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Experimental Design of Extractive Question-Answering Systems: Influence of Error Scores and Answer Length

Journal of Artificial Intelligence Research 2024年 80卷 87-125页

作者： Farea, Amer Emmert-Streib, Frank Predictive Society and Data Analytics Lab Faculty of Information Technology and Communication Sciences Tampere University Tampere33100 Finland Faculty of Engineering and Information Technology Taiz University Taiz Yemen

Question-answering (QA) systems are becoming more and more important because they enable human-computer communication in a natural language. In recent years, significant progress has been made with transformer-based models that leverage deep learning in combination with large amounts of text data. However, a significant challenge with QA systems lies in their complexity rooted in the ambiguity and flexibility of a natural language. This makes even their evaluation a formidable task. For this reason, in this study, we focus on the evaluation of extractive question-answering (EQA) systems by conducting a large-scale analysis of distilBERT using benchmark data provided by the Stanford Question Answering dataset (SQuAD). Specifically, the main objectives of this paper are fourfold. First, we study the influence of the answer length on the performance and we demonstrate that there is an inverse correlation between both. Second, we study differences in exact match (EM) measures because there are different definitions commonly used in the literature. As a result, we find that despite the fact that all of those measures are named"exact match" these measures are actually different from each other. Third, we study the practical relevance of these different definitions because due to the ambivalent meaning of"exact match" in the literature, it is often unclear if reported improvements are genuine or only due to a change in the exact match measure. Importantly, our results show that differences between differently defined EM measures are in the same order of magnitude as reported differences found in the literature. This raises concerns about the robustness of reported results. Fourth, we provide guidelines to improve the experimental design of general EQA studies, aiming to enhance performance evaluation and minimize the potential for spurious results. ©2024 The Authors. Published by AI Access Foundation under Creative Commons Attribution License CC BY 4.0.

关键词： Large datasets

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LSTM based Soft-Sensor for Estimating Nitrate Concentration in Aquaponics pond 3

LSTM based Soft-Sensor for Estimating Nitrate Concentration ...

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3rd International Conference for Innovation in Technology, INOCON 2024

作者： Dharshan, A. Kumar, Purushottam Ravimaran, S. Srinivasulu Reddy, U. Saranathan College of Engineering Department of Artificial Intelligence and Data Science Trichy India National Institute of Technology Artificial Intelligence Machine Learning & Data Analytics Lab Trichy India National Institute of Technology Department of Computer Applications Machine Learning & Data Analytics Lab Trichy India

ISBN: (纸本)9798350381931

In the field of aquaponics, where fish and plants coexist in a symbiotic environment, closely monitoring nitrate levels in the water is crucial due to their profound impact on aquatic and plant well-being. Traditional nitrate measurement methods are often time-consuming and costly. Various approaches, including first principles, IoT-based sensors, and machine learning-based soft sensors, have been attempted to address this challenge. However, these efforts face challenges such as expensive sensors, infrequent data collection, multistage data processing using limited sensor types, and the need for regular maintenance like cleaning and calibration. Additionally, varied environmental conditions affect sensor suitability for different water environments, and even some machine learning-based soft sensors have proven inaccurate. In response, soft sensors, especially deep learning-based ones, have gained prominence in industrial applications for their adaptability and accuracy. These sensors provide real-time insights into complex processes without requiring expensive hardware. In this study, an innovative solution was introduced using Long Short-Term Memory (LSTM) technology, a neural network architecture in deep learning known for capturing complex temporal patterns. LSTM is well-suited for modeling and predicting nitrate concentration changes in aquaponics, trained with extensive data collected from various aquaponic ponds. Through rigorous evaluation, a remarkable MSE value of 0.00074 and an impressive R-squared score of 0.98 were achieved, holding potential for scaling up to commercial applications, benefiting aquaponics operations, supporting researchers, and enhancing sustainability and productivity in aquaponic systems. © 2024 IEEE.

