Reviewerly/exHarmony huggingface dataset:
THINK-ALOUD PROTOCOLS, EYE-TRACKING AND KEY-LOGGING IN IDENTIFICATION AND ADDRESSING TRANSLATION CHALLENGES

Purpose of the study: The research is focused on studying the translation process using the methods of allied sciences for improving a translation end product. This article deals with the identification of the most challenging prospective translation difficulties and analysis of the main macro- and micro strategies of translation on the basis of conducted experiments with combination of eye-tracking and think-aloud protocols and an experiment using keystroke logging. In the article we also make an attempt to undercover the basic principles which influence decision-making.&#x0D; Methodology: Conclusions based on the results of the study have both theoretical and practical implications. The theoretical significance of the research is expressed with the summarizing of the main translation difficulties and translation strategies of the tested. It was also revealed that the translation difficulty is not always predictable and is not always explained theoretically. As a result of the experiments the theory of H. Krings on 3 stages of translation Micro Strategy has been confirmed. Finally, the analysis of the experiments made it possible to determine the factors influencing a particular translation solution: grammatical, contextual and stylistic accuracy and euphony of the translation end product.&#x0D; Results: Regarding the first experiment, TAPs required a voice recorder, and respectively, eye-tracking assumed the usage of eye-tracker, procured by the Laboratory of Small Computer Engineering of Kazan Federal University. As equipment for the second experiment, we used a laptop with a loaded keystroke logging program recording each keystroke, as well as the time intervals between these presses. In this case, the task of the tested was to submit a written translation of the given text.&#x0D; Applications of this study: This research can be used for the universities, teachers, and students.&#x0D; Novelty/Originality of this study: In this research, the model of the Think-Aloud Protocols, Eye-Tracking, and Key-Logging in Identification and Addressing Translation Challenges are presented in a comprehensive and complete manner.
Performance Comparison of Forensic Software for Carving Files using NIST Method

Data lost due to the fast format or system crash will remain in the media sector of storage. Digital forensics needs proof and techniques for retrieving data lost in storage. This research studied the performance comparison of open-source forensic software for data retrieval, namely Scalpel, Foremost, and Autopsy, using the National Institute of Standards Technology (NIST) forensic method. The testing process was carried out using the file carving technique. The carving file results are analyzed based on the success rate (accuracy) of the forensic tools used in returning the data. Scalpel performed the highest accuracy for file carving of 100% success rate for 20 document files in pdf and Docx format, and 90% for 10 image files in png and jpeg format.
Learning a Lattice Planner Control Set for Autonomous Vehicles.

This paper introduces a method to compute a sparse lattice planner control set that is suited to a particular task by learning from a representative dataset of vehicle paths. To do this, we use a scoring measure similar to the Frechet distance and propose an algorithm for evaluating a given control set according to the scoring measure. Control actions are then selected from a dense control set according to an objective function that rewards improvements in matching the dataset while also encouraging sparsity. This method is evaluated across several experiments involving real and synthetic datasets, and it is shown to generate smaller control sets when compared to the previous state-of-the-art lattice control set computation technique, with these smaller control sets maintaining a high degree of manoeuvrability in the required task. This results in a planning time speedup of up to 4.31x when using the learned control set over the state-of-the-art computed control set. In addition, we show the learned control sets are better able to capture the driving style of the dataset in terms of path curvature.
A nonlinear lumped parameter model for designing a capacitive MEMS microphone

The MEMS microphone, which is widely used for mobile devices, includes electro-mechanical energy conversion as well as acousto-mechanical conversion through the structures such as the back plate with many holes and an extremely flexible membrane with nonlinear characteristics. Hence, it is a time-consuming job to build an appropriate finite element model to predict the dynamic behaviors of the microphone. The equivalent circuit models are frequently used to check the linear behaviors of the MEMS microphone. However, it is sometimes inadequate to predict the nonlinear behaviors due to the nonlinear deformation of the sensing membrane, which usually determine the acoustic overload point. In this study, a nonlinear four-degree-of-freedom model is developed to design the MEMS microphone. Instead of the conventional equivalent circuit model, a model based on mechanical analysis is constructed and the state equations for the model is derived in the form of a set of ordinary differential equations. The responses of the model can be easily predicted in the time and frequency domain both by solving the equations numerically. With the time domain analysis used, the sensitivity can be easily shown as a function of frequency even including the nonlinear dynamic behaviors. [Work supported by DB Kim Jun-ki Cultural Foundation.]
The Audience Experience in Immersive Art Spaces

