Reviewerly/exHarmony huggingface dataset:
Learning experience design for augmented reality

Recent years have seen a growing interest in augmented reality (AR) technologies due to their potential for simulating real-life situations and creating authentic learning tasks. Studies have shown that AR enables engaging and interactive learning experiences (e.g. Bressler and Bodzin 2013; Klopfer and Sheldon 2010) and can benefit student learning (e.g. Bonner and Reinders 2018; Siegle 2019). However, although research in AR for education is not scarce, educators often do not have a learning experience design (LXD) approach that is supported by the recent findings of learning sciences and instructional design models. To bridge this gap, the present study introduces an AR-learning prototype developed by using SAM I (Successive Approximation Model I), and the Threshold Concepts Framework, employed for meaningful integration of AR into the learning process. A pre-survey and a post-survey method were utilised in the data gathering process to gauge students' experience with the AR module. The findings show that the majority of students have not had educational experiences with AR prior to the study, and they struggled to find ways to incorporate this technology into their content areas in a meaningful way. Nonetheless, participants realised the value of AR and stated that they most likely would use this technology in the future. Based on the findings, the authors present a set of suggestions for instructors and LXDs, and provide recommendations for future research. This article is part of the special collection: Mobile Mixed Reality Enhanced Learning edited by Thom Cochrane, James Birt, Helen Farley, Vickel Narayan and Fiona Smart. More papers from this collection can be found here.
Head Pose Estimation through Keypoints Matching between Reconstructed 3D Face Model and 2D Image

Mainstream methods treat head pose estimation as a supervised classification/regression problem, whose performance heavily depends on the accuracy of ground-truth labels of training data. However, it is rather difficult to obtain accurate head pose labels in practice, due to the lack of effective equipment and reasonable approaches for head pose labeling. In this paper, we propose a method which does not need to be trained with head pose labels, but matches the keypoints between a reconstructed 3D face model and the 2D input image, for head pose estimation. The proposed head pose estimation method consists of two components: the 3D face reconstruction and the 3D–2D matching keypoints. At the 3D face reconstruction phase, a personalized 3D face model is reconstructed from the input head image using convolutional neural networks, which are jointly optimized by an asymmetric Euclidean loss and a keypoint loss. At the 3D–2D keypoints matching phase, an iterative optimization algorithm is proposed to match the keypoints between the reconstructed 3D face model and the 2D input image efficiently under the constraint of perspective transformation. The proposed method is extensively evaluated on five widely used head pose estimation datasets, including Pointing’04, BIWI, AFLW2000, Multi-PIE, and Pandora. The experimental results demonstrate that the proposed method achieves excellent cross-dataset performance and surpasses most of the existing state-of-the-art approaches, with average MAEs of 4.78∘ on Pointing’04, 6.83∘ on BIWI, 7.05∘ on AFLW2000, 5.47∘ on Multi-PIE, and 5.06∘ on Pandora, although the model of the proposed method is not trained on any of these five datasets.
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Drifting data streams and multi-label data are both challenging problems. Multi-label instances may simultaneously be associated with many labels and classifiers must predict the complete set of labels. Learning from data streams requires algorithms able to learn from potentially unbounded data that is constantly changing. When multi-label data arrives as a stream, the challenges of both problems must be addressed, but additional challenges unique to the combined problem also arise. Each label may experience different concept drifts, simultaneously or distinctly, and parameter optimizations may be different for each label. In this paper we present a self-adapting algorithm for drifting, multi-label data streams, that can adapt to a variety of concepts drifts, is robust to data-level difficulties, and mitigates the necessity to tune multiple parameters. The window of retained instances self-adjusts in size to retain only the current concept, enabling efficient response to abrupt concept drift. The value k is self-adapting for each label, relieving the necessity to tune and allowing it to change, over time, for each label individually. A novel, label-based mechanism disables individual labels that contribute to error, while another punitive measure removes erroneous instances entirely, increasing robustness to noise, concept drift and label differences. Extensive experiments on 35 multi-label streams and generators demonstrate the superiority and advantages of the self-adapting mechanisms proposed compared to existing state-of-the-art methods.
Intra-Camera Supervised Person Re-Identification

