Reviewerly/exHarmony huggingface dataset:
J.K. ROWLING LA GRAN DESCONOCIDA DE LA CRÍTICA LITERARIA ANGLOSAJONA: ¿CONTAMINACIONES CULTURALES? J.K. ROWLING, THE MOST UNKNOWN OF THE ANGLO-SAXON LITERARY CRITICISM: CULTURAL POLLUTIONS?

Resumen En ocasiones, la valoracion literaria de un autor parece clara y asumida no solo por la audiencia sino, tambien, por la Academia. Sin embargo, al mirar de cerca esta valoracion, su metodologia y sus fuentes, las razones por las cuales se tilda de “bueno” o “malo” a un autor pueden quedar muy diluidas o, a veces, invisibles. Este es el caso de J.K. Rowling. Los resultados de este estudio sugieren posibles contaminaciones culturales como motivo ultimo del escrache a la obra de esta autora, quizas la gran desconocida en su faceta mas puramente literaria. Palabras clave: monocultura, multicultural, transcultural, dificultad social. Abstract Sometimes, a writer’s literary assessment appears to be crystal clear and it is assumed not only by the audience but also by the Academia. However, when looking closely at its evaluation, its methodology and its sources, the reasons why it is labelled as ‘good’ or ‘bad’ remain blurry or, most of the times, invisible. This is the case of J.K. Rowling. Findings of this research suggest possible cultural pollutions as the ultimate reason for the boycott of this author’s work, who might be the most unknown regarding her literary facet. Key words: monoculture, multicultural, transcultural, social dis-ease. [
STUDENTS’ WRITING ESSAY ABILITY OF “ILMU PERPUSTAKAAN ISLAM”

This research was conducted to see how the ability of students majoring in Islamic Library Science, Faculty of Islamic Law Adab and the Da'wah IAIN Batusangkar in writing discourse. In general, the components of writing discourse that are of concern are Organization, Content, Grammar and Sentence structure, Mechanics and Vocabulary. This research focuses on organization and grammar and sentence structure. This research uses descriptive method. The data of this study were taken from the test of the ability to write discourse by students of Islamic Library Science in the first year. Then, the data is analyzed using the qualitative method. The results of this study indicate that of 38 students majoring in Islamic Library Science, 20 students were able to write discourse while 18 students were only able to write a few sentences. Of the 20 discourses, 1 discourse has used writing components very well. 4 discourses are already using writing components well. 5 discourses are categorized as moderate. 10 discourses are categorized as lacking and there are no discourses categorized as very lacking in using writing components, especially organization and grammar and sentence structure.
Efficient Migration of Large-Memory VMs Using Private Virtual Memory

Recently, Infrastructure-as-a-Service clouds provide virtual machines (VMs) with a large amount of memory. Such large-memory VMs can be migrated to other hosts on host maintenance, but it is costly to always preserve hosts with sufficient free memory as the destination of VM migration. Using virtual memory in destination hosts is a possible solution, but the performance of VM migration largely degrades because traditional general-purpose virtual memory causes frequent paging during the migration. This paper proposes VMemDirect, which achieves efficient migration of large-memory VMs using private virtual memory. VMemDirect creates private swap space for each VM on fast NVMe SSDs. Then it transfers likely accessed memory data to physical memory and the other data to the private swap space directly. This direct memory transfer can completely avoid paging during VM migration. We have implemented VMemDirect in KVM and showed that the performance of VM migration and the migrated VM was improved dramatically.
Gathering Identification Using Image Metrics for Intelligent Situation Awareness System in Real Time Scenarios

The advancement of image processing in the field of Artificial Intelligence has created various research prospects in the area of object detection, pattern recognition, etc. Face detection technology is applied for biometric authentication systems for face recognition and verification. This work aims at developing a tool that can be used in an Intelligent Situation Awareness System for a given Region of Interest. This has been done by identifying human face as objects from digital video frames through real time video streaming by using Recurrent Convolution Neural Network (RCNN) classifiers. The key frames from a video are identified and machine learning algorithm is being applied on it for performing the object identification. After the facial objects are being identified in a given frame, this can be utilized for performing semantic analysis in a given spatiotemporal scenario. The metrics identified in this work include object count, hand gestures, relative distance as well as density of objects for developing a robust system that could function in real time.
Stretching the Rubber Band: Centralized vs. Cooperative Management of the Alma Community Zone

