2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)最新文献_第5页

Variability and Trend Analysis of a Grid-Scale Solar Photovoltaic Array above the Arctic Circle 北极圈以上电网规模太阳能光伏阵列的变率和趋势分析

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00049

Henry Toal, A. K. Das

{"title":"Variability and Trend Analysis of a Grid-Scale Solar Photovoltaic Array above the Arctic Circle","authors":"Henry Toal, A. K. Das","doi":"10.1109/IRI58017.2023.00049","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00049","url":null,"abstract":"As solar photovoltaic (PV) power generation continues to grow in popularity, the variability in solar irradiance caused by weather effects such as clouds poses an increasing challenge to maintaining grid stability. Characterizing the variability and trends present in historical PV production data is vital to the development of effective models for predicting rapid changes. This is particularly important at higher latitudes where seasonal changes in PV generation are more extreme. In this paper, we analyse data from a small, grid-scale PV array in Kotzebue, Alaska (66.8969° N, 162.5931° W), located above Arctic Circle. We also successfully validate the variability index (VI), a previously proposed metric which quantifies the volatility of solar PV data over a given time span using a synthetic cloudless (clear-sky) dataset as a reference. We also include an examination of the stationarity of the PV production data at various timescales as well as the efficacy of using clear-sky models as a reference for de-trending solar irradiance data via, showing that better results can be obtained from data closer to solar noon. To the best of our knowledge, this is the first use of VI to assess PV production data from above the Arctic Circle.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129006258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Meme it Up: Patterns of Emoji Usage on Twitter 恶搞:推特上表情符号的使用模式

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00041

Mohammad Shiri, Oleksii Dubovyk, Golbarg Roghaniaraghi, S. Jayarathna

{"title":"Meme it Up: Patterns of Emoji Usage on Twitter","authors":"Mohammad Shiri, Oleksii Dubovyk, Golbarg Roghaniaraghi, S. Jayarathna","doi":"10.1109/IRI58017.2023.00041","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00041","url":null,"abstract":"As emojis have grown in their popularity in social media over the last decades, they not only enrich messaging with emotional connotations but offer a convenient system for studying the effects of memes on ideas’ survival. In this work, we treat emojis like standardized memes to test the impact of their usage on different facets of success within social media. Specifically, we extracted random individual tweets from Twitter to construct a list of emojis used within each tweet. With this dataset, we aimed to address three distinct questions: (1) whether there are specific patterns of emoji usage that increase tweet popularity; (2) whether emojis usage on tweeter can be a good predictor of the stock market trading volume; and (3) whether there is a specific subset of emojis associated with low-quality tweets (e.g., spam). We found no evidence of the positive effects of emoji usage on tweet popularity. However, there was a reason to claim that negative emojis may trigger an intensive response from the audience. For some companies, we were able to accurately predict stock patterns based on emoji usage. Finally, there clearly was a specific subset of emojis used in low-quality tweets. This work may serve as a starting point for a deep investigation of the emoji-meme system, as this topic seems to be relatively fresh in the literature.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127361629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Weakly Supervised Confidence Aware Probabilistic CAM multi-Thorax Anomaly Localization Network 弱监督置信度感知概率CAM多胸异常定位网络

