Reviewerly/exHarmony huggingface dataset:
THE ROLE OF AUGMENTED REALITY IN DESTINATION BRANDING

Purpose – The goals of this study are to find out which factors contribute to AR applications generating satisfactory tourism experiences; analyze the impact of AR on destination brand communication; and examine whether the act of communicating the destination brand also increases satisfactory tourist experiences. Design – The research is based on a case study of an AR application that belongs to the city of Tarragona (Spain) that reproduces its main monuments dating from the Roman era. Methodology – The study includes one survey conducted on 150 participants (75 tourists, 75 residents) and 15 in-depth interviews with respondents who used the Imageen application. Approach – The analysis provides detailed information on the users’ experiences and opinions, highlighting the different variables that provide the most satisfactory tourist experiences. Findings – The study shows that the AR application creates highly satisfactory tourism experiences, although not extraordinary. The main contribution of this study has been to demonstrate that the AR application has the potential to communicate the destination brand. Originality of the research – This study provides knowledge on the AR role in constructing a destination brand and its relationship with the tourism experience. Particularly, the findings of this paper have significant implications for DMOs and their marketing and communication strategies.
Face alignment based on fusion subspace and 3D fitting

The traditional face alignment approaches based on cascade regression have achieved satisfactory result on the frontal face, but for the face with large changes in posture and expression, a single initial shape will lead to the result falling into local optimum. In order to solve this problem, a two-stage cascade regression model for face alignment is proposed, which generates coarse initial shape from the aligned salient shape. The first stage is used to align the salient shape that contains some prominent landmarks. To enhance the robustness of authors' method, the fusion subspace is used to divide the samples, and each subset trains cascade regression model separately. The alignment results of the first stage are used to generate the coarse initial shapes for the second stage through 3D fitting. The second stage is still based on cascade regression, which is used to further predict the full shape. The experimental results demonstrate the proposed method can achieve state-of-art performance, especially in unconstrained conditions with various poses.
Coding as a tooL for forming ComputationaL thinking of students

The article outlines the developmental and social aspects of teaching coding to schoolchildren: the development of thinking, the formation of new values of the digital society, understanding the rules of behavior in the digital environment. Coding is a powerful tool for developing computational (algorithmic) thinking. The article shows a variant of teaching coding, which allows schoolchildren to independently invent algorithms, develop their thinking, and improve their abilities. Namely, an example of a sequence of coding tasks on the topic "Integers and operations on them" for elementary school students is considered in detail. Tasks solutions (programs) are given in Python. The emphasis is on how, on the basis of a specially organized sequence of tasks, to provide the student with the opportunity to develop their abilities to perform decomposition, abstraction, pattern recognition, algorithmization, modeling, assessment.
Load Transportation by Quadrotors in Crowded Workspaces

Load transportation by unmanned aerial vehicles has many advantages in comparison with load transportation by unmanned ground vehicles. With the vehicle in the air obstacles in the ground and hard terrains are not a problem anymore, for instance. Moreover, higher speeds can be developed, allowing faster task accomplishment. The disadvantage is the possibility of load oscillations during the flight, which should be compensated through using suitable controllers. Furthermore, aiming at a real application, such as the delivery of medical kits for victims of disasters, the control system responsible for the navigation should embed an obstacle avoidance technique that works in harmony with the compensation of the load oscillations. In this work a load delivery task in an environment crowded with obstacles is addressed, in which the aerial vehicle should deliver the cargo at a specified target point. Euler-Lagrange equations are used to model the vehicle-load system, and a feedback-linearization controller is designed based on such a model, which is able to reject the load oscillations. The obstacle avoidance module embedded in the controller is also concerned to the efficiency in the task accomplishment, such that it avoids abrupt movements and reduction of the forward velocity of the vehicle. Several real experiments in which the proposed control system is adopted have been run, some of them presented here, whose results validate the proposed controller.
Multi-Tasking U-Shaped Network for Benign and Malignant Classification of Breast Masses

