Reviewerly/exHarmony huggingface dataset:
AVIRIS-NG Data for Geological Applications in Southeastern Parts of Aravalli Fold Belt, Rajasthan

Advanced techniques using high resolution hyperspectral remote sensing data has recently evolved as an emerging tool with potential to aid mineral exploration. In this study, pertinently, five mosaicked scenes of Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) hyperspectral data of southeastern parts of the Aravalli Fold belt in Jahazpur area, Rajasthan, were processed. The exposed Proterozoic rocks in this area is of immense economic and scientific interest because of richness of poly-metallic mineral resources and their unique metallogenesis. Analysis of high resolution multispectral satellite image reveals that there are many prominent lineaments which acted as potential conduits of hydrothermal fluid emanation, some of which resulted in altering the country rock. This study takes cues from studying those altered minerals to enrich our knowledge base on mineralized zones. In this imaging spectroscopic study we have identified different hydrothermally altered minerals consisting of hydroxyl, carbonate and iron-bearing species. Spectral signatures (image based) of minerals such as Kaosmec, Talc, Kaolinite, Dolomite, and Montmorillonite were derived in SWIR (Short wave infrared) region while Iron bearing minerals such as Goethite and Limonite were identified in the VNIR (Visible and Near Infrared) region of electromagnetic spectrum. Validation of the target minerals was done by subsequent ground truthing and X-ray diffractogram (XRD) analysis. The altered end members were further mapped by Spectral Angle Mapper (SAM) and Adaptive Coherence Estimator (ACE) techniques to detect target minerals. Accuracy assessment was reported to be 86.82% and 77.75% for SAM and ACE respectively. This study confirms that the AVIRIS-NG hyperspectral data provides better solution for identification of endmember minerals.
Comparisons of Parents’Trust in Teachers for School Levels and School Types

학교의 교육목적을 달성하기 위해서는 학교공동체 구성원 간의 신뢰는 매우 중요하다. 특히 교사에 대한 학부모의 신뢰는 학부모의 학교 활동 참여를 유도하여 학생의 전인교육과 학업성취를 위한 기본 조건 이다. 이에 본 연구의 목적은 학교급과 혁신학교 여부에 따른 교사에 대한 학부모의 신뢰 차이를 비교하는 데 있다. 현재 초·중·고등학교에 재학 중인 자녀를 둔 443명의 학부모가 유용한 자료를 제공했다. 기술통계, 독립 t-검정, 일원 분산 분석을 수행하여 데이터를 분석하였다. 그 결과, 학교급과 혁신 학교 여부에 따라 학부모의 교사에 대한 신뢰 정도에 통계적으로 유의한 차이가 나타났다. 학교급이 낮을수록, 일반학교보다는 혁신학교 학부모일수록 교사에 대한 신뢰도가 높게 나타났다. 연구 결과는 향후 학교급과 혁신학교에서의 학부모-교사 신뢰에 관한 연구의 토대를 제공할 것이다. 마지막으로, 연구결과에 대한 논의 및 향후 연구를 위한 제언을 제시하였다.The purpose of this study was to compare the degree of parents’ trust in teachers at elementary, middle, and high schools in South Korea for school levels (elementary, middle, and high school) and school types (Heoksin or non-Heoksin) as measured by the Parents’ Trust in Teachers Scale. A total of 443 Korean parents of elementary, middle, and high schools provided usable data. Descriptive statistics, independent t-tests, and One-Way ANOVA were conducted to analyze data. Findings suggested that there were statistically significant differences in parents’ trust in teachers depending on school types and levels. The lower the level of the school, combined with being a Heoksin school rather than a non-Heoksin school, enabled a higher the level of trust in teachers. The results of the study can provide researchers and professional development practitioners with information regarding the trust between parents and teachers. Implications and suggestions for further studies are also discussed.
Customer Review-driven Function Formulation for Design Education

Product design is rapidly shifting from the conventional paradigm of creativity-oriented and model-based design toward a new paradigm of user-centered and data-driven design.Against such a background, this paper discusses the values of massively crowdsourced customer reviews for engineering design education.Recently, customers become growingly motivated to share their first-hand experience of using a product on various ecommerce and social platforms in the format of customer reviews (CRs).In practice, these informative CRs are increasingly employed by professional designers to support their early-stage design decision making.Unlike expert designers who are more comfortable at comprehending ill-structured CRs without any predefined hypothesis or structured design process, student designers should be carefully guided to uncover the implicit values of CRs for their design projects.Based on a novel integration of relevant design methods and recommendation approaches, this paper presents a CR-driven function formulation method.Student designers can follow the new method to abstract useful information from readily available customer reviews to formulate a unique engineering design problem as a set of functional requirements.
An Empirical Investigation of Real-World QoS of Web Services

