Reviewerly/exHarmony huggingface dataset:
The Design Of The Spk In Fulfilling The Production Target Of Vegetable Availability In Lut Tawar Sub-District Uses The SAW Method

Efforts to meet the availability of vegetables in fulfilling vegetable intake, especially for people in Lutawar sub-district, Central Aceh district, community agriculture requires a technology development that supports the calculation of production at each harvest and can also determine the fulfillment of community food availability. In this case, the fulfillment of production targets for the availability of vegetables will be very effective if it is calculated using a decision support system application, using the Simple Additive weighting (SAW) method.The process of collecting and analyzing data is carried out systematically and logically using a survey, which is to collect as much data as possible about the vegetables found in Lut Tawar Subdistrict, which is a decision support for the quality of the research. Then analyze vegetables in meeting production targets and food availability. Application of the Simple Additive Weighting (SAW) method into a decision support system to create a decision support system for food availability that is assessed from its production, including 4 criteria used to make an assessment, C1: Productivity, C2: Harvest Area, C3: Area Planting, C4: Population, then decision making assigns weight to each criterion. The types of vegetables as an alternative in this study are as follows
Finding Flow in Training Activities by Exploring Single-Agent Arcade Game Information Dynamics

This paper incorporates discussion about game refinement theory and the flow model to analyze simulation data collected from two types of arcade games. A mathematical model of the arcade game processes is formulated. The essence of the arcade games is verified through the game-playing processes of players. In particular, challenge setup could contribute to the addictiveness when the mode is close to flow channel. Risk frequency ratio is applied to measure the process and verified the more entertaining mode of training activities.
Representation of 212 rallies in the Jakarta Post articles: A hybridity of CDA and SFL analysis

For decades, newspapers have become a daily need for people across the globe to update information. There is a tendency of the people to believe in the news published in newspapers, for media is considered neutral. In Indonesia, 212 rallies are the events that were widely reported as headlines for weeks by national and international newspapers. This study showcases the brief portrait of The Jakarta Post representations on the 212 rallies by its use of linguistic properties, to see whether The Jakarta Post is impartial in delivering the news. This study employs Critical Discourse Analysis (CDA) strategies and applies the analytical tools drawn from Systemic Functional Grammar (SFG). The data were taken from sixteen 212 rallies related news, including seven headlines, collected from The Jakarta Post archives. The result of this study reveals that by using transitivity and conceptual metaphor, The Jakarta Post tends to stand on the side of the one being protested, and oppose the rallies. This finding suggests newspaper readers to read the information in newspapers more critically, to understand the use of lexicon as well as the structure of sentences to conclude the right interpretation of the news and to realize the representation. By doing so, newspaper readers will not simply accept the news they read.
Application of Data Mining Technology under K-means Algorithm Combined with BIM Technology in Management Engineering

The data mining technology of the K-means algorithm combined with BIM (Building Information Modeling) technology is applied to management engineering, which is convenient for project management personnel. Method: The K-means clustering algorithm is combined with the support vector machine algorithm. The support vector machine is used to ensure the high accuracy of the anomaly detection algorithm. The K-means clustering algorithm is used to divide the support vector machine into blocks. It also analyzes the different needs of the facility management staff, and clearly defines the content and level of detail required to build the BIM model. It not only meets the data requirements for operation and maintenance but also avoids waste caused by excessive modeling. Result: Compared with traditional support vector machines, the improved algorithm in this paper has a higher detection rate and lower false alarm rate. Also, it can shorten the detection time of large-scale data to provide an effective method for abnormal detection of sensor networks and processing of large-scale data sets. The improved method increases the detection accuracy by 8.13% and decreases the false alarm rate by 89.08%. In terms of detection time, the improved method increases by 3.82s, which is 4.67 times the traditional method. Conclusion: The structural health monitoring system can efficiently and accurately monitor the accuracy of the data. BIM can provide rich operation and maintenance data for facility management to effectively improve the efficiency of facility management.
Securing Govt Research Content using QR Code Image

