Reviewerly/exHarmony huggingface dataset:
Perbaikan Citra Dokumen Hasil Pindai Menggunakan Metode Simple, Adaptive-Gaussian, dan Otsu Binarization Thresholding

The use of digital images from scanned documents is commonly used both for data backup and for further processing. However, often the digital image obtained is not optimum due to various factors like noise. The method to improve the quality of digital images is to filter images using the Thresholding method. This study compares three Thresholding methods, which are Simple Thresholding, Adaptive-Gaussian Thresholding, and Otsu Binarization. All three methods have advantages and disadvantages. However, using the MSE and PSNR assessment parameters, the Simple Thresholding method shows better quality with an MSE value of 5,196.76, followed by Otsu Binarization with a value of 5,934.10, and Adaptive-Gaussian Thresholding with a value of 9,025.29. Meanwhile, by using PSNR, the value for Simple Thresholding is 13.37, followed by Otsu Binarization with a value of 12.47, and Adaptive-Gaussian Thresholding with a value of 10.31.
Recent Advances in Reinforcement Learning in Finance

The rapid changes in the finance industry due to the increasing amount of data have revolutionized the techniques on data processing and data analysis and brought new theoretical and computational challenges. In contrast to classical stochastic control theory and other analytical approaches for solving financial decision-making problems that heavily reply on model assumptions, new developments from reinforcement learning (RL) are able to make full use of the large amount of financial data with fewer model assumptions and to improve decisions in complex financial environments. This survey paper aims to review the recent developments and use of RL approaches in finance. We give an introduction to Markov decision processes, which is the setting for many of the commonly used RL approaches. Various algorithms are then introduced with a focus on value and policy based methods that do not require any model assumptions. Connections are made with neural networks to extend the framework to encompass deep RL algorithms. Our survey concludes by discussing the application of these RL algorithms in a variety of decision-making problems in finance, including optimal execution, portfolio optimization, option pricing and hedging, market making, smart order routing, and robo-advising.
Robust Video Steganography Technique Against Attack Based on Stationary Wavelet Transform (SWT) and Singular Value Decomposition (SVD)

The interchanging of multimedia data over the Internet is growing exponentially. These data need to be kept safe from being hacked or corrupted. Steganography techniques are used to hide the important data by embedding it into a cover object. The proposed steganography algorithm uses the stationary wavelet transform (SWT) and the singular value decomposition (SVD) in the YCbCr color space to hide an image in video frames. The algorithm is tested using different images and under different types of attacks. The qualitative and quantitative results proved that the imperceptibility and the robustness of the algorithm are very high as the average normalized cross-correlation is 0.9. The average similarity for the secret image is 0.9 without attacks and 0.85 after adding different types of attacks on the stego-video. The structural similarity between the cover video and the stego-video is 0.99.
Blockchain Plus Finance: Application, Dilemma and Solution

Blockchain technology has attracted great attention in various fields because of its advantages of distributed network. Its rational application in enterprise's financial activities can optimize financial processes and control operating costs; improve the quality of financial information, reduce information asymmetry; promote the transformation and progress of financial personnel. But there may be difficulties in the practical application of new technology, and this paper will put forward feasible solutions based on these problems.
Analysis of Different Neural Network Techniques Used for Image Caption Generation

Artificial intelligence has open doors to new opportunities for research and development. And the recent development in machine learning and deep learning has paved a way to deal with complex problem easily. Now a day’s every aspect of human life can now be thought of as a problem statement that can be implemented and is useful in one way or the other. One such aspect is human ability to understand and describe the surrounding to which they interact and take decision accordingly. This ability can also be used in machines or bots to make human and machine interaction easier. Generating captions for images is same we need to describe images based on what you see. This task can be considered as a combination of computer vision and natural language processing. In this paper we performs a survey of various methods and techniques that can be useful in understanding how this task can be done. The survey mainly focuses on neural network techniques, because they give state of the art results.
Desa Peduli Pendidikan melalui pendampingan penulisan Karya Ilmiah (laporan PTK)

The publication of scientific papers is an essential element in promoting educators. The problem is that the average teacher at SD Negeri 45 has not written and published independently. Some teachers are not proficient in operating computers, especially Microsoft Office Word. Even though writing scientific papers requires these skills to facilitate the work of teachers during writing. In this service, several points are trained, namely optimizing the use of Microsoft Office Word, the rules of writing scientific reports and publications, and techniques for implementing CAR. This activity was carried out on September 18, 2021. The partners of this service were all teachers at the 45 Ternate State Elementary School. The method used is demonstration, lecture, and question and answer. The results of community service activities show that teachers at SD Negeri 45 Ternate City can write CAR scientific reports even though only three people have the best writing skills. At the evaluation stage, several corrections were found, such as spelling errors, data analysis techniques, and the use of references. The obstacle in this research is the limitations of laptops and computers. It takes a long time because there are three available laptop facilities during practice, so the teacher is divided into 3 study groups. However, overall, this service can provide understanding and motivation to teachers at SD Negeri 45 Ternate City to write scientific papers
Lateral scanning logging while drilling data processing using convolutional neural networks

