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arXiv 2024年

作者： Shahin, Nada Ismail, Leila Intelligent Distributed Computing and Systems Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates Emirates Center for Mobility Research United Arab Emirates University Abu Dhabi United Arab Emirates

With the growing Deaf and Hard of Hearing population worldwide and the persistent shortage of certified sign language interpreters, there is a pressing need for an efficient, signs-driven, integrated end-to-end translation system, from sign to gloss to text and vice-versa. There has been a wealth of research on machine translations and related reviews. However, there are few works on sign language machine translation considering the particularity of the language being continuous and dynamic. This paper aims to address this void, providing a retrospective analysis of the temporal evolution of sign language machine translation algorithms and a taxonomy of the Transformers architectures, the most used approach in language translation. We also present the requirements of a real-time Quality-of-Service sign language machine translation system underpinned by accurate deep learning algorithms. We propose future research directions for sign language translation *** Codes I.2, I.2.7, I.4, I.4.9 © 2024, CC BY.

关键词： Neural machine translation

Insights into the Advancements of Artificial Intelligence and Machine Learning, the Present State of Art, and Future Prospects: Seven Decades of Digital Revolution 4th

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Insights into the Advancements of Artificial Intelligence an...

4th International Conference on Smart Computing and Informatics, SCI 2020

作者： Bindra, Prabhleen Kshirsagar, Meghana Ryan, Conor Vaidya, Gauri Gupt, Krishn Kumar Kshirsagar, Vivek Government College of Engineering Aurangabad Maharashtra India Biocomputing and Developmental Systems Lab Lero the Science Foundation Ireland Research Centre for Software University of Limerick Limerick Ireland Limerick Institute of Technology Limerick Ireland

ISBN: (纸本)9789811608773

The desire of human intelligence to surpass its potential has triggered the emergence of artificial intelligence and machine learning. Over the last seven decades, these terms have gained much prominence in the digital arena due to its wide adoption of techniques for designing affluent industry-enabled solutions. In this comprehensive survey on artificial intelligence, the authors provide insights from the evolution of machine learning and artificial intelligence to the present state of art and how the technology in future can be exploited to yield solutions to some of the challenging global problems. The discussion centers around successful deployment of diverse use cases for the present state of affairs. The rising interest among researchers and practitioners led to the unfolding of AI into many popular subfields as we know today. Through the course of this research article, the authors provide brief highlights about techniques for supervised as well as unsupervised learning. AI has paved the way to accomplish cutting-edge research in complex competitive domains ranging from autonomous driving, climate change, cyber-physical security systems, to healthcare diagnostics. The study concludes by depicting the growing share in market revenues from artificial intelligence-powered products and the forecasted billions of dollars worth of market shares ahead in the coming decade. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Machine learning

Semi-Supervised Image Captioning Considering Wasserstein Graph Matching

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arXiv 2024年

作者： Yang, Yang The Nanjing University of Science and Technology Nanjing210094 China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China Ministry of Education State Key Lab. for Novel Software Technology Nanjing University China

Image captioning can automatically generate captions for the given images, and the key challenge is to learn a mapping function from visual features to natural language features. Existing approaches are mostly supervised ones, i.e., each image has a corresponding sentence in the training set. However, considering that describing images always requires a huge of manpower, we usually have limited amount of described images (i.e., image-text pairs) and a large number of undescribed images in real-world applications. Thereby, a dilemma is the "Semi-Supervised Image Captioning". To solve this problem, we propose a novel Semi-Supervised Image Captioning method considering Wasserstein Graph Matching (SSIC-WGM), which turns to adopt the raw image inputs to supervise the generated sentences. Different from traditional single modal semi-supervised methods, the difficulty of semi-supervised cross-modal learning lies in constructing intermediately comparable information among heterogeneous modalities. In this paper, SSIC-WGM adopts the successful scene graphs as intermediate information, and constrains the generated sentences from two aspects: 1) inter-modal consistency. SSIC-WGM constructs the scene graphs of the raw image and generated sentence respectively, then employs the wasserstein distance to better measure the similarity between region embeddings of different graphs. 2) intra-modal consistency. SSIC-WGM takes the data augmentation techniques for the raw images, then constrains the consistency among augmented images and generated sentences. Consequently, SSIC-WGM combines the cross-modal pseudo supervision and structure invariant measure for efficiently using the undescribed images, and learns more reasonable mapping function. Experiments show that our method can outperform state-of-the-art comparison methods on the MS-COCO "Karpathy" offline test split, under different complex semi-supervised scenarios. Copyright © 2024, The Authors. All rights reserved.

