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Landscape of sign language research based on smartphone apps: coherent literature analysis, motivations, open challenges, recommendations and future directions for app assessment

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Universal Access in the Information Society 2024年第2期23卷 687-702页

作者： David, Dianes Alamoodi, A.H. Albahri, O.S. Zaidan, B.B. Zaidan, A.A. Garfan, Salem Ismail, Amelia Ritahani Albahri, A.S. Alsinglawi, Belal Malik, R.Q. Perak Malaysia Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Faculty of Engineering and IT The British University in Dubai Dubai United Arab Emirates International Islamic University Malaysia Kuala Lumpur Malaysia Baghdad Iraq School of Computer Data and Mathematical Sciences Western Sydney University Sydney Australia Medical Intrumentation Techniques Engineering Department Al-Mustaqbal University College Babylon Iraq Computer Techniques Engineering Department Mazaya University College Thi-Qar Nassiriya Iraq

Numerous nations have prioritised the inclusion of citizens with disabilities, such as hearing loss, in all aspects of social life. Sign language is used by this population, yet they still have trouble communicating with others. Many sign language apps are being created to help bridge the communication gap as a result of technology advances enabled by the widespread use of smartphones. These apps are widely used because they are accessible and inexpensive. The services and capabilities they offer and the quality of their content, however, differ greatly. Evaluation of the quality of the content provided by these applications is necessary if they are to have any kind of real effect. A thorough evaluation like this will inspire developers to work hard on new apps, which will lead to improved software development and experience overall. This research used a systematic literature review (SLR) method, which is recognised in gaining a broad understanding of the study whilst offering additional information for future investigations. SLR was adopted in this research for smartphone-based sign language apps to understand the area and main discussion aspects utilised in the assessment. These studies were reviewed on the basis of related work analysis, main issues, discussions and methodological aspects. Results revealed that the evaluation of sign language mobile apps is scarce. Thus, we proposed a future direction for the quality assessment of these apps. The findings will benefit normal-hearing and hearing-impaired users and open up a new area where researchers and developers could work together on sign language mobile apps. The results will help hearing and non-hearing users and will pave the way for future collaboration between academicians and app developers in the field of sign language technology. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Smartphones

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DCHM: Dynamic Collaboration of Heterogeneous Models Through Isomerism Learning in a Blockchain-Powered Federated Learning Framework 39

DCHM: Dynamic Collaboration of Heterogeneous Models Through ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Hao, Zhihao Zhang, Bob Li, Haisheng School of Computer Science and Artificial Intelligence Beijing Key Laboratory of Commercial Data Security Protection and Intelligent Governance Beijing Technology and Business University Beijing China Department of Computer and Information Science Faculty of Science and Technology University of Macau Taipa China Centre for Artificial Intelligence and Robotics Institute of Collaborative Innovation University of Macau Taipa China

ISBN: (纸本)157735897X

Solutions to time-varying problems are crucial for research areas such as predicting changes in human body shape over time. While recurrent neural networks have made significant advancements in this field, their reliance on centralized processing has led to challenges such as model silos and data isolation. In response, distributed AI systems like federated learning have emerged to facilitate dynamic collaboration among models;however, they still depend on central coordinators, which pose risks to system security and efficiency. Moreover, traditional federated learning primarily supports homogeneous models and lacks effective strategies for the interaction of heterogeneous models. To address these limitations, we propose a novel method called Dynamic Collaboration of Heterogeneous Models (DCHM), based on Isomerism Learning, which leverages a consortium blockchain network to enhance model credibility and facilitate coordination among heterogeneous models. Additionally, we introduce a Distributed Hierarchical Aggregation (DHA) algorithm that enables permissioned nodes within each group to aggregate local model results and share them for standardized processing. After several iterative cycles, these nodes perform secondary integration of local results to produce global outcomes. Experimental results demonstrate that DCHM effectively analyzes the temporal variability of body shape changes with high efficiency. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Federated learning

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Adaptive Modeling for Real-Time data Analysis with Machine Learning 3

Adaptive Modeling for Real-Time Data Analysis with Machine L...

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3rd International Conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Mahesh, T.R. Singh, Shweta Srivastav, Vaibhav Kalra, Hitesh Surulivel Rajan, T. Yadav, Gitanjali B. Computer Science and Engineering Faculty of Engineering and Technology Karnataka Bangalore India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Computer Science & Engineering Jaipur India Chitkara University Institute of Engineering and Technology Chitkara University Centre of Interdisciplinary Research in Business and Technology Punjab India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Chennai127 India Vishwakarma Institute of Information Technology Department of Artificial Intelligence & Data Science Pune India

ISBN: (纸本)9798350319125

Adaptive Modeling for actual-Time information evaluation with system studying is a critical studies area in the modern-day technology of information-in depth evaluation. Conventional gadget studying strategies are static, meaning that the models do no longer evolve as information scientist interactions occur or because the data changes over time. As a result, models can grow to be obsolete and further downstream machine overall performance will become constrained. Consequently, the want for adaptive modeling algorithms has arisen with the intention to permit dynamic, system-stage getting to know from statistics. Such strategies look to provide affordable predictions for real-time inputs, while also looking forward to enough destiny reaction behavior to make more accurate predictions for destiny, unseen facts. The purpose of this approach is to create a solution which optimally balances accuracy and flexibility for an ever converting environment. One of these system could be carried out for predictive upkeep, diagnostic analysis, cyber security detection or self-sustaining vehicle decision making. © 2023 IEEE.

