Reviewerly/exHarmony huggingface dataset:
A Study on the Effect of Social Media Factors on Product Innovation Through Business Performance

Social media has a great influence on the domestic and global business development and manages the new products. Organizations can utilize the social media platforms for carrying their messages as well as for promoting them to the market. Malaysia tops the region in terms of utilizing social media channels for reading comments made by others regarding their brands, services or products with nearly 94 percent Innovation plays a significant role in helping business to attain a sustainable competitive edge, especially in developing countries such as Malaysia, where industrial growth has become an integral part of the economic development. Despite the importance of social media, the dimensions and their synergistic role in ensuring product innovation and organizational performance in Malaysian manufacturing industries are yet to be explored and established. Hence in this study this problem will be addressed by producing a comprehensive model within Malaysian content. This research identifies the dimensions for the proposed model for social media, product innovation and business performance. This study is important mainly because of the fact that social media is gaining considerable attention in the research field as well as in the business world with considerable significance. A significant contribution of this research is identified the factors of social media, and product innovation will help to achieve business performance. Another significant contribution of this study is the construction of a theoretically based model which assimilates the determinants of social media, product innovation and business performance.
Distributed Query Evaluation over Large RDF Graphs

RDF is increasingly being used to encode data for the semantic web and data exchange. There have been a large number of studies that address RDF data management over different distributed platforms. In this paper we provide an overview of these studies. This paper divide the studies of existing distributed RDF systems into two categories: partitioning-based approaches and cloud-based approaches. We also introduce a partition-tolerant distributed RDF system, gStore $$^D$$ .
Analysis of attribute domain for geometrical gesture performed by arm movements

&lt;span&gt;Hand gesture recognition commonly uses a camera to track hand movements and transformed into gesture database by using various computational approaches. Motion tracking utilized to map coordinate point of the subject movement, either in skeletal model or marker tracing. Data from motion trackers usually contains massive coordinate sequences of marker movement. A reliable method is required to select best features and analyze these data. However, the current issue whether the selected features and data presentation are significant for the research or not. This research brings the concept of ontology design for arm gesture recognition systems by utilizing the motion capture system. Ontology is the conceptual structure mainly used to retrieve information by establishing relation in complex data model. The proposed ontology framework is divided into three domains which are knowledge domain, attribute domain and process domain. Knowledge domain holds pre-processed gestural data from motion capture. The attribute domain is that the level where all the attribute elements were presented. This paper shows the analysis of the datasets in attribute domain. The analysis is divided into two parts which is precision measure and ANOVA test. Both analyses are to prove the reliability of datasets in attribute domain. The precision measure is used to remove all the common data for all gesture. A statistical analysis of p-value is lower than 0.01 which means the gestural data are statistically significant to be used for the similarity measure.&lt;/span&gt;
Performance Comparison Among Deep Neural Networks Consisting of Various Constituents

