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A Framework for Facilitating Reproducible News Sentiment Impact Analysis 2022

A Framework for Facilitating Reproducible News Sentiment Imp...

引用

5th International Conference on software engineering and Information Management, ICSIM 2022

作者： Chen, Weisi Al-Qudah, Islam Rabhi, Fethi School of Software Engineering Xiamen University of Technology China Department of Computer Science University of Sharjah United Arab Emirates School of Computer Science and Engineering University of New South Wales Australia

ISBN: (纸本)9781450395519

The proliferation of outlets for news media in recent decades has contributed to faster issuance of news data. News analysis has been one of the key activities conducted by researchers in a broad variety of research disciplines. In general, the analysis process used in these studies includes interpreting the content of the news items, and then discovering their impact in a specific area. In this paper, we delve into the field of news analysis applied to the financial domain and explore news sentiment impact analysis in the context of financial markets. Existing studies lack systematic methods to assimilate financial context and evaluate the impact of a given news dataset on relevant entities financial market performance. We introduce an improved version of the framework called News Sentiment Impact Analysis (NSIA) that encompasses models, supporting software architecture and processes for defining various financial contexts and conducting news sentiment impact analysis. The framework is then evaluated using a prototype implementation and a case study that investigates the impact of extremely negative news on the stock price of the related entities. The results demonstrate the functionality, usability and reproducibility of the framework, and its capability to bridge the gap between generating news sentiment and evaluating its impact in selected financial contexts. © 2022 ACM.

关键词： software architecture

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Examinations Timetabling System Based on A Genetic Algorithm

Examinations Timetabling System Based on A Genetic Algorithm

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2022 International Conference on engineering and MIS, ICEMIS 2022

作者： Mohammed, Mousa F Abobaker, Rema A. Aljalil, Asma M. Maatuk, Abdelsalam M Omar Al-Mukhtar University Faculty of Science Department of Computer Science Al-Bayda Libya University of Benghazi Department of Software Engineering Benghazi Libya

ISBN: (数字)9781665454360

ISBN: (纸本)9781665454360

Examination timetabling is a complicated issue that is part of the larger field of scheduling. Timetabling comes in a variety of forms based on the field, for which it is used. In this paper, we explore the problem of exam timetabling in a university. This problem had been defined as difficult to solve since it is generally done by hand over several days or weeks of iterative correction based on feedback from staff and students. We designed a computer-based application to generate an automated exam scheduler that was implemented on the data collected from the Faculty of science at Omar Al-Mukhtar University. We study the problem of examination scheduling for the faculty intensely and propose a genetic algorithm based on a strategy for generating examination schedules that prioritize students' achievement while meeting the hard constraints required for feasibility. Finally, an application entitled 'Examination Scheduler' has been developed and evaluated to confirm that it generates satisfactory results. © 2022 IEEE.

关键词： Genetic algorithms

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Usability Evaluation of Kids' Learning Apps 2

Usability Evaluation of Kids' Learning Apps

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Ibrahim, Amer Al-Rajab, Murad Hamid, Khalid Aqeel, Muhamamd Muneer, Salman Parveen, Mehvish Saleem, Maryam American University in the Emirates College of Computer and Information Technology Dubai United Arab Emirates Abu Dhabi University Computer Scienc and IT College of Engineering United Arab Emirates Superior University Department of Computer Science Lahore Pakistan Superior University Department of Software Engineering Lahore Pakistan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan NCBAE School of Computer Science Lahore Pakistan

ISBN: (纸本)9798350335644

The main objective of learning applications is to use modern electronic technologies to impart knowledge, communicate knowledge, and Direct learning models with their learning activities in a timely and effective manner. The preponderance of the apps were designed to up-skill the kids in the fundamentals of letters and numbers. Generally, they were grounded and practice skills in nature, based on a lack of abilities in critical thinking, favoring parrot-fashion skills, and contributing to a deep conceptual comprehension of more specific concepts are unable. By using appropriate evaluation methodologies, the purposed study observes essential characteristics and aspects that affect the usability of interfaces. An evaluation of the app's features will figure out to ensure user contentment. When a kid interacts with a learning interface, the ease of use provided by the interface is validated. Because a large number of the apps were designed to train kids' fundamentals of alphabetic and numeric. The focus of this research is to see whether self-proclaimed learning apps for kids are built according to developmentally appropriate standards to help kids in formal and informal learning settings develop socially, emotionally, and academically. © 2023 IEEE.

