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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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A secure two-party PIR for privacy applications

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International Journal of Intelligent information and Database Systems 2022年第4期15卷 450-467页

作者： Bhat, Radhakrishna Reddy, N.V. Subba Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Karnataka Manipal576104 India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

The major drawback of existing information retrieval schemes in preserving user privacy is that they either exhibit computationally bounded privacy with intractability assumptions or perfect privacy with high bandwidth utilisation. Today, the essential requirement is to have a bandwidth efficient perfect privacy preserving information retrieval scheme in order to provide effective and guaranteed service to the information retrieving user. Therefore, in this paper, we have constructed a new single database perfect privacy preserving private block retrieval scheme called as ‘pepperPBR’ using quadratic residuosity as the underlying primitive where private block retrieval (PBR) is a natural extension to private information retrieval (PIR). In this paper, user generates O(6k) bit query and server generates O(2ku + o(n)) bit response where n is the database size, o(n) is the non-trivial server communication cost, u is the number of database blocks, k is the security parameter. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： information retrieval

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Enhancing Maize Leaf Disease Prediction with Advanced Machine Learning Models

Enhancing Maize Leaf Disease Prediction with Advanced Machin...

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Congress on Smart Computing Technologies, CSCT 2023

作者： Kanakaprabha, S. Venu Gopal, G. Saranya, N. Ganesh Kumar, G. Sape, Chittibabulu Raju, Yallapragada Ravi Department of Computer Science and Engineering Rathinam Technical Campus Anna University Coimbatore India Department of Computer Science and Engineering KL Education Foundation Guntur India Department of Computer Science and Engineering Dr. N.G.P Institute of Technology Coimbatore India Department of Information Technology Hindusthan Institute of Technology Coimbatore India Department of Computer Science and Engineering Aditya College of Engineering Surampalem India Department of Computer Science and Engineering Madanapalle Institute of Technology and Science Andrapradesh Madanapalle India

ISBN: (纸本)9789819750801

The main goal of this research is to apply cutting-edge machine learning methods to predict maize leaf disease more accurately. Crucial staple crop maize is susceptible to a number of leaf diseases that can have a major negative influence on output and food security. Traditional disease detection techniques frequently lack accuracy and speed. We use cutting-edge machine learning methods, especially deep learning algorithms and Convolutional Neural Networks (CNNs), to solve this. We train and fine-tune our models using a large dataset that includes multiple disease categories, such as Common Rust, Gray Leaf Spot, Blight, and Healthy leaves. Our results show that these sophisticated models provide a significant improvement in the accuracy of disease prediction, allowing for earlier interventions and better crop health management techniques. This study advances precision agriculture by giving farmers and agronomists a trustworthy tool to increase the sustainability of maize output. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Adversarial machine learning

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Integrating Image Recognition and Board Games into Financial education 16

Integrating Image Recognition and Board Games into Financial...

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16th Asia-Pacific Workshop on Mixed and Augmented Reality, APMAR 2024

作者： Hung, Yu-Han Chao, Hao-Bin Liou, Jie-Sin Han, Ping-Hsuan Hsieh, Yi-Zeng Fan, Ping-Lin Chou, Chien-Hsing Department of Mathematics and Information Education National Taipei University of Education Taiwan Department of Interaction Design National Taipei University of Technology Taiwan Department of Electrical and Computer Engineering National Taiwan University of Science and Technology Taiwan Department of Digital Technology Design National Taipei University of Education Taiwan

As financial products diversify and market structures become more complex, promoting financial education has become increasingly important. Given that the foundation of financial literacy should be established from childhood, how to cultivate students' correct financial management concepts from a young age has become a significant issue in modern society. Considering the substantial advantages of game-based learning in providing high interactivity and real-world scenario simulations, this paper aims to develop an innovative financial education board game that incorporates elements of risk assessment and insurance planning, and digitalizes it. With the assistance of an app and the application of image recognition technology, we could enhance the convenience and fun of the gameplay. Research results show that integrating digital apps into board games as a game-based learning strategy significantly improves the learning outcomes and motivation of upper elementary school students in the areas of risk and insurance knowledge, compared to traditional lecture methods. It also enhances their understanding and application abilities. Students hold a positive attitude toward this teaching approach, demonstrating its potential and advantages in financial education. © 2024 Copyright for this paper by its authors.

关键词： Decentralized finance

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An Exploration of Contagion Infection in Citrus Plant with Augmented Data using ConvNet 2

An Exploration of Contagion Infection in Citrus Plant with A...