The benign and malignant (BM) classification of breast masses based on mammography is a key step in the diagnosis of early breast cancer and an effective way to improve the survival rate of patients.Nevertheless, due to the differences in size, shape and texture of breast masses and the visual similarity between masses of the same category, it is difficult to obtain a robust classification model using conventional deep learning methods.To address this problem, we proposed a Multi-Tasking U-shaped Network (MT-UNet), which contains three key ideas: 1) the U-shaped classification architecture constructed can well adapt to the heterogeneity of breast masses; 2) the combination of the proposed truncated normalization method and adaptive histogram equalization method can enhance the contrast of image; 3) training with label smoothing method can alleviate the problem of convergence difficulty caused by insufficient training data.The performance of the proposed scheme is evaluated on the public dataset of DDSM and INbreast.On the DDSM dataset, the Area Under Curve (AUC) and accuracy (ACC) reached 0.9963 and 0.9817, respectively.On the INbreast dataset, the AUC and ACC reached 0.9767 and 0.9391, respectively.Experimental results show that the proposed method can obtain a competitive performance. INDEX TERMS classification of benign and malignant, image enhance, label smoothing, transfer learning, computer aid diagnosisJunyan Tong received her master degree from Nanjing University of technology in 2009.She currently is an algorithm application expert of Hikvision Research Institute, and her main research direction is AI and its application.
Weakly-Supervised Action Localization, and Action Recognition Using Global–Local Attention of 3D CNN

3D Convolutional Neural Network (3D CNN) captures spatial and temporal information on 3D data such as video sequences. However, due to the convolution and pooling mechanism, the information loss seems unavoidable. To improve the visual explanations and classification in 3D CNN, we propose two approaches; i) aggregate layer-wise global to local (global-local) discrete gradients using trained 3DResNext network, and ii) implement attention gating network to improve the accuracy of the action recognition. The proposed approach intends to show the usefulness of every layer termed as global-local attention in 3D CNN via visual attribution, weakly-supervised action localization, and action recognition. Firstly, the 3DResNext is trained and applied for action classification using backpropagation concerning the maximum predicted class. The gradients and activations of every layer are then up-sampled. Later, aggregation is used to produce more nuanced attention, which points out the most critical part of the predicted class's input videos. We use contour thresholding of final attention for final localization. We evaluate spatial and temporal action localization in trimmed videos using fine-grained visual explanation via 3DCam. Experimental results show that the proposed approach produces informative visual explanations and discriminative attention. Furthermore, the action recognition via attention gating on each layer produces better classification results than the baseline model.
A robust deep learning approach for glasses detection in non‐standard facial images

Automated glasses detection is a cardinal component in facial/ocular analysis that powers forensic, surveillance and biometric authentication systems. Throughout literature, glasses detection was always experimented by either utilizing hand-crafted or deep learning features. Nevertheless, in both cases, highly standard face/ocular images were needed to derive the suggested technique. Both working methods performed reasonably well, but the results were bonded to the quality of the facial image and extracted features, where a slight shift and/or rotation in the input face image negatively affects the results. In addition, the obtained performance is even worse on real-world (non-standard) images, especially when compared to recent achievements in other computer vision research areas. In this paper, we present a robust deep learning approach for glasses detection from selfie photos full/partial frontal body non-standard images captured in real-life uncontrolled environments that do not utilize any facial landmarks. To the best of our knowledge this paper is the first to experiment detecting glasses from selfie photos, using a robust deep learning approach. Experimental results on various benchmark facial analysis datasets demonstrated the superior performance of the proposed technique with 96% accuracy.
On improving the memorability of system-assigned recognition-based passwords

User-chosen passwords reflecting common strategies and patterns ease memorisation but offer uncertain and often weak security, while system-assigned passwords provide higher security guarantee but suffer from poor memorability. We thus examine the technique to enhance password memorability that incorporates a scientific understanding of long-term memory. In particular, we examine the efficacy of providing users with verbal cues—real-life facts corresponding to system-assigned keywords. We also explore the usability gain of including images related to the keywords along with verbal cues. In our multi-session lab study with 52 participants, textual recognition-based scheme offering verbal cues had a significantly higher login success rate (94.23%) compared to the control condition, i.e. textual recognition without verbal cues (61.54%). When users were provided with verbal cues, adding images contributed to faster recognition of the assigned keywords, and thus had an overall improvement in usability. So, we conducted a field study with 54 participants to further examine the usability of graphical recognition-based scheme offering verbal cues, which showed an average login success rate of 98% in a real-life setting and an overall improvement in login performance with more login sessions. These findings show a promising research direction to gain high memorability for system-assigned passwords.
Cryptography from One-Way Communication: On Completeness of Finite Channels

