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

On the Way to a Framework for Evaluating Creativity in Requirements Engineering 在需求工程中评估创造性的框架之路

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00039

Tanmay Bhowmik, A. Q. Do, Hayden Lam, Md Rayhan Amin, Gary L. Bradshaw, Nan Niu

{"title":"On the Way to a Framework for Evaluating Creativity in Requirements Engineering","authors":"Tanmay Bhowmik, A. Q. Do, Hayden Lam, Md Rayhan Amin, Gary L. Bradshaw, Nan Niu","doi":"10.1109/IRI58017.2023.00039","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00039","url":null,"abstract":"Creativity in requirements engineering (RE) has recently emerged to help innovate “novel and useful” requirements and improve a software system’s sustainability. Existing research has mostly focused on workshops, techniques, and tools to aid creative requirements elicitation. Limited attention, however, has been dedicated to creativity evaluation, despite the current mechanisms being largely restricted to rating requirements for a broad notion of “novelty and appropriateness”. In addition, such mechanisms focus on evaluating creativity from an elicitation perspective, leaving other RE activities widely disregarded. To further advance the literature, we present a preliminary study on developing a framework that aims to evaluate creativity in a precise manner and accounts for the full spectrum of RE activities. In particular, we propose a “creative requirement diagnosis scale (CRDS)” that includes 27 indicators to assess creativity, present a novel framework to evaluate the creative merits of requirements in terms of the complete RE process, and further evaluate requirements using our framework in a study with 53 participants. The results suggest our framework’s potential to capture creativity aspects that would otherwise be undetected by traditional techniques. Our study also indicates the need for further refinement of the framework, thereby opening new avenues for creativity in RE.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"46 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":"127080038","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

Enhancing Noisy Binary Search Efficiency through Deep Reinforcement Learning 通过深度强化学习提高噪声二值搜索效率

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00033

Rui Ma, Yudong Tao, Mohamed M. Khodeiry, Karam A. Alawa, M. Shyu, Richard K. Lee

{"title":"Enhancing Noisy Binary Search Efficiency through Deep Reinforcement Learning","authors":"Rui Ma, Yudong Tao, Mohamed M. Khodeiry, Karam A. Alawa, M. Shyu, Richard K. Lee","doi":"10.1109/IRI58017.2023.00033","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00033","url":null,"abstract":"Noisy binary search (NBS) aims to find the closest element to a target value within a sorted array through erroneous queries. In an ideal NBS environment where the error rate remains constant, and the costs of all queries are the same, the maximum likelihood estimation (MLE) procedure has been proven to be the optimal decision strategy. However, in some non-ideal NBS problems, both the error rates and the costs are dependent on the queries, and in some cases, finding the optimal decision strategies can be intractable. We propose to use deep reinforcement learning to approximate the optimal decision strategy in the NBS problem, in which an intelligent agent is used to interact with the NBS environment. A dueling double deep Q-network guides the agent to take action at each step, either to generate a query or to stop the search and predict the target value. An optimized policy will be derived by training the network in the NBS environment until convergence. By evaluating our proposed algorithm on a non-ideal NBS environment, visual field test, we show that the performance of our proposed algorithm surpasses baseline visual field testing algorithms by a large margin.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"18 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":"115249823","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

Speech-to-speech Low-resource Translation 语音到语音低资源翻译

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00023

Hsiao-Chuan Liu, Min-Yuh Day, Chih-Chien Wang

{"title":"Speech-to-speech Low-resource Translation","authors":"Hsiao-Chuan Liu, Min-Yuh Day, Chih-Chien Wang","doi":"10.1109/IRI58017.2023.00023","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00023","url":null,"abstract":"Speech-to-speech translation (S2ST), particularly in the context of low-resource languages, plays a vital role in facilitating global communication. However, comprehensive research in this emerging field is lacking, especially concerning translation without the use of text. The objective of this study is to bridge the gap by conducting a systematic review of existing literature on S2ST for low-resource languages. We discovered 455 articles by searching the Scopus, IEEE Xplore, and ACM Digital Library databases, focusing on identifying research trends. The results highlight significant topics covered in the literature, marking a transition from traditional neural network methodologies to advanced transformer-based models. Our findings provide a robust overview of the S2ST landscape, identifying challenges and potential solutions for future research, particularly regarding the application of this technology in low-resource settings. The research contribution of this study is the insights gleaned will benefit academics and professionals seeking a comprehensive understanding of S2ST for low-resource languages.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"59 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":"129100613","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 Fully Connected Reproducible SE-UResNet for Multiorgan Chest Radiographs Segmentation 用于多器官胸片分割的全连接可复制SE-UResNet