머신러닝 모델 중에 하나인 딥뉴럴넷 모델은 최근 월등한 성능을 보이며, 자율주행, 자연어 처리 등 다양한 분야에서 많이 연구되고 있다. 딥뉴럴넷 모델은 그 구성요소가 다양하기 때문에, 모델을 어떻게 구성하는지에 따라서 그 성능이 크게 달라진다. 본 연구에서는 딥뉴럴넷 모델의 구성 요소 5 가지 - 가중치 최적화, 활성화 함수, 드롭아웃, 정규화, 가중치 초기화 - 를 정하고, 5 가지 구성요소의 가능한 조합 48가지의 딥뉴럴넷 모델을 만들었다. 만든 모델들의 성능 평가를 위해서, 음식의 성분들을 가지고 20개의 나라 중 하나를 예측하는 Kaggle의 what s cooking 데이터를 활용하였다. 결과적으로, 적응모멘트추정(adaptive moment estimation, Adam) 최적화 기법, 정류선형단위(rectified linear unit, ReLU) 활성화 함수, 릿지(ridge) 정규화 미사용, 드롭아웃 사용, He 가중치 초기화라는 구성으로 딥뉴럴넷을 구성했을 때 가장 좋은 성능(정확도: 79.45%)을 보였다. 각 요소 별로 성능을 비교하였을 때에는, Adam 최적화 기법이 경사하강(gradient descent) 최적화 기법을 사용한 모델들 보다 평균적으로 56%정도의 성능이 좋은 것으로 나타났고, ReLU 활성화 함수가 시그모이드(sigmoid) 활성화 함수를 사용한 모델들 보다 평균적으로 약 3% 정도 성능이 좋은 것으로 나타났다. 이 연구의 결과가 더 성능 좋은 딥뉴럴넷 모델을 만드는데 기여할 수 있기를 기대한다.In these days, deep neural networks (DNN) yield the most remarkable performance among machine-learning algorithms. Many researchers claim that the performance of DNN might be different according to its constituents. In this paper, we thoroughly analyze the performance of 48 DNNs that consist of various constituents in regard to the five factors, i.e. optimizer, activation function, dropout, regularization and weight initialization. What’s cooking data in Kaggle that contains ingredients of food and its nation are employed to train and test the 48 models. As a result, among the 48 DNNs, the DNN composed of Adam optimizer, ReLU activation function, He weight initialization, use of dropout, and no use of ridge regularization produce the best performance (accuracy: 79.45%) in predicting the nation based on food ingredients. The DNNs using Adam optimizer averagely show 56% higher accuracy than those using gradient descent optimizer, and the DNNs using ReLU activation function averagely show 3% higher accuracy than those using sigmoid activation function. We expect that the result of this study can assist to construct better performance DNN models.
Online Anomaly Detection in Microbiological Data Sets

To prevent health risks caused by waterborne bacteria, significant changes of the bacterial community have to be detected as soon as possible. The aim of this study was to research suitable methods and implement a prototype of a system that can immediately detect such anomalous data points in microbiological data sets. The method chosen for the detection of anomalous cell counts was prediction-based outlier detection: auto generated models were used to predict the expected number of cells in the next sample and the real number was compared to the prediction. Significant changes in bacterial communities were identified using Cytometric Fingerprinting, a method that provides functionalities to compare multivariate distributions and quantify their similarity. The prototype was implemented in R and tested. These tests showed that both methods were capable to detect anomalies but have to be optimized and further evaluated.
Reflections on being CCR editor 2008--2012

This article discusses my personal view of being Editor of CCR from 2008 to 2012.
NCTU-GTAV360: A 360° Action Recognition Video Dataset

Despite many action recognition video datasets available right now, none of them are in the spherical projection. NCTU-GTAV360 is a new 360° action recognition video dataset captured from a game, Grand Theft Auto V (GTA V). The spherical video is obtained by stitching 24 views from various angles and combining them into a video. The benefit of using 360° cameras is that it can capture the entire surroundings using one single camera. We captured 200 locations within the Los Santos city (city name in the GTA V). This dataset should benefit researchers working on the spherical images, particularly the human action recognition research using machine learning or deep learning technique, which requires a large amount of training data and the associated ground-truth.
EFEKTIFITAS LAYANAN INFORMASI TERHADAP PEMAHAMAN MAHASISWA MENGGUNAKAN INTERNET SECARA POSITIF DI PRODI BKI FITK UIN SUMATERA UTARA MEDAN

Positive behavior is expected to appear in every student activity at school and outside the school. BK lecturers are expected to work together with lecturers of study and all steakholders to shape positive student behavior such as using internet positively. This research uses Quasi experiment type research. The population of the study were students of BKI FITK UIN Sumatera Utara Medan 142, a sample of 32 people. Data collection techniques are questionnaires, data analysis techniques are data descriptions and hypothesis testing. Data analysis was done with the help of SPSS. The results showed: 1) There was a significant difference of understanding scores on students' understanding of using internet positively on experimental group students between test pree with post test. The difference occurred with the increase of the score of all respondents as many as 20 people. Total score increase of 210 with an average increase of 10.50. 2) There was no significant difference in understanding scores about students' understanding using internet positively on control group students between pree test and post test. From 21 respondents only 7 people who experienced an increase. Total increase of 32.50 with an average of 4.64. 3) There is a significant influence of information services on the understanding of students' understanding of using the internet positively on students BKI FITK Students UIN Sumatera Utara Medan Lesson Year 2017/2018 with Asyim sig. 0,000 <0.05. Keywords: Information service, internet use positively
Development of fall detection and activity recognition using threshold based method and neural network

