Reviewerly/exHarmony huggingface dataset:
Apriori Algorithm on Car Rental Analysis with The Most Popular Brands

Nowadays, vehicle rental has become a common function for companies that have busy operational activities. Every company in carrying out operational activities requires a vehicle that is always there when needed. PT. Agung Solusi Trans is a vehicle rental company that rents various vehicle brands commonly used by customers to rent vehicles. In addition, PT. Agung Solusi Trans is also difficult to get updated information regarding the level of sales per period. Therefore, we need a decision support system and a method that can be used to design a business strategy that can provide an efficient and effective information, namely data mining using the a priori algorithm association method. The researcher specializes in taking only vehicle types as research material by selecting fifteen brands, including Agya, Yaris, Sienta, Calya, Avanza, Innova, Rush, Vios, Altis, Camry, Fortuner, Alphard, Hi Ace, Voxy, and Hilux. In analyzing the data, the writer uses a priori algorithm calculation by testing the hypothesis of two variables between the value of support and the value of confidence. After that, apriori algorithm is calculated using Tanagra. Based on the analysis done by the author, that the brands most sought after by customers are Calya, Avanza, Hilux. From these results can be used by PT. Agung Solusi Trans to prepare vehicle brands that are widely leased by customers and increase brand inventory.
PENGEMBANGAN SISTEM INFORMASI PENJUALAN SEPATU PADA TOKO ANUGERAH JAYA

This study aims to analyze and develop an Anugerah Jaya store sales information system using the PIECES method. The problem faced at this time is that the sales system that is running is still manual, starting from the sales transaction process, the income calculation process, data collection on goods prices and inventory data are also still calculated by manual calculations that are only written on paper or books. This makes the quality of the information obtained is not optimal, less efficient and of course the possibility of misinformation is quite large. The research method used is a field study with data collection conducted through interviews and direct observation of the sales process. The results of the study based on the PIECES parameter analysis found several shortcomings in the sales process including the slow processing of data and the provision of reports. To overcome the existing problems, a new web-based system proposal was designed to overcome the current problems
Load Balancing in Cloud Environment by Implementing Enhanced Dragonfly Algorithm

The breakthroughs in processing and storage technologies as well as the popularity of the Internet have made computer resources more economical, powerful and widely available than ever before. A new computer model known as cloud computing has emerged as a result of this technical advancement. Scheduling is a key cloud application. In contrast, task scheduling in cloud computing is an NP-hard optimization issue. A fundamental part of cloud task planning is the load balancing of non-preventive, autonomous workloads on virtual machines (VMs). The technique of moving tasks from overburdened VMs to under burdened VMs is known as load balancing. When operating an application, load balancing might have an impact on the overall performance of the system. A load balancing algorithm tries to improve the response time of user-submitted applications by guaranteeing maximum utilization of available resources. To address this issue, we developed a novel cloud load balancing approach based on the Enhanced Dragonfly algorithm (EDA). In addition, we create a multi-objective function based on three variables: completion time, processing expenses, and load. The major goal is to perform the task in a shorter amount of time, with lower processing costs and load balancing. The experimental results suggest that the proposed strategy outperforms existing approaches in terms of load balancing (lower cost and time).
TR-SEQ: Named Entity Recognition Dataset for Turkish Search Engine Queries

Recognizing named entities in short search engine queries is a difficult task due to their weaker contextual information compared to long sentences.Standard named entity recognition (NER) systems that are trained on grammatically correct and long sentences fail to perform well on such queries.In this study, we share our efforts towards creating a cleaned and labeled dataset of real Turkish search engine queries (TR-SEQ) and introduce an extended label set to satisfy the search engine needs.A NER system is trained by applying the state-of-the-art deep learning model BERT to the collected data and its high performance on search engine queries is reported.Moreover, we compare our results with the state-ofthe-art Turkish NER systems.
Modelling and analysis of weather radar backscatter from wind energy turbines

The overall goal consists in understanding and overcoming the influence of weather radar returns that stem from wind energy turbines. We tackle this problem on two different levels. First, we model and simulate radar raw data. The goal is to understand the echo characteristics from wind turbine returns and to use this knowledge to construct classifiers that are able to discriminate between “clean” and “contaminated” radar echoes. If possible, raw data-based “decontamination”- routines will be applied. The second approach relies on the analysis of higher moment data. In this approach we are faced with incomplete spatial-temporal data, namely with data gaps that are caused by strong wind turbine backscatter. The goal is to develop and to apply so-called “gap-infilling” routines that deliver physically feasible higher moment recoveries for the missing parts of the data. Mathematically the first approach relies on sophisticated Fourier analysis and classification/learning theory. The second approach involves techniques from inverse problems, sparse recovery principles, partial differential equation based infilling and optical flow.
A Central Limit Theorem for Differentially Private Query Answering

