Reviewerly/exHarmony huggingface dataset:
Multi-Point Collaborative Authentication Method Based on User Image Intelligent Collection in the Internet of Things

With the increasing demand for intelligent services of the Internet of Things (IoT), its security issues have attracted widespread attention recently. Since most of the existing identity authentication policies are based on a single authentication mode, they are highly likely to cause problems such as illegal operation and stealing of sensor information. In order to meet the needs of increasing IoT users for the security management of intelligent services, a multi-point collaborative authentication method based on user image intelligent collection for the security problems faced by IoT in identity authentication is proposed in the paper. This method firstly collects the identity of the legal user through the intelligent collection technology and then realizes the identity authentication of the unidentified user through the collaborative authentication between the local domain management machine, the back-end image management machine, and the cloud server. Compared with the traditional single identity authentication method, our method uses three-party collaborative authentication to avoid the problem of sensor information stealing easily caused by a single authentication method, which makes the user’s identity authentication more secure and effective. The security analysis shows that the method is able to resist multiple attacks and prevent the sensor information from being illegally operated and stolen, protecting the security of the sensor information.
Innovative Ability Mission of the Enterprising University

Comparative research of the entrepreneurial University VS innovator and students who have studied a curriculum that promotes entrepreneurship and those who did not.Hypothesis 1, the student who receives entrepreneurial training has more developed innovative and entrepreneurial skills.Hypothesis 2, there is no difference between one student and another.The population is made up of 522 students from various Universities in Mexico.In order to identify the profile of the entrepreneurial and innovative University, the Ranking 2018 was used as well as an Instrument Barometer INCODE (innovation competences development) to determine the Ez of the students, which is a learning program of the European Union.The results showed the importance of fostering the innovative ability of the student, curricular and extracurricular, since this is a cross-cutting skill that all professionals require to undertake any project or strategy with an emphasis on entrepreneurship and innovation demanded by the environment and the entrepreneurial University in all contexts.The study gives us the opportunity to develop the transversal skills in line with the mission of the University.Also, the challenge of transition from the University of teaching to the entrepreneurial and innovative University.
STEM-education in the mirror of sociological research

The article is devoted to the research of trends and mechanisms of STEM-innovations implementationin Ukraine. STEM – a series or sequence of educational programs designed to solve the problem of thescientific and engineering specialists lack for high-tech industries and raise the competitiveness of thestate. It is a priority in state policy towards economy strengthening and state social order to the educationalsector. Consequently, relevance of STEM in the modern European institutional space is beyond doubt. Inthis regard, attention is focused on measuring the understanding of STEM and its essence, on the prospectsand difficulties of implementing these social technologies in Ukrainian realities. The paper presents theresults of quantitative research conducted on the basis of author’s methodologies, members of teachingstaff, student youth, expert group and parents of high school students. On the basis of conducted monitoringresults calculation, comparing the answers of different categories of respondents, the authors attempt tosummarize the STEM technologies state in modern Ukrainian society and come to the conclusion that theimplementation of relevant innovations contributes to the dissemination of dialogue among all participantsin the educational process, parents and potential employers. Conducted research makes it possible to statethe following. Educators of experimental educational institutions have a sufficiently high level of motivationto innovate, open to cooperation and understand the importance of innovations in the modern Ukrainianschool. STEM topics of interest to students are clearly outlined. Therefore, the majorities of polled studentssupport the STEM direction and understand its prospects but do not understand in what sphere and wherethey will work in the future, even choosing the STEM profession. The results of the study showed thatmodern parents are motivated to influence the future profession choice their own children. However, thedirect parents’ influence on the future profession choosing is limited because a significant number of highschool students have their own opinion on this issue, which is in part or does not coincide, with parents.STEM innovations create a new space for a steady dialogue between educators, parents and employers forthe development of the economies of the states and engage youth in STEM industry.
A Method of Ontology Evolution and Concept Evaluation Based on Knowledge Discovery in the Heavy Haul Railway Risk System