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00052

Debojyoti Pal, Tanushree Meena, S. Roy

{"title":"A Fully Connected Reproducible SE-UResNet for Multiorgan Chest Radiographs Segmentation","authors":"Debojyoti Pal, Tanushree Meena, S. Roy","doi":"10.1109/IRI58017.2023.00052","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00052","url":null,"abstract":"Deep learning (DL) models are a popular choice for resolving intricate issues in medical imaging, such as the classification of diseases, detection of anomalies, and segmentation of tissues in real-world scenarios. To be useful in these contexts, the models must be able to provide accurate results for new, previously untrained data. Existing methods not only fail to consider the intrinsic features of small target lesions but are also not evaluated on separate datasets. To solve these problems we propose a novel architecture, SE-UResNet, capable of segmenting multiple organs having different size and shapes from Chest X-Ray (CXR) images. The proposed architecture introduces a residual module in between the encoding and decoding modules of an attention U-Net architecture for better feature representation of high-level features. The architecture also replaces the attention gates in the decoder module of attention U-Net with Squeeze and Excite (S&E) modules. SE-UResNet is experimented on benchmark CXR datasets such as NIH CXR for lungs, heart, trachea and collarbone segmentation as well as VinDr-RibCXR for ribs segmentation tasks with respect to other state-of-the-art segmentation models. The proposed model achieves an average DSC of 95.9%, 76.8%, 78.7%, 78.8%, and 86.0% for lungs, trachea, heart, collarbone and ribs segmentation for the aforementioned datasets. Furthermore, the proposed model has only been tested on two benchmark CXR datasets: Shenzen and JSRT to establish the reproducibility and robustness of the model. The performance of SE-UResNet on several benchmark CXR datasets demonstrates the model’s ability to generalize, making it a reliable baseline for medical image segmentation. Furthermore, it can also be used for assessing the reproducibility of DL models based on their performance on different datasets.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"3 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":"131534528","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 Deep Learning Based Method for Vitreous Hemorrhage Recognition in Fundoscopic Images 基于深度学习的眼底镜图像玻璃体出血识别方法

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00047

Xiaoliang Wang, Yongjin Lu, Wei-bang Chen, Dominic Baker

引用次数: 0

A Novel Smartphone-Based Human Activity Recognition Approach using Convolutional Autoencoder Long Short-Term Memory Network 基于卷积自编码器长短期记忆网络的智能手机人类活动识别方法

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00032

Dipanwita Thakur, Sandipan Roy, S. Biswas, Edmond S. L. Ho, Samiran Chattopadhyay, Sachin Shetty

{"title":"A Novel Smartphone-Based Human Activity Recognition Approach using Convolutional Autoencoder Long Short-Term Memory Network","authors":"Dipanwita Thakur, Sandipan Roy, S. Biswas, Edmond S. L. Ho, Samiran Chattopadhyay, Sachin Shetty","doi":"10.1109/IRI58017.2023.00032","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00032","url":null,"abstract":"In smart and intelligent health care, smartphone sensor-based automatic recognition of human activities has evolved as an emerging field of research. In many application domains, deep learning (DL) strategies are more effective than conventional machine learning (ML) models, and human activity recognition (HAR) is no exception. In this paper, we propose a novel framework (CAEL-HAR), that combines CNN, Autoencoder and LSTM architectures for efficient smartphone-based HAR operation. There is a natural synergy between the modeling abilities of LSTMs, autoencoders, and CNNs. While AEs are used for dimensionality reduction and CNNs are the best at automating feature extraction, LSTMs excel at modeling time series. Taking advantage of their complementarity, the proposed methodology combines CNNs, AEs, and LSTMs into a single architecture. We evaluated the proposed architecture using the UCI, WISDM public benchmark datasets. The simulation and experimental results certify the merits of the proposed method and indicate that it outperforms computing time, F1-score, precision, accuracy, and recall in comparison to the current state-of-the-art methods.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"35 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":"124670516","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

The Method of Making the Low-dimensional Map that Preserves the Distance Relationships from Selected Data Point 保持与选定数据点的距离关系的低维地图制作方法

