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Building a portable parallel asynchronous PDE solver using Kokkos 31

Building a portable parallel asynchronous PDE solver using K...

引用

31st IEEE International Conference on High Performance Computing, data, and Analytics Workshops, HiPCW 2024

作者： Bhat, Ranjan Aditya, Konduri Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal India Department of Computational and Data Sciences Indian Institute of Science Bengaluru India

ISBN: (纸本)9798331509118

In this study, we outline the design and implementation of a portable massively parallel asynchronous solver for time-dependent partial differential equations (PDEs). The solver is implemented using Kokkos library for portability across different node architectures and is coupled with the communication-avoiding algorithm that relaxes the communication across nodes, thus improving the scalability. Asynchrony-tolerant finite difference schemes for spatial derivatives along with the low storage Runge-Kutta scheme for time integration are used to achieve high-order accuracy. The efficacy of the implementation is demonstrated using a mini-app that solves the three-dimensional diffusion problem. © 2024 IEEE

关键词： Scalability

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The differential meet-in-the-middle attack on FUTURE and CRAFT 13

The differential meet-in-the-middle attack on FUTURE and CRA...

引用

13th International Conference on Communication and Network Security, ICCNS 2023

作者： Lin, Han Zou, Jian Li, Jiayin College of Computer and Data Science Fuzhou University China College of Computer and Cyber Security Fujian Normal University China

ISBN: (纸本)9798400707964

The differential meet-in-the-middle (MITM) attack is a new cryptanalysis technique proposed at Crypto 2023. It has achieved good results in attacking some symmetric encryption algorithms. In this paper, we enhance the differential-meet-in-the-middle attack by utilizing MILP to construct the best differential trail for a distinguisher in the offline phase and optimizing the attack process during the online phase. This optimization leads to a reduction in the time complexity of the attack. We apply the differential meet-inthe- middle attack to 8-round FUTURE with 264data, 248 memory, 2124time complexity. Besides, our attack to the on 14-round CRAFT with 264 data, 274 memory, 2104 time complexity. As far as we know, it is the first key recovery attack to FUTURE. © 2023 ACM.

关键词： Integer programming

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Analyzing Jordanian Higher Education Instructors' Perception Towards Metaverse Based Education 2

Analyzing Jordanian Higher Education Instructors' Perception...

引用

2nd International Conference on Cyber Resilience, ICCR 2024

作者： Omar, Khalil Abu-Aishah, Saif Matar, Izzeddin Al Smadi, Duha Fakhouri, Hussam Abuarqoub, Mohammad University Of Petra Computer Science Department Amman Jordan University Of Petra Information Security Department Amman Jordan University Of Petra Data Science And Ai Department Amman Jordan

ISBN: (纸本)9798350394962

In recent years, many researchers have become interested in how the Metaverse can be applied in higher education. Although the use of Metaverse in education is still in its early stages, ongoing research on virtual worlds in education provides an opportunity to consider potential challenges in exploring the integration of Metaverse in education. Furthermore, there is a lack of empirical studies exploring the factors that impact the acceptance and use of immersive technologies, such as Augmented Reality, Virtual Reality, and Metaverse in higher education, particularly in developing countries such as Jordan. Therefore, the present study aims to create a comprehensive understanding of the current situation, perceptions, needs, and concerns related to integrating the Metaverse into Jordanian higher education institutes. © 2024 IEEE.

关键词： Augmented reality

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Selective Temporal Knowledge Graph Reasoning 30

Selective Temporal Knowledge Graph Reasoning

引用

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

作者： Hou, Zhongni Jin, Xiaolong Li, Zixuan Bai, Long Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9782493814104

Temporal Knowledge Graph (TKG), which characterizes temporally evolving facts in the form of (subject, relation, object, timestamp), has attracted much attention recently. TKG reasoning aims to predict future facts based on given historical ones. However, existing TKG reasoning models are unable to abstain from predictions they are uncertain, which will inevitably bring risks in real-world applications. Thus, in this paper, we propose an abstention mechanism for TKG reasoning, which helps the existing models make selective, instead of indiscriminate, predictions. Specifically, we develop a confidence estimator, called Confidence Estimator with History (CEHis), to enable the existing TKG reasoning models to first estimate their confidence in making predictions, and then abstain from those with low confidence. To do so, CEHis takes two kinds of information into consideration, namely, the certainty of the current prediction and the accuracy of historical predictions. Experiments with representative TKG reasoning models on two benchmark datasets demonstrate the effectiveness of the proposed CEHis. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Forecasting

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Language-agnostic Zero-shot Entity Linking via Entity Prototype Network 15

Language-agnostic Zero-shot Entity Linking via Entity Protot...

