Reviewerly/exHarmony huggingface dataset:
An Adaptive Clustering-Based Algorithm for Automatic Path Planning of Heterogeneous UAVs

Due to the high maneuverability and strong adaptability, autonomous unmanned aerial vehicles (UAVs) are of high interest to many civilian and military organizations around the world. Automatic path planning which autonomously finds a good enough path that covers the whole area of interest, is an essential aspect of UAV autonomy. In this study, we focus on the automatic path planning of heterogeneous UAVs with different flight and scan capabilities, and try to present an efficient algorithm to produce appropriate paths for UAVs. First, models of heterogeneous UAVs are built, and the automatic path planning is abstracted as a multi-constraint optimization problem and solved by a linear programming formulation. Then, inspired by the density-based clustering analysis and symbiotic interaction behaviours of organisms, an adaptive clustering-based algorithm with a symbiotic organisms search-based optimization strategy is proposed to efficiently settle the path planning problem and generate feasible paths for heterogeneous UAVs with a view to minimizing the time consumption of the search tasks. Experiments on randomly generated regions are conducted to evaluate the performance of the proposed approach in terms of task completion time, execution time and deviation ratio.
Specificity-preserving RGB-D Saliency Detection

RGB-D saliency detection has attracted increasing attention, due to its effectiveness and the fact that depth cues can now be conveniently captured. Existing works often focus on learning a shared representation through various fusion strategies, with few methods explicitly considering how to preserve modality-specific characteristics. In this paper, taking a new perspective, we propose a specificity-preserving network (SP-Net) for RGB-D saliency detection, which benefits saliency detection performance by exploring both the shared information and modality-specific properties (e.g., specificity). Specifically, two modality-specific networks and a shared learning network are adopted to generate individual and shared saliency maps. A cross-enhanced integration module (CIM) is proposed to fuse cross-modal features in the shared learning network, which are then propagated to the next layer for integrating cross-level information. Besides, we propose a multi-modal feature aggregation (MFA) module to integrate the modality-specific features from each individual decoder into the shared decoder, which can provide rich complementary multi-modal information to boost the saliency detection performance. Further, a skip connection is used to combine hierarchical features between the encoder and decoder layers. Experiments on six benchmark datasets demonstrate that our SP-Net outperforms other state-of-the-art methods. Code is available at: https://github.com/taozh2017/SPNet.
Generalized Source-free Domain Adaptation

Domain adaptation (DA) aims to transfer the knowledge learned from a source domain to an unlabeled target domain. Some recent works tackle source-free domain adaptation (SFDA) where only a source pre-trained model is available for adaptation to the target domain. However, those methods do not consider keeping source performance which is of high practical value in real world applications. In this paper, we propose a new domain adaptation paradigm called Generalized Source-free Domain Adaptation (G-SFDA), where the learned model needs to perform well on both the target and source domains, with only access to current unlabeled target data during adaptation. First, we propose local structure clustering (LSC), aiming to cluster the target features with its semantically similar neighbors, which successfully adapts the model to the target domain in the absence of source data. Second, we propose sparse domain attention (SDA), it produces a binary domain specific attention to activate different feature channels for different domains, meanwhile the domain attention will be utilized to regularize the gradient during adaptation to keep source information. In the experiments, for target performance our method is on par with or better than existing DA and SFDA methods, specifically it achieves state-of-the-art performance (85.4%) on VisDA, and our method works well for all domains after adapting to single or multiple target domains. Code is available in https://github.com/Albert0147/G-SFDA.
Virtual Reality Metaverse System Supplementing Remote Education Methods: Based on Aircraft Maintenance Simulation

Due to the COVID-19 pandemic, there has been a shift from in-person to remote education, with most students taking classes via video meetings. This change inhibits active class participation from students. In particular, video education has limitations in replacing practical classes, which require both theoretical and empirical knowledge. In this study, we propose a system that incorporates virtual reality and metaverse methods into the classroom to compensate for the shortcomings of the existing remote models of practical education. Based on the proposed system, we developed an aircraft maintenance simulation and conducted an experiment comparing our system to a video training method. To measure educational effectiveness, knowledge acquisition, and retention tests were conducted and presence was investigated via survey responses. The results of the experiment show that the group using the proposed system scored higher than the video training group on both knowledge tests. As the responses given to the presence questionnaire confirmed a sense of spatial presence felt by the participants, the usability of the proposed system was judged to be appropriate.
Hypergraph Contrastive Collaborative Filtering

Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing users and items into latent representation space, with their correlative patterns from interaction data. Among various CF techniques, the development of GNN-based recommender systems, e.g., PinSage and LightGCN, has offered the state-of-the-art performance. However, two key challenges have not been well explored in existing solutions: i) The over-smoothing effect with deeper graph-based CF architecture, may cause the indistinguishable user representations and degradation of recommendation results. ii) The supervision signals (i.e., user-item interactions) are usually scarce and skewed distributed in reality, which limits the representation power of CF paradigms. To tackle these challenges, we propose a new self-supervised recommendation framework Hypergraph Contrastive Collaborative Filtering (HCCF) to jointly capture local and global collaborative relations with a hypergraph-enhanced cross-view contrastive learning architecture. In particular, the designed hypergraph structure learning enhances the discrimination ability of GNN-based CF paradigm, so as to comprehensively capture the complex high-order dependencies among users. Additionally, our HCCF model effectively integrates the hypergraph structure encoding with self-supervised learning to reinforce the representation quality of recommender systems, based on the hypergraph-enhanced self-discrimination. Extensive experiments on three benchmark datasets demonstrate the superiority of our model over various state-of-the-art recommendation methods, and the robustness against sparse user interaction data. Our model implementation codes are available at https://github.com/akaxlh/HCCF.
Multimodal machine learning in precision health: A scoping review

Abstract Machine learning is frequently being leveraged to tackle problems in the health sector including utilization for clinical decision-support. Its use has historically been focused on single modal data. Attempts to improve prediction and mimic the multimodal nature of clinical expert decision-making has been met in the biomedical field of machine learning by fusing disparate data. This review was conducted to summarize the current studies in this field and identify topics ripe for future research. We conducted this review in accordance with the PRISMA extension for Scoping Reviews to characterize multi-modal data fusion in health. Search strings were established and used in databases: PubMed, Google Scholar, and IEEEXplore from 2011 to 2021. A final set of 128 articles were included in the analysis. The most common health areas utilizing multi-modal methods were neurology and oncology. Early fusion was the most common data merging strategy. Notably, there was an improvement in predictive performance when using data fusion. Lacking from the papers were clear clinical deployment strategies, FDA-approval, and analysis of how using multimodal approaches from diverse sub-populations may improve biases and healthcare disparities. These findings provide a summary on multimodal data fusion as applied to health diagnosis/prognosis problems. Few papers compared the outputs of a multimodal approach with a unimodal prediction. However, those that did achieved an average increase of 6.4% in predictive accuracy. Multi-modal machine learning, while more robust in its estimations over unimodal methods, has drawbacks in its scalability and the time-consuming nature of information concatenation.
Advanced Media-Based Smart Big Data on Intelligent Cloud Systems

Today's advanced media technology preaches an enthralling time that will enormously bear on daily life. Moreover, the rapid raise of wireless communications and networking will ultimately bring advanced media to our lives anytime, anywhere, and on any device. According to the National Institute of Standards and Technology (NIST), Cloud Computing (CC) is a scheme for enabling convenient, on-demand network access to a shared pool of configurable computing pores (for example networks, applications, storage, servers, and services) which could be promptly foresighted and delivered with minimal management effort or service provider interaction. This paper proposed an efficient algorithm for advanced scalable Media-basedSmart Big Data (3D, Ultra HD) on Intelligent Cloud Computing systems. The proposed encoding algorithmoutperforms the conventional HEVC standard which demonstrated by the performance evaluations. In order to ratify the proposed approach, in addition, a relative study has been carried out. The proposed method could be used and integrated into HEVC, as a Smart Big Data, without violating the standard.
Robust Image Hashing with Tensor Decomposition

This paper presents a new image hashing that is designed with tensor decomposition (TD), referred to as TD hashing, where image hash generation is viewed as deriving a compact representation from a tensor. Specifically, a stable three-order tensor is first constructed from the normalized image, so as to enhance the robustness of our TD hashing. A popular TD algorithm, called Tucker decomposition, is then exploited to decompose the three-order tensor into a core tensor and three orthogonal factor matrices. As the factor matrices can reflect intrinsic structure of original tensor, hash construction with the factor matrices makes a desirable discrimination of the TD hashing. To examine these claims, there are 14,551 images selected for our experiments. A receiver operating characteristics (ROC) graph is used to conduct theoretical analysis and the ROC comparisons illustrate that the TD hashing outperforms some state-of-the-art algorithms in classification performance between the robustness and discrimination.
Why do people play location-based augmented reality games: A study on Pokémon GO

