Reviewerly/exHarmony huggingface dataset:
SOCIALIZATION AND PERSONAL COMPETENCIES IN THE CONTEXT OF SUCCESSFUL INTERCULTURAL COMMUNICATION

espanolEl articulo analiza los problemas del entorno de la educacion intercultural justificados socialmente. El individuo desarrolla su personalidad interactuando con otras personas. Este es un proceso muy complejo y de multiples capas donde las relaciones entre las personas se ven reforzadas por la necesidad interna de colaboracion y empatia. El proceso en si implica el intercambio de significados, el desarrollo de una estrategia unificada de interaccion, percepcion y comprension del otro / el extrano que esta tan lejos. El sentido social del area de educacion intercultural se manifiesta en su capacidad de transmitir formas de vivencia cultural y social. El sentido de igualdad hace que la presion de supresion y restriccion de identificaciones de personalidad significativas sea menor. El area de educacion intercultural presupone tanto la comprension de la personalidad compleja y multidimensional / identidad de fondo que marca el desarrollo social del individuo como la tolerancia a la diversidad disponible en varias (co) identificaciones. EnglishThe paper discusses the problems of the intercultural education environment as socially justified. The individual develops his/her personality by interacting with other people. This is a very complex and multi-layered process where relations between people are enhanced by the inner necessity for collaboration and empathy. The process itself involves exchange of meanings, development of a unified strategy for interaction, perception and understanding of the other/the stranger that is so far away. The social sense of the intercultural education area reveals in its ability to relay forms of cultural and social experience. The sense of equality makes the pressure of suppression and restriction of significant personality identifications lesser. The intercultural education area presupposes both understanding of the complex and multi-dimensional personality/background identity that marks the social development of the individual and tolerance to diversity available in various (co-)identifications.
Empirical Evaluation of Various Classification Methods

Humans are anxious to know how computer will venture on data, citation of teaching a computer about the data in order to get relieved from the programming at every step by the programmer and computer itself is able to perform those tasks that could not be done manually. It gives us the glimpse of machine learning. Machine learning uses various methods, statistical techniques and algorithms have the skill to understand multidimensional and multi variety of data. In this paper, we discuss about the empirical evaluation of various classification methods on the performance measure such as accuracy, kappa statistics, mean absolute errors working on different benchmarks, datasets. The classification methods we have taken in this paper are Naïve Bayesian, logistic regression, k-Star; sequential minimal optimization and random tree and the datasets are breast cancer, zoo and iris. This helps us too know which classifier is suited best for the concern objective and the parameters on which we have analyzed are accuracy, kappa statistics and absolute mean error.
WITHDRAWN: Public key authentication schemes in asymmetric cryptography

In asymmetric cryptography, public key of an authenticated user is accessible to remaining authenticated users in a communication network. Consequently, an ill minded user can avail and modify public key of a legal user. Therefore, it results the problem of public key authentication. There is a scope to explore the ways to secure the public key of an authorized user together with achieving necessary security services. Several key authentication schemes were developed to assure the public key of authorized users. This paper presents the existing key authentication schemes from the instigation of public-key cryptography to till now.
Shared learning activity labels across heterogeneous datasets

Nowadays, the advancement of sensing and communication technologies has led to the possibility of collecting a large amount of sensor data, however, to build a reliable computational model and accurately recognise human activities we still need the annotations on sensor data. Acquiring high-quality, detailed, continuous annotations is a challenging task. In this paper, we explore the solution space on sharing annotated activities across different datasets in order to enhance the recognition accuracies. The main challenge is to resolve heterogeneity in feature and activity space between datasets; that is, each dataset can have a different number of sensors in heterogeneous sensing technologies and deployed in diverse environments and record various activities on different users. To address the challenge, we have designed and developed sharing data and sharing classifiers algorithms that feature the knowledge model to enable computationally-efficient feature space remapping and uncertainty reasoning to enable effective classifier fusion. We have validated the algorithms on three third-party real-world datasets and demonstrated their effectiveness in recognising activities only with annotations from as little as 0.1% of each dataset.
A study on wavelet selection in power signal denoising

