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arXiv 2023年

作者： Bansal, Yogesh Lillis, David Kechadi, Mohand Tahar School of Computer Science University College Dublin Ireland Insight Centre for Data Analytics Ireland

Western countries rely heavily on wheat, and yield prediction is crucial. Time-series deep learning models, such as Long Short Term Memory (LSTM), have already been explored and applied to yield prediction. Existing literature reported that they perform better than traditional Machine Learning (ML) models. However, the existing LSTM cannot handle heterogeneous datasets (a combination of data which varies and remains static with time). In this paper, we propose an efficient deep learning model that can deal with heterogeneous datasets. We developed the system architecture and applied it to the real-world dataset in the digital agriculture area. We showed that it outperforms the existing ML models. Copyright © 2023, The Authors. All rights reserved.

关键词： Time series

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AI-Informed Development for a Lactate Measurement Tool 32

AI-Informed Development for a Lactate Measurement Tool

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32nd Irish Conference on Artificial Intelligence and Cognitive science, AICS 2024

作者： Kiely, Cian Rossberg, Nicola Krishnamoorthy, Shree Visentin, Andrea School of Computer Science & IT University College Cork Ireland SFI Center for Research Training in Artificial Intelligence University College Cork Ireland Biophotonics@Tyndall IPIC Tyndall National Institute Ireland SFI Insight Centre for Data Analytics University College Cork Ireland

Lactate has been identified as a key biomarker, with spikes co-occurring with high-risk medical conditions including sepsis and hypoxia. Despite its high medical value, current methods of Lactate measurement require repeated blood sampling from the patient, which is both costly and invasive, and consequently tends to be limited to intensive care units. Spectroscopy, a non-invasive light-based system, presents a cost-effective alternative to these traditional methods, which permits continuous measurement and improved patient monitoring. Through the use of machine learning, spectroscopic measurements can be used to estimate blood Lactate levels in an accessible and low-cost manner. In this study, machine learning models were trained on Near-infrared (NIR) spectroscopy data, to identify the best set-up for high-precision estimation of Lactate levels. The results of the analysis are used to determine the best path length for spectroscopic measurements. Feature selection is implemented to establish the most important wavelengths for prediction and inform on the most relevant spectral regions for the given task. Explainability is implemented to analyse feature contributions and allow inference of potentially interfering components that should be considered for further testing. The results showed that by using a random forest, R2 values of 0.9986 can be achieved. Feature selection increased predictive performance considerably with R2 values as high as 0.9996 and the implementation of explainability allowed the identification of important wavelength ranges. © 2024 Copyright for this paper by its authors.

关键词： Near infrared spectroscopy

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Synergy between CSST galaxy survey and gravitational-wave observation: Inferring the Hubble constant from dark standard sirens

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science China(Physics,Mechanics & Astronomy) 2024年第3期67卷 7-19页

作者： Ji-Yu Song Ling-Feng Wang Yichao Li Ze-Wei Zhao Jing-Fei Zhang Wen Zhao Xin Zhang Key Laboratory of Cosmology and Astrophysics(Liaoning)&College of Sciences Northeastern UniversityShenyang 110819China CAS Key Laboratory for Researches in Galaxies and Cosmology Department of AstronomyUniversity of Science and Technology of ChinaChinese Academy of SciencesHefei 230026China School of Astronomy and Space Sciences University of Science and Technology of ChinaHefei 230026China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern UniversityShenyang 110819China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern Universty Shenyang 110819China

Gravitational waves(GWs) from compact binary coalescences encode the absolute luminosity distances of GW sources. Once the redshifts of GW sources are known, one can use the distance-redshift relation to constrain cosmological parameters. One way to obtain the redshifts is to localize GW sources by GW observations and then use galaxy catalogs to determine redshifts from a statistical analysis of redshift information of the potential host galaxies, commonly referred to as the dark siren method. The third-generation(3G) GW detectors are planned to work in the 2030s and will observe numerous compact binary *** these GW events as dark sirens requires high-quality galaxy catalogs from future sky survey projects. The China Space Station Telescope(CSST) will be launched in 2024 and will observe billions of galaxies within a 17500 deg^(2) survey area with redshift up to z ~ 4, providing photometric and spectroscopic galaxy catalogs. In this work, we simulate the CSST galaxy catalogs and the 5-year GW data from the 3G GW detectors and combine them to infer the Hubble constant(H_(0)). Our results show that the measurement precision of H0could reach the sub-percent level, meeting the standard of precision cosmology. We conclude that the synergy between CSST and the 3G GW detectors is of great significance in measuring the Hubble constant.