Government division may be a crucial portion of the nation's economy. Security of government inquire about substance from all sorts of dangers is basic not as it were for trade coherence but too for supporting the economy of the country as a entirety. With the digitization of conventional records, government substances experience troublesome issues, such as government capacity and access. Research office spend significant time questioning the specified information when getting to Government investigate substance subtle elements, but the gotten information are not fundamentally rectify, and get to is some of the time limited. On this premise, this think about proposes a investigate substance which utilize ciphertext-based encryption to guarantee information privacy and get to control of record subtle elements. The investigate head may scramble the put away data for accomplishing get to control and keeping information secure. In this manner AES Rijndael calculation is utilized for encryption. This guarantees security for the data and empowers Protection.
Designing the training students methods of automated documents creation

In modern conditions, informatization permeate into all spheres of society. This also applies to the educational sphere. At the moment, many software products are involved in the educational process. Involve of software products helps to increase the clarity and aesthetics of students' paperwork. At the same time, the dominance of technical means increases the student load. It should also be noted that the speed of paperwork is often reduced. Students spend more time on the results with the help of technical means than on the work done by hand. This contradiction can be resolved by students acquiring skills in working with automated documents. This work is devoted to the development of methods for training students the principles of creating automated documents through the use of automatic elements: styles, objects, labels, links, automatic lists of various directions. The theoretic, practical and individual stages are fixed in the developed method. The thematic plan with covering all parties of automatic elements application at document creation is designed. The main tools used to create an automated document in most of the software products used are highlighted. Chief among these tools are: labels, links, programmable fields. To work with documents, you can use many software products, among which the most common are Microsoft Office, LaTeX, Libre Office. Today, given the current trend of distributing free licenses of software products for educational institutions and education seekers, the Microsoft Office suite remains the most popular among students. Taking this into account, the work provides examples of the implementation of tasks that correspond to the compiled thematic plan using the means of the Microsoft Office package. All examples are accompanied by pictures explaining the use of the selected automatic element. As a result of the work, a method for training students the principles of creating automated documents was formulated. Implementation of the designed training method allows to reduce the time spent on preparing an electronic document, improve the documents design quality and increase the flexibility of the received content for export, import and editing operations.
Use of Claim Graphing and Argumentation Schemes in Biomedical Literature: A Manual Approach to Analysis

Argumentation in an experimental life science paper consists of a main claim being supported with reasoned argumentative steps based on the data garnered from the experiments that were carried out. In this paper we report on an investigation of the large scale argumentation structure found when examining five biochemistry journal publications. One outcome of this investigation of biochemistry articles suggests that argumentation schemes originally designed for genetic research articles may transfer to experimental biomedical literature in general. Our use of these argumentation schemes shows that claims depend not only on experimental data but also on other claims. The tendency for claims to use other claims as their supporting evidence in addition to the experimental data led to two novel models that have provided a better understanding of the large scale argumentation structure of a complete biochemistry paper. First, the claim graph displays the claims within a paper, their interactions, and their evidence. Second, another aspect of this argumentation network is further illustrated by the Model of Informational Hierarchy (MIH) which visualizes at a meta-level the flow of reasoning provided by the authors of the paper and also connects the main claim to the paper’s title. Together, these models, which have been produced by a manual examination of the biochemistry articles, would be likely candidates for a computational method that analyzes the large scale argumentation structure.
Image Enhancement based upon GWO-GA Optimized Algorithm using Kuwahara Filter

Digitization has emerged as a very popular technology. Digital images are taken to capture themoments and other things. However, there are several factors that reduce the quality of the image such asnoise, contrast, low light, and high contrast. To overcome these issues, different approaches have beendesigned by the researchers. Image enhancement is the process of enhancing the low-quality images.Histogram equalization, filtering, contrast enhancement are several methods that are being used to this end.This paper proposed a novel method after performing a narrative review in which limitations of cuckoosearch optimization were observed. The novel model amalgamates gray wolf Optimization and geneticalgorithm to overcome the drawbacks of Cuckoo Search (CS) method. Apart from this, the proposed methodis made more efficacious by introducing Kuwahara Filter. This filter is utilized to remove the extra noisefrom the edges of images attained after applying the enhancement technique. The Results are performedusing MATLAB software and the comparison of the existing Cuckoo Search method and designed GWOGA method showed the supremacy of proposed method over different quality parameters.
REKAYASA PERANGKAT LUNAK PRODUK INOVASI, HAK KEKAYAAN INTELEKTUAL, DAN INKUBATOR BISNIS DENGAN METODE USER CENTERED DESIGN: STUDI KASUS UNIVERSITAS BINA DARMA