关键词： Nitrates

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A Survey and Experimental Review on data Distribution Strategies for Parallel Spatial Clustering Algorithms

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Journal of Computer Science & Technology 2024年第3期39卷 610-636页

作者： Jagat Sesh Challa Navneet Goyal Amogh Sharma Nikhil Sreekumar Sundar Balasubramaniam Poonam Goyal Advanced Data Analytics and Parallel Technologies Laboratory Birla Institute of Technology and Science Pilani 333031India Uber New York 11101U.S.A. Computer Science and Engineering Department University of MinnesotaMinneapolis 55455U.S.A.

The advent of Big data has led to the rapid growth in the usage of parallel clustering algorithms that work over distributed computing frameworks such as MPI,MapReduce,and *** important step for any parallel clustering algorithm is the distribution of data amongst the cluster *** step governs the methodology and performance of the entire *** typically use random,or a spatial/geometric distribution strategy like kd-tree based partitioning and grid-based partitioning,as per the requirements of the ***,these strategies are generic and are not tailor-made for any specific parallel clustering *** this paper,we give a very comprehensive literature survey of MPI-based parallel clustering algorithms with special reference to the specific data distribution strategies they *** also propose three new data distribution strategies namely Parameterized Dimensional Split for parallel density-based clustering algorithms like DBSCAN and OPTICS,Cell-Based Dimensional Split for dGridSLINK,which is a grid-based hierarchical clustering algorithm that exhibits efficiency for disjoint spatial distribution,and Projection-Based Split,which is a generic distribution *** of these preserve spatial locality,achieve disjoint partitioning,and ensure good data load *** experimental analysis shows the benefits of using the proposed data distribution strategies for algorithms they are designed for,based on which we give appropriate recommendations for their usage.

关键词： parallel data mining data distribution parallel clustering spatial locality preservation

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Energy-Aware Blocking Hybrid Flow Shop Scheduling Problem with Sequence-depend Setup Times and Machine Speed Levels 6

Energy-Aware Blocking Hybrid Flow Shop Scheduling Problem wi...

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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Missaoui, Ahmed Ozturk, Cemalettin O'Sullivan, Barry Insight Centre for Data Analytics University College Cork Ireland Process Energy and Transport Engineering Munster Technological University Cork Ireland

The industry sector has the second largest energy demand after electricity generation. Given the high trend of energy prices, scarcity of its supply due to political instability and dependency on fossil fuels for its production makes energy the primary challenge for the manufacturing sector to stay competitive. Therefore, in addition to novel equipment technologies that use less energy, energy-efficient scheduling has also been a priority for manufacturing companies. In this study, the Hybrid Flowshop Scheduling Problem with Blocking Constraints and Sequence-Depend Setup Times (BHFS-SDST) is investigated for the minimization of makespan and total energy consumption (TEC). First, a novel bi-objective Mixed Integer Linear Programming (MILP) model is formulated and solved through augmented epsilon constraints. Then, a novel multi-objective approach based on Iterated Greedy meta-heuristic is developed for larger instances. Efficiency and the scalability of the proposed approaches are tested with small, medium, and large instances. Computational experiments show the effectiveness of developed methods in solving the BHFS-SDST problem. © 2025 The Authors.

关键词： Mixed-integer linear programming

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An Attention-Guided Image Super-Resolution for Scene Text 8

An Attention-Guided Image Super-Resolution for Scene Text

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8th International Conference on Video and Image Processing, ICVIP 2024

作者： Em, Tithnorakneath Aramvith, Supavadee Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Faculty of Engineering Chulalongkorn University Bangkok Thailand