Since the late 20th century, the traditional practices and roles of the established art worldhave experienced a major shift. The emergence of technology and the growth of commercializationare introducing new ways of displaying and consuming art, that are challenging the ‘white cube’ideal and emphasize on the economic value of experience. At the same time, the ‘experienceeconomy’ creates new needs to a diverse art audience, to which the museum exhibition environmentneeds to respond. Under those circumstances, art spaces and museums are challenged to reconceptualizetheir role by bringing individuals in the center of curatorial attention and beingmarket-oriented, while having social and political relevance.Among the innovative changes of art spaces, the digital, immersive art displays areconsidered to generate intellectual and emotional stimulation in a unique and distinct exhibitionenvironment. Taking this into consideration, this study aims to explore immersive art displays ascultural experience goods that can engage visitors and facilitate future museums in the provision of‘experience services’. After conducting a quantitative analysis of 287 surveys regarding theindividual visitor experience in three immersive art exhibitions, the study proved that immersivedisplays were perceived positively, they successfully met different expectations and they stimulated‘leisure learning’ through escapist experiences that motivate individuals to re-visit again in thefuture.Reflecting the analysis in a post-museum context, it is implied that the immersive displayingpractices could be used to address different consumption patterns and increase current, as well asfuture demand, while shaping tastes and conceptualizing the cultural value of the institution.Aiming for inclusiveness, participation and omnivorousness, the immersive museum experienceswould also fit in a democratic context that allows diversity, active participation and meaningmakingpractices in the exhibition environment.
Using Similarity Metrics on Real World Data and Patient Treatment Pathways to Recommend the Next Treatment

Non-small-cell lung cancer (NSCLC) is one of the most prevalent types of lung cancer and continues to have an ominous five year survival rate. Considerable work has been accomplished in analyzing the viability of the treatments offered to NSCLC patients; however, while many of these treatments have performed better over populations of diagnosed NSCLC patients, a specific treatment may not be the most effective therapy for a given patient. Coupling both patient similarity metrics using the Gower similarity metric and prior treatment knowledge, we were able to demonstrate how patient analytics can complement clinical efforts in recommending the next best treatment. Our retrospective and exploratory results indicate that a majority of patients are not recommended the best surviving therapy once they require a new therapy. This investigation lays the groundwork for treatment recommendation using analytics, but more investigation is required to analyze patient outcomes beyond survival.
Research on Fast Kernel Subspace Face Recognition Based on Deep Belief Network

Abstract Face recognition usually uses different features as input signals. There are many conventional techniques being used and these include technical and fundamental analysis. In this paper, the sample data is mapped from low-dimensional space to high-dimensional space by the kernel method, which makes the classification algorithm have the ability to deal with non-linear data and can solve the small sample problem. At the same time, deep belief network is used as feature transformation and classification to mine feature information of high-dimensional face data. The experimental results show that the optimal recognition rate of the proposed algorithm in a specific face database is up to 96%.
Kinerja Instruktur Pendidikan Dan Pelatihan Keterampilan Pada Panti Sosial Bina Remaja Majar Tabela Kota Palangka Raya

This research aims to find out, describe and analyze the performance of educational instructors and skills training at the youth community Development of Majar Tabela City Palangka Raya. While the research method used is qualitative. Researchers want to describe and analyze the performance of educational instructors and skill training at the Majar Tabela Youth Development Community in the six (6) Aspects of quality, quantity, timeliness, cost-effectiveness, need for supervision and Interpersonal impact. Data sources consist of primary data sources, which are interviews with 4 (four) instructors, 2 (two) Students/clients, and the head of Sub administration representing the head of Youth Development (PSBR) Majar Tabela, while the secondary data source is Official document of the Youth Development Community (PSBR) Majar Tabela.&#x0D; The results showed that the instructor's performance was still less than optimal. This can be seen from the quantity aspect is still a lack of instructors there are at least 6 (six) to 8 (eight) people, while the one is now only 4 (four) instructors. Then, from the need for supervision aspect, there are still less independent instructors, especially with regards to the creation of reports and learning evaluations. This is because the instructor is newly recruited so that it is still less experienced in making reports and evaluation activities. Thus causing the timeliness aspect is also impacted by delays in making activity reports. Therefore, the head of Youth Development Social Orphanage (PSBR) Majar Tabela City Palangka Raya is 1) increasing the number of instructors who are experienced in their field, both in the field of automotive and sewing, 2) provide technical guidance to the new instructor So that it can be independent (need for supervision) and timeliness in making final reports as well as evaluation of activities, and 3) reviewing the honor of the instructor to improve the instructor's commitment to improve the performance of instructors.
Stakeholders’ Responses to Disruptive Technologies