关键词： Learning systems

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Difficulty-Aware Data Augmentor for Scene Text Recognition 48

Difficulty-Aware Data Augmentor for Scene Text Recognition

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Meng, Guanghao Dai, Tao Chen, Bin Li, Naiqi Jiang, Yong Xia, Shu-Tao Tsinghua University Tsinghua Shenzhen International Graduate School China Peng Cheng Laboratory Shenzhen China Shenzhen University College of Computer Science and Software Engineering China Harbin Institute of Technology Department of Computer Science and Technology Shenzhen China

ISBN: (纸本)9781728163277

Deep neural network (DNN) based scene text recognition (STR) methods usually require a large amount of annotated data for training, which is time-consuming and cost-expensive in practice. To address this issue, many data augmentation methods have been developed to train recognizers by improving the diversity of training samples. However, most existing methods neglect the difficulty inherent in samples, and easily suffer from the problem of over-diversity, i.e., the distribution of the augmented data significantly deviates from that of clean data. In this paper, we propose a novel difficulty-aware data augmentation framework for scene text recognition, which jointly considers the difficulty of samples and the strength of augmentations. Specifically, our framework first predicts the sample difficulty, followed by an adaptive data augmentation strategy. Furthermore, we build a more diverse set of augmentation methods for STR and integrate it into our augmentation framework. Extensive experiments on scene text recognition benchmarks show that our augmentation framework significantly improves the performance of recognizers. © 2023 IEEE.

关键词： data augmentation deep neural network difficulty-aware Scene text recognition

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A Robust Methodology for Fruit Quality Prediction and Estimation Using Intelligent Learning Based Image Processing Logic

A Robust Methodology for Fruit Quality Prediction and Estima...

引用

2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Malathi, K. Babu, Vunnava Dinesh Ramkumar, M. Ayyadurai, M. Vivek, K. Aljawawdeh, Hamzeh J. Saveetha University Department of Cse Chennai India Rv Institute of Technology Department of Cse Guntur India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Yeshwantrao Chavan College of Engineering Department of Mechanical Engineering Nagpur India Department of software engineering Faculty of information technology Zarqa University Zarqa Jordan University of Business and Technology Jeddah21448 Saudi Arabia

ISBN: (纸本)9798331543617

Fruit quality prediction plays a vital role in agricultural and food industries, ensuring that only high-quality produce reaches the consumer. Traditional methods of assessing fruit quality rely on manual inspection, which is both time-consuming and prone to human error. This paper presents a robust methodology for fruit quality prediction and estimation, utilizing a hybrid deep learning model that integrates DenseN et for feature extraction with LightGBM for efficient classification. The proposed model is designed to automate the process of fruit quality assessment by analyzing key attributes such as color, texture, and shape from high-resolution fruit images. The Fruit360 dataset was used for training and testing, with images classified into categories such as fresh, ripe, overripe, and damaged. A comprehensive comparison with nine existing models, including SVM, Random Forest, and XGBoost, was conducted using performance metrics such as accuracy, precision, recall, and Fl-score. The proposed model achieved an accuracy of 96.78%, significantly outperforming all other models. Furthermore, the model's precision and recall reached 95.33% and 95.67%, respectively, demonstrating its reliability in distinguishing high-quality fruits from low-quality ones. © 2024 IEEE.

关键词： Fruits

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Image Reconstruction Improvement of Variable Coded Aperture using Deep Learning Method for Gamma and Lensless Imaging Applications

Image Reconstruction Improvement of Variable Coded Aperture ...

