Reviewerly/exHarmony huggingface dataset:
Implementing Augmented Reality (AR) on Phonics-based Literacy among Children with Autism

The implementation of Augmented Reality (AR) technology in education has created an interesting approach to enhance the effectiveness and attractiveness of teaching and learning for students in real-life scenarios. This medium offers unique affordances, combining physical and virtual worlds, with continuous and implicit user control of the point of view and interactivity. This paper introduces the technology of augmented reality and its capabilities in facilitating children with autism. AR is a technology that overlays digital information on a live view of the physical world to create a blended experience. AR provides unique experiences and opportunities to learn and interact with information in the physical world. Hence, AR can be one of the effective technologies available in developing tools for teaching and learning with the combination of the virtual world and real objects such as transportation, fruits, numbers, and alphabets. This will facilitate the autistic child to recognize the abstract concepts of the real objects and their descriptions. The purpose of this paper was to investigate the use of AR on mobile devices in fostering literacy in academic and learning skills for children with autism using the phonics learning method. This prototype helps the autistic child to capture and associate the graphics or pictures of the surrounding objects so as improving the literacy and learning skills of the children. The results show that the children can pronounce and to distinguish between vowels “a”, “i” and “u”. The children are also able to answer most of the questions in the exercises provided. The interactivity between the children and the application raises their attention and focus mainly in literacy and learning skills.
Learning Multiple Parameters for Kernel Collaborative Representation Classification

In this article, the problem of automatically learning multiple parameters for kernel collaborative representation classification (KCRC) is considered. We investigate the KCRC and measure its generalization error via leave-one-out cross-validation (LOO-CV). By taking advantage of the specific properties of KCRC, a closed-form expression is derived for the outputs of LOO-CV. Then, a simple classification rule that provides probabilistic outputs is adopted, and thereby, an effective loss function that is an explicit function with respect to the parameters is proposed as the generalization error. The gradients of the loss function are calculated, and the parameters are learned by minimizing the loss function using a gradient-based optimization algorithm. Furthermore, the proposed approach makes it possible to solve the multiple kernel/feature learning problems of KCRC effectively. Experiment results on six data sets taken from different scenes demonstrate the effectiveness of the proposed approach.
Adaptive neural tree exploiting expert nodes to classify high-dimensional data

Classification of high dimensional data suffers from curse of dimensionality and over-fitting. Neural tree is a powerful method which combines a local feature selection and recursive partitioning to solve these problems, but it leads to high depth trees in classifying high dimensional data. On the other hand, if less depth trees are used, the classification accuracy decreases or over-fitting increases. This paper introduces a novel Neural Tree exploiting Expert Nodes (NTEN) to classify high-dimensional data. It is based on a decision tree structure, whose internal nodes are expert nodes performing multi-dimensional splitting. Any expert node has three decision-making abilities. Firstly, they can select the most eligible neural network with respect to the data complexity. Secondly, they evaluate the over-fitting. Thirdly, they can cluster the features to jointly minimize redundancy and overlapping. To this aim, metaheuristic optimization algorithms including GA, NSGA-II, PSO and ACO are applied. Based on these concepts, any expert node splits a class when the over-fitting is low, and clusters the features when the over-fitting is high. Some theoretical results on NTEN are derived, and experiments on 35 standard data show that NTEN reaches good classification results, reduces tree depth without over-fitting and degrading accuracy.
The Preliminary Application of Augmented Reality in Unilateral Orbitozygomatic Maxillary Complex Fractures Treatment

Purpose: Try to apply augmented reality (AR) technique which based on visual recognition artificial marks in the treatment of unilateral Orbitozygomatic Maxillary complex fractures (OZMF), and to explore the feasibility and the application value of the AR for assisting the oral and maxillofacial surgery (OMFS) treatment. Methods: Based on the AR application JSARToolKit developed on the web, and combining with the self-designed tooth-borne type visual recognition artificial marks to display three-dimensional (3D) image of the region of interest (ROI), virtually designed the position and direction of plate on the jaw and helped OZMF surgery. According to some results to evaluate the postoperative effects, such as imaging, occlusion, facial appearance recovery, patients’ satisfaction and operation time. Results: The AR achieved the 3D image of ROI in real time, satisfied information enhancement of real scene, broadened the surgeon's horizons and improved the 3D recognition ability of maxillofacial tissue. After operation, patients had no discomforts, fracture reduction were well healed through imaging examination, and the titanium plate position was consistent with the preoperative planning. What is more, patients’ facial appearance and occlusal function recovered well, significantly shorten the intraoperation time. Conclusion: Augmented reality technology is an effective method for OZMF treatment but there are still many technical difficulties which need to be get over, and its application in maxillofacial surgery and relevant basic theories are both worth further exploring.
Automatic fringe pattern enhancement using truly adaptive period-guided bidimensional empirical mode decomposition