Abstract Existing person re-identification (re-id) methods mostly exploit a large set of cross-camera identity labelled training data. This requires a tedious data collection and annotation process, leading to poor scalability in practical re-id applications. On the other hand unsupervised re-id methods do not need identity label information, but they usually suffer from much inferior and insufficient model performance. To overcome these fundamental limitations, we propose a novel person re-identification paradigm based on an idea of independent per-camera identity annotation. This eliminates the most time-consuming and tedious inter-camera identity labelling process, significantly reducing the amount of human annotation efforts. Consequently, it gives rise to a more scalable and more feasible setting, which we call Intra-Camera Supervised (ICS) person re-id, for which we formulate a Multi-tAsk mulTi-labEl (MATE) deep learning method. Specifically, MATE is designed for self-discovering the cross-camera identity correspondence in a per-camera multi-task inference framework. Extensive experiments demonstrate the cost-effectiveness superiority of our method over the alternative approaches on three large person re-id datasets. For example, MATE yields 88.7% rank-1 score on Market-1501 in the proposed ICS person re-id setting, significantly outperforming unsupervised learning models and closely approaching conventional fully supervised learning competitors.
A Quantum Leap in Microgrids Security: The Prospects of Quantum-Secure Microgrids

Communication has always played a vital role in microgrids to maintain reliable operations and achieve great benefits and will be even more critical with the increasing deployment of renewable energies, information technologies, and real-time automation and control systems. The existing classical cryptographic methods for securing microgrid communication, however, rely on mathematical assumptions, which are vulnerable to attacks from quantum computers. This article reviews the current status of developing quantum-secure microgrids, namely microgrids that are secure against attacks from quantum computers. We introduce some potential issues associated with applying existing quantum cryptography methods in the context of microgrids and provide future perspectives to make quantum security more practical in microgrids.
Alleviating Domain Shift via Discriminative Learning for Generalized Zero-Shot Learning

In zero-shot learning (ZSL) tasks, especially in generalized zero-shot learning (GZSL), the model tends to classify unseen test samples into seen categories, which is well known as the domain shift problem, because the model is trained from seen samples without unseen samples. Recently, generative adversarial network (GAN) based methods have achieved good performance in GZSL, which replace real unseen features by synthesizing fake ones to mitigate the domain shift. However, the domain shift problem is still not well solved, due to the lacking of unseen samples in the training progress of the GAN generator. In this paper, we propose a generative model named discriminative learning GAN (DL-GAN) to alleviate the domain shift in GZSL. Specifically, the DL-GAN is designed with three novel components: a dual-stream embedding model that aligns features to the ground-truth attributes to extract discriminative latent attributes from features, an attribute-based generative model that generates high-quality unseen features from semantic attributes to guarantee inter-class discriminability and semantic consistency, and a seen/unseen classifier that leverages validation samples to distinguish seen samples from unseen ones. Experimental results on four widely used datasets verify that our proposed approach significantly outperforms the state-of-the-art methods under the GZSL protocol.
Sentiment Analysis on Twitter Data Using Deep Learning approach

The recent developments of many social networking websites have created large collections of product reviews and polarity of opinions etc, data for customer around the world. Data collection from these social media can be utilized to solve objectives such as market prediction, product recommendation, and reviewer's sentiment. It is very difficult task to manage of unstructured data available on social media. To handle such type of data, Deep learning algorithms is an appropriate solution for analysis these challenges. In this paper, we propose a CNN- LSTM based deep learning method with pre trained embedding approach learns to extract feature automatically for analysing sentiments and classification of reviews or opinions labelled into two polarity as positive or negative. Our proposed model to implement the result gives better performance on benchmark dataset. The performance of CNN-LSTM based deep learning method has compared with baseline machine learning methods.
Systematic Mapping of the Literature on Secure Software Development

The accelerated growth in exploiting vulnerabilities due to errors or failures in the software development process is a latent concern in the Software Industry. In this sense, this study aims to provide an overview of the Secure Software Development trends to help identify topics that have been extensively studied and those that still need to be. Therefore, in this paper, a systematic mapping review with PICo search strategies was conducted. A total of 867 papers were identified, of which only 528 papers were selected for this review. The main findings correspond to the Software Requirements Security, where the Elicitation and Misuse Cases reported more frequently. In Software Design Security, recurring themes are security in component-based software development, threat model, and security patterns. In the Software Construction Security, the most frequent topics are static code analysis and vulnerability detection. Finally, in Software Testing Security, the most frequent topics are vulnerability scanning and penetration testing. In conclusion, there is a diversity of methodologies, models, and tools with specific objectives in each secure software development stage.
An Autocorrelation-based LSTM-Autoencoder for Anomaly Detection on Time-Series Data