This article discusses a new approach to processing lateral scanning logging while drilling data based on a combination of three-dimensional numerical modeling and convolutional neural networks. We prepared dataset for training neural networks. Dataset contains realistic synthetic resistivity images and geoelectric layer boundary layouts, obtained based on true values of their spatial orientation parameters. Using convolutional neural networks two algorithms have been developed and programmatically implemented: suppression of random noise and detection of layer boundaries on the resistivity images. The developed algorithms allow fast and accurate processing of large amounts of data, while, due to the absence of full-connection layers in the neural networks’ architectures, it is possible to process resistivity images of arbitrary length.
Improving Collaborative Filtering Based Recommender System with Season and Style Features

People have gained greater shopping interest as their living standards have risen, owing to an increase in demand for fashion products. Fashion e-commerce has risen at a rate of 12.2% in the last year as a result of this demand, users often have trouble identifying desired items. The fashion product depends not only rating but also on other features. The season and style are two important features while recommending the fashion products. This research aims to provide recommendations to a wide range of users through collaborative filtering based system by incorporating season and style features of fashion products. The deep learning based technique, convolutional neural network, is used to extract season and style features of fashion product. The dataset used to validate the proposed approach is the amazon product dataset for clothing, shoes, and jewelry. The results are compared using standard evaluation measures like MAE, MSE, RMSE.
Decentralized Library Management System Using Blockchain Technology

Blockchain technology provides a secure and decentralized nature. The existing library management system are completely centralized, i.e., based on a central database solely. This means that in case the database crashes or maybe attacked by some intruder, the data stored in it will be at risk. To conquer, this we are proposing a blockchain-based decentralized and secure library management system. Not only this cover the book owning organization but also this can be implemented to propose a system where any person owning a book can give his book to someone who is in need. The addition of new book owner and new book borrower will be managed by verification at different nodes, hence providing it a secure and authenticated way. We have implemented it in python with the help of its advanced libraries ( https://manavrachna.edu.in ).
The (Un-)Making of Microfascism in Schools and Classrooms

This chapter demonstrates how Deleuze and Guattari’s notion of ‘microfascism’ is of crucial importance to an understanding of the complexities of contemporary pedagogical efforts to combat populism, right-wing extremism, and fascism. In particular, I discuss how ‘affect’ and ‘biopower’ are entangled in everyday processes of discipline and control, and argue that these concepts are pivotal for appreciating the affective relations and capacities of microfascism. To illustrate how affect and biopower are intimately linked to microfascist practices in schools and classrooms, I analyze two examples—one in health education and another in citizenship education. Finally, I suggest pedagogical strategies that could unmake microfascist subjectivities, emphasizing that it is important to understand the complexities involved since fascism is easily disguised in many forms that are often aligned with (neo)liberal values.
Development of Students’ Research Competence in the Study of the Humanities in Higher Educational Institutions

The aim of the study was to experimentally test the effectiveness of didactic conditions for the development of students’ research competence in the study of the humanities. Several complementary methods were used in the experimental study: a comprehensive test to assess the levels of research competence (S.A. Mishyn); Zamfir’s Motivation of Professional Activity modified by A.N. Rean; adapted Starkey’s Critical Thinking test; Simonov’s Education Level technique, author’s questionnaire with open-ended questions to determine the importance of research competence for students in the course of their professional education. The results of the pedagogical experiment revealed the effectiveness of the introduction of innovative forms of theoretical training (lectures and seminars) for the development of research competence in future specialists in the humanities classes. The research found the main directions of innovative experience of improving lectures, seminars as a form of education for the development of students’ research competence. The research showed that it is possible to develop research competence in the process of teaching humanities depending on the types of training (theoretical, practical). It is important to organize the students’ cognitive activity in the course of theoretical training, while it is necessary to apply the problem method of teaching in the practical classes. We consider the study of the problem of continuity in the development of the research culture of the individual under the conditions of continuing education as a prospect for further research.
Flexible recovery of uniqueness and immutability

