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HUSS:A Heuristic Method for Understanding the Semantic Structure of Spreadsheets

Data Intelligence 2023年第3期5卷 537-559页

作者： Xindong Wu Hao Chen Chenyang Bu Shengwei Ji Zan Zhang Victor S.Sheng Key Laboratory of Knowledge Engineering with Big Data(the Ministry of Education of China) Hefei University of TechnologyChinaSchool of Computer Science and Information EngineeringHefei University of TechnologyHefeiChina Research Institute of Artificial Intelligence Zhejiang LabHangzhouChina Department of Computer Science Texas Tech UniversityLubbockTX 79409USA

Spreadsheets contain a lot of valuable data and have many practical *** key technology of these practical applications is how to make machines understand the semantic structure of spreadsheets,e.g.,identifying cell function types and discovering relationships between cell *** existing methods for understanding the semantic structure of spreadsheets do not make use of the semantic information of cells.A few studies do,but they ignore the layout structure information of spreadsheets,which affects the performance of cell function classification and the discovery of different relationship types of cell *** this paper,we propose a Heuristic algorithm for Understanding the Semantic Structure of spreadsheets(HUSS).Specifically,for improving the cell function classification,we propose an error correction mechanism(ECM)based on an existing cell function classification model[11]and the layout features of *** improving the table structure analysis,we propose five types of heuristic rules to extract four different types of cell pairs,based on the cell style and spatial location *** experimental results on five real-world datasets demonstrate that HUSS can effectively understand the semantic structure of spreadsheets and outperforms corresponding baselines.

关键词： Spreadsheet semantic structure Information extraction Heuristics Cell function analysis Table structure analysis

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Accurate diagnosis of non-Hodgkin lymphoma on whole-slide images using deep learning 9

Accurate diagnosis of non-Hodgkin lymphoma on whole-slide im...

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9th IEEE International Conference on sciences of Electronics, Technologies of Information and Telecommunications, SETIT 2022

作者： Khelil, Hathem El Moumene Zerari, Abd Djerou, Leila Algeria University of Biskra LISIA Laboratory Computer Science Department Algeria

ISBN: (数字)9781728184425

ISBN: (纸本)9781728184425

Digital Pathology is the technique of digitizing histology slides to create high-resolution images. One of the important applications for digital pathology is tissue level classification, such as the identification of the three most common kinds of non-Hodgkin lymphomas;Mantle Cell Lymphoma, Follicular Lymphoma and Chronic Lymphocytic Leukemia, which pose a significant problem for pathologists due to their inherent complexity. In this research, deep learning ideas are combined with an improvement of the CNN algorithm to propose a Non-Hodgkin Lymphoma model which is able, effectively, to categorize these subtypes of non-Hodgkin lymphomas. The proposed model was trained using NIA-curated dataset images and achieved a classification accuracy of 98.7%, about 0.6% better than current classification strategies for non-Hodgkin's lymphomas based on deep learning. © 2022 IEEE.

关键词： Classification (of information)

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Optimizing Artificial Neural Networks Trough Weight Adjustments

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Procedia computer science 2024年 246卷 2158-2165页

作者： Syed Muhammad Abrar Akber Agnieszka Szczesna Sadia Nishat Kazmi Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice 44-100 Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice 44-100 Poland

Longer training times pose a significant challenge in Artificial neural networks (ANNs) as it may leads to increasing the computational costs and decreasing the effectiveness of the model. Therefore, it is imperative to reduce training times in ANNs to enhance the computational efficiency. The initialization of the weights between the layers in ANN plays a vital role in reducing training times. Appropriate weight initialization can help the network converge faster during the training by providing an optimum starting point for the network. Therefore, weight initialization techniques are essential for efficient training of ANNs. This paper revisits and implements different popular weight initialization techniques in ANNs and analyzes their impact on training time. Specifically, this paper implements Gaussian-based, Kaming-based, and Xavier-based weight initiation atop a popular DNN-based network. The experiments are conducted by employing a well-known dataset. The results show that the scenario when no weight initiation is applied consumed the highest training time, whereas different weight initiation techniques contribute in reducing the training times for the network.

