Reviewerly/exHarmony huggingface dataset:
Statistical Analysis and Introspection on Research Situation of Foreign Language Teaching in China under CALL Environment

This paper presents an overview of the development of CALL research in the past 11 years (2007-2018). Based on the previous studies published in the most influential eight foreign language teaching journals in China, a statistical analysis is made on the collected research and features and tendencies of CALL research are explored and summarized based on the comparison results of Na Min's research(which also gives a statistical analysis on CALL research published in 1995-2006 with the same collected range).The paper concludes that the past seven years sees a sharp drop of research in CALL. With respect to research areas, the following five are identified: the theoretical discussions relevant to CALL, the classroom practice of CALL, the positioning of teachers and students in CALL, the computer-assisted language testing and the development of computer technologies and software. Finally, the conclusions along with some suggestions for further improvements are raised.
Sebastian Hoffmann, Andrea Sand, Sabine Arndt-Lappe and Lisa Marie Dillmann (eds.), Corpora and lexis (Language and Computers 81). Leiden and Boston: Brill / Rodopi, 2018. Pp. 306. ISBN 9789004361133.

Sebastian Hoffmann, Andrea Sand, Sabine Arndt-Lappe and Lisa Marie Dillmann (eds.), Corpora and lexis (Language and Computers 81). Leiden and Boston: Brill / Rodopi, 2018. Pp. 306. ISBN 9789004361133. - Volume 24 Issue 2
PROFESSIONAL AND PEDAGOGICAL SELF-DESIGN OF FUTURE EDUCATION MANAGERS: THEORETICAL ASPECTS

The article considers self-design as an important element for the professional and pedagogical training of future education managers. It is noted that the specialists’ professional activities cannot be sufficient for quality without the formation of prognostic and design competencies. The concentration of individual experience in solving professional problems provides act-psychological competence. The manager of a modern educational institution is a professional who consciously and constantly improves his/her qualifications not only by means of organized forms, but also in the course of self-knowledge, self-diagnosis, self-design, and self-improvement. The article analyzes the main components of education management, reveals the essence of the concept of “self-design” and shows a comparative analysis of various theoretical and methodological approaches (activity, subject, complex, functional role-playing, acmeological) to describe the theoretical and methodological approach of self-designing future managers in interpreting various scientists. Self-design is considered as the personal ability of the future personnel to the inclusion in the system of self-determined and self-controlled activities which aim at increasing the efficiency of professional activity and self-improvement of personal qualities. The real methodological basis for effective professional and pedagogical self-design for future education managers is synergetic acmeology, which explores the regularity of attaining the maximum perfection by an arbitrary system of self-organization. Synergetic acmeology contains the theory of social synergetics and the theory of acmeological development. Synergetic acmeology is a practical application of the philosophy’s success, which is reflected in its sections: synergetic carherology, synergetic catabology, synergetic ethnology. Based on the analysis, it was concluded that self-design is the process of identifying possible options for combining means, goals, spatial and temporal parameters of activity to find the most acceptable, optimal option for the development and improvement of the personality. The result of self-design is various projects (options) for future activities, various types of Self-concept. Self-design is the desire of a person to improve his/her Self-concept in various areas of life.
Parallel Acceleration of ELAS on ARM

Fast implementation of stereo depth estimation algorithms on low-cost processors is demanding both insight of the algorithmic architecture and efficient parallelization techniques. This paper proposes an ARM-base OpenMP parallel implementation method of ELAS (Efficient Large-Scale Stereo Algorithms), which is very promising for fast computation on embedded systems. Our experiments were conducted under multi-core environments, both on an 8-core computer and an 8-core ARM ODROID-XU4. The parallel performance was examined in two ways: single image's parallel processing and batch processing of an image set containing seven pictures. Experimental results demonstrate the efficiency of our acceleration method, which can achieve a speeding-up for more than four times compared with the original sequential implementation.
Long Distance Sea Surface Images Fast Sparse Matching by Integrated Feature Vector

Correspondence matching is a fundamental task of stereo binocular vision system. This paper proposes a fast spares correspondence matching method used for long distance sea surface images which possesses no obvious structure and color distribution features, simultaneously, requires high processing accuracy and speed. To solve the problem of lacking obvious features, the paper extracts sea waves out from sea surface image as feature points and gathers sea waves’ size, shape, brightness, location and lengths in four orientations to realize descriptors fusing and form feature vector. To complete stereo matching fast and accurately, the paper utilize cosine similarity to match feature vectors generated in first step. The correspondence verification is carried out by the final 3D measurement result. Experiments on various real sea surface image pairs are operated to validate the effectiveness of proposed method. The comparisons between our method and some state-of-the-art methods are carried out to display the advantage of our method in precision.
Robust Face Recognition Method Based on Kernel Regularized Relevance Weighted Discriminant Analysis and Deterministic Approach

