Reviewerly/exHarmony huggingface dataset:
TEND: A Target-Dependent Representation Learning Framework for News Document

Real-time news documents published on the Internet have global financial and political impacts. Pioneering statistical approaches investigate manually defined features to capture lexical, sentiment, and event information, which suffer from feature sparsity. As a remedy, recent work has considered learning dense vector representations for documents. Such representations are general, which can not model target-dependent scenarios, such as stance detection towards a specific claim. There has been work on target-specific word and sentence representations, but little was done on target-dependent document representation. Moreover, documents contain more potentially helpful information, but also noise compared to events and sentences. To address the above issues, we focus on models that are: 1. task-driven, which optimize the neural network representations for the end task; 2. target-specific, learning news representations by considering the influence of specific targets. In particular, we propose a novel document-level target-dependent learning framework TEND. The framework employs the information of the target and the news abstract as clues, obtaining relatively informative sentences from the entire document for our objectives. The framework assembles a document representation by integrating the news abstract representation and a weighted sum of sentence representations in the document. To the best of our knowledge, we are among the first to investigate target-dependent document representation. Existing text representation models can be easily integrated into our TEND framework, and it is general enough to be applied to different target-dependent document representation tasks. We empirically evaluate our framework on two target-dependent document-level tasks, including a cumulative abnormal return prediction task and a news stance detection task. Results show that our models give the best performances compared to state-of-the-art document embedding methods, yielding robust and consistent performances across datasets.
AN ANALYSIS DEIXIS IN RIDWAN KAMIL’S SPEECH AT THE ASIA AFRICA CONFERENCE (KAA)

The central topics of pragmatics, this study is limited on applying deixis on speech Ridwan Kamil at the Asia Africa Conference (KAA). This research deals with the English deixis. The objectives of this study are to analyze type of deixis and to find out the most dominant type of deixis in “Ridwan Kamil’s speech at Asia Africa Conference (KAA)” on April 24th 2015. This study was conducted by using qualitative approach. From the five type of deixis, in Ridwan Kamil’s speech found that there are three types of deixis, namely person deixis (7 data) time deixis (3 data), and the last is social deixis (1 data). From the analysis of deixis, it was also revealed that person deixis as the most dominant type which was found on speech Ridwan Kamil at the Asia Africa Conference. Keywords: Deixis, Asia Africa Conference (KAA), Speech
The Vakt Model Based on Psycholinguistic Review for Overcoming Dyslexia Children

The underlying problems of dyslexia in this research begins when researcher conducted the research in grade five SD 1 Tritis Jepara on December 2017.From total 28 students, there were three students who couldn't read and write, even read one word or write their own name.This case certainly becomes a portrait reality of education that's very sad.That's not so good to hear.Some researchers have worked on VAKT Model to give contribution to overcome those problems.The VAKT Model is a learning model that activates all children modalities including visual, auditory, kinesthetic, and tactile.Learning with VAKT Model reviews from psycholinguistic in dyslexia children have problems in working memory; phonology related to hearing alphabet, numerical, and others; ortography realted to writing; kinesthetic; and reading speed also reading comprehension.But firstly, dyslexia children must understand about right and left directions.Triggered by that model, the researcher tries to hold new research on applying VAKT Model to overcome some difficulties of dyslexia children on reading.This research conducted in "Sekolah Disleksia Cendekia Kudus", because one and only therapy school in Central Java.The research about dyslexia is still rare than another problems.The goal is to know how effective VAKT Model for overcoming dyslexia children based on psycholinguistic review.Total of subjects 10 dyslexia children and how they learning with three therapy teachers.This research is qualitative descriptive analysis with type of case study.Data Collection Techniques are from participant observation, interviews, and documentation.Data will be analyzed by applying data reduction, data presentation, and verification.
MENGEMBANGKAN (PEMBELAJARAN) BLENDED LEARNING DENGAN SISTEM LINGKUNGAN PEMBELAJARAN VIRTUAL (VLE) DI PTKIN

