Reviewerly/exHarmony huggingface dataset:
Integration of virtual reality and 3D modeling use of environments in education

This text deals with the design of virtual reality systems. It is also said here that the results reached with the use of the developed software show the attributes that make the ideal virtual reality for situations of research and learning taking the discipline as a reference of the classroom to the computer labs and making more interesting to the student, making the learning easy. Also of note is the fear that is characteristic only for VR in the field of education. Modern e-learning technology has many advantages, but they gradually reduce the share of the direct dialogue between teacher and student. Teacher replaced by interactive content, and the student instead of the person interacts with its simplified virtual model. The introduction of e-learning technologies must not become an end in itself. It requires a careful and balanced approach when implementing all new in education. Based on the experience of implemented development VR content in education can be noted that technology brings positive results when it is using by short sessions or as simulators and trainers. It is inappropriate to use VR for lectures and seminars. In addition to reviews how virtual reality has been used in education, discusses the advantages and disadvantages of using these technologies in the classroom, and describes how virtual reality and augmented reality technologies can be used to enhance teaching at the Samarkand branch of the Tashkent University of information technologies named after Muhammad al-Khwarizmi.
Automated Detection of Social Roles in Online Communities using Deep Learning

Online communities are an increasingly important aspect in the digital age, for business organizations, diverse industry sectors and overall, in modern society. The social role of each end-user, influencers to followers, and content providers to receivers is a primary consideration when evaluating the purpose and contribution of any online community. Most existing research on the detection of social roles in online communities is based on manual observations and analysis. This paper introduces a technique for automating the detection and extraction of social roles from online communities. Given the large volume of text and value of content, it is no longer viable to manually encode and detect social roles and contributions. The machine learning approach is based on a deep recurrent neural network and a word embedding model. A dataset consisting of over 1.2 million textual posts extracted from an online community on higher education in Australia was used to demonstrate the technique. This technique can be applied to any online community to automatically identify social roles, their influence and interactions.
Intonation in Robot Speech

Human-robot interaction (HRI) research aims to design natural interactions between humans and robots. Intonation, a social signaling function in human speech investigated thoroughly in linguistics, has not yet been studied in HRI. This study investigates the effect of robot speech intonation in four conditions (no intonation, focus intonation, end-of-utterance intonation, or combined intonation) on conversational naturalness, social engagement, and people's humanlike perception of the robot collecting objective and subjective data of participant conversations (n = 120). Our results showed that humanlike intonation partially improved subjective naturalness but not observed fluency, and that intonation partially improved social engagement but did not affect humanlike perceptions of the robot. Given that our results mainly differed from our hypotheses based on human speech intonation, we discuss the implications and provide suggestions for future research to further investigate conversational naturalness in robot speech intonation.
Full Reference Image Quality Assessment by CNN Feature Maps and Visual Saliency

In this paper, we propose a new full reference image quality estimation method by feature maps which are intermediate layer's outputs in convolutional neural network. The novelty of the proposed method is to combine a saliency map reflecting human gaze area with the feature maps of convolutional neural network. In addition, we analyze in detail which layer's feature maps are effective for image quality estimation. Experiments using CID:IQ data set are performed on VGG16 and VGG19 which are deep neural networks for object recognition, and the results show that the image quality estimation accuracy can be significantly improved compared to the conventional method by introducing the saliency map.
Face and occlusion Recognition Algorithm based on Global and Local

Abstract Nowadays, the face recognition direction of unobstructed has been greatly developed, but there is still a huge deficiency in face recognition with occlusion. In order to improve the recognition rate under occlusion, this paper proposes a global and local occlusion recognition algorithm using deep learning for feature learning. Due to occlusion, local significant features will be missing, the impact will cause the accuracy of global recognition to decrease. Therefore, SIFT and SVM algorithms are used to discriminate partial face occlusion, and cut off the occluded local face to form a global face. Global facial features and unmasked local feature are extracted together. In the feature extraction, the LeNet-5 lightweight convolutional neural network and the improved LeNet-5 lightweight convolutional neural network are combined with SoftMax multi-classification. The final classification results use the improved hierarchical voting method to make final decisions on the face, which speeds up the training and prediction speed of the multi-classifier. This experiment will perform occlusion face verification on the AR dataset. The final experimental results show that the method greatly improves the accuracy of occlusion face recognition and has strong robustness to occlusion.
A novel two-dimensional reversible data hiding method with high embedding capacity in H.264/advanced video coding

