Reviewerly/exHarmony huggingface dataset:
A Secure Hybrid Authentication Scheme Using Passpoints and Press Touch Code

With the increasing capabilities of smart devices, keeping them secure has become a major concern. To mitigate that concern, over the last few years, several new classes of authentication schemes have been proposed. Graphical Authentication (GA) is one of those classes and is the focus of this paper. The GA schemes are more popular and preferable for smart devices due to their: heavily graphics-oriented nature, higher memorability over text-based schemes, and no additional hardware requirement. However, most of these GA schemes are unable to resist several prominent attacks, namely shoulder surfing, smudge, and brute force. Therefore, in this paper, a new hybrid authentication scheme is proposed which seamlessly integrates two independent yet popular authentication schemes - Passpoints and Press Touch Code or PTC. The purpose of this new scheme is to ensure a higher level of security by resisting those prominent attacks. The proposed scheme is implemented on the Android operating system and is tested on Huawei P9 plus device, a pressure sensitivity screen enabled device, which is compulsory for the PTC scheme. The performance of the proposed technique is evaluated for security, functionality, and usability. When it is compared with other similar schemes, it outperforms the existing schemes.
Multilingual Language Models for Named Entity Recognition in German and English

We assess the language specificity of recent language models by exploring the potential of a multilingual language model.In particular, we evaluate Google's multilingual BERT (mBERT) model on Named Entity Recognition (NER) in German and English.We expand the work on language model fine-tuning by Howard and Ruder (2018), applying it to the BERT architecture.We successfully reproduce the NER results published by Devlin et al. (2019).Our results show that the multilingual language model generalises well for NER in the chosen languages, matching the native model in English and comparing well with recent approaches for German.However, it does not benefit from the added finetuning methods.
Evaluating the Consistency of Word Embeddings from Small Data

In this work, we address the evaluation of distributional semantic models trained on smaller, domain-specific texts, particularly philosophical text.Specifically, we inspect the behaviour of models using a pretrained background space in learning.We propose a measure of consistency which can be used as an evaluation metric when no in-domain gold-standard data is available.This measure simply computes the ability of a model to learn similar embeddings from different parts of some homogeneous data.We show that in spite of being a simple evaluation, consistency actually depends on various combinations of factors, including the nature of the data itself, the model used to train the semantic space, and the frequency of the learned terms, both in the background space and in the in-domain data of interest.
Eksperimentasi Model Pembelajaran JUCAMA Ditinjau Dari Gaya Belajar Terhadap Prestasi Belajar Mahasiswa Mata Kuliah Berbicara di Prodi PBSI IKIP PGRI Bojonegoro

Focus this is research to determine which are the achievements of better speaking learning: (1), learning model JUCAMA or Conventional, (2) Visual, auditory, or kinesthetic, (3) On models learning JUCAMA, which are the achievements of better speaking learning, visual, auditory, or kinesthetic.This type of research is quasi-experimental with 2 × 3 factorial design. The population of this research is all students of PBSI IKSI PGRI Bojonegoro Study Program, which consists of nine classes with a total of 288 students. Sampling was done by cluster random sampling, selected three classes, namely the level of I-A, IB, and IC. The sample consisted of 93 students, with details of 31 IA students as an experiment, 31 IB level students as a control, and 31 IC level students as a trial. Data collection instruments using learning style questionnaires, and learning achievement tests that have been validated by the validators. The test instrument was tested in PBSI level I study program after being tested and then analyzed its reliability, distinguishing features and difficulty levels. The prerequisite tests include the normality test using the Lilliefors method and the variance homogeneity test using the Bartlett method. With α = 0.05, it can be concluded that the sample comes from populations that are normally distributed and have a homogeneous variance.In this study it can be concluded that: (1) students with learning JUCAMA had speaking learning achievement better than the students with conventional learning, (2) achievement of learning speaking students who have a visual learning style is better than the speaking learning achievement of students who have auditory and kinesthetic learning styles. While the speaking achievement students who have learning styles of auditory as well as students who have a kinesthetic learning style, (3) On learning JUCAMA, there is no difference between speaking achievement in each learning style. Keyword: jucama, learning style, learning achievement, speaking
On Characterization of Joint and Conditional Exponential Survival Entropies

