Reviewerly/exHarmony huggingface dataset:
The Relationship between Interest in Following Sewing Training and Learning Results in PKBM Ganting Pesisir Selatan

This study aims to determine the relationship between interest in participating in sewing training and learning outcomes in the South Coast Ganting District Court. The proposed hypothesis is that there is a positive relationship between interest in attending sewing training and learning outcomes of learning citizens. The more interested the residents learn to take sewing training, the better their learning outcomes will be. And vice versa, the more people are not interested in learning to participate in training, the results obtained will not be good. The type of research that researchers use is correlative research using a quantitative approach. The population in this study were the sewing training participants this year. The data collection technique used was a questionnaire and the tools used were a questionnaire. The data analysis technique used the percentage formula and the Spearman Rho formula. The results of this study indicate that: 1) the participants' interest in learning in sewing is good; 2) the learning outcomes of the sewing training participants are classified as good; 3) There is a significant relationship between the Interests in Participating in Sewing Training and Learning Outcomes in PKBM Ganting Pesisir Selatan. Suggestions from this research are that because there is a significant relationship between interest in attending sewing training and learning outcomes, it is necessary for the tutor to improve the implementation of sewing training, by making interesting learning media, methods and strategies that will increase motivation so that participants are active in participating all training activities so that their implementation can run more conducive. Keywords: Interest, Sewing Training, Learning Results
The role of political instability and corruption on foreign direct investment in the MENA region

The interest of this paper is to show the influence of political instability and corruption on foreign direct investment and its different effects among MENA countries. Political instability and corruption are highlighted as a risk factor for the foreign investor who generates several costs for economic activity and remains a major determinant of FDI. The combination of political instability and corruption contributes to the revolution in these countries such as Tunisia, Egypt, Libya and weak economic integrations in general explain the low attractiveness of MENA countries for foreign investors. It is widely argued that good governance is an important factor of FDI. With the exception of studies of corruption, however, empirical research on the link between governance and FDI is limited, particularly in the context of MENA countries. Corruption and political instability are the governance indicators that seem to have the greatest impact on foreign direct investment (FDI). An increase in FDI has the greatest effect on development in politically stable regimes. Studies of corruption and its relation to foreign direct investment (FDI) have yielded mixed results; some have found that corruption discourages FDI, but others have found the opposite. The study covers the MENA region for the period 1996-2016. Using the panel data technique and the results obtained indicate a negative relationship between political instability and foreign direct investment and between corruption and FDI.
The research data management ecosystem and information professionals: An overview of the issues, methodology and proposed solutions of a Canadian survey

‪This article presents the motivations, the conceptual framework and the methodology of a budding research project aimed at studying research data management. The point of view taken is that of the information professional, with regard to so-called information governance. Various issues in research data management are explored, including tensions related to data sharing and research ethics, and the question of shared responsibility for this management. The discussion motivates the methodological approach (surveys on the current situation and theoretical modeling of the existing ecosystem) and foreshadows the solutions envisioned by the research project‪.
Building Balinese Part-of-Speech Tagger Using Hidden Markov Model (HMM)

Part-of-Speech tagging or word class labeling is a process for labeling a word class in a word in a sentence. Previous research on POS Tagger, especially for Indonesian, has been done using various approaches and obtained high accuracy values. However, not many researchers have built POS Tagger for Balinese. In this article, we are interested in building a POS Tagger for Balinese using a probabilistic approach, specifically the Hidden Markov Model (HMM). HMM is selected to deal with ambiguity since it gives higher accuracy and fast processing time. We used k-fold cross-validation (with k = 10) and tagged corpus around 3669 tokens with 21 tags. Based on the experiments conducted, the HMM method obtained an accuracy of 68.56%.
DEVELOPMENT OF COLLABORATIVE PROBLEM-SOLVING COMPETENCY FOR STUDENTS THROUGH TEACHING METHOD OF WORKING IN CORNERS IN TEACHING CHEMISTRY AT HIGH SCHOOLS

Collaborative problem-solving competency is one of the essential competencies, it is the combined competency of individual and social factors, demonstrating the ability to solve problems through team cooperation. With the organization of learning for students to perform group tasks in the corners, teaching methods of working in corners to have many conditions to develop collaborative problem-solving capabilities for students. However, in practice, when applying this teaching method, teachers only focus on designing tasks that fulfill the goal of knowledge and skills without paying attention to developing problemsolving capabilities through collaborative learning. In addition, teachers also face many difficulties in designing tools to assess students' competency. The paper presents principles and processes for designing learning tasks for collaborative learning activities in cornerbased teaching methods to develop collaborative problem-solving competency and building its assessment tools for students in high schools.
Performance comparison of ant colony system and firefly algorithm for traveling salesman problem

