Reviewerly/exHarmony huggingface dataset:
An Analysis on the Students’ Ability in Writing Conclusion of Argumentative Essay of the Eleventh Grade SMAN 3 Tanjung

Writing is an act to express the idea, feeling and facts in written form, including argumentative essay. This research describes the analysis of students’ ability in writing conclusion of argumentative essay at the eleventh grade of SMAN 3 Tanjung. Descriptive-qualitative research method was used with the total subject of 13 students at XI IPA of SMAN 3 Tanjung. The data were collected through writing test and the results were categorized in percentage.  The result of the research denoted that there were 64% students who were in good category in writing the content of conclusion of argumentative essay. However, it was also found that 76% of the students had low ability in grammar and 90% in diction. It can be concluded that students’ ability in writing the content of conclusion of argumentative essay is in good category but with limited ability in grammar and diction.
Structural Characterization of Quarry Stone in Experimental Testing Using Digital Image Correlation as a Non-destructive Technique

In this research the use of digital image correlation technique (DIC) is applied as a non-destructive monitoring method with the aim to obtain the lineal and non-linear behavior of pink quarry stone similar to those found on masonry historical constructions of the 16th century on the central and northern part of Mexico. Uniaxial compression tests were carried out in order to demonstrate the suitability of the DIC technique to represent the behavior of pink quarry stone in terms of displacements and crack propagation regarding to another monitoring methods such as measurements using LVDT sensors. From the experimental tests it was possible to obtain the mechanical properties of pink quarry stone in terms of stresses, strains, Young’s modulus and Poisson’s ratio. Incongruences during the elastic stage of the compression tests were found in the analysis of quarry stone by DIC technique regarding the traditional sensors due to the brittle behavior of the material. It was possible to demonstrate the capability of the DIC technique to obtain the full-field displacements measurement on the material surface and it has the ability to identify the crack propagation on quarries, which is not possible to obtain using the conventional techniques.
Effective methods for constructing extreme quantum observables

We study extreme points of the set of finite-outcome positive-operator-valued measures (POVMs) on finite-dimensional Hilbert spaces and particularly the possible ranks of the effects of an extreme POVM. We give results discussing ways of deducing new rank combinations of extreme POVMs from rank combinations of known extreme POVMs and, using these results, show ways to characterize rank combinations of extreme POVMs in low dimensions. We show that, when a rank combination together with a given dimension of the Hilbert space solve a particular packing problem, there is an extreme POVM on the Hilbert space with the given ranks. This geometric method is particularly effective for constructing extreme POVMs with desired rank combinations.
Optimization of Neural Networks using Deep Ge-netic Network Algorithm

Optimizing a neural network's performance is a tedious and time taking process, this iterative process does not have any defined solution which can work for all the problems. Optimization can be roughly categorized into - Architecture and Hyperparameter optimization. Many algorithms have been devised to address this problem. In this paper we introduce a neural network architecture (Deep Genetic Network) which will optimize its parameters during training based on its fitness. Deep Genetic Net uses genetic algorithms along with deep neural networks to address the hyperparameter optimization problem, this approach uses ideas like mating and mutation which are key to genetic algorithms which help the neural net architecture to learn to optimize its hyperparameters by itself rather than depending on a person to explicitly set the values. Using genetic algorithms for this problem proved to work exceptionally well when given enough time to train the network. The proposed architecture is found to work well in optimizing hyperparameters in affine, convolutional and recurrent layers proving to be a good choice for conventional supervised learning tasks.
Eye Detection Using Gabor Filter, Neural Network and Skin Color

In this paper, we have proposed a new robust method of eye detection based on Gabor filter, neural network, skin color, and eye template. First, the faces are detected by a system composed of a Gabor filter, and a neural network. Then, in the faces detected, we have removed the skin regions, and keep only the non-skin regions which have a high probability to be eyes. Then, these non-skin regions are compared with an eye template to locate the position of the eyes. The obtained results show that our method is robust and provides superior performance compared to other methods recently published.
About the k-error linear complexity over F <sub>p</sub> of sequences of length 2p with optimal three-level autocorrelation

We investigate the $k$-error linear complexity over $\mathbb{F}_p$ of binary sequences of length $2p$ with optimal three-level autocorrelation. These balanced sequences are constructed by cyclotomic classes of order four using a method presented by Ding et al.
On block bootstrapping for autoregressive time series models

