Reviewerly/exHarmony huggingface dataset:
A novel two-level interactive action recognition model based on inertial data fusion

Currently, the majority of solutions for human interactive action recognition typically rely on machine vision methods, thus leading to limited application scenarios for accurate recognition. In this paper, we proposed a novel two-level multi-head attentional human interaction action recognition model based on inertial measurement units. Considering that individual actions may contribute differently to interaction actions, the overall model of weighting individual actions to identify interaction actions is divided into individual scenes and interaction scenes. A multi-head attention mechanism is added to the model to obtain action weighting information in both scenes. We combine a bi-directional gated recurrent unit (BiGRU) and an improved convolutional neural network constructs to fully capture spatiotemporal features. The experimental results show that the established model can accurately recognize seven interactive actions with an average recognition accuracy of 98.73%, which verifies the excellent performance of the proposed method.
Explainable Artificial Intelligence (XAI) in Manufacturing

This chapter begins by defining explainable artificial intelligence (XAI). A procedure for implementing XAI was also established. Then, through literature analysis, the application of XAI in various fields such as medicine, service, education, finance, medical treatment, manufacturing, food, and military is compared. Some representative cases in these fields are also reported. Subsequently, several applications of XAI in the field of manufacturing are reviewed, including explaining the classification process and results of factory jobs, explaining artificial neural network (ANN)-based cycle time prediction methods, comparing the effect of alloy composition using Shapely additive explanation value (SHAP) analysis, etc.
Explainable AI in medical imaging: An overview for clinical practitioners – Beyond saliency-based XAI approaches

Driven by recent advances in Artificial Intelligence (AI) and Computer Vision (CV), the implementation of AI systems in the medical domain increased correspondingly. This is especially true for the domain of medical imaging, in which the incorporation of AI aids several imaging-based tasks such as classification, segmentation, and registration. Moreover, AI reshapes medical research and contributes to the development of personalized clinical care. Consequently, alongside its extended implementation arises the need for an extensive understanding of AI systems and their inner workings, potentials, and limitations which the field of eXplainable AI (XAI) aims at. Because medical imaging is mainly associated with visual tasks, most explainability approaches incorporate saliency-based XAI methods. In contrast to that, in this article we would like to investigate the full potential of XAI methods in the field of medical imaging by specifically focusing on XAI techniques not relying on saliency, and providing diversified examples. We dedicate our investigation to a broad audience, but particularly healthcare professionals. Moreover, this work aims at establishing a common ground for cross-disciplinary understanding and exchange across disciplines between Deep Learning (DL) builders and healthcare professionals, which is why we aimed for a non-technical overview. Presented XAI methods are divided by a method's output representation into the following categories: Case-based explanations, textual explanations, and auxiliary explanations.
QoE Sustainability on 5G and Beyond 5G Networks

The 5G network not only causes many internet real-time broadband services to become wireless, such as virtual reality and cloud gaming, but also promotes mobile-inherent brand-new emerging services, like in-vehicle real-time video communications. One of the main visions of 5G and beyond 5G is to provide human-centric communications, not only for diverse services, but also better quality of experience (QoE) for users. In the meantime, as with the time variance challenges faced by all wireless networks, the problem of controllability for QoE is still noticeable in 5G. This article presents an AI-enabled QoE predicting and sustainability architecture, cooperating with 5G network data analytic function, network slicing, and MEC technologies to collect cross-layer performance data in real time, as well as adjust network resources accordingly. Thereby, it can support QoE-demanding services, create business innovation, and improve energy efficiency. In addition to illustrating the operation of the proposed architecture with one video streaming scenario, a machine learning QoE predicting model, its performance of the field trial, and research directions are discussed.
An Algorithm of Complete Coverage Path Planning for Unmanned Surface Vehicle Based on Reinforcement Learning

A deep reinforcement learning method to achieve complete coverage path planning for an unmanned surface vehicle (USV) is proposed. This paper firstly models the USV and the workspace required for complete coverage. Then, for the full-coverage path planning task, this paper proposes a preprocessing method for raster maps, which can effectively delete the blank areas that are impossible to cover in the raster map. In this paper, the state matrix corresponding to the preprocessed raster map is used as the input of the deep neural network. The deep Q network (DQN) is used to train the complete coverage path planning strategy of the agent. The improvement of the selection of random actions during training is first proposed. Considering the task of complete coverage path planning, this paper replaces random actions with a set of actions toward the nearest uncovered grid. To solve the problem of the slow convergence speed of the deep reinforcement learning network in full-coverage path planning, this paper proposes an improved method of deep reinforcement learning, which superimposes the final output layer with a dangerous actions matrix to reduce the risk of selection of dangerous actions of USVs during the learning process. Finally, the designed method validates via simulation examples.
Imbalanced data based fault diagnosis of the chiller via integrating a new resampling technique with an improved ensemble extreme learning machine