Quality of service (QoS) is a critical nonfunctional property and a criterion for the selection of web services (WSs); due to its importance, many QoS-aware or QoS-based approaches have been proposed and developed. However, with the existence of numerous approach-based studies of QoS of WSs, we consider that the deficiency in the existing research is the lack of a systematic investigation and analysis of real-world QoS data to discover and understand the characteristics of such data. Therefore, in this paper, we first define a number of research questions related to the properties of WSs’ QoS that could be interesting to WS/QoS researchers. Then, two real-world, large-scale QoS datasets are chosen, and a number of experiments that address the defined research questions are designed and performed on those datasets. Finally, based on the experimental results, the answer to each research question is discussed in detail. The main contribution of this paper is to empirically reveal and confirm several useful and interesting properties of real-world QoS. For example, it is found that the distance between a service consumer and its invoked WS does not influence the invocation failure rates of the WSs; however, this distance is indeed correlated to the consumer-perceived WS performance in that a shorter distance can lead to a shorter response time and higher throughput (i.e., a better performance) of WSs according to our experimental results.
Literature review on depth estimation using a single image

Depth estimation is an important component of understanding geometric relations within a scene. Different depth estimation techniques using a single image are analyzed in this survey paper. Depth estimation from a single image is often described as an ill-posed and inherently ambiguous problem. Recovering depth information in applications like 3D modeling, robotics, autonomous driving, etc. is more important when no other information such as stereo images, optical flow, or point clouds are unavailable. For the task of depth estimation using single images, learning based methods have shown very promising results.
Proposed method to Malayalam Handwritten Character Recognition using Residual Network enhanced by multi-scaled features

Handwritten character recognition demands great importance in the field of bank cheque processing, tax returns, etc. Deep learning techniques for recognition of handwritten characters have surpassed the traditional techniques involving handcrafted feature extraction. Although it achieves around 0.95 recognition rate, some misclassification does exist. This is because the classifier function at the output layer performs classification that does not consider the parameter adjustments using the low and mid-level features. This paper proposes an efficient method of recognition of handwritten Malayalam characters, including compound characters and signs, using deep layered graph neural network, Residual network enhanced by multi-scaled features from lower and middle layers.
Improving Speed and Security When Sharing Protected Health Information of Deceased Organ Donors

Because extensive data from medical records are gathered by organ procurement teams, the process of data transfer between sites created a data security risk. This includes the use of fax machines, courier services, and employees’ vehicles. Even health records transmitted over public Wi-Fi become at risk for a data breach. The Plan-Do-Study-Act method for performance improvement was used to address this time-consuming and labor-intensive process. A secured virtual private network from within the donor hospital site setting was developed to improve the transfer of images and data. The goal was to improve the process and increase data security. Once the new network was in place, the process steps decreased and eliminated data vulnerability. Average process time decreased by approximately 1 hour. Organs procured increased from pre (expected 3, observed 2) to post (expected 3, observed 5) implementation of the new process.
PerLE: A Testbed for Pervasive Middlewares in Learning Environments

Today's classrooms contain an increasing number of smart devices such as displays and smart lighting in the infrastructure. Additionally, many are brought by lecturers and students in the form of laptops, smartphones, or tablets. However, until now, all these devices are not fully utilized for the benefit of the lecture and a better learning experience. While the use of a pervasive middleware is the first step in connecting all the heterogeneous devices and enabling communication among them, the system also needs the right services to utilize this capability. We introduce PerLE (Pervasive Learning Environment), a testbed for pervasive middlewares in classroom scenarios. The testbed has two main goals. First, it enables the evaluation of different pervasive middlewares during lectures. Second, it shows how pervasive services enhance the learning experience of students and help lecturers in holding improved lessons. Therefore, we implemented five different services for a pervasive classroom system together with an application for lecturers and students.
Depth-based saliency estimation for omnidirectional images