In this paper, we propose new Ordinary Least Squares based overlapping and non-overlapping block bootstrap methods for autoregressive time series models. The finite sample properties of the proposed methods are illustrated by an extensive simulation study and two realworld examples. Our findings reveal that the proposed methods have better performances than their conventional counterparts.
Kernel Treelets

A new method for hierarchical clustering is presented. It combines treelets, a particular multiscale decomposition of data, with a projection on a reproducing kernel Hilbert space. The proposed approach, called kernel treelets (KT), effectively substitutes the correlation coefficient matrix used in treelets with a symmetric, positive semi-definite matrix efficiently constructed from a kernel function. Unlike most clustering methods, which require data sets to be numeric, KT can be applied to more general data and yield a multi-resolution sequence of basis on the data directly in feature space. The effectiveness and potential of KT in clustering analysis is illustrated with some examples.
An Optimization Scheme for Demosaicing Algorithm on GPU Using OpenCL

With the popularity of GPU which has the high performance computing feature, more and more algorithms have been successfully transplanted to the GPU platform and achieved high efficiency. But existing videos or images processing methods, such as demosaicing algorithm, have not fully exploited the parallel computing capacity of heterogeneous processing platform and the video frame rates can’t meet real-time requirements. In order to take full advantage of the computing power of GPU under the heterogeneous processing platform, an optimization scheme is proposed in this paper. We use the demosiacing algorithm as a case and modify the algorithm. By exploiting the GPU’s memory hierarchy, the optimization scheme improves the parallelism of the algorithm while reducing the memory access latency, and greatly reduces the execution time. Then we achieve the zero-copy at the same time. The experimental results show that optimization version has a significant performance improvement, the optimized OpenCL version is up to 6x comparing with the basic OpenCL version about kernel execution.
Application of Integrated Reorganization of Science Specimen Data Using Kettle

Standards and specifications are the premise of integrated reorganization of science specimen data, and data integration is the core of the reorganization. ETL [1] which is the abbreviation of extract, transform, and load [2], is very suitable for data integration. Kettle is a kind of ETL software. In this paper, it has been introduced into the integrated reorganization of science specimen data. Multi-source and heterogeneous specimen data are integrated using kettle, and good results have been achieved. It proved the effectiveness of kettle in the integrated reorganization of science specimen data. The application has practical significance, and the method can be referenced when reorganizing other resource data.
PEMBELAJARAN MENULIS CERITA PENDEK DENGAN MENGGUNAKAN METODE THINK TALK WRITE

Short story writing is one of the skills that must be mastered by students especially class XI. Apparently, writing has not become a cultural activity so the role of teachers is very important in improving it. This study aims to (1) to find out the activities of teachers and students in short story learning using Think Talk Write method (2) to see the significant difference after being given treatment with Think Talk Write method. The method used in this research is an experimental method. Samples used are students of class XI SMK BIB Ngamprah. (1) seen from the activity of teacher and student that can be done 86 and 87,5 which mean very good (2) result of research indicate that result of calculation of Mann-with test 0.000, hence significance value ≤ 0,05 can be concluded that there a significant influence.
Face Recognition in Thermal Images based on Sparse Classifier

Despite recent advances in face recognition systems, they suffer from serious problems because of the extensive types of changes in human face (changes like light, glasses, head tilt, different emotional modes). Each one of these factors can significantly reduce the face recognition accuracy. Several methods have been proposed by researchers to overcome these problems. Nonetheless, in recent years, using thermal images has gain more attention among the introduced solutions as an effective and unique solution. This article studies the performance of sparse processing techniques when facing with challenges of face recognition problem in thermal images. Also, the potential of the sparse classifier algorithm to receive information directly from input images without using any feature extraction algorithms was studied. The obtained results indicated that the sparse processing techniques outperform the Eigenface and KNN algorithms in terms of addressing the challenges of thermal images. In this work, USTC NVIN and CBSR NIR face datasets were used for simulation purposes. These datasets include the images with different emotional states (sad, happy, etc.) captured in different light conditions; also the images are captured both with and without wearing glasses. Simulation results have shown that sparse classifier can be an effective algorithm for the face recognition problem in thermal images.
No Such Thing as a Small Leak: Leakage-Abuse Attacks Against Symmetric Searchable Encryption