A novel feature dimensionality reduction strategy based on kernel regularized relevance weighted discriminant analysis is proposed in this paper with some interesting characteristics. First, the proposed method has shown its effectiveness in dealing with a small sample size problem when using the regularized linear discriminant analysis (RLDA) technique and Kernel theory. Second, a new computation method is proposed to solve the complicated and inefficient computation procedure problem in the traditional RLDA technique while using the cross-validation method. The experimental results indicate that the proposed algorithm shows better performance than the other methods.
University of Kentucky TraceLab Component Similarity Matrix Voting Merge

We present one of the University of Kentucky TraceLab components, Similarity Matrix Voting Merge. In this paper, we highlight some particularly interesting aspects of the component such as challenges faced when developing it and while implementing unit testing for the project. Finally, we provide an example of the component being used in a TraceLab experiment. A link for download of the component is provided.
Method of Sea Wave Matching Based on Siamese Network

Tsunami is known as one of the most destructive natural disasters. Aiming to measure Tsunami, our laboratory proposes a method based on the binocular stereo vision. As we calculate the 3D coordinates of sea waves, the sea surface height in surveillance area can be measured in real time. Sea wave matching is the key step in the above method. However, because of the wave deformation caused by uneven illuminance and different filming angles, it is difficult to match the deformed sea waves by conventional image processing methods. In this paper, we propose a method of sea wave matching based on Siamese Network. Firstly, we take photos of the sea surface with two cameras. Given the raw photos, we divide them into small blocks of waves. Blocks contains few waves will be removed. Then the remaining blocks from left and right photos will be connected and saved as image samples of training database. Afterwards we establish a siamese network framework consisting of two identical convolutional neural networks (CNN). After being trained by database, the siamese network can measure the similarity metric between the two inputs and tell whether the input waves are matched or not. Experiments on the test database show that our method can match sea waves with approximately 78% accuracy within several seconds. Besides, our network is self-adaptive to different sea wave photos as well as to different camera settings.
The National and University Library of Iceland 200th Anniversary

The National and University Library of Iceland (Landsbókasafn Íslands — Háskólabókasafn) celebrated its 200th anniversary in 2018. The purpose of the review is to acquaint Russian library specialists with the history of this institution and to show its role in the preservation and promotion of the national cultural heritage. The author considers the main stages of development of the National and University Library of Iceland (NULI): from the establishment of the Icelandic Provincial Library in Reykjavík in 1818 (in 1881 it was renamed to the National Library of Iceland) to its merger with the University Library in 1994. The main sources of the study are the articles of foreign, primarily Icelandic, researchers on the activities, holdings and electronic collections of NULI, published in the late 1990s — 2010s. The author notes the contribution of Jón Árnason (1819—1898), the famous Icelandic researcher-folklorist and the first national librarian, who headed the library for about 40 years, in the development of the institution’s structure and stocks. The paper characterizes the modern organizational and management structure of NULI, composition of collections (including the Manuscript Department and the Icelandic Department), the system of library and bibliographic services. The author emphasizes the role of the Árni Magnússon Institute for Icelandic Studies in Reykjavík as the main repository and centre for the study of Icelandic manuscripts. The institute conducts the intensive work on search for medieval Icelandic documents in the libraries and archives of other states and the digital reconstruction of scattered collections. The article reveals the work of NULI on opening of its holdings and creation of digital collections (“Collection of historical maps of Iceland”, “Digital library of medieval Icelandic manuscripts”, “Digital library of Icelandic Newspapers and magazines”, “Collection of old books of Iceland”, etc.). The author draws particular attention to the activities of the Consortium of Icelandic Libraries led by NULI to create a Unified Information and Library Space for the country and concludes on the significant contribution of NULI to the promotion and popularization of the national cultural heritage of the Northern state.
Introduction to Hand Posture Estimation

Computers are an essential tool for the Information Age in the modern world. They are, essentially, a tool to aid the human mind. To be effective, information needs to get to and from the human mind. For much of the evolution of computers, this was text-based, using simple keyboards and monitors.
Rethinking Temporal Structure Modeling Method for Temporal Action Localization