Abstract In the wavelet threshold denoising of power signal, the selection of wavelet has an important influence on the denoising effect, and the wavelet generating function has diversity, if not selected properly, it will directly lead to the failure of denoising. Firstly, an operator is introduced to modify the threshold of each scale to better reflect the variation of wavelet coefficients of signal and noise with scale. Then a controllable threshold function is proposed to adapt to different soft and hard characteristics, and it is used to denoise the wavelet coefficients. Based on the study of the characteristics of wavelet, such as orthogonality, vanishing moment, support length and symmetry, four principles of wavelet selection in power signal denoising are proposed. The voltage sag and harmonics model are established, and db5, coif1 and sym2 wavelets are selected to decompose the signal to the fourth scale for denoising. The signal-to-noise ratio, mean square error and the detailed features of the reconstructed signal after denoising are compared. The experimental results show that the orthogonal wavelet db5 with high vanishing moment order and long support length has better denoising effect than coif1 and sym2, which proves the correctness of the wavelet selection principles proposed in this paper.
Applying Piecewise Linear Approximation for DNN Non-Linear Activation Functions to Bfloat16 MACs

The efficient implementation of inference engines for the DNN (Deep Neural Network) execution requires low power consumption and a small area. However, the activation functions' implementation is challenging since they require considerable computing resources due to their non-linearity. In this paper, we study the non-linearity of the functions and show that only one MAC execution using our PLA (Piecewise Linear Approximation) scheme is sufficient to guarantee the accuracy in bfloat16. For the evaluation, we applied our proposal to our in-house bfloat16 MACs and achieved that our results were less than 1 LSB difference from ideal values on average.
Distributed secret sharing scheme based on the high-dimensional rotation paraboloid

Secret sharing of (k,n)-threshold is naturally used to assist in security-assurance applications in smart systems. It can ensure a high level of data security and reliability. Most of existing secret sharing schemes are employing interpolating polynomial or Chinese remainder theorem, while the hyperplane geometry of Blakley’ s scheme has not been widely used. One of the reasons is that the hyperplane geometry-based scheme leaks information about the secret even via less than the threshold number of shadows. In this paper, we propose a novel secret sharing scheme based on the high-dimensional rotation paraboloid instead of the hyperplanes to overcome this weakness. The secret is corresponding to the unique focal point, and the shadows are corresponding to points on it. We prove that linearly independent k points can determine a unique (k−1)-dimensional rotation paraboloid, which is the theoretical support of secret distribution and reconstruction. Moreover, we realize verification via two well-known terms: cheater identification and cheating detection. We identify the validity of shadows submitted by participants for cheater identification and verify whether the reconstructed secret is the original one or not for cheating detection, so that dishonest behavior in the secret sharing scheme can be detected. Security analysis shows our scheme is secure against different types of attacks.
Designing IoT based Posture Monitoring System

With the existence of vast increase in the usage of digital equipment such as computers and other smart devices in their work, people tend to face several health issues. This utilization of digital technology makes people spend most of their time by sitting in one place for a long time. This results in creating several illnesses to them. People do not realize their sitting posture which might hurt their body over a long run of inappropriate sitting practice. As a result, this paper aims to design a system to monitor the posture of the neck and body of a person in sitting position. This proposal utilizes the Internet of Things (IoT) such as microcontroller, sensor, etc., to monitor the body posture of a person and help people to correct their body posture.
100 Mbps Reconciliation for Quantum Key Distribution Using a Single Graphics Processing Unit

An efficient error reconciliation scheme is important for post-processing of quantum key distribution (QKD). This paper concerns the improvement of reconciliation performance. Recently, a multi-matrix low-density parity-check code-based reconciliation algorithm which can provide remarkable perspectives for high-efficiency information reconciliation was proposed by Gao (Opt Express 27:14545–14566, 2019). The multi-matrix algorithm is implemented and optimized on the graphics processing unit (GPU) to obtain high reconciliation throughput. Experimental results indicate that the GPU-based algorithm can highly improve reconciliation throughput to an average of 85.67 Mbps and a maximum 102.084 Mbps with typical code rate and efficiency. This is the best performance of reconciliation on GPU platform to our knowledge.
An efficient Byzantine fault-tolerant consensus mechanism based on threshold signature

In the application scenario of PKI system, the amount of written data is huge and the consensus will consume a lot of resources. In order to reduce the communication complexity and optimize the master node election process, this paper proposes Threshold Signature Practical Byzantine Fault Tolerant(TS-PBFT), an efficient Byzantine Fault Tolerant consensus mechanism based on the Threshold Signature. This consensus mechanism firstly adds the Threshold Signature technique to the PBFT algorithm with Byzantine Fault Tolerant, which reduces the communication complexity to O(n) level; secondly, it introduces the external monitoring mechanism and node trustworthiness index to improve the master node election strategy; Finally, it adds the batch processing mechanism to improve the performance of the consensus process. Compared with the PBFT algorithm, the TS-PBFT algorithm increases the throughput, reduces the delay, and decreases the out block time, which improves the performance of the algorithm.
Touchless Gestures for Interactive Messaging

