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Generating Difficult CNF Instances in Unexplored Constrainedness Regions

ACM Journal of Experimental Algorithmics 2020年第pp1–12期25卷 1-12页

作者： Escamocher, Guillaume O'Sullivan, Barry Prestwich, Steven David Insight Centre for Data Analytics School of Computer Science and IT University College Cork Ireland

When creating benchmarks for satisfiability (SAT) solvers, we need Conjunctive Normal Form (CNF) instances that are easy to build but hard to solve. A recent development in the search for such methods has led to the Balanced SAT algorithm, which can create k-CNF instances with m clauses of high difficulty, for arbitrary k and m. In this article, we introduce the No-Triangle CNF algorithm, a CNF instance generator based on the cluster coefficient graph statistic. We empirically compare the two algorithms by fixing the arity and the number of variables, but varying the number of clauses. We find that the hardest instances produced by each method belong to different constrainedness regions. In the 3-CNF case, for example, hard No-Triangle CNF instances reside in the highly-constrained region (many clauses), while Balanced SAT instances obtained from the same parameters are easy to solve. This allows us to generate difficult instances where existing algorithms fail to do so. © 2020 ACM.

关键词： Clustering algorithms

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Incentive Mechanism Design for Federated Learning With Dynamic Network Pricing

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IEEE Transactions on Mobile Computing 2025年

作者： Ding, Ningning Gao, Lin Huang, Jianwei Data Science and Analytics Thrust Internet of Things Thrust Information Hub Guangzhou China Harbin Institute of Technology School of Electronics and Information Engineering Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen China The Chinese University of Hong Kong School of Science and Engineering Shenzhen Research Institute of Big Data Shenzhen Shenzhen518172 China

Federated learning protects users' data privacy by sharing users' local model parameters (instead of raw data) with a server. However, when massive users train a large machine learning model through federated learning, the dynamically varying and often heavy communication overhead can put significant pressure on the network operator. The operator may choose to dynamically change the network prices in response, which will eventually affect the payoffs of the server and users. This paper considers the under-explored yet important issue of the joint design of participation incentives (for encouraging users' contribution to federated learning) and network pricing (for managing network resources). Due to heterogeneous users' private information and multi-dimensional decisions, the optimization problems in Stage I of multi-stage games are non-convex. Nevertheless, we are able to analytically derive the corresponding optimal contract and pricing mechanism through proper transformations of constraints, variables, and functions, under three interaction structures of the participants. We show that the coordinated structure is better than the two uncoordinated structures, as it avoids the selfish behaviors of the network operator and the server;the vertically uncoordinated structure is better than the horizontally uncoordinated structure, as it avoids the interests misalignment between the server and the network operator. We also propose multi-period network pricing to reduce the implementation complexity of dynamic pricing. Numerical results based on real-world datasets show that our proposed mechanisms decrease the server's cost by up to 24.87% and increase the network operator's profit by up to 1245.25%, compared with the state-of-the-art benchmarks. © 2025 IEEE. All rights reserved.

关键词： Federated learning

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Evaluating Outputs Fusion Technique in Multimodal Human Activity Recognition: Impact of Modality Reduction on Performance Efficiency 7

Evaluating Outputs Fusion Technique in Multimodal Human Acti...

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7th International Conference on Signal Processing and Information Security, ICSPIS 2024

作者： Tagmouni, Abdelkader Elmir, Youssef Kechadi, Mohand Tahar Laboratoire LITAN École supérieure en Sciences et Technologies de l'Informatique et du Numèrique RN 75 Amizour Bejaia06300 Algeria SGRE-lab Bechar Algeria University College Dublin Insight Centre for Data Analytics School of Computer Science Dublin 4 Ireland