Recently there has been a great interest invested in maximizing higher education students’ knowledge retention as well as increasing students learning experience using web technologies. Many higher education institutions upgrade their learning facilities by integrating virtual learning system to their education provisions. Educational content management system is now a primary requirement in higher education provisions. However, as for the context of Indonesian higher education, upgrading the learning system may mean a total change of the way classes are delivered. Most of Indonesian higher education teachers are those with minimal exposure of virtual technological use in classrooms. Although these teachers are familiar with the common internet tools especially the electronic mail system and internet social media, they are not yet familiar with managing a content management system. Universitas Islam Negeri (UIN) ar-Raniry is one of Perguruan Tinggi Keagamaan Islam Negeri (PTKIN) that share similar context. This study was conducted to explore UIN Ar-Raniry teachers’ readiness in delivering blended learning using Virtual Learning Environment. 124 teachers were participating in this research. The index of technological acceptance is relatively high (3.78). However, on the contrary digital technology use related to delivering lecture is relatively low. The use of e-mail and facebook is relatively popular among these teachers. The study found that teachers are interested in using more convenient digital tools in their classes if the university supports the trainings and assigns educational technology consultant to help them establish the virtual class and help with their technical troubles.
Trajectory Planning Based on Spatial-Temporal Constraints for MAV/UAV Cooperative System

Manned aerial vehicle (MAV)/ Unmanned aerial vehicle (UAV) cooperative system will be a significant form of aerial engagement in future. Based on convex optimization theory, a trajectory planning method for the cooperative system is proposed. Firstly, the motion model of the system was established and the controlling process was analyzed. Secondly, trajectory planners of MAV and UAV were designed, where the minimum energy consumption and the shortest time to arrival were selected as optimization objective respectively. Then the model of the two planners was approximated and convexified, with the spatial-temporal constraints. After that, the convex optimization algorithm was used to solve the problem. Finally, the feasibility of the proposed method is verified by taking the dual aerial vehicle as a scenario. The results show that the convex optimization algorithm can solve the optimal model of the trajectory planners effectively, and the safety of the cooperative system can be guaranteed by adding the spatial-temporal constraints.
A Power Anomaly Detection Architecture Based on DNN

Different from the traditional power data mining, it is proposed to use DNN (Deep Neural Networks) to deeply process the data, so as to discover power faults and abnormal behaviours in time. The result obtained ensures the normal operation of the power system; according to the characteristics of the user's electricity consumption data provided by the power company, the optimization methods such as oversampling are used to extract the maximum retained data information. At the same time, the accuracy of training is improved and the training complexity is reduced. A deep learning model is used to classify single point anomalies based on time series data. Agile AI (Artificial Intelligence) engineering architecture, offline batch data training and online model real-time detection are combined. Agile AI engineering is constantly improving accuracy. More importantly, the adaptability has been improved to ensure accuracy and efficiency in different scenarios.
Impacts of Entrepreneurial Competence and Government Policy on SMEs’ Non-Oil Export Performance in the Southwest Nigeria

The role of an effective export sector on an economy is of paramount importance in promoting growth and attainments of a nation's developmental objectives.This is due to its capacity to create employment opportunities and improve income generation with consequent reduction in social costs of youth unemployment and other associated crimes (Akeem, 2011).Export is also an important means of ameliorating the balance of payment deficits and contributing to foreign exchange generation needed for general economic growth and development (Mohsen, 2015).A well-performing export sector particularly in the non-oil product market for a country like Nigeria, has a multiplier effect that enhances increase in the national income (Usman & Salami, 2008).However, while the oil sector according to the recent Central Bank of Nigeria data (CBN, 2017, p. 230)still maintains its dominance with 98.2 % of the total exports in Nigeria, the trajectory and vagaries of instabilities in the international market price of crude oil is a well-known challenge to the dynamics of economic growth and development in Nigeria.This is why many research experts (Ehinomen & Adeleke, 2012; Aladejare & Saidi, 2014; Osotimehin, Jegede, Akinlabi & Olajide, 2012; Onodugo, Ikpe & Anowor, 2013; etc.) are of the view that promoting the export potentials of the Nigerian SMEs in the non-oil products sector,will provide a betterand more reliable result in the ongoing economic diversification programme.However, despite that the SME-led non-oil sector employs over 84% of Nigerian labour force according to Premium Times Newspaper report (25.01.2019), its contribution to total exports stillstands at dismal and meager level of 7.70% (CBN, 2017, p. 230).Thiscontrasts widely with the average of 45% and 65% SMEs' export contributions in the Asian Tigers and the European Unionrespectively (SMEDAN &NBS, 2013).Therefore, if Nigeria is to make progress in her quest for the SME non-oil export-led growth alternative, urgent and drastic action needs to be taken regarding proper identification of, and effective decision making on the core determinants of improved trade performance of her SMEs domestically and internationally.1.1.
Pengembangan Konsep Kompensasi Dosen dengan mekanisme reward/punishment ditopang dengan konsep sustainabilitas keuangan dengan menggunakan Sistem Informasi. (Studi Kasus : Universitas X)