引用

15th IEEE International Conference on Knowledge Graph, ICKG 2024

作者： Yang, Haihong Qin, Ming Zhang, Qiang Xu, Renjun Zhejiang University College of Computer Science and Technology Hangzhou China Zhejiang University School of Software Technology Hangzhou China Zhejiang University Center for Data Science Hangzhou China

ISBN: (纸本)9798331508821

Following the recent trend of language understanding, we introduce a novel approach for constructing a general-purpose entity linking system by learning entity prototypes. Our model, the Entity Prototype Network (EPN), generates language-agnostic entity prototypes by reading entity descriptions and aligning diverse surface forms in different languages to the corresponding entity prototype in a unified vector space. To validate our core assumption of learnable language-agnostic entity prototypes, we define a new Zero-shot Cross-lingual Entity Linking (ZXEL) task. Additionally, we propose a simple yet effective auxiliary task termed Entity Identification, which enhances our model's ability to capture both intra-entity similarity and inter-entity dissimilarity. We evaluate our model on the new task in both classic and generalized settings. Experimental results demonstrate consistent improvements (exceeding 30% on average) over competitive baselines. An ablation study confirms the necessity of our model design and highlights the impact of the proposed auxiliary task. © 2024 IEEE.

关键词： Zero-shot learning

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Nested Event Extraction upon Pivot Element Recognition 30

Nested Event Extraction upon Pivot Element Recognition

引用

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

作者： Ren, Weicheng Li, Zixuan Jin, Xiaolong Bai, Long Su, Miao Liu, Yantao Guan, Saiping Guo, Jiafeng Cheng, Xueqi School of Computer Science and Technology University of Chinese Academy of Sciences China Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)9782493814104

Nested Event Extraction (NEE) aims to extract complex event structures where an event contains other events as its arguments recursively. Nested events involve a kind of Pivot Elements (PEs) that simultaneously act as arguments of outer-nest events and as triggers of inner-nest events, and thus connect them into nested structures. This special characteristic of PEs brings challenges to existing NEE methods, as they cannot well cope with the dual identities of PEs. Therefore, this paper proposes a new model, called PerNee, which extracts nested events mainly based on recognizing PEs. Specifically, PerNee first recognizes the triggers of both inner-nest and outer-nest events and further recognizes the PEs via classifying the relation type between trigger pairs. The model uses prompt learning to incorporate information from both event types and argument roles for better trigger and argument representations to improve NEE performance. Since existing NEE datasets (e.g., Genia11) are limited to specific domains and contain a narrow range of event types with nested structures, we systematically categorize nested events in the generic domain and construct a new NEE dataset, called ACE2005-Nest. Experimental results demonstrate that PerNee consistently achieves state-of-the-art performance on ACE2005-Nest, Genia11, and Genia13. The ACE2005-Nest dataset and the code of the PerNee model are available at https://***/waysonren/PerNee. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Character recognition

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Machine Learning Approach for Complaint Prediction in the Telecom Industry 2

Machine Learning Approach for Complaint Prediction in the Te...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Gewaly, Mohamed Mahmoud Saeed, Mahmoud Samy Ammar, Ammar Mohammed FGSSR Cairo University Dept. of Data Science Cairo Egypt Telecom Egypt Cairo Egypt FGSSR Cairo University Dep. of Computer Science Cairo Egypt

ISBN: (纸本)9798350362633

The quality of service of the individual user of telecommunication services is one of the most critical criteria in the telecom industry that affects customer satisfaction. This paper aims to predict customer complaints before escalation and validate current escalated complaints in fixed broadband from telecommunication companies via historical and real-time customer data, including service logs and previous complaints, to optimize resource allocation and maximize customer satisfaction as the challenges are related to the volume of physical complains and churn. The dataset is real-world data provided by a single Egyptian mobile network operator and consists of 12 features pertaining to 100,000 customers. We represent 12 predictive models developed using machine learning algorithms via advanced techniques such as classification algorithms like Random Forest Classifier (RFC), AdaBoost Classifier with a Decision Tree (DT), Logistic Regression (LR), -Nearest Neighbors (KNN), Gaussian Naive Bayes (GNB), Decision Tree Classifier (DTC), Support Vector Machine (SVM) to analyze various features and patterns within the data. The results show that the AdaBoost with a Decision Tree model did well using a variety of assessment indicators, including Accuracy, Precision, Recall, and F1-score;the model aims to classify customers, indicating their likelihood of raising complaints in the future. The results and insights obtained from this study can provide telecom service providers with valuable guidance for implementing data-driven strategies to improve customer experience and take proactive maintenance that will reduce cost and churn. © 2024 IEEE.