In the present communication the multivariate survival function of a random variable is used to define four new classes of exponential survival entropies and their particular cases. Joint and conditional exponential survival entropies for two non-negative random variables are defined and characterized. The generalized exponential survival entropies for some families of continuous distributions are also derived and studied in brief.
SJTU at MRP 2019: A Transition-Based Multi-Task Parser for Cross-Framework Meaning Representation Parsing

This paper describes the system of our team SJTU for our participation in the CoNLL 2019 Shared Task: Cross-Framework Meaning Representation Parsing.The goal of the task is to advance data-driven parsing into graphstructured representations of sentence meaning.This task includes five meaning representation frameworks: DM, PSD, EDS, UCCA, and AMR.These frameworks have different properties and structures.To tackle all the frameworks in one model, it is needed to find out the commonality of them.In our work, we define a set of the transition actions to oncefor-all tackle all the frameworks and train a transition-based model to parse the meaning representation.The adopted multi-task model also can allow learning for one framework to benefit the others.In the final official evaluation of the shared task, our system achieves 42% F 1 unified MRP metric score.
PRACTICAL IMPLEMENTATION OF PROJECT-BASED LEARNING AT THE UNIVERSITY

The modern system of higher education involves the active implementation of the practice-oriented approach, which is based not only on the educational process, but also on the formation of a student’s personality. The introduction of a practice-oriented approach implies students’ acquiring both practical and social competencies, which will be necessary for them in their future professional activity. Project activity in Higher School of Economics (HSE) is presented as a separate type of students’ educational activity within an educational program, credit units for it corresponding to specific forms of organization and control. The project seminar is an educational form of organizational and educational support for project activities. The project seminar is not directly related to the forms of projects implementation. It aims at forming students’ project competencies, skills of project interaction, and project management. The practice of implementing a project seminar within undergraduate programs began in 2014, within graduate programs – in 2018. This paper analyzes the undergraduate students’ practice of project work in educational programs of economic profile. The study is based on the analysis of HSE – Perm. The survey involved 117 respondents, including 8 teachers, 5 customer representatives, and 104 students of different years (the sample corresponds to the confidence accuracy of 95 % with 10 % room for possible error and a total population of 983 students). Research question. Since 2014, HSE has been introducing a special practice of project-oriented training in undergraduate programs, which is to implement projects and a project seminar and to involve students in practical programs (applied projects). This practice has methodological support in the form of local regulations, but, in fact, there are ample opportunities for educational programs, as far as the peculiar features of implementation are concerned. At the same time, a large number of participants have been included in the process of implementing this practice. It is this research that is supposed to identify common benefits and difficulties in implementing this approach. Accordingly, the purpose of the study is to evaluate the practice of introducing project-oriented education on the example of undergraduate economic programs at HSE – Perm, to find out corresponding problems, and to identify ways of improving this process.
Impact of teaching-learning process for brain

This research has based on a theoretical review of the knowledge about the topics, the importance of the brain and its learning styles, which some theorists propose, whose interests and publications focus on the importance of the brain for learning. The purpose was to demonstrate, the impact of these topics on the teaching-learning process and the problems. That arise around them, as well as topics on the need to incorporate flexible models that take into account the diversity of human thought. Develop capacities taking into account the particularities of the student, attention should be paid, manipulate methodological strategies and resources that respond to the different learning styles and systems proposed in this article, so that the teaching of all students benefits, according to their progressive period, level of development and educational needs, without separating them from their environment or context in which they operate. It shows how the brain influences learning, so that knowledge has strengthened in a humanistic way. The objective is to potentiate teaching and learning, to achieve a better academic performance, which seeks to offer significant experiences to adapt to the new demands of the society of the 21st century.
Video Satellite Imagery Super-Resolution via a Deep Residual Network

Recently, as a new remote sensing system, video satellite develops rapidly for long-time observation. Thanks to its high temporal resolution, video satellite has been extensively used for environmental detection, especially for dynamic target monitoring. However, limited by the imaging device, it sacrifices some of its spatial resolution. Therefore, the super-resolution (SR) technology applied to these images is crucial. Based on deep residual learning, which has obtained a great success in the single-image SR, we propose a SR network structure which consists of two main steps. First, we use multi-scale feature extraction to exploit more contextual information on video satellite imagery, which is aimed at inferring high frequency components. Then, we utilize a series of residual blocks to learn the mapping between low resolution and high resolution images in a deeper and more stable network. In our experiment, the SR reconstruction results on Jinlin-1 satellite images greatly indicate the effectiveness of our method and the potential of the residual network for video satellite imagery SR.
When Humanizing Customer Service Chatbots Might Backfire