The Alma Community Zone contains bibliographic and access information for hundreds of thousands of electronic resources as well as authority records representing several international thesauri used in support Alma's automated authority control processes. Two members of the Alma Community Zone Management Group will provide an overview of the Community Zone and will discuss current topics of interest including:Community Zone scope, collections and data sourcesCommunity Zone maintenance and enhancement (both by ExLibris and by customers)Issues of quality control and collaborative maintenance of the Community ZoneThe speakers will also discuss the current work of the Community Zone Management Group, a group charged by the Alma Working Group to advise and collaborate with Ex Libris on Community Zone metadata and its enrichment, and to serve as a resource for the Alma community.
Parameters of Motion for Multi-UGV Control System Performing Joint Transportation

The paper considers an algorithm for calculating the motion parameters for a group of mobile robots performing a joint transport task. The motion of this group of mobile robots is considered on a plane surface. Trajectory of motion passes over different zones with various soil properties. Rectilinear motion and motion along the arc of known radius are considered. The algorithm was successfully tested in previously developed special software for debugging and modelling.
A NOVEL METHODOLOGY FOR TIME PLANNING OF RESOURCE-CONSTRAINED SOFTWARE PROJECTS WITH HESITANT FUZZY DURATIONS: A CASE STUDY

Time planning of software projects is a complex process for almost all companies. Uncertainties about effort required may cause underestimation or overestimation. Thus, enterprises may encounter an unrealistic schedule and choosing the appropriate planning method becomes crucial. This paper presents a novel integrated decision support methodology for time planning of resource constrained software projects. For this aim, after identifying the effective criteria by the help of expert judgments from the Turkish Information Technology industry and literature review, we find their priorities by Hesitant Fuzzy Pairwise Comparison and calculate estimated effort for the projects. Then, we use the priorities of criteria and estimated durations of the projects in a mathematical programming model proposed to find starting and finishing (delivery) time of the projects. Furthermore, we apply the methodology for solving the project time planning problem of a Turkish software company. It is seen that the proposed method provides efficient schedules.
Multi-objective Approach for Semi-Supervised Discriminant Analysis with Relative Distance

Data such as videos, genetic information, etc. from real-world applications reside in a high dimensional space. Before performing classification, it is required to project the data from the high dimensional space to a lower dimensional space without losing too much information. Linear discriminant analysis (LDA) is one of the most widely used methods for dimensionality reduction, that maximizes the ratio of the between-class scatter and total data scatter in the projected space using the labeled information. However, in the real world scenario, labeled information is hardly ever available in large quantities, but an abundant amount of unlabeled data is available. In this paper, we propose a Semi-Supervised Discriminant Analysis method called SSDARD, which considers the unlabeled information in the form of a k-NN graph. Different from the existing semi-supervised dimensionality reduction algorithms, our algorithm is more consistent in propagating the label information from labeled data to unlabeled data due to the use of relative distance function instead of normal Euclidean distance function to generate the k-NN graph. To find an appropriate relative distance function, we use pairwise constraints generated from labeled data and satisfy them using Bregman projection. Since the projection is not orthogonal, we require an appropriate subset of constraints. In order to select such subset of constraints, we have further developed a framework called MO-SSDARD, which uses an evolutionary algorithm while optimizing various cluster validity indices simultaneously. The experimental results on various datasets show that our proposed method is superior than various methods concerning various validity indices.
Experiencing the Future Car: Anticipatory UX as a Social and Digital Phenomenon

In order to be innovative and competitive, the automotive industry seeks to understand how to attract new customers, even before they have experienced the product. User Experience (UX) research oft ...
How we conceptualize climate change: Revealing the force-dynamic structure underlying stock-flow reasoning

How people understand the fundamental dynamics of stock and flow (SF) is an important basic theoretical question with many practical applications. In this paper, we present a universal frame for understanding stock-flow reasoning in terms of the theory of force dynamics. This deep-level analysis is then applied to two different presentation formats of SF tasks in the context of climate change. We can explain why in a coordinate-graphic presentation misunderstandings occur (“SF failure”), whereas in a verbal presentation a better understanding is found. We end up with recommendations for presentation formats that will help people to better understand SF dynamics. Better public SF understanding might in turn also enhance corresponding public action – such as enhancing pro-environmental actions in relation to climate change.
Improvement of QSL by Ontologies of E-Questionnaire, E-Testing, and E-Voting Systems

QSL is the first specification language for specifying various e-questionnaire, e-testing, and e-voting systems. Although the terminologies among systems and services of e-questionnaire, e-testing, and e-voting are different, QSL has explicitly summed up three kinds of terminologies into one, so that causes QSL has poor usability. The ontologies to summarize the terminologies in e-questionnaire, e-testing, and e-voting systems, to find out the corresponding relations with terminology of QSL, and to clarify the relations of e-questionnaire, e-testing, and e-voting systems can improve usability of QSL, so that the stakeholders can communicate and write requirement specifications easily. However, there is no ontology of e-questionnaire, e-testing, and e-voting systems. This paper proposes the ontologies for e-questionnaire, e-testing, and e-voting systems. Based on the ontologies, we present the improvement of QSL, so that stakeholders can use arbitrary specific terminologies to specify the requirement specifications for other kinds of systems and services.
Further Heuristic Discussion on Two Countermeasures against Reaction Attacks on McEliece Cryptosystems with QCLDPC Codes

