Reviewerly/exHarmony huggingface dataset:
Value-Oriented Model of Tuition in the Context of University 3.0 Paradigm

Introduction. This article presents the analysis of transformational changes in the Russian system of higher education. The authors aimed to develop a value-oriented model for the organization of educational activities. The concept of University 3.0 serves as a modern basis for ensuring the global competitiveness of national economies in the digital world. Materials and Methods. The methodological approaches of the study were systematic, comparative and problem-chronological methods. Results. The study expanded the understanding of the main stages of transformation in the higher education system in the Russian Federation. The results show that the destruction of the Soviet academic culture was accompanied by the destruction of the historical institutional balance. As a result, it became necessary to form new norms that define both the rules of interaction within the educational community and the place of this community in the overall social structure. The concept of Education 3.0 is designed to form these normative frameworks and ensure their interiorisation both in the pedagogical community and among students. It is revealed that in the modern Russian educational environment they are formed spontaneously, mainly due to the independent development of new technologies by students, while teachers continue to adhere to the previous educational concepts. Discussion and Conclusion. The practical significance of the study lies in the proposal of specific measures, the implementation of which will form the reputation of the University, attract additional investment, enhance its competitiveness both at the domestic and international level. The timeliness of the study lies in the fact that the analysis of the educational reform was carried out taking into account the needs of value-based management. The results of the study will be useful for the management of higher education institutions to reformulate development concepts, as well as for the executive authorities to further reform the educational system.
Automated brain tumor segmentation from multi-modality MRI data based on Tamura texture feature and SVM model

The precise segmentation of brain tumors is the most important and critical step in the diagnosis and radiotherapy of brain tumors. An automatic segmentation algorithm of brain tumor MRI image based on Tamura texture feature and SVM model is proposed in this paper. Firstly, the local grayscale features of four modal magnetic resonance images are combined with the Tamura texture metrics, which are roughness, contrast, directionality, and regularity. In this way, the information in the image is extracted as much as possible. Then, the known samples are put into the SVM model and classifier training is performed. Finally, other brain tumor images are processed with the trained SVM model. The performance of this method is evaluated using the 2013 BRATS test data-set. The encouraging evaluation results is obtained.
Employee Theft among College Students in the Workforce

Employee theft costs organizations billions of dollars annually. Despite the severity of employee theft, previous research has not focused on the specific demographics of employees who commit this crime. The number of college students in the workforce has increased over the years; thus, the purpose of this study was to investigate the serious problem of employee theft, particularly in relation to the college student population. A survey was used to collect data from college students to indicate employee theft activities among them. Employee theft activities were categorized as follows: time theft, property theft, embezzlement, pilferage, and data and trade secret theft. Survey responses from 92 students indicated that the majority committed time theft, followed by property theft, pilferage, data and trade secret theft, and embezzlement, respectively. This study adds valuable information for businesses and practitioners to understand the severity of employee theft among the increasing number of college students in the workforce.
Phoneme Level Language Models for Sequence Based Low Resource ASR

Building multilingual and crosslingual models help bring different languages together in a language universal space. It allows models to share parameters and transfer knowledge across languages, enabling faster and better adaptation to a new language. These approaches are particularly useful for low resource languages. In this paper, we propose a phoneme-level language model that can be used multilingually and for crosslingual adaptation to a target language. We show that our model performs almost as well as the monolingual models by using six times fewer parameters, and is capable of better adaptation to languages not seen during training in a low resource scenario. We show that these phoneme-level language models can be used to decode sequence based Connectionist Temporal Classification (CTC) acoustic model outputs to obtain comparable word error rates with Weighted Finite State Transducer (WFST) based decoding in Babel languages. We also show that these phoneme-level language models outperform WFST decoding in various low-resource conditions like adapting to a new language and domain mismatch between training and testing data.
Park Recommendation Algorithm based on User Reviews and Ratings

Recommendation systems are widely used in e-commerce websites as they can recommend appropriate movies, songs, books, and other items to users according to users' historical behavior.In traditional collaborative filtering algorithms, users' historical scores are usually used to predict the unknown item rating, while ignoring their textual reviews.Therefore, this paper proposes a park recommendation model based on user reviews and ratings (PRMRR).PRMRR first uses the latent Dirichlet allocation model to extract the statistical distribution of the park features.Secondly, it detects user preference distribution based on park features and user ratings.In order to measure the credibility of user ratings, user rating confidence level is considered to correct user preferences.Thirdly, it uses Kullback-Leibler divergence to calculate the similarity between different users and then predicts the unknown park rating for a specific user.Finally, the proposed algorithm is evaluated on two real park data sets, and the results on two different data sets show that the proposed approach outperforms other traditional approaches.Our recommendation algorithm thus has great potential to improve the quality of park recommendation and effectively handle the data sparsity problem.
Chatbot for University Resource Booking