Perhaps the single most important use case for differential privacy is to privately answer numerical queries, which is usually achieved by adding noise to the answer vector. The central question, therefore, is to understand which noise distribution optimizes the privacy-accuracy trade-off, especially when the dimension of the answer vector is high. Accordingly, extensive literature has been dedicated to the question and the upper and lower bounds have been matched up to constant factors [BUV18, SU17]. In this paper, we take a novel approach to address this important optimality question. We first demonstrate an intriguing central limit theorem phenomenon in the high-dimensional regime. More precisely, we prove that a mechanism is approximately Gaussian Differentially Private [DRS21] if the added noise satisfies certain conditions. In particular, densities proportional to $\mathrm{e}^{-\|x\|_p^\alpha}$, where $\|x\|_p$ is the standard $\ell_p$-norm, satisfies the conditions. Taking this perspective, we make use of the Cramer--Rao inequality and show an uncertainty principle-style result: the product of the privacy parameter and the $\ell_2$-loss of the mechanism is lower bounded by the dimension. Furthermore, the Gaussian mechanism achieves the constant-sharp optimal privacy-accuracy trade-off among all such noises. Our findings are corroborated by numerical experiments.
ELERRANT: Automatic Grammatical Error Type Classification for Greek

In this paper, we introduce the Greek version of the automatic annotation tool ERRANT (Bryant et al., 2017), which we named ELER-RANT.ERRANT functions as a rule-based error type classifier and was used as the main evaluation tool of the systems participating in the BEA-2019(Bryant et al., 2019) shared task.Here, we discuss grammatical and morphological differences between English and Greek and how these differences affected the development of ELERRANT.We also introduce the first Greek Native Corpus (GNC) and the Greek WikiEdits Corpus (GWE), two new evaluation datasets with errors from native Greek learners and Wikipedia Talk Pages edits respectively.These two datasets are used for the evaluation of ELERRANT.This paper is a sole fragment of a bigger picture which illustrates the attempt to solve the problem of lowresource languages in NLP, in our case Greek.
Pengaruh Metode Gallery Walk Terhadap Hasil Belajar Siswa Kelas IV SDN Tladan 2 Tahun Ajaran 2020/2021

Learning must be implemented using methods and media that enable students to improve their learning results. The goal of this study was to see how the Gallery Walk approach affected primary school pupils' learning outcomes. This is a study that is being conducted as an experiment. The result of this research is that the pretest value is 0.222 while the posttest value is 0.223. Based on the t-test, it shows that tcount = 14,601 with ttable = 2,228, 14.601 &gt; 2,228, then H0 is rejected. So it can be concluded that there is an influence of the Gallery Walk learning method on the learning outcomes of elementary school students.&#x0D; &#x0D; Pelaksanaan pembelajaran memerlukan metode dan media yang dapat memfasilitasi siswa guna meningkatkan hasil belajarnya Tujuan dari peneltian berikut adalah mengetahuii pengaruh metodeeGallery Walk terhadp hasill belajar siswa SD. Penelitian berikut termasuk dalam jenis eksperimen. Hasil penelitian ini Lhitung nilai pretest sebesar 0,222 sedangkan Lhitung nilai postest 0,223. Berdasarkan uji t menunjukkan hasil_thitung = 14,601 dengan ttabel = 2,228, 14,601 &gt; 2,228, maka H0 ditolak. Dapat disimpulkan ada pengaruh metode pembelajaran Gallery Walk terhadap hasil belajar siswa Sekolah Dasar.
Face Mask Detection System

Abstract: There are many solutions to prevent the spread of the COVID-19 virus and one of the most effective solutions is wearing a face mask. Almost everyone is wearing face masks at all times in public places during the coronavirus pandemic. Coronavirus disease 2019 has affected the world seriously. One major protection method for people is to wear masks in public areas. The risk of transmission is highest in public places. However, there are only a few research studies about face mask detection based on image analysis. This paper aims to present a review of various methods and algorithms used for human recognition with a face mask. The proposed system to classify face mask detection using COVID-19 precaution both in images and videos using convolution neural network, TensorFlow and OpenCV to detect face masks on people. This system has various applications at public places, schools, etc. where people need to be detected with the presence of a face mask and recognize them and help society. Keywords: COVID-19, Tensorflow, OpenCV, Face Mask, Image Processing, Computer Vision
Understanding Technical and Scientific Translation: A Genre-based Approach

