Reviewerly/exHarmony huggingface dataset:
PENGARUH KUALITAS SISTEM, KUALITAS INFORMASI DAN KUALITAS LAYANAN HER REGISTRASI IGRACIAS TERHADAP KEPUASAN PENGGUNA

This research was carried out by using the model's success Information System which has been designed by DeLone and McLean (2003) by taking several required dimensions or variables. The purpose of this study was to examine the influence of information quality, system quality, service quality Her Registration process to the satisfaction of a user (A Case Study of the iGracias application Telkom University). The sample used in this study were the students of Telkom University class of 2013, which at the time of the school year 2016/2017 be final year students so that more process Her Registration compared to other forces. The sampling technique using probability sampling, with probability sampling technique used is Proportionate stratified random sampling, so that the sample in this study were 371 respondents.&#x0D; The study proves that the information quality, system quality, and service quality significantly influence to user satisfaction simultaneously, as well as proving that the information quality, system quality, and service quality have a positive influence and significant to user satisfaction
Company from the Uncanny Valley: A Psychological Perspective on Social Robots, Anthropomorphism and the Introduction of Robots to Society

There are many issues surrounding the introduction of social robots into society, including concerns about how they may be used to replace true social interaction in personal life, dehumanise formerly social occupations such as elderly care, and be perceived as more human than they actually are. This paper shall present a psychological perspective on the human reception of social robots and apply the gathered information to address these concerns.
A development of stage event management system using virtual reality technique

In order to organize the event such as ceremonies, lectures and academic societies, we have to make plans such as layout of the venue, placement the staff, route of entry and so on. In addition, it is also important to make sure of the lighting and sound effects of the venue, and to carry out simulations assuming troubles. On the other hand, a presenter is likely to be in trouble caused by being unfamiliar with the venue. It is also important to confirm the situation of the venue and the position of the seat as participants. However, from the point of the venue reservation and movement cost, it may be difficult to preview and rehearse at the venue several times. Therefore, the purpose of this research is to develop a stage event support system using virtual reality techniques. By using this system, organizer can simulate placement of the venue and simulate the schedule, presenters can confirm the environment of the venue and practice, and participants can check the situation of the venue. When a system user wears a head mounted display (HMD), she/he can perform rehearsals and simulations before the event such as confirming the environment and practicing events as if she / he is in the real space on.
THE IMPLEMENTATION OF INQUIRY-BASED LEARNING STRATEGY TO ENHANCE STUDENTS’ WRITING ABILITY IN DESCRIPTIVE TEXT

This research was aimed at knowing whether Inquiry-Based Learning strategy enhanced students’ writing ability in descriptive text or not. The research was a classroom action research. The data collected through quantitative and qualitative ways. Quantitative data was gained from the result of pre-test and post-test from writing test. Meanwhile, qualitative data was gained from the result of observation sheets toward students’ and teachers activity. The instrument used by the researcher were test and observation sheet. The finding of the research shows that the strategy can improve the students’ writing ability in descriptive text. the improvement can be seen from the mean score of students from pre-test, post-test one, and post-test two. The mean score of pre-test was 69.5 (35%), there were seven students who passed the minimum passing grade, in the post-test one, 72.2 (60%), twelve students passed the minimum passing grade and in the post-test two, 78.25 (85%), seventeen students passed the minimum passing grade (KKM) from twenty students.
Techniques for GPU-based Color Palette Mapping

This paper presents a GPU-based approach to color quantization by mapping of arbitrary color palettes to input images using Look-Up Tables (LUTs).For it, different types of LUTs, their GPU-based generation, representation, and respective mapping implementations are described and their run-time performance is evaluated and compared.
Research on improved privacy publishing algorithm based on set cover

With the invention of big data era, data releasing is becoming a hot topic in database community. Meanwhile, data privacy also raises the attention of users. As far as the privacy protection models that have been proposed, the differential privacy model is widely utilized because of its many advantages over other models. However, for the private releasing of multi-dimensional data sets, the existing algorithms are publishing data usually with low availability. The reason is that the noise in the released data is rapidly grown as the increasing of the dimensions. In view of this issue, we propose algorithms based on regular and irregular marginal tables of frequent item sets to protect privacy and promote availability. The main idea is to reduce the dimension of the data set, and to achieve differential privacy protection with Laplace noise. First, we propose a marginal table cover algorithm based on frequent items by considering the effectiveness of query cover combination, and then obtain a regular marginal table cover set with smaller size but higher data availability. Then, a differential privacy model with irregular marginal table is proposed in the application scenario with low data availability and high cover rate. Next, we obtain the approximate optimal marginal table cover algorithm by our analysis to get the query cover set which satisfies the multi-level query policy constraint. Thus, the balance between privacy protection and data availability is achieved. Finally, extensive experiments have been done on synthetic and real databases, demonstrating that the proposed method preforms better than state-of-the-art methods in most cases.
A Grounded Theory of the Requirements Engineering Process

