Reviewerly/exHarmony huggingface dataset:
Robust and precise isotropic scaling registration algorithm using bi-directional distance and correntropy

In orthodontics, a patient is collected a lot of 3D oral cavity data, including oral cavity gypsum and scan data sets. To accurately measure the patient's tooth movement, this paper proposes a robust and precise isotropic scaling registration algorithm using bi-directional distance and correntropy. Firstly, because the oral cavity gypsum data sets have a lot of gypsum tumors and bubbles, which can cause the accuracy of registration results to decrease. Then, we introduce the correntropy into the traditional scaling registration model. Secondly, since unconstrained scaling registration is an ill-posed problem, bi-directional distance is used to enhance the robustness. In this way, a registration model using bi-directional distance and correntropy is established. In order to solve this problem, this paper proposes a new registration algorithm with iterative closest point. Moreover, the convergence of the algorithm is proved theoretically. Finally, the proposed algorithm is tested on the orthodontic database, and our experimental results demonstrate that our algorithm performs robust and high accuracy.
The strong substructure and feature attention mechanism for image semantic segmentation

Abstract Semantic segmentation is a hot topic in computer vision and various deep learning networks are designed to achieve higher accuracy on that by fully exploring the capability of neural networks. This paper aims to address the issue and proposes the substructures with novelty for popular networks. Meanwhile, we present a cross‐channel structure, which simultaneously reduces parameter while the kernel size becomes larger. After that, to overcome the weakness of insufficient dataset which refers to satellite image data, we propose a feature attention mechanism with generative adversarial network to enhance the images' features. We show the recognition result on the satellite image dataset with a large picture. This paper evaluates substructures on the PASCAL VOC2012 dataset and improves the mIOU from 74.68% to 88.15%.
Detection of Deepfakes Using Visual Artifacts and Neural Network Classifier

The field of Artificial Intelligence is so advanced that it made the creation and modification of synthetic images and videos very easy. Tampering of videos attained a new level of refinement due to the deep learning techniques and the availability of high computing power. This contributes to the 'deepfake' era. Deepfake is a term coined for the fake videos created using deep learning techniques. With this method, one can create fake videos of people that they never did by replacing their face in some other real videos. There is a great danger of misusing this technique to disseminate false information or fake news. Thus the detection of deepfakes is critical to protect the people's pride and trust in the digital content. Most of the works in detecting deepfakes are using deep learning methods. In this paper, we are proposing an approach to identify deepfake videos with very less computational power. The proposed method exploits visual artifacts present in the face regions in the generated deepfakes. We use a three-layer neural network to classify the videos as deepfake or real. As a second step of confirmation, the variance of laplacian is calculated for different patches in the face, and based on their comparison, detection of deepfakes is assured. Our approach is tested in two datasets, UADF dataset, and the latest DeepFakeDetection dataset released by the Google AI team. The proposed method achieves better results in terms of computational requirements and accuracy, and are explained in detail in the analysis section.
Cryptocurrencies in portfolios: return–liquidity trade-off around China forbidding initial coin offerings

We examine diversification when cryptocurrencies are included in investment portfolios, around China prohibiting initial coin offerings on 4 September 2017. We discover, once we account for liquidity, that all portfolio diversification benefits of cryptocurrencies are eliminated.
Towards Valid Use Cases: Requirements and Supporting Characteristics of Proper Blockchain Applications

Practitioners from various fields discuss potential applications for Blockchain, often, without a clear understanding which class of use cases qualify for this technology. In this paper, we build a framework for classifying suitable Blockchain use cases. We present requirements (mandatory features of all Blockchain use cases) and supporting characteristics (features which make using Blockchain technology recommendable, if requirements are fulfilled) of possible applications, which we group in the stakeholders and objects “users of the system”, “oracles and information”, “agreement”, and “goods”. We justify our framework by applying it to four use cases from different fields: Proof of Publication, Cryptocurrencies, Lotteries, and Provenance Tracking. Our model can explain why those use cases are a good fit for implementation with Blockchain technology.
A Robust Variance Complexity Measure for Stochastic Self-Affine Processes

The complexity of a deterministic differentiable process is lower than that of a stochastic nondifferentiable process. Measuring the complexity of such processes may be useful in extracting objective features from the processes for their classification in either reactive, adaptive, or predictive control. This applies to classifiers based not only on the traditional neural networks, but also on deep learning systems, and particularly in cognitive systems. This paper describes a robust algorithm to measure the variance complexity of a self-affine time series using multiscale and polyscale analyses, and provides new insight in the theoretical and practical aspects of extracting the measure.
Cryptanalysis of a Centralized Location-Sharing Scheme for Mobile Online Social Networks