The risk pre-control of heavy haul railways is a collaborative scenario with multi-department linkage and the risk analysis model relies on multiple data sources. As a tool for knowledge formal modeling, Ontology and knowledge graph can achieve knowledge discovery, reasoning and decision support based on multi-dimensional heterogeneous data. This paper restores unusual context with participant behavior data as the core, establishes a basic Scenario-Risk-Accident Chain (SRAC) ontology framework. Under collaborative relationships formed by reasoning rules between context and risk, this paper establishes evolution mechanism of SRAC to introduce new knowledge, such as knowledge extracted from device detection data. New entities are added to the risk concept tree through semantic similarity algorithms. In addition, researchers added weight attribute to the risk ontology. With quantitative representation of risk concepts, this paper uses risk relevance mining to establish associated-subgraphs, establishes a new method for potential accident level assessment through maximum flow search mechanism.
Sentiment Analysis in Social Networks: A Methodology Based on the Latent Dirichlet Allocation Approach

The detection and analysis of sentiment in textual communication is a topic attracting attention in both academia and industry.In fact, thanks to the explosion of the Social Networks a wealth of information is produced every day.This huge amount of contents can be very helpful in assessing the general public's sentiment and opinions toward products, services and topics.This paper presents a methodology for the detection of sentiment in textual contents using a methodology based on the Latent Dirichlet Allocation (LDA) approach and a word-based graphical model, the mixed graph of terms.The method has been tested in various operative scenarios: on standard datasets, on datasets obtained collecting tweets from twitter and on datasets coming from social networks as Twitter and TripAdvisor.The experimental campaigns show that the proposed approach is effective and furnishes good and reliable results in each context.
On Hidden Conflicts of Belief Functions

Hidden conflict of belief functions in the case where the sum of all multiples of conflicting belief masses being equal to zero was observed.Degrees of hidden conflict are defined and analyzed including their relation to partial and full non-conflictness.The hidden conflict between two belief functions is distinguished from internal hidden conflict(s) of the individual belief function(s).Finally, computational issues of hidden conflict and non-conflictness are presented.
Real-Time Multi-Modal People Detection and Tracking of Mobile Robots with A RGB-D Sensor

A real-time multi-modal people detection and tracking algorithm based on RGB-D information is presented in this paper. The color image and the depth image is calibrated and aligned firstly with each other. At texture level a SVM classifier based on HOG features is used to detect and identify human body in color images, while at depth level an Adaboost classifier with Haar-like features is used to detect human head in depth images respectively. With the space relationship between human body and head, a combined multi-modal cascade detector is then constructed, which can greatly improve the detection accuracy. For tracking tasks, the KCF(Kernelized Correlation Filter) algorithm is adopted to realize the real-time people tracking, in which color and depth features are both considered and used in template matching to guarantee the robustness of the tracking algorithm. The proposed algorithm is robust to object occlusion and tracking lost caused by fast movement or cross walking. Extensive experiments are carried out on a wheeled mobile robot mounted with a Kinect2 sensor, and the result shows the feasibility and effectiveness of the method.
Fake Indian Currency Recognition System by using MATLAB

Counterfeit notes are one of the biggest problem occurring in cash transactions. For country like India, it is becoming big hurdle. Because of the advances in printing, scanning technologies it is easily possible for a person to print fake notes with use of latest hardware tools. Detecting fake notes manually becomes time-consuming and untidy process hence there is need of automation techniques with which currency recognition process can be efficiently done. Here we implemented our proposed idea in two ways: one by using analysis through hyper spectral imaging and the second one is the Extraction of different features in fake and real currency notes and through comparing with each other we can able to differentiate the fake note from the real note. We integrated these two systematic approaches in our proposed work. The different colour lights used for hyper spectral imaging are Ultraviolet (UV) light, Normal LED Bulb, Red LED light, Green LED light and Blue LED light with different wavelengths ranging from 360 nm to 800nm respectively. In image processing part, The different features present in the testing input note is identified and compared with the real note and results as the given note is fake or not. Entropy is measured for all the features. The Aspect Ratio is calculated initially for the input notes in order to classify the given note is 100,500 or 2000.This all modules are implemented in MATLAB.We have implemented a fake note detection unit with image processing algorithms.The experimental results indicate that the results achieved are nearly accurate, since the textural background which looks very complex has similar intensity levels.
EFL Learners’ Perceptions towards their Self-Efficacy in Learning Public Speaking Course

