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Robust white matter hyperintensity segmentation on unseen domain

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arXiv 2021年

作者： Zhao, Xingchen Sicilia, Anthony Minhas, Davneet S. O’Connor, Erin E. Aizenstein, Howard J. Klunk, William E. Tudorascu, Dana L. Hwang, Seong Jae Intelligent Systems Program University of Pittsburgh Department United States Computer Science Radiology Psychiatry - University of Pittsburgh United States Department of Diagnostic Radiology Nuclear Medicine University of Maryland Baltimore United States

Typical machine learning frameworks heavily rely on an underlying assumption that training and test data follow the same distribution. In medical imaging which increasingly begun acquiring datasets from multiple sites or scanners, this identical distribution assumption often fails to hold due to systematic variability induced by site or scanner dependent factors. Therefore, we cannot simply expect a model trained on a given dataset to consistently work well, or generalize, on a dataset from another distribution. In this work, we address this problem, investigating the application of machine learning models to unseen medical imaging data. Specifically, we consider the challenging case of Domain Generalization (DG) where we train a model without any knowledge about the testing distribution. That is, we train on samples from a set of distributions (sources) and test on samples from a new, unseen distribution (target). We focus on the task of white matter hyperintensity (WMH) prediction using the multi-site WMH Segmentation Challenge dataset and our local in-house dataset. We identify how two mechanically distinct DG approaches, namely domain adversarial learning and mix-up, have theoretical synergy. Then, we show drastic improvements of WMH prediction on an unseen target domain. © 2021, CC BY.

关键词： Image segmentation

Revised JNLPBA corpus: A revised version of biomedical NER corpus for relation extraction task

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arXiv 2019年

作者： Huang, Ming-Siang Lai, Po-Ting Tsai, Richard Tzong-Han Hsu, Wen-Lian Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang Ming University Taipei Taiwan Department of Computer Science National Tsing-Hua University Hsinchu Taiwan Intelligent Agent Systems Laboratory Institute of Information Science Academia Sinica Taipei Taiwan Intelligent Information Service Research Laboratory Department of Computer Science and Information Engineering National Central University Taoyuan Taiwan

The advancement of biomedical named entity recognition (BNER) and biomedical relation extraction (BRE) researches promotes the development of text mining in biological domains. As a cornerstone of BRE, robust BNER system is required to identify the mentioned NEs in plain texts for further relation extraction stage. However, the current BNER corpora, which play important roles in these tasks, paid less attention to achieve the criteria for BRE task. In this study, we present Revised JNLPBA corpus, the revision of JNLPBA corpus, to broaden the applicability of a NER corpus from BNER to BRE task. We preserve the original entity types including protein, DNA, RNA, cell line and cell type while all the abstracts in JNLPBA corpus are manually curated by domain experts again basis on the new annotation guideline focusing on the specific NEs instead of general terms. Simultaneously, several imperfection issues in JNLPBA are pointed out and made up in the new corpus. To compare the adaptability of different NER systems in Revised JNLPBA and JNLPBA corpora, the F1-measure was measured in three open sources NER systems including BANNER, Gimli and NERSuite. In the same circumstance, all the systems perform average 10% better in Revised JNLPBA than in JNLPBA. Moreover, the cross-validation test is carried out which we train the NER systems on JNLPBA/Revised JNLPBA corpora and access the performance in both protein-protein interaction extraction (PPIE) and biomedical event extraction (BEE) corpora to confirm that the newly refined Revised JNLPBA is a competent NER corpus in biomedical relation application. The revised JNLPBA corpus is freely available at ***/BNER/Content/Revised_***. Copyright © 2019, The Authors. All rights reserved.

关键词： Proteins

Range Measurement by computer Vision systems Based on Invariant Moments

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Range Measurement by Computer Vision Systems Based on Invari...