Data quality significantly impacts the results of data analytics. Researchers have proposed machine learning based anomaly detection techniques to identify incorrect data. Existing approaches fail to (1) identify the underlying domain constraints violated by the anomalous data, and (2) generate explanations of these violations in a form comprehensible to domain experts. We propose IDEAL, which is an LSTM-Autoencoder based approach that detects anomalies in multivariate time-series data, generates domain constraints, and reports subsequences that violate the constraints as anomalies. We propose an automated autocorrelation-based windowing approach to adjust the network input size, thereby improving the correctness and performance of constraint discovery over manual and brute-force approaches. The anomalies are visualized in a manner comprehensible to domain experts in the form of decision trees extracted from a random forest classifier. Domain experts can then provide feedback to retrain the learning model and improve the accuracy of the process. We evaluate the effectiveness of IDEAL using datasets from Yahoo servers, NASA Shuttle, and Colorado State University Energy Institute. We demonstrate that IDEAL can detect previously known anomalies from these datasets. Using mutation analysis, we show that IDEAL can detect different types of injected faults. We also demonstrate that the accuracy improves after incorporating domain expert feedback.
MPC-MPNet: Model-Predictive Motion Planning Networks for Fast, Near-Optimal Planning Under Kinodynamic Constraints

Kinodynamic Motion Planning (KMP) is to find a robot motion subject to concurrent kinematics and dynamics constraints. To date, quite a few methods solve KMP problems and those that exist struggle to find near-optimal solutions and exhibit high computational complexity as the planning space dimensionality increases. To address these challenges, we present a scalable, imitation learning-based, Model-Predictive Motion Planning Networks framework that quickly finds near-optimal path solutions with worst-case theoretical guarantees under kinodynamic constraints for practical underactuated systems. Our framework introduces two algorithms built on a neural generator, discriminator, and a parallelizable Model Predictive Controller (MPC). The generator outputs various informed states towards the given target, and the discriminator selects the best possible subset from them for the extension. The MPC locally connects the selected informed states while satisfying the given constraints leading to feasible, near-optimal solutions. We evaluate our algorithms on a range of cluttered, kinodynamically constrained, and underactuated planning problems with results indicating significant improvements in computation times, path qualities, and success rates over existing methods.
Group Navigation for Guided Tours in Distributed Virtual Environments

Group navigation can be an invaluable tool for performing guided tours in distributed virtual environments. Related work suggests that group navigation techniques should be comprehensible for both the guide and the attendees, assist the group in avoiding collisions with obstacles, and allow the creation of meaningful spatial arrangements with respect to objects of interest. To meet these requirements, we developed a group navigation technique based on short-distance teleportation (jumping) and evaluated its usability, comprehensibility, and scalability in an initial user study. After navigating with groups of up to 10 users through a virtual museum, participants indicated that our technique is easy to learn for guides, comprehensible also for attendees, non-nauseating for both roles, and therefore well-suited for performing guided tours.
Distributed Robust Kalman Filtering for Markov Jump Systems With Measurement Loss of Unknown Probabilities

This article is concerned with a distributed filtering problem for Markov jump systems subject to the measurement loss with unknown probabilities. A centralized robust Kalman filter is designed by using variational Bayesian methods and a modified interacting multiple model method based on information theory (IT-IMM). Then, a distributed robust Kalman filter based on the centralized filter and a hybrid consensus method called hybrid consensus on measurement and information (HCMCI) is designed. Moreover, boundedness of the estimation errors and the estimation error covariances are studied for the distributed robust Kalman filter.
A Survey on Sentiment Analysis

Sentiment analysis is one of the recent technologies under NLP (an application of Al and ML). Sentiment analysis is used in many applications for recommendation and feedback analysis. In this paper, from defining the sentiment analysis to algorithms for sentiment analysis and from the first step of sentiment analysis to evaluating the predictions of sentiment classifiers, additional feature extractions to boost performance are discussed with practical results. A brief description of complex sequence-based Neural Network sentiment classifiers with reasonable analytics is provided. The practical results declared in this paper are from the implantation of sentiment analysis on the IMDB movie reviews dataset. Evaluation metrics such as accuracy, precision, recall, and f1-score are used. This Research-based survey has been divided into different sections, each section concerning the stepwise process of sentiment analysis.
On the effect of dropping layers of pre-trained transformer models

