Reviewerly/exHarmony huggingface dataset:
Improvement of Enhanced Image Using Mean and Standard Deviation Increment Method Based on Visual Representation Statistics

Processes of image quality enhancement often leave deficiencies in the image result. These deficiencies in the form of loss of local contrast and loss of detail in some parts of the image. These deficiencies resulted in some important information on the image become unreadable. The deficiency that caused from processes of image enhancement can be minimized by taking information back from the original image. Taking this information can be done by combining the original image with the image of the improvements. Before the fusion of image, the means of average value and standard deviation value from the result on image should be improved first enhancement, so that fusion of image can be maximum. From the tested of 500 (five hundred) images that consist of image lacks brightness, image lacks contrast, and image lacks brightness and contrast, there were 74 (seventy four) image that can not be full repaired by using the proposed method. But for the image of the other experiments, the proposed method could improve image deficiencies. In this success level from method which is proposed reaches 85 %. Key Word : image improvement, mean increament,  standard deviation increament.
Storytelling and Culture ---- A three Sixty Degree Transformation in the Young Generation.

Story telling sessions have always provided entertainment to the young generations. Our culture has been weaved with the concept of embedding the difference of good and evil into the soft minds through stories and this process has been an effort since ages with the beginners. The grandparents, and the book shops have played the key role in keeping the children engaged introducing them with the Indian culture and social patterns. The stories they narrated and published, hovered around Epics, Aesop’s Fables, Panchatantras and European Folk Tales.Folk tales and stories have always revolved around the ancient times in multifaceted forms. And story time sessions always opened up the pitarrie for these fresh brains. The characters were crafted in a way to feel them very close to their young hearts and were devoid of any cultural clash or racial fervor. These narrations awakened the cells of creativity and delved the morals of life.The transformation stepped in with the evolution of science and technology. And moreover the concept of Globalization flooding in with the Western life styles , largely influenced the Indian Societal aspects. Similar resultant effects were observed in Bengal, a region always well known for its culture and intellectualism. Gradually the family concept shrinked into nuclear ones and the senior citizens were shifted to Old Age Homes. The story telling sessions faded away and the paradigms of story writing and narrating changed dramatically.Digitisation took over. Social media pawned the free space and the Y generation immediately stuck to the technology magic. All the stories which used to enchant them couched into Compact Discs and reading story books no more remained a passion. The cultural paradigm had a three sixty degree transformation.The objective of this topic would be dissecting the aftermaths of the Technical modes and how it has affected the reading habits of the “Y” class. The research shall revolve around the degeneration and loss of value system in the Bengali culture.There would be areas highlighting the genres of Bengali stories with the methodology being restricted to secondary sources such as story reviews and other narratives
An overview of mobile robot path planning

The emerging trend of modern industry automation requires intelligence to be embedded into mobile robot for ensuring optimal or near-optimal solutions to execute certain task. This yield to a lot of improvement and suggestions in many areas related to mobile robot such as path planning. The purpose of this paper is to review the mobile robots path planning problem, optimization criteria and various methodologies reported in the literature for global and local mobile robot path planning. The classical approaches such as cell decomposition, roadmap approach, artificial potential field (AFP), and heuristics approaches such as genetic algorithm, particle swarm optimization (PSO) approach and ant colony optimization (ACO) method are considered in this study. It is observed that heuristics approaches are more popular and widely used compared to classical approaches due to its robustness and perform well in various environmental condition. Finally, few suggestions for future research work in this field are addressed at the end of this paper.
I puntini di sospensione nelle grammatiche italiane tra fine Settecento e inizio Novecento

This paper provides an overview of the treatment of the ellipsis 〈...〉 in the history of Italian grammars, manuals of orthography and punctuation guides published between the late 18th century and early 20th century. In these works, the ellipsis is acknowledged with a certain regularity only from the 1820s. On the functional side, most works approach the ellipsis through the use of mixed descriptive categories, combining prosodic, rhetoric and pragmatic features. Between the late 19th and early 20th century, some more modern outlines of the ellipsis come to light, where the focus is set on the functional flexibility of the mark. The paper also considers some formal properties of the ellipsis, such as the terms used in grammars to refer to the mark and the number of characters employed to represent it.
Comparison of the number of training data for Pix2Pix voice conversion system