Abstract : The Directorate of Innovation and Business Incubator is part of Bina Darma University which facilitates tenants to get incubations from partners who collaborate with Bina Darma University. This research was conducted with the aim of creating an information system that can solve existing problems, namely building a web-based information system and Progressive Web Apps (PWA) using the Codeigniter and Materialize framework, which is an information system designed to manage all data collection. Tenants, activity data collection, document storage, and other data in the Directorate of Innovation and Business Incubator, and can display the required information reports quickly and efficiently. By using the Focus Group Discussion (FGD) method, researchers get feedback from clients by discussing, and researchers can create systems with the best implementation to meet client needs. The results of this study are the availability of a information system that can assist users in managing all tenant data collection, activity data collection, documentation, and data on innovation products and intellectual property rights at the Directorate of Innovation and Business Incubator at Bina Darma University.&#x0D;
ADDRESSING THE NEED OF TEACHING ENGLISH FOR SPECIFIC PURPOSE AT SMK KESEHATAN BAKTI ASSYUKUR

CTL method is one of the teaching techniques where students work in a group. It is used to improve the student’s skill in mastering and understanding English vocabulary. This study tries to determine the effectiveness of teaching and learning at the tenth-grade students of Vocational School. The main goal of this research is to improve English language skills, especially in vocabulary understanding. The method of this research is classroom action research and the populations are the tenth-grade students of pharmacy which consist of nine students as sample randomly. The data collection was done by pre-test and post-test as research instruments. The results of this research that had been carried out during the study showed that the post-test average score (71,5%) is higher than the pre-test average value (49,3%). It shows that the CTL method is very effective in increasing students’ understanding of English, especially in the homonym vocabulary pronounciation and spelling words. Keywords: ESP, CTL, Vocabulary, Teaching
A Symmetric Loss Perspective of Reliable Machine Learning

When minimizing the empirical risk in binary classification, it is a common practice to replace the zero-one loss with a surrogate loss to make the learning objective feasible to optimize. Examples of well-known surrogate losses for binary classification include the logistic loss, hinge loss, and sigmoid loss. It is known that the choice of a surrogate loss can highly influence the performance of the trained classifier and therefore it should be carefully chosen. Recently, surrogate losses that satisfy a certain symmetric condition (aka., symmetric losses) have demonstrated their usefulness in learning from corrupted labels. In this article, we provide an overview of symmetric losses and their applications. First, we review how a symmetric loss can yield robust classification from corrupted labels in balanced error rate (BER) minimization and area under the receiver operating characteristic curve (AUC) maximization. Then, we demonstrate how the robust AUC maximization method can benefit natural language processing in the problem where we want to learn only from relevant keywords and unlabeled documents. Finally, we conclude this article by discussing future directions, including potential applications of symmetric losses for reliable machine learning and the design of non-symmetric losses that can benefit from the symmetric condition.
The Implementation of Online Training for Library Employees as an Activity Increasing Competencies in the Promotion of Culture on the Internet

Thesis / purpose of the article ‒ The aim of the article is to analyse the process of on-line training for library employees as an activity increasing competencies in the field of promoting culture on the Internet. Research methods ‒ The training process concerning areas that have a direct or indirect impact on the subsequent activities of library employees on the Internet is discussed. Various forms of online training are indicated, among which priority is given to the division into synchronous and asynchronous training. Results and conclusions ‒ Libraries, as cultural institutions, play a special role in providing access to information and cultural events. This scope of activity is of exceptional importance in the case of local communities, in particular those located outside large urban centres. Therefore, business continuity is very important, even in unusual situations such as the COVID-19 pandemic
Neural News Recommendation with Negative Feedback

News recommendation is important for online news services. Precise user interest modeling is critical for personalized news recommendation. Existing news recommendation methods usually rely on the implicit feedback of users like news clicks to model user interest. However, news click may not necessarily reflect user interests because users may click a news due to the attraction of its title but feel disappointed at its content. The dwell time of news reading is an important clue for user interest modeling, since short reading dwell time usually indicates low and even negative interest. Thus, incorporating the negative feedback inferred from the dwell time of news reading can improve the quality of user modeling. In this paper, we propose a neural news recommendation approach which can incorporate the implicit negative user feedback. We propose to distinguish positive and negative news clicks according to their reading dwell time, and respectively learn user representations from positive and negative news clicks via a combination of Transformer and additive attention network. In addition, we propose to compute a positive click score and a negative click score based on the relevance between candidate news representations and the user representations learned from the positive and negative news clicks. The final click score is a combination of positive and negative click scores. Besides, we propose an interactive news modeling method to consider the relatedness between title and body in news modeling. Extensive experiments on real-world dataset validate that our approach can achieve more accurate user interest modeling for news recommendation.
Defenses Against Multi-Sticker Physical Domain Attacks on Classifiers