In the recent past, 30 or 40 years ago, the terms technical and scientific were usedinterchangeably when discussing about communication, texts, terms, vocabulary or writing.Due mainly to this reason, the lines separating technical and scientific translation becameincreasingly blurred and little or no distinction was made between the two terms; as such,technical and scientific translation was regarded as one type of LSP translation. Using genreknowledge, in this article we show that technical translation and scientific translation can andshould be treated separately, as two distinct types of LSP translation, for both educational andresearch purposes. We present two genre-based models for setting-up the technical andscientific curricula, aimed at familiarising students with these inter-related, yet different typesof LSP translation, and which can also be used as starting points for research in these twofields.
Chapter 1. A Confidence-Building Toolkit

Chapter 1 of Library Technology Reports (vol. 57, no. 8), “Using the Toward Gigabit Libraries Toolkit,” covers the history of the toolkit, describes its open licensing, and explains how a group of subject matter experts were pivotal to its success. It also credits leaders and advisory boards for both the Toward Gigabit Libraries and the Gigabit Libraries and Beyond projects.
Optimizing STEM+C Education With Data-Driven Intelligent Tutoring Systems

One of the most effective ways to learn is through problem solving. In recent years, it is widely known that problem solving is a central subject and fundamental ability in the teaching and learning. Besides, problem solving is integrated in the STEM+C (Science, Technology, Engineering, and Math plus Computing, Coding or Computer Science) fields. Intelligent tutoring systems (ITSs) have been shown to be effective in supporting students' domain-level learning through guided problem solving practice. Intelligent tutoring systems provide personalized feedback (in the form of hints) to students and improve learning at effect sizes approaching that of human tutors. However, creating an ITS to adapt to individual students requires the involvement of experts to provide knowledge about both the academic domain and novice student behavior in that domain’s curriculum. Creating an ITS requires time, resources, and multidisciplinary skills. Because of the large possible range of problem solving behavior for any individual topic, the amount of expert involvement required to create an effective, adaptable tutoring system can be high, especially in open-ended problem solving domains. Data-driven ITSs have shown much promise in increasing effectiveness by analyzing past data in order to quickly generate hints to individual students. However, the fundamental long term goal was to develop “better, faster, and cheaper” ITSs. The main goal of this paper is to: 1) presents ITSs used in the STEM+C education; and 2) introduce data-driven ITSs for STEM+C education.
Antitrust Rules and Competition Violations. The Evolution of Consumer Protection

According to the order of the Court of Verona of 01.10.2018, No. 3763, a prohibited agreement pursuant to Art. 2, Law No. 287/1990, can also be harmful to consumer or entrepreneur, who has not taken part in it. In order to recognize an interest in invoking the protection referred to in Art. 33, para. 2, Law No. 287/1990, it is not sufficient to allege the nullity of the agreement itself but it is also necessary to specify the consequence that this failure has produced regarding the right to an effective choice between a plurality of competing products. This paper intends to investigate the institutions of the omnibus guarantee and its consequent nullity for violation of the discipline that governs agreements restricting competition. It also provides an analysis of the remedies and safeguards available to consumers who have remained extraneous to the competitive agreement, and who have entered into a subsequent contract of the latter.
Low-dimensional Structure in the Space of Language Representations is Reflected in Brain Responses

How related are the representations learned by neural language models, translation models, and language tagging tasks? We answer this question by adapting an encoder-decoder transfer learning method from computer vision to investigate the structure among 100 different feature spaces extracted from hidden representations of various networks trained on language tasks. This method reveals a low-dimensional structure where language models and translation models smoothly interpolate between word embeddings, syntactic and semantic tasks, and future word embeddings. We call this low-dimensional structure a language representation embedding because it encodes the relationships between representations needed to process language for a variety of NLP tasks. We find that this representation embedding can predict how well each individual feature space maps to human brain responses to natural language stimuli recorded using fMRI. Additionally, we find that the principal dimension of this structure can be used to create a metric which highlights the brain's natural language processing hierarchy. This suggests that the embedding captures some part of the brain's natural language representation structure.
Time-varying parameter estimation and adaptive Kalman filter in computer aided control application

Estimating the unknown parameter vector of the system model is the most important problems in system identification. Especially in cases where the system's parameters are time-variable, it is observed that estimations obtained using estimator have deviated from the actual values, and therefore that the estimator must be corrected to some extent. In this paper, some methods for the parameter estimation in cases where a system is modelled with ARX Autoregressive Exogenous Input) are considered. After reviewing the problems, a simulation study has been made on comparing different estimation methods. Corrected (Adaptive) Kalman Filter (CKF) gives results more accurately than Normal Kalman Filter (NKF) for time varying parameter estimation. Moreover, after an introduction to the method of minimum variance feed-back control, using this method and CKF, a heating control is done in computer aided experimental study. CKF ensures that the system is kept under control by correctly estimating the parameter that changes over time.
Research of Bibliographic References in Math-Net.Ru using the Journal Citation Graph