关键词： CSST galaxy survey galaxy catalogs gravitational waves dark sirens the Hubble constant

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A transformer-based unified multimodal framework for Alzheimer's disease assessment

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Computers in Biology and Medicine 2024年 180卷 108979页

作者： Yu, Qi Ma, Qian Da, Lijuan Li, Jiahui Wang, Mengying Xu, Andi Li, Zilin Li, Wenyuan Department of Big Data in Health Science School of Public Health and Center of Clinical Big Data and Analytics of The Second Affiliated Hospital Zhejiang University School of Medicine Zhejiang Hangzhou China School of Mathematics and Statistics Northeast Normal University Jilin Changchun130024 China

In Alzheimer's disease (AD) assessment, traditional deep learning approaches have often employed separate methodologies to handle the diverse modalities of input data. Recognizing the critical need for a cohesive and interconnected analytical framework, we propose the AD-Transformer, a novel transformer-based unified deep learning model. This innovative framework seamlessly integrates structural magnetic resonance imaging (sMRI), clinical, and genetic data from the extensive Alzheimer's Disease Neuroimaging Initiative (ADNI) database, encompassing 1651 subjects. By employing a Patch-CNN block, the AD-Transformer efficiently transforms image data into image tokens, while a linear projection layer adeptly converts non-image data into corresponding tokens. As the core, a transformer block learns comprehensive representations of the input data, capturing the intricate interplay between modalities. The AD-Transformer sets a new benchmark in AD diagnosis and Mild Cognitive Impairment (MCI) conversion prediction, achieving remarkable average area under curve (AUC) values of 0.993 and 0.845, respectively, surpassing those of traditional image-only models and non-unified multimodal models. Our experimental results confirmed the potential of the AD-Transformer as a potent tool in AD diagnosis and MCI conversion prediction. By providing a unified framework that jointly learns holistic representations of both image and non-image data, the AD-Transformer paves the way for more effective and precise clinical assessments, offering a clinically adaptable strategy for leveraging diverse data modalities in the battle against AD. © 2024 Elsevier Ltd

关键词： Neuroimaging

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Influence of Convolutional Neural Network Depth on the Efficacy of Automated Breast Cancer Screening Systems 28

Influence of Convolutional Neural Network Depth on the Effic...

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28th IEEE Symposium on Computers and Communications, ISCC 2023

作者： Nalla, Vineela Pouriyeh, Seyedamin Parizi, Reza M. Hwang, Inchan Brown-Mulry, Beatrice Zhang, Linglin Woo, Minjae Kennesaw State University Department of Information and Technology MariettaGA United States Kennesaw State University Decentralized Science Lab MariettaGA United States School of Data Science and Analytics Kennesaw State University KennesawGA United States

ISBN: (纸本)9798350300482

Breast cancer is a global health concern for women. The detection of breast cancer in its early stages is crucial, and screening mammography serves as a vital leading-edge tool for achieving this goal. In this study, we explored the effectiveness of Resnet 50v2 and Resnet 152v2 deep learning models for classifying mammograms using EMBED datasets for the first time. We preprocessed the datasets and utilized various techniques to enhance the performance of the models. Our results suggest that the choice of model architecture depends on the dataset used, with ResNet152 outperforming ResNet50 in terms of recall score. These findings have implications for cancer screening, where recall is an important metric. Our research highlights the potential of deep learning to improve breast cancer classification and underscores the importance of selecting the appropriate model architecture. © 2023 IEEE.

关键词： Diseases

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Generating interesting song-to-song segues with dave 21

Generating interesting song-to-song segues with dave

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29th ACM Conference on User Modeling, Adaptation and Personalization, UMAP 2021

作者： Gabbolini, Giovanni Bridge, Derek Insight Centre for Data Analytics School of Computer Science and IT University College Cork Ireland

ISBN: (纸本)9781450383660

We introduce a novel domain-independent algorithm for generating interesting item-to-item textual connections, or segues. Pivotal to our contribution is the introduction of a scoring function for segues, based on their ĝ€ interestingness'. We provide an implementation of our algorithm in the music domain. We refer to our implementation as Dave. Dave is able to generate 1553 different types of segues, that can be broadly categorized as either informative or funny. We evaluate Dave by comparing it against a curated source of song-to-song segues, called The Chain. In the case of informative segues, we find that Dave can produce segues of the same quality, if not better, than those to be found in The Chain. And, we report positive correlation between the values produced by our scoring function and human perceptions of segue quality. The results highlight the validity of our method, and open future directions in the application of segues to recommender systems research. © 2021 ACM.

关键词： Recommender systems

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AN INTERPRETABLE MUSIC SIMILARITY MEASURE BASED ON PATH INTERESTINGNESS 22

AN INTERPRETABLE MUSIC SIMILARITY MEASURE BASED ON PATH INTE...

