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arXiv 2025年

作者： Gungor, Cagri Eppinger, Derek Kovashka, Adriana Intelligent Systems Program Department of Computer Science University of Pittsburgh United States

Tactile sensing, which relies on direct physical contact, is critical for human perception and underpins applications in computer vision, robotics, and multimodal learning. Because tactile data is often scarce and costly to acquire, generating synthetic tactile images provides a scalable solution to augment real-world measurements. However, ensuring robust generalization in synthesizing tactile images—capturing subtle, material-specific contact features—remains challenging. We demonstrate that overlapping training and test samples in commonly used datasets inflate performance metrics, obscuring the true generalizability of tactile models. To address this, we propose a leakage-free evaluation protocol coupled with novel, reference-free metrics—TMMD, I-TMMD, CI-TMMD, and D-TMMD—tailored for tactile generation. Moreover, we propose a vision-to-touch generation method that leverages text as an intermediate modality by incorporating concise, material-specific descriptions during training to better capture essential tactile features. Experiments on two popular visuo-tactile datasets, Touch and Go and HCT, show that our approach achieves superior performance and enhanced generalization in a leakage-free setting. © 2025, CC BY.

关键词： Information leakage

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A Two-stage Reconfiguration in Network Function Virtualization: Toward Service Function Chain Optimization

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IEEE Transactions on Network and Service Management 2025年

作者： Assis, Karcius D. R. Almeida, Raul C. Baghban, Hojjat Santos, Alex F. Boutaba, Raouf Electrical and Computer Engineering Department Salvador Brazil University of Waterloo David R. Cheriton School of Computer Science Canada Department of Electronics and Systems Recife Brazil Chang Gung University Department of Artificial Intelligence Taiwan Chang Gung University College of Intelligent Computing Taiwan Center of Science and Technology in Energy and Sustainability Brazil Postgraduate Program in Computer Science Bahia Brazil

Network Function Virtualization (NFV), as a promising paradigm, speeds up the service deployment by separating network functions from proprietary devices and deploying them on common servers in the form of software. Any service in NFV-enabled networks is achieved as a Service Function Chain (SFC) which consists of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within a dynamic NFV-enabled network is a key challenge to be addressed. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF instances. In this paper, we assume that each deployed VNF is used by multiple SFCs and deal with the optimal placement for the contemporaneous migration of VNFs based on the actual network situation. We formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the VNF migration between nodes or the total number of core changes per node. The approach is a two-stage MILP based on optimal order to solve the reconfiguration. Extensive evaluation shows that the proposed approach can reduce the change in terms of location or number of cores per node in a 6-node and 14-node networks while ensuring network latency compared with the model without reconfiguration. © 2004-2012 IEEE.

关键词： Network function virtualization

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Model for Anomaly Detection in Reducing Water Losses in the Distribution Network of Peru using Machine Learning Algorithms

Model for Anomaly Detection in Reducing Water Losses in the ...

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Software Engineering and computer science (CSECS), International Conference on

作者： Mayra Rivera Pricila Lucero José Luis Castillo-Sequera Lenis Wong Information Systems Engineering Program Universidad Peruana de Ciencias Aplicadas Lima Peru Department of Computer Science Group: Health Computing and Intelligent System Universidad de Alcalá Alcalá de Henares Spain

ISBN: (数字)9798331522216

ISBN: (纸本)9798331522223

Water leakage in distribution networks is a significant challenge, especially in regions with limited infrastructure like Huancayo, Peru, where losses account for 32.82% of the distributed volume. This study introduces a machine learning-based approach to detect leaks using four algorithms: Autoencoder LSTM, Isolation Forest, One-Class SVM, and K-Nearest Neighbors (KNN). The methodology involved preprocessing historical consumption data (2018–2024) into 12-month temporal sequences per client and evaluating the models based on F1 Score, Precision, and Mean Absolute Error (MAE). Among the algorithms, the Autoencoder LSTM demonstrated superior performance with the highest precision (0.89) and the lowest MAE (0.00402). Its robustness in high-variability contexts enables early and reliable leak detection, providing a cost-effective solution for optimizing water management in resource-constrained environments.