Visual saliency estimation aims at identifying and localizing the areas of images and videos that are attractive for a human subject. In this work, a novel approach for estimating the visual saliency in omnidirectional images is proposed. It is based on the identification of low-level image feature descriptors (i.e., the presence of texture, edges, etc.) coupled with the information about the local depth of the scene. To evaluate the performances of the proposed method, the estimated saliency map is compared with the available ground truth through two objective metrics: the correlation coefficient and the Kullback-Leibler divergence. The analysis of the achieved results confirms the validity of the proposed approach.
Discrete Wavelet Design for Target Classification in Pulse-Doppler Surveillance Radar

Wavelets are powerful signal classification tools that show promise for classification in Pulse-Doppler radar applications. A wavelet decomposition of match filtered radar data is performed for a surveillance radar in the Czech Republic. A custom discrete wavelet is designed based on the range profiles of the dataset with and without targets and clutter present. The entropy and energy for different levels of the DWT decomposition are used to determine the presence of various targets. Targets are differentiated from static clutter by thresholding the DWT decomposition levels.
A Relation Extraction Method Based on Entity Type Embedding and Recurrent Piecewise Residual Networks

Relation extraction is an important while challenging task in information extraction. We find that existing solutions can hardly extract correct relation when the sentence is long and complex or the firsthand trigger word does not show. Inspired by the idea of fusing more and deeper information, we present a new relation extraction method that involves the types of entities in the joint embedding, namely, Entity Type Embedding (ETE). An architecture of Recurrent Piecewise Residual Networks (RPRN) is also proposed to cooperate with the joint embedding so that the relation extractor acquires the latent representation underlying the context of a sentence. We validate our method by experiments on public data set of New York Times. Experiment results show that our method outperforms the state-of-the-art models.
Using R with SQL Server 2016 and 2017

In 2016, Microsoft announced the possibility of writing R codes inside SQL Server Management Studio. To be able to write R code in SQL Server 2016, we must install R Services first. In 2017, the ability to write Python codes inside SQL Server 2017 was provided. A developer can write the R and Python codes inside SQL Server Management Studio, using Machine Learning Server and accessing the different R or Python packages. In 2017, instead of R services, we have machine learning services, which allow us to embed R or Python codes in SQL scripts. In this chapter, the process of how we can set up SQL Server Management Studio to write R or Python scripts is explained. A brief explanation of some essential packages is also provided. In addition, best practices for how we can create a model and reuse it for another data set are explained.
Improving Security in Ciphertext-Policy Attribute-Based Encryption with Hidden Access Policy and Testing

Ciphertext-policy attribute-based encryption (CP-ABE) is one of the practical technologies to share data over cloud since it can protect data confidentiality and support fine-grained access control on the encrypted data.However, most of the previous schemes only focus on data confidentiality without considering data receiver privacy preserving.Recently, Li et al.(in TIIS, 10(7), 2016.7)proposed a CP-ABE with hidden access policy and testing, where they declare their scheme achieves privacy preserving for the encryptor and decryptor, and also has high decryption efficiency.Unfortunately, in this paper, we show that their scheme fails to achieve hidden access policy at first.It means that any adversary can obtain access policy information by a simple decisional Diffie-Hellman test (DDH-test) attack.Then we give a method to overcome this shortcoming.Security and performance analyses show that the proposed scheme not only achieves the privacy protection for users, but also has higher efficiency than the original one.
Research of Power User Load Classification Method Based on K-means and FSVM

In order to realize the differentiation and refinement management of power users, the paper proposes a power user load classification method based on k-means and FSVM. The method takes the power use daily load curve index as the original sample, extracts the typical industry category by k-means clustering algorithm, combines the FSVM classification algorithm to train and test the samples of the known industry labels, and obtains the FSVM classification model of power user load. The simulation experiments show that the power user load classification model implemented by this method has higher classification accuracy and can effectively identify users in unknown industries.
Face Recognition Using Gabor Wavelet in MapReduce and Spark

Face recognition has become one of the important research areas and is used in wide range of applications. In addition to accuracy, traditional face recognition methods face challenges on time-consuming to identify and apply to distributed systems in a large data environment. To solve these problems, we proposed a facial recognition method using the Gabor wavelet technique and the MapReduce parallel processing model. We performed parallel processing at the extraction and recognition stage with the MapReduce model in the Spark environment. Experimental results show that the proposed method significantly improves the computing time and the accuracy of face recognition.
How Do Medical Researchers Make Causal Inferences?

