检索结果-内蒙古大学图书馆

8th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2024

作者： Hussain, Shahid Wang, Wen-Li Khan, Saif Ur Rahman Rahim, Muhammad Abdul Basit Ur Ibrahim, Naseem Zhao, Jie Department of Computer Science and Software Engineering Penn State University Erie United States Department of Software Engineering Shifa Tameer-e-Millat University Islamabad Pakistan Department of Computer Science California State University Long Beach United States

ISBN: (纸本)9789819757985

The long-term participation of trained and competent employees is required to improve the morale, productivity, safety, value, and autonomy of an organization. In small and medium-sized software industries, the long-term involvement of developers will not only boost the efficiency of software development but also help train their team members. Although software industries have managed to stimulate developers’ productivity and reduce their turnover with a better compensation plan, the incentive through money has caused contradictory arguments. Hence, there is a need to employ a more systematic way to explore, analyze, and calibrate the developer’s needs, so that the managerial roles can make informed decisions and incorporate influential motivators to increase a developer’s job satisfaction level. In this respect, we are conducting an empirical study using the basics of Maslow's theory, a motivational theory in psychology, to (1) explore the developer’s motivation level in software industries, (2) benchmark the developer’s needs for job satisfaction at each level, and (3) analyze the job-hunting factors for developers’ turnover. We conducted a questionnaire-based study to gather feedback from 128 developers in 10 software industries with four types of analyses being conducted. The results indicate that satisfaction levels can vary across software industries, but the management can anticipate developers’ high-level needs from their low-level needs to determine influencing and motivating factors for job satisfaction improvements. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Job satisfaction

来源：评论

学校读者我要写书评

暂无评论

Test-Driven Development (TDD) in Small software Development Teams: Advantages and Challenges 5

Test-Driven Development (TDD) in Small Software Development ...

引用

5th International Conference on Advancements in Computational sciences, ICACS 2024

作者： Ramzan, Hafiz Arslan Ramzan, Sadia Kalsum, Tehmina Institute of Computer and Software Engineering Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan Pakistan National University of Modern Languages Department of Computer Science Multan Pakistan University of Engineering and Technology Department of Software Engineering Taxila Pakistan

ISBN: (纸本)9798350394764

In the context of small software development teams, this research article gives a thorough investigation of the adoption of test-driven development (TDD) approaches. It aims to highlight the benefits that TDD offers, such as improved code quality through modularization and proactive defect spotting which results in effective debugging and development processes. It also discusses the complex issues that arise when TDD is implemented in smaller teams, such as the learning curve and resource constraints. This study significantly advances the understanding of how TDD can be used to optimize software development techniques in organizations or software houses having small development teams. It also explores the possible advantages and challenges. © 2024 IEEE.

关键词： Modular construction

来源：评论

学校读者我要写书评

暂无评论

Evaluating Transparency: A Cross-Model Exploration of Explainable AI in Financial Forecasting

Evaluating Transparency: A Cross-Model Exploration of Explai...

引用

2023 IEEE Asia-Pacific Conference on computer science and Data engineering, CSDE 2023

作者： Valensky, Dylan Mohaghegh, Mahsa Auckland University of Technology Computer Science and Software Engineering Auckland New Zealand

ISBN: (纸本)9798350341072

Advances in machine learning and deep learning have assisted progress in stock market predictions, which have presented unique opportunities for investors and traders to benefit from predictions. However, understanding how these models work is crucial to trusting their predictions. This research applies three prominent Explainable AI (XAI) techniques - SHAP, LIME, and Permutation Importance - to three distinct forecasting models: Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Decision Tree. This work fills a gap in the literature by comparing XAI methods across a combination of 'black-box' and 'white-box' models. Two key research questions guide this research: the performance of these XAI methods across the chosen models and the consistency between global and local explanations across these models. Findings reveal interesting model-specific behaviors, like CNN's emphasis on slightly older data and LSTM's focus on immediate past data. In the S&P 500 dataset context, features such as 'Close' and 'Adj Close' prices emerged as significant across models, while 'Volume' remained insignificant. This study offers a broader perspective on applying XAI in financial time series forecasting and helps enhance trust and comprehension among stakeholders relying on these model predictions. © 2023 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Graph Enhanced Transformer for Aspect Category Detection

引用

Journal of computer science & Technology 2023年第3期38卷 612-625页

作者：陈晨王厚峰朱晴晴柳军飞 Office of the Cyberspace Affairs Commission Peking UniversityBeijing 100871China School of Computer Science Peking UniversityBeijing 100871China School of Software and Microelectronics Peking UniversityBeijing 100871China National Engineering Research Center for Software Engineering Peking UniversityBeijing 100871China

Aspect category detection is one challenging subtask of aspect based sentiment analysis, which categorizes a review sentence into a set of predefined aspect categories. Most existing methods regard the aspect category detection as a flat classification problem. However, aspect categories are inter-related, and they are usually organized with a hierarchical tree structure. To leverage the structure information, this paper proposes a hierarchical multi-label classification model to detect aspect categories and uses a graph enhanced transformer network to integrate label dependency information into prediction features. Experiments have been conducted on four widely-used benchmark datasets, showing that the proposed model outperforms all strong baselines.