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00061

Tanushree Meena, Anwesh Kabiraj, Pailla Balakrishna Reddy, S. Roy

{"title":"Weakly Supervised Confidence Aware Probabilistic CAM multi-Thorax Anomaly Localization Network","authors":"Tanushree Meena, Anwesh Kabiraj, Pailla Balakrishna Reddy, S. Roy","doi":"10.1109/IRI58017.2023.00061","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00061","url":null,"abstract":"Most anatomical information and anomalies are provided by chest X-ray (CXR) images and are sometimes adequate for the early diagnosis. However, by observing the radiographs it is a challenging task to recognize multiple occurring thorax diseases. As a result, there is a trend toward developing deep learning systems to assist radiologists. Motivated by this, we propose a generalized weakly supervised Confidence-Aware Probabilistic Class Activation Map (CAPCAM) classification model that localizes anomalies for 13 different thoracic disease. The CAPCAM used CX-Ultranet as the backbone with the combination of Confidence Aware Network (CAN) and Anomaly Detection Network (ADN) which helps the model to utilise all components of feature extracted therefore eliminating the necessity to train them individually and time taken. We experimentally shown that our proposed CAPCAM method sets a new state-of-the-art benchmark by achieving accuracy in terms of Intersection of Bounding Box (IoBB) in the range of 85% - 94%, and F1 scores in the range of 88% - 90% for all thirteen diseases on publicly available large-scale CXR NIH dataset. The proposed CAPCAM pooling also achieves better results than other state of the art (SOTA) pooling methods.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123231195","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unsupervised Anomaly Detection of Class Imbalanced Cognition Data Using an Iterative Cleaning Method 基于迭代清洗方法的类不平衡认知数据无监督异常检测

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00060

Robert K. L. Kennedy, Zahra Salekshahrezaee, T. Khoshgoftaar

引用次数: 0

State Classification in Injection Molding Cycles using Transformation of Acceleration Data into Images 基于加速数据图像转换的注塑周期状态分类

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00027

K. Pichler, J. Brunthaler, W. Lubowski, P. Grabski, Veronika Putz, S. Breitenberger, C. Kastl

引用次数: 0

Optimal latent space for Low-shot Face Recognition 低镜头人脸识别的最优潜在空间

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00029

Anvaya Rai, B. Lall, Astha Zalani, Raghawendra Prakash Singh, Shikha Srivastava

{"title":"Optimal latent space for Low-shot Face Recognition","authors":"Anvaya Rai, B. Lall, Astha Zalani, Raghawendra Prakash Singh, Shikha Srivastava","doi":"10.1109/IRI58017.2023.00029","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00029","url":null,"abstract":"The ability of humans to learn to classify objects after seeing a very few examples of them in the past, has given rise to the field of Low-shot learning. The idea is to be able to train a deep learning model to differentiate between same and different pairs and then generalise these ideas to evaluate new categories. As shapes, structure and low level visual features of human faces are similar in nature, so we can make use of extensive public face data sets to initially train a deep neural network (DNN), to learn generalised features of human face. We call this face space as Latent Feature Space. Then we demonstrate the use of probablistic interpretation of principal component analysis (PPCA) along with Extreme Learning Machine (ELM) algorithms, as an efficient technique to transform this space for representing our novel dataset classes with limited number of available samples. We avoid any kind of network re-training, while enforcing the network to learn a distance function between images rather than explicitly classifying them. The proposed algorithm couples a deep neural network (DNN) based feature representation with a low-dimensional manifold extraction to address the Low-shot classification and verification problems. We call this low-dimensional subspace as Feature Transformed Latent Space. Also, in addition to providing performance improvements in terms of accuracy, the suggested approach provides significant advantages in terms of memory, computation and speed during classification/ verification tasks, while being agnostic to occlusion, pose, expression and illumination conditions.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131407855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sequence Separation-Based Modeling of Denoised Implicit Feedback Behavior 基于序列分离的去噪隐式反馈行为建模

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00056

Shibo Ji, Bo Yang

{"title":"Sequence Separation-Based Modeling of Denoised Implicit Feedback Behavior","authors":"Shibo Ji, Bo Yang","doi":"10.1109/IRI58017.2023.00056","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00056","url":null,"abstract":"This paper analyzes Click-through rate prediction (CTR), a critical component within recommender systems aiming to forecast the personalized probability of user-item click events. Recent advancements have shown that incorporating user behavior sequences into CTR prediction models can yield significant performance improvements. However, CTR prediction models primarily rely on implicit positive feedback, such as clicks, from user-item interactions while overlooking the negative feedback, such as unclicks. Moreover, the implicit feedback obtained from users often contains noisy data, which hampers the accuracy of user interest modeling. As a solution, we propose a novel framework for estimating click-through rates, leveraging the modeling of Denoised Implicit feedback Behavior (DIB). DIB integrates the complete implicit feedback behavior of users into the click-through rate estimation task and aims to mitigate the influence of noise in implicit feedback on the model’s accuracy. Through extensive experiments conducted on real-world, largescale datasets, we demonstrate that DIB outperforms state-of-the-art models by a substantial margin, resulting in an approximate 5% improvement in Area Under the Curve (AUC).","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"387 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132257370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Completeness of Natural Language Requirements: A Comparative Study of User Stories and Feature Descriptions 自然语言需求的完备性:用户故事与特征描述的比较研究