Developing the technology of reversible data hiding based on video compression standard, such as H.264/advanced video coding, has attracted increasing attention from researchers. Because it can be applied in some applications, such as error concealment and privacy protection. This has motivated us to propose a novel two-dimensional reversible data hiding method with high embedding capacity in this article. In this method, all selected quantized discrete cosine transform coefficients are first paired two by two. And then, each zero coefficient-pair can embed 3 information bits and the coefficient-pairs only containing one zero coefficient can embed 1 information bit. In addition, only one coefficient of each one of the rest coefficient-pairs needs to be changed for reversibility. Therefore, the proposed two-dimensional reversible data hiding method can obtain high embedding capacity when compared with the related work. Moreover, the proposed method leads to less degradation in terms of peak-signal-to-noise ratio, structural similarity index, and less impact on bit-rate increase.
Spline functions for Arabic morphological disambiguation

We have developed in this paper a morphological disambiguation hybrid system for the Arabic language that identifies the stem, lemma and root of a given sentence words. Following an out-of-context analysis performed by the morphological analyser Alkhalil Morpho Sys, the system first identifies all the potential tags of each word of the sentence. Then, a disambiguation phase is carried out to choose for each word the right solution among those obtained during the first phase. This problem has been solved by equating the disambiguation issue with a surface optimization problem of spline functions. Tests have shown the interest of this approach and the superiority of its performances compared to those of the state of the art.
Primary Steps in Analyzing Data: Tasks and Tools for a Systematic Data Exploration

Understanding the structure, basic properties and relationships in a given dataset is a fundamental prerequisite for an appropriate statistical analysis. Here, we highlight the major principles of data exploration and provide a roadmap for a systematic and reproducible analysis based on key questions. Using an exemplary dataset on throughfall measurements, we demonstrate how several techniques can be used and evaluated in order to address common tasks of data analysis such as understanding the structure of the dataset, detecting temporal and spatial dependence among observations, identifying outliers and influential observations, checking normality and homogeneity of model residuals and exploring the relationships of variables. Finally, it is discussed when and when not data transformations can be used as potential actions to overcome restrictions in the application of a statistical method. The electronic supplement offers the sample dataset as well as fully documented computer code, which aims to serve as a guideline for conducting an exploratory data analysis using the statistical software environment R.
Research and improvement of fraud identification model of Chinese A-share listed companies based on M-score

Purpose The purpose of this paper is to start with the background of the construction of the M-score model, find the variables that can represent the fraud characteristics of Chinese companies, and use the data of Chinese A-share listed companies to modify the M-score model. Design/methodology/approach In this paper, the fraud behavior of Chinese enterprises that M-score cannot detect is summarized as the basis of adding variables. Then, based on the data of Chinese listed companies, a modified M-score model including nine variables is constructed by the logistic regression method based on Wald. Findings Based on the original 8 variables of M-score, this paper adds 10 new variables that can represent the fraud characteristics of Chinese listed companies, and finally, constructs a modified M-score model with 9 variables. Results indicated that indexes such as gross profit margin, fixed assets depreciation rate, equity concentration and audit opinion can characterize the financial fraud of Chinese listed companies. Practical implications The modified M-score model based on the characteristics of Chinese enterprises’ fraud is more suitable for Chinese market, which can help investors avoid fraud risks, protect their own rights and interests and reduce losses. Originality/value Starting from the background of the model, this paper looks for variables that can characterize the characteristics of fraud in Chinese listed companies. Then, subdivides the research samples into specific fiscal years in which fraud occurs, so that the modified M-score model can be more suitable for the Chinese market.
Annotated 360-Degree Image and Video Databases: A Comprehensive Survey

The advances in 5G mobile networks are expected to enable immersive interconnected mobile multimedia systems. As humans are the final judges of the quality of immersive multimedia, it is essential to engage a suitable ground truth in the design of such systems. Databases annotated with results from subjective tests constitute such ground truth given as opinion scores, head movements, eye tracking data, psychophysiological data, and other data related to the viewers’ behavior. On this basis, perception-based quality assessment of algorithms, systems, and services can be performed, and objective perceptual quality models can be developed. In this paper, a comprehensive survey of publicly available annotated 360-degree image and video databases is provided. The survey may guide the selection of ground truth on 360-degree images and videos to support quality assessment and modeling research. Further, the survey reveals the need for establishing new annotated databases that address the full range of subjective aspects of immersive multimedia.
MaskPlus: Improving Mask Generation for Instance Segmentation