In recent past, due to extensive development of mobile Internet and GPS technology, mobile online social networks (mOSNs) have gained more popularity over traditional online social networks (OSNs). mOSN provides supports to various day-to-day online social network operations like establishing friend relationship, providing location-based services, location sharing among friends, etc. Very recently, in 2018, Xiao et al. proposed a centralized location-sharing scheme where social network server and location-based server are integrated into a single entity (future generation computer systems). In this paper, we analyze that though the scheme of Xi Xiao et al. is efficient and incurs lesser communication and storage cost compared to existing schemes, it has several security weaknesses. As, for example, it cannot resist man-in-the-middle attack and replay attack. Moreover, due to incorrect strategy in location updates phase, user suffers from denial-of-service attack querying friend’s location phase. The cryptanalysis of the scheme of Xi Xiao et al. shows that it is not suitable for practical applications. We verify the attack on the protocol using widely accepted ProVerif and AVISPA simulation tools. Finally, we hint at some possible improvements that can be adopted by their scheme to make it more secured against various possible known attacks.
Research on badminton action feature recognition based on improved HMM model

The badminton movement speed is fast, and the movement is complicated. Therefore, it is difficult to effectively recognize the athlete’s movement through the monitoring level in the competition and training, which makes it difficult for the athlete to effectively improve his skill. In order to effectively improve the training effect and the quality of the athletes, this study uses badminton as the research object, analyzes the sports characteristics research algorithm through literature review, and finds the shortcomings of traditional algorithms. At the same time, this paper combines the actual situation to improve the algorithm and combines GMM and HMM to builds the GMM-HMM model. In addition, this paper uses the Baum-Welch unsupervised learning algorithm for data processing, and based on the learning machine training, the recognition results are obtained. Finally, in order to verify the validity of the model, this study uses the mobile phone badminton action as the data foundation and performs training recognition in the model to summarize the recognition results. The research shows that the algorithm has good performance and can meet the actual needs and can be used as a reference for the subsequent related research corporal punishment theory.
Determinants of digital skills in Northeast Anatolia, Turkey

Digital divide is among the most important problems required to be overcome by our contemporary information society, where skills are among the principle determinants of such inequalities. This study examines the digital divide in a non-Western population which has not been studied before, and specifically it measures the digital skill levels of the people living in the Northeastern Anatolia region of Turkey. The primary contribution of the study is confirmatory of previous research regarding digital skills, though in the context of a previously-unexamined population. By using a sample representing the region (n = 400), the digital skill levels of the participants were measured. Data were collected through performance tests developed by van Deursen, A.J.A.M., and J.A.G.M. van Dijk (2010. Internet skills and the digital divide. New Media & Society 13, no. 6: 893–911. doi:10.1177/1461444810386774). Findings indicate that the digital skill level of the participants is generally low. The users are most successful at the formal level, followed by operational, informational and strategic skills, respectively. The level of strategic skills is quite low, which hints at the fact that the users studied are not able to translate their Internet use into real-world tangible benefits. It is also found that age, gender, education, household income significantly predict digital skill levels.
Implementation of K-Nearest Neighbor for Classification Teacher Engagement Profiling and Interventions

In this study we used the K-Nearest Neighbor approach to classify teacher profiles based on the Index values obtained previously through the Indonesian Teacher Engagement Index application. We found that the accuracy of the K-Nearest Neighbor is quite good if it is tested using a small data set that is equal to 88.94% and RMSE 0.300. Whereas if tested again into greater data the resulting accuracy decreases to 79.75% and RMSE 0.373. Some intervention designs related to meaningfulness to increase teacher engagement are through Meaningful and Fulfilling Education programs, Meaningful and Fulfilling Work Life, Meaningful and Fulfilling Character, and Meaningful and Fulfilling Leaders.
Network Obfuscation for Net Worth Security

Research community has been highlighting recently security issues related to network traffic analysis. Using machine learning techniques, an eavesdropper can exploit traffic features to determine useful information threatening privacy of users. In this paper, we propose an efficient defense against network fingerprinting attacks where we obfuscate information leaked by traffic features specifically packet sizes. First, we model the packet lengths probability distribution of the source app to be protected and that of the target app that the source app will resemble. Then, we define a security model that mutates the packet lengths of a source app to those lengths from the target app having similar bin probability. This would confuse a classifier and make it identify a mutated source app as the target app. A comprehensive simulation study of the proposed model, using real apps traffic traces, shows considerable obfuscation efficiency with relatively acceptable overhead. We were able to reduce a classification accuracy of 91.1% to 0.22% using the proposed algorithm, with only 11.86% padding overhead.
A Novel Nonlinear Second Order Hyperbolic Partial Differential Equation-Based Image Restoration Algorithm With Directional Diffusion