Falls are dangerous and contribute to over 80% of injury-related hospitalization especially amongst the elderly. Hence, fall detection is important for preventing severe injuries and accidental deaths. Meanwhile, recognizing human activity is important for monitoring health status and quality of life as it can be applied in geriatric care and healthcare in general. This research presents the development of a fall detection and human activity recognition system using Threshold Based Method (TBM) and Neural Network (NN). Intentional forward fall and six other activities of daily living (ADLs), which include running, jumping, walking, sitting, lying, and standing are performed by 15 healthy volunteers in a series of experiments. There are four important stages involved in fall detection and ADL recognition, which are signal filtering, segmentation, features extraction and classification. For classification, TBM achieved an accuracy of 98.41% and 95.40% for fall detection and activity recognition respectively whereas NN achieved an accuracy of 97.78% and 96.77% for fall detection and activity recognition respectively.
A Case Study Based Research on the Oranization of Vocational Colleges in Chinese Higer Education System

Chinese vocational education plays a very important role to train professinal talents to serve the economy and industry development. A case study of Shenzhen Polytechnic is adopted to demonstrate a clear pattern that a Chinese vocationa college run in the higher education system. The student recourses, financial resources, programs, guiding philosophies are described to show a picture of modern Chinese vocational colleges.
Evaluating Deep Contextual Description of Superpixels for Detection in Aerial Images

In several applications, the use of pixel contextual information has led to significant effectiveness results in remote sensing image classification tasks. In this paper, we investigate the use of deep superpixel context representation for detection in aerial images. Experimental results considering the widely used ISPRS Postdam and Munich Vehicle datasets demonstrate that max pooling and standard deviation are the most promising pooling methods, while the concatenation of contextual features from different pooling approaches leads to an effective characterization of superpixel contextual information in remote sensing images.
Bracelet-based Monitoring and Analysis Tool for Daily Life Behaviors

Abstract As China’s economy develops and people’s attitudes change, the birth rate of newborns in China is decreasing year by year, but the population over 65 is increasing year by year. China is moving towards an aging society. The problem of providing for the aged is gradually becoming a national problem. Faced with such a social problem, we want to solve such a problem through the development of science and technology. In response to the fall of an elderly person and the fact that he was left unattended, This paper presents a monitoring and analysis tool of daily life behavior based on bracelet, The tool collects data about the human body through a bracelet sensor, The machine learning algorithm is used to recognize the human motion behavior, and then the neighboring people or relatives are notified by alarms or text messages, so as to achieve the effect of guardianship, which not only avoids the elderly falling unnoticed, but also reduces the difficulty of guardianship without watching all the time.
Efficient Evolutionary Approach for Virtual Machine Placement in Cloud Data Center

Administering energy and resource management are two vital managing components of cloud data centers. From last two decades, most of cloud data centers (CDC) are suffering from these two; the former has become a serious issue nowadays. In this paper, we focused on effective virtual machine placement (VMP). Evolutionary approach is applied to place the virtual machine in an effective way which properly utilizes the underutilized resources and reduced the active physical servers. After experiencing the performance of particle swam optimization (PSO) algorithm for combinatorial problems, a distributed PSO approach is modeled to minimize energy consumption of CDCs. The proposed PSO and DPSO algorithms are applied on VMP over large distributed cloud data centers. Experimental results of PSO and distributed PSO algorithms are presented. The model is applied with variety of placement problems with varying data center network topology. The performance of the model outperforms the traditional heuristic and several optimizations approaches.
Use of J Measures Reflecting the Direction of Association