ISBN: (纸本)9798350368673

Human Activity Recognition (HAR) using deep learning has seen significant advancements, particularly in the development of multimodal approaches that integrate diverse data sources to enhance recognition accuracy. This study introduces a novel multimodal architecture designed to fuse skeletal and inertial data for improved HAR performance. The proposed methodology employs dedicated Convolutional Neural Networks (CNNs) for each modality, followed by a late fusion of their outputs to capture spatial-temporal patterns more effectively. We evaluate the robustness and generalizability of the proposed approach using two multimodal datasets, UTD-MHAD and CZU-MHAD, across 11 distinct experimental trials. The results show that our model outperforms existing state-of-the-art methods, achieving an accuracy of 99.42% on the CZU-MHAD dataset and 99.22% on the UTD-MHAD dataset. This significant improvement demonstrates the efficacy of our multimodal fusion strategy. This work underscores the potential of combining skeletal and inertial data in deep learning frameworks to achieve high accuracy in HAR, particularly in scenarios requiring remote patient monitoring and elderly care. © 2024 IEEE.

关键词： Convolutional neural networks

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Overview of Second Shared Task on Homophobia and Transphobia Detection in Social Media Comments 3

Overview of Second Shared Task on Homophobia and Transphobia...

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3rd Workshop on Language Technology for Equality, Diversity and Inclusion, LTEDI 2023

作者： Chakravarthi, Bharathi Raja Ponnusamy, Rahul Subramanian, Malliga Buitelaar, Paul García-Cumbreras, Miguel Ángel Jiménez-Zafra, Salud María García-Díaz, José Antonio Valencia-García, Rafael Jindal, Nitesh School of Computer Science University of Galway Ireland Insight SFI Research Centre for Data Analytics University of Galway Ireland Kongu Engineering College Tamil Nadu India Universidad de Jaén Spain Universidad de Murcia Spain University of Galway Ireland

ISBN: (纸本)9789544520847

We present an overview of the second shared task on homophobia/transphobia Detection in social media comments. Given a comment, a system must predict whether or not it contains any form of homophobia/transphobia. The shared task included five languages: English, Spanish, Tamil, Hindi, and Malayalam. The data was given for two tasks. Task A was given three labels, and Task B fine-grained seven labels. In total, 75 teams enrolled for the shared task in Codalab. For Task A, 10 teams submitted systems for English, 7 for Tamil, 4 for Spanish, 7 for Malayalam, and seven teams for Hindi. For Task B, 8 teams were submitted for English, 7 for Tamil, and 6 for Malayalam. We present and analyze all submissions in this paper. © 2023 LTEDI 2023 - 3rd Workshop on Language Technology for Equality, Diversity and Inclusion, associated with the 14th International Conference on Recent Advances in Natural Language Processing, RANLP 2023 - Proceedings. All rights reserved.

关键词： Social networking (online)

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CNN-Based Analytical Approach for Liver Disease Classification and Prediction

CNN-Based Analytical Approach for Liver Disease Classificati...

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Advancement in Computation & Computer Technologies (InCACCT), International Conference on

作者： Abrar Ahmed Syed Asia Banu Shaik Sukanya Konatam Maddi Venkata Phani Kumar Neha Gupta Data and Analytics Gainwell Technologies LLC Dallas USA Data Architecture and Analytics LTIMindtree Limited Dallas USA Artificial Intelligence The University of Texas at Austin Austin USA Sr. Technical Architect Tech Mahindra Americas Inc. Plano USA School of Computer Science and Engineering Geeta University Panipat

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

Liver disease is a major global health concern, and effective treatment requires an early and accurate diagnosis. Deep learning (DL) and Machine Learning (ML) techniques have been widely applied to improve disease prediction and classification. A range of models, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Logistic Regression (LR), and K-Nearest Neighbours (KNN), were trained using the Indian Liver Patient dataset (ILPD) from Kaggle in order to diagnose cases of liver illness. With an accuracy rate of 96.21%, precision rate of 74.76%, recall rate of 92.77%, and F1-score rate of 82.80%, the CNN model outperforms all others, demonstrating its ability to extract complex patterns for accurate categorization. Experimental results prove that Deep Learning (DL) methodologies, especially CNN, can enhance predictive power extensively compared to Machine Learning (ML) methods.