关键词： Visual languages

An Adaptive Decision-Making Approach for Better Selection of Blockchain Platform for Health Insurance Frauds Detection with Smart Contracts: Development and Performance Evaluation 14

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An Adaptive Decision-Making Approach for Better Selection of...

14th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Kaafarani, Rima Ismail, Leila Zahwe, Oussama ICCS-Lab Computer Science Department American University of Culture and Education Beirut1507 Lebanon Laboratory School of Computing and Information Systems The University of Melbourne Melbourne Australia Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates National Water and Energy Center United Arab Emirates University Abu Dhabi United Arab Emirates

Blockchain technology has piqued the interest of businesses of all types, while consistently improving and adapting to business requirements. Several blockchain platforms have emerged, making it challenging to select a suitable one for a specific type of business. This paper presents a classification of over one hundred blockchain platforms. We develop smart contracts for detecting healthcare insurance frauds using the top two blockchain platforms selected based on our proposed decision-making map approach which selects the top suitable platforms for healthcare insurance frauds detection application. Our classification shows that the largest percentage of platforms can be used for all types of application domains, the second biggest percentage for financial services, and a small number is to develop applications in specific domains. Our decision-making map and performance evaluations reveal that Hyperledger Fabric surpassed Neo in all metrics for detecting healthcare insurance frauds. © 2023 Elsevier B.V.. All rights reserved.

关键词： Smart contract

Data-Filtered Prediction with Decomposition and Amplitude-Aware Permutation Entropy for Workload and Resource Utilization in Cloud Data Centers

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IEEE Internet of Things Journal 2025年

作者： Yuan, Haitao Hu, Qinglong Wang, Meijia Wang, Shen Bi, Jing Buyya, Rajkumar Shi, Shuyuan Yang, Jinhong Zhang, Jia Zhou, MengChu Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Computer Science Hong Kong999077 Hong Kong Beijing University of Technology School of Software Engineering The Faculty of Information Technology Beijing100124 China Lab School of Computing and Information Systems MelbourneVIC3010 Australia Beihang University School of Integrated Circuit Science and Engineering Beijing China CSSC Systems Engineering Research Institute Beijing China Southern Methodist University Department of Computer Science The Lyle School of Engineering DallasTX75205 United States New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States

In recent years, cloud computing has witnessed widespread applications across numerous organizations. Predicting workload and computing resource data can facilitate proactive service operation management, leading to substantial improvements in quality of service and cost efficiency. However, these data often exhibit non-linearity, high volatility, and interdependencies across different categories, presenting challenges for accurate forecasting. Consequently, there is a critical need to develop a method that thoroughly and comprehensively analyzes all available data to forecast future trends effectively. This work proposes a novel integrated data-enhanced prediction model named SVAPI for achieving high-accuracy workload prediction in cloud computing systems. SVAPI employs the Savitzky-Golay filter, Variational mode decomposition, and the mode selection based on Amplitude-aware Permutation entropy for feature processing, whose features are subsequently utilized by Informer for multivariate joint analysis of the enhanced data, achieving high-precision prediction. Ablation and comparative experiments with advanced prediction models are conducted on the Google cluster trace and other typical datasets. Realistic data-driven results indicate that SVAPI improves the prediction accuracy by 37.7% compared to the original Informer, with each module contributing to the performance enhancement. Furthermore, compared with Autoformer, SVAPI enhances the prediction accuracy of workload, CPU, and memory by 65.6%, 66.9%, and 70.8%, respectively, demonstrating that SVAPI owns strong abilities in noise filtering, feature processing, and multivariate joint analysis for achieving higher prediction accuracy. © 2014 IEEE.