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2nd International Conference on Augmented Intelligence and Sustainable Systems, ICAISS 2023

作者： Shanmugapriya, S. Dhanasekaran, S. Murugan, B.S. Kalasalinagam Academy of Research and Education Departement of Computer Science and Engineering India Kalasalinagam Academy of Research and Education Departement of Information Technology India M. Kumarasamy College of Eengineering Department of Computer Science and Engineering Karur India

ISBN: (纸本)9798350325799

Citrus is widely cultivated and consumed fruit across the global. It gains its popularity from its nutritional benefits. Citrus fruit has gained its international trading due to freshly consumed, processed products, oil production and other by-products. Citrus production is cut short mainly due its diseases. Identifying the diseases at early stage and stepping forward to cure them is vital role in improving the production. Automated disease identification cuts down the manual effort and also improves the efficiency in identification. With various degrees of accuracy, both machine learning and deep learning algorithms have been successful in identifying citrus leaf and fruit diseases. This research study proposes a novel disease classification method using CNN and also provides a comparative study with models like SVM, k-means clustering, ANN and CNN for disease identification. © 2023 IEEE.

关键词： Citrus fruits

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Counterfactual discriminative micro-expression recognition

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Visual Intelligence 2024年第1期2卷 350-359页

作者： Yong Li Menglin Liu Lingjie Lao Yuanzhi Wang Zhen Cui Department of Computer Science City University of Hong KongHong Kong999077China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing210094China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information Ministry of EducationSchool of Computer Science and EngineeringNanjing University of Science and TechnologyNanjing210094China

Micro-expressions are spontaneous,rapid and subtle facial movements that can hardly be suppressed or ***-expression recognition(MER)is one of the most challenging topics in affective *** aims to recognize subtle facial movements which are quite difficult for humans to perceive in a fleeting ***,many deep learning-based MER methods have been ***,how to effectively capture subtle temporal variations for robust MER still perplexes *** propose a counterfactual discriminative micro-expression recognition(CoDER)method to effectively learn the slight temporal variations for video-based *** explicitly capture the causality from temporal dynamics hidden in the micro-expression(ME)sequence,we propose ME counterfactual reasoning by comparing the effects of the facts *** ME sequences and the counterfactuals ***-revised ME sequences,and then perform causality-aware prediction to encourage the model to learn those latent ME temporal *** experiments on four widely-used ME databases demonstrate the effectiveness of CoDER,which results in comparable and superior MER performance compared with that of the state-of-the-art *** visualization results show that CoDER successfully perceives the meaningful temporal variations in sequential faces.

关键词： Affective computing Micro-expression recognition Temporal variation Counterfactual reasoning Causal graph

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Image Emotion Classification Network Based on Multilayer Attentional Interaction,Adaptive Feature Aggregation

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computers, Materials & Continua 2023年第5期75卷 4273-4291页

作者： Xiaorui Zhang Chunlin Yuan Wei Sun Sunil Kumar Jha Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Nanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China Adani University AhmedabadGujaratIndia

The image emotion classification task aims to use the model to automatically predict the emotional response of people when they see the *** have shown that certain local regions are more likely to inspire an emotional response than the whole ***,existing methods perform poorly in predicting the details of emotional regions and are prone to overfitting during training due to the small size of the ***,this study proposes an image emotion classification network based on multilayer attentional interaction and adaptive feature *** perform more accurate emotional region prediction,this study designs a multilayer attentional interaction *** module calculates spatial attention maps for higher-layer semantic features and fusion features through amultilayer shuffle attention *** layer-by-layer up-sampling and gating operations,the higher-layer features guide the lower-layer features to learn,eventually achieving sentiment region prediction at the optimal *** complement the important information lost by layer-by-layer fusion,this study not only adds an intra-layer fusion to the multilayer attention interaction module but also designs an adaptive feature aggregation *** module uses global average pooling to compress spatial information and connect channel information from all ***,the module adaptively generates a set of aggregated weights through two fully connected layers to augment the original features of each ***,the semantics and details of the different layers are aggregated through gating operations and residual connectivity to complement the lost *** reduce overfitting on small datasets,the network is pre-trained on the FI dataset,and further weight fine-tuning is performed on the small *** experimental results on the FI,Twitter I and Emotion ROI(Region of Interest)datasets show that the proposed network exceeds existing image emotion classification methods

关键词： Attentionmechanism emotional region prediction image emotion classification transfer learning

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A Review on Detection of Lung Cancer Using Ensemble of Classifiers with CNN 2

A Review on Detection of Lung Cancer Using Ensemble of Class...