Recently, physical domain adversarial attacks have drawn significant attention from the machine learning community. One important attack proposed by Eykholt et al. can fool a classifier by placing black and white stickers on an object such as a road sign. While this attack may pose a significant threat to visual classifiers, there are currently no defenses designed to protect against this attack. In this paper, we propose new defenses that can protect against multi-sticker attacks. We present defensive strategies capable of operating when the defender has full, partial, and no prior information about the attack. By conducting extensive experiments, we show that our proposed defenses can outperform existing defenses against physical attacks when presented with a multi-sticker attack.
THE EFFECT OF LEARNING MOTIVATION AND SELF-EFFICACY ON ENGLISH LEARNING ACHIEVEMENT

This study aims to analyze the effect of learning motivation and self-efficacy on English learning achievement. It was motivated by the efforts of Al-Lughah Bogor to continue to strive to follow the development of the world of education in order to improve the quality of services, human resources and the quality of its graduates. The research object used was Al-Lughah Bogor students representing all levels of education as many as 56 students. To analyze the research data, SPSS Version 20 was used. The results showed that (1) Learning Motivation had a positive and significant effect on Student Achievement in Al-Lughah Bogor Students, (2) Self Efficacy had no significant effect on Student Achievement in Al-Lughah Bogor Students, (3)) Learning motivation and self-efficacy together have a significant effect on the learning achievement of Al-Lughah students in Bogor
Elaborating the Bayesian Priors in Unsupervised Graph Embedding via Graph Concepts

Unsupervised Graph Embedding yields specific importance because it performs well with inputs limited to the graph structure only. Proximity-preserving models, including link-preserving and Skip-Gram models, prove to be good approaches in both efficiency and accuracy on unsupervised tasks, even compared with state-of-the-art deep models. We first show that the optimization problem these models solve is equivalent to a Bayesian Inference problem, however, these models generally assume a uniform distribution for the target node representations, that is, the representations of nodes are not further constrained. In our paper, we elaborate this Bayesian prior resorting to potential concepts underlying a graph. These graph concepts can be communities in a graph, nodes with different interaction patterns et al. We further derive the optimization objective according to this elaborated prior, and proposed our learning objective. Intuitively, graph nodes of the same concept are embedded close to each other. Our paper proposes a flexible framework which is adaptable to any other proximity-based models. Experiments show that our model significantly elevates the baseline performances of proximity-preserving models, yielding state-of-the-art results on unsupervised learning tasks.
Next Release Problem: A Systematic Literature Review

Agile software development is a widely used software development methodology which welcomes highly changing customers’ requirements. In Agile software development process, the whole software is delivered into a series of small releases. Each release incorporates a subset of whole software requirements. The selection of requirements to be incorporated in the next release is a complex activity. It was first termed as “Next Release Problem” by Bagnal. Many techniques were proposed later on to solve NRP The main objectives of this research are: (1) to classify NRP papers according to four criteria: techniques used, datasets used, objectives (either single or multiple), publication channels and trends; and (2) to analyze these studies from three perspectives: study objectives, optimization techniques to solve NRP and limitations of study. We performed a systematic literature review on NRP studies published in the period 2010-2018 and reviewed them on an automated four electronic databases. We identified a total of 27 studies published between 2010 and 2018 and classified them on predefined classification criteria. Based on the findings of this research it is concluded that multi-objective optimization techniques are the most widely used techniques. Among multiobjective optimization techniques applied in the context of NRP, NSGA-II provides the best solution both in term of convergence speed and solution quality while for the single-objective optimization problem, Simulated Annealing provides promising results. It is also observed that customer’s satisfaction is widely used objectives to be maximized in either single objective or multi-objective optimization techniques. Furthermore, 10 real-world datasets were identified during this research. It is observed that the latest optimization techniques are given less attention to solving NRP which have shown promising results in many cases as compared to techniques applied to cater NRP.