Recently two countermeasures against reaction Attacks on McEliece cryptosystems with QC-LDPC and QC-MDPC codes were proposed. In this paper further heuristic discussion and elements of the protocol proposals are presented which can bring a system with these techniques closer to practical applications and also can enhance the overall security of the systems which will use them.
Anonymity in the Age of Hate

The age of anonymous communication has given a voice to those who would otherwise have been silent. The internet has given people the ability to speak ...
GPU Algorithms for K-Anonymity in Microdata

GPU computing, nowadays widely and readily available on the cloud, has opened up novel opportunities for the parallelization of computationally-intensive tasks, such as data anonymization. The development of effective techniques that help to guarantee data anonymity is a critical enabler for data sharing activities, as well as to enforce compliance-think about the European GDPR. In this scenario, we focus on personal data stored in microdata sets. Before releasing such microdata to the general public, statistical agencies and the like have to sanitize them by using a variety of Microdata Protection Techniques (MPTs)that aim at keeping data utility while preserving some kind of anonymity. In particular, microaggregation is a specific MPT arisen in the field of statistical disclosure control. We analyze the microaggregation anonymization issues and propose three GPU-based parallel approaches for a well-known microaggregation technique: the Maximum Distance to Average Vector (MDAV)algorithm. The experimental results demonstrate the feasibility of our proposal and emphasize the benefits of using GPUs to speed-up the execution of privacy preserving algorithms for microdata.
Homogeneity Index as Stopping Criterion for Anisotropic Diffusion Filter

The Anisotropic Diffusion Filter is an image smoothing method often applied to improve segmentation and classification tasks. Because it is an adaptive and iterative method, one should define some stopping criterion in order to avoid unnecessary computational cost while producing the desired output. However, state-of-the-art methods in this regard consider costly comparative functions computed at each iteration or allowing extra iterations before actually stopping. Therefore, in this paper we propose a new stopping criterion to overcome this difficulty that defines the number of iterations without additional comparisons during the image processing. Our stopping criterion is based on the image homogeneity index and the constants included in the filter definition, which can be calculated before the first iteration. Using three different measures of similarity in grayscale and colorful images from different domains with variation of tonality, our results indicate that the proposed stopping criterion reduces the number of iterations and, simultaneously, maintains the quality of the diffused images. Consequently, our method can be applied to images from different sources, color composition, and levels of noise.
A Enhancing Students' Reading Comprehension Through Jigsaw at Vocational High School

The objective of this research is to increase students’ reading comprehension through jigsaw and to describe classroom situation when jigsaw is applied in teaching reading. Classroom action research was carried out through observasion, interview, test consisting of two cycles. 35 Multi Media students of tenth grade at SMKN 2 Kediri was assigned as participant. Analyses resulted that jigsaw could enhance the students’ reading comprehension and classroom situation. It can be seen that the students were eager, interested and motivated to participate in reading class. During teaching reading, the students showed more active than before and the class atmosphere seems more alive. Then, students’ reading achievement in cycle two improved than in cycle one. The students’ average score was higher from 71,85 in the first cycle to 79,85 in the second cycle.Keywords: Reading Comprehension, Jigsaw, Vocational High School
The Effects of Corpus Type and Size on the Classification of News

A tremendous amount of data is produced and stored in every second. This increase causes problems in controlling, storing and analyzing the data. These data have to be classified in order to access information rapidly. We researched word embedding tools, the effects of corpus types and size on the success of classification. We present the large volume data set of Turkish news that will be classified, unlabeled 2 million and 20 million Turkish tweets to researchers. Word embedding tools GloVe and fastText success were inspected on 40 thousand Turkish news that were labeled 13 different classes, 2 millions of tweets and 20 millions of tweets.
Online and Hate Harassment

Abstract The Universities UK Conference, on tackling online harassment, recently took place at the BCS offices in London. ‘It was clear from the speakers that we must do more as a society to safeguard the individual and prevent online harassment,’ writes Daniel Aldridge, Senior Policy Programme Manager at BCS
Recurrent Control Nets as Central Pattern Generators for Deep Reinforcement Learning