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22nd International Conference on Music Information Retrieval, ISMIR 2021

作者： Gabbolini, Giovanni Bridge, Derek Insight Centre for Data Analytics School of Computer Science & IT University College Cork Ireland

ISBN: (纸本)9781732729902

We introduce a novel and interpretable path-based music similarity measure. Our similarity measure assumes that items, such as songs and artists, and information about those items are represented in a knowledge graph. We find paths in the graph between a seed and a target item;we score those paths based on their interestingness;and we aggregate those scores to determine the similarity between the seed and the target. A distinguishing feature of our similarity measure is its interpretability. In particular, we can translate the most interesting paths into natural language, so that the causes of the similarity judgements can be readily understood by humans. We compare the accuracy of our similarity measure with other competitive path-based similarity baselines in two experimental settings and with four datasets. The results highlight the validity of our approach to music similarity, and demonstrate that path interestingness scores can be the basis of an accurate and interpretable similarity measure. © 2021 Proceedings of the 22nd International Conference on Music Information Retrieval, ISMIR 2021. All Rights Reserved.

关键词： Information retrieval

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Re-designing the Interaction of Day-to-Day Applications to Support Sustained Attention Level 25th

Re-designing the Interaction of Day-to-Day Applications to S...

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25th International Conference on Human-Computer Interaction, HCII 2023

作者： Hacioglu, Naile Leva, Maria Chiara Lee, Hyowon School of Computing Dublin City University Glasnevin Dublin 9 Ireland School of Food Science and Environmental Health Technological University Dublin Grangegorman Dublin Ireland Insight Centre for Data Analytics Dublin City University Glasnevin Dublin 9 Ireland

ISBN: (纸本)9783031480379

Digital distractions in decreasing people’s attentional abilities have become a subject of increasing concern and scrutiny in recent years. Based on the existing literature regarding the negative impact of technology on attention, this paper examines various solutions, encompassing mostly reactive strategies. It also questions the prevailing design practices prioritising learnability and efficiency and proposes a shift towards designing interactions that minimise distractions and promote sustained attention while emphasising the negative consequences of distraction-prone interactions on users’ attentional resources. As a proactive solution, we present a novel interaction strategy called "Attention mode," which selectively reduces distractions when using apps. We developed three prototypes that consistently incorporate this strategy, each used in a different application on a different device: a news website on a desktop, an e-book reading app on a tablet, and a video-watching app on a smartphone. We conducted usability testing with 13 participants to evaluate this design feature and address the implications of these in the interaction design knowledge and practice today. By advocating for considering users’ attentional abilities in design, this work contributes to the field of Human-Computer Interaction (HCI) and calls for a balance between cognitive well-being and traditional usability criteria. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Human computer interaction

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Positive and negative length-bound reachability constraints 27

Positive and negative length-bound reachability constraints

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27th International Conference on Principles and Practice of Constraint Programming, CP 2021

作者： Quesada, Luis Brown, Kenneth N. Insight Centre for Data Analytics School of Computer Science University College Cork Ireland

ISBN: (纸本)9783959772112

In many application problems, including physical security and wildlife conservation, infrastructure must be configured to ensure or deny paths between specified locations. We model the problem as sub-graph design subject to constraints on paths and path lengths, and propose length-bound reachability constraints. Although reachability in graphs has been modelled before in constraint programming, the interaction of positive and negative reachability has not been studied in depth. We prove that deciding whether a set of positive and negative reachability constraints are satisfiable is NP complete. We show the effectiveness of our approach on decision problems, and also on optimisation problems. We compare our approach with existing constraint models, and we demonstrate significant improvements in runtime and solution costs, on a new problem set. © Luis Quesada and Kenneth N. Brown.

关键词： Constraint theory

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A Multistage Framework for Detection of Very Small Objects 23

A Multistage Framework for Detection of Very Small Objects

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6th International Conference on Machine Vision and Applications, ICMVA 2023

作者： Don, Duleep Rathgamage Aygun, Ramazan Karakaya, Mahmut School of Data Science and Analytics College of Computing and Software Engineering Kennesaw State University MariettaGA30060 United States Department of Computer Science College of Computing and Software Engineering Kennesaw State University MariettaGA30060 United States

ISBN: (纸本)9781450399531

Small object detection is one of the most challenging problems in computer vision. Algorithms based on state-of-the-art object detection methods such as R-CNN, SSD, FPN, and YOLO fail to detect objects of very small sizes. In this study, we propose a novel method to detect very small objects, smaller than 8×8 pixels, that appear in a complex background. The proposed method is a multistage framework consisting of an unsupervised algorithm and three separately trained supervised algorithms. The unsupervised algorithm extracts ROIs from a high-resolution image. Then the ROIs are upsampled using SRGAN, and the enhanced ROIs are detected by our two-stage cascade classifier based on two ResNet50 models. The maximum size of the images used for training the proposed framework is 32×32 pixels. The experiments are conducted using rescaled German Traffic Sign Recognition Benchmark dataset (GTSRB) and downsampled German Traffic Sign Detection Benchmark dataset (GTSDB). Unlike MS COCO and DOTA datasets, the resulting GTSDB turns out to be very challenging for any small object detection algorithm due to not only the size of objects of interest but the complex textures of the background as well. Our experimental results show that the proposed method detects small traffic signs with an average precision of 0.332 at the intersection over union of 0.3. © 2023 ACM.

关键词： Object detection

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