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2023 Conference on Lasers and Electro-Optics Europe and European Quantum Electronics Conference, CLEO/Europe-EQEC 2023

作者： Schwarz, Ariel Shemer, Amir Danan, Eliezer Cohen, Noa E. Danan, Yossef Department of Electrical and Electronics Engineering Azrieli College of Engineering Jerusalem9103501 Israel Faculty of Engineering Bar-Ilan University Ramat-Gan5290002 Israel School of Software Engineering and Computer Science Azrieli College of Engineering Jerusalem9103501 Israel

ISBN: (纸本)9798350345995

In gamma ray imaging for nuclear medicine, coded aperture is used to improve sensitivity. one of the main reconstructing methods is inverse filtering (deconvolution), where the recorded image is cross-correlated with periodic inverse filter of coded array. The reconstruction is free of coding noise for arbitrary array. However, amplification of quantum noise affect the reconstructed image. Although it is improved by Wiener filtering, the major problem is small terms in the spectral distribution of coded masks. Statistically and experimentally pinhole arrays have at least one term which is zero, resulting unacceptably noisy reconstruction. In our previous research we presented new approach of variable coded aperture (VCA) design for far and near field imaging applications [1-4]. The imaging system is based on time multiplexing method using variable multi pinhole array. The unique variable design enables to overcome the spatial frequencies cutoff and small terms in the Fourier transform exists in static multi pinhole array. The overall pinholes positions are designed to avoid spatial frequencies loss. However, traces of duplications can still be detected in reconstruction using Wiener filtering. Furthermore, since coded aperture blocks most of the photons that enter the detector, the dynamic range of the image is limited, thus leading to low contrast image and inadequate colors gamut. © 2023 IEEE.

关键词： Quantum noise

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Evaluating Accuracy and Efficiency of Fruit Image Generation Using Generative AI Diffusion Models for Agricultural Robotics

Evaluating Accuracy and Efficiency of Fruit Image Generation...

引用

International Conference on Image and Vision Computing New Zealand, IVCNZ

作者： Kun Zhao Minh Nguyen Weiqi Yan Department of Computer Science & Software Engineering Auckland University of Technology Auckland New Zealand

ISBN: (数字)9798331518776

ISBN: (纸本)9798331518783

The automation of fruit picking using robotic systems is rapidly advancing within precision agriculture, relying heavily on the accurate detection and segmentation of fruit objects through computer vision. This study explores the use of generative artificial intelligence (AI) to create extensive fruit image datasets, concentrating on cherries, bananas, oranges, apples, and pineapples. Five generative models—Stable Diffusion v1.4, Stable Diffusion v1.5, Stable Diffusion v2, Stable Diffusion XL Refiner 1.0, and SDXL Turbo—were employed to generate 1000 images per fruit type, each set against a white background. Thresholding techniques were used to automatically extract fruit boundaries, which were then utilized to train segmentation models. The performance of these models was evaluated based on their accuracy and processing speed. SDXL Turbo consistently delivered the highest accuracy across all fruit types, though it required more processing time per image. Stable Diffusion XL Refiner 1.0 also exhibited strong accuracy but balanced performance differently. In contrast, Stable Diffusion v2 showed significant shortcomings, particularly in producing accurate images for cherries and oranges. This comparative analysis highlights the potential of advanced generative models in enhancing synthetic dataset creation for fruit object detection and segmentation in agricultural robotics. Future research will focus on refining these models to improve accuracy, broaden their applicability to a wider variety of fruits and environments, and optimize the trade-off between image quality and generation speed to meet the practical needs of agricultural automation.

关键词： Precision agriculture Image quality Image segmentation Accuracy Automation Generative AI Image synthesis Refining Object detection Synthetic data

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Comparative Analysis of Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Transformer Models in Predicting Stock Prices

Comparative Analysis of Long Short-Term Memory (LSTM), Gated...