关键词： Forecasting

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HySpecNet-11k: a Large-Scale Hyperspectral dataset for Benchmarking Learning-Based Hyperspectral Image Compression Methods

HySpecNet-11k: a Large-Scale Hyperspectral Dataset for Bench...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Martin Hermann Paul Fuchs Begüm Demir Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Germany

The development of learning-based hyperspectral image compression methods has recently attracted great attention in remote sensing. Such methods require a high number of hyperspectral images to be used during training to optimize all parameters and reach a high compression performance. However, existing hyperspectral datasets are not sufficient to train and evaluate learning-based compression methods, which hinders the research in this field. To address this problem, in this paper we present HySpecNet-11k that is a large-scale hyperspectral benchmark dataset made up of 11,483 nonoverlapping image patches. Each patch is a portion of 128 × 128 pixels with 224 spectral bands and a ground sample distance of 30 m. We exploit HySpecNet-11k to benchmark the current state of the art in learning-based hyperspectral image compression by focussing our attention on various 1D, 2D and 3D convolutional autoencoder architectures. Nevertheless, HySpecNet-11k can be used for any unsupervised learning task in the framework of hyperspectral image analysis. The dataset, our code and the pre-trained weights are publicly available at https://***.

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Transformer-Based Multi-Modal Learning for Multi-Label Remote Sensing Image Classification

Transformer-Based Multi-Modal Learning for Multi-Label Remot...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： David Sebastian Hoffmann Kai Norman Clasen Begüm Demir Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Germany

In this paper, we introduce a novel Synchronized Class Token Fusion (SCT Fusion) architecture in the framework of multi-modal multi-label classification (MLC) of remote sensing (RS) images. The proposed architecture leverages modality-specific attention-based transformer encoders to process varying input modalities, while exchanging information across modalities by synchronizing the special class tokens after each transformer encoder block. The synchronization involves fusing the class tokens with a trainable fusion transformation, resulting in a synchronized class token that contains information from all modalities. As the fusion transformation is trainable, it allows to reach an accurate representation of the shared features among different modalities. Experimental results show the effectiveness of the proposed architecture over single-modality architectures and an early fusion multi-modal architecture when evaluated on a multi-modal MLC dataset. The code of the proposed architecture is publicly available at https://***/rsim/sct-fusion.

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Optimal bounds for ℓp sensitivity sampling via ℓ2 augmentation 24

Optimal bounds for ℓp sensitivity sampling via ℓ2 augmenta...

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Proceedings of the 41st International Conference on Machine Learning

作者： Alexander Munteanu Simon Omlor Dortmund Data Science Center Faculties of Statistics and Computer Science TU Dortmund University Dortmund Germany Faculty of Statistics TU Dortmund University Dortmund Germany and Lamarr-Institute for Machine Learning and Artificial Intelligence Dortmund Germany

data subsampling is one of the most natural methods to approximate a massively large data set by a small representative proxy. In particular, sensitivity sampling received a lot of attention, which samples points proportional to an individual importance measure called sensitivity. This framework reduces in very general settings the size of data to roughly the VC dimension d times the total sensitivity S while providing strong (1±ε) guarantees on the quality of approximation. The recent work of Woodruff & Yasuda (2023c) improved substantially over the general $\tilde O(\varepsilon^{-2}\mathfrak Sd)$ bound for the important problem of ℓp subspace embeddings to $\tilde O(\varepsilon^{-2}\mathfrak S^{2/p})$ for p ∈ [1, 2]. Their result was subsumed by an earlier $\tilde O(\varepsilon^{-2}\mathfrak Sd^{1-p/2})$ bound which was implicitly given in the work of Chen & Derezinski (2021). We show that their result is tight when sampling according to plain ℓp sensitivities. We observe that by augmenting the ℓp sensitivities by ℓ2 sensitivities, we obtain better bounds improving over the aforementioned results to optimal linear $\tilde O(\varepsilon^{-2}(\mathfrak S+d)) = \tilde O(\varepsilon^{-2}d)$ sampling complexity for all p ∈ [1, 2]. In particular, this resolves an open question of Woodruff & Yasuda (2023c) in the affirmative for p ∈ [1, 2] and brings sensitivity subsampling into the regime that was previously only known to be possible using Lewis weights (Cohen & Peng, 2015). As an application of our main result, we also obtain an Õ(ε-2µd) sensitivity sampling bound for logistic regression, where µ is a natural complexity measure for this problem. This improves over the previous Õ (ε-2µ2d) bound of Mai et al. (2021) which was based on Lewis weights subsampling.

