Reviewerly/exHarmony huggingface dataset:
AWARENESS OF ENERGY CONSUMPTION IN CLOUD DATA CENTERS: A SURVEY ON EMPLOYEE ENGAGEMENT IN ENERGY MANAGEMENT

The processing demand for high-performance application has increased adoption of Cloud computing among organizations. Such increment in Cloud usages has made Cloud data center incur high energy consumption due to high processing capacity that needed for longer time. It leads to several affect such as emission of heat, high carbon dioxide released and elevation of energy cost. Therefore, it is important to ensure the Cloud data centers are really have a good energy management policies and rules in the organizations. In respect to that, the organization needs to prepare their employees for realization of efficient energy consumption. In this work, a survey is conducted in the form of questionnaires that distributed to employees who have responsible for operating the Cloud data center. This survey aims to analyze level of awareness and satisfaction of energy management scopes such as technique, policy, auditing, and training that managed in the organization. It covers two main factors are the employeeÂs roles and years of experience. The result shows that there is a significant increase of awareness in efficient energy management for operating the data center. Meanwhile, there is diverse feedback in satisfaction level of employees towards energy policy and training provided in the organization. Most of the respondents with higher roles and long experiences shown better awareness level and satisfaction against the specified energy management scopes.
Semiparametric Bayesian Markov analysis of personalized benefit–risk assessment

The development of systematic and structured approaches to assess benefit-risk of medical products is a major challenge for regulatory decision makers.Existing benefit-risk methods depend only on the frequencies of mutually exclusive and exhaustive categories in which the subjects fall, and the responses of individuals are allowed to belong to any of the other categories during their postwithdrawal visits.In this article we introduce a semiparametric Bayesian Markov model (SBMM) that treats the withdrawal category as an absorbing state and analyzes subject-level data for multiple visits, accounting for any within-patient dependencies in the response profiles.A log-odds ratio model is used to model the subject-level effects by assuming a ratio of transition probabilities with respect to a "reference" category.A Dirichlet process is used as a semiparametric model for the subject-level effects to flexibly capture the underlying distributions of the personalized response profiles without making strong parametric assumptions.This also allows the borrowing of strength between the patients and achieves dimension reduction by allocating similar response profiles patterns into an unknown number of latent clusters.We analyze a motivating clinical trial dataset to assess the personalized benefit-risks in each arm and evaluate the aggregated benefits and risks associated with the drug Exalgo.
FM-Index Reveals the Reverse Suffix Array

Given a text T[1,n] over an alphabet Σ of size σ, the suffix array of T stores the lexicographic order of the suffixes of T. The suffix array needs Θ(nlog n) bits of space compared to the n log σ bits needed to store T itself. A major breakthrough [FM - Index, FOCS'00] in the last two decades has been encoding the suffix array in near-optimal number of bits (≈ log σ bits per character). One can decode a suffix array value using the FM-Index in log^{O(1)} n time.We study an extension of the problem in which we have to also decode the suffix array values of the reverse text. This problem has numerous applications such as in approximate pattern matching [Lam et al., BIBM' 09]. Known approaches maintain the FM - Index of both the forward and the reverse text which drives up the space occupancy to 2nlog σ bits (plus lower order terms). This brings in the natural question of whether we can decode the suffix array values of both the forward and the reverse text, but by using nlog σ bits (plus lower order terms). We answer this question positively, and show that given the FM - Index of the forward text, we can decode the suffix array value of the reverse text in near logarithmic average time. Additionally, our experimental results are competitive when compared to the standard approach of maintaining the FM - Index for both the forward and the reverse text. We believe that applications that require both the forward and reverse text will benefit from our approach.
Extension and Expression of OWL Modeling Primitives Based on Spatio-Temporal Ontology of Ocean Flow Field

For the issue that it was difficult to describe and express the dynamic, multi-dimensional and time-varying characteristics of ocean flow field by existing ontology language, by introducing spatio-temporal ontology, the paper constructed a three-tier architecture which was composed of ocean flow field concept ontology, spatio-temporal object ontology and spatio-temporal process ontology. And based on this, the paper proposed a spatio-temporal ontology model for the dynamic variability characteristics of ocean flow field, and realized the semantic expression of ocean flow domain knowledge. Based on the semantic analysis of ocean flow field spatial primitives and temporal primitives, the paper established the criteria for determining the topological relationship, the sequential relationship, the distance relationship and the temporal relationship between ocean flow field objects. In order to give the formal expression of the spatial-temporal ontology for ocean flow field, based on Web Ontology Language (OWL) syntactic rules, the paper constructed spatio-temporal predicates and terms, extended OWL spatio-temporal modeling key nodes, and formed Ocean Current Spatio-temporal Web Ontology Language (OST OWL). And taking the cold vortex as an example, the spatio-temporal process has modeled. It showed that OST OWL can express ocean flow field more effectively.
The effect of the drama-based German foreign language course application on motivation and flow experience