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00035

Koki Yoshioka, Gen Niina, H. Dozono

{"title":"The Method of Making the Low-dimensional Map that Preserves the Distance Relationships from Selected Data Point","authors":"Koki Yoshioka, Gen Niina, H. Dozono","doi":"10.1109/IRI58017.2023.00035","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00035","url":null,"abstract":"In recent years, data analyses have been conducted in various fields. If the data distribution is unknown before analysis, it is often necessary to determine it. Based on the obtained distribution, hypotheses are set up and the analysis is conducted according to the task. In general, data are high-dimensional with many elements. Therefore, dimensionality reduction is performed to determine the data distribution. Dimensionality reduction maps high-dimensional data onto a low-dimensional space of two or three dimensions while preserving the data features. This allows humans to easily grasp the data distribution. However, in a low-dimensional space, the number of dimensions that can be used for expression is reduced; thus, there will inevitably be gaps in the distance relationships between data in high-dimensional and low-dimensional spaces. As a result, the gaps lead to misinterpretation of the data distribution and analyses, based on incorrect hypotheses. To solve this problem, one possible method is to select a data point with a large gap in the distance relationships as a candidate and check the low-dimensional map that preserves the distance relationships from the candidate data point to the other data points, while preserving the distance relationships between noncandidate data points as much as possible. In this paper, we propose a method that creates a low-dimensional map in which the distance relationships from one selected data point to the other data points are preserved. As a result of the experiment, we confirmed that the proposed method preserves the distance relationships from the candidate data point to the other data points, while preserving the distance relationships between noncandidate data points as much as possible.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"144 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":"127296756","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

Fine Tuning Vision Transformer Model for Facial Emotion Recognition: Performance Analysis for Human-Machine Teaming 面向面部情感识别的微调视觉变压器模型:人机协作的性能分析

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00030

Sanjeev Roka, D. Rawat

引用次数: 0

An Approach to Testing Banking Software Using Metamorphic Relations 一种利用变质关系测试银行软件的方法

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00036

Karishma Rahman, C. Izurieta

{"title":"An Approach to Testing Banking Software Using Metamorphic Relations","authors":"Karishma Rahman, C. Izurieta","doi":"10.1109/IRI58017.2023.00036","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00036","url":null,"abstract":"Software systems used for banking are crucial for daily operations and are considered to be part of critical infrastructure; however, testing the functions of these highly reusable systems can be difficult due to the project’s complexity and the absence of a reliable oracle. In software testing, the Oracle problem directs to the difficulty of deciding whether the software’s observed behavior is correct. To address this issue, we suggest utilizing metamorphic testing (MT), which tests the banking system’s functionalities based on their properties. Metamorphic testing is a software testing technique where multiple inputs are generated for a program, then those inputs are transformed based on a pre-defined set of rules. The resulting outputs are then compared to the original outputs to verify that the program works correctly. Metamorphic relations (MRs) are a fundamental concept in metamorphic testing. They define the relationships between the input and output of a system under test and specify how they should change in response to input transformations. Through a case study, we introduce new metamorphic relations to test banking functions and demonstrate the effectiveness of using these MRs. The study results indicate that this is a feasible and efficient approach using an alternative to a test oracle when testing complex E-type (i.e., real-world) software.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"358 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":"116132960","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

Real-time web-based International Flight Tickets Recommendation System via Apache Spark 基于Apache Spark的实时网络国际机票推荐系统

2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI) Pub Date : 2023-08-01 DOI: 10.1109/IRI58017.2023.00055

Malek Malkawi, R. Alhajj

{"title":"Real-time web-based International Flight Tickets Recommendation System via Apache Spark","authors":"Malek Malkawi, R. Alhajj","doi":"10.1109/IRI58017.2023.00055","DOIUrl":"https://doi.org/10.1109/IRI58017.2023.00055","url":null,"abstract":"Traveling by airplane has become more popular with advanced technology. The tickets can be booked effortlessly via airlines corporation’s online platforms. However, recommending the best airline ticket according to the buyer’s demands is a challenging task owing to the unexpected fluctuations in the price depending on various reasons. Traditional recommender suggestions are optimized for predicting the price for a specific time or estimating the period of the lowest price. However, considering the sudden changes is an essential matter to increase the accuracy. In this work, we present a web-based real-time system to recommend the most suitable ticket regardless of the continuous changes in the prices. Apache Spark has been used to analyze the data obtained from the international airline web pages. Besides the ease of use of the system, it helps the customer to buy the flight ticket at the lowest price for the desired period and destination. Based on the proposed model, using Python programming language, Flask web server, and Apache Spark, we design and implement the international ticket recommendation system with the MVC design pattern.","PeriodicalId":290818,"journal":{"name":"2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science (IRI)","volume":"34 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":"128101523","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