Instance segmentation is a promising yet challenging topic in computer vision. Recent approaches such as Mask R-CNN typically divide this problem into two parts - a detection component and a mask generation branch, and mostly focus on the improvement of the detection part. In this paper, we present an approach that extends Mask R-CNN with five novel techniques for improving the mask generation branch and reducing the conflicts between the mask branch and the detection component in training. These five techniques are independent to each other and can be flexibly utilized in building various instance segmentation architectures for increasing the overall accuracy. We demonstrate the effectiveness of our approach with tests on the COCO dataset.
MIHNet: Combining N-gram, Sequential and Global Information for Text Classification

Abstract Pre-training language model has achieved amazing results in many NLP tasks. In Particular, BERT (Bidirectional Encoder Representations from Transformers) create a new era in NLP tasks. Despite the success, these model perform well at Global Information but weak on n-gram and Sequential information. In this paper, we conduct exhaustive experiments of classical text classification models upon BERT in text classification task and provide a general guide for BERT+ models. Finally, we propose a new text classification model called MIHNet (Multi-dimension Information Integration using Highway network), which integrates Global, n-gram and Sequential information together and get a better performance. Notably, our model obtains new state-of-the-art results on eight widely-studied text classification datasets.
ReStGAN: A step towards visually guided shopper experience via text-to-image synthesis

E-commerce companies like Amazon, Alibaba and Flip-kart have an extensive catalogue comprising of billions of products. Matching customer search queries to plausible products is challenging due to the size and diversity of the catalogue. These challenges are compounded in apparel due to the semantic complexity and a large variation of fashion styles, product attributes and colours. Providing aids that can help the customer visualise the styles and colours matching their "search queries" will provide customers with necessary intuition about what can be done next. This helps the customer buy a product with the styles, embellishments and colours of their liking. In this work, we propose a Generative Adversarial Network (GAN) for generating images from text streams like customer search queries. Our GAN learns to incrementally generate possible images complementing the fine-grained style, colour of the apparel in the query. We incorporate a novel colour modelling approach enabling the GAN to render a wide spectrum of colours accurately. We compile a dataset from an e-commerce website to train our model. The proposed approach outperforms the baselines on qualitative and quantitative evaluations.
Online dimensional control of rolled steel profiles using projected fringes

Abstract Fringe projection is a versatile method for mapping the topography of surfaces. In this paper, it is used to measure the defects on the head of railroad rails while the rails are moving. Railroad rails are made by hot rolling. The quality of the finished product is generally good, but surface texture will deteriorate with increasing temperature. A method for online inspection therefore is very desirable. In the present experiment, dimensional inspection of the railroad rails was made online while moving at a speed of 1–2 m/s. Therefore, it is important to minimize the registration time. To achieve this, we apply a method of fringe location with sub-pixel accuracy that requires only a single exposure for each registration.
On the Robustness of Support Vector Machines against Adversarial Examples

In this paper, the robustness of Support Vector Machines (SVMs) against adversarial instances is considered in relation to the design parameters. After generating adversarial instances using convex programming, it is shown through extensive numerical analysis that the robustness is significantly affected by parameters which change the linearity of the models. Interestingly, robustness is only slightly sensitive to the parameter determining the margin between classes. It is shown that adversarial robustness not only depends on the geometric properties of the classifier but is also subject to the accuracy of the model. The results are discussed in the light of the so-called linearity hypothesis, regarding adversarial robustness of machine learning algorithms.
Null test of large convex aspheres by subaperture stitching with replaceable holograms

Null test is preferred for measuring surface error of aspheres. However for large convex aspheres, it is challenging to fabricate larger null optics. Subaperture stitching is hence introduced but requires different null optics for subapertures of different off-axis distances. It gets easier by using replaceable computer generated holograms (CGHs). In this paper, a convex high-order even asphere is taken as an example and multiple CGHs are designed for null test of different subapertures. The same transmission flat and tilt carrier are used to facilitate alignment of CGHs. Disturbance orders of diffraction are confirmed to be separated by the tilt carrier without ghost interference. A configuration-based stitching algorithm is applied to successfully retrieve the full aperture surface error. The CGH substrate error is calibrated out before stitching optimization by measuring its transmitted wavefront with a high precision reference flat. Finally, the stitching result is verified by through-the-back null test of the same asphere.
Learning to Navigate Robotic Wheelchairs from Demonstration: Is Training in Simulation Viable?

