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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Yun-Chi Chen Tung Chao Wimaya Nitya Phandita Tzu-Yu Sun Huai-Zhi Wang Yi-Hsien Hsieh Le-Yin Hsu Che Lin Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University (NTU) Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan Data Science Degree Program NTU Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients.

关键词： Attention mechanisms Histopathology Computational modeling Precision medicine Small satellites Immunotherapy Colorectal cancer Predictive models Transformers Genetics

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Dynamic programming Algorithm using Furniture Industry 0/1 Knapsack Problem

Dynamic Programming Algorithm using Furniture Industry 0/1 K...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Mietha Anditta Natashya Amartya Laurens Spits Warnars Harco Leslie Hendric Spits Warnars Arief Ramadhan Teddy Siswanto Teddy Mantoro Nurulhuda Noordin Computer Science Departement School of Computer Science Bina Nusantara University Jakarta Indonesia Information Systems Departement School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Departement BINUS Graduate Program - Computer Science Bina Nusantara University Jakarta Indonesia PJJ Informatika Telkom University Bandung Indonesia Information Systems Department Trisakti University Jakarta Indonesia Faculty of Engineering and Technology Sampoerna University Jakarta Indonesia School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Malaysia

Dynamic programming is a fundamental algorithm that can be found in our daily lives easily. One of the dynamic programming algorithm implementations consists of solving the 0/1 knapsack problem. A 0/1 knapsack problem can be seen from industrial production cost. It is prevalent that a production cost has to be as efficient as possible, but the expectation is to get the proceeds of the products higher. Thus, the dynamic programming algorithm can be implemented to solve the diverse knapsack problem, one of which is the 0/1 knapsack problem, which would be the main focus of this paper. The implementation was implemented using C language. This paper was created as an early implementation algorithm using a Dynamic program algorithm applied to an Automatic Identification System (AIS) dataset.

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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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Collecting Child Psychiatry Documents of Clinical Trials from PubMed by the SVM Text Classification Method with the MATF Weighting Scheme 15th

Collecting Child Psychiatry Documents of Clinical Trials fro...

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15th International Conference on Computing and Information Technology, IC2IT 2019

作者： Polpinij, Jantima Kachai, Tontrakant Nasomboon, Kanyarat Bheganan, Poramin Intellect Laboratory Department of Computer Science Faculty of Informatics Mahasarakham University Mahasarakham Province Thailand Computer Science Program Mahidol University International College Mahidol University Nakhonpathom Province Thailand

ISBN: (纸本)9783030198602

Child psychiatry is a branch of psychiatry focused on the diagnosis, treatment, and prevention of mental health issues in children and their families. In many countries, the study of disorders such as ADHD (Attention-Deficit/Hyperactivity Disorder) by child and adolescent psychiatry is still in its infancy, with the result that children’s mental health issues can be the source of embarrassment for the family and of shame for many children. Misunderstanding, denying, and ignoring children’s mental health issues by parents are the main problem encountered in diagnosis and treatment of mental health issues in children. To help parents and extended families understand this problem better, and thus help them to better care for children with mental health issues, starting with seeking help from a psychiatrist without embarrassment, an easily accessible and reliable source of information is urgently needed. To develop such a single source of information, relevant documents need to be gathered together. This study presents a method of gathering reports of clinical trials from PubMed which describe diagnosis and treatment of child mental health issues. The main mechanism of the proposed method is a Support Vector Machine with a Multi Aspect TF (MATF) weighting scheme. After testing by recall, precision, and F1, it can return satisfactory results of 0.82, 0.79, and 0.80 respectively. © 2020, Springer Nature Switzerland AG.

关键词： Support vector machines

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SWING-BY DYNAMICS IN CONCEPT LEARNING AND COMPOSITIONAL GENERALIZATION

arXiv

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arXiv 2024年

作者： Yang, Yongyi Park, Core Francisco Lubana, Ekdeep Singh Okawa, Maya Hu, Wei Tanaka, Hidenori CBS-NTT Physics of Intelligence Program Harvard University CambridgeMA United States Physics & Informatics Laboratories NTT Research Inc. SunnyvaleCA United States Computer Science and Engineering University of Michigan Ann ArborMI United States Department of Physics Harvard University CambridgeMA United States

