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IEEE International Conference on Big data

作者： ML Tlachac Walter Gerych Kratika Agrawal Benjamin Litterer Nicholas Jurovich Saitheeraj Thatigotla Jidapa Thadajarassiri Elke A. Rundensteiner Department of Information Systems & Analytics Center for Health & Behavioral Sciences Bryant University Smithfield USA Data Science and Computer Science Departments Worcester Polytechnic Institute (WPI) Worcester USA School of Information University of Michigan Ann Arbor USA University of Tennessee Knoxville TN

ISBN: (纸本)9781665480468

Corpuses of unstructured textual data, such as text messages between individuals, are often predictive of medical issues such as depression. The text data usually used in healthcare applications has high value and great variety, but is typically small in volume. Generating labeled unstructured text data is important to improve models by augmenting these small datasets, as well as to facilitate anonymization. While methods for labeled data generation exist, not all of them generalize well to small datasets. In this work, we thus perform a much needed systematic comparison of conditional text generation models that are promising for small datasets due to their unified architectures. We identify and implement a family of nine conditional sequence generative adversarial networks for text generation, which we collectively refer to as cSeqGAN models. These models are characterized along two orthogonal design dimensions: weighting strategies and feedback mechanisms. We conduct a comparative study evaluating the generation ability of the nine cSeqGAN models on three diverse text datasets with depression and sentiment labels. To assess the quality and realism of the generated text, we use standard machine learning metrics as well as human assessment via a user study. While the unconditioned models produced predictive text, the cSeqGAN models produced more realistic text. Our comparative study lays a solid foundation and provides important insights for further text generation research, particularly for the small datasets common within the healthcare domain.

关键词： Systematics Medical services Machine learning Predictive models Big data Depression Generative adversarial networks

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T-Cell Receptor Optimization with Reinforcement Learning and Mutation Polices for Precision Immunotherapy 27th

T-Cell Receptor Optimization with Reinforcement Learning an...

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27th International Conference on Research in Computational Molecular Biology, RECOMB 2023

作者： Chen, Ziqi Min, Martin Renqiang Guo, Hongyu Cheng, Chao Clancy, Trevor Ning, Xia Computer Science and Engineering The Ohio State University ColumbusOH43210 United States Machine Learning Department NEC Labs PrincetonNJ08540 United States Digital Technologies Research Centre National Research Council Canada Ontario Canada Department of Medicine Baylor College of Medicine HoustonTX77030 United States NEC Oncolmmunity AS Oslo Cancer Cluster Innovation Park Ullernchausséen 64 Oslo0379 Norway Biomedical Informatics The Ohio State University ColumbusOH43210 United States Translational Data Analytics Institute The Ohio State University ColumbusOH43210 United States

ISBN: (纸本)9783031291180

T cells monitor the health status of cells by identifying foreign peptides displayed on their surface. T-cell receptors (TCRs), which are protein complexes found on the surface of T cells, are able to bind to these peptides. This process is known as TCR recognition and constitutes a key step for immune response. Optimizing TCR sequences for TCR recognition represents a fundamental step towards the development of personalized treatments to trigger immune responses killing cancerous or virus-infected cells. In this paper, we formulated the search for these optimized TCRs as a reinforcement learning (Formula Presented) problem, and presented a framework (Formula Presented) with a mutation policy using proximal policy optimization. (Formula Presented) mutates TCRs into effective ones that can recognize given peptides. (Formula Presented) leverages a reward function that combines the likelihoods of mutated sequences being valid TCRs measured by a new scoring function based on deep autoencoders, with the probabilities of mutated sequences recognizing peptides from a peptide-TCR interaction predictor. We compared (Formula Presented) with multiple baseline methods and demonstrated that (Formula Presented) significantly outperforms all the baseline methods to generate positive binding and valid TCRs. These results demonstrate the potential of(Formula Presented) for both precision immunotherapy and peptide-recognizing TCR motif discovery. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Reinforcement learning

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A Novel Framework for the Development of Quality-Aware Autonomic IoT Applications