关键词： Quality of service

Zero-Shot Stance Detection via Sentiment-Stance Contrastive Learning

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Zero-Shot Stance Detection via Sentiment-Stance Contrastive ...

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Jiaying Zou Xuechen Zhao Feng Xie Bin Zhou Zhong Zhang Lei Tian School of Computer National University of Defense Technology Changsha China Key Lab. of Software Engineering for Complex Systems National University of Defense Technology Changsha China

Zero-shot stance detection (ZSSD) is an important research problem that requires algorithms to have good stance detection capability even for unseen targets. In general, stance features can be grouped into two types: target-invariant and target-specific. Target-invariant features express the same stance regardless of the targets they are associated with, and such features are general and transferable. On the contrary, target-specific features will only be directly associated with specific targets. Therefore, it is crucial to effectively mine target-invariant features in texts in ZSSD. In this paper, we develop a method based on contrastive learning to mine certain transferable target-invariant expression features in texts from two dimensions of sentiment and stance and then generalize them to unseen targets. Specifically, we first grouped all texts into several types in terms of two orthogonal dimensions: sentiment polarity and stance polarity. Then we devise a supervised contrastive learning-based strategy to capture each type's common and transferable expressive features. Finally, we fuse the above-mentioned expressive features with the semantic features of the original texts about specific targets to deal with the stance detection for unseen targets. Extensive experiments on three benchmark datasets show that our proposed model achieves the state-of-the-art performance on most datasets. Code and other resources are available on GitHub 1 1 https://***/zoujiaying1995/sscl-project.

关键词： Training Analytical models Codes Fuses Semantics Benchmark testing Feature extraction

Domain-adaptive Graph based on Post-hoc Explanation for Cross-domain Hate Speech Detection

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Domain-adaptive Graph based on Post-hoc Explanation for Cros...

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yushan Jiang Bin Zhou Xuechen Zhao Jiaying Zou Feng Xie Liang Li School of Computer National University of Defence Technology Changsha China Key Lab. of Software Engineering for Complex Systems National University of Defence Technology Changsha China

Hate speech detection is hampered by the scarcity and topical and lexical biases of annotated data, leading to poor generalization. It is imperative to devise a cross-domain approach to solve this problem. The ability to learn transferable knowledge is critical for cross-domain hate speech detection. In this work, We propose a domain-adaptive dependency graph method based on post-hoc explanation (DPDG). We extract post-hoc explanations from fine-tuned BERT classifiers as the importance score for hate representation. Based on these, we construct in-domain graph and cross-domain graph to better learn in-domain hate representation and adapt to the target domain respectively. Finally, we use interactive GCN blocks to interactively and adaptively learn and adjust the domain adaptive graph representation. The results of cross-domain experiments on multiple domains show that our proposed model outperforms competitive baselines in cross-domain hate speech detection.

关键词： Weight measurement Adaptation models Analytical models Image edge detection Hate speech Neural networks Natural languages

Shaping History: Advanced Machine Learning Techniques for the Analysis and Dating of Cuneiform Tablets over Three Millennia

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arXiv 2024年

作者： Kapon, Danielle Fire, Michael Gordin, Shai Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Be'er Sheva Israel Department of Land of Israel Studies and Archaeology Digital Pasts Lab Ariel University Ariel Israel Digital Humanities and Social Sciences Hub Open University of Israel Ra'anana Israel