关键词： Support vector machines Machine learning algorithms Autoencoders Distribution networks Forestry Nearest neighbor methods Robustness Leak detection Long short term memory Water resources

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Exploring Augmented Reality's Influence on Cognitive Load and Emotional Dynamics within UAV Training Environments

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IEEE Transactions on Learning Technologies 2025年

作者： Rahimi, Fatema Sadeghi-Niaraki, Abolghasem Song, Houbing Wang, Huihui Choi, Soo-Mi Sejong University Department of Computer Science and Engineering Convergence Engineering for Intelligent Drone XR Research Center Seoul Korea Republic of Department of Information Systems BaltimoreMD21250 United States St. Bonaventure University Cybersecurity Program St. BonaventureNY14778 United States

This study investigates the cognitive and emotional processes involved in augmented reality (AR)-based learning. The study looks at learning outcomes, emotional responses, meditation, and attention using a comprehensive approach that includes self-assessment, EEG data gathering, and post-experiment questionnaires. Twelve participants, selected based on their English proficiency and lack of prior knowledge of the course material, engaged in AR-based learning, while a baseline reading condition was included to contextualize cognitive and emotional engagement. The study findings indicate that the AR group's participants demonstrated notably elevated attention and meditation levels, indicating heightened engagement and focus that is advantageous for efficient assimilation and retention of knowledge. Furthermore, AR learners reported feeling less tired and exhausted, which may have mitigated the negative emotional states that are frequently connected to learning activities. However, no significant differences in negative emotions were observed between the reading and AR groups. These results emphasize the value of customized AR environments for education goals and the need for more study to maximize learning outcomes and affective experiences in AR learning contexts. © 2008-2011 IEEE.

关键词： Engineering education

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A Multimodal Recaptioning Framework to Account for Perceptual Diversity in Multilingual Vision-Language Modeling

arXiv

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arXiv 2025年

作者： Buettner, Kyle Emmerson, Jacob Kovashka, Adriana Intelligent Systems Program Department of Computer Science University of Pittsburgh United States

There are many ways to describe, name, and group objects when captioning an image. Differences are evident when speakers come from diverse cultures due to the unique experiences that shape perception. Machine translation of captions has pushed multilingual capabilities in vision-language models (VLMs), but data comes mainly from English speakers, indicating a perceptual bias and lack of model flexibility. In this work, we address this challenge and outline a data-efficient framework to instill multilingual VLMs with greater understanding of perceptual diversity. We specifically propose an LLM-based, multimodal recaptioning strategy that alters the object descriptions of English captions before translation. The greatest benefits are demonstrated in a targeted multimodal mechanism guided by native speaker data. By adding produced rewrites as augmentations in training, we improve on German and Japanese text-image retrieval cases studies (up to +3.5 mean recall overall, +4.7 on non-native error cases). We further propose a mechanism to analyze the specific object description differences across datasets, and we offer insights into cross-dataset and cross-language generalization. Copyright © 2025, The Authors. All rights reserved.

关键词： Computational grammars

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How can NLP tasks mutually benefit sentiment analysis? A holistic approach to sentiment analysis 7

How can NLP tasks mutually benefit sentiment analysis? A hol...

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7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2016 at the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2016

作者： Deng, Lingjia Wiebe, Janyce Intelligent Systems Program University of Pittsburgh Intelligent Systems Program Department of Computer Science University of Pittsburgh

ISBN: (纸本)9781941643822

Existing opinion analysis techniques rely on the clues within the sentence that focus on the sentiment analysis task itself. However, the sentiment analysis task is not isolated from other NLP tasks (co-reference resolution, entity linking, etc) but they can benefit each other. In this paper, we define dependencies between sentiment analysis and other tasks, and express the dependencies in first order logic rules regardless of the representations of different tasks. The conceptual framework proposed in this paper using such dependency rules as constraints aims at exploiting information outside the sentence and outside the document to improve sentiment analysis. Further, the framework allows exception to the rules. © 2016 Association for Computational Linguistics

关键词： Sentiment analysis

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Towards Safe AI Clinicians: A Comprehensive Study on Large Language Model Jailbreaking in Healthcare

arXiv

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arXiv 2025年

作者： Zhang, Hang Lou, Qian Wang, Yanshan Intelligent Systems Program School of Computing and Information University of Pittsburgh PA United States Department of Computer Science University of Central Florida OrlandoFL United States Department of Health Information Management University of Pittsburgh PA United States Department of Biomedical Informatics University of Pittsburgh PA United States Clinical and Translational Science Institute University of Pittsburgh PittsburghPA United States University of Pittsburgh Medical Center PittsburghPA United States