A Novel Framework for the Development of Quality-Aware Auton...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： Ankur Singh Bisht Manjunatha R M. P. Karnik A. Amudha Nimesh Raj Shalini M Computer Science and Engineering Graphic Era Hill University Bhimtal Graphic Era Deemed to be University Dehradun Uttarakhand India School of Sciences Data Analytics and Mathematical Sciences JAIN (Deemed to be University) Bangalore Karnataka Computer Engineering Vishwakarma Institute of Technology Pune India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

A tremendous quantity of data is being generated via the Internet of Things (IoT). While our knowledge of how to assess the quality of this data at the network and device levels is expanding, there are still significant obstacles to overcome when it comes to interpreting and assessing the quality at the informational and application levels. If you want to know what aspects impact the overall system quality, this post will go over some of the problems with IoT systems and how to fix them at various OSI levels. Using two digital health apps made possible by the internet of things (IoT), the authors look at how semantics may be used to measure the system’s data quality and how to integrate multimodal information to clinical decision support. Important data quality issues arise from the fact that the Internet of Things (IoT) ecosystem generates massive amounts of data collected from a variety of heterogeneous sources. The reason for this is that the whole operation of the internet of things system is badly affected by low data quality standards. data quality has been the subject of much research outside of the Internet of Things (IoT) domain, and the idea is backed by massive standardization efforts. However, data quality management inside the IoT has received little attention. An essential idea with many existing applications for issue solving, data quality (DQ) is used in the creation and execution of information, handling data, decision making, as well as engineering systems. But the ways DQ is now integrated into data management pipelines aren’t flexible enough to accommodate all kinds of uses, which limits its use in the IoT space. A generic framework is introduced in this research that allows for the implementation of DQ assessment in a wide range of Internet of Things applications. A knowledge graph serves as the foundation of the framework, making it easy to incorporate into new uses. The study’s findings highlight many gaps in the current state of IoT quality a

关键词： Training Support vector machines Accuracy data integrity Semantics Vectors Classification algorithms Internet of Things Cultural differences Standards

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Using multiple drug similarity networks to promote adverse drug event detection

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Heliyon 2024年第22期10卷 e39728页

作者： Padhi, Biswajit Liu, Ruoqi Yang, Yuedi Peng, Xueqiao Li, Lang Zhang, Pengyue Zhang, Ping Department of Computer Science and Engineering The Ohio State University 2015 Neil Ave Columbus 43210 OH United States Department of Biostatistics and Health Data Science Indiana University School of Medicine 410 W. 10th Street HITS 3000 Indianapolis 46202 IN United States Department of Biomedical Informatics The Ohio State University 1800 Cannon Drive Columbus 43210 OH United States Translational Data Analytics institute The Ohio State University 1760 Neil Ave Columbus 43210 OH United States

The occurrence of an adverse drug event (ADE) has become a serious social concern of public health. Early detection of ADEs can lower the risk of drug safety as well as the expense of the drug. While post-market spontaneous reports of ADEs remain a cornerstone of pharmacovigilance, most existing signal detection algorithms rely on substantial accumulated data, limiting their applicability to early ADE detection when reports are scarce. To address this issue, we propose a label propagation model for generating enhanced drug safety signals using multiple drug features. We first construct multiple drug similarity networks using a range of drug features. We then calculate initial drug safety signals using conventional signal detection algorithms. These original signals are subsequently propagated across each drug similarity network to obtain enhanced drug safety signals. We evaluate our proposed model using two common signal detection algorithms on data from the FDA Adverse Event Reporting System (FAERS). Results demonstrate that enhanced drug safety signals with pre-clinical information outperform the standard safety signal detection algorithms on early ADE detection. In addition, we systematically evaluate the performance of different drug similarities against different types of ADEs. Furthermore, we have developed a web interface (http://***/) to display our multiple drug similarity scores, facilitating access to this valuable resource for drug safety monitoring. © 2024 The Authors

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Quantum Machine Learning for Software Supply Chain Attacks: How Far Can We Go?