关键词： Artificial neural network Deep neural network weight initiation

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Stochastic Event-Triggered Estimation With Smart Sensors Over Packet-Dropping Links

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IEEE Transactions on Control of Network Systems 2025年第2期12卷 1756-1768页

作者： Deng, Yahan Yang, Nachuan Li, Yuzhe Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Clear Water Bay SAR Hong Kong999077 Hong Kong

The event-triggered scheme (ETS) has been widely used for sensor data scheduling in cyber-physical systems (CPS). Existing literature on the design of ETSs for packet drops deals with the issue of non-Gaussianity of the a posteriori distribution in the system state. On the one hand, the Gaussian assumption only derives an approximate result, while on the other hand, exact results can be obtained by numerical integration but with excessive computational complexity. To this end, in this paper, we propose a stochastic ETS based on acknowledgment (ACK) information for remote state estimation with smart sensors and packet drops. The transmission decision is jointly driven by the holding time at the remote end and the accumulated innovative information. Then, we inductively derive the exact probability density function of the augmented innovative information vector by the Bayesian rule, which is used to obtain the explicit form of the estimation error covariance. These exact theoretical results mean that the design of scheduling parameter sequences no longer relies on experience. Finally, numerical simulations are provided to demonstrate that the empirical results agree with the theoretical results. © 2014 IEEE.

关键词： Packet loss

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Opinions Are Not Always Positive: Debiasing Opinion Summarization With Model-Specific and Model-Agnostic Methods 30

Opinions Are Not Always Positive: Debiasing Opinion Summariz...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Yanyue Lai, Yilong Wang, Zhenglin Li, Pengfei Zhou, Deyu He, Yulan School of Computer Science and Engineering Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China Department of Informatics King's College London United Kingdom The Alan Turing Institute United Kingdom

ISBN: (纸本)9782493814104

As in the existing opinion summary data set, more than 70% are positive texts, the current opinion summarization approaches are reluctant to generate the negative opinion summary given the input of negative opinions. To address such sentiment bias, two approaches are proposed through two perspectives: model-specific and model-agnostic. For the model-specific approach, a variational autoencoder is proposed to disentangle the input representation into sentiment-relevant and sentiment-irrelevant components through adversarial loss. Therefore, the sentiment information in the input is kept and employed for the following decoding which avoids interference of content information with emotional signals. To further avoid relying on some specific opinion summarization frameworks, a model-agnostic approach based on counterfactual data augmentation is proposed. A dataset with a more balanced emotional polarity distribution is constructed using a large pre-trained language model based on some pairwise and mini-edited principles. Experimental results show that the sentiment consistency of the generated summaries is significantly improved using the proposed approaches, while their semantics quality is unaffected. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Semantics

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An Approach for UML Sequence Diagram Models/MetaModels Co-evolution Using Transformation Rules 8th

An Approach for UML Sequence Diagram Models/MetaModels Co-ev...

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8th International Symposium on Modelling and Implementation of Complex Systems, MISC 2024

作者： Chouikh, Asma Khalfaoui, Khaled Kerkouche, ELHillali Chaoui, Allaoua LaRIA Laboratory University of Jijel Jijel18000 Algeria Department of Computer Science University of Jijel Jijel18000 Algeria MISC Laboratory Abdelhamid Mehri University Constantine 2 Constantine Algeria

ISBN: (纸本)9783031821110

Like all software artifacts, metamodels frequently evolve in response to various factors, such as technological advances or changing user requirements. When a metamodel evolves, a simple change can affect all associated artifacts, including models, code generators, transformation rules and consistency constraints. As a result, these artifacts may no longer be compliant. The main challenge is how to adapt and maintain the consistency and conformity of the initial artifacts to the updated metamodel. Several approaches have been proposed, focusing exclusively on model co-evolution during metamodel evolution. In this paper, we propose an approach to address the evolution of the UML Sequence Diagram metamodel. Our approach consists in calculating and analyzing the differences between the initial metamodel and the evolved metamodel. These differences are used to define transformation rules that enable the models to co-evolve. We illustrate our approach with a practical example. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Eclipse Modeling Frameworks Metamodeling Model Transformation Model-Driven Engineering (MDE) Model/metamodel Co-evolution UML Sequence Diagram

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What-If XAI Framework (WiXAI): From Counterfactuals towards Causal Understanding

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Journal of computer and Communications 2024年第6期12卷 169-198页

作者： Neelabh Kshetry Mehmed Kantardzic Department of Computer Science Data Mining Laboratory University of Louisville Louisville USA

People learn causal relations since childhood using counterfactual reasoning. Counterfactual reasoning uses counterfactual examples which take the form of “what if this has happened differently”. Counterfactual examples are also the basis of counterfactual explanation in explainable artificial intelligence (XAI). However, a framework that relies solely on optimization algorithms to find and present counterfactual samples cannot help users gain a deeper understanding of the system. Without a way to verify their understanding, the users can even be misled by such explanations. Such limitations can be overcome through an interactive and iterative framework that allows the users to explore their desired “what-if” scenarios. The purpose of our research is to develop such a framework. In this paper, we present our “what-if” XAI framework (WiXAI), which visualizes the artificial intelligence (AI) classification model from the perspective of the user’s sample and guides their “what-if” exploration. We also formulated how to use the WiXAI framework to generate counterfactuals and understand the feature-feature and feature-output relations in-depth for a local sample. These relations help move the users toward causal understanding.

