Reviewerly/exHarmony huggingface dataset:
Task Scheduling In Cloud Environment Using Enhanced Resource Monitor And Scheduler Algorithm

Cloud Computing is a concept for virtualized resources. It is a technology which delivers plenty of services to service users through internet. It takes users request as input and delivers the service in good manner. Cloud computing manages the numerous variety of virtualized resources and user request which makes scheduling an important one in order to manage it effectively. For every task thousands of virtualized assets are used. Task scheduling in cloud computing is the process of organizing the user task and allocating it with the suitable and available resources so that the available resources will be properly used and scheduling will be done effectively. There are numerous task scheduling techniques. This paper presents an alternative approach to task scheduling problem. It mainly focused on the time utilization and resource monitoring. The proposed method is Enhanced Resource Monitor and Scheduler (ERMS) which focus on both monitoring of available resources and scheduling of user task.Periodic push action method is used to get the status of virtual machine. The fitness value is calculated and scheduling is done. The process is implemented using ClouldSim 3.0 Simulator in NetBeans IDE.
Secure Voting System Using Distributed Ledger Technology

Blockchain and distributed ledger technologies have increasingly been gaining interest over the last few years. The promise of a completely secure environment for data to be stored has caught the attention of everyone, including individuals and institutions, who, in their turn, have started to invest generously in related research and development of such systems, especially those concerned with finance and crypto-currency. In this research, we focus on the applications beyond these topics, aiming to solve the problem of tampering with vote counting in elections around the world. By designing and constructing a system of a distributed ledger with a permissioned consensus algorithm, we are composing a tamper-proof method to provide a secure environment for voting.
An Object Surveillance Algorithm Based on Batch-Normalized CNN and Data Augmentation in Smart Home

Smart home technology powered by cutting-edge intelligent algorithms creates a secure and comfortable living environment for electricity end users. In this paper, we propose an object surveillance algorithm and apply it to smart home environment. A combination usage of batch-normalized convolutional neural network (CNN) and data augmentation technique has been introduced for accomplishing object surveillance task by overseeing an object for long duration. Three aspects are considered including algorithm convergence, model creation, and dataset preparation. Firstly, mathematical demonstrations are offered to study the vanishing gradient phenomenon, and batch normalization is proposed for resolving this issue. Secondly, a revised version of AlexNet with batch normalization layer added after each convolution layer significantly boosts training speed and retains test set accuracy. Thirdly, only one picture taken on-premise is required for dataset preparation, and satisfactory predictive results are obtained.
Companion Cognitive Systems: Design Goals and Some Lessons Learned

The companion cognitive architecture supports experiments in achieving human-level intelligence. This article describes seven key design goals of companions, relating them to properties of human reasoning and learning, and to engineering concerns raised by attempting to build large-scale cognitive systems. We summarize our experiences with companions in two domains: test taking and game playing.
Calorie Consumer by using Barcode

People are facing difficulties in getting obsess. A healthy lifestyle can be achieved by maintaining calorie intake for which ones has to track his daily calorie requirement firstly and then work on maintenance. The issue has become difficult as it requires great time effort for calculations. To resolve the problem an application is being made to work on these calculations and help in tracking daily calorie requirement. This application works on BMR (basal Metabolic Rate) of end user and suggest daily calorie requirement. As the end user scans barcode of the product he wants to consume, the calories of the consumed product recorded his profile and system further suggest required calories on the basis of usage.
From Explicit to Implicit Entity Linking: A Learn to Rank Framework

Implicit entity linking is the task of identifying an appropriate entity whose surface form is not explicitly mentioned in the text. Unlike explicit entity linking where an entity is linked to an observed phrase within the input text, implicit entity linking is concerned with determining specific yet implied entities. Existing work in the literature have already identified appropriate features that can be used for ranking relevant entities for explicit entity linking. In this paper, we (1) consider the applicability of such features for implicit entity linking, (2) introduce features that are suited for this task, (3) compare our work with the state of the art in implicit entity linking, and (4) and report on feature importance values and present their interpretations.
Random Feature Based Attribute-weighed Kernel Fuzzy Clustering for Non-linear Data