Central Pattern Generators (CPGs) are biological neural circuits capable of producing coordinated rhythmic outputs in the absence of rhythmic input. As a result, they are responsible for most rhythmic motion in living organisms. This rhythmic control is broadly applicable to fields such as locomotive robotics and medical devices. In this paper, we explore the possibility of creating a self-sustaining CPG network for reinforcement learning that learns rhythmic motion more efficiently and across more general environments than the current multilayer perceptron (MLP) baseline models. Recent work introduces the Structured Control Net (SCN), which maintains linear and nonlinear modules for local and global control, respectively. Here, we show that time-sequence architectures such as Recurrent Neural Networks (RNNs) model CPGs effectively. Combining previous work with RNNs and SCNs, we introduce the Recurrent Control Net (RCN), which adds a linear component to the, RCNs match and exceed the performance of baseline MLPs and SCNs across all environment tasks. Our findings confirm existing intuitions for RNNs on reinforcement learning tasks, and demonstrate promise of SCN-like structures in reinforcement learning.
한국어 모국어 화자의 영어 파생접사 학습 및 사용 오류에 관한 연구

The purpose of this study is to investigate Korean L1 students’ awareness of L2 English derivational affixes and the relationship between one’s morphological knowledge and overall language proficiency. A total of 150 high school students participated in the study. A diagnostic test was developed to assess their morphological knowledge with a set of high-frequency prefixes and suffixes. The results indicate that the degree of ease and difficulty in acquisition of an affixal morpheme is related to its frequency and compositionality but not with its productivity or uniformity; the acquisition of prefixes appear to differ in function of frequency while that of suffixes is more closely associated with grammatical categories. Korean students show some characteristic error patterns. Most notably, they tend to overgeneralize the prefix in- to a wide range of stems, often producing morphologically ill-formed words. A student’s ability to use suffixes predicts the student’s overall English proficiency to a significant degree, but no such relationship is found between one’s knowledge of prefixes and proficiency. Since Korean students can hardly notice and acquire L2 English derivational morphology only on the basis of natural input, explicit instructional interventions and consciousness raising seem to be necessary.
Implikatur Kampanye Calon Walikota dan Wakil Walikota Bengkulu

This research aims to describe the purpose and the function of the candidate of mayor and deputy mayor’s campaign discourse implicature in Bengkulu for 2012 – 2017 period. The method used was descriptive method. The result showed that the implicature occurred on the campaign discourse was aimed to persuade and influence the readers. There are some purpose of this implicature, those are: (1) to promise the flagship program, (2) to assure people towards the candidate that is going to be voted, (3) to persuade people to vote the candidate, (4) to satirize. Additionally, the functions of the implicature are: (1) to make people agree with the program which is offered, (2) to persuade people to vote the candidates, (3) to criticize.Key Words: Discourse, Implicature, the candidate of mayor in Bengkulu
The Image Shape Recovery on Communication Data Lost Damage

This paper presents a fast efficient error concealment method for recovering information related to shape. The proposed technique comprises block classification, edge direction interpolation and filtering interpolation. Missing blocks are classified in four categories: transparent, opaque, edge and isolated blocks. Most of the computation is spent on edge blocks and isolated blocks to maximize the cost and performance tradeoff. For the recovery of edge blocks, the edge slope is computed by referring to the nearest available block, from which the missing shape is interpolated parallel to the edge. Isolated blocks are dealt with using a cascade filter to approximate the actual shape.
What are our existing and developed senses in and as a result of virtual space?

What are our existing and developed senses in and as a result of virtual space? As more and more acts of human beings are transposed into the virtual world, one of the questions that can arise is, what about our senses? The discussion might seem pointless since these virtual users, all of us, are human beings living in the real world!
Multiple-point Statistics Based on Gaussian Pyramids of the Training Image

Summary In this work, we present a new multiple-point statistics (MPS) method combining the direct sampling algorithm and the use of multiple-resolution representations of the training image (TI) through Gaussian pyramids. First, the pyramid is built by applying convolution with a Gaussian-like kernel, which provides versions of the TI at lower resolutions. Then, successive MPS simulations are performed within a pyramid: 1) a simulation is done in the lowest resolution level, 2) the result is used to condition a simulation in the next (finer) level, and 3) this last step is repeated until the initial resolution is simulated. This technique allows to guide the MPS simualtions and to obtain results that better reproduce the spatial statistics of the TI, compared to the results of MPS without pyramids.
Automatic transcription of the Polish newsreel

Abstract This paper describes an automatic transcription system for the Polish Newsreel, which is a collection of mid to late 20th century news segments presented in audio and video form. They are characterized by their use of archaic language and poor audio quality, which makes them a demanding problem for speech recognition systems. Acoustic and language models had to be retrained using data from in-domain corpora. During the adaptation of the models, experiments were carried out to select optimal adaptation parameters. The experiments showed that the adaptation of the speech recognition system to a narrow and clearly defined domain significantly increases its efficiency. The final word error rate obtained for this domain was 10.97%.