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Transformer-based Multi-Modal Learning for Multi Label Remote Sensing Image Classification

arXiv

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arXiv 2023年

作者： Hoffmann, David Sebastian Clasen, Kai Norman Demir, Begüm Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Germany

In this paper, we introduce a novel Synchronized Class Token Fusion (SCT Fusion) architecture in the framework of multimodal multi-label classification (MLC) of remote sensing (RS) images. The proposed architecture leverages modality-specific attention-based transformer encoders to process varying input modalities, while exchanging information across modalities by synchronizing the special class tokens after each transformer encoder block. The synchronization involves fusing the class tokens with a trainable fusion transformation, resulting in a synchronized class token that contains information from all modalities. As the fusion transformation is trainable, it allows to reach an accurate representation of the shared features among different modalities. Experimental results show the effectiveness of the proposed architecture over single-modality architectures and an early fusion multi-modal architecture when evaluated on a multi-modal MLC dataset. The code of the proposed architecture is publicly available at https://***/rsim/sct-fusion. © 2023, CC BY.

关键词： Image classification

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Capturing the Complexity of Human Strategic Decision-Making with Machine Learning

arXiv

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arXiv 2024年

作者： Zhu, Jian-Qiao Peterson, Joshua C. Enke, Benjamin Griffiths, Thomas L. Department of Computer Science Princeton University United States Faculty of Computing and Data Science Boston University United States Department of Economics Harvard University NBER United States Department of Psychology Princeton University United States

Understanding how people behave in strategic settings–where they make decisions based on their expectations about the behavior of others–is a longstanding problem in the behavioral sciences. We conduct the largest study to date of strategic decision-making in the context of initial play in two-player matrix games, analyzing over 90,000 human decisions across more than 2,400 procedurally generated games that span a much wider space than previous datasets. We show that a deep neural network trained on these data predicts people’s choices better than leading theories of strategic behavior, indicating that there is systematic variation that is not explained by those theories. We then modify the network to produce a new, interpretable behavioral model, revealing what the original network learned about people: their ability to optimally respond and their capacity to reason about others are dependent on the complexity of individual games. This context-dependence is critical in explaining deviations from the rational Nash equilibrium, response times, and uncertainty in strategic decisions. More broadly, our results demonstrate how machine learning can be applied beyond prediction to further help generate novel explanations of complex human behavior. © 2024, CC BY.

关键词： Adversarial machine learning

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Critical Evaluation of the Role of Cloud Systems and Networking in the Security and Growth of the Business Market

Critical Evaluation of the Role of Cloud Systems and Network...

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Cyber Resilience (ICCR), International Conference on

作者： Hussam N. Fakhouri Basim Alhadidi Mohammad A. AlSharaiah Heba Al Naddaf Ashraf Saleh Alomoush data Data Science and Artificial Intelligence Department Faculty of Information Technology University of Petra Amman Jordan Department of Computer Information Systems Al-Balqa Applied University Salt Jordan Data Science and Artificial Intelligence Department Al-Ahliyya Amman University Amman Jordan Deprtment of Electronic Media Faculty of Media Damascus University Damascus Syria Science and Artificial Intelligence Department Faculty of Information Technology University of Petra Amman Jordan

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

This paper aims to assesses the multifaceted role of cloud systems in stimulating and protecting business market growth, with the goal of exploring the transformative benefits of cloud computing, such as lower costs, increased flexibility, improved data security, efficient collaboration, and greater integration, while also highlighting the challenges and risks associated with it. These include data protection, legal concerns, and compliance, which are especially critical given the growing incidence of cybersecurity risks. Furthermore, the paper dives into the many types of cloud services, such as infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS), outlining their distinct benefits, potential downsides, and applicability in various business situations. It also looks at new trends and solutions, particularly the rise of hybrid cloud systems.

关键词： Cloud computing Technological innovation Costs Social networking (online) data security Software as a service Platform as a service

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Annotation Cost Efficient Active Learning for Content Based Image Retrieval

Annotation Cost Efficient Active Learning for Content Based ...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Julia Henkel Genc Hoxha Gencer Sumbul Lars Möllenbrok Begüm Demir Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Germany

Deep metric learning (DML) based methods have been found very effective for content-based image retrieval (CBIR) in remote sensing (RS). For accurately learning the model parameters of deep neural networks, most of the DML methods require a high number of annotated training images, which can be costly to gather. To address this problem, in this paper we present an annotation cost efficient active learning (AL) method (denoted as ANNEAL). The proposed method aims to iteratively enrich the training set by annotating the most informative image pairs as similar or dissimilar, while accurately modelling a deep metric space. This is achieved by two consecutive steps. In the first step the pairwise image similarity is modelled based on the available training set. Then, in the second step the most uncertain and diverse (i.e., informative) image pairs are selected to be annotated. Unlike the existing AL methods for CBIR, at each AL iteration of ANNEAL a human expert is asked to annotate the most informative image pairs as similar/dissimilar. This significantly reduces the annotation cost compared to annotating images with land-use/land cover class labels. Experimental results show the effectiveness of our method. The code of ANNEAL is publicly available at https://***/rsim/ANNEAL.

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