Traditional kernel clustering methods are useful in dealing with non-linear data, but the high-dimensional kernel space obtained by kernel mapping is an abstract concept, which is difficult to be determined. The kernel mapping between raw data space and kernel space needs high computational complexity which is burdensome for hardware. At the same time, due to the unknown nature of kernel space, traditional kernel clustering methods cannot process data with the consideration of different importance among dimensions, i.e., discover the hidden feature subset of high-dimensional sparse data. To overcome these limitations, we put forward a novel random Fourier feature based attribute-weighed kernel fuzzy c-means clustering algorithm (RFF-WKFCM). This method employs RFF map to generate low-rank random features, and performs fuzzy c-means clustering with attribute weight entropy regularization in this feature space, which greatly reduces the computational complexity. What is more, the adoption of the maximum entropy technique ensures the optimal distribution of attribute weights, which stimulate important dimensions play a greater role in the clustering process. The proposed method shows good performance on the experiments of ring data set compared with other fuzzy clutering methods.
Multi-objective Optimal Control for Proactive Decision Making with Temporal Logic Models

The operation of today’s robots entails interactions with humans, in settings ranging from autonomous driving amidst human-driven vehicles to collaborative manufacturing. To effectively do so, robots must proactively decode the intent or plan of humans and concurrently leverage such a knowledge for safe, cooperative task satisfaction—a problem we refer to as proactive decision making. However, the problem of proactive intent decoding coupled with robotic control is computationally intractable as a robot must reason over several possible human behavioral models and resulting high-dimensional state trajectories. In this paper, we address the proactive decision making problem using a novel combination of algorithmic and data mining techniques. First, we distill high-dimensional state trajectories of human-robot interaction into concise, symbolic behavioral summaries that can be learned from data. Second, we leverage formal methods to model high-level agent goals, safe interaction, and information-seeking behavior with temporal logic formulae. Finally, we design a novel decision-making scheme that simply maintains a belief distribution over high-level, symbolic models of human behavior, and proactively plans informative control actions. Leveraging a rich dataset of real human driving data in crowded merging scenarios, we generate temporal logic models and use them to synthesize control strategies using tree-based value iteration and reinforcement learning (RL). Results from cooperative and adversarial simulated self-driving car scenarios demonstrate that our data-driven control strategies enable safe interaction, correct model identification, and significant dimensionality reduction.
Hardware Implementation of Parallelized Fuzzy Adaptive Resonance Theory Neural Network

A hardware implementation design of parallelized fuzzy Adaptive Resonance Theory neural network is described and simulated. Parallel category choice and resonance are implemented in the network. Continuous-time and discrete-time winner-take-all neural circuits identifying the largest of M inputs are used as the winner-take-all units. The continuous-time circuit is described by a state equation with a discontinuous right-hand side. The discrete-time counterpart is governed by a difference equation. Corresponding functional block-diagrams of the circuits include M feed-forward hard- limiting neurons and one feedback neuron, which is used to compute the dynamic shift of inputs. The circuits combine arbitrary finite resolution of inputs, high convergence speed to the winner-take-all operation, low computational and hardware implementation complexity, and independence of initial conditions. The circuits are also used for finding elements of input vector with minimal/maximal values to normalize them in the range [0,1].
СУЧАСНІ МОДЕЛІ ТА ФОРМИ ВИКОРИСТАННЯ ІНФОРМАЦІЙНО-КОМП’ЮТЕРНИХ ТЕХНОЛОГІЙ В ОСВІТІ