ISBN: (数字)9781510689244

ISBN: (纸本)9781510689237

Scene Text Image Super-Resolution (STISR) has become a significant pre-processing technique to enhance text recognition accuracy in low-resolution scene text images, particularly when addressing the challenges of low-resolution (LR) images in real-world applications. Despite advancements in general and text-focused methods, current techniques often fail to retain the fine-grained and text-specific details necessary for downstream recognition tasks. This paper presents a novel Attention-Guided Scene Text Image Super-Resolution (AG-STISR) framework designed to overcome these challenges. Our approach integrates advanced channel attention and spatial attention mechanisms within a shallow feature extraction module, enabling precise enhancement of key text features such as character edges, contours, and fine structural details. Channel attention helps prioritize the features rich in text-specific details by focusing the model's attention on the channels with the most important data for text recognition. On the other hand, Spatial attention emphasizes locating and highlighting the most significant spatial areas in the image, like character outlines and strokes, ensuring that key elements are maintained while minimizing background distractions. We extensively evaluated our model on the TextZoom dataset and assessed it against various scene text recognition standards. The results demonstrate that our AG-STISR model consistently outperforms state-of-the-art methods, showing significant improvements in PSNR, SSIM, and text recognition accuracy across varying levels of image degradation. This research introduces a compelling solution to STISR, enhancing not only image clarity but also the reliability of text recognition in real-world applications. © 2025 SPIE.

关键词： Image enhancement

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Rethinking Node-wise Propagation for Large-scale Graph Learning 24

Rethinking Node-wise Propagation for Large-scale Graph Learn...

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33rd ACM Web Conference, WWW 2024

作者： Li, Xunkai Ma, Jingyuan Wu, Zhengyu Su, Daohan Zhang, Wentao Li, Rong-Hua Wang, Guoren Beijing Institute of Technology Beijing China Peking University National Engineering Laboratory for Big Data Analytics and Applications Beijing China

ISBN: (纸本)9798400701719

Scalable graph neural networks (GNNs) have emerged as a promising technique, which exhibits superior predictive performance and high running efficiency across numerous large-scale graph-based web applications. However, (i) Most scalable GNNs tend to treat all nodes with the same propagation rules, neglecting their topological uniqueness;(ii) Existing node-wise propagation optimization strategies are insufficient on web-scale graphs with intricate topology, where a full portrayal of nodes' local properties is required. Intuitively, different nodes in web-scale graphs possess distinct topological roles, and therefore propagating them indiscriminately or neglecting local contexts may compromise the quality of node representations. To address the above issues, we propose Adaptive Topology-aware Propagation (ATP), which reduces potential high-bias propagation and extracts structural patterns of each node in a scalable manner to improve running efficiency and predictive performance. Remarkably, ATP is crafted to be a plug-and-play node-wise propagation optimization strategy, allowing for offline execution independent of the graph learning process in a new perspective. Therefore, this approach can be seamlessly integrated into most scalable GNNs while remaining orthogonal to existing node-wise propagation optimization strategies. Extensive experiments on 12 datasets have demonstrated the effectiveness of ATP. © 2024 ACM.

关键词： Graph neural networks

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Efficient Deep Attentive Pixels Network in Face Super-Resolution at Scale Factor of 16 20

Efficient Deep Attentive Pixels Network in Face Super-Resolu...

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20th International Conference on Electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON 2023

作者： Aung, Hein Htet Aramvith, Supavadee Faculty of Engineering Chulalongkorn University Multimedia Data Analytics and Processing Research Unit Department of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9798350310467

Nowadays, Face Super-Resolution (FSR) models utilize the fusion approach, which combines the attention technique with the super-resolution network. The fusion approach has been proposed and solves the problem of FSR. Facial attributes have been effectively used to guide low-level information of the face to perform robust face image restoration. Iterative techniques appraised the value of facial landmarks to boost the reconstruction capability of the super-resolution network. Nevertheless, the network parameters in FSR are high, while the learning rate is still low. This paper proposes an attention mechanism combined with the Face Alignment Network (FAN). The proposed attention mechanism consists of channel attention and a non-local module. Our proposed model outperforms at the scale of $\times 16$ compared to the other state-of-the-art models. © 2023 IEEE.

关键词： Image reconstruction

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Double U-Net: Improved multiscale modeling via fully convolutional neural networks

Double U-Net: Improved multiscale modeling via fully convolu...