J 측도는 연관 규칙의 정보량을 다루는 값으로 엔트로피를 기반으로 계산된다. 연관성은 항목 간 관련성을 파악하기 위해 이용되는 분석인데 항목 간 연관성의 방향(음의 연관 또는 양의 연관)과 강도를 파악하고 활용한다. 하지만 J 측도는 연관성의 방향에 관계없이 해당 항목 간 연관 규칙이 갖고 있는 정보량만을 수치화한다. 따라서 연관성 분석을 수행할 때는 통상적으로 수행되는 연관성 방향은 알 수 없다. 이에 본 연구에서는 J 측도에 방향성을 부여한 수정된 J 측도를 제안하였다. J 측도가 나타내고 있는 정보량의 의미를 유지하는 동시에 연관성의 방향을 파악할 수 있도록 하였다. 아울러 항목의 선후에 관계없이 연관 규칙의 정보량을 측정하는 대칭적 J 측도에 대해서도 기존의 방향을 고려한 대칭적 J 측도의 대안이 될 수 있는 수정된 대칭적 J 측도를 제안하였다. 기존에 개발되어 있는 방향을 고려한 대칭적 J 측도의 경우 J 측도가 0인 경우임에도 불구하고 0이 아닌 값을 갖게 되는 문제와 J 측도값 0 주변에서 변화의 정도가 기대보다 크게 나타나는 문제가 있었다. 이에 수정된 대칭적 J 측도의 제안을 통해 이와 같은 문제를 해결할 수 있음을 살펴보았다. 수치 예를 통해 수정된 J 측도와 수정된 대칭적 J 측도의 활용가능성을 확인하였다.The J measure is a value that deals with the amount of information in the association rule. It quantifies the amount of information, regardless of the direction of association (negative or positive association) between items. The calculation of the amount of information is based on entropy. When conducting an association analysis, the direction of association is usually identified. However, the J measure alone does not determine the direction of the association. Therefore, a modified J measure that gives directionality to the J measure is proposed. Also, an another version of modified symmetric J measure is proposed. It is an alternative to the existing modified symmetric J measure considering the direction of association. In the case that J measure is 0, the proposed symmetric J measure solved the problem of having a non-zero value of existing modified symmetric J measure and steep change of the value around 0. Numerical examples show applicability of the proposed measures.
PERSEPSI MAHASISWA LAMA DAN MAHASISWA BARU TERHADAP PENGGUNAAN SISTEM INFORMASI AKADEMIK (Studi Empiris pada Sistem Informasi Akademik (SIMAWEB) Fakultas Ekonomika dan Bisnis Universitas Diponegoro)

Th is study aimed to examine differences in perceptions of old and new students on the use of academic information system using DeLone and McLeane models (2003). There are five factors used in this research those are : content, accuracy, format, ease of use and timeliness. Variables used in this research are content, accuracy, format, ease of use and timeliness. This research uses survey method by questionnaire in collecting the data. The questionnaires are given to students faculty of economics and business Diponegoro University Semarang. Afterwards, the data gained is analysed using SPSS. The results of this research only the variable accuracy and timeliness that affect student’s perceptions of the use of information systems. While the variable content, format and ease of use doesn’t affect students’ perceptions of the use of information systems.
Inverting airborne gamma-ray spectrometry data of Maricá calibration range, Brazil

We present an inverse analysis of the Maricá Calibration Range data, located in Rio de Janeiro -Brazil.As a gamma-ray detector does not admit a fixed field of view, there is significant overlap between successive fields of view.For this reason, standard height and sensitivity corrections may lead to incorrect estimates of the concentrations of the radioelements, thus a practical solution is to invert the data.By inverting the airborne gamma-ray spectrometric data we obtained smoothed results compared to the conventional procedure, especially in potassium and uranium channels.
A Study on the Pre-service Teachers' Opinion on the Contents of 'Function and Structure of Robots' in Practical Arts Textbooks in the 2015 Revised Curriculum