Epidemiological inference is complex. It is rarely obvious what statistically correlated factors are causally responsible for others. It is typical for multiple possible causes to be viable explanations. Partial evidence is usually misleading. Some of the identified associations normally conflict with expected theory. The complexity of epidemiological inference suggests a coherentist interpretation. Evidence for a claim emerges from a body of information in which the relations of support between claims are bi-directional and may involve rejecting some of the originally accepted claims. To determine overall coherence, the computer program ECHO uses a neural network algorithm for approximately maximizing coherence. ECHO represents each proposition by a unit, a simplified artificial neuron that is connected to other units by excitatory and inhibitory links. As in real neurons, an excitatory link is one that enables one neuron to increase the firing of another, whereas an inhibitory link decreases firing.
Reforming Cyber Security with Coding

Today, a large number of transactions and data exchanged over internet through different data transaction layers among network. There is need to put a security or some patches to private data among military, commercial or financial sector. A number of cryptographic methods have recently proposed and implemented because of strong demand for encryption and decryption process against cyber-attacks. Most recent encrypting or encoding methods include many iterations and substitution processes to encounter some issues such as lack of robustness and valuable amount of time. In our proposed research, we introduce method based upon idea of encoding with programming to achieve cyber security. We use ASCII to build an encoding table with different way to enhance security and save cost and time.
Can the Null Hypothesis Be “Proven” by Using Large Numbers of Predictor Variables?

Multivariate techniques have become commonplace in the social sciences.Some scholars have attempted to "prove" the null hypothesis by predicting one variable from a host of others.At least three risks or dangers accompany such attempts.First, the addition of multiple predictors may eventually render the apparent significance of any one independent variable to non-significance even if the underlying effect size remains medium to large.Second, the use of multiple predictors implies the potential for hundreds to millions of alternative models that could have been tested along with the models reported in an article.Third, ruling out the direct effects of a variable does not necessarily prove that the variable has no indirect effects on the outcome(s).Using a small data set of survey research in which truck drivers participated as respondents, we show how a theoretically and empirically strong relationship between income satisfaction and job satisfaction could be rendered non-significant (despite a medium or larger effect size) by adding enough other predictor variables, even if those additions did not make much theoretical sense.Thus, it is possible that other misinterpretations of the null hypothesis have occurred in the scientific literature when models with dozens of predictor variables have been used by authors who have had a goal of "proving" the null hypothesis.
Особенности влияния цифровых коммуникаций на профессиональную культуру и адаптацию студентов социально-политических специальностей

The article presents the results of the study of the impact of Internet communication, including social networks, on the formation of the professional culture of applicants and students of political, economic and managerial educational profiles, as well as a professional adaptation of young professionals who started work immediately after graduation from high school. The professional culture of students as the subject of the research we understood as a set of sustainable values, norms, attitudes and behavioural strategies that emerge mainly in the digital environment in the process of choosing a profession, gaining professional competencies by a student at a university and supporting a career as a young specialist. The adaptation of young professionals is the process of forming of the ideas of their mind about a particular profession through the formation of the image of a professional, identification with a professional group, as well as the implementation of the installation on entering the professional community. Using a combination of quantitative and qualitative research methods, we identified and classified destructive and design features of the influence of digital communications on these phenomena. The vector of influence in the area of destruction is caused by the negative factors of the digital environment that can catalyse the regressive forms of professional adaptation. Accordingly the influence of the vector of influence in the constructive area is due to the positive factors of the digital environment achieved through the targeted development of digital competencies at all levels of education. In conclusions and recommendations, the article suggests methods and techniques for implementing anti-destructive immunities in the digital environment.
Performance Comparison of Detection, Recognition and Tracking Rates of the different Algorithms

This article discusses the approach of human detection and tracking in a homogeneous domain using surveillance cameras.This is a vast area in which significant research has been taking place from more than a decade and the paper is about detection of a human and its face in a given video and stores Local Binary Pattern Histogram (LBPH) features of the detected faces.Once a human is detected in the video, that person will be given a label and him/her is tracked in different video taken by multiple cameras by the application of machine learning and image processing with the help of OpenCV.Many algorithms were used for detecting, recognizing and tracking till date, thus in this paper, main thing is the comparison of the proposed algorithm with some among the state-of-the-art algorithms.And also shows how the proposed algorithm is better than the other chosen algorithms.