The investigation of feelings, attitudes, and perceptions with regard to learning a foreign language takes an essential place for learning quality and success.In addition to traditional methods of learning a foreign language, the tendency towards more interactive and student-centered methods such as drama affects the teaching process positively.This study aims to examine the effect of the drama-based German foreign language course application on motivation and flow experience.The study was carried out with drama-based activities for 12 weeks in the "Drama in German Teaching" course at the Department of German Teaching of a state university in Turkey in the fall semester of the 2019 -2020 academic year.The study was conducted with fourteen 2 nd -grade students who took this course.The data were collected using the "Motivation Scale for Learning German" and the "Flow Experience Scale-Short Form" as pretest and posttest.Upon examining the average scores obtained from the scales, significant differences were determined in terms of motivation and flow experience in the drama-based German foreign language course.Accordingly, it was found that students experienced positive emotions against the dramabased German foreign language course and achieved high motivation and also had flow experience.
Analysis of Max Morden’s Identity Crisis and Self-construction in The Sea Based on Psychoanalytic Theory

The Sea, one of the most representative works about identity crisis and self-construction, shows Max Morden's absence of life and struggling in memory.Based on the Psychoanalytic Theory of Literature, this paper aims to study the main character Max Morden's self-identity crisis and come up with feasible ways to solve the problem.By analyzing Max Morden's identity crisis and self-construction in The Sea, it reveals the author John Banville's thinking of value and the meaning of life.It will help people understand the self-identity crisis, its cause, and how to solve it in modern society.
An Evaluation of Nucleated Red Blood Cell Count by an Automated Haematology Analyser- A Tertiary Centre Experience

BACKGROUNDnRBCs also referred to as normoblasts are seen in the peripheral blood films in variable numbers; both in physiological as well as pathologic states.Enumeration of these cells by modern day automated analysers remains a challenge.We wanted to assess the precision of the Beckman Coulter LH 755 & 780 haematology analysers TM in enumerating the nRBC count. METHODSThis is a retrospective study conducted in the Clinical Laboratory and Haematology Division of Kasturba Hospital, Manipal over a 3-month period on the Beckman Coulter LH 755 & 780 haematology analysers TM (Beckman Coulter Inc., Miami, FL, USA) after obtaining requisite clearance from the Institutional Ethics Committee.A data of 47,332 random blood samples run on the analysers was collected.Both descriptive and analytical statistics were performed using the SPSS software version 22.0 TM (Chicago, IL, USA).The sensitivity, specificity and kappa agreement were calculated using the same. RESULTSA total of 797 cases from the 47,332 samples showed a "flag" for nRBCs.Two Twenty of these cases were confirmed microscopically to have had nRBCs in the peripheral smear (true positives).137 cases had nRBCs in the smear but were not definitively evaluated by the instrument (false negatives).A vast majority of the cases (577) did not reveal any nRBC on microscopic examination despite a flag generated by the machine (false positives).Additionally, a sensitivity of 27.6% and specificity of 99.7% were also noted.The kappa agreement was 0.376 which showed a fair agreement between the two methods. CONCLUSIONSThe automated haematology analysers Beckman Coulter LH 755 & 780 were found to be less sensitive in adequately enumerating the nucleated red blood cells.The number of false positives can be reduced by noting the 'cellular interference' flag.A manual review of such smears is necessary to confirm or refute such instrument messages.
Interaction by Taking a Picture for Smartphone Generation

In recent years, smartphones have come to pervade people’s lives, and people carry their smartphones for whatever they do. With the spread of smartphones, new people called smartphone generation have emerged, and information sharing on SNS using smartphones has become popular among smartphone generations. They began to take pictures in every situation with smartphones, triggered by information sharing on SNS. Considering the characteristics of the smartphone generation like this can lead to computer designs that are easier for them to use. In addition, the number of users having multiple computers has increased, and the opportunity to transfer information between computers has increased. A technology that can quickly and intuitively transfer information between computers is required. In this study, we focus on taking pictures, which is a daily activity, and aim to show the effectiveness of interaction by taking a picture. In this paper, we focused on information transfer between computers as the first step, and we developed a system that is familiar to smartphone generation and that can be operated intuitively. The system allows users to transfer files and web pages displayed on the PC screen to the smartphone by taking a picture of the PC screen with the smartphone. An evaluation experiment of the system showed that the system was effective in three ways. As a result, we showed the effectiveness of the interaction by taking a picture for information transfer.
Adult's perception of health care after myocardial infarction