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00017

Aurek Chattopadhyay, Ganesh Malla, Nan Niu, Tanmay Bhowmik, J. Savolainen

{"title":"Completeness of Natural Language Requirements: A Comparative Study of User Stories and Feature Descriptions","authors":"Aurek Chattopadhyay, Ganesh Malla, Nan Niu, Tanmay Bhowmik, J. Savolainen","doi":"10.1109/IRI58017.2023.00017","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00017","url":null,"abstract":"Checking the completeness of requirements is critical for software validation, as incomplete requirements can adversely affect the delivery of high-quality software within budget. Many existing methods rely on the domain model that defines the correct and relevant constructs, against which the requirements completeness is checked. However, building accurate and updated domain models requires considerable human effort, which is often challenging in practical settings. To operate in the absence of domain models, we propose to measure a textual requirement's completeness based on a universal linguistic theory, namely Fillmore's frame semantics. Our approach treats the frame elements (FEs) associated with a requirement's verb as the roles that should participate in the syntactic structure evoked by the verb. The FEs thus give rise to a linguistic measure of completeness, through which we compute a requirement's actual completeness. Using our linguistic-theoretic approach allows for a fully automatic completeness check of different real-world requirements. The comparisons show that our studied feature descriptions are more complete than user stories.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"270 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134091787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Building and Validating a Clinical Ultrasound Image Reporting Model 建立和验证临床超声图像报告模型

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00024

Meng-Che Tsai, Kuo-Chung Chu, Yi-Xian Li

{"title":"Building and Validating a Clinical Ultrasound Image Reporting Model","authors":"Meng-Che Tsai, Kuo-Chung Chu, Yi-Xian Li","doi":"10.1109/IRI58017.2023.00024","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00024","url":null,"abstract":"The main job of a radiologist is to understand the essential information hidden in medical images and write diagnostic reports, which are very helpful for subsequent clinical treatment. However, due to the difficulty of interpreting medical images, it requires long-term training. If the training time is short and the experience is insufficient, it will lead to errors in subsequent clinical diagnosis. In addition, the aging population has increased the workload for radiologists, especially with more elderly patients. Therefore, in the case of insufficient workforce and time costs, this study established an Encoder-Decoder architecture for ultrasound image report generation. The Encoder used Faster RCNN to extract lesion-related features from the image, while the Decoder used LSTM to describe the lesion features in words. This approach can effectively assist radiologists in writing diagnostic reports. Faster RCNN and LSTM have shown superior performance in computer vision and natural language processing, but their performance may not be as expected when the dataset is insufficient. Especially, the collection of medical images and reports is difficult, which may result in generated reports that cannot be used. Therefore, this study introduces the idea of prior knowledge, which integrates the lesion organs classified by Faster RCNN and the lesion image features into LSTM to improve the accuracy of describing the lesion organ names and reduce the model’s description errors of organs in small samples, thus increasing the trust of physicians in the report. Finally, in the experimental results, introducing prior knowledge of lesion organ names has a better effect, and the generated reports all contain organ names related to ultrasound images.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130948184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Teacher-Student Knowledge Distillation Framework for Enhanced Detection of Anomalous User Activity 一种用于增强异常用户活动检测的师生知识蒸馏框架

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/iri58017.2023.00011

Chan Hsu, Chan-Tung Ku, Yuwen Wang, Minchen Hsieh, Jun-Ting Wu, Yunhsiang Hsieh, PoFeng Chang, Yimin Lu, Yihuang Kang

引用次数: 0