Symmetric Searchable Encryption ( $$\mathrm {SSE}$$ ) schemes enable clients to securely outsource their data while maintaining the ability to perform keywords search over it. The security of these schemes is based on an explicit leakage profile [1], has initiated the investigation into how much information could be deduced in practice from this leakage. In this paper, after recalling the leakage hierarchy introduced in 2015 by Cash et al. and the passive attacks of [1] on $$\mathrm {SSE}$$ schemes. We demonstrate the effectiveness of these attacks on a wider set of real-world datasets than previously shown. On the other hand, we show that the attacks are inefficient against some types of datasets. Finally, we used what we learned from the unsuccessful datasets to give insight into future countermeasures.
FILTERAPP: An Android-Based Profanity Control Application Using WU-Manber Algorithm with Fault-Tolerant Searching Capability

This study utilized a multi-pattern string matching algorithm to develop an effective android-based mobile profanity control application for children. Initially, three (3) algorithms were subjected to an experiment using a simulation program to determine which algorithm has the highest accuracy rate particularly in providing a fault-tolerant search result. To analyze the performance of each algorithm, pre-defined datasets were used in the experiment for comparative analysis. Three measures were used: Precision, Recall and F-Measure. Based on the conducted experiment, Wu-Manber algorithm has the highest accuracy rate against Aho-Corasick and Set-Horspool algorithms.
A Visualization Technique to Support Searching Filtering

This paper discusses the need for an integrative literature review on Information Visualization for exploratory search particularly in handling data overload. The paper analyses many applications and web sites across disciplines. Certain search engines incorporate visualization to allow for better understanding of the information and at the same time reduce information overload. Current search engines use the query and response (lookup) process. Exploratory search allows for open-ended search. Visual representation is one feature in exploratory search that can be used to improve the overall search. The main contribution of this paper is the review of previous exploratory-search-based works, the utilised features as well as its existing applications, visualizations as the mechanism for developing filters to narrow down the results of searching. Many studies have shown that replacing traditional search engines with exploratory search by using the features of exploratory search can reduce the data overload.
Evaluating the influence of error management culture factors on human resources productivity and strategic human capital - the case of Mobarakeh Steel Company

The main aim of this study is to determine factors influencing the error management culture and investigating the impact of such factors on the development of strategic human capital and human resources productivity. Mobarakeh Steel Company has been selected as the case study and mixed (qualitative-quantitative) method has been applied for data analysis. In the qualitative and quantitative analyses, 25 executive managers and 153 experienced employees have been selected, respectively. The influencing factors as prerequisites for effective management of error in the organisation's culture were determined as changing the attitude and belief of management toward error, paying attention to all dimensions of error management, balanced learning in all departments of the company, integrated actions for establishing an error management culture and a fundamental development in the human resources system of the company. Findings indicate that the managers focused on the importance and stronger support of the importance of the main factors affecting the error management culture. Also, a platform was created for the development of strategic human capital with a learning-from-errors approach and encouraging the organisation to improve human resource productivity.
Assessing Written Work and Working with Errors

The article deals with the issue of assessing written work of students and working with errors. As to the article, assessment is a part of the lesson during which the teacher evaluates how students have mastered the material and use it in reception and production of texts in the oral and written forms. Also, author studies theoretically the specific features of the assessment, characteristics of writing technique and types of errors and working with during the lesson.
e-channel

Traditional journal venues are not typically receptive to publishing manuscripts or objects devoted to innovation. As a result, innovators are left without a platform for announcing their contributions and having their work validated and valued by institutional administrators and funding bodies. The Spencer S. Eccles Health Sciences Library (EHSL) at the University of Utah addressed this imbalance by offering a reputable channel where innovators can disseminate their work. Partnering with other innovation centers, libraries, and professional associations, the EHSL has created e-channel—a forum for innovators to receive credit for their work and gauge the impact of their ideas or projects.1 This chapter discusses the path taken by the EHSL to create the e-channel venue, highlights the various partners who have contributed content, and describes additional projects underway.
RECENT ADVANCES IN MODELLING AND OPTIMIZING COMPLEX SYSTEMS BASED ON INTELLIGENT ALGORITHMS

The paper tries to understand recent research advances in modelling and optimizing complex systems to give references for interested researchers. Intelligent models have the capability of self-adapting to the changing environment, which may generate effective solutions for complex dynamic systems. Firstly, critical techniques and significant events in the development process of complex systems are reviewed. Then classical practical applications of modelling and optimizing complex systems over recent years based on several representative intelligent models are introduced and analysed. Thirdly, methodology of each intelligent M & O model is analysed and compared. Finally, this paper concludes and prospects relevant future work.
Development of a prototype for referees to monitor whip use in horse racing

