Reviewerly/exHarmony huggingface dataset:
VAT Fraud, Cryptocurrencies and a Future for the VAT System

In this article, the author outlines the existing interactions between cryptocurrencies, money laundering and organized VAT fraud, evaluating whether a growing use of virtual currencies may result in the development of more sophisticated fraud patterns. The adoption of blockchains, split-payment mechanisms and the definitive VAT regime are discussed as possible long-term measures to address the issue.
The Development Of English For Beginner Through Pocketbook Media To Improve Student Learning Primary Results

Students' difficulty in understanding English language as a subject usually leads to poor learning outcomes. The problem is, however, observed to be getting worse due to the unavailability of suitable learning media for educators and students. This research was, therefore, conducted to develop an English for Beginner pocketbook media to improve student learning outcomes. Meanwhile, pocketbook media is an alternative packaging of information through the use of user efficiency elements. This research was developed based on the Borg &amp; Gall model with the data collected using questionnaires, tests, and documentation and analyzed through Independent Sample T-test. The results showed the sig. (2-tailed) &lt; sig. level (0,000 &lt; 0.05), therefore, Ha was accepted while H0 was rejected. It was discovered that average students learning outcomes increased to 83,4 and learning completeness by 83.33% after receiving the treatment in the form of the English for Beginner pocketbook media. This shows this material is feasible to be implemented as a learning medium due to its effectiveness in improving the learning outcomes of fifth-grade students of MI Masaran 1 Trenggalek.
Government-based Cryptocurrencies

The objectives in this study were 1) to investigate the state of art of government-based cryptocurrencies, and 2) to discuss government-based cryptocurrency in sovereignty and control of government, government with her problems in possible unintentional failures and opportunism with third-party status, and information sharing. Documentary research was employed in this study. The findings revealed that government-based cryptocurrencies had a few interchangeable titles as follows: country-owned digital currency, national cryptocurrency, government-issued cryptocurrency/state-backed cryptocurrencies, and digital government backed cryptocurrency; the general characteristics of government-based cryptocurrencies were as follows: 1) The main objective of government-based cryptocurrencies was used against non-government-based cryptocurrencies, 2) Value and amount of currency were fixed by central bank, 3) Banning all or some existing nongovernment-based cryptocurrencies was conducted by government, 4) Government-based cryptocurrency could be invented by government or jointed venture, 5) The development of government-based cryptocurrency could be terminated by her government or by external entity’s order on her government, 6) The classification of government-based cryptocurrency could be fully activated currency, non-activated currency, and pilot-project-based currency, and 7) Some governments used their valuables for backing up government-based cryptocurrencies. In addition, Information sharing with equity of public, private, and people sector; formulation about rule and regulation of trade with attractive tax rate for legalized cryptocurrencies; AI in public policy analysis; and the foundation of an autonomous public organization were suitable solutions of macro economy and public financial management for lessening two important problems-loss of sovereignty and control of government and government’s unintentional failures and opportunism with third-party status.
POLITENESS AND UCHI-SOTO IN CROSS-CULTURAL PERSPECTIVES

every language has a polite manner of expression to the speaker. Both the indonesian and japanese expressions of this polite form are influenced by their culture. Politeness in Japanese is influenced in part by the uchi-soto culture. The form of respect used by paying attention to the interlocutor is an insider (Uchi) or an outsider (Soto), also paying attention to whether it is higher or lower, and age or position with the interlocutor. That forms of politeness in Japanese have interesting linguistic forms and processes to be studied in comparison with the disclosure in Indonesian. This paper discusses cultural understanding of the politeness in both languages ​​expressed through various language markers, both grammatically and/or lexically, and morphemic processes.
The Analysis of the Innovation of College English Phonetics Teaching Methods Under the "Internet +" Environment

The 21st century is an open era. With the continuous development of the Internet environment, network information technology has penetrated into all aspects of people’s production and life, and education is no exception. At this stage, my country attaches great importance to the development of the “Internet +” environment. “Internet + education” has become an important choice for the development of education in the new era. The organic combination of the Internet and college English phonetics can fully mobilize students’ interest in learning, to break the limitations of time and space, let students more actively participate in the learning process. Below, this article will conduct a series of explorations on the methods of college English phonetics education and teaching in the “Internet +” environment, hoping to effectively promote the reform and development of college English teaching.
Blockchain: A use case in smart farming