As the term disruptive technologies itself signifies, these are technologies capable of fundamentally disrupting the way things are done in the present.   The impact of disruptive technologies provokes various kinds of responses in different stakeholders.   Moving beyond the scope of the primary stakeholders comprised of the customers, complementary innovators, suppliers, and investors, these technologies can also create impact and provoke responses from a secondary set of stakeholders, namely, the competitors, activists, governments, media, public interest groups, local community and trade associations. This study was conceived with the belief that mapping the response trends in the environment of disruptive technologies can aid in a better understanding of how disruptive technologies serve the stakeholders. The earlier studies in the area were predominantly on the incumbent's response to disruptive technologies.  Other rare studies that have attempted to analyze multiple stakeholders have done so from the perspective of a single technology.   In this study, we attempt to analyze multiple stakeholder responses to various disruptive technologies in order to glean the existence of a pattern in these responses. This paper is a conceptual one, and the methodology followed is qualitative.
A New Stochastic Limited Memory BFGS Algorithm

In this paper, a new limited memory BFGS is proposed for solving stochastic optimization problems. Since the cost of storing and manipulating $H_k$ is prohibitive in the large scale setting, the L-BFGS algorithms use the strategy of keeping the most recent correction pairs. Besides, in the stochastic regime, due to some noisy information in both gradient vector and Hessian approximation, the second-order model is not an accurate estimation of the function. To overcome this problem, our L-BFGSemploys memory in an optimal manner by storing the correction pairs that have the least violation in the secant equation. Under some standard assumptions, the convergence property of the new algorithm is established for strongly convex functions. Numerical results on the problems arising in machine learning show that the new method is competitive and effective in practice.
Evidential deep learning for arbitrary LIDAR object classification in the context of autonomous driving.

In traditional LIDAR processing pipelines, a point-cloud is split into clusters, or objects, which are classified afterwards. This supposes that all the objects obtained by clustering belong to one of the classes that the classifier can recognize, which is hard to guarantee in practice. We thus propose an evidential end-to-end deep neural network to classify LIDAR objects. The system is capable of classifying ambiguous and incoherent objects as unknown, while only having been trained on vehicles and vulnerable road users. This is achieved thanks to an evidential reformulation of generalized logistic regression classifiers, and an online filtering strategy based on statistical assumptions. The training and testing were realized on LIDAR objects which were labelled in a semi-automatic fashion, and collected in different situations thanks to an autonomous driving and perception platform.
An Improved Method of Joint Extraction

Named entity recognition (NER) and relation extraction (RE) are the basic tasks of Natural Language Processing (NLP). However, previous works always treat them as two separated subtasks, a novel improved method of joint extraction was present in this paper to solve the problem of internal relationship and error propagation in traditional pipeline model, Named entity recognition is regarded as a sequential annotation problem. In relation extraction task, the relationship between two entities and their relationship types are predicted at the same time, the possible multiple pairs of relationships in sentences are identified. Finally, the work innovatively use sequential patterns to correct the results. The experiment on two authoritative datasets verifies the advancement in our model.
Modelling Context Awareness for a Situated Semantic Agent

This paper presents a model of contextual awareness implemented for a social communicative robot Leolani. Our model starts from the assumption that robots and humans need to establish a common ground about the world they share. This is not trivial as robots make many errors and start with little knowledge. As such, the context in which communication takes place can both help and complicate the interaction: if the context is interpreted correctly it helps in disambiguating the signals, but if it is interpreted wrongly it may distort interpretation. We defined the surrounding world as a spatial context, the communication as a discourse context and the interaction as a social context, which are all three interconnected and have an impact on each other. We model the result of the interpretations as symbolic knowledge (RDF) in a triple store to reason over the result, detect conflicts, uncertainty and gaps. We explain how our model tries to combine the contexts and the signal interpretation and we mention future directions of research to improve this complex process.
Media Pembelajaran Jaringan Komputer Berbasis Android

With the development of information technology especially smartphone technology, Android based this time, learning media capable of being deployed to help students study computer network science. Difficulties of students in the process of remembering and understand the functions of the computer network components. is research coupled with software design methods. The results achieved are the availability of applications supporting teaching and learning activities which can be obtained without being bound by time and place. Computer network science which had seemed difficult learned will feel easy and fun to be learned through an Android smartphone.
Pelatihan pembuatan media pembelajaran berbasis ICT untuk guru SD Muhammadiyah Ambarketawang 3