© 2018 Elsevier B.V. We present an imperative object calculus where types are annotated with qualifiers for aliasing and mutation control. There are two key novelties with respect to similar proposals. First, the type system is very expressive. Notably, it adopts the recovery approach, that is, using the type context to justify strengthening types, greatly improving its power by permitting to recover uniqueness and immutability properties even in presence of other references. This is achieved by rules which restrict the use of such other references in the portion of code which is recovered. Second, execution is modeled by a non standard operational model, where properties of qualifiers can be directly expressed on source terms, rather than as invariants on an auxiliary structure which mimics physical memory. Formally, this is achieved by the block construct, introducing local variable declarations, which, when evaluated, play the role of store.
Conclusion

Reducing organizational cybersecurity risk while simultaneously keeping up with the business is a challenge for many organizations. When addressing cybersecurity, some basic foundational components can help focus attention and organize around the ability to understand, manage, and properly measure cybersecurity risk.
ANALYSIS OF THE INFLUENCE METRIC OF DEVELOPERS IN THE R LANGUAGE IN THE GITHUB

Social Programming Sites (SPSs) are social networking services for sharing software devel-opment projects on the Internet. Many open source projects are currently being developed on the SPSs. One of the characteristics of SPSs is that they provide a social media platform that encourages collaboration between developers with similar interests and goals. The article proposes an approach to the formation of a Github SPSs graph as a heterogeneous network. On the basis of this approach, the metric of determining the developers of the most used R packages is investigated.
Supporting the Reflective Learning of Conceptual Modeling Using Interactive Timelines and Replays

It is not a trivial task to teach "good" conceptual modeling in a structured way.One problem we encountered in our previous exploratory studies is the predominant reflection of the quality of finished models and less of the modeling process itself.As a result, many phenomena like the emergence of errors or the coordination and collaboration phases in group modeling usually get out of focus and are subsequently not thoroughly considered in teaching.Thus, we have developed a tool-supported timeline method, which allows us to review and discuss the collaborative development process of models after they have been finished or submitted.We evaluated the method and employed a variety of tool features in our regular undergrad courses.Considering the perspective and experiences of teachers and learners alike, we discovered that teachers can use our method to better analyze strategies and made mistakes when enough time to prepare the reflection, even if this increases the time distance to the event it is reflected on.Learners were motivated to share and reflect on their actions.Furthermore, we identified further steps to facilitate our reflective modeling teaching method.
Efficient transformation capabilities of single database private block retrieval

Abstract Private information retrieval (PIR) is one of the promising techniques to preserve user privacy in the presence of trusted-but-curious servers. The information theoretically private query construction assures the highest user privacy over curious and unbounded computation servers. Therefore, the need for information-theoretic private retrieval was fulfilled by various schemes in a variety of PIR settings. But, there is a lack of efficient encryption switching scheme which supports efficient switching between information-theoretic to/from computationally bounded PIRs. We propose a combination of new bit connection methods called rail-shape and signal-shape and new quadratic residuosity assumption based family of trapdoor functions for generic single database private block retrieval (PBR). The main goal of this work is to show that the possibility of mapping from computationally bounded privacy to information theoretic privacy or vice-versa in a single database setting using newly constructed bit connection and trapdoor function combinations. Notably, the proposed schemes are single round, memoryless and plain database schemes (at their basic constructions).
Boundary-Aware Hashing for Hamming Space Retrieval

Hamming space retrieval is a hot area of research in deep hashing because it is effective for large-scale image retrieval. Existing hashing algorithms have not fully used the absolute boundary to discriminate the data inside and outside the Hamming ball, and the performance is not satisfying. In this paper, a boundary-aware contrastive loss is designed. It involves an exponential function with absolute boundary (i.e., Hamming radius) information for dissimilar pairs and a logarithmic function to encourage small distance for similar pairs. It achieves a push that is bigger than the pull inside the Hamming ball, and the pull is bigger than the push outside the ball. Furthermore, a novel Boundary-Aware Hashing (BAH) architecture is proposed. It discriminatively penalizes the dissimilar data inside and outside the Hamming ball. BAH enables the influence of extremely imbalanced data to be reduced without up-weight to similar pairs or other optimization strategies because its exponential function rapidly converges outside the absolute boundary, making a huge contrast difference between the gradients of the logarithmic and exponential functions. Extensive experiments conducted on four benchmark datasets show that the proposed BAH obtains higher performance for different code lengths, and it has the advantage of handling extremely imbalanced data.
Blockchain-Based Decentralized Electronic Voting System