This paper explores the requirements engineering (RE) process by conducting interviews with RE professionals and applying grounded theory to determine whether a theory of RE emerges.Analysis of the interviews revealed prominent data patterns that were used to model the RE process.The model depicts the RE process as one of establishing a match through discovery and streamlining, and which utilizes corrective measures in order to manage threats to establishing a match.The process involves many entities but is mainly conducted by RE professionals whose experience plays a major role in extracting complete requirements and detecting occasions of mismatch between customer needs and the software requirements, which represent their main concern during the process.This paper contributes to the empirical analysis of RE by presenting evidence of the RE process in its basic form as carried out in industry, which may form as a building block for further RE research.
Triplet Deep Hashing with Joint Supervised Loss Based on Deep Neural Networks

In recent years, with the explosion of multimedia data from search engines, social media, and e-commerce platforms, there is an urgent need for fast retrieval methods for massive big data. Hashing is widely used in large-scale and high-dimensional data search because of its low storage cost and fast query speed. Thanks to the great success of deep learning in many fields, the deep learning method has been introduced into hashing retrieval, and it uses a deep neural network to learn image features and hash codes simultaneously. Compared with the traditional hashing methods, it has better performance. However, existing deep hashing methods have some limitations; for example, most methods consider only one kind of supervised loss, which leads to insufficient utilization of supervised information. To address this issue, we proposed a triplet deep hashing method with joint supervised loss based on the convolutional neural network (JLTDH) in this work. The proposed method JLTDH combines triplet likelihood loss and linear classification loss; moreover, the triplet supervised label is adopted, which contains richer supervised information than that of the pointwise and pairwise labels. At the same time, in order to overcome the cubic increase in the number of triplets and make triplet training more effective, we adopt a novel triplet selection method. The whole process is divided into two stages: In the first stage, taking the triplets generated by the triplet selection method as the input of the CNN, the three CNNs with shared weights are used for image feature learning, and the last layer of the network outputs a preliminary hash code. In the second stage, relying on the hash code of the first stage and the joint loss function, the neural network model is further optimized so that the generated hash code has higher query precision. We perform extensive experiments on the three public benchmark datasets CIFAR-10, NUS-WIDE, and MS-COCO. Experimental results demonstrate that the proposed method outperforms the compared methods, and the method is also superior to all previous deep hashing methods based on the triplet label.
Super-resolution imaging design for CX6-02 microsatellite

This paper aims at a novel super-resolution imaging design scheme for CX6-02 micro-satellite. In the design of super resolution reconstruction algorithm, under the framework of the hierarchical bayesian, an improved L1 norm prior model based on gradient adaptation is proposed, to avoid the amplification of noise while deconvolution. In imaging mode and system design, the satellite attitude control deviation and ground velocity compensation are used to obtain multi-frame images with sub-pixel information, and in order to exploit the potential of the reconstruction algorithm, the parameters of the optical system are designed by means of high availability redundancy. These make the CX6-02 satellite’s imaging resolution increased from 2.8 to 1.4 meters in the 700km orbit altitude, the whole satellite is 66kg.
Referring under Uncertainty

In this paper, we study the referring expression generation problem (REG) when the available information about the properties of objects is uncertain, in the sense that we are not sure about the actual properties an object has. We formalize the problem by extending the conventional REG framework through the use of possibility distributions, represented by fuzzy sets. We show the potential benefits of this proposal in the assessment of the referential success for referring expressions. This approach opens a new line of research in the application of fuzzy sets to the REG problem, complementary to those approaches that use fuzzy sets as a suitable bridge between language and raw data.
Lokative pronominer og præpositionalfrasens struktur

In this paper, I investigate the syntax of the prepositional phrase in Danish and the locative pronouns her ‘here’, der ‘there’ and hvor ‘where’ in e.g. hertil ‘hereto’. I argue that (a) the directional adverb p is a functional head that projects a pP on top of a PP and takes this PP as its complement; that (b) the locative pronoun moves via an intermediate position within the prepositional phrase before it leaves the prepositional phrase; and that (c) the preposition moves to p (P-to-p-incorporation).
An Automatic Translator from the Florentine Vernacular Language to Modern Italian Language

