Reviewerly/exHarmony huggingface dataset:
Issues Of Establishing Systemic Communications With Alumni (On The Materials Of Spbstu)

Communities of University Graduates have a special position in the communication system of Universities. These communities connect education, science and society, and they play key communication role for generations, knowledge and practice. Also they demonstrate the competitiveness of the University and affect the brand characteristics. The key aspects of interaction between Peter the Great St. Petersburg Polytechnic University and its graduates were detected as results of research. The features of interaction between graduates and the University, ways of possible communication and key value, identification and practice-oriented interests of respondents in relation to the University were revealed in the opinion poll. The results show us that it is important to find new forms of communication, which include professional connections with university graduates and let alumni toparticipate in university’s everyday life as specialists, as people, who really represent the professional community. Also alumni are interested both in commercial and uncommercial communications with university. As well we may say that the key factor of alumni’s interest in alma mater is communication with new generation of student as new generation of specialists. Alumni prefer roles in communication, which let them play expert role, to be employer of graduates or to take care about practice of students Analysis of results became the basis of University contemporary corporate culture creation and established the other forms of interaction in graduate’s community, including the Endowment Fund.
Analisis dan Evaluasi Penerapan Sistem Informasi Smart Library AMIKOM Resource Centre dengan Metode Pieces Framework

Technological developments are currently running so fast along with the times. The information system is one of the technological developments. With the existence of information system of course very give big influence to institute or institution good of profit and non profit like library. Of course, the system used has advantages and disadvantages that need to be done a renewal so that the system can work effectively and efficiently. For that we need an analysis of the level of effectiveness and efficiency of a system. One of the methods that can be used in analyzing the system is PIECES Fremwork where there are 6 related indicators about performance, information, control / scuritu, efficiency, service. This study uses quantitative descriptive approach, with data collection techniques using questionnaires and observations. The results of this study in the form of statements about the information system in carefully. With PICES method is expected to be able to provide an overview of the shortcomings of the system used so that later the system can be improved and developed towards a better direction again.
Enhanced Object Representation on Moving Objects Classification

A feature representation approach is proposed for discriminative features extraction and this object representation tend to handle the large amount of local features in feature correspondence. Object representation with shape and color feature tends to certify the strength of proposed feature extraction method. In the proposed method, HOG are extracted on 300 corner points which are the strongest points on detected corners and these points are supposed as in one block to get the HOG vector. As a second portion of feature extraction, the moments on HSI are extracted on each separated channel. The proposed feature extraction method is tested intensively on the different sequences of the Online Benchmark Tracking dataset, CAVIAR Test Case Scenarios and Change Detection dataset (CDnet 2014) with the comparison of other related feature extraction methods. Classification of proposed approach receives 98.1%, 93.8%, 96.8%, 97.7% and 90.5% for walking, crossing, walk1, pedestrians and twopositionPTZCam respectively.
Empirical Study of Easy and Hard Examples in CNN Training

Deep Neural Networks (DNNs) generalize well despite their massive size and capability of memorizing all examples. There is a hypothesis that DNNs start learning from simple patterns and the hypothesis is based on the existence of examples that are consistently well-classified at the early training stage (i.e., easy examples) and examples misclassified (i.e., hard examples). Easy examples are the evidence that DNNs start learning from specific patterns and there is a consistent learning process. It is important to know how DNNs learn patterns and obtain generalization ability, however, properties of easy and hard examples are not thoroughly investigated (e.g., contributions to generalization and visual appearances). In this work, we study the similarities of easy and hard examples respectively for different Convolutional Neural Network (CNN) architectures, assessing how those examples contribute to generalization. Our results show that easy examples are visually similar to each other and hard examples are visually diverse, and both examples are largely shared across different CNN architectures. Moreover, while hard examples tend to contribute more to generalization than easy examples, removing a large number of easy examples leads to poor generalization. By analyzing those results, we hypothesize that biases in a dataset and Stochastic Gradient Descent (SGD) are the reasons why CNNs have consistent easy and hard examples. Furthermore, we show that large scale classification datasets can be efficiently compressed by using easiness proposed in this work.
Sistema web para la evaluación 360° de docentes de nivel superior de la Universidad Autónoma de Nayarit, México

The following article presents a proposal for evaluating professors based on the 360 degrees’ methodology using an application for data handling. The bases of this integral evaluation process are indicated. There is research that said bases have been previously applied to the same work group – to the Tronco Básico Universitario (TBU) in the Autonomous University of Nayarit. The initiative to generate an automated system of data processing for the evaluated area of Ciencias Económico-Administrativas is also presented. A PHP web-based application interacting with a MySQL database engine was developed as a result of the research. Access to information is controlled by the system and delivers immediate evaluation results feedback to the professors.
Klasifikasi Anjing dan Kucing menggunakan Algoritma Linear Discriminant Analysis dan Support Vector Machine