India is a constitutional democracy, and it holds regular, free and fair elections. According to the current voting system of India, every person casts a vote using an electronic voting machine, in a voting booth. Once the voting time has elapsed, the votes are counted. However, the transparency is not maintained. There is also a possibility of system rigging and delay in results. Blockchain being a distributed ledger and a transparent system is hence a better alternative to the current voting system. This ensures a secure, reliable and decentralized voting environment. E-voting system using blockchain works as a step towards creating a secure and transparent environment for elections where the users will be able to cast their votes only once and will be able to view the total votes cast in real time without having the permission to edit the same after elections get over. The working of blockchains will ensure the votes are maintained and the systems are not rigged by any third party. The secure electronic voting system uses blockchain which is a decentralized, peer-to-peer transaction ledger. The system will create permissioned blocks from the authorized nodes where the casted votes will be considered as individual transactions. These blocks are counted, and the results are then announced.
A proactive population dynamics load balancing algorithm in cloud computing for QoS optimisation

Load unbalancing is a grim concern among cloud service providers that has adverse consequences on Quality of Service (QoS) and profit turnout. Load balancing tries to overcome load imbalances by ensuring proper task deployment among cloud resources, yielding productive resource utilisation, throughput, and other QoS metrics. A proactive load balancing approach featuring population dynamics model called as PPDLB model is proposed to limit the tasks within the maximum carrying capacity of VM. The PPDLB algorithm was compared with the existing Improved Weighted Round Robin (IWRR), Harris Hawk Optimisation (HHO) and Spider Monkey Optimisation (SMO) algorithms. The PPDLB algorithm was found to be efficient than existing algorithms in terms of makespan, resource utilisation, degree of balance and number of task migrations. The dominance of the proposed algorithm over existing load balancing techniques lies in the fact that it eliminates the need to solve migration associated metrics. Moreover, it has greater convergence rate when search space becomes large which makes it feasible for large scale scheduling problems.
Readiness of Elementary Teachers in Minimum Competency Assessment: Teachers' Competence in Arranging Literature and Numeration Tests

Minimum competency assessment is a new challenge for classroom teachers in implementing and integrating it in learning to maximize its diagnostic and curative function on the quality of Indonesian education. This is the background of this research to analyze the readiness of teachers in arranging literacy and numeracy tests in elementary schools. This study used a content analysis design to reveal a test prepared by elementary school teachers for minimum competency assessment involving 30 elementary school teachers. Data analysis results are presented in the form of percentage accuracy of items with quantitative descriptive and examples of items that are described qualitatively. The results showed that the competence of school teachers in compiling literacy and numeracy assessment questions in elementary schools was still not optimal in terms of form, content, context, and cognitive level, especially for reflect and evaluate levels for literacy assessments and reasoning levels for required numeracy assessments. Especially for literary content on literacy, scientific context on literacy and numeracy, and third-level literacy and numeracy. Based on the results of the study, it can be concluded that the competency of elementary school teachers in implementing a minimum competency assessment needs to be carried out to meet the standards and functions of the Indonesian national assessment. This study provides an overview of teacher readiness in carrying out minimum competency assessments in elementary schools related to their role as learning evaluators
Generic constructions of MDS Euclidean self-dual codes via GRS codes

<p style='text-indent:20px;'>Recently, the construction of new MDS Euclidean self-dual codes has been widely investigated. In this paper, for square <inline-formula><tex-math id="M1">\begin{document}$ q $\end{document}</tex-math></inline-formula>, we utilize generalized Reed-Solomon (GRS) codes and their extended codes to provide four generic families of <inline-formula><tex-math id="M2">\begin{document}$ q $\end{document}</tex-math></inline-formula>-ary MDS Euclidean self-dual codes of lengths in the form <inline-formula><tex-math id="M3">\begin{document}$ s\frac{q-1}{a}+t\frac{q-1}{b} $\end{document}</tex-math></inline-formula>, where <inline-formula><tex-math id="M4">\begin{document}$ s $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M5">\begin{document}$ t $\end{document}</tex-math></inline-formula> range in some interval and <inline-formula><tex-math id="M6">\begin{document}$ a, b \,|\, (q -1) $\end{document}</tex-math></inline-formula>. In particular, for large square <inline-formula><tex-math id="M7">\begin{document}$ q $\end{document}</tex-math></inline-formula>, our constructions take up a proportion of generally more than 34% in all the possible lengths of <inline-formula><tex-math id="M8">\begin{document}$ q $\end{document}</tex-math></inline-formula>-ary MDS Euclidean self-dual codes, which is larger than the previous results. Moreover, two new families of MDS Euclidean self-orthogonal codes and two new families of MDS Euclidean almost self-dual codes are given similarly.