Touchless gesture interaction has been featured in some recent smartphone models. We extend the domain of application of touchless gestures in smartphones by adopting gesture recognition for sending emoji. A hand-resembling emoji can be sent in just two seconds using a touchless interface. The proposed solution allows enhancing the expressiveness of the communication by sending an image of the user's hand instead of an emoji pictogram. The usability study reveals a strong user preference towards touchless gesture input as compared to the conventional touch.
VEHlA: Safety enhancement of non-premium vehicle assisting system for personal vehicle users

“Vehia” is a personal vehicle driving assistant which renders a massive support to the driver while drive the vehicle. As it is already known that, humans have great relationship with the automobiles since the innovation of the first wheel in stone age era. As a result of the development of the technology many vehicle manufacturing industries were established. In current society, there are many modern cars with tremendous features in it like, Mercedes, BMW, Audi, Tesla etc. Those cars are highly technical and even higher in price. Majority of the people are not affordable on buying those vehicles since the middle range vehicles are not capable of much features like, visualizing performance, driver safety etc. “Vehia” is a device with a multi-informative display which can easily connect to the vehicle that supports the driver in many ways. This device is basically reducing the discrimination of high-tech cars and the middle range cars. So, it provisions utmost same feeling which can gain by an advanced high price car to the driver via a middle range vehicle. This device increases the driver safety by various features like giving alerts and consists with a smartphone based mobile application to interconnect with the mechanics in case of troubleshooting.
Is Anxiety-Inducing VR Experienced Differently Depending on Personality? The Mediating Role of Presence

This study attempted to investigate the role of presence in an anxiety-inducing virtual reality (VR) experience and explain possible individual differences in the feeling of presence. More specifically, it was investigated if the feeling of presence (a) differed by personality and (b) affected individual levels of anxiety. This study utilized the psychobiological model of the Temperament and Character Inventory (TCI) to study personality in the context of virtual reality exposure therapy (VRET). In total, thirty six individuals participated in a lab experiment in which anxiety-inducing VR content was experienced through a head-mounted display. The results suggested that individuals with high levels of cooperativeness and self-transcendence experienced increased levels of presence, which further enhanced cognitive anxiety levels. On a physiological level, cooperativeness and reward dependence enhanced somatic anxiety (respiration), but presence was not a significant mediating factor.
Using Software Engineering and Agile Methods to Improve Inclusion and Team Diversity

Agile Methodologies have brought insights into how to develop software by focusing on individuals and relationships, and valuing diversity teams. Moreover, only professional diversity is considered, where different skills and knowledge facilitate self-sufficiency and consequently self-performance. However, the concept of diversity goes beyond this perspective.
Fast Monte Carlo Dropout and Error Correction for Radio Transmitter Classification

Monte Carlo dropout may effectively capture model uncertainty in deep learning, where a measure of uncertainty is obtained by using multiple instances of dropout at test time. However, Monte Carlo dropout is applied across the whole network and the use of it significantly increases the computational complexity, proportional to the number of instances. To reduce the computational complexity, at test time we enable dropout layers only near the output of the neural network and reuse the computation from prior layers while keeping, if any, other dropout layers disabled. Additionally, we leverage the side information about the ideal distributions for various input samples to do `error correction' on the predictions. We apply these techniques to the radio frequency (RF) transmitter classification problem and show that the proposed algorithm is able to provide better prediction uncertainty than the simple ensemble average algorithm and can be used to effectively identify transmitters that are not in the training data set while correctly classifying transmitters it has been trained on.
The optimization of parameters and matching point pairs in the 3D reconstruction of coronary artery

In coronary angiography, the accuracy of the geometric transformation matrix and the matching of corresponding points between two angiographic images with different angles is the key to achieve 3D reconstruction of coronary. However, due to the movement of heart and hospital bed which commonly occurs during clinical practice, the geometric transformation matrix obtained directly from the imaging system can not well reflect the spatial geometric relationship between different imaging coordinate systems. Therefore, according to the principle of minimizing the error between the actual vessel and projection of the 3D reconstructed vessel, this paper proposes a parameter-adjusting Levenberg-Marquarelt(PALM) algorithm combined with a trust region method to optimize the geometric transformation matrix. As for the matching of corresponding points, the traditional method is achieved by epipolar constraint, which easily leads to wrong matching point pairs. Therefore, this paper further adopts the error matrix defined by the epipolar matching error to achieve global optimal matching point pairs of vascular through dynamic programming while considering the constraints of the vascular smoothness. The experiments show that the 3D reconstruction accuracy has been dramatically improved in terms of mean back projection errors, which confirms the validity and applicability of the algorithm put forward in this paper.
Optical imaging of surface strain distribution up to 1500°C: Development of micro‐speckle pattern