关键词： Support vector machines Deep learning Logistic regression Accuracy Liver diseases Nearest neighbor methods Feature extraction Convolutional neural networks Medical diagnosis Diseases

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Low-cost surface modification of a biomedical Zr-2.5Nb alloy fabricated by electron beam melting

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Journal of Materials science & Technology 2023年第12期143卷 178-188页

作者： Caixu Wang Xiaoli Zhao Shujun Li Lu Liu Deliang Zhang Mitsuo Niinomi School of Materials Science and Engineering Northeastern UniversityShenyang 110819China Shi-changxu Innovation Center for Advanced Materials Institute of Metal ResearchChinese Academy of SciencesShenyang 110016China Key Laboratory of Data Analytics and Optimization for Smart Industry(Ministry of Education) Northeastern UniversityShenyang 110819China Tianjin Key Laboratory of Bone Implants Interface Functionalization and Personalization Tianjin 300190China Institute for Materials Research Tohoku UniversitySendaiMiyagi 9808577Japan Division of Materials and Manufacturing Science Graduate School of EngineeringOsaka UniversityOsaka 565-0871Japan Department of Materials Science and Engineering Graduate School of Science and TechnologyMeijo UniversityNagoya 468-8502Japan Faculty of Chemistry Materials and BioengineeringKansai UniversityOsaka 564-860Japan

The Zr-2.5Nb alloy with a fine microstructure consisting ofαlaths was successfully prepared by electron beam melting(EBM).The thermal oxidation behaviors and kinetics of the as-built,and the EBM-built and hot isostatically pressed(HIPed)Zr-2.5Nb materials in a temperature range of 450-600°C were in-vestigated and compared with those of the alloy prepared by conventional casting and *** was found that the oxidation kinetics of the as-built and the forged materials followed the parabolic rate law during isothermal oxidation at 550°C,but the HIPed materials exhibited a parabolic-to-linear kinetic transition,suggesting that the larger grain sizes enhanced the *** oxide layers of all materials were composed of a large fraction of monoclinic zirconia phase(m-ZrO_(2))and a small fraction of tetrago-nal zirconia phase(t-ZrO_(2)),and transformed from t-ZrO_(2)to m-ZrO_(2)with increasing oxidation *** surface hardness of the as-built,the forged and the HIPed materials increased from 215,204,and 188 HV before oxidation to 902,1070,and 1137 HV after oxidation,*** cross-sections of the materi-als showed the presence of micropores and microcracks inside the oxide layers with thicknesses ranging from 4 to 8μ*** the oxidation temperature of 600°C and oxidation time duration of 3 h,a dense black m-ZrO_(2)oxide layer with smooth surface and 902 HV hardness was obtained on the EBM as-built Zr-2.5Nb materials.

关键词： Zr-2.5Nb alloy Electron beam melting Thermal oxidation Oxidation behaviors and kinetics Grain size

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A Semantic Mention Graph Augmented Model for Document-Level Event Argument Extraction

arXiv

引用

arXiv 2024年

作者： Zhang, Jian Yang, Changlin Zhu, Haiping Lin, Qika Xu, Fangzhi Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University China National Engineering Lab for Big Data Analytics Xi’an China National University of Singapore Singapore

Document-level Event Argument Extraction (DEAE) aims to identify arguments and their specific roles from an unstructured document. The advanced approaches on DEAE utilize prompt-based methods to guide pre-trained language models (PLMs) in extracting arguments from input documents. They mainly concentrate on establishing relations between triggers and entity mentions within documents, leaving two unresolved problems: a) independent modeling of entity mentions;b) document-prompt isolation. To this end, we propose a semantic mention Graph Augmented Model (GAM) to address these two problems in this paper. Firstly, GAM constructs a semantic mention graph that captures relations within and between documents and prompts, encompassing co-existence, co-reference and co-type relations. Furthermore, we introduce an ensembled graph transformer module to address mentions and their three semantic relations effectively. Later, the graph-augmented encoder-decoder module incorporates the relation-specific graph into the input embedding of PLMs and optimizes the encoder section with topology information, enhancing the relations comprehensively. Extensive experiments on the RAMS and WikiEvents datasets demonstrate the effectiveness of our approach, surpassing baseline methods and achieving a new state-of-the-art performance. © 2024, CC BY.