&#x0D; &#x0D; &#x0D; &#x0D; The paper systematizes and describes the leading world approaches to the use of information and computer technologies in educational institutions in the process of modernization of current educational systems in accordance with the latest requirements of the information society. It is noted that the information society gives the humanity a fundamentally new tool of life that is characterized by the possibility of operating large volumes of information without territorial and time boundaries and displays information-knowledge at the level of the leading producer of public goods. All this leads to a review of the key functions of modern education, when the possession of information and computer technology becomes the main competence requirement for the pupil, student, and every person at each stage of his/her life.The authors substantiate the necessity of creating a system of "total computer literacy" and total informatization of the national education system, wide introduction in modern educational institutions of modern models and forms of use of information and computer technologies, introduction of the national education system to the global educational space at the institutional, environmental and personal levels. It is concluded that today our state faces a difficult task of ensuring the conformity of the basic approaches, principles and instruments of the educational process to the requirements of the transition to the era of the information society in regard to the modernization of the system of national education. We have to invest in the development of domestic innovative ICT products ahead of schedule. Technology and informatization of the educational process must become priority tasks of education reforms from pre-school education institutions to higher education and research institutions, in order not to stay away from the advancement towards the global information society.&#x0D; &#x0D; &#x0D; &#x0D;
АКЦЕНТУАЦІЇ ХАРАКТЕРУ УЧНІВ ТА СТУДЕНТІВ СУЧАСНИХ НАВЧАЛЬНИХ ЗАКЛАДІВ РІЗНИХ ТИПІВ У КОНТЕКСТІ СТРЕСОСТІЙКОСТІ

This work shows relevance of research into stress resistance of students at modern educational establishments. It describes methods of studying personality accentuations by G. Shmishek, examines peculiarities of stress resistance, accentuations of character and the way they affect young people. It was established that the highest values of the indicators are found in boys and girls hyperthymic type, and the lowest ones are among both the young males and females of the dysthymic type. The gender and age diversities were observed in the following accentuations: cycloid type, demonstrative type, affective-exalted, anxious, emotive, excitable. Moreover, the values of the indicators were in all groups higher for girls than for boys. The work demonstrates that each person has a certain set of features which under certain conditions can be accentuated and determine significantly the human reaction. These qualities are especially vivid in adolescence due to the imperfection and vulnerability of the nervous system, the formation of character, the change of values, selectivity and vulnerability in relation to the environment, parents and friends. Therefore, the development of ways to correct accentuated character traits among students of modern educational institutions of different types is becoming relevant.
Efficient Morphing of Shape-preserving Star Coordinates

Data tours follow an exploratory multi-dimensional data visualization concept that provides animations of projections of the multidimensional data to a 2D visual space. To create an animation, a sequence of key projections is provided and morphings between each pair of consecutive key projections are computed, which then can be stitched together to form the data tour. The morphings should be smooth so that a user can easily follow the transformations, and their computations shall be fast to allow for their integration into an interactive visual exploration process. Moreover, if the key projections are chosen to satisfy additional conditions, it is desirable that these conditions are maintained during morphing. Shape preservation is such a desirable condition, as it avoids shape distortions that may otherwise be caused by a projection. We develop a novel efficient morphing algorithms for computing shape-preserving data tours, i.e., data tours constructed for a sequence of shape-preserving linear projections. We propose a stepping strategy for the morphing to avoid discontinuities in the evolution of the projections, where we represent the linear projections using a star-coordinates system. Our algorithms are less computationally involved, produce smoother morphings, and require less user-defined parameter settings than existing state-of-the-art approaches.
SAFEGUARD IDENTIFICATION-Safety as an Essential Aspect

As technology has brought a drastic change in the lives of human and adaptions to these technologies has risen to the peak, as we all are familiar with the concept of object detection which has brought an extraordinary ease in many of the fields of computer as well as in industry. Many of the researches are being done on it and still in process but it is not implemented for the purpose of industrial safety. Therefore, being embolden by the idea of object detection we have introduced our idea comparable to it known as `Safeguard Identification', a model designed for safety purpose of the employees working in an industry where safety is a fundamental approach. Our model comprises of a camera which is trained for image classification and object detection from a Custom model using Tensorflow Object Detection API. For our project we have used MySql database for maintaining the data of workers. Future upgrades may lead this project by generating safety alerts on the phones of the individuals or by adding more features like detecting workers smoking in a prohibited area.
The generalized Pitman measure of similarity and hierarchical clustering