Abstract More and more companies are using chatbots in customer service. Instead of with a human employee, customers interact with a machine. Many companies give these chatbots human traits through names, human-like appearances, a human voice or even character descriptions. Intuitively such a humanization strategy seems to be a good idea. Studies show, however, that the humanization of chatbots is perceived in a nuanced way and can also backfire. Especially in the context of customer complaints, human-like chatbots can intensify negative reactions of angry customers, because their performance is judged more critically compared to non-humanized chatbot variants. Service managers should therefore consider very carefully whether and in which situations they should use humanized service chatbots.
Self-Adaptive Scaling for Learnable Residual Structure

Residual has been widely applied to build deep neural networks with enhanced feature propagation and improved accuracy.In the literature, multiple variants of residual structure are proposed.However, most of them are manually designed for particular tasks and datasets and the combination of existing residual structures has not been well studied.In this work, we propose the Self-Adaptive Scaling (SAS) approach that automatically learns the design of residual structure from data.The proposed approach makes the best of various residual structures, resulting in a general architecture covering several existing ones.In this manner, we construct a learnable residual structure which can be easily integrated into a wide range of residual-based models.We evaluate our approach on various tasks concerning different modalities, including machine translation (IWSLT-2015 EN-VI and WMT-2014 EN-DE, EN-FR), image classification (CIFAR-10 and CIFAR-100), and image captioning (MSCOCO).Empirical results show that the proposed approach consistently improves the residual-based models and exhibits desirable generalization ability.In particular, by incorporating the proposed approach to the Transformer model, we establish new state-of-thearts on the IWSLT-2015 EN-VI low-resource machine translation dataset.
Sparseness embedding in bending of space and time; a case study on unsupervised 3D action recognition

Human action recognition from skeletal data is one of the most popular topics in computer vision which has been widely studied in the literature, occasionally with some very promising results. However, being supervised, most of the existing methods suffer from two major drawbacks; (1) too much reliance on massive labeled data and (2) high sensitivity to outliers, which in turn hinder their applications in such real-world scenarios as recognizing long-term and complex movements. In this paper, we propose a novel unsupervised 3D action recognition method called Sparseness Embedding in which the spatiotemporal representation of action sequences is nonlinearly projected into an unwarped feature representation medium, where unlike the original curved space, one can easily apply the Euclidean metrics. Our strategy can simultaneously integrate the characteristics of nonlinearity, sparsity, and space curvature of sequences into a single objective function, leading to a more robust and highly compact representation of discriminative attributes without any need to label information. Moreover, we propose a joint learning strategy for dealing with the heterogeneity of the temporal and spatial characteristics of action sequences. A set of extensive experiments on six publicly available databases, including UTKinect, TST fall, UTD-MHAD, CMU, Berkeley MHAD, and NTU RGB+D demonstrates the superiority of our method compared with the state-of-the-art algorithms.
Bidirectional Tracking Scheme for Visual Object Tracking Based on Recursive Orthogonal Least Squares

Visual object tracking in unconstrained environments is a challenging task in computer vision. How to design an efficient discriminative feature representation is one challenging issue. To improve the adaptability of the tracker to large object appearance changes, the observation model needs to be updated online. However, a bad model update using inaccurate training samples can lead to model drift problem. Therefore, how to design an efficient online observation model and a model update strategy are two other challenging issues. This paper proposes the concatenation of histogram of oriented gradients variant (HOGv) and color histogram as the feature representation to balance discriminative power and efficiency. The single-hidden-layer feedforward neural network (SFNN) is used as an observation model, and the recursive orthogonal least squares (ROLS) algorithm is used to update the model online. A bidirectional tracking scheme is designed to alleviate the model drift problem during online tracking. The proposed bidirectional tracking scheme consists of three modules: the forward tracking module, the backward tracking module and the integration module. The forward tracking module first finds all the candidate regions, and then, the backward tracking module calculates the respective confidence of each candidate region according to historical information. Finally, the integration module integrates both of the first two modules' results to determine the final tracked object and the model update strategy for the current frame. Extensive evaluations of the existing tracking benchmarks have shown that the proposed tracking framework results in significant performance improvements compared with the base tracker, and it outperforms most of the state-of-the-art trackers.
Multi-class Text Complexity Evaluation via Deep Neural Networks