Many algorithms are applied for optimization of logistic and scheduling problem, and one of the most known techniques is swarm intelligence. In years, a number of swarm based optimizations are increased such as Ant Colony System (ACS) and Firefly Algorithm (FA) with a new movement scheme. ACS provides a better balancing between exploration of new edge and exploitation of the shortest route in Travelling Salesman Problem (TSP). This strategy significantly improved performance of Ant System (AS) that often trapped into local optimum. Meanwhile, discrete FA is metaheuristic algorithm that works very well to solve the TSP. By using a new movement scheme, FA gives better accuracy with much faster average time to solve the TSP. This paper presents a performance comparison between ACS and FA. In ACS and FA, a set of particle cooperate to find the best solution to TSP. The result shows that FA gives better performances in terms of optimal solution and faster convergence rather than that of ACS.
PENGARUH EFEKTIVITAS DAN KUALITAS PENERAPAN SISTEM INFORMASI AKUNTANSI TERHADAP KINERJA PENGGUNANYA

This research aimed to examine the effectiveness of accounting information system implementation and quality of accounting information system on users’ performance. While, the population was employees who applied accounting system information. Moreover, the data collection technique used purposive sampling technique, in which the sample was based on criteria given. In line with, there were 44 respondents which were under supervision of Senior Manager of Finance Management at PDAM Surya Sembada Surabaya. The instrument in data collection technique used survey through questionnaires which were distributed to respondents. Furthermore, the data analysis technique used multiple linear analysis with SPSS (Statistical Product and Service Solution) 20. The research results concluded the effectiveness of accounting information system implementation as well as accounting information system either partially or simultaneously had positive effect on user’s performance. As simultaneously, this was proven with F test which was about F-count 28.230 > F-table 3.23 with significance lvel less then 0.05. Meanwhile, as partially it could be proven with t-test which showed effectiveness of accounting information system had significant value of 0.023 or < 0.05. In addition, quality of accounting information system had significance value of 0.000 which smaller than 0.05.Keywords: effectiveness implementation, accounting information system, user’s performance
Rancang Bangun Sistem Informasi Manajemen Arsip Surat

Mail archives are the center of memory for each organization or agency, if the processing is not good as a result it will affect the reputation of an organization or agency. One of the uses of archives is to manage important documents such as incoming mail and outgoing mail. The Information System for Filing In and Out Letters is developed based on the Waterfall method and is expected to make it easier for government agencies of the Perbecel Kelod Village Office in West Denpasar District, Denpasar City to run more optimally to serve the community quickly and precisely.
Human Perception Modeling for Automatic Natural Image Matting.

Natural image matting aims to precisely separate foreground objects from background using alpha matte. Fully automatic natural image matting without external annotation is quite challenging. Well-performed matting methods usually require accurate handcrafted trimap as extra input, which is labor-intensive and time-consuming, while the performance of automatic trimap generation method of dilating foreground segmentation fluctuates with segmentation quality. In this paper, we argue that how to handle trade-off of additional information input is a major issue in automatic matting, which we decompose into two subtasks: trimap and alpha estimation. By leveraging easily-accessible coarse annotations and modeling alpha matte handmade process of capturing rough foreground/background/transition boundary and carving delicate details in transition region, we propose an intuitively-designed trimap-free two-stage matting approach without additional annotations, e.g. trimap and background image. Specifically, given an image and its coarse foreground segmentation, Trimap Generation Network estimates probabilities of foreground, unknown, and background regions to guide alpha feature flow of our proposed Non-Local Matting network, which is equipped with trimap-guided global aggregation attention block. Experimental results show that our matting algorithm has competitive performance with current state-of-the-art methods in both trimap-free and trimap-needed aspects.
Studi Deskripsi SpesifikasiTehnis dari Kamera Mirroless untuk Mengambil Foto Secara Artistik

The Mirorrless camera is a photographic device used to take pictures through mechanical and electronic processes. It can be interpreted as a combination of technology and art, making photography a complex art and image medium, as well as providing meaning and information. In general, what distinguishes digital SLR cameras from mirrorless cameras is how the image is displayed in the viewfinder. The camera has different composition settings to make it easier to understand each type of photography. In 2009, Olympus introduced a new type of mirrorless camera.
The Improvement of NDF(No Defect Found) on Mobile Device Using Datamining