Pitman measure of closeness (PMC) is a criterion to show how much an estimator is close to its parameter with respect to another estimator. We develop a new similarity measure which uses PMC on the basis of the similarity measure for hierarchical clustering algorithm that has better performance for heavy-tailed data.
A browserless architecture for extracting web prices

There are many businesses interested in following prices in different web sites which use time-consuming web scrappers embedded in a browser to render web pages and to simulate user actions. The recent appearence of browserless wrappers provides a more efficient solution because they access a web server through HTTP requests and take the desired data directly from the raw replies. However, creating and mantaining browserless wrappers of high precision is very labor intensive. In this paper, we propose a browserless architecture which uses easy to specify rules for extracting the correct price of the entities of detail pages. This architecture has given exceptional results in terms of precision and specificity from a dataset obtained from 735 of the most prominent e-commerce websites.
An analysis of speaker dependent models in replay detection

Most research on replay detection has focused on developing a stand-alone countermeasure that runs independently of a speaker verification system by training a single spoofed model and a single genuine model for all speakers. In this paper, we explore the potential benefits of adapting the back-end of a spoofing detection system towards the claimed target speaker. Specifically, we characterize and quantify speaker variability by comparing speaker-dependent and speaker-independent (SI) models of feature distributions for both genuine and spoofed speech. Following this, we develop an approach for implementing speaker-dependent spoofing detection using a Gaussian mixture model (GMM) back-end, where both the genuine and spoofed models are adapted to the claimed speaker. Finally, we also develop and evaluate a speaker-specific neural network-based spoofing detection system in addition to the GMM based back-end. Evaluations of the proposed approaches on replay corpora BTAS2016 and ASVspoof2017 v2.0 reveal that the proposed speaker-dependent spoofing detection outperforms equivalent SI replay detection baselines on both datasets. Our experimental results show that the use of speaker-specific genuine models leads to a significant improvement (around 4% in terms of equal error rate (EER)) as previously shown and the addition of speaker-specific spoofed models adds a small improvement on top (less than 1% in terms of EER).
An optimization approach for transportation problem of transplant organ using self-adaptive bear hibernation algorithm

In this paper, we propose a new algorithm for transportation problem considering the multiple organ transplant scenario which is based on the concept of hibernation in bears. Hibernation is a state of inactivity based on localized adaptation instead of migrating out. Organ transplant is a major achievement of modern medicine,and it is helping in savingmany lives every day. In our proposed work, we consider a donor bank where all the required organs are brought for their transportation to the different organ transplant patients around the city and nearby areas. Also, each organ has its own preservation time before which the organ must be transplanted. Inspired from hibernation in bears, the proposed algorithm optimizes the time duration, and the priority of the organs to be transplanted. Here, we migrate or hibernate the organs according to the factors that affect the scenario the most. The factors include time constraints of the organs, risk of navigation in the wrong direction, etc. It has been tested on some benchmark functions and compared with other migration algorithms like AMO. The proposed algorithm removes the limitations of the optimization algorithms based on migrating models.
Assessing the Determinants of Millennials’ Online Protective Behavior: How Their Protection Motivation Translates into Actual Use Behavior

This research assesses the determinants of Millennials protection motivation (or security intentions) on their actual use behavior when navigating online, considering the protective measures they adopt. Accordingly, we propose a model integrating variables from two widely accepted behavioral theories, the Protection Motivation Theory and the Reasoned Action Approach. An online survey was conducted, relying on the responses of 236 participants from different nationalities, which were analyzed through hierarchical multiple regression. Results show a gap between security intentions and use behavior and indicate safety habit strength and actual control as significant predictors of Millennials’ use behavior. By focusing not only on the users’ behavioral intentions but also on their actual use behavior, this research seeks to extend the previous literature on the topic.
Arts high school of the Korean traditional music major Curriculum comparison analysis and efficient operation plan

