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Human-Computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Daniel Schneider Marcos Antonio De Almeida Ramon Chaves Benjamim Fonseca Hesam Mohseni António Correia Postgraduate Program in Informatics (PPGI) Federal University of Rio de Janeiro Rio de Janeiro Brazil Systems Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil INESC TEC and University of Trás-os-Montes e Alto Douro Vila Real Portugal Faculty of Information Technology University of Jyväskylä Jyväskylä Finland

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

Interest in artificial intelligence (AI)-driven crowd work has increased during the last few years as a line of inquiry that expands upon prior research on microtasking to represent a means of scaling up complex tasks through AI mediation. Despite the increasing attention to the macrotask phenomenon in crowdsourcing, there is a need to understand the processes, elements, and constraints underlying the infrastructural and behavioral aspects in such form of crowd work when involving collaboration. To this end, this paper provides a first attempt to characterize some of the research conducted in this direction to identify important paths for an agenda comprising key drivers, challenges, and prospects for integrating human-centered AI in collaborative crowdsourcing environments.

关键词： Crowdsourcing Human computer interaction Collaboration Artificial intelligence Mediation Robots Optimization

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A computational framework for longitudinal medication adherence prediction in breast cancer survivors: A social cognitive theory based approach

arXiv

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arXiv 2025年

作者： Kaur, Navreet Gonzales, Manuel Alcaraz, Cristian Garcia Gong, Jiaqi Wells, Kristen J. Barnes, Laura E. Department of Systems and Information Engineering University of Virginia CharlottesvilleVA United States SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology San DiegoCA United States Department of Computer Science The University of Alabama TuscaloosaAL United States Department of Psychology San Diego State University San DiegoCA United States

Non-adherence to medications is a critical concern since nearly half of patients with chronic illnesses do not follow their prescribed medication regimens, leading to increased mortality, costs, and preventable human distress. Amongst stage 0-3 breast cancer survivors, adherence to long-term adjuvant endocrine therapy (i.e., Tamoxifen and aromatase inhibitors) is associated with a significant increase in recurrence-free survival. This work aims to develop multi-scale models of medication adherence to understand the significance of different factors influencing adherence across varying time frames. We introduce a computational framework guided by Social Cognitive Theory for multi-scale (daily and weekly) modeling of longitudinal medication adherence. Our models employ both dynamic medication-taking patterns in the recent past (dynamic factors) as well as less frequently changing factors (static factors) for adherence prediction. Additionally, we assess the significance of various factors in influencing adherence behavior across different time scales. Our models outperform traditional machine learning counterparts in both daily and weekly tasks in terms of both accuracy and specificity. Daily models achieved an accuracy of 87.25% (Precision – 92.04%, Recall – 93.15%, Specificity – 77.50%), and weekly models, an accuracy of 76.04% (Precision – 75.83%, Recall – 85.80%, Specificity – 72.30%). Notably, dynamic past medication-taking patterns prove most valuable for predicting daily adherence, while a combination of dynamic and static factors is significant for macro-level weekly adherence patterns. While our models exhibit strong predictive performance, they are constrained by potential cohort-specific biases, reliance on self-reported adherence data, and a limited understanding of the context around non-adherence. Future research will focus on external validation across diverse populations and explore the real-world implementation of sensor-rich systems for a more compre

关键词： Endocrinology

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Generative AI-Driven Data Augmentation for Robust Virtual Metrology: GANs, VAEs, and Diffusion Models

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IEEE Transactions on Semiconductor Manufacturing 2025年

作者： Lin, Chin-Yi Tseng, Tzu-Liang Emon, Solayman Hossain Tsai, Tsung-Han Department of Industrial Manufacturing and Systems Engineering University of Texas at El Paso El PasoTX79968 United States Computational Science Program University of Texas at El Paso El PasoTX79968 United States Institute of Information and Decision Sciences National Taipei University of Business Taipei10051 Taiwan

Advanced semiconductor manufacturing increasingly depends on Virtual Metrology (VM) for real-time quality monitoring, yet conventional data-driven models rarely capture the scarce or extreme process conditions critical for robust predictions. We propose a Multi-Stage Constrained Data Generative Augmentation (MSC-DGA) framework that integrates Variational Autoencoders (VAE), Normalizing Flows, and Constrained Diffusion to systematically expand coverage of seldom-seen regimes. By embedding strict engineering constraints during generation and applying a two-stage quality filter, MSCDGA ensures physically plausible synthetic samples. We further present theoretical proofs showing that multi-stage generation can approximate complex sensor distributions while enforcing domain validity, thereby improving coverage and preserving essential process physics. Empirically, we demonstrate the approach on real WBG SiC data, incorporating these curated samples into a Generative Foundation Model (GFA-VM) with few-shot finetuning and uncertainty-based active sampling, yielding significant accuracy gains for rarely observed conditions. Experiments confirm notable performance improvements over single-stage augmentation and naive oversampling. By rigorously balancing distribution realism with engineering feasibility, MSC-DGA offers a practical and theoretically grounded advancement for VM, enhancing adaptive control and product quality in next-generation power semiconductor manufacturing. © 2024 IEEE.