Recently, with the development of technologies for the fourth industrial revolution, convergence and complex technology are being applied to aircraft, electronic home appliances and mobile devices, and the number of parts used is increasing. Increasing the number of parts and the application of convergence technologies such as HW (hardware) and SW (software) are increasing the No Defect Found (NDF) phenomenon in which the defect is not reproduced or the cause of the defect cannot be identified in the subsequent investigation systems after the discovery of the defect in the product. The NDF phenomenon is a major problem when dealing with complex technical systems, and its consequences may be manifested in decreased safety and dependability and increased life cycle costs. Until now, NDF-related prior studies have been mainly focused on the NDF cost estimation, the cause and impact analysis of NDF in qualitative terms. And there have been no specific methodologies or examples of a working-level perspective to reduce NDF. The purpose of this study is to present a practical methodology for reducing NDF phenomena through data mining methods using quantitative data accumulated in the enterprise. In this study, we performed a cluster analysis using market defects and design-related variables of mobile devices. And then, by analyzing the characteristics of groups with high NDF ratios, we presented improvement directions in terms of design and after service policies. This is significant in solving NDF problems from a practical perspective in the company.
Sentiment analysis in social media based on English language

Numerous numbers of companies have utilized the web to offer their services and products. Web customers dependably look through the reviews of other customers towards a product or service before they chose to buy the things or viewed the films. The company needs to analyse their customers’ sentiment and feeling based on their comments. The outcome of the sentiment analysis makes the companies easily discover either the expression of their users is more to positive or negative. There are numerous numbers of sentiment analysis techniques available in the market today. However, only three (3) techniques will be used in this research which are the Python NLTK Text Classification, Miopia and MeaningCloud. These techniques used to analyse the sentiment analysis of the reviews and comments from English language in social media. 2400 datasets from Amazon, Kaggle, IMdB, and Yelp were used to analyse the accuracy of these techniques. From this analyses, average accuracy for sentiment analysis using Python NLTK Text Classification is 74.5%, meanwhile only 73% accuracy achieved using Miopia technique. The accuracy achieved when using MeaningCloud technique is 82.1% which is the highest compared to other techniques. This shows that hybrid technique offers a greatest accuracy for sentiment analysis on social reviews
Statistical Governance and FDI in Emerging Economies

The importance of institutional settings for economic development outcomes is broadly acknowledged nowadays. This paper investigates the role of official statistics in alleviating financing constraints in emerging and developing economies, with a particular focus on Sub-Saharan Africa. Official statistics has a major dual role: it directly adds to the information set of investors regarding the general state of the economy and it is a key commitment and signalling device as to future good governance. Empirically, the paper investigates, for a sample of 98 emerging and developing countries, the relationship between the adoption of the IMF General Data Dissemination Standard (GDDS) for statistical data production and the net incurrence of foreign direct investment liabilities. Direct investment is considerably higher under GDDS. Controlling also for time and country effects, using fixed effects and quantile panel regression, the relationship ceases to be uniformly positive. Heterogeneity matters: There is a large and significant difference between poorer and richer countries, as well as between countries in Sub-Saharan Africa and elsewhere. Given the information asymmetry problems in poor developing countries, this is not unexpected. Furthermore, it becomes evident that the relationship between the adoption of GDDS and net incurrence of FDI liabilities is negative for richer countries and outside Sub-Saharan Africa. For richer countries, the relevant alternative might have been the more demanding SDDS, turning the adoption of GDDS into an unfavourable signal. Quantile regression is carried out using the quantile panel estimator of Canay (2011).
Are Axioms and Inference Rules Sufficient to Find any True Statement in Science? GÃ¶del, a Great Logician with Psychological Problems Gave the Answer

Until 1931 eminent intellectuals, like Russell, Ackermann and Hilbert were trying to find formal systems based of a finite number of axioms and generally accepted rules of inference in which every statement could be proved or disproved, independently from the objects considered. However, to apply this in an endless variety of objects they had to consider them as mere signs drained from any content. This had as a result a statement to be proved or disproved only syntactically, i.e. only formally, without caring if is true or not. Kurt GA¶del in 1930 made a distinction between syntactic provability and semantic truth, the latter resulting from the consideration of properties coming from the content of the objects. Finally, in 1931 GA¶del, with his two incompleteness theorems, proved that there are true statements in mathematics including elementary arithmetic of natural numbers [0,1,2â€¦] which are impossible to be proved or disproved, demolishing the optimistic opinion prevailing until then. The consequences of these theorems puzzle logicians, mathematicians and physicists ever since. Although GA¶del was a leading logician, ironically he was tormented by lifelong illogical fears which finally caused his death.
Continuous-variable error correction for general Gaussian noises