Temporal action localization in untrimmed videos is an important but difficult task. Difficulties are encountered in the application of existing methods when modeling temporal structures of videos. In the present study, we developed a novel method, referred to as Gemini Network, for effective modeling of temporal structures and achieving high-performance temporal action localization. The significant improvements afforded by the proposed method are attributable to three major factors. First, the developed network utilizes two sub-nets for effective modeling of temporal structures. Second, three parallel feature extraction pipelines are used to prevent interference between the extractions of different stage features. Third, the proposed method utilizes auxiliary supervision, with the auxiliary classifier losses affording additional constraints for improving the modeling capability of the network. As a demonstration of its effectiveness, the Gemini Network was used to achieve state-of-the-art temporal action localization performance on two challenging datasets, namely, THUMOS14 and ActivityNet.
Hybrid Function Sparse Representation Towards Image Super Resolution

Sparse representation with training-based dictionary has been shown successful on super resolution(SR) but still have some limitations. Based on the idea of making the magnification of function curve without losing its fidelity, we proposed a function based dictionary on sparse representation for super resolution, called hybrid function sparse representation (HFSR). The dictionary we designed is directly generated by preset hybrid functions without additional training, which can be scaled to any size as is required due to its scalable property. We mixed approximated Heaviside function (AHF), sine function and DCT function as the dictionary. Multi-scale refinement is then proposed to utilize the scalable property of the dictionary to improve the results. In addition, a reconstruct strategy is adopted to deal with the overlaps. The experiments on ‘Set14’ SR dataset show that our method has an excellent performance particularly with regards to images containing rich details and contexts compared with non-learning based state-of-the art methods.
Web security training [at] UniGe

Although cybersecurity is becoming more and more central in every software development lifecycle, nowadays, developers often lack a precise understanding of the risks and impacts of cyberattacks. Discovering vulnerabilities in production environments is hard, therefore making developers aware of common vulnerabilities leads to more reliable software and an easier and more effective vulnerability assessment process.
Multiple External Representations (MERs) as a Component of Special Language in Biology

Dealing with multiple external representations (MERs) in science education is the key to students’ understanding of science communication and becoming scientifically literate. It is generally accepted that learning scientific concepts, processes, and principles requires understanding and interacting with MERs. Science can be understood as a multimodal discourse, and dealing with MERs becomes a premise for learning and developing representational competence. This study analyzes the use of different representations and the relationship between representational competence and different biological contexts. A competence model with the components integration by information selection and interpretation (MERI), integration by construction (MERII), and translation and transformation of different scientific representations (MERIII) was operationalized in three different biological task contexts (ecology, physiology, genetics) and two types of representation (diagram, schema). Data collection was carried out via test booklets. Participants were 548 first-year university students from various fields. The findings indicate that the components MERI and MERII of the competence model have a significant influence on item difficulty, while there was only a significant influence for the biological task context genetics, but no significant influence for the type of representation.
Blind Print-Cam Data Hiding Exploiting Color Perception

Augmented Reality is becoming a fundamental technique to provide an easy access to additional information directly from the surrounding environment. It is however crucial that the mean through which the information is accessed is as integrated in the environment as possible. To this end, several data hiding techniques have been devised in the years to encode information in images in an imperceptible way. However, these techniques are frequently strongly affected by printing and re-acquisition process. This work presents an application developing a data hiding technique robust to printing and camera acquisition (print-cam), thus allowing to recover inserted data (hundreds or thousands of information bits) from printed images in a robust way (e.g., for different size of printed cover image, in different illumination conditions, with various geometric distortions). Performance and robustness of the proposed solution are tested with respect to different metrics to prove the feasibility of the technique.
A Collaborative Framework for Structure Identification over Print Documents

We describe Texture, a framework for data extraction over print documents that allows end-users to construct data extraction rules over an inferred document structure. To effectively infer this structure, we enable developers to contribute multiple heuristics that identify different structures in English print documents, crowd-workers and annotators to manually label these structures, and end-users to search and decide which heuristics to apply and how to boost their performance with the help of ground-truth data collected from crowd-workers and annotators. Texture's design supports each of these different user groups through a suite of tools. We demonstrate that even with a handful of student-developed heuristics, we can achieve reasonable precision and recall when identifying structures across different document collections.
T-Shirt as the Media of Learning the Nias Culture ( Study of Gamagama Nias T-Shirt)

Abstract In this modern era, human endevour to make the innovation in many aspects of life. Gama-Gama is one of the brands of t-shirt that attempt to make innovation in order to introduce Nias culture. On that t-shirt printed vocabularies that uses terms in Nias language The purpose of this research is to describe how GamagamaNias t-shirt can be the media of learning Nias Culture. Through interview and documentation the researcher collecting the data. Base on analysing the data the result of the research that Gama-Gama is a good media of learning Nias culture especially for the millenial generation. Thus the function of t-shirt is not only for fashion but also the media of learning. The suggestion could be given is innovation should be a tool for introducing the culture. The researcher hope there is another innovation way for introducing the culture.
Advanced Moving Camera Object Detection