Fringe patterns encode the information about the result of a measurement performed via widely used optical full-field testing methods, e.g., interferometry, digital holographic microscopy, moiré techniques, structured illumination etc. Affected by the optical setup, changing environment and the sample itself fringe patterns are often corrupted with substantial noise, strong and uneven background illumination and exhibit low contrast. Fringe pattern enhancement, i.e., noise minimization and background term removal, at the pre-processing stage prior to the phase map calculation (for the measurement result decoding) is therefore essential to minimize the jeopardizing effect the mentioned error sources have on the optical measurement outcome. In this contribution we propose an automatic, robust and highly effective fringe pattern enhancement method based on the novel period-guided bidimensional empirical mode decomposition algorithm (PG-BEMD). The spatial distribution of the fringe period is estimated using the novel windowed approach and then serves as an indicator for the truly adaptive decomposition with the filter size locally adjusted to the fringe pattern density. In this way the fringe term is successfully extracted in a single (first) decomposition component alleviating the cumbersome mode mixing phenomenon and greatly simplifying the automatic signal reconstruction. Hence, the fringe term is dissected without the need for modes selection nor summation. The noise removal robustness is ensured employing the block matching 3D filtering of the fringe pattern prior to its decomposition. Performance validation against previously reported modified empirical mode decomposition techniques is provided using numerical simulations and experimental data verifying the versatility and effectiveness of the proposed approach.
Secure Data Provenance in IoT Network using Bloom Filters

Internet of Things (IoT) network are considered the future in monitoring and surveillance. With the advent of autonomous organization, and network and service management, conventional networks are fast integrated with IoT networks. With minimal human interaction, the security of IoT data travelling through the networks becomes more critical. Data Provenance ensures the path through which data was received for providing integrity and confidence. However, the provenance frameworks are also prone to attacks, as the attacker can manipulate the data and the meta-date used by the provenance mechanism. Therefore, a secure provenance mechanism is required for IoT networks, which is efficient and light-weight as the IoT devices have limited computational and storage resources. In this paper, we propose a secure provenance mechanism which utilizes less storage, specifically designed for IoT devices, by using Bloom-filters and outsourcing attribute based cryptographic mechanisms. The proposed scheme helps in reducing the storage requirement and computational time in IoT devices. The performance of the proposed mechanism is evaluated, and the results show that the proposed solution is best suited for resourced constrained IoT network.
Leveraging Global and Local Topic Popularities for LDA-Based Document Clustering

Document clustering is of high importance for many natural language technologies. A wide range of computational traditional topic models, such as LDA (Latent Dirichlet Allocation) and its variants, have made great progress. However, traditional LDA-based clustering algorithms might not give good results due to such probabilistic models require prior distributions which are always difficult to define. In this paper, we propose a probabilistic model named tpLDA, which incorporates different levels of topic popularity information to determine the prior LDA distribution, discover the latent topics and achieve better clustering. Specifically, global topic popularity is introduced to reduce the potential distraction in local cluster popularity and the local cluster popularity draws more attention on certain parts of the global topic popularity. The two popularities contribute complementary information and their integration can dynamically adjust statistical parameters of the model. Experimental evaluations on real data sets show that, compared with state-of-the-art approaches, our proposed framework dramatically improves the accuracy of documents clustering.
Seeing Beyond Appearance - Mapping Real Images into Geometrical Domains for Unsupervised CAD-based Recognition

While convolutional neural networks are dominating the field of computer vision, one usually does not have access to the large amount of domain-relevant data needed for their training. Therefore, it has become common practice to use available synthetic samples along domain adaptation schemes to prepare algorithms for the target domain. Tackling this problem from a different angle, we introduce a pipeline to map unseen target samples into the synthetic domain used to train task-specific methods. Denoising the data and retaining only the features these recognition algorithms are familiar with, our solution greatly improves their performance. As this mapping is easier to learn than the opposite one (i.e., to generate realistic features to augment the source samples), we demonstrate how our whole solution can be trained purely on augmented synthetic data and still performs better than methods trained with domain-relevant information (e.g., real images or realistic textures for the 3D models). Applying our approach to object recognition from texture-less CAD data, we present a custom generative network which fully utilizes the purely geometrical information to learn robust features and to achieve a more refined mapping for unseen color images.
LevelSpace: A NetLogo Extension for Multi-Level Agent-Based Modeling