Chatbots are programs that mimic human conversation using Artificial Intelligence (AI). It is designed to be the ultimate virtual assistant, entertainment purpose, helping one to complete tasks ranging from answering questions, getting driving directions, turning up the thermostat in a smart home, to playing one’s favorite tunes etc. Chatbot has become more popular in business groups right now as they can reduce customer service cost and handles multiple users at a time. But yet to accomplish many tasks there is need to make chatbots as efficient as possible. To address this problem, in this paper we provide the design of a chatbot, which is efficient and timesaving in booking University/Institution resources like auditoriums, seminar halls, projectors etc which is built using Dialogflow, Nodejs, and PostgreSQL. This chatbot can be used by any University/Institution to book resources.
Construction of emotional intelligent service system for the aged based on Internet of things

With the acceleration of global aging, the service of the aged has been widely concerned by the whole society. The traditional service mode has been challenged, and it is difficult to meet the needs of the aged. In order to provide better services for the aged, the construction of emotional intelligent service based on Internet of things is proposed in this article. First, the framework is introduced, and the architecture of the whole system is given. Second, the key technologies of emotional intelligence and Internet of things are described, which mainly include affective computing and data collection of Internet of things. Third, the implementation of the software and hardware is given, and the core functional modules are analyzed. As a whole, there are two main contributions of this article. One is to give the framework of emotional intelligence service system for the aged based on Internet of things, and the other is to analyze the emotional state and the physical data of the aged so that the early-warning and intelligent services can be provided for them.
ECKO: Ensemble of Clustered Knockoffs for Robust Multivariate Inference on fMRI Data

Continuous improvement in medical imaging techniques allows the acquisition of higher-resolution images. When these are used in a predictive setting, a greater number of explanatory variables are potentially related to the dependent variable (the response). Meanwhile, the number of acquisitions per experiment remains limited. In such high dimension/small sample size setting, it is desirable to find the explanatory variables that are truly related to the response while controlling the rate of false discoveries. To achieve this goal, novel multivariate inference procedures, such as knockoff inference, have been proposed recently. However, they require the feature covariance to be well-defined, which is impossible in high-dimensional settings. In this paper, we propose a new algorithm, called Ensemble of Clustered Knockoffs, that allows to select explanatory variables while controlling the false discovery rate (FDR), up to a prescribed spatial tolerance. The core idea is that knockoff-based inference can be applied on groups (clusters) of voxels, which drastically reduces the problem’s dimension; an ensembling step then removes the dependence on a fixed clustering and stabilizes the results. We benchmark this algorithm and other FDR-controlling methods on brain imaging datasets and observe empirical gains in sensitivity, while the false discovery rate is controlled at the nominal level.
Application of Eye Tracking in Intelligent User Interface

Aiming at the requirement of intelligent user interface state awareness and intention prediction, a method of interactive state classification and intention prediction based on eye tracking is proposed. 5 states of interaction are defined, which are monitoring state, tracking state, decision state, burst state and off-loop state, design induced experiments were conducted to collect eye movement data in each state, a single factor analysis of variance shows that the 7 eye movements in the 5 interactive states has a significant difference. The prediction accuracy of the SVM classification model under category 5 conditions is 77.2%, while the accuracy rate of the classification under category 4 conditions is 85.9%, individual differences also have important effects on prediction accuracy, the accuracy rate of single subjects under category 5 conditions are more than 84%, while under category 4 conditions up to 90%. The research results are of reference value to the design and application of intelligent user interface.
Highway Visual Tracking System using Thresholding and Hough Transform