关键词： Customer satisfaction

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The Implementation of Integration of ML Algorithm for the Design of Stability System for Grid System

The Implementation of Integration of ML Algorithm for the De...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Chandani, Priyanka Vijay, Sandip Soumya, K. Noida Institute of Engineering and Technology Department of Data Science Uttar Pradesh Greater Noida India Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Science Bangalore India

ISBN: (纸本)9798350357769

A modern decentralized electric grid is a groundbreaking system that integrates demand response effortlessly and doesn't need major infrastructure changes. Within the decentralized domain, users independently control their power consumption according to the frequency of the grid. This is made possible by the use of reasonably priced devices like smart meters, which allow grid frequency to be measured from almost anywhere. Different data-level resampling strategies have been used to address the problem of data imbalance, while data normalization approaches have been used to reduce biased behavior among characteristics. The findings clearly show that in terms of the performance of classifiers, a balanced dataset performs better than an unbalanced one. Specifically, for unbalanced datasets, oversampling approaches are more effective than under sampling ones. With a precision level of 94.7 percent, the XGBoost algorithm is the best performer within the range of deep learning algorithms that are taken into consideration. Remarkably, XGBoost's accuracy forecast rises to 96.8% when paired with random oversampling. This improved model manages the volatility of renewable energy supplies and maximizes their use by accurately forecasting frequency variations in decentralized power networks. This model's predictive capacities have great potential to support the stability of distributed electricity grids, which will improve the distribution and administration of energy on a larger scale. © 2023 IEEE.

关键词： data Normalization data Resampling Decentralized Power Grid Demand Response and Classifiers Performance Energy Policy Grid Stability Machine Learning Renewable Energy Integration Renewable Sources Smart Meters Stability Prediction XGBoost Algorithm

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Multimodal Machine Learning for Early Detection of Autism in Children: Integrating Gestures and Mood Analysis 2

Multimodal Machine Learning for Early Detection of Autism in...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Puri, Chetan Ghodeswar, Ujwala Reddy, K.T.V. Sharma, Mansi Warudkar, Sahil Department of Computer Science and Design Wardha India Yeshwantrao Chavan College of Engineering Department of Electronics Engineering Nagpur India Department of Artificial Intelligence and Data Science Wardha India

ISBN: (纸本)9798331528713

Autism spectrum disorder (ASD) is characterized by neurological disorders and challenges with interpersonal communication, communication, and schedule behaviour. Early distinguishing proof of ASD is vital to optimize the intervention and progress long-term results. This paper analyses the potential of numerous machine learning procedures for early location of ASD in children. We propose a novel approach that combines gesture assessment with cognitive evaluation to capture a comprehensive picture of a child's behaviour. Side effect analysis will focus on highlights such as body language, hand movements, and eye contact, which can give profitable bits of knowledge into the social and communication designs of children with ASD. Therapy will utilize facial expressions, tone of voice, and body language to survey a child's emotional state. The combination of these strategies empowers the machine learning show to recognize complex structures that will be characteristic of ASD. This consider has the potential to move forward the exactness and productivity of ASD diagnosis in children. The proposed strategy can be connected in different situations, such as during routine testing or through a portable application. Early and precise conclusion can clear the way for convenient intervention and back for children with ASD, eventually driving to positive long-term results. © 2024 IEEE.

关键词： Diseases

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News-Driven Price Movement Forecasting with Label-Prior Graph Attention 24

News-Driven Price Movement Forecasting with Label-Prior Grap...

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33rd Companion of the ACM World Wide Web Conference, WWW 2023

作者： Liou, Yi-Ting Chen, Chung-Chi Huang, Hen-Hsen Chen, Hsin-Hsi Graduate Program of Data Science National Taiwan University Academia Sinica Taiwan AIST Japan Institute of Information Science Academia Sinica Taiwan Department of Computer Science and Information Engineering National Taiwan University Taiwan

ISBN: (纸本)9798400701726

This paper introduces a novel approach to stock movement prediction using multi-label classification, leveraging the interconnections between news articles and related company stocks. We present the Label-Prior Graph Attention (LPGA) model, which significantly enhances the performance of news-driven stock price movement forecasting. This model is comprised of a unique graph attention architecture, incorporating a label encoder and a text encoder, designed to effectively capture and utilize the relationships between labels in a graph-based context. Our model demonstrates superior performance over several benchmark models. The LPGA model’s efficacy is further validated through experiments on two multilabel datasets. The model outperforms established baseline models across various evaluation metrics. The success of the LPGA model in both stock movement prediction and general multi-label classification tasks indicates its potential as a versatile tool in the realm of machine learning and financial analysis. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Graphic methods

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