Prior work has shown that text-conditioned diffusion models can learn to identify and manipulate primitive concepts underlying a compositional data-generating process, enabling generalization to entirely novel, out-of-distribution compositions. Beyond performance evaluations, these studies develop a rich empirical phenomenology of learning dynamics, showing that models generalize sequentially, respecting the compositional hierarchy of the data-generating process. Moreover, concept-centric structures within the data significantly influence a model’s speed of learning the ability to manipulate a concept. In this paper, we aim to better characterize these empirical results from a theoretical standpoint. Specifically, we propose an abstraction of prior work’s compositional generalization problem by introducing a structured identity mapping (SIM) task, where a model is trained to learn the identity mapping on a Gaussian mixture with structurally organized centroids. We mathematically analyze the learning dynamics of neural networks trained on this SIM task and show that, despite its simplicity, SIM’s learning dynamics capture and help explain key empirical observations on compositional generalization with diffusion models identified in prior work. Our theory also offers several new insights—e.g., we find a novel mechanism for non-monotonic learning dynamics of test loss in early phases of training. We validate our new predictions by training a text-conditioned diffusion model, bridging our simplified framework and complex generative models. Overall, this work establishes the SIM task as a meaningful theoretical abstraction of concept learning dynamics in modern generative models. © 2024, CC BY.

关键词： Mapping

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Feature Comparison for Automatic Bug Report Classification 15th

Feature Comparison for Automatic Bug Report Classification

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15th International Conference on Computing and Information Technology, IC2IT 2019

作者： Luaphol, Bancha Srikudkao, Boonchoo Kachai, Tontrakant Srikanjanapert, Natthakit Polpinij, Jantima Bheganan, Poramin Intellect Laboratory Department of Computer Science Faculty of Informatics Mahasarakham University Mahasarakham province Thailand Computer Science Program Mahidol University International College Mahidol University Nakhonpathom province Thailand

ISBN: (纸本)9783030198602

Nowadays, various bug tracking systems (BTS) such as Jira, Trace, and Bugzilla have been developed and proposed to gather the issues from users worldwide. This is because those issues, called bug reports, contain a significant information for software quality maintenance and improvement. However, many bug reports with poor quality might have been submitted to the BTS. In general, the reported bugs in the BTS are firstly analyzed and filtered out by bug triagers. However, with the increasing amount of bug reports in the BTS, manually classifying bug reports is a time-consuming task. To address this problem, automatically distinguishing of bugs and non-bugs is necessary. To the best of our knowledge, this task is never easy for bug reports classification because the problem of bug reports misclassification still occurs to date. The background of this problem may be arise from using inappropriate or confusing features. Therefore, this work aims to study and discover the most proper features for binary bug report classification. This study compares seven features such as unigram, bigram, camel case, unigram+bigram, unigram+camel case, bigram+ camel case, and all features together. The experimental results show that the unigram+camel case should be the most proper features for binary bug report classification, especially when using with the logistic regression algorithm. Consequently, the unigram+camel case should be the proper feature to distinguish bug reports from the non-bugs ones. © 2020, Springer Nature Switzerland AG.

关键词： Support vector machines

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Prediction of Consumer Purchase Intention on Green Product

Prediction of Consumer Purchase Intention on Green Product

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2021 International Seminar on Application for Technology of Information and Communication, iSemantic 2021

作者： Hariyadi, Guruh Taufan Purwanto Isnanto, R Rizal Wibowo, Adi Warsito, Budi Universitas Diponegoro Doctoral Program of Information Systems Semarang Indonesia Universitas Diponegoro Faculty of Engineering Department of Chemical Engineering Semarang Indonesia Universitas Diponegoro Faculty of Engineerig Department of Computer Engineering Semarang Indonesia Universitas Diponegoro Faculty of Science and Mathematics Departement of Informatics Semarang Indonesia

ISBN: (纸本)9781665428040

Education to the public on green products has been widely carried out. Consumers who buy green products can be profiled by segmenting green consumers. In green marketing, consumer education on sustainable consumption is increasingly needed in recent years due to the problem of environmental pollution which poses a serious threat to the world population. Concern for the environment is an important topic in most countries, and individuals also study environmental issues. Individuals or groups of individuals who are 'ecologically aware' are also referred to as 'green consumers.' Such consumers are considered to have more concern for the environment. This study examined the predictions on the effect of the Green Perceived Value variables consisting of functional, conditional, and social values on the purchase intention of green consumers. It also tested the comparisons with other classification models. The models used in this research were Random Forest, Decision Tree, and Neural Network.. © 2021 IEEE.

关键词： Consumer behavior

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Development and Validation of Customer Intention Factors for Live-Stream Shopping

Development and Validation of Customer Intention Factors for...