The role of technology in the field of education that is popular and growing rapidly at this time is the media or learning resources based on ICT (Information and Communication Technology). One of the opportunities and challenges in facing the industrial era 4.0 in the field of education is innovation in the teaching and learning process from conventional learning to modern learning by utilizing information and computer technology. One form of innovation in learning is the development of multimedia applications for learning media. Muhammadiyah Ambarketawang 3 Primary School has adequate equipment facilities to organize learning by utilizing technology such as computers and LCD projectors. However, the learning process is still carried out classically, not yet utilizing technology-based learning media because of the inability of teachers to create instructional media. This community service program is carried out to increase the capacity of Muhammadiyah Ambarketawang elementary school teachers in making ICT-based learning media. The method of implementation carried out is in the form of training in making instructional media. The results of these community service activities include increasing the understanding and ability of teachers in making ICT-based learning media. This indicator of increasing understanding and ability reached 92.4% from 27% before the training was held
KRITIK SOSIAL PADA NOVEL â€œDilan : dia adalah Dilanku Tahun 1990â€ KARYA PIDI BAIQ

This research title isÂ SocialCriticism in the Novel "Dilan: dia adalah Dilanku Tahun 1990" by Pidi Baiq. The aim of this study is (1) To find out the social criticism contained in the novel, (2 ) To find out the results of the social criticism analysis contained in the novel. This research is included in library techniques and content analysis. The method used is descriptive qualitative research methods. Data sources used in this study is the text of the novel "Dilan: dia adalah Dilanku Tahun 1990" by Pidi Baiq, the second print in 2016 and published by Pastel Books Bandung. The study used a sociological literary approach that considered social aspects to better understand the social symptoms contained in the novel "Dilan: dia adalah Dilanku Tahun 1990" which focused on issues related to social criticism.The results of this study are aimed at the world of education, especially among adolescents who are still educated in schools which cause variousÂ social problems that occur in the midstÂ the environment of society. As for social criticism found in the novel " Dilan: dia adalah Dilanku Tahun 1990", that isÂ social criticism of juvenile delinquency in school, criminalization, attitudes and behavior, educators (teachers), power, and injustice and low.Â
COLLECTIVE ACTION AND SOCIAL CHANGE: HOW DO PROTESTS INFLUENCE SOCIAL MEDIA CONVERSATIONS ABOUT IMMIGRANTS?

This study aims to improve our understanding of using protest as an intervention strategy to reduce online users’ prejudiced speech against immigrants by: 1) developing a reliable method to measure for online users’ prejudiced speech against immigrants; 2) examining the role of temporal and geographic exposure to protest in online users’ prejudiced speech against immigrants; and 3) examining the role of group identity in the relationship between protest and online users’ prejudiced speech against immigrants. It hypothesizes that protest would reduce online users’ prejudiced speech against immigrants. After collecting 31,210,740 tweets from 102, 094 Twitter users, machine learning techniques were leveraged into developing a reliable measurement for online users’ prejudiced speech against immigrants. Repeated measures of users’ prejudiced speech were taken in a two-week window to establish baseline before and after protest. Analyses examined group differences across different levels of geographic exposure to local protest and between users identified with different groups using analysis of variance procedures. Overall, this research did not provide evidence supporting the claim that protest can reduce online prejudiced speech. However, it was found that users expressed more prejudiced speech after protest compared to baseline before protest. This change was more pronounced among users located furthest (in geographic distance) from the cities where protests occurred. It was also more pronounced among users who did not identify with immigrants. Further research is needed to determine if these results call into question the effectiveness of protest in reducing prejudiced speech or are peculiar to social media, and if so, how these negative effects can be mitigated.
Measuring Nonclassicality of Mesoscopic Twin-Beam States with Silicon Photomultipliers †

The study of nonclassical properties of quantum states is a relevant topic for fundamental Quantum Optics and Quantum Information applications. In the mesoscopic domain, promising results have been obtained using photon-number-resolving detectors. Here we show recent results achieved with the class of Silicon Photomultipliers: by a proper analysis of the output signal, the nonclassicality of twin-beam states can be detected and exploited.
Output Power Forecasting for 2kW Monocrystalline PV System using Response Surface Methodology

Photovoltaic (PV) system is a renewable energy source that not only able to reduce the effect of greenhouse gas towards the environment, but also a highly profitable industry nowadays. To determine the Return of Investment (ROI) of a newly installed system, forecasting is crucial. Thus, the purpose of this study is to produce a prediction model for the yearly output power of the PV system using three environmental elements; irradiance, module temperature and ambient temperature by Response Surface Methodology (RSM). To do so, MATLAB RStool which is consisting of four models; multiple linear regression (MLR), interaction, pure quadratic, and full quadratic is used. The 5 minute sampling size of yearly 2014 weather station data the three environmental elements and output power of a 2kW Monocrystalline  real PV system are used for training. Whereas, yearly 2015 data of the aforementioned elements are used for validation. The coefficient of determination (R 2 ) method and root mean square error (RMSE) approach were used to determine the most accurate prediction model. Results show that, full quadratic is the most accurate prediction model with R 2  value of 0.999 5  and RMSE of 8%. It is hoped that the prediction model introduced can be a viable method to be used by the PV system installer.