Abstract Micro‐speckle patterns were fabricated using α‐Al 2 O 3 and SiC micro‐particles for in‐plane strain distribution measurement at temperatures up to 1500°C using an ultraviolet high‐temperature observation system (UV‐HTOS) combined with a digital image correlation (DIC) technique. The experimental results showed that the measured mean surface strains of α‐Al 2 O 3 were in good agreement with the calculated values obtained from thermal expansion coefficient and temperature difference. The proposed speckle pattern could be used for strain distribution measurement without degradation even after heating up to 1500°C. The results also demonstrated that the pattern fabricated in this study is effective to understand the heterogeneous deformation mechanism in composite materials applied in high‐temperature structural components.
Systematic Review on Recommendation Systems

Nowadays recommendation systems have become significant as it supports e-commerce applications and many research communities. Recommendation systems are used in the field of knowledge discovery techniques and search engines. It helps in decision making on personalized recommendation of information. There are key challenges to a recommendation system which arises due to the huge growth of data on the internet as well as the no of users visiting the websites. Some of the challenges are generating accurate recommendation, manipulating many recommendations efficiently Hence, new recommendation system technologies are required that can do the computations even for huge data sets. In this paper, we will provide an explanation for approximately the recommendation system, its processes and the technology used.. The study shows that item-based recommendation system is an efficient recent method to make recommendation where user could provide the whole documents instead of just keywords and make internal and external rating to make recommendation system.
Physics in pocket: Learning physics is easy and fun

Technology in the 21st century produces diverse teaching materials in support of a quality learning process. Pocket books become one of the simple teaching materials and are widely used in supporting independent learning. The addition of technology to display multi-media in a printed pocket book will improve the quality of its use. This article will describe the results of research that produces physics pocket books equipped with multi-media based on QR-Code technology. The pocket book was developed for high school physics class X material. This research and development method uses the Dick and Carey model. The feasibility of the resulting pocket book was tested material and media by experts using a valid and reliable Likertz-scale questionnaire instrument. This research succeeded in producing a product in the form of a physics pocket book equipped with media-based QR-Code technology that is feasible as a practical teaching material and can be used as a source of independent teaching material by high school students in grade X.
Can we estimate the perceived comfort of virtual human faces using visual cues?

The sense of strangeness (or discomfort) perceived in certain virtual characters, discussed in Uncanny Valey (UV) theory, can be a key factor in our perceptual and cognitive discrimination.Understanding how this strangeness happens is essential to avoid it in the process of modeling virtual humans.In this paper, we investigate the relationship between images features and the discomfort that human beings can perceive.We extract image features based on Hu Moments (Hum) and Histogram Oriented Gradient (Hog).The saliency detection is also extracted in the specific parts of the virtual face.Finally, a model using Support Vector Machine (SVM) to provide binary classification is suggested.The results indicate accuracy of around 80% in the image estimation process comparing with subjective classification.As a contribution, some areas may benefit from this study for avoiding the creation of characters that may cause strangeness, such as the games, conversational agents and cinema industry.
Analyzing Customer Sentiments Using Machine Learning Techniques to Improve Business Performance

In today's digital world, advancement in machine learning has changed the traditional perspective towards business analysis. Orthodox business analysts did not consider customer reviews as cost-effective input for analysis because earlier fetching customer reviews was costly. The emergence of the internet turned the whole world upside down. Now, the customer's sentiment analysis is the new friend of all business analysts. In this paper, we will be doing a comparative study between the two methods. One in which we use usual parameters like average rating, average review counts. In second, we use a text review as a parameter for sentiment classification. The effectiveness of these methods is evaluated, and the optimal model is selected.
Analysis of Generative Shape Modeling Approaches

Generative shape models are crucial for many medical image analysis tasks. In previous studies, it has been shown that conventional methods like PCA-based statistical shape models (SSMs) and their extensions are thought to be robust in terms of generalization ability but have rather poor specificity. On the contrary, deep learning approaches like autoencoders, require large training set sizes, but are comparably specific. In this work, we comprehensively compare different classical and deep learning-based generative shape modeling approaches and demonstrate their limitations and advantages. Experiments on a publicly available 2D chest X-ray data set show that the deep learning methods achieve better specificity and similar generalization abilities for large training set sizes. Furthermore, an extensive analysis of the different methods, gives an insight on their latent space representations.