关键词： aspect based sentiment analysis aspect category detection hierarchical multi-label classification transformer network

来源：评论

学校读者我要写书评

暂无评论

Identifying Athletics Tracks using Keypoint Detection 38

Identifying Athletics Tracks using Keypoint Detection

引用

38th International Conference on Image and Vision Computing New Zealand, IVCNZ 2023

作者： Harcombe, Gareth Green, Richard Neshatian, Kourosh University of Canterbury Department of Computer Science and Software Engineering Christchurch New Zealand

ISBN: (纸本)9798350370515

This paper proposes the first known approaches to locating an athletics track from GPS data of a running workout. This is done by mapping the GPS points onto an image and applying computer Vision methods, including: performing keypoint detection using a Keypoint Region-Based Convolutional Neural Network (R-CNN) model with a Residual Neural Network backbone;and applying Convolutional Kernels over the image. The Keypoint R-CNN model achieved a Root Mean Square Error (RMSE) of 1.29 on synthetic data, which is lower than the error of other keypoint detection models of 2.23, but performed poorly on real data. The Convolutional Method achieved an RMSE of 4.20. The methods and results of this paper can be used in future applications for error correction and feature extraction of GPS data for running workout analysis. © 2023 IEEE.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

Analyzing the Impact of Preprocessing Techniques on the Efficiency and Accuracy of Sentiment Classification Algorithms 15

Analyzing the Impact of Preprocessing Techniques on the Effi...

引用

15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Nikhila Kanigiri, Sai Mekuriyaw, Chachi Goodman, Garrett Alexiou, Michail S. Kennesaw State University Department of Computer Science MariettaGA United States Miami University Computer Science and Software Engineering OxfordOH United States

ISBN: (纸本)9798350368833

In the era of data-driven decision-making, the ability to rapidly and accurately analyze vast amounts of textual data is essential. This paper examines the role of text pre-processing techniques in enhancing the effectiveness of sentiment classification algorithms. Such algorithms are pivotal in interpreting the emotional tone of texts and serving as a preliminary step towards reverse-engineering complex data systems. Through comprehensive analysis, we assess the impact of various preprocessing strategies such as keyword removal, lemmatization, and stemming and their combination on the performance of both traditional and advanced machine learning models, including Random Forest, Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Bidirectional Encoder Representations from Transformers (BERT). The current study evaluates the balance between efficiency and accuracy in sentiment analysis algorithms, illustrating the impact of preprocessing techniques. In addition, it aims to serve as a guideline for selecting preprocessing methods that optimize model performance in both sentiment analysis and textual data reverse-engineering. © 2024 IEEE.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

On Site Personnel Access Monitoring and Early Warning in Photovoltaic Field Based on AI Image Recognition 16

On Site Personnel Access Monitoring and Early Warning in Pho...

引用

16th IEEE PES Asia-Pacific Power and Energy engineering Conference, APPEEC 2024

作者： Zheng, Wenjin Wang, Yajun Xu, Xiaochen Chu, Hao Jin, Danyang Fang, Shibiao Powerchina Huadong Engineering Corporation Limited Hangzhou China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9798350386127

The study aims to enhance the management and monitoring of photovoltaic (PV) construction sites, which are often located in remote areas with challenging conditions. Traditional monitoring methods are inefficient, costly, and prone to errors, necessitating the integration of advanced image recognition technology to improve site management. The research introduces image recognition technology, a core aspect of artificial intelligence (AI) and computer vision (CV), to automate the detection and monitoring of equipment, personnel behavior, materials, and construction progress at PV sites. The process involves real-time data collection via high-definition cameras, automated anomaly detection, reporting, and historical data analysis using advanced deep learning models like YOLO. The study developed an efficient and adaptive image recognition model tailored to the complexities of PV construction sites. The model successfully improves the accuracy of detecting safety hazards, optimizes resource utilization, and enhances the overall safety and efficiency of the construction process. The findings highlight the potential of image recognition technology to significantly improve the management of PV construction sites. As the technology advances and becomes more cost-effective, its application is expected to drive higher levels of efficiency, reliability, and safety in the renewable energy sector, supporting the industry's sustainable growth. © 2024 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Binary Decomposition for Multi-Class Classification Problems: Development and Applications

Binary Decomposition for Multi-Class Classification Problems...