Along several centuries hundreds of books were written in Florentine vernacular language.The latter, however, is not easy to understand even for native Italian speakers.In 2016-2018 the author created a PC software which provides the possibility to automatically translate entire texts from Florentine vernacular language, as it is found in the literature, into modern Italian language.In this article the author intends to describe the phases of the realization of this software as well as the results of its use.The software in its dictionary currently includes about 25 000 definitions of the vernacular language (where "definitions" mean the presence of terms or phrases in vernacular literature that are replaced in the respective terms and phrases of modern Italian).Numerous studies over the years have demonstrated the limitations of machine translation, often using the error rates of translation software.Even translators that use complex algorithms created with statistical methods frequently end up generating unreliable results, sometimes questioning the very usefulness of translation software.However, if used with the necessary precautions, automatic translators can simplify a job or can help in understanding of texts even for those who know little or no foreign languages.The translator described in this article, although not immune from the defects of machine translation, can be useful both to scholars of Italian literature of past centuries, as well as to those who, while knowing Italian, want to approach texts that cannot be fully understood without the support of footnotes.
Map Reduce Implementation for Malicious Websites Classification

Due to the rapid growth of the internet, malicious websites [1] have become the cornerstone for internet crime activities.There are lots of existing approaches to detect benign and malicious websitessome of them giving near 99% accuracy.However, effective and efficient detection of malicious websites has now seemed reasonable enough in terms of accuracy, but in terms of processing speed, it is still considered an enormous and costly task because of their qualities and complexities.In this project, We wanted to implement a classifier that would detect benign and malicious websites using network and application features that are available in a data-set from Kaggle, and we will do that using Map Reduce to make the classification speeds faster than the traditional approaches.[2].
On task effects in NLG corpus elicitation: a replication study using mixed effects modeling

Task effects in NLG corpus elicitation recently started to receive more attention, but are usually not modeled statistically.We present a controlled replication of the study by Van Miltenburg et al. (2018b), contrasting spoken with written descriptions.We collected additional written Dutch descriptions to supplement the spoken data from the DIDEC corpus, and analyzed the descriptions using mixed effects modeling to account for variation between participants and items.Our results show that the effects of modality largely disappear in a controlled setting.2 The original study Van Miltenburg et al. (2018b) aimed to identify 1 All our code and data is publicly available online.
A blockchain-based fact-checking registry: Enhancing trust in the fact-checkers

This paper introduces a project to build a blockchain-based registry for fact checks.It describes why the project was initiated and briefly argues for the use of blockchain to store fact checks compared to traditional storage methods.The project uses the Fact Check Assistant web application that was developed to streamline fact checking processes for individual fact checkers whether they work independently or as part of an institution or project.The paper is a mere introduction with in-depth research expected to follow so as to assess the project and identify what worked and what didn't after the implementation of the project.The project is essentially an experiment to see if blockchain technology can help make fact checking processes more reliable and effective.
Discrimination of cervical cancer cells via cognition-based features

Computer-assisted cervical screening is an effective method to save the doctors’ workload and improve their work efficiency. Usually, the correct classification of cervical cells depends on the nuclear segmentation effect and the extraction of nuclear features. However, the precise nucleus segmentation remains a huge challenge, especially for densely distributed nucleus. Moreover, previous cellular classification methods are mostly based on morphological features of nucleus size or color. Those individual features can make accurate classification for severe lesions, but not for mild lesions. In this paper, we propose an accurate instance segmentation algorithm and propose cognition-based features to identify cervical cancer cells. Different from previous individual nucleus features, we also propose population features and cognition-based features according to the Bethesda System (TBS) for reporting cervical cytology and the diagnostic experience of the cytologists. The results showed that the segmentation achieves better success in complex situations than that by traditional segmentation algorithms. Besides, the cell classification via cognition-based features also help us find out more about less severe lesions’ nuclei than that based on conventional features of individual nucleus, meaning an improvement of classification accuracy for cervical screening.
Development of a Security Information System in a Hospital

The purpose of this study was to identify technological developments in information security systems in hospitals.The method used in this study was a review method with techniques for reviewing and analyzing several papers related to the topic of discussion about the safety of hospital information systems.The results of the study are that the highest threat to the security of hospital information systems is the threat of hackers.The development of technology makes it easy for people, especially in business.In general, this research is in accordance with the expected objectives, namely, to know the security of the health information system network.
Structural adaptive anisotropic NAS-RIF for biomedical image restoration