关键词： Extraction

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Recurrent Neural Networks and Long Short-Term Memory Networks: Tutorial and Survey

arXiv

引用

arXiv 2023年

作者： Ghojogh, Benyamin Ghodsi, Ali Department of Statistics and Actuarial Science David R. Cheriton School of Computer Science Data Analytics Laboratory University of Waterloo WaterlooON Canada

This is a tutorial paper on Recurrent Neural Network (RNN), Long Short-Term Memory Network (LSTM), and their variants. We start with a dynamical system and backpropagation through time for RNN. Then, we discuss the problems of gradient vanishing and explosion in long-term dependencies. We explain close-to-identity weight matrix, long delays, leaky units, and echo state networks for solving this problem. Then, we introduce LSTM gates and cells, history and variants of LSTM, and Gated Recurrent Units (GRU). Finally, we introduce bidirectional RNN, bidirectional LSTM, and the Embeddings from Language Model (ELMo) network, for processing a sequence in both directions. Copyright © 2023, The Authors. All rights reserved.

关键词： Long short-term memory

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Performance and Energy Savings Trade-Off with Uncertainty-Aware Cloud Workload Forecasting

Performance and Energy Savings Trade-Off with Uncertainty-Aw...

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International Conference on Network Protocols

作者： Diego Carraro Andrea Rossi Andrea Visentin Steven Prestwich Kenneth N. Brown Insight Centre for Data Analytics and Centre for Research Training in Artificial Intelligence School of Computer Science University College Cork Ireland

Cloud computing has seen widespread adoption because it increases the productivity and efficiency of industries and allows for effective scalability of their business [1]. Guaranteeing performance levels is at the core of cloud services and requires huge computational resources, especially with the latest advances in technologies such as Artificial Intelligence and the Internet of Things [2]. Typically, customers subscribe to agreements where cloud providers ensure specific levels of reliability, availability and responsiveness to systems and applications and describe penalties if the service levels are not met. At the same time, massive computational resources are a cost for providers and have a significant environmental impact, which will increase in the future. It is estimated that the energy consumption of data centres (which host cloud services) will grow from 292 TWh in 2016 to 353 TWh in 2030 [3], and greenhouse gas emissions will increase over 14% in 2040, compared to a 1-1.6% increase in the 2007–2016 [4].

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data-Driven Analysis of Climate Change Impacts on Public Health: A Systematic Review and Future Directions

Data-Driven Analysis of Climate Change Impacts on Public Hea...

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IEEE International Conference on Big data

作者： Sahar Yarmohammadtoosky Ying Xie School of Data Science and Analytics Kennesaw State University Marietta Georgia USA College of Computing and Software Engineering Kennesaw State University Marietta Georgia USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Climate change profoundly impacts human health, ecosystems, and food and water security. This review provides a comprehensive analysis of climate change's effects on human health using data science methods, including statistical and machine learning models, to assess current knowledge. It identifies critical gaps in data collection and methodological limitations across different regions and climates. Key recommendations include improving data resolution and duration to enhance model accuracy, as well as fostering collaboration between data scientists, public health experts, and climate specialists. The review underscores the need for innovative, data-driven models to better predict and address the health impacts of climate change.

关键词： Climate change Analytical models Biological system modeling Ecosystems Machine learning data science data models Thermal pollution Public healthcare Meteorology

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