引用

IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Chinecherem Umezuruike Deborah Olaniyan Julius Olaniyan Abidemi Emmanuel Adeniyi Adedoyin Oyebade David Abaneme Department of Software Engineering Bowen University Iwo Department of Computer Science Bowen University Iwo Department of Computer Science University of Ilorin Ilorin Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

This research explores the application of LSTM, GRU and Transformer models for predicting stock prices, aiming to enhance accuracy in financial forecasting. Stock price prediction is crucial for investment decision-making, yet challenging due to market volatility and complex patterns. The objectives are to evaluate the performance of LSTM, GRU and Transformer models using key metrics such as test loss, MAE, and MSE, and to compare their predictive capabilities. The LSTM model demonstrates robust performance with low test loss and MAE, indicating precise predictions and effective pattern recognition in financial data. In contrast, the Transformer model also shows promising results with relatively low test loss and MAE, albeit with larger errors in MSE and MAE metrics. Both models highlight the potential for accurate stock price prediction, suggesting avenues for future research to optimize model performance and reliability in financial forecasting applications. The experimental results show that GRU Outperformed LSTM and Transformer with an MSE of 0.0008, MAE of 0.0023, and high test accuracy of 0.9833.

关键词： Measurement Analytical models Accuracy Refining Predictive models Transformers Pattern recognition Reliability Forecasting Long short term memory

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TBSR: Tile-Based 360° Video Streaming with Super-Resolution on Commodity Mobile Devices

TBSR: Tile-Based 360° Video Streaming with Super-Resolution...

引用

IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Lei Zhang Haobin Zhou Haiyang Wang Laizhong Cui College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer Science University of Minnesota Duluth USA

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Streaming 360° videos demands excessive bandwidth. Tile-based streaming and super-resolution are two widely studied approaches to alleviate bandwidth shortage and enhance user experience in such real-time video streaming systems. The former prioritizes the transmission of a fraction of the 360° video according to the user viewport, while the latter enhances the streamed video in higher resolutions through computations. However, these two approaches bring substantial complexity and computation overhead and thus suffer from resource bottlenecks due to the constrained mobile hardware. This paper proposes TBSR, a practical mobile 360° video streaming system that incorporates in-time super-resolution with tile-based streaming on commodity mobile devices. We present the designs of three key mechanisms, including a rate adaptation method with macro tile grouping to reduce decoding computations, a decoding and SR scheduler for different types of tasks to achieve the best cost efficiency, and the workload adjustment method to control the amount of tasks given the available capabilities. We further implement the TBSR prototype. Our performance evaluation shows that TBSR outperforms the existing methods, improving QoE quality by up to 32% and bandwidth savings by 26%.

关键词： Costs Superresolution Prototypes Bandwidth Streaming media Mobile handsets Decoding

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A Mathematical Model for the Selection of software Requirements Elicitation Techniques 6th

A Mathematical Model for the Selection of Software Requireme...

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6th International Conference on Information Systems and Management science, ISMS 2023

作者： Saxena, Virat Raj Yadav, Rajeev Parveen, Azra Sadiq, Mohd. Department of Computer Science and Engineering Mangalayatan University Uttar Pradesh Aligarh202001 India Indraprastha Research Laboratory Indraprastha Institute of Information Sciences Private Limited New Delhi110025 India Software Engineering Laboratory Computer Engineering Section UPFET Jamia Millia Islamia A Central University New Delhi110025 India

ISBN: (纸本)9783031664090

Various techniques have been developed for the identification of different types of requirements like interview, questionnaire, group elicitation techniques, attributed goal-oriented requirements analysis, fuzzy based goal-oriented requirements analysis method, non-functional requirements framework, etc. Based on the critical analysis of requirements elicitation methods, we found that single elicitation technique is not suitable to understand the need of the stakeholders. In real life applications, multiple elicitation techniques are required to elicit and select the requirements of an information system because each technique has its strength and limitations. Therefore, to address this issue, this paper presents a mathematical model for the selection of requirements elicitation techniques so that complete set of requirements along with its priority can be identified before the development of an information system. The institute examination system is considered to show the applicability of the proposed mathematical model. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： software testing

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