arXiv

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arXiv 2022年

作者： Masum, Mohammad Nazim, Mohammad Jobair Hossain Faruk, Md. Shahriar, Hossain Valero, Maria Abdullah Hafiz Khan, Md. Uddin, Gias Barzanjeh, Shabir Saglamyurek, Erhan Rahman, Akond Ahamed, Sheikh Iqbal Analytics and Data Science Institute Kennesaw State University Kennesaw United States Department of Computer Science Kennesaw State University MariettaGA United States Department of Software Engineering Kennesaw State University MariettaGA United States Department of Information Technology Kennesaw State University Marietta United States Electrical and Software Engineering Institute for Quantum Science and Technology University of Calgary Canada Department of Computer Science Tennessee Tech University Tennessee United States Department of Computer Science Marquette University Wisconsin United States

Quantum Computing (QC) has gained immense popularity as a potential solution to deal with the ever-increasing size of data and associated challenges leveraging the concept of quantum random access memory (QRAM). QC promises- quadratic or exponential increases in computational time with quantum parallelism and thus offer a huge leap forward in the computation of Machine Learning algorithms. This paper analyzes speed up performance of QC when applied to machine learning algorithms, known as Quantum Machine Learning (QML). We applied QML methods such as Quantum Support Vector Machine (QSVM), and Quantum Neural Network (QNN) to detect Software Supply Chain (SSC) attacks. Due to the access limitations of real quantum computers, the QML methods were implemented on open-source quantum simulators such as IBM Qiskit and TensorFlow Quantum. We evaluated the performance of QML in terms of processing speed and accuracy and finally, compared with its classical counterparts. Interestingly, the experimental results differ to the speed up promises of QC by demonstrating higher computational time and lower accuracy in comparison to the classical approaches for SSC attacks. © 2022, CC BY.

关键词： Supply chains

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The Effect of Prolonged Conditionally Automated Driving on Fatigue, Physiological Activity, and Takeover Performance: A Driving Simulator Experiment

SSRN

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SSRN 2025年

作者： Coyne, Rory Khan, Bilal Alam Shiel, Matthew Ur Rahman, Shams Vlad, Victor Lillis, Conor Usman, Muzna Kielty, Paul Parsi, Ashkan Lemley, Joseph Smeaton, Alan F. Corcoran, Peter Walsh, Jane C. School of Psychology University of Galway Ireland School of Psychology Trinity College Dublin Ireland FotoNation Ltd Block 5 Parkmore East Galway Ireland Insight Research Ireland Centre for Data Analytics Dublin City University Dublin Ireland School of Computer Science University of Galway Ireland Department of Electrical and Electronic Engineering University of Galway Ireland School of Computing Dublin City University Dublin Ireland

The transition from manual to conditionally automated driving involves the driver’s role changing from an active operator to a ‘fallback-ready’ user, who is free to disengage from driving, but must remain ready to take over when prompted. Driver fatigue, which can be distinguished from drowsiness and already accounts for as many as 20% of all road traffic accidents, is likely to remain a pervasive problem during conditionally automated driving, as reductions in attention and vigilance due to fatigue could imperil safe transitions of control between automated system and user. While considerable research exists concerning drivers’ responses under states of drowsiness or distraction, fewer studies have investigated the effect of automation on fatigue. This driving simulator experiment was conducted to examine the effect of prolonged automation on fatigue during conditionally automated driving. Drivers’ self-reported fatigue and workload were measured, in addition to a suite of physiological measures and takeover reaction time and quality. Findings show that fatigue was significantly higher following 50 minutes of automated driving than at baseline, or while participants performed a non-driving-related task. Strikingly, 80% of participants experienced signs of sleepiness, and almost half had to exercise effort to stay awake. Fatigue also resulted in decreases in heart rate and relative beta power derived from electroencephalography, and an increase in blink rate and heart rate variability. Overall, the findings advance knowledge in this area by supporting the idea of fatigue as failure to adequately self-regulate during automation. Several physiological measures have also been identified as possible markers of fatigue to inform emerging monitoring technology. © 2025, The Authors. All rights reserved.