Automatic Text Complexity Evaluation (ATE) is a natural language processing task which aims to assess texts difficulty taking into account many facets related to complexity. A large number of papers tackle the problem of ATE by means of machine learning algorithms in order to classify texts into complex or simple classes. In this paper, we try to go beyond the methodologies presented so far by introducing a preliminary system based on a deep neural network model whose objective is to classify sentences into more of two classes. Experiments have been carried out on a manually annotated corpus which has been preprocessed in order to make it suitable for the scope of the paper. The results show that a higher detail level of the classification makes the ATE problem much harder to resolve, showing the weaknesses of the model to accomplish the task correctly.
Data-Driven Day-Ahead PV Estimation Using Hybrid Deep Learning

Ongoing smart grid activities and associated automation resulted in rich set of data. These data can be utilized for monitoring and estimation of real time photovoltaic (PV) generation. Inherent variability in PV and related impact on power systems is a challenging problem. Improving the accuracy of PV generation estimation is beneficial for both the PV owners and the grid operators. Recently, deep learning algorithms possible by the availability of data have shown its advantages for time series estimation; however, its application on PV generation estimation is still in the early stage. In this paper, a hybrid estimation model with a combination of long-short-term-memory network (LSTM) and persistence model (PM) is developed to provide day-ahead PV estimation at 15-minute time interval with high accuracy and robustness. Simulation results show the superior performance of the proposed method over existing methods for most of the test cases.
Speckle Reduction by Directional Coherent Anisotropic Diffusion

To effectively balance speckle smoothing and preservation of edges and radiation, a novel anisotropic diffusion filter was developed that uses a directional coherent coefficient. The proposed filter effectively improves the edge detection operator of a traditional anisotropic diffusion filter. The new edge detection operator calculates 16 direction coherence coefficients to avoid the interference of the edge direction. For the diffusion function, the proposed method directly uses the detected directional coherent edge as the diffusion coefficient, which simplifies the calculation of the diffusion function and avoids the adverse effects of inaccurate estimation of the diffusion function threshold for a traditional anisotropic diffusion filter. The influence of the number of iterations and time steps on the proposed filter was analyzed. A series of experiments was conducted with a simulated image and three real synthetic-aperture radar images from different sensors. The results confirmed that the proposed method not only significantly reduces speckle but also effectively preserves the edge and radiation information of images.
Face Liveness Detection Based on the Improved CNN with Context and Texture Information

Face liveness detection, as a key module of real face recognition systems, is to distinguish a fake face from a real one. In this paper, we propose an improved Convolutional neural network (CNN) architecture with two bypass connections to simultaneously utilize low-level detailed information and high-level semantic information. Considering the importance of the texture information for describing face images, texture features are also adopted under the conventional recognition framework of Support vector machine (SVM). The improved CNN and the texture feature based SVM are fused. Context information which is usually neglected by existing methods is well utilized in this paper. Two widely used datasets are used to test the proposed method. Extensive experiments show that our method outperforms the state-of-the-art methods.
Genetic Programming for Imputation Predictor Selection and Ranking in Symbolic Regression with High-Dimensional Incomplete Data

Incompleteness is one of the challenging issues in data science. One approach to tackle this issue is using imputation methods to estimate the missing values in incomplete data sets. In spite of the popularity of adopting this approach in several machine learning tasks, it has been rarely investigated in symbolic regression. In this work, a genetic programming (GP) based feature selection and ranking method is proposed and applied to high-dimensional symbolic regression with incomplete data. The main idea is to construct GP programs for each incomplete feature using other features as predictors. The predictors selected by these GP programs are then ranked based on the fitness values of the best constructed GP programs and the frequency of occurrences of the predictors in these programs. The experimental work is conducted on high-dimensional data where the number of features is greater than the number of instances.
Augmented object selection through smart glasses

Several interaction methods for smart glasses have been studied, but it is not clear which method could be the best to interact with augmented objects. In this study, an Augmented Reality (AR) marker-based application for smart glasses was developed, implementing three different interaction methods for marker selection: one using mid-air hand gestures, other using head movements and double-tap in the glasses, and the last one using a handheld controller. We studied the users' interaction times, preferences, and their willingness to perform the interaction method in public. We find that users' felt more comfortable using a familiar handheld controller. However, the solution with the smart glass's tap function and head movement got results close to the results of smart glass's controller.