One of the factors driving technological development is the increase in computers ability to complete various jobs. One of them is doing image processing, which is widely used in our daily life, such as the use of fingerprints, face/iris recognition barcodes, medical needs, and various other uses. Classification is one of the applications of image processing that is used the most. One algorithm that can be used for the development of image classification systems is Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM). LDA is a feature extraction algorithm to find a subspace that separates classes well. SVM is a classification algorithm, based on the idea of finding a hyperplane that best divides a dataset into classes. In this study, LDA and SVM algorithms were tested on the dog and cat classification system, with the highest F- score calculation results being 0.69 with 200 training data and 50 testing data for cats and 0.64 with 200 training data and 30 testing data for dogs.
Association Metrics in Neural Transition-Based Dependency Parsing

Lexical preferences encoded as association metrics have been shown to improve performance on structural ambiguities that are still challenging for modern parsers.This paper introduces a mechanism to include lexical preferences into a neural transition-based dependency parser for German.We compare pointwise mutual information (PMI) and embedding-based scores.Both the PMI-based model and the embedding-based model outperform the baseline significantly.The best model is PMI-based and increases overall performance by 0.26 LAS points over the baseline.
Investigation of the Determinants and Consequences of Employee Motivation: PLS Approach

This study examines the mediating influence of employee motivation on the relationships among performance appraisal satisfaction, work performance and employee commitment. To ensure this mediating influence the textile sector of Faisalabad was studied. Participants of this research were 450 employees of different organizations of textile sector who were selected via convenient sampling. Structural Equation Modeling (SEM) through PLS were used for data analysis. Results of this study indicate that there is positive and significant relationship among studied variables.
Streaming generalized cross entropy

We propose a new method to combine adaptive processes with a class of entropy estimators for the case of streams of data. Starting from a first estimation obtained from a batch of initial data, model parameters are estimated at each step by combining the prior knowledge with the new observation (or a block of observations). This allows to extend the maximum entropy technique to a dynamical setting, also distinguishing between entropic contributions of the signal and the error. Furthermore, it provides a suitable approximation of standard GME problems when the exacted solutions are hard to evaluate. We test this method by performing numerical simulations at various sample sizes and batch dimensions. Moreover, we extend this analysis exploring intermediate cases between streaming GCE and standard GCE, i.e., considering blocks of observations of different sizes to update the estimates, and incorporating collinearity effects as well. The role of time in the balance between entropic contributions of signal and errors is further explored considering a variation of the Streaming GCE algorithm, namely Weighted Streaming GCE. Finally, we discuss the results: In particular, we highlight the main characteristics of this method, the range of application, and future perspectives.
Sampling for Bayesian Mixture Models: MCMC with Polynomial-Time Mixing

We study the problem of sampling from the power posterior distribution in Bayesian Gaussian mixture models, a robust version of the classical posterior. This power posterior is known to be non-log-concave and multi-modal, which leads to exponential mixing times for some standard MCMC algorithms. We introduce and study the Reflected Metropolis-Hastings Random Walk (RMRW) algorithm for sampling. For symmetric two-component Gaussian mixtures, we prove that its mixing time is bounded as $d^{1.5}(d + \Vert θ_{0} \Vert^2)^{4.5}$ as long as the sample size $n$ is of the order $d (d + \Vert θ_{0} \Vert^2)$. Notably, this result requires no conditions on the separation of the two means. En route to proving this bound, we establish some new results of possible independent interest that allow for combining Poincaré inequalities for conditional and marginal densities.
Adoption of International Financial Reporting Standards (IFRSs) and their Impact on Loan Terms: Kosovo Case

As part of the major international developments on financial reporting and auditing, the International Financial Reporting Standards (IFRS), which are now applied in many countries around the world, are creating an international harmonization and a common language for financial reporting and accounting between firms that apply and report in accordance to these standards. This paper empirically tests whether adoption of IFRS in developing and transitional Kosovo, has impact on bank`s loan terms and conditions for companies that are mandatory adopters of IFRSs. To test the hypothesis, the authors analyzed the opinions of the banking sector and of companies that are mandatory adopters of IFRSs in Kosovo. The research results suggest that the adoption of IFRSs in Kosovo has impact on interest rates offered by banks to mandatory adopters, as well as on non-financial loan terms such as: loan time limits, mortgage requirements, lower administrative costs and renegotiating loan terms. Mandatory IFRS adopters have a better loan rating compared to other companies in Kosovo and the percentage of non-performing loans is lower for mandatory IFRS adopters in Kosovo. Based on this research, the aim is to demonstrate to policy makers and other stakeholders that only the mere implementation of high quality financial reporting standards, such as IFRSs, is not sufficient to improve the quality of accounting and financial reporting in Kosovo, especially since there are no institutional mechanisms to empower the implementation of IFRSs. For companies in Kosovo receiving better loan terms from banks can be an incentive to improve their financial reporting systems, but the focus is also on other benefits that should be considered as well.
The Investigation of LSB based Image Steganographic Techniques in Spatial Domain for Secure Communication