关键词： Heart

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Combining K-means type algorithms with hill climbing for joint stratification and sample allocation designs

arXiv

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arXiv 2021年

作者： O'Luing, Mervyn Prestwich, Steven Tarim, S. Armagan Insight Centre for Data Analytics Department of Computer Science University College Cork Ireland Cork University Business School University College Cork Ireland

In this paper we combine the k-means and/or k-means type algorithms with a hill climbing algorithm in stages to solve the joint stratification and sample allocation problem. This is a combinatorial optimisation problem in which we search for the optimal stratification from the set of all possible stratifications of basic strata. Each stratification being a solution the quality of which is measured by its cost. This problem is intractable for larger sets. Furthermore evaluating the cost of each solution is expensive. A number of heuristic algorithms have already been developed to solve this problem with the aim of finding acceptable solutions in reasonable computation times. However, the heuristics for these algorithms need to be trained in order to optimise performance in each instance. We compare the above multi-stage combination of algorithms with three recent algorithms and report the solution costs, evaluation times and training times. The multi-stage combinations generally compare well with the recent algorithms both in the case of atomic and continuous strata and provide the survey designer with a greater choice of algorithms to choose from. © 2021, CC BY.

关键词： Heuristic algorithms

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GNN’s FAME: Fairness-Aware MEssages for Graph Neural Networks 25

GNN’s FAME: Fairness-Aware MEssages for Graph Neural Networ...

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Proceedings of the 33rd ACM Conference on User Modeling, Adaptation and Personalization

作者： Erasmo Purificato Hannan Javed Mahadik Ludovico Boratto Ernesto William De Luca European Centre for Algorithmic Transparency (ECAT) European Commission Joint Research Centre (JRC) Ispra Italy Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig Leipzig Germany Department of Mathematics and Computer Science University of Cagliari Cagliari Italy Otto von Guericke University Magdeburg Magdeburg Germany and Leibniz Institute for Educational Media | George Eckert Institute Brunswick Germany

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Towards privacy-anomaly detection: Discovering correlation between privacy and security-anomalies 17

Towards privacy-anomaly detection: Discovering correlation b...

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17th International Conference on Mobile Systems and Pervasive Computing, MobiSPC 2020

作者： Khan, Muhammad Imran Foley, Simon N. O'Sullivan, Barry Insight Centre for Data Analytics School of Computer Science and Information Technology University College Cork Ireland Department of Information Security and Communication Technology Norwegian University of Science and Technology Gjøvik Norway

In this paper a notion of privacy-anomaly detection is presented where normative privacy is modelled using k-anonymity. Based on the model, normative privacy-profiles are constructed, and deviation from normative privacy-profile at runtime is labelled as a privacy-anomaly. Furthermore, the paper investigates whether there is a correlation between security-anomalies and privacy-anomalies, that is, whether the privacy-anomalies labelled by privacy-anomaly detection system are detected by conventional security-anomaly detection system used for detecting malicious accesses to databases by insiders. © 2020 The Authors.

关键词： Anomaly detection

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Behavior change in response to subreddit bans and external events

arXiv

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arXiv 2021年

作者： Thomas, Pamela Bilo Riehm, Daniel Glenski, Maria Weninger, Tim Department of Computer Science & Engineering University of Notre Dame United States Data Sciences and Analytics Group Pacific Northwest National Laboratory United States

As more people flock to social media to connect with others and form virtual communities, it is important to research how members of these groups interact to understand human behavior on the Web. In response to an increase in hate speech, harassment and other antisocial behaviors, many social media companies have implemented different content and user moderation policies. On Reddit, for example, communities, i.e., subreddits, are occasionally banned for violating these policies. We study the effect of these regulatory actions as well as when a community experiences a significant external event like a political election or a market crash. Overall, we find that most subreddit bans prompt a small, but statistically significant, number of active users to leave the platform;the effect of external events varies with the type of event. We conclude with a discussion on the effectiveness of the bans and wider implications for the online content moderation. © 2021, CC BY.

关键词： Social networking (online)

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