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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Geethu Lakshmi, G. Nagaraj, P. Subhashini, S.J. Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Viruthanagar Krishnankovil India Srm Madurai College of Engineering and Technology Department of Information Technology Madurai Sivagangai India

ISBN: (纸本)9798350347579

A thorough understanding of lung cancer and tumor development pathways has made significant advancements in lung cancer treatment. Lung cancer diagnosis and treatment in its early stages still require new techniques. Recent advancements in genetics, computational biology, and other technologies provide an opportunity to better understand the immunological landscape associated with early-stage lung carcinogenesis and the mechanism of lung cancer evolution. This review focuses on immunoediting and discusses new research on immunological alterations and biomarkers in pulmonary premalignancy and early-stage non-small cell lung cancer. By concentrating on developing innovative techniques for intercepting cancer before it advances to later stages, researchers have the potential to revolutionize lung cancer therapy and significantly improve clinical outcomes. The use of an Ensemble of Classifiers with a Convolution Neural Network could further enhance this approach. © 2023 IEEE.

关键词： Support vector machines

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PACE: Reputation-Driven Hierarchical Incentive Mechanism for Trusted Healthcare Transactions in Model Marketplace

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1571-1583页

作者： Lu, Jianfeng Dou, Xiaochi Rathore, Bharati Jhaveri, Rutvij H. Seid, Abegaz Mohammed Erbad, Aiman Wuhan University of Science and Technology School of Computer Science and Technology Wuhan430065 China Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology China United Kingdom Pandit Deendayal Energy University Department of Computer Science and Engineering School of Technology India Hamad Bin Khalifa University College of Science and Engineering Doha Qatar Qatar University College of Engineering Doha Qatar

Federated learning-based model marketplaces have the potential to securely leverage healthcare data for efficient healthcare transactions. However, the willingness to participate in this marketplace is severely hindered by self-interested model sellers, who maximize their personal gains via distorting the quality of their models. To this end, we propose a rePutation-driven hierArchical inCentive mEchanism, called PACE, which combines contract cooperation and game pricing to motivate model sellers to be honest, thereby improving the sustainability of the model marketplace via two-layer cooperation. Specifically, at the training layer, we design a reward payment rule based on multidimensional contracts, while at the trading layer, we use a competitive market pricing game to develop the accuracy pricing rule. In particular, the above rules cleverly combine the fine-grained model sellers' reputations with the willingness of healthcare devices to indirectly affect the model sellers' utilities. Theoretically, we prove that PACE can effectively incentivize model sellers to truthfully report model quality while satisfying both individual rationality and incentive compatibility with a fairness guarantee. Experimental results conducted on four real-world datasets show that our PACE improves the global performance by up to 12.85% and reduces the global loss by at least 19.23% compared with the state-of-the-art baselines. © 1975-2011 IEEE.

关键词： Marketplaces

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Hybrid Dipper Throated and Grey Wolf Optimization for Feature Selection Applied to Life Benchmark Datasets

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computers, Materials & Continua 2023年第2期74卷 4531-4545页

作者： Doaa Sami Khafaga El-Sayed M.El-kenawy Faten Khalid Karim Mostafa Abotaleb Abdelhameed Ibrahim Abdelaziz A.Abdelhamid D.L.Elsheweikh Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Department of System Programming South Ural State UniversityChelyabinsk454080Russia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Faculty of Specific EducationMansoura UniversityEgypt

Selecting the most relevant subset of features from a dataset is a vital step in data mining and machine *** feature in a dataset has 2n possible subsets,making it challenging to select the optimum collection of features using typical *** a result,a new metaheuristicsbased feature selection method based on the dipper-throated and grey-wolf optimization(DTO-GW)algorithms has been developed in this *** can result when the selection of features is subject to metaheuristics,which can lead to a wide range of ***,we adopted hybrid optimization in our method of optimizing,which allowed us to better balance exploration and harvesting chores more *** propose utilizing the binary DTO-GW search approach we previously devised for selecting the optimal subset of *** the proposed method,the number of features selected is minimized,while classification accuracy is *** test the proposed method’s performance against eleven other state-of-theart approaches,eight datasets from the UCI repository were used,such as binary grey wolf search(bGWO),binary hybrid grey wolf,and particle swarm optimization(bGWO-PSO),bPSO,binary stochastic fractal search(bSFS),binary whale optimization algorithm(bWOA),binary modified grey wolf optimization(bMGWO),binary multiverse optimization(bMVO),binary bowerbird optimization(bSBO),binary hysteresis optimization(bHy),and binary hysteresis optimization(bHWO).The suggested method is superior 4532 CMC,2023,vol.74,no.2 and successful in handling the problem of feature selection,according to the results of the experiments.

关键词： Metaheuristics dipper throated optimization grey wolf optimization binary optimizer feature selection

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