The performance of X University in addition to being measured by academic productivity, is also measured by the competence of academic staff. Career development, productivity and performance of X University employees (especially lecturers) is the key to X University development strategies towards 2034. So there is a need to build a fix take home pay compensation concept solution with a more valid concept, more effective as a reward / punishment mechanism, and supported by concepts financial sustainability. In addition, the concept must be implemented to an information system that supports fix take home pay compensation to enter lecturer work plans and provides reports on the results of lecturer achievements each semester.
Local Image Descriptor Based Phishing Web Page Recognition as an Open-Set Problem

With the advent of e-commerce, digital services and social media, scammers have changed their way to gain illegal benefits in various forms such as capturing the credit card information or exploiting personal cloud accounts which is termed as phishing.For this reason, against this cyber crime, last two decades have witnessed a variety of combatting methodologies like HTML content based similarity analysis, URL based classification and recently visual similarity based matching since phishing web pages visually mimic to their legitimate counterparts in order to create an illusion to deceive innocent users.To this end, in this study, we propose a computer vision and machine learning based approach in order to classify whether a suspicious web page is phishing and further recognize its original brand name.In this regard, we have utilized and investigated two different local image descriptors namely Scale Invariant Feature Transform (SIFT) and DAISY.Apart from their common properties such as scale invariance, the aforementioned descriptors have apparent differences such that in addition to rotational invariance, SIFT employs key-point based sampling whereas DAISY applies dense sampling by default.Therefore, we first aimed to investigate the feasibility of these two local image descriptors in addition to revealing the effects of sampling strategy and rotational invariance in problem domain.Furthermore, in order to create a discriminative representation of a web page, we followed the bag of visual words (BOVW) approach having different vocabulary sizes such as 50, 100, 200 and 400.In order to evaluate the proposed approach, we have utilized a publicly available phishing dataset including snapshots of webpages sampled from both 14 different highly phished brands and ordinary legitimate web pages yielding a challenging open-set problem.The aforementioned dataset involves 1313 training and 1539 testing image samples in total.The visual features extracted via SIFT and DAISY were first transformed to a BOVW histogram and fed to three different machine learning methods such as SVM, Random Forest and XGBoost.According to the conducted experiments, based on a 400-D visual vocabulary, SIFT descriptor along with XGBoost has been found as the best descriptor-learner configuration having reached up to 89.34% validation accuracy with 0.76% false positive rate.Moreover, SIFT has outperformed DAISY descriptor in all settings.As a result, it has been shown that SIFT descriptors equipped with BOVW representation can be effectively used for brand identification of phishing web pages.
Prediction of Critical Clearing Time for Transient Stability Based on Ensemble Extreme Learning Machine Regression Model

To improve the accuracy of critical clearing time (CCT) prediction model, an ensemble regression learning method is employed, and a CCT prediction method based on the ensemble extreme learning machine regression model is proposed. First, the static power flow values are utilized as the original feature set, and the least absolute shrinkage selection operator (Lasso) is used for determining the input features. The extreme learning machine, regarded as the base learner, is used to establish the mapping relationship between the input features and CCT value. On this basis, the AdaBoost.RT algorithm is employed as the ensemble regression learning framework to construct multiple base learners. When performing CCT prediction, the predicted values of each base learner are weight integration to obtain the final prediction result. Simulation results on New England 39-bus system demonstrate the effectiveness of the proposed method.
Decidability of Several Concepts of Finiteness for Simple Types

If we consider as “member” of a simple type the outcome of any successful (possibly infinite) run of bottom-up proof search that starts from the type, then several concepts of “finiteness” for simple types are possible: the finiteness of the search space, the finiteness of any member, or the finite ness of the number of finite members (in other words, the inhabitants). In this paper we show that these three concepts are instances of the same parameterized notion of finiteness, and that a single, parameterized proof shows the decidability of all of them. One instance of this result means that termination of proof search is decidable. A separate result is that emptiness is also decidable (where emptiness is absence of “members” as above, not just absence of inhabitants). This fact is an ingredient of the main decidability result, but it also has a different application, the definition of the pruned search space - the one where branches leading to failure are chopped off. We conclude with our version of König’s lemma for simple types: a simple type has an infinite member exactly when the pruned search space is infinite.
Analysis of the Impact of the Conclusion of the Collaborative Professional Master's Program on Embraer and ITA