Garg et al. (Crypto 2015) initiated the study of cryptographic protocols over noisy channels in the non-interactive setting, namely when only one party speaks. A major question left open by this work is the completeness of finite channels, whose input and output alphabets do not grow with the desired level of security. In this work, we address this question by obtaining the following results:
Preliminary Experiment on Emotion Detection in Illustrations Using Convolutional Neural Network

The paper describes an experiment on emotion detection in images, specifically illustrations and cartoon images. Usually, detection and classification of emotions are performed on human faces so the algorithm can learn, for example, what a “happy human face” looks like. These algorithms probably can’t transfer their understanding of happiness features onto different types of objects, like animals or cartoon illustrations. We, humans, can recognize and detect signs of emotions (although maybe falsely) in new and unusual objects. Developing an algorithm capable of recognizing emotions in objects it wasn’t trained on would allow for better human-like robots and systems. This is a preliminary study on how well knowledge gained by a typical neural network detection system on a set of objects transfers to new, unknown objects. The neural network detection system used in this study is YOLO. We collected small training datasets using cartoon illustrations of several animals of two categories: happy and sad. We tested the trained network on a set of illustrations depicting a different animal the network hasn’t seen in training. The best performance achieved is 0.69 F1-score.
A Novel Approach to Data Encryption Based on Matrix Computations

In this paper, we provide a new approach to data encryption using generalized inverses. Encryption is based on the implementation of weighted Moore–Penrose inverse A†MN(nxm) over the nx8 constant matrix. The square Hermitian positive definite matrix N8x8 p is the key. The proposed solution represents a very strong key since the number of different variants of positive definite matrices of order 8 is huge. We have provided NIST (National Institute of Standards and Technology) quality assurance tests for a random generated Hermitian matrix (a total of 10 different tests and additional analysis with approximate entropy and random digression). In the additional testing of the quality of the random matrix generated, we can conclude that the results of our analysis satisfy the defined strict requirements. This proposed MP encryption method can be applied effectively in the encryption and decryption of images in multi-party communications. In the experimental part of this paper, we give a comparison of encryption methods between machine learning methods. Machine learning algorithms could be compared by achieved results of classification concentrating on classes. In a comparative analysis, we give results of classifying of advanced encryption standard (AES) algorithm and proposed encryption method based on Moore–Penrose inverse.
Image Stitching Based on Nonrigid Warping for Urban Scene

Image stitching based on a global alignment model is widely used in computer vision. However, the resulting stitched image may look blurry or ghosted due to parallax. To solve this problem, we propose a parallax-tolerant image stitching method based on nonrigid warping in this paper. Given a group of putative feature correspondences between overlapping images, we first use a semiparametric function fitting, which introduces a motion coherence constraint to remove outliers. Then, the input images are warped according to a nonrigid warp model based on Gaussian radial basis functions. The nonrigid warping is a kind of elastic deformation that is flexible and smooth enough to eliminate moderate parallax errors. This leads to high-precision alignment in the overlapped region. For the nonoverlapping region, we use a rigid similarity model to reduce distortion. Through effective transition, the nonrigid warping of the overlapped region and the rigid warping of the nonoverlapping region can be used jointly. Our method can obtain more accurate local alignment while maintaining the overall shape of the image. Experimental results on several challenging data sets for urban scene show that the proposed approach is better than state-of-the-art approaches in both qualitative and quantitative indicators.
Exploiting prunability for person re-identification

Abstract Recent years have witnessed a substantial increase in the deep learning (DL) architectures proposed for visual recognition tasks like person re-identification, where individuals must be recognized over multiple distributed cameras. Although these architectures have greatly improved the state-of-the-art accuracy, the computational complexity of the convolutional neural networks (CNNs) commonly used for feature extraction remains an issue, hindering their deployment on platforms with limited resources, or in applications with real-time constraints. There is an obvious advantage to accelerating and compressing DL models without significantly decreasing their accuracy. However, the source (pruning) domain differs from operational (target) domains, and the domain shift between image data captured with different non-overlapping camera viewpoints leads to lower recognition accuracy. In this paper, we investigate the prunability of these architectures under different design scenarios. This paper first revisits pruning techniques that are suitable for reducing the computational complexity of deep CNN networks applied to person re-identification. Then, these techniques are analyzed according to their pruning criteria and strategy and according to different scenarios for exploiting pruning methods to fine-tuning networks to target domains. Experimental results obtained using DL models with ResNet feature extractors, and multiple benchmarks re-identification datasets, indicate that pruning can considerably reduce network complexity while maintaining a high level of accuracy. In scenarios where pruning is performed with large pretraining or fine-tuning datasets, the number of FLOPS required by ResNet architectures is reduced by half, while maintaining a comparable rank-1 accuracy (within 1% of the original model). Pruning while training a larger CNNs can also provide a significantly better performance than fine-tuning smaller ones.
Removing Atmospheric Turbulence Effects Via Geometric Distortion and Blur Representation