Multi-Level Agent-Based Modeling (ML-ABM) has been receiving increasing attention in recent years. In this paper we present LevelSpace, an extension that allows modelers to easily build ML-ABMs in the popular and widely used NetLogo language. We present the LevelSpace framework and its associated programming primitives. Based on three common use-cases of ML-ABM â€“ coupling of heterogeneous models, dynamic adaptation of detail, and cross-level interaction - we show how easy it is to build ML-ABMs with LevelSpace. We argue that it is important to have a unified conceptual language for describing LevelSpace models, and present six dimensions along which models can differ, and discuss how these can be combined into a variety of ML-ABM types in LevelSpace. Finally, we argue that future work should explore the relationships between these six dimensions, and how different configurations of them might be more or less appropriate for particular modeling tasks.
Efficient Intelligent Automatic Image Annotation Method based on Machine Learning Techniques

This study proposes an image automatic annotation method based on machine learning techniques. The method can automatically analyze the unlabeled videos, and extract the interesting samples that the user needs. The new labeled image will be added to the training sets, and the current model will become the pretrained model for our new one, by this method we can improve the recognition performance for various machine learning models. In addition, this research provides a machine learning module interface which allow users to choose proper model, such as SVM or deep learning techniques, in different situation.
On Motor Performance in Virtual 3D Object Manipulation

Fitts's law facilitates approximate comparisons of target acquisition performance across a variety of settings. Conceptually, also the index of difficulty of 3D object manipulation with six degrees of freedom can be computed, which allows the comparison of results from different studies. Prior experiments, however, often revealed much worse performance than one would reasonably expect on this basis. We argue that this discrepancy stems from confounding variables and show how Fitts's law and related research methods can be applied to isolate and identify relevant factors of motor performance in 3D manipulation tasks. The results of a formal user study (n=21) demonstrate competitive performance in compliance with Fitts's model and provide empirical evidence that simultaneous 3D rotation and translation can be beneficial.
Detecting Cyberbullying in Social Commentary Using Supervised Machine Learning

This paper addresses the problem of cyberbullying on various online discussion forums in the form of social commentary. Here, supervised machine learning algorithms are employed to detect whether a particular comment is an insult, threat or a hate message. First of all, a machine learning model is developed with Logistic Regression, Random forest and naive bayes algorithms for classification and then, both Voting and AdaBoost classifiers are applied on the developed model to observe which works best in this case. In Japan, the members of PTA (Parent Teacher Association) perform net-petrol with a manual website monitoring in order to catch and stop cyberbullying activities; however, doing all this manually is very time consuming and hectic process. The main contribution of this paper includes a mechanism to detect cyberbullying and by using supervised machine learning with logistic regression algorithm, model has achieved an accuracy of 82.7%. With voting classifier, an accuracy of 84.4% was observed. The evaluation results show that voting classifier outperforms all other algorithms in detecting cyberbullying.
3D Hand Tracking in the Presence of Excessive Motion Blur

We present a sensor-fusion method that exploits a depth camera and a gyroscope to track the articulation of a hand in the presence of excessive motion blur. In case of slow and smooth hand motions, the existing methods estimate the hand pose fairly accurately and robustly, despite challenges due to the high dimensionality of the problem, self-occlusions, uniform appearance of hand parts, etc. However, the accuracy of hand pose estimation drops considerably for fast-moving hands because the depth image is severely distorted due to motion blur. Moreover, when hands move fast, the actual hand pose is far from the one estimated in the previous frame, therefore the assumption of temporal continuity on which tracking methods rely, is not valid. In this paper, we track fast-moving hands with the combination of a gyroscope and a depth camera. As a first step, we calibrate a depth camera and a gyroscope attached to a hand so as to identify their time and pose offsets. Following that, we fuse the rotation information of the calibrated gyroscope with model-based hierarchical particle filter tracking. A series of quantitative and qualitative experiments demonstrate that the proposed method performs more accurately and robustly in the presence of motion blur, when compared to state of the art algorithms, especially in the case of very fast hand rotations.
Digital image correlation with improved efficiency by pixel selection

With the increase in digital image correlation (DIC) applications, the computational efficiency of DIC is becoming increasingly important. In previous studies, real-time DIC was realized with a relatively small subset. However, a small subset does not always include sufficient gray gradient information. In this paper, a pixel selection strategy is proposed to improve the computational efficiency of DIC further, allowing a real-time deformation measurement with a large subset. Within the subset, zero weight is assigned to unreliable pixels as a way of pursuing maximum efficiency. The modulus of the local intensity gradient vector of each pixel in the reference image is used as the criterion for reliability. Numerical and real experiments conducted to validate the feasibility and effectiveness of the strategy showed that the computational speed of DIC could be improved about 2 times.
Secure Healthcare Framework Using Blockchain and Public Key Cryptography