The creation of Embraer resulted from a long-term investment of the Brazilian Government in developing the necessary conditions for designing and manufacturing airplanes in the country.In each decade after its creation in 1969, Embraer launched a different type of aircraft.In 1999, Embraer launched the program for Regional Jets ranging from 70 -110 passengers, which implied a high demand of new engineers.One alternative to cope with that was the creation of the Professional Master's Program in Aeronautical Engineering in 2002, in partnership with ITA (Aeronautics Institute of Technology).The objective of the program has been to provide specialized knowledge to recently graduated engineers, in order to prepare them to work in multidisciplinary teams in Embraer engineering areas.The collaboration between Embraer and ITA is present from the selection process, through curriculum design up to Master's theses.The objective of this paper is to present results of an evaluation of the impact of the Master's Program on alumni's performance on their work, on the company (technology, process, products, services and patents) and on the Institute.Since 2002, the program has already prepared more than 1400 students.Out of them, more than 800 are still working for Embraer (Feb 2019) and more than 400 have their Master's theses approved.The methodology adopted in this paper consisted in interviews and surveys with former students of the program, their direct supervisors and both their industrial and academic advisors.This paper presents, as results, the most significant contributions both to the company and to ITA.
Features of value orientations of students of future teachers

The relevance of the study is due to the fact that life priorities, professional preferences of Russian youth have changed, new values have been formed.However, higher education is rarely conducted in accordance with the values and motivations of students.The aim of our study was to determine the structure and content of the values and motivations of student.The study of value orientations of students was conducted using the "polar" questionnaire, revealing the life, material, moral, aesthetic and professional values of students.B. S. Kruglov's method "Determination of formation of value orientations" was also used in our study.The study revealed the structure and content of values and motives of studentsfuture teachers of one of the major universities.The results of the study are applicable for the pedagogical process of higher education.
Sistem Pendukung Keputusan dalam Penentuan Beasiswa dengan Logika Fuzzy Tsukamoto di STMIK Sumedang

STMIK Sumedang is one of the high schools that has a scholarship system for Academic Achievement Improvement (PPA). Where PPA or Academic Achievement Improvement Scholarships are scholarships that are given to increase equity and learning opportunities like students who have academic achievements. Based on data collected, the number of scholarship applications is quite large, with various criteria students appear to have difficulties in determining the winners of student scholarships with almost the same number of indicators and criteria. On the basis of these findings, it is necessary to have a system that is able to help determine scholarship recipients that fit the criteria specified. The results achieved in the form of Decision Support Systems in Determining Scholarships are expected to help STMIK Sumedang in making scholarship decisions.
Sign Language Interpreter for Deaf and Dumb People

For a person with disability (deaf and dumb) faces difficulty in communication with normal person. Because of this, a person who lacks in hearing (deaf) and speaking ability (dumb) is not able to communicate with normal person. This paper aims to lower this barrier in communication. Sign Language is used by deaf and dumb people for communication. A gesture in a sign language is a particular movement of the hands with a specific shape made out of them. A sign language usually provides sign for letters. Sensors in the glove pick up gestures and convert that to text data with the help of Analog to Digital convertor on arduino nano board. This converted text data will be sent wirelessly via WIFI module to a cell phone. The WIFI module transmits the gesture to a smartphone. Using Text to Speech software, incoming text message will be converted to voice. The arduino nano board will recognize the sign language for Alphabets and Numbers and it will display the message on computer and mobile. Sign Language interpreter system recognizes American Sign Language (ASL) alphabets, numbers in the form of letter, word or sentence based on sensor movement. A system is developed that can translate different signs using American SignLanguage to text as well as voice format.
Research on Abnormal Detection Technology of Real-Time Interaction Process in New Energy Network

Efficient and accurate detection of abnormal interaction information at network-related terminals of new energy plants and stations can effectively improve the network security and protection capabilities of new energy plants and stations. Firstly, the network attack scenarios, malformed messages and irregular business instructions of new energy plants and stations are analyzed. Secondly, the deep parallel parsing technology of real-time interactive protocol for new energy plants and stations is proposed to improve the efficiency of protocol parsing. Thirdly, a real-time interactive process anomaly detection technology based on feature matching is proposed. K-NN classification algorithm is used to match the characteristic vectors of network data packets in the new energy plant and station system, which realizes the anomaly detection of network attack scenarios, malformed messages and irregular business instructions in the new energy plant and station system. Finally, a simulation experiment environment of the new energy plant and station system is built to verify the method proposed in this paper. The experimental results show that the algorithm has high ability of anomaly detection and low false alarm rate. It is of great significance to improve the level of network security protection of new energy plants and stations, and to ensure the safe and stable operation of new energy plants and stations.