Reviewerly/exHarmony huggingface dataset:
Analysis of The Motivational Effect of Gamified Augmented Reality Apps for Learning Geometry

Gamification has been recognised for its motivational power and been increasingly integrated into Augmented Reality (AR)-based educational applications. However, results of the existing studies on the effect of gamified AR apps on learning motivation remain inconclusive. To address this issue, we developed an AR learning app and its three gamified variants - Badges, Points, and Timer. Each of the apps was used by a group of 30 students aged 12-16 years to learn 3D geometry. To assess the impact of these AR apps on learning motivation, we constructed a three-item questionnaire, measuring Interest, Confidence and Intention to Use, which was administered after each of the six rounds of interaction with the apps. Results showed that there were no other significant differences in the motivational effect between the gamified and non-gamified apps, and Points had a stronger learning effect than Badges or Timer. Implications for designing the gamification elements are drawn.
A Reinforcement Learning-Based Classification Symbiont Agent for Dynamic Difficulty Balancing

AdaptiveSGA is a mechanism for achieving Adaptive Game AI-based Dynamic Difficulty Balancing in games. AdaptiveSGA is based on the Symbiotic Game Agent model and, therefore, leverages the advantages of biological symbiosis. Within the AdaptiveSGA architecture, the classification symbiont agent is responsible for the dynamic difficulty balancing component. Current work proposes the use of a classification symbiont agent that makes use of reinforcement learning to optimise dynamic difficulty balancing in order to match the opponent's skill. Current work also introduces three different types of decision-making algorithms that can be used by decision-making symbiont agents to display different kinds of behaviour. The ability to reproduce different kinds of NPC behaviour forms the adaptive game AI component of AdaptiveSGA. Experimental results showed that the reinforcement learning-based classification symbiont agent can achieve an even game with opponents and can further help minimise the number of draws.
Tangible Lighting Proxies: Brokering the Transition from Classroom to Stage

Supporting Practice-based vocational skills development in the classroom can be logistically challenging, however tangible interfaces present natural affordances for supporting such skill development. In the context of teaching stage lighting specifically, we present an open source toolkit based around tangible hardware proxies to support the teaching of practice-based skills. Our tangible proxies embody key configuration, interaction and optical properties of real stage equipment. Drawing on notions of representation and bi-directional digital-to-physical transformation, we design a toolkit that specifically supports a gradual transition between virtual, simulated and real equipment during the learning journey, and open the door to embedding stage craft into schools. Through reporting on deployments in two schools, we discuss the affordances of such proxies and their potential for supporting the teaching and learning of practice-based skills in the classroom.
A Novel Variational Autoencoder based Radar Signal Reconstruction Algorithm Using Polluted Data

In the transmission process, radar signal could be polluted easily, and the radar individual recognition process will be undermined by the polluted data. Some existing algorithms so far use the image denoising method to deal with this issue while ignoring the essential characteristics of the signal, and thus the noise reduction effect is poor. To address this issue, a novel variational autoencoder based radar signal reconstruction algorithm is proposed in this paper to reconstruct high quality data from the polluted ones by compressing the one-dimensional polluted signal and learning the essential characteristics. The experiments prove that the algorithm indeed improves the quality of the reconstructed data compared with the polluted signal.
Adaptive Fusion and Mask Refinement Instance Segmentation Network for High Resolution Remote Sensing Images

Instance segmentation of remote sensing images (RSIs) is an active yet challenging task because of the huge scale variation and arbitrary complex shapes of objects. To address these issues, we propose an adaptive fusion and mask refinement (AFMR) instance segmentation network for RSIs in this paper. More precisely, AFMR consists of an adaptive fusion module to learn multi-scale complementary spatial features in an unsupervised manner, and a content-aware module for segmentation mask refinement. These two modules enable a better feature learning of convolutional neural network and boost the performance by 1.5% compared with the baseline method. In iSAID, a large-scale dataset for RSIs instance segmentation, our AFMR framework achieves the state-of-the-art accuracy, which verifies the superiority of the proposed method.
Network Representation Learning Algorithm Combined with Node Text Information