Fault diagnosis of the chiller is essential to guarantee chiller's safe operation and reduce building energy consumption. However, the existing fault diagnosis methods rarely consider the chiller's imbalanced data conditions, which always leads to low diagnosis accuracy of the minority class samples. To figure out the issue of imbalanced fault pattern data during the chiller fault diagnosis, a hybrid resampling based improved extreme learning machine (HRIELM) is developed in our work. A new hybrid resampling technique (HRT) is first proposed to balance the majority and minority classes in the imbalanced fault pattern datasets. The HRT is then carried out repetitively to obtain multiple diverse rebalanced training datasets using different benchmark datasets. Subsequently, various primary extreme learning machine (ELM) models are established utilizing these rebalanced training datasets. Furthermore, an improved ELM model based on a novel selective ensemble learning strategy is presented to enhance the effectiveness of the chiller fault diagnosis. The basic ELM models with superior performance for diagnosing the neighbor subset constructed according to the fault snapshot sample are first picked out, and a weight factor is further defined to build the final selective ensemble ELM model. Finally, the fault pattern of the snapshot sample is identified according to the weighted voting mechanism. Detailed experimental results on the ASHRAE Research Project RP-1043 experimental datasets certify the effectiveness of the presented HRIELM scheme for the chiller fault diagnosis under the imbalanced data environments.
Conversation-based assessment: A novel approach to boosting test-taking effort in digital formative assessment

Conversational agents such as chatbots have recently gained a lot of traction in various industries, including education, marketing, and healthcare. In K–12 and higher education, conversational agents have been used to boost student learning and motivation. Recently, new applications of conversational agents, such as conversation-based assessments, have emerged in the context of educational assessments. Such applications aim to enhance students' assessment experiences through the use of conversational agents in administering items (or tasks) and providing feedback to students. To discuss the utility of conversational agents in assessments, this paper puts a special emphasis on test-taking effort in low-stakes assessments. It provides a brief introduction to educational assessments and student test-taking motivation, presents the concept of conversational agents for learning and assessment along with several examples, examines the benefits and challenges of conversational agents used for learning and assessment purposes, and discusses the concept of conversation-based assessment. This position paper contributes to the literature by discussing the utility of conversation-based assessments as a novel tool to enhance test-taking effort among students and thereby obtain a more accurate representation of students’ academic performance. Specifically, we describe the advantages and disadvantages of using conversation-based assessments for addressing the lack of test-taking effort in digital formative assessments and discuss how conversation-based assessments can foster a more engaging testing experience for students.
Explainable AI in medical imaging: An overview for clinical practitioners – Saliency-based XAI approaches

Since recent achievements of Artificial Intelligence (AI) have proven significant success and promising results throughout many fields of application during the last decade, AI has also become an essential part of medical research. The improving data availability, coupled with advances in high-performance computing and innovative algorithms, has increased AI's potential in various aspects. Because AI rapidly reshapes research and promotes the development of personalized clinical care, alongside its implementation arises an urgent need for a deep understanding of its inner workings, especially in high-stake domains. However, such systems can be highly complex and opaque, limiting the possibility of an immediate understanding of the system's decisions. Regarding the medical field, a high impact is attributed to these decisions as physicians and patients can only fully trust AI systems when reasonably communicating the origin of their results, simultaneously enabling the identification of errors and biases. Explainable AI (XAI), becoming an increasingly important field of research in recent years, promotes the formulation of explainability methods and provides a rationale allowing users to comprehend the results generated by AI systems. In this paper, we investigate the application of XAI in medical imaging, addressing a broad audience, especially healthcare professionals. The content focuses on definitions and taxonomies, standard methods and approaches, advantages, limitations, and examples representing the current state of research regarding XAI in medical imaging. This paper focuses on saliency-based XAI methods, where the explanation can be provided directly on the input data (image) and which naturally are of special importance in medical imaging.
AA-trans: Core attention aggregating transformer with information entropy selector for fine-grained visual classification