Blind image deconvolution is an ill-posed problem that attempts to restore an acquired image degraded by unknown PSF.A variational BID implementation, called NAS-RIF, is known for being robust but prone to poor convergence under low SNR and unrealistic support.Motivated by simple yet effi cient fi delity metric, this paper presents an improved NAS-RIF by reducing adverse effect of inverse high-pass fi lter and computationally intensive pre-deterministic noise removal, by adaptively incorporating anisotropic structural property within local neighborhood seamlessly in NAS-RIF cost function.With an automatic support region estimation, the entire deconvolution process was fully automatic.The experimental results reported herein indicated that the enhanced structural adaptive anisotropic NAS-RIF had better convergence condition,while maintaining the underlying image fi delity.
Data archiving for dissemination within a Gulf nation

Since 2008, Qatar University’s Social and Economic Survey Research Institute (SESRI), has been collecting nationally representative survey data on social and economic issues. In 2017, SESRI leadership established an Archiving Unit tasked with data preservation and dissemination both for internal purposes and with the intent of disseminating select data to the public for secondary analysis.&#x0D; &#x0D; This paper reviews the lessons learned from creating a data archive in an emerging economy where both cultural and political sensitivities exist amid varied groups of stakeholders. Challenges have included recruiting trained personnel, developing policies for data selection and workflow objectives, processing restricted and non-restricted datasets and metadata, data security issues, and promoting usage. Additionally, there is hope that the presence of the Archiving Unit adds value for other SESRI research staff involved in the design, collection, documentation, and processing of studies. After successfully addressing these challenges over the past year, the Archive met its objective to launch a data center at the Institute’s website (http://sesri.qu.edu.qa) and to make multiple datasets available for public download from it. Also, to be discussed are the tools, processes and leveraging of resources that are being implemented as the archiving process continues to evolve.
Hybrid methods of working with databases in JEE applications

The article presents the research on hybridization of methods of working with databases in JEE applications. The test applications were made based on the JDBC interface, Hibernate and Spring framework. Application performance analysis covered the execution time and RAM usage for basic CRUD operations in the database.
Improving Unsupervised Learning With Exemplar CNNS

Most recent unsupervised learning methods explore alternative objectives, often referred to as self-supervised tasks, to train convolutional neural networks without the supervision of human annotated labels. This paper explores the generation of surrogate classes as a self-supervised alternative to learn discriminative features, and proposes a clustering algorithm to overcome one of the main limitations of this kind of approach. Our clustering technique improves the initial implementation and achieves 76.4% accuracy in the STL-10 test set, surpassing the current state-of-the-art for the STL-10 unsupervised benchmark. We also explore several issues with the unlabeled set from STL-10 that should be considered in future research using this dataset.
ISTEM INFORMASI PEMBERIAN BONUS UPAH DAN PENJADWALAN KARYAWAN MENGGUNAKAN METODE ALGORITMA GENETIKA

By following the development of the market and the needs of the community, it is increasingly needed a technology to improve the quality and results of its production with careful planning, in order to avoid the level of loss and reduce production in the company. This is experienced by companies UD. Muria PS who is engaged in animal husbandry. Which has several problems in scheduling employees to take their products. Which also has an impact on the gap between employees on the bonus wages they receive. By building a scheduling information system and providing bonus wages, this research uses php programming language and website-based programming and applies genetic algorithms in it to more easily manage employee schedules and provide bonus rewards fairly and evenly. This genetic algorithm process has a parameter of the vehicle, coolie, and time in the scheduling process. and schedule parameters in the employee bonus wage process. Based on the results of the user trial, the respondents strongly agree with the existence of this application. Which of the 14 respondents said it was feasible to apply to the company UD. Muria PS with a result of 87.37%. It is expected that with this application, employee scheduling and bonus wage issues can help solve it. Keywords: Scheduling, Bonus Salary, Genetic Algorithm, PHP
A GAN-based Method for SAR Image Despeckling

Synthetic Aperture Radar (SAR) images are contaminated by multiplicative noise known as speckle. Most filtering methods are limited to noise statistics and requires complex parameter tuning to achieve the desired visual effects. To solve the above problem, a generative adversarial network (GAN) based method is proposed for SAR image despeckling. Firstly, homogeneous regions are selected manually and speckle samples are produced. Then, GAN is trained to learn the distribution of the speckle samples and generate the “realistic-looking” ones. Third, a convolutional neural network (CNN) is designed that specializes in removing the speckle. Experiments on simulated SAR images and real SAR images show the good performance of the proposed method with respect to both the visual effect and quantitative analysis.