关键词： Computer control

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Adaptive Contextual Caching for Mobile Edge Large Language Model Service

arXiv

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arXiv 2025年

作者： Liu, Guangyuan Liu, Yinqiu Wang, Jiacheng Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui College of Computing and Data Science The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

Mobile edge Large Language Model (LLM) deployments face inherent constraints, such as limited computational resources and network bandwidth. Although Retrieval-Augmented Generation (RAG) mitigates some challenges by integrating external knowledge bases, inefficient cache management can still result in high retrieval latency and frequent cache updates. To address these issues, we propose an Adaptive Contextual Caching (ACC) framework that anticipates user needs by proactively caching semantically relevant data for mobile-edge LLMs. ACC utilizes a deep reinforcement learning (DRL) module to refine cache replacement policies, balancing user context, document similarity, and the overhead associated with cache misses. Experimental results demonstrate that ACC increases cache hit rates to over 80% after only 11 training episodes, outperforming FIFO, LRU, and semantic-only caching while reducing retrieval latency by up to 40%. In particular, ACC also reduces local caching overhead (i.e., the cost of updating the cache when a miss occurs) by as much as 55%, enabling scalable, low-latency LLM services in resource-constrained edge environments. © 2025, CC BY.

关键词： Deep reinforcement learning

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Entity-relationship and object-oriented data modeling - An experimental comparison of design quality

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DATA & KNOWLEDGE engineering 1997年第3期21卷 297-315页

作者： Shoval, P Shiran, S Information Systems Program Department of Industrial Engineering and Management Ben-Gurion University of the Negev Beer-Sheva 84105 Israel

We compare EER and OO data models from the point of view of design quality. Quality is measured in terms of (a) correctness of the conceptual schemas being designed, (b) time to complete the design task, and (c) designers' preferences of the models. Result of an experimental comparison of the two models reveal that the EER model surpasses the OO model for designing unary and ternary relationships, it takes less time to design EER schemas, and the EER model is preferred by designers. We conclude that even if the objective is to implement an OO database schema, the recommended procedure is to: (1) create an EER conceptual scheme, (2) map it to an OO schema, and augment the target schema with behavioral constructs that are unique to the 00 approach.

关键词： data modeling entity relationship model object oriented model database design conceptual schema experimentation schema transformation quality of design

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STRUCTURED METHOD FOR DESIGNING SUBsystems

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information AND SOFTWARE TECHNOLOGY 1995年第2期37卷 79-85页

作者： SHOVAL, P Information Systems Program Department of Industrial Engineering and Management Ben-Gurion University of the Negev Beer-Sheva 84105 Israel

This paper describes a structured method for designing subsystems from a whole system, which extends ADISSA methodology for systems analysis, design and prototyping, The design of a subsystem includes: (a) definition of the transactions, or processes, that belong to the subsystem, thus determining the boundaries of the subsystem;(b) design of the subsystem user-interface, a sub-menu-tree, derivable from the menu-tree interface of the global system;and (c) design of the database sub-schema, derivable from the global database schema. The method is supported by a CASE tool, enabling automatic and interactive design and prototyping of the subsystems.

关键词： SUBSYSTEM ANALYSIS AND DESIGN METHODOLOGY CASE PROTOTYPING TRANSACTIONS MAN-MACHINE INTERFACE MENU-TREE DATABASE SCHEMA SUB-SCHEMA

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Determination of an implementation order for IS projects

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information & MANAGEMENT 1996年第2期31卷 67-74页

作者： Shoval, P Giladi, R Information Systems Program Department of Industrial Engineering and Management Faculty of Engineering Sciences Ben-Gurion University of the Negev Beer-Sheva 84105 Israel

The process of information systems (IS) planning in an organization occurs when there is a set of IS projects that require implementation, generally within certain time frames and budgetary constraints. We propose a method for determining an optimal order of project implementation. The proposed method utilizes the cost-benefit graph, which considers the expected cost and benefit of each of the projects comprising the set, as well as the weight (importance) of the cost and benefit factors to the decision-makers. The method allows us to analyze the sensitivity of a preferred implementation order to changes in cost and benefits, and the importance of these factors.