Learning from demonstration (LfD) enables robots to learn complex relationships between their state, perception and actions that are hard to express in an optimization framework. While people intuitively know what they would like to do in a given situation, they often have difficulty representing their decision process precisely enough to enable an implementation. Here, we are interested in robots that carry passengers, such as robotic wheelchairs, where user preferences, comfort and the feeling of safety are important for autonomous navigation. Balancing these requirements is not straightforward. While robots can be trained in an LfD framework in which users drive the robot according to their preferences, performing these demonstrations can be time-consuming, expensive, and possibly dangerous. Inspired by recent efforts for generating synthetic data for training autonomous driving systems, we investigate whether it is possible to train a robot based on simulations to reduce the time requirements, cost and potential risk. A key characteristic of our approach is that the input is not images, but the locations of people and obstacles relative to the robot. We argue that this allows us to transfer the classifier from the simulator to the physical world and to previously unseen environments that do not match the appearance of the training set. Experiments with 14 subjects providing physical and simulated demonstrations validate our claim.
Transfer Learning for Image Classification Using Hebbian Plasticity Principles

Transfer learning is a deep learning technique has proved to be of great importance. However, most of the standard transfer learning algorithms are designed to repeat the same method for fine-tuning of the weights on the target domain. If we try to investigate the human brain's mechanism of learning a new complex concept based on a simple and basic concept, we can say, it is different from just the repetition of the same method of learning on a different dataset. In this article, we have introduced a novel transfer learning algorithm referred to as HTL (Hebbian transfer learning) using synaptic plasticity. The Hebbian theory, introduced by Donald Hebb, explains the "associative learning" in which the simultaneous activation of the brain cells positively affects the increase in the synaptic connection strength between the individual cells. This particular behaviour of Hebbian learning, makes it a very viable candidate for discriminative learning for the search of the specific feature for the task of object recognition or image classification. It helps connection weights of the learned model to adapt as per task dataset using numerical methods defining plasticity principles. Learning to discriminate between instances of different classes, over a variable number of classes within the dataset space defined by the task at hand, can be the result-oriented approach for classification problem. Extensive experiments verify that HTL, using synaptic plastic behaviour in heterogeneous transfer learning task does better than the standard state of the art methods of transfer learning on the cross-domain image classification task.
Detection of Bots in CAPTCHA as a Cloud Service Utilizing Machine Learning

In recent years, the damage caused by unauthorized access using bots has increased. Compared with attacks on conventional login screens, the success rate is higher and detection of them is more difficult. CAPTCHA is commonly utilized as a technology for avoiding attacks by bots. But user's experience declines as the difficulty of CAPTCHA becomes higher corresponding to the advancement of the bot. As a solution, adaptive difficulty setting of CAPTCHA combining with bot detection technologies is considered. In this research, we focus on Capy puzzle CAPTCHA, which is widely used in commercial service. We use a supervised machine learning approach to detect bots. As a training data, we use access logs to several Web services, and add flags to attacks by bots detected in the past. We have extracted vectors fields like HTTP-User-Agent and some information from IP address (e.g. geographical information) from the access logs, and the dataset is investigated using supervised learning. By using XGBoost and LightGBM, we have achieved high ROC-AUC score more than 0.90, and further have detected suspicious accesses from some ISPs that has no bot discrimination flag.
A multimodal generative and fusion framework for recognizing faculty homepages

Multimodal data consist of several data modes, where each mode is a group of similar data sharing the same attributes. Recognizing faculty homepages is essentially a multimodal classification problem in which a target faculty homepage is determined from three different information sources, including text, images, and layout. Conventional strategies in previous studies have been either to concatenate features from various information sources into a compound vector or to input them separately into several different classifiers that are then assembled into a stronger classifier for the final prediction. However, both approaches ignore the connections among different feature sets. We argue that such relations are essential to enhance multimodal classification. Besides, recognizing faculty homepages is a class imbalance problem in which the total number of samples of a minority class is far smaller than the sample numbers of other classes. In this study, we propose a multimodal generative and fusion framework for multimodal learning with the problems of imbalanced data and mutually dependent feature modes. Specifically, a multimodal generative adversarial network is first introduced to rebalance the dataset by generating pseudo features based on each mode and combining them to describe a fake sample. Then, a gated fusion network with the gate and fusion mechanisms is presented to reduce the noise to improve the generalization ability and capture the links among the different feature modes. Experiments on a faculty homepage dataset show the superiority of the proposed framework.