Abstract In a network data analysis task, network representation learning play an important role. It can obtain a low-dimensional representative vector from high dimensional features of nodes which will promote the efficiency of downstream analysis task. Many algorithms mainly based on the structure information of the network but for a complex network, there is a lot kinds of feature information available for representation learning which has an important impact on network data analysis task. Therefore, we propose a network representation learning algorithm that combines node text information. Before our algorithm, we encode the information of the nodes including structure and text, and the strategy of fusion is adopted in advance which splice structure and text information. And then we feed the splicing results into the neural network which is a multi-layer feedforward neural network with a tower structure to learn the nonlinear structure information of complex networks. Finally, we design the structure loss function that preserve structure similarity and text loss function that preserve text information respectively. And we use the joint training to make two aspects information interact continuously. We conduct experiment to evaluate our algorithm. The results on two real word networks show our algorithm has better performance compared with traditional algorithms.
Estimating Loss Due to Cyber-Attack in the Presence of Uncertainty

Cyber-security risk assessment includes estimation of losses possible to a system due to cyber-attacks. As there are uncertain elements to this and as we model uncertainty using probability, we seek to estimate the attack loss distribution. In particular, the tail of the distribution represents the low-probability but high-impact events. However, quantifying those events using standard Monte Carlo techniques is inefficient due to the low probability. This paper proposes a novel cyber-security risk assessment approach based on uncertain graphs, with an emphasis on modeling losses due to cyber-attacks. Under rare event realizations where the attack loss is greater than a selected threshold, we (i) derive the analytically optimal importance sampling scheme for the loss tail probability and (ii) propose an approximation to the optimal importance sampling scheme which has the assurance of bounded relative error. While the approximation scheme requires solving an NP-hard problem, we use a search procedure that becomes more efficient as the attack loss threshold increases. A case study on a medium-sized network demonstrates the use and performance of our approach.
Development of Exo-Glove for Measuring 3-axis Forces Acting on the Human Finger without Obstructing Natural Human-Object Interaction

Measuring the forces that humans exert with their fingers could have many potential applications, such as skill transfer from human experts to robots or monitoring humans. In this paper we introduce the "Exo-Glove" system, which can measure the joint angles and forces acting on the human finger without covering the skin that is in contact with the manipulated object. In particular, 3-axis sensors measure the deformation of the human skin on the sides of the finger to indirectly measure the 3-axis forces acting on the finger. To provide a frame of reference for the sensors, and to measure the joint angles of the human finger, an exoskeleton with remote center of motion (RCM) joints is used. Experiments showed that with the exoskeleton the quality of the force measurements can be improved.
Research on the Application of an Image Translation Technology Based on GAN

Abstract Image translation is a field with broad prospects. Recently, diverse research methods have been developed and applied in this field, among which GAN is the most potential technology. GAN belongs to artificial neural network, which not only has the advantages of artificial neural network, but also has its own unique characteristics. Although it still has some shortcomings, with the continuous improvement of researchers, it has been widely used as an effective algorithm in the field of image translation.
Comment on ‘ElGamal cryptosystem‐based secure authentication system for cloud‐based IoT applications’

This comment is presented to identify the drawbacks in a recently demonstrated scheme by Maitra et al., SAS-Cloud: doi:10.1049/iet-net.2019.0004, which adopted an ElGamal cryptosystem-based technique for biometric authentication in cloud-based IoT applications. In this protocol, the authors claim that their scheme provides mutual authentication. However, it is demonstrated in this article that the protocol merely supports unilateral authentication, which may result in clogging attack on the server's end. This is because the latter is unable to verify the authentication request in absolute terms, which might lead to resource clogging as well as denial of service attack affecting its Quality of Service (QoS).
3D Human Pose Estimation Based on a Fully Connected Neural Network With Adversarial Learning Prior Knowledge

3D human pose estimation is more and more widely used in the real world, such as sports guidance, limb rehabilitation training, augmented reality, and intelligent security. Most existing human pose estimation methods are designed based on an RGB image obtained by one optical sensor, such as a digital camera. There is some prior knowledge, such as bone proportion and angle limitation of joint hinge motion. However, the existing methods do not consider the correlation between different joints from multi-view images, and most of them adopt fixed spatial prior constraints, resulting in poor generalizations. Therefore, it is essential to build a multi-view image acquisition system using optical sensors and customized algorithms for a 3D reconstruction of the human pose in the image. Inspired by generative adversarial networks (GAN), we used a data-driven method to learn the implicit spatial prior information and classified joints according to the natural connection characteristics. To accelerate the proposed method, we proposed a fully connected network with skip connections and used the SMPL model to make the 3D human body reconstruction. Experimental results showed that compared with other state-of-the-art methods, the joints’ average error of the proposed method was the smallest, which indicated the best performance. Moreover, the running time of the proposed method was 1.3 seconds per frame, which may not meet real-time requirements, but is still much faster than most existing methods.
Multi-level feature fusion and multi-loss learning for person Re-Identification