ABSTRACT Objectives: to describe the Adult's perception of healthcare after Myocardium Infarction. Methods: qualitative descriptive analysis carried out with 12 adults who had myocardial infarction and conducted from February to May 2018. The research design was based on the “Consolidated Criteria for Reporting Qualitative Research”. Data collection was performed through semi-structured questionnaire, interview script and field diary. The interviews were encoded, and their content was analyzed using software support. Results: three classes were obtained: “Fear, (un) certainty and (un) awareness”; “Lifestyle and beliefs of illness” and “Health care search”. Unhealthy lifestyle habits, lack of health prevention / promotion, difficulties in accessing services and lack of knowledge about the disease generated fear and uncertainty about the future. Final Considerations: the perception of acute myocardial infarction is related to the trajectory of illness, lifestyle and the belief that the infarction only affects others.
Design Principles and Privacy in Cloud Computing

Cloud computing is considered as the utilization of different services, such as software development platforms, servers, and storage, as well as software, over the Internet. In this chapter, we first discuss the evolution of the cloud computing and its applications in several domains. Next, we discuss various design principles in cloud computing, and the privacy and security issues that are needed in such environment. Various security requirements and attacks along with a generalized threat model are also considered in the cloud computing environment. After that, we discuss the security and privacy preservation protocols in cloud computing that are essential to provide security and privacy to the stored data and also data in transit. Finally, we discuss various challenges in security and privacy of cloud computing followed by highlighting some potential future research directions.
Detection Effectiveness Estimation Based on Multi-Angle Data and Visualization Analysis

In intelligent confrontation games, how to estimate the detection effectiveness of the early-warning detection (EWD) system of systems (SoS) is the most important issue that has been studied in hopes of a breakthrough for the long time. The conventional approaches to effectiveness estimation have been reductionism or the linear estimation methods, which are not suitable for the estimation of the effectiveness of EWD SoS. Effectiveness estimation methods and ideas based on complex networks have been proposed and studied, which can inspire that the logic relationship in the SoS can be analyzed by using the network thoughts. As a development of some research based on the Data mode, data and visualization analysis methods have been proposed. In these approaches, the multi-angle data and visualization analysis methods can be utilized to directly show some significant relationships in the SoS from the different aspects, especially from different angles of sight. These statistics and suggested results can be employed to analyze the situation of SoS, so they have potentially important capabilities in terms of the estimation of EWD SoS. Therefore, in this paper, these new ideas are introduced into the study and solution of the problem of the detection effectiveness estimation of EWD SoS. On the basis that the running characteristics of the detecting work of the EWD SoS are described, the data method and idea with multi-angles for EWD SoS are proposed and discussed, and the visualization analysis method and ideas about EWD SoS are suggested and analyzed. Furthermore, a typical application is employed to estimate the detection effectiveness of EWD SoS, based on the data and visualization analysis methods stated in this paper. As the results of the estimate are generally consistent with the actual situation, the validity of the proposed methods is considered proven. The main work in this paper can provide new ideas on the study of the issue of the detection effectiveness estimation of EWD SoS, and it also helpful for SoS analysis and other estimations of effectiveness.
Research on Image Feature Processing Based on FPGA

Regarding present problems of low computational efficiency of the existing large-scale image content retrieval schemes, this paper designs and implements a deep learning image classification system based on FPGA. Utilizing the characteristics of FPGA’s programmable hardware to integrate SATA data communication and CNN algorithm in one FPGA chip. At the same time, the vector comparison method is used to perform feature retrieval and sorting on images when reading data, thereby achieving high-performance integrated image classification and retrieval functions. The experimental results show that when the main frequency of the FPGA is 200 MHz, the characteristic processing capacity is 85 images/second, the power consumption of the algorithm is 24 W, the storage scale is not less than 1 TB, and the SATA read speed is 482 MB/s, and the write speed is 273 MB/s.
?뺤떊吏덊솚??媛吏??숇즺吏?먭????щ쭩 寃쏀뿕

Purpose : This study is to understand the meaning and discover the structure by examining the living experience of hope of peer supporters with mental...
Optimizing Design of College Teaching Based on C4.5 Algorithm

This article discusses the methods of using data mining technology to optimize college English multimedia teaching. It mainly introduces the three major processes of data preparation, data mining, and result evaluation in multimedia teaching, and briefly describes the role of four data mining models Numerical experiments show the effect of data mining on college English multimedia teaching.
Exploring Effects of Auditory Stimuli on CAPTCHA Performance