引用

2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Li, Peiwei Liu, Han College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9798350303780

Binary decomposition of a multi-class classification problem is a widely used method in the field of machine learning, which involves using an ensemble of binary classifiers to undertake multi-class classification tasks. The motivation of binary decomposition lies in two main aspects. Firstly, some learning algorithms such as support vector machine can not achieve directly learning a multi-class classifier. Secondly, in some cases, the adoption of binary decomposition for training an ensemble of binary classifiers can even achieve better performance than the direct learning of a multi-class classifier. There are some existing methods that can be applied in achieving binary decomposition and have been proven to be effective. Some of these methods may also be applied in other tasks beyond classification. This paper presents a comprehensive review of the development and applications of binary decomposition methods. Specifically, these methods are put into two categories, namely, ordered and unordered decomposition, which each is introduced in terms of the essence and strategies of decomposition. These methods are also compared through theoretical analysis of their effectiveness and efficiency. On the basis of the comparative analysis, we suggest some future directions towards achieving further advances in binary decomposition. © 2023 IEEE.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

A Model Driven Framework Enabling the Development of Standardized Text-Free User Interface for Smartphones 21

A Model Driven Framework Enabling the Development of Standar...

引用

21st International Bhurban Conference on Applied sciences and Technology, IBCAST 2024

作者： Rasheed, Uzair Khalid, Sadia Mehmood, Rabia Ahmad, Muhammad Muneer, Mishal College of Electrical and Mechanical Engineering NUST Department of Computer and Software Engineering Islamabad Pakistan Lahore College for Women University Department of Computer Science Lahore Pakistan International Univesity of Rostock Department of Computer science Germany

ISBN: (纸本)9798331516680

Smartphones offer a wide range of applications globally, and usability is a key factor in their quality. Human-computer Interaction (HCI) continuously evolves to enhance usability by improving user interface (UI) components. However, most UI trends are designed for literate users, neglecting a significant portion of the population who cannot read or write. These users often rely on others for assistance with smartphone applications, and text-based UIs hinder their learning and usage, reducing overall usability. This research addresses the limitations of text-based standards by introducing a standardized framework for developing text-free user interfaces for smartphones. The framework utilizes static and dynamic images instead of text to convey widget or button functions, aiming to enhance usability for illiterate users, including preschoolers. Developed with Obeo Designer and Sirius, the framework's Platform Independent Metamodel (PIM) was validated through case studies representing the target user groups. Future work involves Model to Text (M2T) transformation, and results show the framework's potential for automating applications with text-free UIs. © 2024 IEEE.

关键词： Smartphones

来源：评论

学校读者我要写书评

暂无评论

Determining the Critical Factors of Antenatal Care Follow-Up Using Two-Phase Machine Learning 27

Determining the Critical Factors of Antenatal Care Follow-Up...

引用

27th International Conference on Soft Computing and Measurements, SCM 2024

作者： Bessmertny, Igor A. Enkomaryam, Bidru C. Mamuye, Adane L. Itmo University Faculty of Software Engineering and Computer Systems Saint Petersburg Russia University of Gondar Department of Computer Science Gondar Ethiopia

ISBN: (纸本)9798350363708

Determining the critical factors affecting antenatal visits will greatly contribute to reducing maternal and infant mortality. This study, thus attempted to construct a cluster-based predictive model to determine the critical factors of antenatal care follow-up among women in Ethiopia using two-step machine learning techniques. The 5,990 preprocessed obstetric dataset with 13 selected attributes were used that was collected from the Dabat Research Center at the University of Gondar, Ethiopia. To solve the study problem, six cluster models were developed using K-means, Expectation Maximization and Make Density clustering algorithms, which are well-suited for analyzing health datasets, providing scalability, flexibility, robustness to noise, and the ability to handle uneven cluster sizes. K-means out-formers Density based and Expectation Maximization clustering algorithms with minimum number of sum of square errors (17094) and number of iteration (3). Then, output of the K-means cluster model was used as input to segment the dataset and build a total of 21 predictive models using the tree-based J48 classifier, PART and JRip rule generators while adjusting their parameters. The model for predicting antenatal care follow-up is constructed by induction of the truncated PART rule (accuracy of 99.93%) and extracts attributes of interest such as antenatal care attendance, antenatal care location, antenatal care frequency, current vaccination, woman's age, and HIV testing to predict antenatal care follow-up in the study area. © 2024 IEEE.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：