Cuneiform tablets, emerging in ancient Mesopotamia around the late fourth millennium BCE, represent one of humanity's earliest writing systems. Historically characterized by wedge-shaped marks on clay tablets, these artifacts provided insight into Mesopotamian civilization across administrative, commercial, legal, literary, and scientific domains. The traditional analysis and dating of these tablets still mainly rely on manual, subjective appreciation of shape and writing style. This approach, however, leads to many uncertainties in pinpointing the exact period the tablets originated from. Recent advances in digitization have revolutionized the study of cuneiform by enhancing accessibility and analytical capabilities. Our research uniquely focuses on the physical shapes of tablets as significant indicators of their historical periods, diverging from most studies that concentrate on textual content. Utilizing an unprecedented dataset of over 94,000 images from the Cuneiform Digital Library Initiative (CDLI) collection, our effort applies deep learning methods to the classification of cuneiform tablets, covering more than 3,000 years of history. By leveraging statistical, computational techniques, and innovative generative modeling through Variational Auto-Encoders (VAEs), we achieve substantial advancements in the automatic classification of these ancient documents, by focusing on the tablets' silhouette as a main predictor. Our classification approach begins with a Decision Tree using the height-to-width ratios of the tablets as the sole predictor which achieved 8% macro F1-score, and culminates with a ResNet50 model, which achieved a 61% macro-F1 score for tablet silhouettes. Additionally, we introduce a novel tool-set, powered by VAEs, to enhance the interpretability of our models and enable researchers to explore the changes in tablet shapes across different eras and genres. This research contributes to the fields of document analysis and diplomatics by demonstra

关键词： Decision trees

Multitask Learning Neural Networks for Pandemic Prediction with Public Stance Enhancement

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Multitask Learning Neural Networks for Pandemic Prediction w...

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Zhong Zhang Feng Xie Xuechen Zhao Bin Zhou Jian Chen Lei Tian School of Computer National University of Defence Technology Changsha China Key Lab. of Software Engineering for Complex Systems National University of Defence Technology Changsha China

State and local governments have imposed health policies to contain the spread of COVID-19 since it had a serious impact on human daily life. However, the public stance on these measures may be time-varying. It is likely to escalate the infection in the area where the public is negative or resistant. To take advantage of the correlation between public stance on health policies and the COVID-19 statistics, we propose a novel framework, Multitask Learning Neural Networks for Pandemic Prediction with Public Stance Enhancement (MP3), which is composed of three modules: (1) Stance awareness module to make stance detection on health policies from users' tweets in social media and convert them into a stance time series. (2) Temporal feature extraction module that applies Convolution Neural Network and Recurrent Neural Network to extract and fuse local patterns and long-term correlations from COVID-19 statistics. Moreover, a Stance Latency-aware Attention is proposed to capture dynamic social effects and fuse them with temporal features. (3) Multi-task prediction module to adopt Graph Convolution Network to model the spread of pandemic and employ multi-task learning to simultaneously predict COVID-19 statistics and the trend of public stance on health policies. The proposed framework outperforms state-of-the-art baselines on both confirmed cases and deaths prediction tasks.

关键词： COVID-19 Correlation Pandemics Social networking (online) Fuses Convolution Multitasking

Pursuit-evasion differential games of players with different speeds in spaces of different dimensions

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arXiv 2022年

作者： Li, Shuai Wang, Chen Xie, Guangming The State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing100871 China The National Engineering Research Center of Software Engineering Peking University Beijing100871 China

We study pursuit-evasion differential games between a faster pursuer moving in 3D space and an evader moving in a plane. We first extend the well-known Apollonius circle to 3D space, by which we construct the isochron for the considered two players. Then both cases with and without a static target are considered and the corresponding optimal strategies are derived using the concept of isochron. In order to guarantee the optimality of the proposed strategies, the value functions are given and are further proved to be the solution of Hamilton-Jacobi-Isaacs equation. Simulations with comparison between the proposed strategies and other classical strategies are carried out and the results show the optimality of the proposed strategies. Copyright © 2022, The Authors. All rights reserved.

关键词： Game theory