CAPTCHAs have been widely used as an anti-bot means for well over a decade. Unfortunately, they are often hard and annoying to use, and human errors have been blamed mainly on overly complex challenges, or poor challenge design. However, errors can also occur because of ambient sensory distractions, and performance impact of these distractions has not been thoroughly examined. The goal of our work is to explore the impact of auditory distractions on CAPTCHA performance. To this end, we conducted a comprehensive user study. Its results, discussed in this paper, show that various types of auditory stimuli impact performance differently. Generally, simple and less dynamic stimuli sometimes improve subject performance, while highly dynamic stimuli have a negative impact. This is troublesome since CAPTCHAs are often used to protect web sites offering tickets for limited-quantity events, that sell out very quickly, i.e., within seconds. In such settings, introduction of even a small delay can make the difference between obtaining tickets from the primary source, and being forced to use a secondary market. Our study was conducted in a fully automated experimental environment to foster uniform and scalable experiments. We discuss both benefits and limitations of unattended automated experiment paradigm.
영재교육 분야별 영재교육 변화요구도 분석

The purpose of this study was to examine teachers’ perception of educational needs and educational change in gifted education depending on gifted...
A Trust Model for Evaluating Trust and Centrality Based on Entropy Weight Method in Social Commerce

Trust of the nodes plays an important role in users’ purchase decisions and trusted nodes can influence other nodes in the social commerce. The evaluation and prediction of users’ trustworthiness is also of great importance for social commerce marketing and promotion. However, analysis of trust and detection of what factors influence trust between users need investigation. This paper proposes a trust model and divides trust into direct trust and indirect trust which presented in the form of familiarity trust, similarity trust, prestige trust and ability trust. The respective weights of four attributes are calculated through entropy weight method. Different centrality approaches such as Eigenvector Centrality, PageRank and Katz Centrality are used to find influential nodes. We found similarity and prestige is relatively more important than familiarity and ability trust in users’ connections. Besides, there is a strong linear relationship between user nodes’ trust and their centrality.
Feature point matching of infrared and visible image

Feature point matching has been widely applied in image registration, image fusion, remote sensing and other fields. The relation between the pixels of infrared images and pixels of visible images is complex due to the images were taken by different sensors. Different sensors images also contain some common information which can been depended to achieve point matching. Scale Invariant Feature Transform (SIFT) algorithm is an effective and popular feature extraction algorithm. SIFT algorithm can be used in point matching, it can get feature descriptor vectors of the feature points which extracted from the images. But in some scenes, SIFT algorithm can’t achieve the accurate feature point matching. Different from SIFT algorithm, Edge-Oriented-Histogram (EOH) algorithm characters the orientation information of the edge and EOH feature descriptor can integrate the boundary information in different directions around the feature points, so that we can realize the describe of the edge direction and amplitude by EOH algorithm. To achieve the accurate feature point matching of infrared image and visible image, we propose a feature point matching method based on SIFT algorithm and EOH algorithm. Firstly, we use image enhancement to increase contrast of the image and then we use SIFT algorithm to extract feature points. Next, we utilize the EOH algorithm to get the 80 bins descriptors of the feature points which detected by SIFT algorithm. We calculate Euclidean distance to get the similarity of the descriptors to achieve point matching. In order to improve the matching accuracy, we adopt Random Sample Consensus (RANSAC) algorithm to eliminate the mismatching points. Nevertheless, RANSAC can’t get all correct points, we use another measure of Euclidean to select correct pairs, then we combine the two parts to match the feature points again. Experimental results demonstrate that our method can effectively improve the accuracy of matching and find more correct matching point pairs.
Face Recognition Method for Enterprise Workstations Based on Convolutional Neural Network Optimization Algorithm

Nowadays, the world economy is developing rapidly, new Internet companies are emerging. Based on the consideration of effectively improving the working efficiency of employees, enhance the competitiveness of enterprises, and facilitate managers to grasp the working status of employees at any time, this paper proposed a face recognition method for enterprise positions based on a convolutional neural network (CNN) optimization algorithm. At first, this paper established enterprise employee face classification model based on the TensorFlow deep learning framework, then used convolutional neural network to extract employee face image features, and introduced Keras deep learning library to train face recognition model, finally used TensorFlow-supported momentum gradient descent optimization method to effectively optimize the CNN model and used the loss function to effectively evaluate the performance of the model, thereby effectively improving the recognition accuracy of the face recognition algorithm. The algorithm proposed in this paper is used to identify the working status of employees in practice. The validity of the algorithm is verified by questionnaire results, and compared with typical face recognition algorithms. The experiment results clearly show that to some extent the method we proposed has higher recognition accuracy and better practicality, which will help companies find out the working status of their employees.