In the current scenario, healthcare organizations are facing a serious problem in sharing medical information among different stakeholders without sacrificing the privacy and integrity of information. To store and share such a large volume of healthcare information securely is an important research issue. Blockchain has been used successfully in the bitcoin for the decentralized exchange of cryptocurrency without the involvement of the third party. The new structure of blockchain has been designed to accommodate the need of another field such as healthcare. Blockchain technology has the potential of immutability, integrity and decentralized architecture to manage the health records of the patient. This paper aims to address the issues of data security and authentication in healthcare. We have proposed a blockchain based secure framework using public key cryptography.
Enhancing nonclassical and entanglement properties of trio coherent states by photon-addition

Trio coherent states as a kind of three-mode entangled state were introduced and studied. Some nonclassical characteristics of them such as higher-order antibunching, three-mode sum-squeezing and intermodal entanglement have been well revealed. In this paper, we further confirm their nonclassicality by examining negativity of Wigner function and non-Gaussian feature. However, our main purpose is to show that all the above-mentioned quantum properties of the trio coherent state can be enhanced by simultaneously adding photons to each mode of the state. In particular, we demonstrate that such so-called photon-added trio coherent states can possess three-mode sum-squeezing while the original trio coherent state (without photon-addition) cannot. In general, we show analytically and graphically how the degree of nonclassicality and/or entanglement depends on the number of added photons. Furthermore, we also consider a beamsplitter-based scheme for generation of the photon-added trio coherent states and evaluate the fidelity as well as the success probability as functions of the beamsplitter's parameter and the number of added photons.
Light verb variations and varieties of Mandarin Chinese: Comparable corpus driven approaches to grammatical variations

Abstract This article presents a classification and clustering based study to account for the differences among five Chinese light verbs ( congshi , gao , jiayi , jinxing , and zuo ) as well as their variations in Mainland China Mandarin (ML) and Taiwan Mandarin (TW). Based on 13 linguistic features, both competition and co-development of these light verbs are studied in terms of their distinct and shared collocates. The proposed method discovers significant new grammatical differences in addition to confirming previously reported ones. Most significant discoveries include selectional restrictions differentiating deverbal nominals and event nouns, and degrees of transitivity of VO compounds. We also find that most variations between Mainland China Mandarin and Taiwan Mandarin are in fact differences in tendencies or preferences in contexts of usage of shared grammatical rules.
Knowledge Sharing and Organizational Culture Dimensions: Does Job Satisfaction Matter?

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A Study on Self-efficacy and Its Role in Mobile-assisted Language Learning

In this era of digital technology, time and place no longer limit access to information, communication, and learning. Following the trend and popularity of CALL (Computer-assisted Language Learning), MALL (Mobile-assisted Language Learning) is gaining increasing attention and application. Many teachers as well as language learners turn to these mobile devices for various language learning purposes due to easy access to resources, convenience, and less language anxiety. Despite the comprehensive functions and sophisticated devices, a high percentage of learners still lag behind and even give up the apps after some time of use. Some research shows that the unsuccessful learners’ lack of motivation constitutes a major hinderance in their self-regulated learning. This study tries to analyze an important component in learners’ motivation, that is self-efficacy (especially computer self-efficacy), and seek for good solutions to the current problems.
RACRec: Review Aware Cross-Domain Recommendation for Fully-Cold-Start User

Traditional recommendation algorithms such as matrix factorization, collaborative filtering perform poorly when lack of interactive information of user and product, known as the user cold-start problem, which may cut down the revenue of E-Commerce platform.Moreover, it is more challenging to generate recommendation lists for users who have no information at all because there is no preference information about them that could be leveraged, which is the user fully-cold-start problem.In this paper, a review aware cross-domain recommendation algorithm, called RACRec, is proposed to address the fullycold-start problem in the field of product recommendation.Firstly, reviews are dynamically selected by using the adjacency matrix.Secondly, domain-specific preference vectors and domain-shared preference vectors of the cold start user are extracted by a migration model.On the other hand, the product feature vector in the target domain, which is generated from review texts by encoder and decoder, is combined with preference vectors of the cold-start user to make the rating prediction.Experiments on the Amazon datasets reveal that RACRec outperforms the state-of-the-art recommendation algorithms.