In this study, we aimed to use a voice conversion system to improve broadcast over an outdoor public address system, and evaluated the optimum number of training data according to subjective evaluation by voice conversion using Pix2Pix image generation with a generative adversarial network. Accordingly, we compared the number of spoken sentences to be trained in an acoustic features convert model used in a voice conversion model, and in a high-resolution spectrum convert model, through subjective evaluation using mean opinion score in multiple patterns. As a results, the acoustic features convert model requires 150 spoken sentences, and the high-resolution spectrogram model requires 750 sentences for model training.
Modernization Of The E-Learning Management System At University

The article describes the management system of e-learning in the higher education institute "Samara State Transport University" in order to determine innovative areas of education, integration of online resources in the modern information educational space according to international standards. The authors reveal the possibilities of popularization of scientific knowledge through the introduction of modern educational technologies in the educational process. The article shows the importance of the matrix structure application, its relevance in comparison with the existing linear-functional structure, which does not correspond to the dynamic processes occurring in the modern University. In turn, the matrix structure contributes to the imposition of a constant linear-functional scheme of the head University on purposefully organized target structures (matrix elements). The article proves the essence of the target structures providing the implementation of e-learning. It defines the elements of the e-learning management strategy, namely, the emphasis of the management of the higher education institute on the ideas of introduction of information and communication technologies into the organizational environment of the University; this implies the organization of the Department with clearly defined functional responsibilities. It becomes relevant to improve the skills of teachers and staff, monitoring the activity of teachers who apply and develop EA; introduction of various kinds of incentives, as well as the creation of a legal framework for the virtual use of intellectual property of the teaching staff.
Implementation of Boyer Moore Algorithm to Search Criminal Law Based on Criminal Code

Balai Pemasyarakatan (BAPAS) or Penitentiary is one of the institutions to implement guidances.The Penitentiary ("Badan Pemasyarakatan in Indonesia.BAPAS") has a role to make ex Prisoner are not committing a crime or a similar offence.KUHP is to provide knowledge of the articles of the Criminal Code.KUHP (Kitab Undang-Undang Hukum Pidana) consist of many kinds of violations of law such as crimes and abuse, etc.The aim of those guidelines is to provide counselling a deterrent effect to the Ex-Prisoner or "Klien Pemasyarakatan".Information retrieval is a material document search form or another that is not structured to meet the needs of information in a collection of large text that is stored in the computer.Boyer Moore algorithm is an algorithm that does the matching characters from the rightmost position to the left.The results of Boyer Moore algorithm that is implemented into the search clauses of a felony offence under the Criminal Code and provides quick results.Speed in terms of doing a keyword search of article Clients criminal offences committed in total 422.194characters and 1.459 chapters with proven character in the search for the worst and best quickly in few minutes running time.
WHATSAPP AS A GERMAN LANGUAGE LEARNING MEDIA

This paper describes the idea of the WhatsApp application as a medium of learning German. The Whatsapp application is an application that can be accessed using a smartphone ( das Handy ) for the benefit of learning German. The presentation contains: i) WhatsApp background as a German language learning media, ii) WhatsApp understanding, iii) the benefits of using WhatsApp in learning German for lecturers, students, universities, parents or guardians of students, iv) the weaknesses in using WhatsApp in learning German, v)  WhatsApp in learning writing skills ( Schreibfertigkeit ), both writing essays ( der Aufsatz ), writing daily activities ( das Tagebuch schreiben ) or writing personal letters ( der personliche Brief ), vi) Whatsapp in German vocabulary and grammar learning ( deutsche Vokabeln und deutsche Grammatik ), and vii) the evaluation, reflection and follow-up. Keywords: whatsapp, learning German
Practical Mixing of Virtual and Real

Virtual reality (VR) has the potential to provide office working environments with essentially limitless customizations, including screens, whiteboards or tables of any size and number, and the abi ...
Negative Examples Sampling Based on Factorization Machines for OCCF

Some applications of recommender system such as news recommendation and music recommendation, are considered as One-Class Collaborative Filtering (OCCF). In OCCF, dataset for training consists only of binary data that reflects whether the user has behavior on the item. In other words, we can only observe user's positive examples through implicit feedback in the system(such as click, collect). As a result of that, ambiguity arises when interpreting non-positive examples. There are two main solutions to solve this problem: one is to find a algorithm suitable for OCCF, and the other is to perform negative examples sampling algorithm to balance the positive and negative samples and convert OCCF into traditional collaborative filtering problem. In this paper, we apply the factorization machines algorithm to negative examples sampling. Experimental results show that the method we proposed effectively solves the problem that OCCF has no negative examples, and this method is better than other basic negative examples sampling methods.
Feature Selection Based on Neighborhood Entropy for Data Hiding Analysis