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Conference on New Media Studies (CoNMedia)

作者： Erick Fernando Desman Hidayat Christian Haposan Pengaribuan Yulius Denny Prabowo Department of Information System Faculty of Engineering and Informatics Universitas Multimedia Nusantara Tangerang Indonesia Management Department BINUS Entrepreneurship Center Bina Nusantara University Jakarta Indonesia Management Study Program Faculty of Business Sampoerna University Jakarta Indonesia Computer Science Department Binus Online Learning Bina Nusantara University Jakarta Indonesia

Along with technological improvements, online shopping is currently developing quickly. Online shops started by selling electronics, clothing, food, and home appliances and continue to evolve, selling various things. Currently, digital marketing has a novel technique, namely live shopping, where sellers can present, promote and offer their products directly through live streaming on social media and e-commerce platforms. This research will focus on gaining insight into the factors influencing customers to watch and purchase through live streaming. This research is an early-stage study of exploring and searching for research models. The study used a literature review approach by reviewing similar previous research articles. Several variable components were used in this study. The findings of the factors include visibility capacity, meta-vocation capacity, purchase orientation capacity, social presence, price, perceived product quality, perceived enjoyment, purchase orientation, perceived usefulness, and intention to buy. These factors were tested and proved to be valid with Cronbach's Alpha value being more than 0.5 and reliable with the value of the loading factor a greater than 0.6. Criteria The factors of the findings for further analysis of the relationship between factors and other variables.

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A Position Control System for a Single-port Laparoscopic Manipulating Robot

A Position Control System for a Single-port Laparoscopic Man...

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2022 International Electrical Engineering Congress, iEECON 2022

作者： Sonsilphong, Amornthep Sonsilphong, Suphachoke Soemphol, Chaiyong Hormdee, Daranee Khampitak, Kovit Rajamangala University of Technology Isan Department of Electrical Engineering Khon Kean Thailand Khon Kaen University Data Science Health Informatics Program Faculty of Medicine Khon Kaen Thailand Mahasarakham University Faculty of Engineering Department of Electrical Engineering Mahasarakham Thailand Khon Kaen University Embedded System RD Group Computer Engineering Faculty of Engineering Khon Kaen Thailand Khon Kean University Faculty of Medicine Department of Obstetrics and Gynecology Khon Kean Thailand

ISBN: (纸本)9781665402064

The research herein proposes a position control system, designed for a single-port surgical robot, based on the lockup table method. The programmable logic controller (PLC) was implemented to move the single- port surgical robot and handle laparoscope and surgical instruments with feedback control. The output responses of the desired angle were investigated under several conditions. The rotating angle allows movement of the laparoscope in the range of -10o to +10o. The error of the desired position of the designed system was less than approximately ± 0.1o. Moreover, the output data from several experiments may be employed to improve the open-loop control systems. This system is also capable of providing computer vision task implementation, enabling semi- automatic or automatic surgery in the future. © 2022 IEEE.

关键词： Laparoscopy

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Dynamic Healthcare Embeddings for Improving Patient Care

Dynamic Healthcare Embeddings for Improving Patient Care

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Hankyu Jang Sulyun Lee D. M. Hasibul Hasan Philip M. Polgreen Sriram V. Pemmaraju Bijaya Adhikari Department of Computer Science University of Iowa Iowa City Iowa USA Interdisciplinary Graduate Program in Informatics University of Iowa Iowa City Iowa USA Department of Internal Medicine University of Iowa Iowa City Iowa USA

As hospitals move towards automating and integrating their computing systems, more fine-grained hospital operations data are becoming available. These data include hospital architectural drawings, logs of interactions between patients and healthcare professionals, prescription data, procedures data, and data on patient admission, discharge, and transfers. This has opened up many fascinating avenues for healthcare-related prediction tasks for improving patient care. However, in order to leverage off-the-shelf machine learning software for these tasks, one needs to learn structured representations of entities involved from heterogeneous, dynamic data streams. Here, we propose DECENT, an auto-encoding heterogeneous co-evolving dynamic neural network, for learning heterogeneous dynamic embeddings of patients, doctors, rooms, and medications from diverse data streams. These embeddings capture similarities among doctors, rooms, patients, and medications based on static attributes and dynamic interactions. DECENT enables several applications in healthcare prediction, such as predicting mortality risk and case severity of patients, adverse events (e.g., transfer back into an intensive care unit), and future healthcare-associated infections. The results of using the learned patient embeddings in predictive modeling show that DECENT has a gain of up to 48.1% on the mortality risk prediction task, 12.6% on the case severity prediction task, 6.4% on the medical intensive care unit transfer task, and 3.8% on the Clostridioides difficile (***) Infection (CDI) prediction task over the state-of-the-art baselines. In addition, case studies on the learned doctor, medication, and room embeddings show that our approach learns meaningful and interpretable embeddings.

关键词： Hospitals Social networking (online) Neural networks Machine learning Predictive models Back Software

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