Transformer-based NLP models are trained using hundreds of millions or even billions of parameters, limiting their applicability in computationally constrained environments. While the number of parameters generally correlates with performance, it is not clear whether the entire network is required for a downstream task. Motivated by the recent work on pruning and distilling pre-trained models, we explore strategies to drop layers in pre-trained models, and observe the effect of pruning on downstream GLUE tasks. We were able to prune BERT, RoBERTa and XLNet models up to 40%, while maintaining up to 98% of their original performance. Additionally we show that our pruned models are on par with those built using knowledge distillation, both in terms of size and performance. Our experiments yield interesting observations such as, (i) the lower layers are most critical to maintain downstream task performance, (ii) some tasks such as paraphrase detection and sentence similarity are more robust to the dropping of layers, and (iii) models trained using a different objective function exhibit different learning patterns and w.r.t the layer dropping.
Secure data transmission in IoTs based on CLoG edge detection

Cyber–physical Systems (CPS) have revolutionized the industry by utilizing the Internet of Things (IoT) for interconnecting various system components. IoT plays a significant role in the development of the smart system paradigm by building a link between physical components with the virtual world for improving smart services and quality of life. It significantly improves people's activities, be it their personalized health care, living, or the way they monitor control, and organize the businesses. However, data streaming/communication over an open IoT environment creates several security issues that need to be taken care of, for transferring data securely. This paper presents an efficient information embedding solution for ensuring data security in a cyber–physicalnetwork. In this work, a new efficient edge detector called CLoG, (based on Canny and Laplacian of Gaussian detectors) has been developed and is used for the detection of edge areas in digital images. The secret information has been embedded in detected edges. The proposed detector finds finer edge details compared to state-of-art, making it possible to hide more information in a cover image. This, in turn, reduces the number of cover images required to transmit secret data and as such fulfills the requirements of resource-constrained platforms like IoT. Experimental results show that the proposed scheme is capable of providing high-quality stego-images with an average peak signal-to-noise ratio (PSNR) of 48.12 dB for a payload of 2 bits per pixel (bpp) payload. Further, the scheme has been analyzed for its efficacy in terms of histogram analysis, normalized cross-correlation (NCC), and other objective parameters. Besides, we show that the proposed scheme supports blind extraction, has less computational complexity, and is suitable for IoT based systems.
BL-ALM: A Blind Scalable Edge-Guided Reconstruction Filter for Smart Environmental Monitoring Through Green IoMT-UAV Networks

Today, low-cost radar/optical visual sensing for monitoring of environment and remote sensing through a network of unmanned aerial vehicles (UAVs) has received more attention such that many different intelligent and pervasive computing techniques are being used to make vision-assisted UAV Networks more reliable and dependable in order to distributed surveillance. On the other hand, the issue of visual information reconstruction has been an interesting topic of machine vision and UAV-borne remote sensing in recent years. To do this, a non-linear blind edge-guided spatial filter based on linear minimum mean square error-estimation (LMMSE) theory has recently been proposed for video sequence reconstruction problems in green monitoring with radars. Although performance of this technique is acceptable compared to the conventional linear techniques in the area, the strategy behind this visual estimator is not generally flexible on which it is not applicable in scalable zooming and its corresponding reconstruction templates. The main objective of this research is to introduce a new version of this filter towards scalable zooming and flexibility in different templates with arbitrary scales. Thus, the proposed algorithm can simultaneously benefit from both edge-directed and distance-weighted adaptation. The proposed approach is theoretically proven, and with using experiments through real UAV visual data, its suitability for low-cost sensing in green Internet of multimedia things (G-IoMT) is shown.
Mobile learning applications to improve invertebrate zoology online teaching

Abstract The use of new technologies including personal mobile devices has become an indispensable tool in our daily lives, and thus its presence in education is becoming ever more ubiquitous. In the current scenario imposed by the COVID‐19 pandemic, in which in‐person presence in classrooms has been enormously reduced at all educational levels, the use of mobile learning and cutting‐edge methods can greatly improve the way students learn and enhance their online‐learning experience. Mobile applications, combined with extended reality technologies such as virtual reality (VR) and augmented reality (AR), are powerful tools that connect real and virtual environments and allow higher interaction for the user. We have leveraged the advantages of mobile learning and extended reality technologies to develop a series of mobile applications and associated educational activities for university‐level courses involving invertebrate zoology field work. In particular, we have developed (a) a VR SCUBA diving video to explore the diversity of a marine protected area; (b) an AR mobile app to visualize 3D models of marine invertebrates; and (c) a mobile‐based catalogue to explore the terrestrial biodiversity of one of the most diverse regions of Spain. Here we provide detailed information describing the design and creation of these tools, as well as their application in class, to facilitate and encourage their use in higher education. Despite the relatively recent application of these technologies in education, they have an enormous potential: they improve student motivation and learning, can be adapted to different learning styles, reduce social inequalities, and facilitate inclusiveness and diversity practices in the classroom.