Alice and Bob want to run a protocol over a noisy channel, where some bits are flipped adversarially. Several results show how to make an L-bit noise-free communication protocol robust over such a channel. In a recent breakthrough, Haeupler described an algorithm sending a number of bits that is conjecturally near optimal for this model. However, his algorithm critically requires prior knowledge of the number of bits that will be flipped by the adversary.We describe an algorithm requiring no such knowledge, under the additional assumption that the channel connecting Alice and Bob is private. If an adversary flips T bits, our algorithm sends L+O(L(T+1)log⁡L+T) bits in expectation and succeeds with high probability in L. It does so without any a priori knowledge of T. Assuming a lower bound conjectured by Haeupler, our result is optimal up to logarithmic factors.
Guides: Printing, Scanning and Photocopying: Installing EDC Lab Printers (MAC)

How to print, scan and photocopy at the RRC Libraries Instructions on how to set up the PaperCut client on your Mac for the Exchange District Campus
Microblog topic identification using Linked Open Data

Much valuable information is embedded in social media posts (microposts) which are contributed by a great variety of persons about subjects that of interest to others. The automated utilization of this information is challenging due to the overwhelming quantity of posts and the distributed nature of the information related to subjects across several posts. Numerous approaches have been proposed to detect topics from collections of microposts, where the topics are represented by lists of terms such as words, phrases, or word embeddings. Such topics are used in tasks like classification and recommendations. The interpretation of topics is considered a separate task in such methods, albeit they are becoming increasingly human-interpretable. This work proposes an approach for identifying machine-interpretable topics of collective interest. We define topics as a set of related elements that are associated by having posted in the same contexts. To represent topics, we introduce an ontology specified according to the W3C recommended standards. The elements of the topics are identified via linking entities to resources published on Linked Open Data (LOD). Such representation enables processing topics to provide insights that go beyond what is explicitly expressed in the microposts. The feasibility of the proposed approach is examined by generating topics from more than one million tweets collected from Twitter during various events. The utility of these topics is demonstrated with a variety of topic-related tasks along with a comparison of the effort required to perform the same tasks with words-list-based representations. Manual evaluation of randomly selected 36 sets of topics yielded 81.0% and 93.3% for the precision and F1 scores respectively.
Malignant Melanoma Classification with Deep Learning

Malignant melanoma is the deadliest form of skin cancer. If correctly diagnosed in time, the expected five-year survival rate can increase up to 97 %. Therefore, exploring various methods for early ...
Why you need a content strategy

Content is at the core of what a company does, and an effective content strategy is aligned with business strategy and works to power all marketing; is your company embracing the opportunities to promote?
EDINA, Education of International Newly Arrived Migrant pupils

Abstract The goals of the project were (1) to reduce disparities proposing adequate strategies and activities to help teachers, schools and municipalities to support newly arrived migrant pupils, (2) to increase the efficiency of investment in the education of this group and, and (3) to strengthen training paths of school staff fostering the collaboration between schools, municipalities, and researchers nationally and internationally. An interdisciplinary and international team developed a curriculum to be used by teachers, schools, municipalities and policy makers for pre- and in-service teacher education. The curriculum is available online at https://edinaplatform.eu
FS-CARS: fast and scalable context-aware news recommender system using tensor factorisation

Matrix factorisation is a widely adopted approach of collaborative filtering technique which factorises user-item rating matrix to generate recommendations. User-item rating matrix can be extended to incorporate user's context, resulting in rating tensor which can be factorised to generate better quality context-aware recommendations. Tensor factorisation is computationally intensive task; computational time can be significantly reduced using a distributed and scalable framework. This paper proposes a context-aware news recommender system which classifies news items into different categories and incorporates user's context resulting in rating tensor which is then factorised to generate recommendations. The news items are highly dynamic and are generated in large numbers which can further increase the computational time many fold. To fix the computation time of the process, the proposed system is implemented on distributed and scalable framework of Apache Spark using MLlib library. The proposed recommender system is evaluated for performance and computational time.
BLOCKCHAIN – A Boon for KYC

Know Your Customers (KYC) works like a backbone of Banks and other Financial Institutions for fighting against Money Laundering and Financing of Terrorism. As such, it becomes necessary that KYC should be properly implemented. In India, KYC was introduced in 2002 by Reserve Bank of India but was made mandatory in 2010. Moreover, if the banks and other financial institutions do not follow KYC, they are penalized with hefty amounts. As such, Banks need to follow KYC strictly. Here Blockchain technology can help Banks and other Financial Institutions, not only in maintaining proper KYC records but also helps in detecting fraud activities along with elimination of duplication of records. In this paper, an attempt has been made to find out how Blockchain can be a Boon for KYC.