Pokémon GO brought the location-based augmented reality games into the mainstream. To understand why people play these games, we created an online survey (n = 2612) with open questions about the reasons to start, continue, and quit playing Pokémon GO, and composed categories of the answers through a thematic analysis. Earlier experiences especially with the same franchise, social influence, and popularity were the most common reasons to adopt the game, while progressing in the game was the most frequently reported reason to continue playing. The player's personal situation outside the game and playability problems were the most significant reasons to quit the game. In addition to shedding more light on the Pokémon GO phenomenon, the findings are useful for both further studying and designing location-based augmented reality game experiences.
Assessing Teacher Digital Competence: the Construction of an Instrument for Measuring the Knowledge of Pre-Service Teachers

Assessing competences always poses a challenge and can be even more complicated when tackling a multidimensional competence like teacher digital competence (TDC). TDC is understood to consist of different dimensions linked to its components. This complexity gives rise to the need to organize and systematize both TDC training and its evaluation through a standard based on validated benchmark indicators. Designing and developing an instrument for TDC assessment has been a two-phase process. The COMDID-A self-assessment tool was developed in the first phase and COMDID-C, an instrument for assessing knowledge related to TDC, in the second. In this article we present the process of constructing the COMDID-C instrument. For this first stage, we worked with two samples, an expert validation and a pilot test sample. Due to the complexity of the test, we conducted a preliminary evaluation of the validity of its content, construction and reliability. Our results indicate that the test is well designed and consistent with its intended purpose. The next step will be administering the test to a larger sample that will allow the instrument to be externally validated.
Effect of augmented reality applications on secondary school students' reading comprehension and learning permanency

The aim of this study was to analyze the effect of augmented reality (AR) applications on reading comprehension and learning permanency and to examine 5th grade students' attitudes towards AR applications. In addition, student opinions about these applications were examined. A mixed method was used with a sample composed of 89 5th grade students (43 girls, 46 boys). The experimental group participated in reading activities using AR applications, while traditional methods were used for the control group. It was found that the experimental group students showed a higher level of reading comprehension and learning permanency when measured on a weekly basis than the control group. The students experienced satisfaction from their participation in AR-based reading activities, and expressed a desire to see similar applications offered in other courses. They also reported low anxiety levels. These qualitative results indicate that AR applications can be used effectively as educational aids for reading-related courses.
High-Level Multiple-UAV Cinematography Tools for Covering Outdoor Events

Camera-equipped unmanned aerial vehicles (UAVs), or “drones,” are a recent addition to standard audiovisual shooting technologies. As drone cinematography is expected to further revolutionize media production, this paper presents an overview of the state-of-the-art in this area, along with a brief review of current commercial UAV technologies and legal restrictions on their deployment. A novel taxonomy of UAV cinematography visual building blocks, in the context of filming outdoor events where targets (e.g., athletes) must be actively followed, is additionally proposed. Such a taxonomy is necessary for progress in intelligent/autonomous UAV shooting, which has the potential of addressing current technology challenges. Subsequently, the concepts and advantages inherent in multiple-UAV cinematography are introduced. The core of multiple-UAV cinematography consists in identifying different combinations of multiple single-UAV camera motion types, assembled in meaningful sequences. Finally, based on the defined UAV/camera motion types, tools for managing a partially autonomous, multiple-UAV fleet from the director's point of view are presented. Although the overall focus is on cinematic coverage of sports events, the majority of our contributions also apply in different scenarios, such as movies/TV production, newsgathering, or advertising.
The Limits of Earthquake Early Warning Accuracy and Best Alerting Strategy

Abstract We explore how accurate earthquake early warning (EEW) can be, given our limited ability to forecast expected shaking even if the earthquake source is known. Because of the strong variability of ground motion metrics, such as peak ground acceleration (PGA) and peak ground velocity (PGV), we find that correct alerts (i.e., alerts that accurately estimate the ground motion will be above a predetermined damage threshold) are not expected to be the most common EEW outcome even when the earthquake magnitude and location are accurately determined. Infrequently, ground motion variability results in a user receiving a false alert because the ground motion turned out to be significantly smaller than the system expected. More commonly, users will experience missed alerts when the system does not issue an alert but the user experiences potentially damaging shaking. Despite these inherit limitations, EEW can significantly mitigate earthquake losses for false-alert-tolerant users who choose to receive alerts for expected ground motions much smaller than the level that could cause damage. Although this results in many false alerts (unnecessary alerts for earthquakes that do not produce damaging ground shaking), it minimizes the number of missed alerts and produces overall optimal performance.
An Efficient Authentication Scheme for Blockchain-Based Electronic Health Records