The task of fine-grained visual classification (FGVC) is to distinguish targets from subordinate classifications. Since fine-grained images have the inherent characteristic of large inter-class variances and small intra-class variances, it is considered an extremely difficult task. Most existing approaches adopt CNN-based networks as feature extractors, which causes the extracted discriminative regions to contain most parts of the object in this way, thus failing to locate the really important parts. Recently, the vision transformer (ViT) has demonstrated its power on a wide range of image tasks, which uses an attention mechanism to capture global contextual information to establish a remote dependency on the target and thus extract more powerful features. Nevertheless, the ViT model still focuses more on global coarse-grained information rather than local fine-grained information, which may lead to its undesirable performance in fine-grained image classification. To this end, we redesigned an attention aggregating transformer (AA-Trans) to better capture minor differences among images by improving the ViT structure in this paper. In detail, we propose a core attention aggregator (CAA), which enables better information sharing between each transformer layer. Besides, we further propose an innovative information entropy selector (IES) to guide the network in acquiring discriminative parts of the image precisely. Extensive experiments show that our proposed model structure can achieve a new state-of-the-art performance on several mainstream datasets.
Smart Contract-Enabled Secure Sharing of Health Data for a Mobile Cloud-Based E-Health System

Healthcare comprises the largest revenue and data boom markets. Sharing knowledge about healthcare is crucial for research that can help healthcare providers and patients. Several cloud-based applications have been suggested for data sharing in healthcare. However, the trustworthiness of third-party cloud providers remains unclear. The third-party dependency problem was resolved using blockchain technology. The primary objective of this growth was to replace the distributed system with a centralized one. Therefore, security is a critical requirement for protecting health records. Efforts have been made to implement blockchain technology to improve the security of this sensitive material. However, existing methods depend primarily on information obtained from medical examinations. Furthermore, they are ineffective for sharing continuously produced data streams from sensors and other monitoring devices. We propose a trustworthy access control system that uses smart contracts to achieve greater security while sharing electronic health records among various patients and healthcare providers. Our concept offers an active resolution for secure data sharing in mobility computing while protecting personal health information from potential risks. In assessing existing data sharing models, the framework valuation and protection approach recognizes increases in the practicality of lightweight access control architecture, low network expectancy, and significant levels of security and data concealment.
Image thresholding approaches for medical image segmentation - short literature review

Medical image analysis is an invaluable tool in medicine. Different imaging modalities provide an effective means for mapping images that can feed machine and deep learning models which can significantly contribute to increase knowledge of diseased anatomy for medical research, being an important component in diagnosis and treatment planning. Accurate segmentation of medical images is a key step in the use of Artificial Intelligence as a tool to support physicians. Although there are many approaches to medical image segmentation, thresholding is one of the simplest methods that can be used, being of great interest to the scientific community. Over the years, several approaches to segmentation of medical images based on threshold methods have been proposed, which have evolved both in computational complexity, with shorter processing times, and in efficiency and accuracy of results. This manuscript summarizes the most common threshold-based approaches for medical image segmentation and discuss some of the methods and algorithms proposed in recent years, analyzing their advantages, disadvantages, and limitations.
Convolutional neural network pruning based on multi-objective feature map selection for image classification

Deep convolutional neural networks (CNNs) are widely used for image classification. Deep CNNs often require a large memory and abundant computation resources, limiting their usability in embedded or mobile devices. To overcome this limitation, several pruning methods have been proposed. However, most of the existing methods focus on pruning parameters and cannot efficiently address the computation costs of deep CNNs. Additionally, these methods ignore the connections between the feature maps of different layers. This paper proposes a multi-objective pruning based on feature map selection (MOP-FMS). Unlike previous studies, we use the number of floating point operations (FLOPs) as a pruning objective in addition to the accuracy of the pruned network. First, we propose an encoding method based on feature map selection with a compact and efficient search space. Second, novel domain-specific crossover and mutation operators with reparation are designed to generate new individuals and make them meet the constraint rules. Then, decoding and pruning methods are proposed to prune networks based on the results of feature map selection. Finally, multi-objective optimisation is used for evaluation and individual selection. Our method has been tested with commonly used network structures. Numerical results demonstrate that the proposed method achieves better results than other state-of-the-art methods in terms of pruning rate without decreasing the accuracy rate to a high degree.
Storytelling and user experience in the cultural metaverse