There are a lot of bottleneck in traversing the food origin. The buyer is always not sure about what is used in this product. Food safety is becoming more and more serious problem around the world. To tackle this from the technical point of view people need to ensure food traceability that can track and monitor the whole lifespan of the food production including the processing of food, raw material cultivation, breeding, processing, transportation, warehouse and selling. Block chain adaptation in agriculture is tailored to solve numerous problems. Majority of the current solution lies on heavily centralised cloud infrastructure where there is usually lack of transparency and security threats including availability of data lock in, confidentiality and availability. The main aim of this research is to highlight the issues that can be solved by blockchain and precision agriculture. Block chain is nothing but a distributed ledger technology allowing members to record transactions in a decentralised data log in computer networks. The transactions are to be approved through authorised partners and every operation gets secured through cryptography. The applications are mostly applicable in agriculture, which is already stressed up with numerous challenges, with rising consumer consciousness towards food safety. The vision of prime minister is to increase double the income of farmer by 2022 can only be realised through block chain only.
A new nail in the coffin: the Booking.com judgement of 2020

The short working paper looks at the recent Booking.com judgement of the ECJ (C-59/19. Wikingerhof GmbH & Co. KG v Booking.com BV. ECLI:EU:C:2020:950) and after a short introduction to recent problems regarding OTAs, the working paper argues that the ECJ has established a very important principle which might lead to more claims against a dominant travel platform, thereby leading to a possible remedy on the digital travel market via private actions.
Multi-Scale Hourglass Hierarchical Fusion Network for Single Image Deraining

Rain streaks bring serious blurring and visual quality degradation, which often vary in size, direction and density. Current CNN-based methods achieve encouraging performance, while are limited to depict rain characteristics and recover image details in the poor visibility environment. To address these issues, we present a Multi-scale Hourglass Hierarchical Fusion Network (MH2F-Net) in end-to-end manner, to exactly captures rain streak features with multi-scale extraction, hierarchical distillation and information aggregation. For better extracting the features, a novel Multi-scale Hourglass Extraction Block (MHEB) is proposed to get local and global features across different scales through down- and up-sample process. Besides, a Hierarchical Attentive Distillation Block (HADB) then employs the dual attention feature responses to adaptively recalibrate the hierarchical features and eliminate the redundant ones. Further, we introduce a Residual Projected Feature Fusion (RPFF) strategy to progressively discriminate feature learning and aggregate different features instead of directly concatenating or adding. Extensive experiments on both synthetic and real rainy datasets demonstrate the effectiveness of the designed MH2F-Net by comparing with recent state-of-the-art deraining algorithms. Our source code will be available on the GitHub: https://github.com/cxtalk/MH2F-Net.
PENGARUH PENGGUNAAN MEDIA ONLINE EDMODO TERHADAP KETERAMPILAN MENULIS CERPEN MAHASISWA BAHASA DAN SASTRA INDONESIA YAYASAN ABDI PENDIDIKAN PAYAKUMBUH

The background of this research is that in this era of globalization, not a few students have been able to use the internet to study or play. So the development of this technology can be utilized in learning. The development of science and technology increasingly encourages efforts to renew the use of technological results in the learning process. Lecturers are required to be able to use the tools that can be provided, and it is possible that these tools are in accordance with the development and demands of the times. Lecturers can at least use laptops and projectors. Utilization of Edmodo as a learning media for Short Story Writing Skills, especially in the eyes of kulaia, writing skills are more interesting than the lecture method. By presenting material briefly, densely and clearly, it makes students not lazy to read the material. The ease of accessing material whenever and wherever makes students able to determine for themselves when the right and comfortable time to learn so that it is better to absorb lessons. This type of research is quantitative research using the experimental method. In this study two classes were used, namely the experimental class and the control class. Experimental research is an investigation that is designed in such a way that the phenomenon or event can be socialized from the influence of other influences. This is evidenced by the change in student grades, which in the control class the mean / mean is 71.25. After using Edmodo online media in the learning process in the experimental class, changes in student learning outcomes were obtained with an average value () of 80.5. Based on the results of the t test (21.31> 1.67) it was concluded that there was a significant difference between the results of short story writing skills in the experimental class using Edmodo online media and without using Edmodo online media in the control class. This can be seen from the results of the ratio of tcount to ttable (21.31> 1.67), because tcount is greater than ttable, Ha (alternative hypothesis) is accepted, meaning that there is influence of edmodo's online media use Indonesian Level Literature and Student Level Five STKIP Foundation Payakumbuh Education Assistant.
Biased Edge Dropout for Enhancing Fairness in Graph Representation Learning.