Removing the geometric distortion and space-time-varying blur caused by atmospheric turbulence from a given image sequence remains a challenge. Since geometric distortion and blur are two different kinds of distortions and interact with each other in the process of image restoration, it is difficult to extract the features that are useful to the restoration process when the images experience multiple distortions. In this article, we propose a new scheme based on geometric distortion and blur representation. The blur invariants and maximum gradient are used to represent the geometric distortion and sharpness of an image frame, respectively. The proposed scheme consists of three parts. First, two fast frame selection algorithms based on independent evaluations of the sharpness and geometric distortion are proposed to subsample a sharp subsequence and obtain a reference image. Next, to suppress the geometric distortion, a moment-blur-invariant-based method is presented to estimate the deformation vector between two degraded frames, and the selected sharp frames are registered to the reference image. Finally, a blind deconvolution method is applied to deblur the fused image, generating a final restoration result. Various experimental results show that the proposed method can effectively alleviate distortion and blur, as well as significantly improve the visual quality of real atmospheric turbulence-degraded images.
Modeling User Interests With Online Social Network Influence by Memory Augmented Sequence Learning

Online social networks, such as Facebook and Twitter, enable users to share their shopping/travel experiences with their friends. However the influence on users' decision-making on next visit/buy has sparse research exposure, by accurately modeling long-term user behaviors from historical data. The existing methods do not fully take advantage of the underlying social networks to model user interests, nor they have not modeled long-term transitional behavior patterns. In this paper, we propose a novel Social Influence aware and Memory augmented Sequence learning (SIMS) model, on what a user will likely buy/visit next. Specifically, SIMS first learns a representation for the visiting/purchasing sequence of each user using the sequence-to-sequence learning method. Then it predicts the interest of a user by integrating the representation of his/her own sequence, with another representation of the corresponding social influence, which is learned using an autoencoder-based model. In addition, we leverage an emerging memory augmented neural network, Differentiable Neural Computer (DNC), to further improve prediction accuracy. We conduct extensive experiments to evaluate the proposed model using three real-world datasets, Yelp, Epinions and Ciao. When compared with 10 other baselines and state-of-the-art solutions, the experimental results show that 1) the proposed model significantly outperforms all other methods in terms of various accuracy-related metrics; 2) the proposed social influence modeling and memory augmentation do lead to the performance gain.
Binary Hybrid Differential Evolution Algorithm for Multi-label Feature Selection

Driven by the recent technological advancements within the machine learning field, multi-label classification has been introduced as one of the challenging tasks to assign more than one label to each instance in a dataset. Feature selection is one of the predominant feature engineering methodologies which being extensively used as a vital step in predictive model construction to enhance the multi-label classification performance. Many metaheuristic algorithms have been tailored to choose the optimal subset of features in datasets but as a challenging problem, such algorithms suffer from a slow process during fine-tuning. Objective of this paper is to propose a hybrid mechanism by which an obtained feature subset from a Binary Differential Evolution (BDE) algorithm will be further enhanced to minimize the classification error using a local search methodology. Key motivation behind the proposed model is to address the weakness in exploitation of metaheuristic feature selection algorithms with the help of classical feature selection method such as Sequential Backward Selection (SBS) as a local search strategy. The classical feature selection method eliminates more redundant and irrelevant features of obtained subset using the BDE to decrease the classification error. The empirical results obtained on eight various multi-label datasets show that the proposed hybrid approach, which is a fusion of both evolutionary and classical feature selection methods, can minimize the classification error on the obtained feature subset using the BDE.