Enthusiasm for the metaverse is intensifying in academia and industries. The metaverse is a complex concept, combining many technologies to create many different types of user experiences (UX), depending on the intended use. The cultural metaverse was first introduced in this study. This study is an initial attempt to fill the gaps in the practical research and storytelling research in the metaverse. Augmented reality (AR) technology is an applicative tool in cultural experience, which displays computer - generated virtual information on a real-world scene. AR displays digital information realistically, making it appear to be part of the actual environment, deepening or expanding the user's understanding of "reality". This study constructed a cultural metaverse using the innovative AR storytelling. The cultural metaverse is a new cultural ecology in which advanced information technologies are deeply integrated with cultural spaces and exhibits. It combines digital technologies and cultural industries, mixing virtual space and physical space to facilitate the UX in cultural experiences. In this study, the existing AR e-book and the innovative AR version were compared while measuring multiple aspects of UX, including presence, flow, enjoyment, education, and engagement. By analyzing questionnaire data from two groups with a total of 368 participants, the results indicated that the innovative AR storytelling produced a better UX across all variables compared to the AR e-book application. Overall, innovative AR storytelling allows visitors to transition between real and virtual spaces, enriches their interactive experience, and improves user engagement with the metaverse exhibition of cultural experience. Therefore, practitioners can construct a primary cultural metaverse through innovative AR storytelling.
Understanding Digital Inequality: A Theoretical Kaleidoscope

Abstract The pandemic affected more than 1.5 billion students and youth, and the most vulnerable learners were hit hardest, making digital inequality in educational settings impossible to overlook. Given this reality, we, all educators, came together to find ways to understand and address some of these inequalities. As a product of this collaboration, we propose a methodological toolkit: a theoretical kaleidoscope to examine and critique the constitutive elements and dimensions of digital inequalities. We argue that such a tool is helpful when a critical attitude to examine ‘the ideology of digitalism’, its concomitant inequalities, and the huge losses it entails for human flourishing seems urgent. In the paper, we describe different theoretical approaches that can be used for the kaleidoscope. We give relevant examples of each theory. We argue that the postdigital does not mean that the digital is over, rather that it has mutated into new power structures that are less evident but no less insidious as they continue to govern socio-technical infrastructures, geopolitics, and markets. In this sense, it is vital to find tools that allow us to shed light on such invisible and pervasive power structures and the consequences in the daily lives of so many.
Machine Learning and AI Technologies for Smart Wearables

The recent progress in computational, communications, and artificial intelligence (AI) technologies, and the widespread availability of smartphones together with the growing trends in multimedia data and edge computation devices have led to new models and paradigms for wearable devices. This paper presents a comprehensive survey and classification of smart wearables and research prototypes using machine learning and AI technologies. The paper aims to survey these new paradigms for machine learning and AI for wearables from various technological perspectives which have emerged, including: (1) smart wearables empowered by machine learning and AI; (2) data collection architectures and information processing models for AI smart wearables; and (3) applications for AI smart wearables. The review covers a wide range of enabling technologies for AI and machine learning for wearables and research prototypes. The main findings of the review are that there are significant technical challenges for AI smart wearables in networking and communication aspects such as issues for routing and communication overheads, information processing and computational aspects such as issues for computational complexity and storage, and algorithmic and application-dependent aspects such as training and inference. The paper concludes with some future directions in the smart wearable market and potential research.
A Self-Supervised-Driven Open-Set Unsupervised Domain Adaptation Method for Optical Remote Sensing Image Scene Classification and Retrieval

Unsupervised domain adaptation (UDA) is an important solution to reduce the bias between the labeled source domain and the unlabeled target domain. It has attracted more attention for optical remote sensing image scene classification and retrieval. Currently, most of the previous work is devoted to closed-set UDA. In fact, the target domain often contains unknown classes. Moreover, some open UDA methods mine structural information of the target domain directly from the type knowledge of the source domain, and less directly from the unlabeled data of the target domain. In this paper, we propose a new self-supervised-driven open-set UDA method combining contrastive self-supervised learning with consistency self-training for optical remote sensing scene classification and retrieval. Specifically, a contrastive self-supervised learning network is introduced to learn discriminative features from the unlabeled target domain data. Moreover, a novel open-set class learning module is developed based on two-level confidence rules and the consistency self-training strategy, which can obtain reliable unknown class samples for co-training. Finally, an open-set dataset including six cross-domain scenarios is constructed based on three public datasets and several experiments are conducted with eleven state-of-the-art domain adaptation methods. Experimental results demonstrate that our proposed method achieves superior performances on the six open-set cross-domain scenarios in both scene classification and retrieval. Especially, our method improves the overall classification accuracies by 9.72% to 24.06% and improve mean average retrieval precisions by 8.06% to 16.21% on the complex UCMD (source domain) → NWPU (target domain) scenario, compared with the other eleven state-of-the-art methods.