Graph representation learning has become a ubiquitous component in many scenarios, ranging from social network analysis to energy forecasting in smart grids. In several applications, ensuring the fairness of the node (or graph) representations with respect to some protected attributes is crucial for their correct deployment. Yet, fairness in graph deep learning remains under-explored, with few solutions available. In particular, the tendency of similar nodes to cluster on several real-world graphs (i.e., homophily) can dramatically worsen the fairness of these procedures. In this paper, we propose a biased edge dropout algorithm (FairDrop) to counter-act homophily and improve fairness in graph representation learning. FairDrop can be plugged in easily on many existing algorithms, is efficient, adaptable, and can be combined with other fairness-inducing solutions. After describing the general algorithm, we demonstrate its application on two benchmark tasks, specifically, as a random walk model for producing node embeddings, and to a graph convolutional network for link prediction. We prove that the proposed algorithm can successfully improve the fairness of all models up to a small or negligible drop in accuracy, and compares favourably with existing state-of-the-art solutions. In an ablation study, we demonstrate that our algorithm can flexibly interpolate between biasing towards fairness and an unbiased edge dropout. Furthermore, to better evaluate the gains, we propose a new dyadic group definition to measure the bias of a link prediction task when paired with group-based fairness metrics. In particular, we extend the metric used to measure the bias in the node embeddings to take into account the graph structure.
Statistical Guarantees for Transformation Based Models with applications to Implicit Variational Inference

Transformation-based methods have been an attractive approach in non-parametric inference for problems such as unconditional and conditional density estimation due to their unique hierarchical structure that models the data as flexible transformation of a set of common latent variables. More recently, transformation-based models have been used in variational inference (VI) to construct flexible implicit families of variational distributions. However, their use in both non-parametric inference and variational inference lacks theoretical justification. We provide theoretical justification for the use of non-linear latent variable models (NL-LVMs) in non-parametric inference by showing that the support of the transformation induced prior in the space of densities is sufficiently large in the $L_1$ sense. We also show that, when a Gaussian process (GP) prior is placed on the transformation function, the posterior concentrates at the optimal rate up to a logarithmic factor. Adopting the flexibility demonstrated in the non-parametric setting, we use the NL-LVM to construct an implicit family of variational distributions, deemed GP-IVI. We delineate sufficient conditions under which GP-IVI achieves optimal risk bounds and approximates the true posterior in the sense of the Kullback-Leibler divergence. To the best of our knowledge, this is the first work on providing theoretical guarantees for implicit variational inference.
Multi-Image Segments Based Data Communication

Verification is fundamental in every single point of view of data access and data sharing. the association or the framework need to verify the clients prior to letting them to get to the data put away in the framework. To concede the offices to the client to get to the framework, it is vital to approve the character of the client as a real client. Along these lines, there is consistently a necessity of the appropriate medium or strategy for confirming the client. In the proposed work, the idea of the client distinguishing proof approval is proposed, in this the remarkable idea of the picture division is taken, in which the two pictures are picked , which are fragmented into the parts and muddled up in course of action , the client needs to organize the example in first picture parts are orchestrated and afterward decide for the second picture part to be plan , every plan will create the example of the content , based on the parts moved or traded and in this manner two password are produced. Aside from the approval part, the proposed work likewise makes utilize the idea of the encoded pictures utilizing the key, which can be utilized for dividing the data among the clients. The key example which is created after the trading of the two pictures sections at that point tried utilizing the different sorts of solidarity checking devices and programming , the outcomes which are gotten after the correlation are very intriguing..
Improvements Based on Feature Fusion Single Shot Multibox Detector

As people have paid more and more attention to deep learning, the development of deep learning is also faster and faster. However, when the object is detected, the accuracy and real-time of the object result is still a challenging problem. For the problem of scale variation in object detection, we mainly use the method of feature pyramid to solve this problem, which has been applied in many detectors. At present, there is an object detection algorithm which is very high speed and high precision. FSSD (Fusion Single Shot Multibox Detector) algorithm has an obvious disadvantage that it cannot obtain more accurate positioning information for medium-sized objects and lower-level information. Especially after feature fusion, the semantic information expression in the shallow layer of its backbone network is not complete enough. This paper proposes a combination of bottom-up pathway, fusing-splitting pathway and RFB (Recepive Field Block) module, through the combination of the above modules, it is actually a relatively effective FSSD model architecture. RFB module can expand receptive field and improve target detection accuracy. Furthermore, in the whole FSSD model network, the architecture is built by merging the split path and RFB module through the bottom-up path. Two different approaches of the multi-scale feature pyramid introduce both better positioning tips and more medium-sized objects information. RFB module introduce finer high-level semantics on shallow layers. On the primary detector FSSD, we used the above method to carry out placement detection. The results show that our method is feasible and the result is the most advanced.
Curious Exploration and Return-based Memory Restoration for Deep Reinforcement Learning