The feature extraction process in data hiding analysis produces a large number of high-dimensional, redundant and uncertain data, resulting in high computational overload and low recognition rate of data hiding analysis systems. Therefore, this paper proposes a feature selection method for data hiding analysis based on neighborhood granulation and neighborhood entropy, exploiting the theory of neighborhood granular computing and the uncertainty measurement method with information entropy. First, the neighborhood granular computing theory is used to granulate the features of data hiding analysis systems. Second, the information entropy measurement of neighborhood granules is defined, and the feature importance is constructed by neighborhood entropy to evaluate the importance of features in data hiding analysis. Furthermore, a feature selection algorithm is designed to select a few features with strong discriminative characteristic. Finally, an SVM classifier is trained using the selected features under different algorithm configurations including one data hiding method, two feature extraction methods and different information hiding amounts. Experimental results show that the proposed method can obtain compact features for data hiding analysis and improve the performance of the classifier on these features.
Curriculum Development as a Task of Optimizing the Educational Process in Educational Institutions

This article discusses a software product for automatic scheduling for a higher educational institution (in particular, for TUIT). The program was written using an algorithm for assessing the freedom of location of each lesson in the schedule. The application provides the following functionality: adding, editing, deleting and working with the database, as well as using it to schedule both full-time and parttime courses.
New role of systems analysts in Agile requirements engineering

The chapter examines the changes being introduced to the role of systems analysts while moving from the traditional Waterfall model to Agile in information systems development. The chapter reveals that various changes in functions, knowledge, skills and people competencies are required for systems analysts in the Agile environment. The research reveals that the functions are also broader in Agile requirements engineering compared to Waterfall. Reliability and personal communication are identified as the core competencies for systems analysts in Agile systems development.
Riflessioni sull’onomastica nel romanzo del dittatore ispano-americano

Dictators are the main characters in many Hispanic-American literary works. The representation of the despots shows a variety of recurring features in which onomastic choices make a significant contribution. For instance, the messianic quality often attributed to dictators is already present in the titles of novels such as Yo, el Supremo, by Augusto Roa Bastos, or El Supremisimo, by Luis Ricardo Alonso. My paper investigates the role of the names, epithets and titles attributed to tyrants in the so-called ‘dictator novel’.
Performance Analysis of Efficient Content Based Image Retrieval Frameworks

Presently, the massive growth in the generation of digital images has initiated the requirement for the enhancement in the way of identifying and retrieving images from a huge dataset. A major issue exist in the process is the way to retrieve the essential images from a huge dataset with high precision with less amount of retrieval time. The choice of proper features acts as a vital part in the enhancement of the results of image retrieval models. Numerous image retrievals have been presented and exist in the literature. In this paper, a detailed comparative study between two image retrieval models namely Fused Information Feature (FIF) and color, texture and edge features (CTE) based image retrieval model. To analyze the better retrieval performance of these two models, a detailed experimentation takes place on the benchmark Corel 10-K dataset. The experimental analysis ensured that the FIF model is found to be an efficient model which is proved by attaining a maximum average precision value of 83.33% whereas the CTE model achieves slightly lower performance by obtaining an average precision value of 82.22%
E-learning in the activities of Polish public libraries

The article characterizes the specificity of public library activities and their role in education. The possibilities of using e-learning in the education process were also presented, with particular emphasis on public education. Attention was also paid to the growing importance and interest in this type of education. The paper discusses the possibilities of using e-learning in teaching, with particular emphasis on education conducted as part of the activities of public libraries. The article presents the offer of e-learning courses mainly in provincial and pedagogical libraries. The purpose of the analysis was to verify the scope of e-learning use by libraries, check the availability of training offered and the ease of finding them on library websites. The number of courses offered and their topics were also examined. Moreover, it was taken into account to whom the prepared courses were addressed. The paper presents the current state, possibilities for further development and suggestions of the authors, which are based on the growing technological possibilities, with more common access to the Internet. Research shows that the use of e-learning platforms by provincial public and pedagogical libraries is unfortunately not common. This situation may be dictated by the lack of funds to prepare such projects. This limitation should, however, lose its significance due to the decreasing costs of platform implementation as well as the increasing funds supporting library activities. In addition, the pressure of potential users can motivate the development of e-learning platforms in libraries.
IMPLEMENTATION OF MACHINE LEARNING IN AIR LINE DATA

