracted from these signals and assessed their correlation to stress-induced changes in mean arterial pressure (MAP) derived from a validated brachial pressure cuff. MAP changes correlated significantly with changes in heart rate (p © 2024 IEEE.

关键词： Blood pressure

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Anomaly Detection of Patient Data in public Hospital used Internet of Things Sensors 7

Anomaly Detection of Patient Data in Public Hospital used In...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Kadir, Evizal Abdul Putri, Lenni Dianna Rosa, Sri Listia Siswanto, Apri Assidiqi, Farhan Evizal, Muhammad Fikri Universitas Islam Riau Department of Informatics Engineering Pekanbaru28284 Indonesia Universitas Prima Indonesia Department of Public Health Medan20118 Indonesia Telkom University Department of Electrical Engineering Bandung40257 Indonesia Eng National Dong Hwa University Computer Science And Information Hualien County974 Taiwan

ISBN: (纸本)9798350359299

Increasing number of patients doing treatment in a public hospital become as issue for the management. Limited number of equipment to detect abnormal patient and support from medical staff. This research aims to detect on the anomaly detection of hospital patient data through the utilization of Internet of Things (IoT) sensors. With the increasing integration of IoT devices in healthcare settings, such as sensors monitoring vital signs like temperature, blood pressure, and heart rate, the need for robust anomaly detection mechanisms becomes imperative. The research explores advanced algorithms and machine learning techniques to identify irregular patterns or outliers in patient data, aiming to enhance the early detection of potential health issues or abnormalities. Leveraging the vast amount of data generated by IoT sensors in hospital environments, the research aims to contribute to the development of more efficient and accurate anomaly detection systems, ultimately improving the quality of patient care and facilitating timely intervention by healthcare professionals. The findings of this research have significant implications for the evolving landscape of healthcare technologies, emphasizing the importance of ensuring data integrity and patient safety in the era of IoT-driven healthcare solutions. Patient data shows in graph to see how the anomaly indication for the patient. Multi sensor with parallel system will develop in future research plan. © 2024 IEEE.

关键词： Hospitals

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