All the conveying bodies need the confidentiality, integrity and authenticity of their secret data. Distinctive methodologies are utilized to adapt these security issues like digital certificate, digital signature and cryptography. But these techniques alone can't be traded off. Steganography is the best answers for these issues as it hide the presence of secret information. Steganography will get its importance in view of its exponential advancement and riddle data of possible PC customers over the web. It can likewise be depicted as the examination of indistinct information that usually manages the methodologies for concealing the closeness of the passed on message. For the most a piece of data covering is refined in correspondence, image, content, voice or sight and sound substance for copyright, military correspondence, confirmation and various diverse purposes. Image steganography strategies have been talked about in this exploration Least Significant Bit (LSB) strategy is watched for different cases. Result for various situations has been looked at and condition of the science assessment and examination of various accessible strategies for steganography have been given. The principles pursued are equivalent to given in the writing. This paper gives a condition of-the – science assessment and examination of the diverse accessible techniques for steganography along the side with some normal models and procedures drawn from the literature. However in this survey concludes with some recommendations and sponsors for the carries-oriented mechanism. Keywords: Steganography, Data concealing, Cover writing
A Circle of Friends

Cardiovascular disease (CVD) is the leading causes of death in the United States and worldwide. While CVD risk factors are well-known and many can be changed with diet and exercise, more research is needed to understand how to design effective interventions that help patients reduce CVD risk. In this paper, we present the results of a content analysis of the Health Freedom Circle of Friends (COF) Walking Program, a community-based health program run by a public health non-profit that has been shown to reduce CVD risks. We examine the design to better understand the persuasive tools used as well as parts of the design that might benefit from a technological intervention.
EVALUATING THE LEARNING, DESIGN AND ENGAGEMENT VALUE OF MOBILE APPLICATIONS: THE MOBILE APP EVALUATION SCALE

EVALUATING THE LEARNING, DESIGN AND ENGAGEMENT VALUE OF MOBILE APPLICATIONS: THE MOBILE APP EVALUATION SCALE
Neural Network-Based Detection of Ultrasonic Targets with Respect to Noise and Number of Sampling Positions

A neural network-based approach for detection of sonar targets in air is presented in this contribution. Our approach may facilitate autonomous mobile systems to reliably detect and classify objects in their surrounding by using sonar information. This task might be extremely important in changing as well as unorganized environments. We perform target iden-tification with long short-term memory networks as classifiers. Such are capable of dealing with variable numbers of echoes from multiple positions per input sequence, which facilitates more flexible operation. The impact of the number of recording positions per sequence and of noise is investigated. Furthermore, we demonstrate the improvement in classification performance in comparison to previously obtained results from multi-layer-perceptrons.
The Use of Fuzzy Logic in Automatic Control Systems

The paper deals with application of fuzzy controllers in automatic control systems.The use of fuzzy logic in automatic control systems (ACS) is specific, associated with essential features of organizational nature, in implementation of structures and operating algorithms.Intellectual systems of industrial automation in their development encounter certain difficulties: unsettled terminology, lack of conventional classification, immature methodology for designing fuzzy automatic control systems.The article formulates the definition of artificial intelligence and intelligent fuzzy ACS, based on the conventional terminology used in the theory of control and artificial intelligence.Classification of structures of automatic control systems is submitted according to the degree of introduction of fuzzy controllers (FC) in automatic control system.Block diagrams and their description are presented.The specific structure of FC, based on classical principles, is explained.It is emphasized that adaptive ACS is implemented using adaptive FC.The method of designing fuzzy controllers for ACS implementation is considered.
Learning Mixtures of Linear Regressions in Subexponential Time via Fourier Moments