关键词： cost-benefit analysis cost-benefit graph decision-making process information systems planning and selection project management

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Interaction Modeling for High-Dimensional Mixed Data with Numerical and Categorical Features 28th

Interaction Modeling for High-Dimensional Mixed Data with Nu...

引用

28th International Conference on Knowledge Based and Intelligent information and engineering systems, KES 2024

作者： Hoshinoa, Sara Itoa, Taiki Matsuzawaa, Takuya Ozawaa, Haruki Arimab, Sumika Degree Program in Systems and Information Engineering University of Tsukuba Japan Institute of Systems and Information Engineering University of Tsukuba Japan

This study aimed to develop an interaction modeling method for high-dimensional industrial data with sparsity. Particularly, we discussed the potential and limitations of Sparse Factorization Machines (SFM) with feature selection capabilities after examining the applicability of Factorization Machines (FMs) to numerical and categorical mixed data like industrial data. FMs has been already a major recommendation engine outperforms SVM because of robustness to the sparse data. However, conventional FMs and SFM based on the L2 norm regularization tolerate huge False-Positives (FPs), which is fatal in the application to real data to which the oracle model is unknown. Therefore, in this study, we focus on the way to automatically reduce the several millions of FPs in interactions while keeping high True-Positives (TPs). For the purpose, SFM with trigonometric inequality (TI) upper boundaries (Atarashi et al., 2021) is improved by two directions. The first is the development of TI_SFM (L1) with an L1 norm for selection of main factors, particularly for FPs reduction of the main factors. The second is the application of adaptive technique for reducing FPs of interactions (combinatorial features). We newly developed "Adaptive SFM" with adaptive technique to introduce data-driven penalty of the interaction term. As the result of numerical evaluations using a mass production oracle interaction model and several simulation data, False-Positives of the main factors (F) and the interactions (F) are significantly reduced, while keeping high level of True-Positives of the main factor () and the interactions (). Concretely, our proposed Adaptive SFM (L1) outperforms the original TI_SFM (L2) as much reducing F and F by over 99% when applying our proposed penalty considering not only the relationship between the explanatory variable and the objective variable, as basic adaptive technique, but also the factor loading indicating the relationship between the latent vector internally opt

关键词： Factorization

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The design and implementation of a corporate householding knowledge processor to improve data quality

引用

JOURNAL OF MANAGEMENT information systems 2003年第3期20卷 41-69页

作者： Madnick, S Wang, R Xian, X John Norris Maguire Professor of Information Technology at the Sloan School of Management MIT Professor of Engineering Systems School of Engineering MIT Co-Director for MIT Total Data Quality Management Program Director MIT Information Quality Program Oracle Corporation

Advances in corporate householding are needed to address certain categories of data quality problems caused by data misinterpretation. In this paper, we first summarize some of these data quality problems and our more recent results from studying corporate householding applications and knowledge exploration. Then we outline a technical approach to a corporate householding knowledge processor (CHKP) to solve a particularly important type of corporate householding problem-entity aggregation. We illustrate the operation of the CHKP by using a motivational example in account consolidation. Our CHKP design and implementation uses and expands on the COntext INterchange (COIN) technology to manage and process corporate householding knowledge.

关键词： context mediation corporate household corporate householding database interoperability data quality enterprise knowledge management

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AN APPROACH TO NONLINEAR FEEDBACK-CONTROL WITH APPLICATIONS TO ROBOTICS

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IEEE TRANSACTIONS ON systems MAN AND CYBERNETICS 1984年第6期14卷 879-884页

作者： GILBERT, EG HA, IJ Department of AeroSpace Engineering Robot Systems Division University of Michigan Ann Arbor MI USA Computer Information and Control Engineering Program Robot Systems Division College of Engineering University of Michigan Ann Arbor MI USA

A control problem involving a mechanical system with generalized coordinates q ∈ R m is considered. The error in tracking a desired input y d ∈ R p is e = E (q,y d )∈ R m . If E satisfies simple conditions, it leads to a nonlinear control law that assures e (t)→0 as t →∞. The law is robust in that small changes in it do not produce large steady-state errors or loss of stability. In this theory a unified framework is presented for treating a number of problems in the control of mechanical manipulators.

关键词： Manipulators Measurement uncertainty Vectors Joints Approximation methods Feedback control

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