Assuming a moving camera, detection of moving objects is a challenging task. This is mainly due to the difficulties to distinguish between objects motion and background motion, introduced by the camera. The proposed real time system, based on previous work without camera movement, is able to discriminate well the two kind of motions, thanks to a robust global motion vector removal, which preserves objects identified in the previous steps. The system reaches high performances just using input Optical Flow, without any assumptions about environmental conditions and camera motion.
Optimization of surveillance cost of a neighborhood using soft computing techniques

This work presents a genetic algorithm for the optimization of surveillance cost of a neighborhood. For a given map view (Google map view, satellite view, etc.) of any neighborhood, the aim is to estimate the minimum number of CCTV monitoring poles and their position required for complete surveillance. We will demonstrate how this problem models as a minimum vertex cover problem (MVCP). As MVC problem is NP-Hard problem, to deal efficiently with this optimization problem, we will apply a genetic based approach. Performance of the proposed genetic algorithm is also compared with the clever greedy algorithm and the natural heuristic for vertex cover. The modeling approach is illustrated with a college campus named Indian Institute of Technology (Indian School of Mines), Dhanbad, Jharkhand-826004.
Sens-e-Motion: Capturing and Visualising Emotional Status of Computer Users in Real Time

Emotion has been demonstrated to play an important role in the learning process. The capturing of these emotions is increasingly being digitally automated as researchers explore how technology can enhance student learning as well as teaching methodologies. More specifically, what emotions should be captured and how information about the captured emotions should be visualised has not been well researched in the context of teaching and learning. The study presented in this paper aims to produce a proof of concept prototype, called Sens-e-motion, that integrates multiple types of digital emotional monitoring and visualises the results in a meaningful way to the users. The monitoring includes facial expression recognition, blink-rate, text sentiment analysis, and keyword analysis. The prototype was designed and implemented as a web chat application with a RESTful web service backend and a NoSQL database. In this paper, we present the design of our system and describe technical details of it. Possible future research and development is also discussed.
Solar Power Resource Assessment Using Light Detection and Ranging Data and Open Source Geographic Information System

There is growing trend for distributed solar generation. To determine if it is a possible aid to a more sustainable power system, accurate assessment of solar potential on rooftops must be done. This study provides a fast and reliable method of solar power resource assessment using remote sensing technologies, Light Detection and Ranging (LiDAR) data and Geographic Information System (GIS). 1x1 m resolution Digital Elevation Model (DEM) is generated from point cloud data. An open-source software, System for Automated Geoscientific Analyses Geographic Information System (SAGA GIS), is used to effectively operate LiDAR point cloud and produce solar insolation maps. Monthly insolation maps of the tested area, Iligan City, and its building rooftops are created. Results show an average value 1,088.76 kWh/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> solar insolation and the total value of 3.61 TWh of energy is found in the building rooftop of the study area. Data validation indicates a 7.66% absolute relative error.
BOOST: A novel algorithm <u>B</u>ased <u>O</u>n m <u>O</u>dified preference function and weight factor for ranking of cloud <u>S</u>ervices efficien <u>T</u>ly

In the present scenario, building the best quality cloud computing applications that fulfill demand services of customers is the toughest challenge for researchers. The services which perform the non-functional activities are considered as Quality of Service (QoS). Many sectors offer various cloud services such as Amazon, HP, and IBM to the customers. No technique is available to measure the real-world usage accurately and provide the efficient results for ranking the cloud services. Therefore, it is a very hard job for the client to select the optimal cloud service provider (CSP), which fulfills all the requirements of the customers. To avoid the conflict for selecting the right CSP, this paper proposes a novel QoS ranking prediction method called BOOST. Various experiments are done on the real-world QoS data by using EC2 services of Amazon. The results reveal that the proposed BOOST provides more efficient results than the competitor approaches.
Detection of Stator Faults in Induction Motors Using Alpha-Beta Transform and Image Analysis

In the present work, the diagnosis of incipient faults in three-phase induction motors is proposed. The system consists in to submit the three-phase induction motor signals to the alpha-beta transform. After that, the pattern obtained from the alpha-beta transform is submitted to the Hough transform in order to find the set of the parameters that describe the pattern. Finally, the set of parameters obtained by the Hough transform is used as input to the KNN algorithm to classify into three groups: Not fault, Medium fault, and Severe fault. The Classifier was tested obtaining 93.9% of accuracy.