Quantum error correction is essential for robust quantum information processing with noisy devices. As bosonic quantum systems play a crucial role in quantum sensing, communication, and computation, it is important to design error correction codes suitable for these systems against various different types of noises. While most efforts aim at protecting qubits encoded into the infinite dimensional Hilbert space of a bosonic mode, [Phys. Rev. Lett. 125, 080503 (2020)] proposed an error correction code to maintain the infinite-dimensional-Hilbert-space nature of bosonic systems by encoding a single bosonic mode into multiple bosonic modes. Enabled by Gottesman-Kitaev-Preskill states as ancilla, the code overcomes the no-go theorem of Gaussian error correction. In this work, we generalize the error correction code to the scenario with general correlated and heterogeneous Gaussian noises, including memory effects. We introduce Gaussian pre-processing and post-processing to convert the general noise model to an independent but heterogeneous collection of additive white Gaussian noise channels and then apply concatenated codes in an optimized manner. To evaluate the performance, we develop a theory framework to enable the efficient calculation of the noise standard deviation after the error correction, despite the non-Gaussian nature of the codes. Our code provides the optimal scaling of the residue noise standard deviation with the number of modes and can be widely applied to distributed sensor-networks, network communication and composite quantum memory systems.
Towards Lifelong Learning of End-to-end ASR

Automatic speech recognition (ASR) technologies today are primarily optimized for given datasets; thus, any changes in the application environment (e.g., acoustic conditions or topic domains) may inevitably degrade the performance. We can collect new data describing the new environment and fine-tune the system, but this naturally leads to higher error rates for the earlier datasets, referred to as catastrophic forgetting. The concept of lifelong learning (LLL) aiming to enable a machine to sequentially learn new tasks from new datasets describing the changing real world without forgetting the previously learned knowledge is thus brought to attention. This paper reports, to our knowledge, the first effort to extensively consider and analyze the use of various approaches of LLL in end-to-end (E2E) ASR, including proposing novel methods in saving data for past domains to mitigate the catastrophic forgetting problem. An overall relative reduction of 28.7% in WER was achieved compared to the fine-tuning baseline when sequentially learning on three very different benchmark corpora. This can be the first step toward the highly desired ASR technologies capable of synchronizing with the continuously changing real world.
APLIKASI TAGGING LOKASI PELANGGAN MENUNGGAK DENGAN METODE GEOLOCATION TAGGING BERBASIS MOBILE

As one of the providers of electrical energy in Indonesia, which will supply electrical energy to every customer of PT PLN (Persero) trying to provide the best service. Currently the media is marking the location of customers is still in the form of photos of manual documentation that is stored on android every officer. So the location tagging application was made especially for customers who have arrears, to facilitate officers in conducting field surveys. The use of an android smartphone is considered to be very suitable for completing photo documentation manually by utilizing the Global Positioning System (GPS) feature. This study aims to design the Tagging application for Arrears Customer Locations with the Geolocation Tagging Method Mobile-Based.The results of this study can provide photo documentation of each location with coordinates together with detailed customer information.
Comparative Analysis of Multiplicative and Additive Noise Based Automated Regularizations in Non-Linear Diffusion Image Reconstruction

Multiplicative and additive noises are often introduced in image signals during the image acquisition process and result into degradation of image features. The work done by Perona and Malik in 1990 and its modified versions revolutionized the way through which noises or speckles are removed. The Perona-Malik model requires tuning of the regularization parameter to control and prevent staircase artifacts in restored images. The current manual tuning is a challenging and time consuming practice when a long queue of images is registered for processing. Attempt to automate the regularization parameter appeared in Perona-Malik model with self-adjusting shape-defining constant. Although both multiplicative and additive noise based automated regularizations were presented, the paper stayed silent on matters concerning the automation method that fits with speckle reduction. This paper therefore, presents a comparative analysis of additive and multiplicative noise based automated regularizations. Simulation results and paired samples T-tests reveal that the multiplicative noise based automation outperforms the additive noise based automation for small speckle variances. However, the two automation methods do not significantly differ when large speckle variances are assumed.