According to the portal Math-Net.Ru a graph of journal citation is constructed. To increase the reliability of the model, the citation time interval was chosen from 2010 to 2021, when the distribution of citation articles stabilized at the level of 3500-4500 citations per year. The structure of link aging is studied and it is shown that their half-life is equal to 8 years. Therefore, the publication date of the cited articles was limited to 2002. For the constructed citation graph, the main properties, such as a small diameter and a high density, are obtained, indicating a high level of scientific communication in the Math-Net.Ru. The absence of the “Matthew effect” as a pronounced advantage in quoting established journals in relation to less well-known ones is shown. Adequacy of the journal citation graph Math-Net.Ru as a model of scientific communication confirmed by comparing the ranking of journals in the citation graph with their SCIENCE INDEX rating in eLIBRARY.RU. A direct moderate relationship between the two rankings is shown. A number of meaningful conclusions are drawn from the analysis of the citation graph.
Contrastive Code Representation Learning

Machine-aided programming tools such as automated type predictors and autocomplete are increasingly learning-based. However, current approaches predominantly rely on supervised learning with task-specific datasets. We propose Contrastive Code Representation Learning (ContraCode), a self-supervised algorithm for learning task-agnostic semantic representations of programs via contrastive learning. Our approach uses no human-provided labels, only the raw text of programs. ContraCode optimizes for a representation that is invariant to semantic-preserving code transformations. We develop an automated source-to-source compiler that generates textually divergent variants of source programs. We then train a neural network to identify variants of anchor programs within a large batch of non-equivalent negatives. To solve this task, the network must extract features representing the functionality, not form, of the program. In experiments, we pre-train ContraCode with 1.8M unannotated JavaScript methods mined from GitHub, then transfer to downstream tasks by fine-tuning. Pre-training with ContraCode consistently improves the F1 score of code summarization baselines and top-1 accuracy of type inference baselines by 2% to 13%. ContraCode achieves 9% higher top-1 accuracy than the current state-of-the-art static type analyzer for TypeScript. Finally, representations learned through a hybrid contrastive and reconstruction objective transfer in zero-shot to code clone detection with +10% AUROC over a static text similarity measure and +5% over reconstruction alone.
Domain Adaptation for Hindi-Telugu Machine Translation using Domain Specific Back Translation

In this paper, we present a novel approach for domain adaptation in Neural Machine Translation which aims to improve the translation quality over a new domain.Adapting new domains is a highly challenging task for Neural Machine Translation on limited data, it becomes even more difficult for technical domains such as Chemistry and Artificial Intelligence due to specific terminology, etc.We propose Domain Specific Back Translation method which uses available monolingual data and generates synthetic data in a different way.This approach uses Out Of Domain words.The approach is very generic and can be applied to any language pair for any domain.We conduct our experiments on Chemistry and Artificial Intelligence domains for Hindi and Telugu in both directions.It has been observed that the usage of synthetic data created by the proposed algorithm improves the BLEU scores significantly.
A Critical Look at the Consistency of Causal Estimation with Deep Latent Variable Models

Using deep latent variable models in causal inference has attracted considerable interest recently, but an essential open question is their ability to yield consistent causal estimates. While they have demonstrated promising results and theory exists on some simple model formulations, we also know that causal effects are not even identifiable in general with latent variables. We investigate this gap between theory and empirical results with analytical considerations and extensive experiments under multiple synthetic and real-world data sets, using the causal effect variational autoencoder (CEVAE) as a case study. While CEVAE seems to work reliably under some simple scenarios, it does not estimate the causal effect correctly with a misspecified latent variable or a complex data distribution, as opposed to its original motivation. Hence, our results show that more attention should be paid to ensuring the correctness of causal estimates with deep latent variable models.
Decentralized Q-learning in Zero-sum Markov Games

We study multi-agent reinforcement learning (MARL) in infinite-horizon discounted zero-sum Markov games. We focus on the practical but challenging setting of decentralized MARL, where agents make decisions without coordination by a centralized controller, but only based on their own payoffs and local actions executed. The agents need not observe the opponent's actions or payoffs, possibly being even oblivious to the presence of the opponent, nor be aware of the zero-sum structure of the underlying game, a setting also referred to as radically uncoupled in the literature of learning in games. In this paper, we develop for the first time a radically uncoupled Q-learning dynamics that is both rational and convergent: the learning dynamics converges to the best response to the opponent's strategy when the opponent follows an asymptotically stationary strategy; the value function estimates converge to the payoffs at a Nash equilibrium when both agents adopt the dynamics. The key challenge in this decentralized setting is the non-stationarity of the learning environment from an agent's perspective, since both her own payoffs and the system evolution depend on the actions of other agents, and each agent adapts their policies simultaneously and independently. To address this issue, we develop a two-timescale learning dynamics where each agent updates her local Q-function and value function estimates concurrently, with the latter happening at a slower timescale.