With the rise of deep learning technology, person re-identification (Re-id) technology has been developed rapidly. During the training process, many recent methods are susceptible to target misalignment and without sufficient discriminative features. Aiming at these two problems, a simple and potent model is proposed by us. A new self-attention module and a multi-loss function with relative weight are designed to integrate the multi-level features of pedestrians in our network. Specifically, the goal of the self-attention is to instruct the baseline network to learn robust features from the resized images. The key of the self-attention model is the weighting of the importance of different person regions. Therefore, the non-local features of the different levels in the baseline network will be paid more attention, which is conducive to learn discriminative features from the baseline network. Finally, a multi-loss function with relative weight is introduced to enhance the feature learning ability and integrate more features reasonably. Many experiments have been done on the three datasets (Market1501, DukeMTMC-reID and CUHK03-NP) and the results explain that the new model gets a higher accuracy than many other recent approaches.
Convolution Filter Pruning for Transfer Learning on Small Dataset

In this paper, we propose a scheme to reduce the size of a pre-trained full-scale model with a domain-specific dataset. This scheme combines model compression and transfer learning. First, it identifies the sensitive parts of a full model using the target dataset. Then it applies transfer learning on the identified part of the network to construct a reduced and customized model. Our scheme can correct structure and parameters to prune for a target dataset, which makes the following transfer learning more efficient.We apply our scheme on image classification applications using convolutional neural networks. We observe that different image categories activate different filters, so we can identify sensitive parts of the model for different categories of images.We use VGG-16 (pre-trained by ImageNet dataset) as the full model and we apply transfer learning on two different datasets (Flowers-102 and Cats vs. Dogs). We apply three pruning criteria in our scheme. The accuracy of Flowers-102 dataset by the best criterion drops less than 2% while pruning 60% of the network. We also observe the effects of different settings of our scheme and examine their feasibility by experiments.
Virtual, Augmented and Mixed Reality: What are the Benefits for SMEs?

Abstract Extended reality (XR) has the potential to be a game‐changer for SMEs. It can enhance competitiveness while supporting sustainable development and labour safety. The outbreak of COVID‐19 has further forced businesses to reflect on how technology might spur creativity and how innovation can affect their future survival. XR can facilitate product development, training, marketing and problem‐solving. We argue that policy should focus on increasing XR awareness in SMEs. Diffusion of XR is also contingent on R&amp;D teams’ ability to exploit cultural assets to allow SMEs to take full advantage of their potential.
Ensemble CNN in Transform Domains for Image Super-resolution from Small Data Sets

In this paper, we develop a method to recover high-resolution images from small training data sets by combining ideas inspired by compressive sensing, super-resolution neural networks, and ensemble learning. The key idea is that the frequency domain, and other transformed domains, where images can be presented sparsely can be used to ensemble a robust model and improve the performance of super-resolution convolutional neural networks. Fourier and wavelet representations are popular candidates since they provide sparse image representations but, the selection of an optimal transform domain is challenging since each type of transformation may be superior for different sub-image patches in different application domains. Herein, we propose an ensemble method for robustly selecting the appropriate sparsifying transform from limited available data. Our proposed ensemble CNN decides the best choice of transform domains for each sub-image patch and combines the patches to provide a general optimal solution for the entire image.
Video Vectorization via Bipartite Diffusion Curves Propagation and Optimization

We propose a new video vectorization approach for converting videos in the raster format to vector representation with the benefits of resolution independence and compact storage. Through classifying extracted curves in each video frame into salient ones and non-salient ones, we introduce a novel bipartite diffusion curves (BDCs) representation in order to preserve both important image features such as sharp boundaries and regions with smooth color variation. This bipartite representation allows us to propagate non-salient curves across frames such that the propagation, in conjunction with geometry optimization and color optimization of salient curves, ensures the preservation of fine details within each frame and across different frames, and meanwhile, achieves good spatial-temporal coherence. Thorough experiments on a variety of videos show that our method is capable of converting videos to the vector representation with low reconstruction errors, low computational cost, and fine details, demonstrating our superior performance over the state of the art. We also show that, when used for video upsampling, our method produces results comparable to video super-resolution.
Re-encrypted Data Access Control Scheme Based on Blockchain