A highway visual tracking system using UAV based digital image.This highway tracking system created by using computer vision.The methods used in this system are the RGB -HSV conversion, colour detection, edge detection, thresholding, dilation canny edge detection, Hough transform, and moments.Prior to image processing, capture video of the highway.In image processing, highways video is prepared to enter image preprocessing, then administer the thresholding and dilation process.This was followed by the method of moments to get the coordinates of the first phase tracking.Next, the X-coordinate is used as a reference to determine the region of interest (ROI).ROI is processed using the Canny edge detection followed hough transform to refine the detected lines into the desired line.Coordinates x line average can be computed which then shows the position of the highway.The programs used in the process of road tracking are using OpenCV 2.3.1 and Visual Studio 2010.The programming language used is C++.Tests carried out using video of highway taken directly from Youtube.The highway HSV colour values are in the range of 20, 3, 30-180, 32.169 and the environment around the highway consists of green vegetation, terrain, office buildings, houses, and trade stalls.The materials that affect the outcome of visual tracking are the presence of a vehicle on the highway and other objects that exist around the highway.
Introduction to forensic voice comparison

This chapter provides a brief introduction to forensic voice comparison. It describes different approaches that have been used to extract information from voice recordings: auditory, spectrographic, acoustic-phonetic, and automatic approaches. It also describes different frameworks that have been used to draw inferences from such information: likelihood-ratio, posterior-probability, identification/exclusion/inconclusive, and the UK framework. In addition, the chapter describes empirical validation of forensic voice comparison systems and briefly discusses legal admissibility.
Careful-Packing

Ensuring the correct behaviour of an application is a critical security issue. One of the most popular ways to modify the intended behaviour of a program is to tamper its binary. Several solutions have been proposed to solve this problem, including trusted computing and anti-tampering techniques. Both can substantially increase security, and yet both have limitations. In this work, we propose an approach which combines trusted computing technologies and anti-tampering techniques, and that synergistically overcomes some of their inherent limitations. In our approach critical software regions are protected by leveraging on trusted computing technologies and cryptographic packing, without introducing additional software layers. To illustrate our approach we implemented a secure monitor which collects user activities, such as keyboard and mouse events for insider attack detection. We show how our solution provides a strong anti-tampering guarantee with a low overhead: around 10 lines of code added to the entire application, an average execution time overhead of 5.7% and only 300KB of memory allocated for the trusted module.
Time-Varying IIR Notch Filter with Reduced Transient Response Based on the Bézier Curve Pole Radius Variability

In this paper a concept of the second order digital infinite impulse response narrow band-reject filter with reduced transient response is proposed. In order to suppress the transient response of the considered infinite impulse response (IIR) notch filter its pole radius is temporarily varied in time using the Bézier parametric curve. Computer simulations verifying the effectiveness of the proposed pole-radius-varying notch filter are presented and compared to the performance of the traditional time-invariant filter using ECG signals distorted by unwanted powerline interference.
Noisy Accelerated Power Method for Eigenproblems With Applications

This paper introduces an efficient algorithm for finding the dominant generalized eigenvectors of a pair of symmetric matrices. Combining tools from approximation theory and convex optimization, we develop a simple scalable algorithm with strong theoretical performance guarantees. More precisely, the algorithm retains the simplicity of the well-known power method but enjoys the asymptotic iteration complexity of the powerful Lanczos method. Unlike these classic techniques, our algorithm is designed to decompose the overall problem into a series of subproblems that only need to be solved approximately. The combination of good initializations, fast iterative solvers, and appropriate error control in solving the subproblems lead to a linear running time in the input sizes compared to the superlinear time for the traditional methods. The improved running time immediately offers acceleration for several applications. As an example, we demonstrate how the proposed algorithm can be used to accelerate canonical correlation analysis, which is a fundamental statistical tool for learning of a low-dimensional representation of high-dimensional objects. Numerical experiments on real-world datasets confirm that our approach yields significant improvements over the current state of the art.
A dataset of attributes from papers of a machine learning conference

In this work, we present a dataset which provides information on the scientific program of a set conferences of Machine Learning. Data were extracted from the IEEE Xplore Digital Library and the official web site of the International Conference on Machine Learning Applications (ICMLA). We include data of four different editions (from 2014 to 2017). Web scrapping techniques were used to mine the data contained in these web sites. The dataset covers 448 papers presented in the conference and every paper contains 6 attributes including information about the thematic session in which they were presented in the conference. The dataset is hosted in the Mendeley Dataset Repository.
Appointment of Directors and Performance of Deposit Money Banks in Sub Saharan Africa: Do We Need More Executive or Non-Executive Directors?