关键词： XAI AI WiXAI Causal Understanding Counterfactuals Counterfactual Explanation

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EduPar 2024 Keynote Speaker

EduPar 2024 Keynote Speaker

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Charles E. Leiserson MIT Department of Electrical Engineering and Computer Science MIT Computer Science and Artificial Intelligence Laboratory

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Aside from pure intellectual interest, why do we teach our students parallel computing? Most people would agree that the primary goal is to produce greater application performance. Yet students frequently parallelize code only to discover that it runs disappointingly slower because they don't understand performance. To exploit parallelism effectively, it must operate synergistically with a host of other techniques, including caching, vectorization, algorithms, bit tricks, loop unrolling, using compiler switches, tailoring code to the architecture, exploiting sparsity, changing data representation, metaprogramming, etc. Software performance engineering, which encompasses these techniques, is the science and art of making code run fast or otherwise limiting its consumption of resources, such as energy, memory footprint, network utilization, response time, etc. In this talk, I will argue that the end of Moore's Law makes software performance engineering a critical skill for our students to learn.

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Automated Machine Learning for Breast Cancer Diagnosis Using TPOT

Automated Machine Learning for Breast Cancer Diagnosis Using...

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iNnovative Informatics of Biskra (ISNIB), International Symposium on

作者： Oumaima Heriz Computer Science Department LINFI Laboratory Biskra Algeria

ISBN: (数字)9798331509576

ISBN: (纸本)9798331509583

Breast cancer is a major global health issue, emphasizing the need for accurate diagnostic tools to improve patient outcomes. This paper presents an approach that utilizes Automated Machine Learning (AutoML), specifically the Tree-based Pipeline Optimization Tool (TPOT) Classifier, to streamline the model selection and so that optimizing breast cancer diagnosis process. The objective of the work is to develop a robust predictive model that classifies tumors as benign or malignant based on features derived from medical imaging data. The final model, a Random Forest Classifier, achieved an important accuracy score of 96.49%, demonstrating its effectiveness and reliability in clinical settings. Comprehensive evaluation metrics, including confusion matrix analysis and Receiver Operating Characteristic (ROC) curve assessment, validate the model's performance, highlighting its potential for enhancing breast cancer diagnosis.

关键词： Accuracy Pipelines Streaming media Automated machine learning Breast cancer Web sites Reliability Usability Random forests Optimization

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Modified simulated annealing for university teacher course assignment considering socio-cultural constraints

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1533-1549页

作者： Brahimi, Samiha Sabba, Sara Elhussein, Mariam Alqahtani, Mohammed Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Faculty of New Technologies of Information and Communication Laboratory of Data Science and Artificial Intelligence (LISIA) Abdelhamid Mahri Constantine-2 University Constantine 25016 Algeria Department of Mathematics and Computing Mount Royal University Calgary AB Canada

This paper aims to address the complex challenge of course assignment for faculty members within a Saudi university, taking into account the socio-cultural constraints imposed by gender-based segregation between students and faculty. To tackle this challenge, a combinatorial optimization model is proposed, which includes a binary representation of the problem, constraints reflecting gender segregation, and an objective function designed to maximize faculty satisfaction. The model employs a Simulated Annealing (SA) algorithm, supplemented with two neighborhood search heuristics (H1 and H2), to explore diverse combinations. Additionally, a greedy randomized adaptive search procedure Greedy Randomized Adaptive Search Procedure (GRASP) algorithm is implemented for comparative analysis, utilizing heuristic 1 for local search. Through the application of simulated annealing alongside both heuristics (SA_H1_H2), the optimal solution is achieved, resulting in a notable equilibrium of courses among faculty members. Objective 1 demonstrates the standardized deviation in workload distribution among faculty members, yielding a deviation value of 0.08. This study contributes to the field by proposing a novel approach to course assignment that explicitly addresses the gender-based segregation prevalent in Saudi universities. By integrating socio-cultural constraints into the optimization model and employing advanced algorithms, the study offers a pioneering solution to a previously unexplored problem domain. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： GRASP Mathematical model Operations Research Optimization Simulated annealing Teacher_Course assignment

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