본 연구는 2015개정 교육과정에 따라 출판된 6종의 초등 실과 교과서에 제시된 로봇의 형상과 작동 형태, 센서의 종류, 로봇 작동 프로그램 내용에 대하여 예비교사의 견해를 분석하여 로봇 관련 내용의 개선과 효율적 교수ㆍ학습 방법을 제시하기 위한 것으로 다음과 같은 결과를 얻었다. 1. 예비교사들은 교과서에 제시된 로봇의 조립 난이도는 대부분의 교과서에서 어렵지 않게 제시되었지만 일부 교과서의 로봇에서는 조립 과정의 그림이나 사진이 이해하기 어렵게 제시되어 있어 보다 더 상세하게 제시할 필요가 있다고 하였다. 2. 예비교사들은 대부분의 교과서에서 제시된 로봇의 작동 형태는 작동 원리를 이해하는데 적절하였으며, 센서의 종류와 작동 특성 역시 적절하였지만, 일부의 교과서에서는 센서 의 감지 상황에 따른 로봇의 작동을 보다 더 확실하게 할 필요가 있다고 하였다. 3. 예비교사들은 로봇의 제어 장치에 내장된 프로그램 선택으로 작동시키는 것도 소프트웨어와 연계된 작동이라고 판단하였고, 로봇 작동을 위한 실제 프로그램 작성 내용은 초등학생들에게 적절한 수준이라고 하였다. 4. 예비교사들은 대부분의 교과서에서 제시된 로봇의 작동 내용이 초등학생들의 흥미를 불러일으킬 수 있지만 게임 형태의 작동 내용 등 더 많은 흥미를 불러일으킬 수 있는 내용이 필요하다고 하였다. 5. ‘로봇의 기능과 구조’ 내용 요소의 효율적 지도를 위한 교육대학에서의 강의 내용과 방법은 로봇의 하드웨어 이해 및 다양한 형태의 로봇 조립과 로봇을 실제 작동시키는 실천적 활동 시간의 확대이다. 6. 예비교사의 약 70%는 ‘로봇의 기능과 구조’ 내용 요소 실제 교육 현장에서의 지도에 대 하여 자신감을 갖고 있었다.This study is to analyze the pre-service teachers opinions on the appropriateness of the contents of the function and structure of robots in elementary practical arts in the 2015 revised curriculum and the contents and methods of lectures at the educational college for efficient education of this content., Pre-service teachers evaluated as follows. 1. Although the difficulty of assembly of robots presented in textbooks is not difficult in most textbooks, it is necessary to present more detailed pictures or pictures because it is difficult for elementary school students to assemble because pictures or pictures of assembly process are presented in some textbooks. 2. The operation form of robots presented in most textbooks is appropriate to understand the operation principles, and the types and operation characteristics of sensors are also appropriate, but some textbooks need to make the operation of robots more certain according to the sensor detection situation. 3. Operation with program selection built into the robot s control device is also linked to software, and actual programming content for robot operation is appropriate for elementary school students 4. Although the behavior of the robot presented in most textbooks may be of interest to elementary school students, it is necessary to present various forms of operation such as game form in order to be more interested. 5. Lecture contents and method for effective instruction of Function and structure of robots unit at the university is to understand the hardware of robot, to assemble various types of robots, and to enlarge practical activity time to realize the actua operation of robots. 6. Approximately 70% of the pre-service teachers had confidence in the actual education of the robot s function and structure unit.
Comparison of geospatial support in RDF stores: Evaluation for ICOS Carbon Portal metadata

The evolution of World Wide Web (WWW) into semantic web is happening with the aid of standards like Resource Description Framework (RDF), SPARQL and a few others from World Wide Web Consortium (W3C). Over the years, semantic data management technologies have been introduced as software platforms commonly known as RDF stores. Lately these RDF stores have been tested for processing and maintenance of large data sets complying with Linked Data principles. In order to standardize geographic capabilities in these RDF stores, Open Geospatial Consortium (OGC) adopted GeoSPARQL as an extension to SPARQL query. Our study aims to discuss the geospatial capabilities, and the conformance to GeoSPARQL standard, of the five RDF stores: Eclipse RDF4J 2.4.0, Apache Jena 3.9.0, Openlink Virtuoso 7.2.4, Stardog 6.0.1 and GraphDB 8.8.0. Along with the investigation of features, the performance evaluation of these RDF stores has also been conducted by measuring the execution times of a set of GeoSPARQL queries. The evaluation query set consists of non- topological, spatial selection as well as spatial join queries adopted from a spatial benchmark, Geographica. The geospatial component of Integrated Carbon Observation System (ICOS) Carbon Portal (CP) metadata has been used for performance evaluation in order to establish the suitability of the RDF stores for ICOS-CP requirements. Java Programs have been developed in order to interact with all the RDF stores for upload of data and execution of benchmark queries. Some result set disparities amongst the RDF stores as well as variation in performance metrics on different hardware platforms have also been highlighted in our research. (Less)