This study mainly examined the effect of Corporate governance on the performance of deposit money banks in Sub Saharan Africa (SSA). Specifically, the effect of appointment of more non- executive directors-BNEDDUM on return on assets (ROA) and net interest margin (NIM) of the banks in SSA was determined. Conversely, the work examined the effect of appointment of more executive directors on the ROA and NIM of the banks. Secondary data on six SSA countries and twelve banks collected for the period 2004 to 2016 were used. Panel data regression approach was employed to analyze the data. Fixed effects and Random effects models were adopted based on the results of Hausman tests. The study revealed among others that appointment of more number of non-executive directors has a positive but insignificant effect on ROA. It also indicated a strong positive correlation with both ROA and NIM. A hypothetical appointment of more executive directors showed a positive and significant effect on NIM while indicating the negative and insignificant effect on ROA. The positive effect of BNEDDUM on ROA coupled with its strong positive correlation with ROA and NIM seem to strongly suggest that appointment of more non-executive directors in deposit money banks in SSA is more beneficial to the banks than the appointment of more executive directors. However, the global conflict in the findings associated with appointment of directors is not yet fully resolved. The study recommends that while more non-executive directors may be appointed, banks in SSA must put in place internal control systems which promote a culture of professionalism in management.
A Framework for Adaptive Personalized E-learning Recommender Systems

With the undergoing technological revolution in education, adapting recommender systems to the personalized elearning is an emerging topic in the education sector.Detecting the student model offers a potential to recommend a learning material that is adequate to the student progress.Accordingly, the learning objects and hypermedia can be adapted to each individual student to meet the personalized learning needs.This paper proposes a framework for applying recommender systems in personalized e-learning domain.Furthermore, the recommender system previous examples, opportunities, and associated challenges are discussed.
Sentiments Analysis for Governor of East Java 2018 in Twitter

The East Java Governor Election which will be held in 2018 is also felt in the virtual world especially Twitter social media. All people freely argue about their respective governor candidates, the memorandum raises many opinions, not only positive or neutral but also negative opinions. Media growth is so rapid, revealing a lot of online media from the news media to social media. Social media alone is Facebook, Twitter, Path, Instagram, Google+, Tumblr, Linkedin and many more. Today's social media is not only used as a means of friendship or making friends, but also for other activities. Promos of trading or buying and selling, until political party promos or campaigns of candidates for regents, governors, legislative candidates until presidential candidates. The research objective is to conduct a method of analyzing the sentiments of 2018 East Java Governor candidates on Twitter social media with optimal and maximum optimization. While the benefits are to help the community conduct research on opinions on twitter which contains positive, neutral or negative sentiments. Analysis of the sentiments of East Java Governor candidates in 2018 on twitter social media using non-conventional processes that save costs, time and effort. The results of Khofifah's dataset are 77% accuracy, 79.2% precision value, 77% recall value, 98.6% TP rate and 22.2% TN rate. For the results of Gus dataset, the accuracy is 76%, the precision value is 74.4%, the recall value is 76%, the TP rate is 93.8% and the TN rate is 52.9%.
A Classroom Atmosphere Management System for Analyzing Human Behaviors in Class Activities

Deep learning can considerably improve the processing of human behavior signals. This paper presents a system that used computer vision processing of human behavior signals to analyze the behaviors of students in class activities. CIassFu system can be used in physical and online teaching environments. It used image sensors to capture student class behaviors, analyzing classroom atmosphere and the level of teacher-student interaction. The system presents objective data on a teaching setting, which can be used to assess students' learning conditions more effectively and enhance teacher-student interactions.
Asynchronous Multisonar Data Integration Approach Used to Detect Sparse Targets

Due to the advantage of the distributed multisensor detection system which makes sonar detect further and more accurate estimation of the target state, distributed multisensor data fusion algorithms are widely applied to the sonar detection system. However, on the one hand, the most asynchronous algorithms focus on how to convert asynchronous data fusion into synchronous data fusion. On the other hand, sonar detection system suffers from more serious asynchronous data problems (such as more serious random delay and packet loss defaults) than radar and other fields. Therefore, the traditional asynchronous fusion method has some limitations. When the targets are sparse, this paper proposed a novel asynchronous multisonar data integration approach, in which the Gaussian mixture probability hypothesis density (GMPHD) filter is used to filter clutter for local sonar sensor. Then, the Gaussian mixture model (GMM) algorithm is used to model asynchronous data over a period of time. Finally, all local sonar detection data are integrated into a surveillance region image to help to detect the target. Several simulation tests and a sea test are presented in this paper to test this approach performance.