International computer science and Engineering Conference (ICSEC)

作者： Mikhail Khachumov Vyacheslav Khachumov Nikolay Abramov Vitaly Fralenko Intelligent Control Laboratory Ailamazyan Program Systems Institute of Russian Academy of Sciences Pereslavl-Zalessky Russia Federal Research Center “Computer Science and Control” of RAS Moscow Russia RUDN University Moscow Russia

ISBN: (纸本)9781665411981

We review range measurement methods based on various configurations of computer vision sensors. We discuss the mathematical model of a stereoscopic pair and the conditionality of its fundamental matrix, which is essential for correct recognition and range measurement. A lot of researchers study the measurement based on single video or photo cameras, they are often installed on small mobile flying vehicles. We focus on the methods based on invariant moments. It is demonstrated that range measurement can be performed by a single camera. A complex of experiments was conducted in order to study single camera range measurement based on invariant moments. The deviation of experimental values of distance measurement that we have done didn't exceed 9%. The error can be explained by the inaccuracy in measuring the distance from the camera lens to the object, unstable position of the camera, and low quality of the obtained image due to the camera restrictions.

关键词： computer science computer vision Stereo image processing Measurement uncertainty Position measurement Cameras Mathematical models

Using LLMs to Discover Legal Factors

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arXiv 2024年

作者： Gray, Morgan Savelka, Jaromir Oliver, Wesley Ashley, Kevin Intelligent Systems Program University of Pittsburgh United States School of Law University of Pittsburgh United States School of Computer Science Carnegie Mellon University United States School of Law Duquesne University United States

Factors are a foundational component of legal analysis and computational models of legal reasoning. These factor-based representations enable lawyers, judges, and AI and Law researchers to reason about legal cases. In this paper, we introduce a methodology that leverages large language models (LLMs) to discover lists of factors that effectively represent a legal domain. Our method takes as input raw court opinions and produces a set of factors and associated definitions. We demonstrate that a semi-automated approach, incorporating minimal human involvement, produces factor representations that can predict case outcomes with moderate success, if not yet as well as expert-defined factors can. © 2024, CC BY.

关键词： Case based reasoning

Generalized Associative Memory Models: Their Memory Capacities and Potential Application

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Journal of Advanced Computational Intelligence and intelligent Informatics 2004年第1期8卷 56-64页

作者： Yap, Tfcddy N. Azcarraga, Arnulfo E. Software Technology Department College of Computer Studies De La Salle University Professional Schools Inc. 2401 Taft Avenue Manila1004 Philippines Program for Research into Intelligent Systems School of Computing Department of Computer Science 3 Science Drive 2 Singapore117543 Singapore De La Salle University Canlubang Laguna Philippines

The Hopfield and bi-directional associative memory (TJAM) models are well developed and carefully studied models tor associative memory that are patterned after the memory structure of the animal brain. Their basic limitation is that they can only perform associations between at most two sets of patterns. Several different models for generalized associative memory are proposed. These models are all extensions or generalizations of the Hopfield and RAM models that can perform multiple associations. Extensive software simulations are conducted to evaluate the different models, using the memory capacity as basis tor comparing their performance. Lastly, potential application of these models as data fusion systems is explored. © 2004 Fuji Technology Press. All rights reserved.

关键词： Data fusion

An Approach to Detect Abnormal Submissions for CodeWorkout Dataset

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arXiv 2024年

作者： Hicks, Alex Shi, Yang Arun-Balajiee Yan, Wei Marwan, Samiha Dept of Computer Science Virginia Tech BlacksburgVA United States Dept of Computer Science Utah State University LoganUT United States Lekshmi-Narayanan Intelligent Systems Program University of Pittsburgh PittsburghPA United States School of Informatics Computing and Cyber Systems North Arizona University FlagstaffAZ United States Dept. of Computer Science University of Virginia CharlottesvilleVA United States

Students’ interactions while solving problems in learning environments (i.e. log data) are often used to support students’ learning. For example, researchers use log data to develop systems that can provide students with personalized problem recommendations based on their knowledge level. However, anomalies in the students’ log data, such as cheating to solve programming problems, could introduce a hidden bias in the log data. As a result, these systems may provide inaccurate problem recommendations, and therefore, defeat their purpose. Classical cheating detection methods, such as MOSS, can be used to detect code plagiarism. However, these methods cannot detect other abnormal events such as a student gaming a system with multiple attempts of similar solutions to a particular programming problem. This paper presents a preliminary study to analyze log data with anomalies. The goal of our work is to overcome the abnormal instances when modeling personalizable recommendations in programming learning environments. © 2024, CC BY-NC-SA.