Recently, variational and partial differential equation (PDE)-based algorithms have become very important for image restoration. In this study, we propose a new second order hyperbolic PDE model based on directional diffusion for image restoration. This hyperbolic PDE restoration model can simply diffuse along the edge's tangential direction in the observed image, thereby removing noise while preserving the image edges and fine details, which avoids the staircase effect in the restored image. An effective numerical scheme is proposed for handling the computation of our approach using the finite difference method. Successful image restoration experiments demonstrated that the proposed second order hyperbolic PDE-based model obtains superior performance compared with other models at preserving edges and it avoids the staircase effect.
General and complete description of temporal photon correlations in cavity-enhanced spontaneous parametric down-conversion

Heralded single-photon sources are the most commonly used sources for optical quantum technology applications. There is strong demand for accurate prediction of their spectral features and temporal correlations with ever-increasing precision. This is particularly important in connection with the intrinsically stochastic photon-pair generation process in heralded sources. Here we present a complete theoretical description of the temporal correlation of a signal-idler, signal-signal, and signal-signal-idler coincidences of photons generated by continuous-wave pumped cavity-enhanced spontaneous parametric down-conversion. The theory excellently predicts the measurements, which has been experimentally confirmed in our setup utilizing single-photon detectors with high temporal resolution. This enables us to resolve and analyze the multiphoton correlation functions in great detail.
Blending Convergent Encryption and Access Control Scheme for Achieving A Secure and Storage Efficient Cloud

Conventional encryption schemes are being used over the years for securing outsourced data to cloud. However, this impedes deduplication- the ability to identify and remove duplicate data from storage server. The idea of Convergent Encryption was introduced to overcome this problem which ensures that identical plaintext files will always produce identical ciphertexts and thus enabling deduplication. Nonetheless, this scheme is vulnerable to a side-channel attack called “confirmation-of-a-file” and its variant “learn-the-remaining-information” attack which breach user privacy by observing the deduplication operation. To resolve the above two seemingly contrasting issues, we propose a scheme which blends convergent encryption with a traditional access control scheme for simultaneously achieving confidentiality and deduplication. Both theoretical security analysis and experimental results show that our scheme is semantically secure and resilient against attacks. It incurs minor storage and latency overhead while performing file and block level deduplication. Furthermore, it ensures secure and fine-grained access control of outsourced data by efficiently handling key-management process.
Sound Field Reconstruction from Incomplete Data by Solving Fuzzy Relational Equations

The approach to solving inverse problems of source identification in acoustics is proposed based on fuzzy relational calculus. The compositional rule of inference connects the real and observed fuzzy acoustic image using the relationship matrix, which reflects the degree of completeness of the microphone array measurement data. The fuzzy model of the acoustic field is based on 3D membership functions, for which the degree of membership decreases in proportion to the square of the distance to the source. The problem of reconstructing the acoustic field is formulated as the problem of inverse logical inference. The method for reconstructing the acoustic field from incomplete data is proposed based on solving fuzzy relational equations. The problem consists in finding such a number of sound sources, their locations and powers, which minimize the difference between the model and observed fuzzy acoustic image. The solutions of the equation system represent the variants of the acoustic field reconstruction in the form of the main acoustic surface and a set of secondary acoustic surfaces. The main acoustic surface is generated by the least number of sources. The set of secondary acoustic surfaces represents the variants of the sound field reconstruction generated by the upper solutions for the number of sources. Since the sources distribution is completely determined by the properties of the solution set, the proposed approach allows avoiding the generation and selection of candidate sources, that provides simplification of the reconstruction process and reduction of time costs. The genetic and neural algorithm provides accurate and fast reconstruction of the acoustic field for an unknown number of sources and their configuration.
Application of machine learning and image target recognition in English learning task