This article reports on EFL learners’ perception towards their self-efficacy in public speaking course. Self-ef?cacy has been de?ned as a person’s perceptions of his or her capabilities to complete a speci?c task with the skills that s/he possesses (Bandura, 1997). Thus, It is important for students to measure their skill in order to improve achievement in public speaking course. This research employed a survey study by means of two data collection techniques; questionnaire and interview. The sample of this research was 60 third year students of English department Galuh University. The research result showed that students’ self-efficacy improved after learning public speaking course and the students felt confident using English in front of people. Furthermore, self-efficacy affected not only in public speaking course but also in the other courses. Meanwhile, the high and low self-efficacy might be affected students’ achievements, however, they have similar purpose in public speaking courses, which they want to speak formally without feeling nervous. They have various efforts and strategies to achieve success in this course. This research concludes that self-efficacy in public speaking learning process to third year students of English department Galuh University was important to improve their achievement in speaking skill and improve their confident.
A Logistic Factorization Model for Recommender Systems With Multinomial Responses

Yu Wanga, Xuan Bib & Annie Quc* a Electrical and Computer Engineering, Duke University, Durham, NC; b Carlson School of Management, University of Minnesota, Minneapolis, MN; c Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, IL
A theoretical framework to study long-term use of smart eyewear

Wearable displays with augmented reality are in an early phase of adoption by the public. The uptake is slow and user studies regarding acceptance and use are scarce and have limitations to understand long-term use and their influence on daily life. The complexity of understanding long-term use requires a multidisciplinary approach to different stakeholder perspectives. A variety of complementary theoretical perspectives are needed to create a framework to study the impact and perils of the use of smart eyewear. In this position paper, we develop such a framework, integrating theoretical perspectives from Philosophy, Psychology, Science and Technology Studies and Information Systems. The resulting framework has concrete implications for future studies regarding the design, acceptance and long-term use of smart eyewear.
Computational Linguistics and Its Implementation in e-Learning Platforms

We have experienced in the past few years massive use of various kinds of e-Learning methodology, such as m-learning and blended learning etc. Despite this ubiquitous implementation of these modern approaches, we still lack their proper use regarding their full exploitation in the virtual learning environment. M-learning platforms are ubiquitous, however, they lack the potential which is given to us from computational linguistics, deep learning, and artificial intelligence. The paper attempts to highlight this fact and brings this neglected topic to the attention of the creators and designers of these mobile apps because their potential is much greater and we should put our undivided attention to this issue if we want to improve the technology which we use.
Real-Time Detection of Food Consumption Activities Using Wearable Wireless Sensors

This paper addresses research challenges associated with development of a wearable sensor system for detecting the food consumption activities of a subject. The objective is to automatically detect the occurrence of food consumption whenever it occurs, in order to use this activity detection to record a representative camera image of the food and count the number of bites of food consumed. The wearable system consists of two elastic bands - one each on the upper arm and wrist - instrumented with wireless inertial and magnetic sensors. Two major technical challenges include i) singularity issues with Euler angle estimation due to arm rotations that can exceed 90 degrees, and ii) the need to differentiate between eating and non-eating activities involving close hand-mouth proximity. The singularity challenge is addressed by using a direction cosine matrix estimation technique that utilizes a linear Kalman Filter. The differentiation between eating and non-eating activities is done using a support-vector-machine (SVM) based machine learning algorithm. Experimental results using wearable prototype bands show that both the DCM estimation and machine learning components work reliably and have the potential to be useful for home-based automated food intake detection.
Real-time Hand Gesture Recognition Based on Deep Learning in Complex Environments

Real-time hand gesture recognition in complex environments has many challenges, such as poor real-time performances and robustness to environmental changes. This paper takes the hand gesture control of the unmanned vehicle as the application background, and focuses on the gesture detection and recognition of video streams based on deep learning in the complex environment. In this paper, we detect the hand in a complex environment by training the ssd_mobilenet model, and initialize the tracking with kalman filter. Then, we detect the hand keypoints by following the architecture of Convolutional Pose Machines (CPMs), in order to obtain the belief maps for all keypoints that are used as the train sets of Convolutional Neural Networks (CNNs). Finally, based on the results obtained by our classification, this paper proposes a method of multi-frame recursion to minimize the influences of redundant frames and error frames. In this paper, eight kinds of gestures for controlling vehicle are identified. The experimental results show that our method can successfully realize real-time hand gesture recognition in the video streams. The recognition accuracy can reach 96.7%, and the average recognition speed reaches 12 fps, which basically meets the real-time requirements and successfully applys to mobile terminals such as TX2 for engineering practice.
THOMAS: The Hegemonic OSU Morphological Analyzer using Seq2seq