전국에 국악 교육을 하는 예술계 고등학교는 국악을 전문으로 교육하는 학교와 예술고등학교 안에 국악 전공을 선발하는 학교가 있어 다양한 형태로 운영되고 있다. 이에 따라 본연구는 예술계 고등학교의 국악 전공 전문교육과정을 분석하고 비교하여 이에 대한 교육과정의 차이점을 밝히고, 그 결과를 토대로 효율적인 운영방안을 모색 하는데 목적이 있다. 논의 결과를 정리하면 다음과 같다. 첫째, 예술계 고등학교는 전문예술인을 양성하기 위한 목표에 걸맞게 실기의 수업이 체계적으로 운영되어야 한다. 둘째, 실기는 음악적 지식을 바탕으로 길러지는 만큼 음악이론, 음악사, 시창·청음 등 기초 교과에 비중을 두어 실기와 기초 교과를 균형 있게 편성하여야 한다. 셋째, 예술계 고등학교는 학교의 특성이 드러나는 다양한 교과를 편성하여 교육해야 한다. 넷째, 예술고등학교에서는 국악 전공자의 정원과 상관없이 국악을 전공한 전임 교사가 확보되어야 한다. 이에 대한 결과는 국악 전공을 위한 예술계 고등학교의 교육과정을 개선하기 위한 기초적인 자료를 제공할 수 있을 것이다. 학교의 교육과정에 대해 옳고 그름을 판단하는 것보다는 학교 운영 별로 교육과정의 문제점을 토대로 서로 교육되어야 할 내용과 음악가로 성장하고 배워나갈 수 있는 교육과정을 조정하고 정리하여 서로 상생해 나가 지역별 균등한 교육이 이루어지기를 기대해본다.The arts high school, which teaches Korean traditional music, is operated in various forms because there are schools that specialize in Korean traditional music and schools that select Korean traditional music majors in art high schools. Accordingly, the purpose of this study is to analyze and compare the professional curriculum of art high school for Korean traditional music major, to find out the difference of curriculum for this, and to find an efficient operation plan based on the educational problems of art high school. The results of the discussion are as follows. First, practical skills classes should be systematically strengthened to meet the goal of cultivating professional artists in art high schools. Second, since practical skills are cultivated on the basis of musical knowledge, the practical skills and basic subjects should be organized in a balanced manner with emphasis on basic subjects such as music theory, music history, poetry and listening. Third, art high schools must organize and educate various subjects that reveal the characteristics of the school. Fourth, a full-time teacher who majored in Korean traditional music should be secured in art high school regardless of the number of Korean traditional music majors. The results of this study can provide basic data for improving the curriculum of art high school for Korean traditional music major. High school is an important time to develop musical qualities and basic skills. Therefore, rather than judging the right or wrong of the curriculum of the school, based on the problems of the curriculum, each school s operation should be coordinated with each other and the curriculum that grows and learns as a musician. I look forward to the education.
Narrative Inquiry of Teacher's Experiences in Using Multimedia in Vocational High School

In recent years, the implementation of using multimedia in English language learning in vocational high school is consistenly limited. Therefore, in Indonesia vocational high school the English teachers still use traditional media such as chalk, blackboard, and printed media as the main sources of learning. Meanwhile, in the 21th-century challenges and demands in education are increasingly higher, so teachers are required to be more up to date. Multimedia has many advantages in English language learning such as giving more information, encouraging students' imagination and creativity, and so on, thus the multimedia need to be utilize with particular mechanism. The purpose of this research is to describe the implementation of using multimedia in English language learning, in accordance with giving and asking opinion expression material at Vocational High School.