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93rd Annual Meeting of the International Association of Applied Mathematics and Mechanics, GAMM 2023

作者： Lißner, Julian Fritzen, Felix Data Analytics in Engineering Institute of Applied Mechanics University of Stuttgart Stuttgart Germany

In multiscale modeling, the response of the macroscopic material is computed by considering the behavior of the microscale at each material point. To keep the computational overhead low when simulating such high performance materials, an efficient, but also very accurate prediction of the microscopic behavior is of utmost importance. Artificial neural networks are well known for their fast and efficient evaluation. We deploy fully convolutional neural networks, with one advantage being that, compared to neural networks directly predicting the homogenized response, any quantity of interest can be recovered from the solution, for example, peak stresses relevant for material failure. We propose a novel model layout, which outperforms state-of-the-art models with fewer model parameters. This is achieved through a staggered optimization scheme ensuring an accurate low-frequency prediction. The prediction is further improved by superimposing an efficient to evaluate U-net, which captures the remaining high-level features. © 2023 The Authors. Proceedings in Applied Mathematics & Mechanics published by Wiley-VCH GmbH.

关键词： Forecasting

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Evaluating E-Recruitment data Quality in Employee Hiring Process 8th

Evaluating E-Recruitment Data Quality in Employee Hiring Pro...

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8th Future of Information and Computing Conference, FICC 2023

作者： Olaniyan, Adelanke H. Hemlata, Sharma Big Data Analytics College of Business Technology and Engineering Sheffield Hallam University Sheffield United Kingdom

ISBN: (纸本)9783031280757

This study provides recommendations on how Human Resources (HR) can utilize e-recruitment sources of employment while estimating electronic platforms for hiring employees. E-hiring had setbacks, such as ineffective measurement and attracting flawed and fraudulent candidates. It is therefore important to employ quality employees since they play a significant role in the success of an organization. With a sample population of 311 employees, secondary sources of data were used to accomplish the study’s objectives. Descriptive statistics were used, and presentation, analysis and visualization were performed using Python and Tableau. Mann-Whitney U test and Crosstabulation were the inferential statistics used to test the hypotheses of the study. The findings identified that Human Resource Managers used approximately 73% of e-recruitment sources and e-recruitment platforms have no significant impact on employee job satisfaction or performance. The study found that e-recruitment channels perform better than non-e-recruitment channels based on job satisfaction ratings, and the performance of employees or relevance is the main consideration for e-recruitment. Therefore, Human Resource Managers and employers should employ e-recruitment platforms effectively to make better hiring decisions. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Big data

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Applying Competition-Based Learning to Stimulate Students' Practical and Competitive AI Ability in a Machine Learning Curriculum

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IEEE Transactions on Education 2024年第2期67卷 256-265页

作者： Chang, Hui-Tzu Lin, Chia-Yu National Yang Ming Chiao Tung University Center for Institutional Research and Data Analytics Hsinchu30010 Taiwan National Central University Department of Computer Science and Information Engineering Taoyuan City320317 Taiwan

Contribution: This study incorporates competition-based learning (CBL) into machine learning courses. By engaging students in innovative problem-solving challenges within information competitions, revealing that students' participation in online problem-solving competitions can improve their information technology, and showcase competitions can enhance their competition ability. Background: The CBL model seamlessly integrates project-based learning and competition, placing a strong emphasis on both collective learning and outcomes. This approach cultivates motivation among team members, driving them to enhance their learning and translate knowledge into practical experience. Research Questions: The objective is to examine the disparities in the development of theoretical knowledge, information technology, AI practical ability, and competition ability among students participating in online problem-solving competitions and showcase competitions, and discusses the potential moderating effect of competition type on the relationships between variables in the hypothetical model. Methodology: The study involved 74 students enrolled in machine learning course at a university. The students were given theoretical knowledge and information technology pretests and posttests in the 2nd and 17th weeks, respectively. In the 18th week, the students presented their projects using slideshows and were graded by judges while also submitting their final competition proposal and slides. Findings: Students in online problem-solving competitions can enhance their information technology, while those participating in showcase competitions can improve their competitive ability. Moreover, the competition type was found to moderate the relationships among theoretical knowledge, information technology, and AI model accuracy. The findings suggest that incorporating CBL into machine learning courses effectively cultivates students' AI practical and competitive abilities. © 1963-2012 IEEE.

关键词： Competition

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