We consider the problem of learning a mixture of linear regressions (MLRs). An MLR is specified by $k$ nonnegative mixing weights $p_1, \ldots, p_k$ summing to $1$, and $k$ unknown regressors $w_1,...,w_k\in\mathbb{R}^d$. A sample from the MLR is drawn by sampling $i$ with probability $p_i$, then outputting $(x, y)$ where $y = \langle x, w_i \rangle + \eta$, where $\eta\sim\mathcal{N}(0,\varsigma^2)$ for noise rate $\varsigma$. Mixtures of linear regressions are a popular generative model and have been studied extensively in machine learning and theoretical computer science. However, all previous algorithms for learning the parameters of an MLR require running time and sample complexity scaling exponentially with $k$. In this paper, we give the first algorithm for learning an MLR that runs in time which is sub-exponential in $k$. Specifically, we give an algorithm which runs in time $\widetilde{O}(d)\cdot\exp(\widetilde{O}(\sqrt{k}))$ and outputs the parameters of the MLR to high accuracy, even in the presence of nontrivial regression noise. We demonstrate a new method that we call "Fourier moment descent" which uses univariate density estimation and low-degree moments of the Fourier transform of suitable univariate projections of the MLR to iteratively refine our estimate of the parameters. To the best of our knowledge, these techniques have never been used in the context of high dimensional distribution learning, and may be of independent interest. We also show that our techniques can be used to give a sub-exponential time algorithm for learning mixtures of hyperplanes, a natural hard instance of the subspace clustering problem.
Poisson Multi-Bernoulli Mixtures for Sets of Trajectories

For the standard point target model with Poisson birth process, the Poisson Multi-Bernoulli Mixture (PMBM) is a conjugate multi-target density. The PMBM filter for sets of targets has been shown to have state-of-the-art performance and a structure similar to the Multiple Hypothesis Tracker (MHT). In this paper we consider a recently developed formulation of multiple target tracking as a random finite set (RFS) of trajectories, and present three important and interesting results. First, we show that, for the standard point target model, the PMBM density is conjugate also for sets of trajectories. Second, based on this we develop PMBM trackers (trajectory RFS filters) that efficiently estimate the set of trajectories. Third, we establish that for the standard point target model the multi-trajectory density is PMBM for trajectories in any time window, given measurements in any (possibly non-overlapping) time window. In addition, the PMBM trackers are evaluated in a simulation study, and shown to yield state-of-the-art performance.
The design of speaking materials for hospitality students of Trans Ocean Floating Hotel program based on competency based language teaching approach

The objectives of this study are to (1) describe the designing of speaking materials for hospitality students in Trans Ocean Floating Hotel Program based on Competency-Based Language Teaching. (2) Reveal the efficacy of the speaking teaching materials in improving students communication skills. This research belongs to Educational Research and Development (R&amp;D) using a simplified six steps of Borg and Gall’s model (1983). The six steps are as follows. (1) Research and information collecting was conducted by performing document analysis, class observation, interview, product testing, as well as experts’ judgments, (2) Planning and drafting, (3) Draft revision by experts, (4) Field testing which was conducted in basic class of the Trans Ocean Floating Hotel Program, (5) Revision, and (6) Final product. The technique of collecting data used in this research consists of (1) document analysis, (2) classroom observation and (3) interview. It was made for obtaining the data for the target situation analysis and appropriateness of the design speaking materials for hospitality students. The findings show that (1) learning materials that were designed in this research fulfilled the criteria of the R&amp;D model, which is based on the students’ need lack and want. The purpose of the learning is preparing the students in their future job field as hotel staff and need to learn more about speaking because speaking skill is more useful for working as hotel staff. (2) The efficacy of the speaking materials is that it serves the explicit instruction and encourage the students in doing activities, especially speaking, besides, there are various activities in the textbook. This textbook is designed based on the Trans Ocean Floating Hotel student’s need, lack, and want. Furthermore, the speaking materials as the product of this study provide additional learning resources for students.
Eliminating cross-camera bias for vehicle re-identification

Vehicle re-identification (reID) often requires recognize a target vehicle in large datasets captured from multi-cameras. It plays an important role in the automatic analysis of the increasing urban surveillance videos, which has become a hot topic in recent years. However, the appearance of vehicle images is easily affected by the environment that various illuminations, different backgrounds and viewpoints, which leads to the large bias between different cameras. To address this problem, this paper proposes a cross-camera adaptation framework (CCA), which smooths the bias by exploiting the common space between cameras for all samples. CCA first transfers images from multi-cameras into one camera to reduce the impact of the illumination and resolution, which generates the samples with the similar distribution. Then, to eliminate the influence of background and focus on the valuable parts, we propose an attention alignment network (AANet) to learn powerful features for vehicle reID. Specially, in AANet, the spatial transfer network with attention module is introduced to locate a series of the most discriminative regions with high-attention weights and suppress the background. Moreover, comprehensive experimental results have demonstrated that our proposed CCA can achieve excellent performances on benchmark datasets VehicleID and VeRi-776.
Predicting utilization of healthcare services from individual disease trajectories using RNNs with multi-headed attention

Machine Learning for Health (ML4H) at NeurIPS 2019. - An accepted paper to be included in the 2019 ML4H Proceedings to be published in PMLR (Proceedings of Machine Learning Research).