This paper describes the OSU submission to the SIGMORPHON 2019 shared task, Crosslinguality and Context in Morphology.Our system addresses the contextual morphological analysis subtask of Task 2, which is to produce the morphosyntactic description (MSD) of each fully inflected word within a given sentence.We frame this as a sequence generation task and employ a neural encoder-decoder (seq2seq) architecture to generate the sequence of MSD tags given the encoded representation of each token.Followup analyses reveal that our system most significantly improves performance on morphologically complex languages whose inflected word forms typically have longer MSD tag sequences.In addition, our system seems to capture the structured correlation between MSD tags, such as that between the verb V tag and TAM-related tags.
Acquisition and Classification System of EMG Signals for Interpreting the Alphabet of the Sign Language

Taking into account that in Peru, there is an increase in people with difficulties in speaking or communicating.According to the National Institute of Statistics and Informatics of Peru (INEI for its acronym in Spanish), around 80000 people use the gesturing language.For this reason, this research proposes to use the electromyography (EMG) signals to detect the hand movement and identify the alphabet of the sign language to provide essential communication to people who need it.The idea is to classify the signals and recognize the letters of the Spanish alphabet, interpreted in the Peruvian sign language.The results show the classification of the 27 letters of the alphabet with a general success rate of 93.9%.
An Acoustic and Lexical Analysis of Emotional Valence in Spontaneous Speech: Autobiographical Memory Recall in Older Adults

Analyzing emotional valence in spontaneous speech remains complex and challenging.We present an acoustic and lexical analysis of emotional valence in spontaneous speech of older adults.Data was collected by recalling autobiographical memories through a word association task.Due to the complex and personal nature of memories, we propose a novel coding scheme for emotional valence.We explore acoustic properties of speech as well as the use of affective words to predict emotional valence expressed in autobiographical memories.Using mixed-effect regression modelling, we compared predictive models based on acoustic information only, lexical information only, or a combination of both.Results show that the combined model accounts for the highest proportion of explained variance, with the acoustic features accounting for a smaller share of the total variance than the lexical features.Several acoustic and lexical features predicted valence.As a first attempt at analyzing spontaneous emotional speech in older adults autobiographical memories, the study provides more insight in which acoustic features can be used to predict valence (automatically) in a more ecologically valid setting.
Double Bayesian Smoothing as Message Passing

Recently, a novel method for developing filtering algorithms, based on the interconnection of two Bayesian filters and called double Bayesian filtering, has been proposed. In this manuscript we show that the same conceptual approach can be exploited to devise a new smoothing method, called double Bayesian smoothing. A double Bayesian smoother combines a double Bayesian filter, employed in its forward pass, with the interconnection of two backward information filters used in its backward pass. As a specific application of our general method, a detailed derivation of double Bayesian smoothing algorithms for conditionally linear Gaussian systems is illustrated. Numerical results for two specific dynamic systems evidence that these algorithms can achieve a better complexity-accuracy tradeoff and tracking capability than other smoothing techniques recently appeared in the literature.
An Analysis of Spam Email Detection Performance Assessment Using Machine Learning

Spam email is very annoying for email account users to get relevant information. Detection of email spam has actually been applied to email services for the public with various methods. But for the use of a limited number of company's e-mail accounts, not all e-mail servers provide spam e-mail detection features. The server administrator must add a separate or modular spam detection feature so that e-mail accounts can be protected from spam e-mail. This study aims to get the best method in the process of detecting spam emails. Some machine learning methods such as Logistic Regression, Decision Tree, and Random Forest are applied and compared results to get the most efficient method of detecting spam e-mail. Efficiency measurements are obtained from the speed of training and testing processes, as well as the accuracy in detecting spam emails. The results obtained in this study indicate that the Random Forest method has the best performance with a test data speed of 0.19 seconds and an accuracy of 98%. This result can be used as a reference for the development of spam detection using other methods.
Pembelajaran Berbasis Multimedia: Upaya Memahami Keberagaman Gaya Belajar Anak

&lt;em&gt;This writing discusses about a way to understand the varieties of children’s learning styles in environment of formal school. One of the ways that will be discussed is by designing a learning based on multimedia. The research uses an approach which vies some concepts existed in the variable. It is called by library research. The result of this research shows that children’s learning styles generally are divided into three styles, they are auditory, visual, and kinesthetic. By using multimedia approach in teaching, it will help teacher in understanding various styles of children’s learning. It is because of multimedia consist of the components, such as media and audio media and visual media have been integrated in multimedia. Instead of multimedia which is designed well can stimulate children’s attention in learning process so the children are interested to follow the learning and all of aspects that are needed by children.&lt;/em&gt;