Reward engineering and designing an incentive reward function are non-trivial tasks to train agents in complex environments. Furthermore, an inaccurate reward function may lead to a biased behaviour which is far from an efficient and optimised behaviour. In this paper, we focus on training a single agent to score goals with binary success/failure reward function in Half Field Offense domain. As the major advantage of this research, the agent has no presumption about the environment which means it only follows the original formulation of reinforcement learning agents. The main challenge of using such a reward function is the high sparsity of positive reward signals. To address this problem, we use a simple prediction-based exploration strategy (called Curious Exploration) along with a Return-based Memory Restoration (RMR) technique which tends to remember more valuable memories. The proposed method can be utilized to train agents in environments with fairly complex state and action spaces. Our experimental results show that many recent solutions including our baseline method fail to learn and perform in complex soccer domain. However, the proposed method can converge easily to the nearly optimal behaviour. The video presenting the performance of our trained agent is available at http://bit.ly/HFO_Binary_Reward.
Politica linguistica, democrazia e plurilinguismo: quali prospettive?

From the point of view of political philosophy, the relationship between literacy and democracy appears quite problematic, if the issue of linguistic diversity is set aside. The debate between equalitarian liberals and multiculturalists emerging with the rise of multiculturalism as a theoretical paradigm has highlighted that the historical affirmation of the Western nation-state has been built upon the negation of internal linguistic diversity, favoring some languages over others, a trend which has not lost its strength with the advent of democracy. However, one of the main results of such debate, largely indebted to the Rawlsian conception of justice, has been seemingly the idea that the extended promotion of plurilingualism (that is, a kind of promotion which does not aim to support second or third language acquisition only for the benefit of the so called ‘minority’ or ‘disadvantaged’ groups and languages) could not be a viable alternative for contemporary democratic theory to ameliorate its performance. Drawing upon the field of language policy, this article envisages showing that such a result is tied to a certain number of theoretical assumptions concerning language, which are controversial both in political philosophy and in linguistic theory. In the final section, the article tries to provide some suggestions to develop a reflection more open towards the extended promotion of plurilingualism and to acknowledge the human ability to learn more than one linguistic code.
ENHANCING THE PERFORMANCE OF SMES IN SAUDI ARABIA THROUGH HUMAN CAPITAL AND SOCIAL-MEDIA TECHNOLOGY: A CONCEPTUAL MODEL

Small and medium enterprises (SMEs) are one of the critical components of national economic growth and development in both developed and developing economies. However, the performance of SMEs in developing countries is lower than expected; therefore, it is crucial to understand the factors that improve performance. This study aims to develop a conceptual model to explain the relationship between the organizational performance of SMEs, human capital, social media technology use in Saudi Arabia. The author proposes a model in which human capital and social media technology use may positively influence organization performance in SMEs in Saudi Arabia context.
PERANCANGAN GAME TRADISIONAL MACANAN BERBASIS ANDROID

Macanan is a strategy game originating from Central Java, the game of macanan is played by two people who act as tigers or humans, where the tiger player must set a strategy to eat or eliminate all human pieces while the human player must set a strategy to lock the tiger. In some conditions the player does not have a partner to play the game macanan, therefore we need a computer (AI) which can be used as an alternative gameplay game when there are only opponents to play partners. This research will apply the Minimax algorithm as a game against the computer (AI), this research also adds optimization with alpha beta pruning to increase the level of game or intelligence from the computer (AI) in traditional Javanese macanan games..  Keywords  :  Game, Minimax, Macanan, AI
Quantum Quantitative Trading: High-Frequency Statistical Arbitrage Algorithm

Quantitative trading is an integral part of financial markets with high calculation speed requirements, while no quantum algorithms have been introduced into this field yet. We propose quantum algorithms for high-frequency statistical arbitrage trading in this work by utilizing variable time condition number estimation and quantum linear regression.The algorithm complexity has been reduced from the classical benchmark O(N^2d) to O(sqrt(d)(kappa)^2(log(1/epsilon))^2 )). It shows quantum advantage, where N is the length of trading data, and d is the number of stocks, kappa is the condition number and epsilon is the desired precision. Moreover, two tool algorithms for condition number estimation and cointegration test are developed.
A study on efficient implementation method of defense cloud computing encryption

This study aims to propose a method for efficient implementation through a method of applying isomorphic encryption based on the analysis of the strengths and weaknesses of isomorphic encryption based on the research results of existing literature on isotype encryption for cloud computing encryption. For this, it is necessary to implement and standardize homomorphic cryptography in IT technology. In this study, we intend to understand the concept of homomorphic cryptography, analyze the advantages and disadvantages of implementing homomorphic cryptography, and introduce efficient implementation methods.