Artificial intelligence speech recognition mostly judges the accuracy of grammar or sentence in the detection of pronunciation error, but has little research on pronunciation judgment, so it cannot effectively correct the pronunciation. This study analyzes the application of image target recognition in English learning task. Task-based approach emphasizes the process of English learning, not the result, the purposeful communication and meaning expression, encourages learners to open their mouths, and emphasizes that English language learning activities and their tasks are realistic in life. In addition, this paper introduces the DNN adaptive technique based on KL divergence regularization to adapt the acoustic model. Finally, this paper uses the experimental contrast method to compare and analyze the algorithm of this research with the traditional algorithm. The research shows that the recognition ability of the algorithm for confusing phonemes is improved than that of traditional algorithms, and this conclusion provides a powerful result for the introduction of error correction algorithms into education networks. By using the platform of autonomous learning center, students can improve their English level by completing the tasks chosen by teachers or by themselves and through training.
Convolution Encoders for End-to-End Action Tracking With Space-Time Cubic Kernels

Convolution is good at extracting per-frame representations but discards the temporal order. Most previous Convolution Neural Network (CNN) based video action tracking handle space and time dynamics separately. To remedy this issue, we extend the convolution kernel with a time dimension and formulate the problem to an encoder-decoder structure, which generates the bounding boxes of action regions for each frame by leveraging the pixel-wise annotations. By using the same measure, Cubic Tracker achieves mAP of 64.9% (frame IoU threshold = 0.5), 78.6%(video IoU threshold = 0.2), and 78.3% (video IoU threshold = 0.5) with gains of 3.6%, 0.2%, and 1.4% over the best competitors on J-HMDB. More remarkably, on 2 benchmarks without pixel-level supervision (UCF-Sports and UCF101-24), Cubic Tracker also approaches the performance of the state of the arts, by simply transferring learned model from J-HMDB, with no further adaptive fine-tuning techniques applied.
Recent Advances in Reinforcement Learning

In this paper, we give a brief review of Markov Decision Processes (MDPs), and how Reinforcement Learning (RL) can be viewed as MDP where the parameters are unknown. Specific topics discussed include the Bellman equation and the Bellman operator, and value and policy iterations for MDPs, together with recent "empirical" approaches to solving the Bellman equation and applying the Bellman iteration. In addition to the well-established method of Q-learning, we also discuss the more recent approach known as Zap Q-learning.
A new approach to enhance the estimation of blur parameters in blurred images

One of the main objectives of blur parameters estimation is the restoration of blurred images. A novel blur parameters estimation algorithm is presented in the present paper. For estimating the blur angle of the degraded image, Hough transform is applied upon two parallel linear edges on two sides of the central bright stripe that appear in the frequency domain. For blur length estimation, the proposed method has two sequential phases. In the first phase, the frequency domain is used for feature extraction. Also, for obtaining a superior performance in the algorithm in the second stage, a wavelet-based parameter was used, which can determine the extent of the blur in the image. For the prediction of the blur length in a particular blurred image, RBFNN is employed with frequency-domain features of the related blurred image and blur extent parameter, coming from the wavelet transform as the input. Blur length in blurred images is estimated at a higher accuracy using this method. The concentration of this approach is on motion blur parameter estimation. Through comparison with available approaches, the results of simulations indicate the ability and strength of the suggested approach in predicting blur parameters.
Offline Signature Recognition and Verification Using ORBKey Point Matching Techniques

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The Use of ICTs in Second Language Education

As many aspects of our lives have been increasingly digitized, education has likewise taken its share from this digitization. Consequently, information and communication technologies (ICTs) have been progressively incorporated into education to improve the effectiveness of educational practices in formal and informal educational settings. ICTs hold great promise for second language education since they have the potential of providing language learners with increased opportunities to engage with the target language and culture even with a mouse click or a tap on their smartphones. This chapter presents an overview of how ICTs have been employed in the field of second language education over the decades by specifically elaborating on the rationale behind ICT adoption, the properties of ICTs utilized, advantages and disadvantages of ICT adoption in the field, and factors that might impede or facilitate ICT adoption in second language education. The chapter concludes with practical considerations that might affect the efficiency of ICT adoption in second language education.
An adaptive neuroevolution-based hyperheuristic

According to the No-Free-Lunch theorem, an algorithm that performs efficiently on any type of problem does not exist. In this sense, algorithms that exploit problem-specific knowledge usually outperform more generic approaches, at the cost of a more complex design and parameter tuning process. Trying to combine the best of both worlds, the field of hyperheuristics investigates the automatized generation and hybridization of heuristic algorithms. In this paper, we propose a neuroevolution-based hyperheuristic approach. Particularly, we develop a population-based hyperheuristic algorithm that first trains a neural network on an instance of a problem and then uses the trained neural network to control how and which low-level operators are applied to each of the solutions when optimizing different problem instances. The trained neural network maps the state of the optimization process to the operations to be applied to the solutions in the population at each generation.