关键词： Students

Implementation of Rössler chaotic system through inherent exponential nonlinearity of a diode with two-channel chaotic synchronization applications

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Implementation of Rössler chaotic system through inherent e...

International Conference on intelligent Control and Information Processing (ICICIP)

作者： S. Larptwee W. San-Um Engineering Program and Basic Science Department Faculty of Engineering Thai-Nichi Institute of Technology Thailand Intelligent Electronic Systems Research laboratory and Computer Engineering Department Faculty of Engineering Thai-Nichi Institute of Technology Bangkok Thailand

ISBN: (纸本)9781467362481

This paper presents a new Rössler chaotic system using exponential nonlinearity and its application to two-channel synchronization. The proposed chaotic system exhibits a chaotic attractor that resembles the original Rössler system with only six-term in three-dimensional ordinary equation systems using the exponential nonlinearity. Chaotic dynamics are described in terms of equilibria, Jacobian matrix, time domain waveforms, chaotic attractors, and bifurcation diagram. The circuit implementation is relatively compact and simple sine the exponential nonlinearity can be achieved by an inherent nonlinearity of single diode. An application to a two-channel secure communication are also demonstrated, showing a fast, low-error and robust synchronization processes.

关键词： Chaotic communication Synchronization Equations Transmitters Receivers Mathematical model

Comparison of Lexical Alignment with a Teachable Robot in Human-Robot and Human-Human-Robot Interactions

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arXiv 2022年

作者： Asano, Yuya Litman, Diane Yu, Mingzhi Lobczowski, Nikki Nokes-Malach, Timothy Kovashka, Adriana Walker, Erin Intelligent Systems Program University of Pittsburgh United States Department of Computer Science University of Pittsburgh United States Learning Research and Development Center University of Pittsburgh United States Department of Psychology University of Pittsburgh United States

Speakers build rapport in the process of aligning conversational behaviors with each other. Rapport engendered with a teachable agent while instructing domain material has been shown to promote learning. Past work on lexical alignment in the field of education suffers from limitations in both the measures used to quantify alignment and the types of interactions in which alignment with agents has been studied. In this paper, we apply alignment measures based on a data-driven notion of shared expressions (possibly composed of multiple words) and compare alignment in one-on-one human-robot (H-R) interactions with the H-R portions of collaborative human-human-robot (H-H-R) interactions. We find that students in the H-R setting align with a teachable robot more than in the H-H-R setting and that the relationship between lexical alignment and rapport is more complex than what is predicted by previous theoretical and empirical work. © 2022, CC BY.

关键词： Alignment

Explaining Legal Concepts with Augmented Large Language Models (GPT-4)

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arXiv 2023年

作者： Savelka, Jaromir Ashley, Kevin D. Gray, Morgan A. Westermann, Hannes Xu, Huihui Computer Science Department Carnegie Mellon University Pittsburgh United States Intelligent Systems Program University of Pittsburgh Pittsburgh United States Cyberjustice Laboratory Faculté de droit Université de Montréal Québec Canada

Interpreting the meaning of legal open-textured terms is a key task of legal professionals. An important source for this interpretation is how the term was applied in previous court cases. In this paper, we evaluate the performance of GPT-4 in generating factually accurate, clear and relevant explanations of terms in legislation. We compare the performance of a baseline setup, where GPT-4 is directly asked to explain a legal term, to an augmented approach, where a legal information retrieval module is used to provide relevant context to the model, in the form of sentences from case law. We found that the direct application of GPT-4 yields explanations that appear to be of very high quality on their surface. However, detailed analysis uncovered limitations in terms of the factual accuracy of the explanations. Further, we found that the augmentation leads to improved quality, and appears to eliminate the issue of hallucination, where models invent incorrect statements. These findings open the door to the building of systems that can autonomously retrieve relevant sentences from case law and condense them into a useful explanation for legal scholars, educators or practicing lawyers alike. © 2023, CC BY.

关键词： Laws and legislation