Nowadays, massive amounts of data are stored in the cloud, how to access control the cloud data has become a prerequisite for protecting the security of cloud data. In order to address the problems of centralized control and privacy protection in current access control, we propose an access control scheme based on the blockchain and re-encryption technology, namely PERBAC-BC scheme. The access control policy is managed by the decentralized and immutability characteristics of blockchain, while the re-encryption is protected by the trusted computing characteristic of blockchain and the privacy is protected by the identity re-encryption technology. The overall structure diagram and detailed execution flow of the scheme are given in this paper. Experimental results show that, compared with the traditional hybrid encryption scheme, the time and space consumption is less when the system is expanded. Then, the time and space performance of each part of the scheme is simulated, and the security of blockchain is proved. The results also show that the time and space performance of the scheme are better and the security is stronger, which has certain stability and expandability.
Polsar Image Classification via Complex-Valued Multi-Scale Convolutional Neural Network

Convolutional neural networks (CNNs) have achieved promising results in polarimetric SAR image classification. Generally, the semantic segmentation of a large image is conducted using the image slices, for which the small slices may be in a single pure class but with insufficient information, and the large ones may contain the mixed class. Therefore, a complex-valued multi-scale CNN (CVMS-CNN) architecture is proposed to extract the hierarchical multi-scale information, i.e. local and global features, and adapt to the complex PolSAR data format, simultaneously. Moreover, the optimal feature fusion mechanism is given through comprehensive comparisons. Experiments are carried out on two benchmark datasets to verify the effectiveness. Numerical simulations show that the classification results have been significantly improved via CVMS-CNN compared with the state-of-the-arts.
Privacy-Encoding Models for Preserving Utility of Machine Learning Algorithms in Social Media

Social media has become a vital platform in our daily life, where users can interact with their friends and other people throughout the world. The vast data generated by these platforms is unique in its variety and sensitivity, and although it potentially has significant utility, but also the potential for misuse. Although social media providers apply some existing privacy techniques, such as encryption and anonymization, the techniques cannot achieve a solid level of data privacy while maintaining the highest level of data utility. This paper proposes new Privacy-Encoding (PE) models that contain two-levels of data privacy: 1) data perturbation-based encoding techniques, and 2) data normalization-based scaling techniques. The data perturbation-based encoding techniques involve label encoder and one-hot encoder ones, while data normalization-based scaling techniques include min-max and z-score normalization ones. The aim of the two-levels is to transform original data into perturbed data, along with balancing the high level of data utility using machine learning algorithms. To evaluate the data utility, the proposed models are applied on the adult dataset as well as a simulated social media dataset and the accuracy of the results is compared with several machine learning algorithms. The experiment results reveal that the models could achieve high privacy and utility levels in terms of variance, accuracy and f-measure metrics.
Design and Implementation of an autonomous vehicle with LIDAR-based navigation

A terrestrial automat has been implemented whose navigation system is based on the reconstruction in two dimensions of its surroundings from multiple sensors such as: a Lidar type sensor, a GPS module, an electronic compass and various proximity sensors. Further, the characterization of the system and all these sensors were carried out in the Raspberry pi 3 B platform.The different types of sensors and the user interface allow the prototype to have great versatility for different specific applications within the engineering world such as zone mapping and interior reconstruction at low cost.
Research on Prediction of the Cash Usage in Banks Based on LSTM of Improved Grey Wolf Optimizer

Abstract In the real production and operation, it is impossible to predict the amount of cash in daily use. Therefore, the prediction model of improved LSTM neural network is proposed to cope with the problem for preparing excessive cash. Hence, the improved Grey Wolf Optimizer is most effective in searching for the optimal solution by optimizing the impact factors of Grey Wolf Optimizer. Combining the improved Grey Wolf Optimizer with LSTM neural network, the neural networking learning rate parameters are set reasonably by optimizing the algorithm to reduce the impact of inappropriate parameters on the prediction results of either over-fitting or under-fitting. What’s more, the neural network topological structure, weighing the number of LSTM network layers and the number of the neural units in each layer, determines the neural network’s description of data. If the network topology is too simple, the prediction results may not be enough to describe the real data. However, if it is too complex, it will not only waste the computing resources, but also make the prediction results over-fitting with only good description of training data. Therefore, it avoids the problem of large errors in predicting results caused by the parameters of neural network and realizes the prediction the daily cash usage. Finally, the test is completed on the data of a sub-branch network of bank with mean square error (MSE) 0.016. Compared with the traditional time series model ARAM and the unimproved LSTM, the improved LSTM predicts cash usage more accurately and efficiently.