Large language models (LLMs) are increasingly utilized in healthcare applications. However, their deployment in clinical practice raises significant safety concerns, including the potential spread of harmful information. This study systematically assesses the vulnerabilities of seven LLMs to three advanced black-box jailbreaking techniques within medical contexts. To quantify the effectiveness of these techniques, we propose an automated and domain-adapted agentic evaluation pipeline. Experiment results indicate that leading commercial and open-source LLMs are highly vulnerable to medical jailbreaking attacks. To bolster model safety and reliability, we further investigate the effectiveness of Continual Fine-Tuning (CFT) in defending against medical adversarial attacks. Our findings underscore the necessity for evolving attack methods evaluation, domain-specific safety alignment, and LLM safety-utility balancing. This research offers actionable insights for advancing the safety and reliability of AI clinicians, contributing to ethical and effective AI deployment in healthcare. © 2025, CC BY-NC-SA.

关键词： Patient treatment

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Heuristic refinements of approximate linear programming for factored continuous-state Markov decision processes

Heuristic refinements of approximate linear programming for ...

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Proceedings of the 14th International Conference on Automated Planning and Scheduling, ICAPS 2004

作者： Kveton, Branislav Hauskrecht, Milos Intelligent Systems Program Department of Computer Science University of Pittsburgh

ISBN: (纸本)1577352009

Approximate linear programming (ALP) offers a promising framework for solving large factored Markov decision processes (MDPs) with both discrete and continuous states. Successful application of the approach depends on the choice of an appropriate set of feature functions defining the value function, and efficient methods for generating constraints that determine the convex space of the solution. The application of the ALP in continuous state-space settings poses an additional challenge - the number of constraints defining the problem is infinite. The objective of this work is to explore various heuristics for selecting a finite subset of constraints defining a good solution policy and for searching the space of such constraints more efficiently. The heuristics that we developed rely upon: (1) the structure of the factored model and (2) stochastic state simulations to generate an appropriate set of constraints. The improvements resulting from such heuristics are illustrated on three large factored MDP problems with continuous states.

关键词： Linear programming

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Joint prediction for entity/event-level sentiment analysis using Probabilistic Soft Logic models

Joint prediction for entity/event-level sentiment analysis u...

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Conference on Empirical Methods in Natural Language Processing, EMNLP 2015

作者： Deng, Lingjia Wiebe, Janyce Intelligent Systems Program University of Pittsburgh United States Intelligent Systems Program Department of Computer Science University of Pittsburgh United States

ISBN: (纸本)9781941643327

In this work, we build an entity/event-level sentiment analysis system, which is able to recognize and infer both explicit and implicit sentiments toward entities and events in the text. We design Probabilistic Soft Logic models that integrate explicit sentiments, inference rules, and +/-effect event information (events that positively or negatively affect entities). The experiments show that the method is able to greatly improve over baseline accuracies in recognizing entity/event-level sentiments. © 2015 Association for Computational Linguistics.

关键词： Sentiment analysis

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Recognizing opinion sources based on a new categorization of opinion types 25

Recognizing opinion sources based on a new categorization of...

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25th International Joint Conference on Artificial Intelligence, IJCAI 2016

作者： Lingjia, Deng Wiebe, Janyce Intelligent Systems Program University of Pittsburgh United States Intelligent Systems Program Department of Computer Science University of Pittsburgh United States

Recognizing sources of opinions is an important task in sentiment analysis. Different from previous works which categorize an opinion according to whether the source is the writer or the source is a noun phrase, we propose a new categorization of opinions according to the role that the source plays. The source of a participant opinion is a participant in the event that triggers the opinion. On the contrary, the source of a non-participant opinion is not a participant. Based on this new categorization, we classify an opinion using phrase-level embeddings. A transductive learning method is used for the classifier since there is no existing annotated corpora of this new categorization. A joint prediction model of Probabilistic Soft Logic then recognizes the sources of the two types of opinions in a single model. The experiments have shown that our model improves recognizing sources of opinions over baselines and several state-of-the-art works.

关键词： Sentiment analysis

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