In traditional electronic health records (EHRs), medical-related information is generally separately controlled by different hospitals and thus it leads to the inconvenience of information sharing.Cloud-based EHRs solve the problem of information sharing in the traditional EHRs.However, cloudbased EHRs suffer the centralized problem, i.e., cloud service center and key-generation center.This paper works on creating a new EHRs paradigm which can help in dealing with the centralized problem of cloudbased EHRs.Our solution is to make use of the emerging technology of blockchain to EHRs (denoted as blockchain-based EHRs for convenience).First, we formally define the system model of blockchainbased EHRs in the setting of consortium blockchain.In addition, the authentication issue is very important for EHRs.However, existing authentication schemes for blockchain-based EHRs have their own weak points.Therefore, in this paper, we also propose an authentication scheme for blockchain-based EHRs.Our proposal is an identity-based signature scheme with multiple authorities which can resist collusion attack out of N from N -1 authorities.Furthermore, our scheme is provably secure in the random oracle model and has more efficient signing and verification algorithms than existing authentication schemes of blockchain-based EHRs.
Optimizing wavelet neural networks using modified cuckoo search for multi-step ahead chaotic time series prediction

Determining the optimal number of hidden nodes and their proper initial locations are essentially crucial before the wavelet neural networks (WNNs) start their learning process. In this paper, a novel strategy known as the modified cuckoo search algorithm (MCSA), is proposed for WNNs initialization in order to improve its generalization performance. The MCSA begins with an initial population of cuckoo eggs, which represent the translation vectors of the wavelet hidden nodes, and subsequently refines their locations by imitating the breeding mechanism of cuckoos. The resulting solutions from the MCSA are then used as the initial translation vectors for the WNNs. The feasibility of the proposed method is evaluated by forecasting a benchmark chaotic time series, and its superior prediction accuracy compared with that of conventional WNNs demonstrates its potential benefit.
WekaDeeplearning4j: A deep learning package for Weka based on Deeplearning4j

Deep learning is a branch of machine learning that generates multi-layered representations of data, commonly using artificial neural networks, and has improved the state-of-the-art in various machine learning tasks (e.g., image classification, object detection, speech recognition, and document classification). However, most popular deep learning frameworks such as TensorFlow and PyTorch require users to write code to apply deep learning. We present WekaDeeplearning4j, a Weka package that makes deep learning accessible through a graphical user interface (GUI). The package uses Deeplearning4j as its backend, provides GPU support, and enables GUI-based training of deep neural networks such as convolutional and recurrent neural networks. It also provides pre-processing functionality for image and text data.
Hyperparameter optimization of deep neural network using univariate dynamic encoding algorithm for searches

This paper proposes a method to find the hyperparameter tuning for a deep neural network by using a univariate dynamic encoding algorithm for searches. Optimizing hyperparameters for such a neural network is difficult because the neural network that has several parameters to configure; furthermore, the training speed for such a network is slow. The proposed method was tested for two neural network models; an autoencoder and a convolution neural network with the Modified National Institute of Standards and Technology (MNIST) dataset. To optimize hyperparameters with the proposed method, the cost functions were selected as the average of the difference between the decoded value and the original image for the autoencoder, and the inverse of the evaluation accuracy for the convolution neural network. The hyperparameters were optimized using the proposed method with fast convergence speed and few computational resources, and the results were compared with those of the other considered optimization algorithms (namely, simulated annealing, genetic algorithm, and particle swarm algorithm) to show the effectiveness of the proposed methodology.
A Survey on Blockchain-Based Internet Service Architecture: Requirements, Challenges, Trends, and Future

The emergence of Internet protocol suites and packet-switching technologies tends to the considerations of security, privacy, scalability, and reliability in layered Internet service architectures. The existing service systems allow us to access big data, but few studies focus on the fundamental security and stability in these systems, especially when they involve large-scale networks with overloaded private information. In this paper, we explored the blockchain-based mechanism that aims to improve the critical features of traditional Internet services, including autonomous and decentralized processing, smart contractual enforcement of goals, and traceable trustworthiness in tamper-proof transactions. Furthermore, we provide a comprehensive review to conceptualize the blockchain-based framework to develop the decentralized protocols for the extensive number of Internet services. This comprehensive survey aims to address blockchain integration to secure Internet services and identify the critical requirements of developing a decentralized trustworthy Internet service. Additionally, we present a case study of the blockchain-based Internet of Things (IoT) for neuro-informatics to illustrate the potential applications of blockchain architectures. Finally, we summarize the trends and challenges of blockchain architectures that benefit a multitude of disciplines across all the Internet service fields of interest.
Cattle detection and counting in UAV images based on convolutional neural networks

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