The main objective of the paper is to implement classification and clustering algorithm such as Naive Bayes, K-Means, and Farthest First clustering algorithm using Weka to analyse airline dataset. In addition to that, performance analysis of these algorithms shows that Farthest First outperforms the other algorithms. Data set has been obtained from UCI machine learning repository. The algorithms has been discussed with the results obtained from the tool. The confusion matrix for classification algorithm and the cluster instances for clustering algorithm is also shown in the result.
A Privacy Preserving Algorithm for Mining Rare Association Rules by Homomorphic Encryption

Privacy-preserving data mining have greate significance in the era of big data. The Privacy-preserving condition on rare association rules mining is about the sensitive information regarding participants. Each side have a private dataset, aims to collaboratively find rare association rules on data set like a logically unified frame, but actually composed of distributed private data set. We proposed a new efficient algorithm to discover privacy-preserving rare association rule mining technique. The main principle idea is that with the secure two-party computation theory we employ homomorphic encryption to hide the private information.
Empowering Density-based Micro-clusters In Dynamic Data Stream Clustering

Data stream is a continuous sequence of data generated from various sources and continuously transferred from source to target. Streaming data needs to be processed without having access to all of the data. Some of the sources generating data streams are social networks, geospatial services, weather monitoring, e-commerce purchases, etc. Data stream mining is the process of acquiring knowledge structures from the continuously arriving data. Clustering is an unsupervised machine learning technique that can be used to extract knowledge patterns from the data stream. The mining of streaming data is challenging because the data is in huge amounts and arriving continuously. So the traditional algorithms are not suitable for mining data streams. Data stream mining requires fast processing algorithms using a single scan and a limited amount of memory. The micro clustering has a good role in this. In itself, density based micro clustering has its own unique place in data stream mining. This paper presents a survey on different data clustering algorithms, realizes and empowers the use of density-based micro clusters.
CATHODIC PROTECTION IN ONSHORE PIPE NETWORKS GAS PAGARDEWA RECEIVING STATION

The use of pipelines is one element that plays an important role as a production chain The pipeline network is used as the main component to distribute gas from the producer to the shipper and then distributed to industrial and household customers. In accordance with the regulations of the Ministry of Mines and Energy No. 300. K / 38 / M.PE / 1997 concerning the Occupational Safety of Petroleum and Gas Pipeline Pipes that the gas and main pipe transmission pipes held on land must be planted with a depth of at least 1 (one) meter above ground level.  However, the placement of steel pipes in the ground can cause pipe material to be threatened by corrosion due to interaction with the environment. This happens in the Pipeline Line of the Gas Pagardewa receiving station. To prevent corrosion from continuing on the pipeline, a precautionary step is needed where one of them is by applying the cathodic protection method. At Pagardewa Gas Receiver Station, which has the role of distributing gas to PLN's power plan in South Sumatra by using a 4875 m pipeline that is embedded in the ground so it needs to be designed for a cathodic protection system to be corrosion resistant. The purpose of this study is to design a cathodic protection system that uses sacrificial anodes so that the analysis can be obtained to be applied in the field.
The Kernel Wiener Filter Algorithm Based on Patch Self-similarity for Image Denoising

Image nonlocal self-similarity (NSS) based methods have achieved impressive success in image denoising with additive white Gaussian noise (AWGN). However, preserving the details and the texture features still is a valid challenge of denoising, especially for degraded images with heavy noise. In this paper, a new algorithm based on NSS is proposed to preserve the textures and details of the image. It uses the restored image of External Patch Prior Guided Internal Clustering (PCLR) for image denoising as preprocessed image, then take it as a reference image and minimize the mean square error to improve the result. Numerical results show that our method is superior compared to other denoising algorithms based on patch self-similarity.
P4PCN: Privacy-Preserving Path Probing for Payment Channel Networks

Recent advances in security and cryptography have enabled new paradigms for secure networking in various scenarios. The payment channel network (PCN) is a notable example, which has emerged from the combination of the traditional credit network in economics and the latest blockchain technology. PCN provides a secure and efficient way for conducting payments, by addressing both the intrinsic financial risk of the credit network and the scalability issue of the blockchain. A crucial challenge in PCN is routing, i.e., to find a set of paths that fulfill a payment request. Due to the fully distributed and dynamic nature of PCN, existing routing algorithms utilize active probing to improve routing success probability. However, while the payment itself is privacy-preserving through existing protocols, the probing process can leak sensitive information including the location of the sender or the recipient. In this paper, we address the privacy of the users in the path probing process, filling in the last piece of the privacy puzzle in PCN. We propose P4PCN, a cryptographic protocol for anonymous active probing without knowing the identities or public keys of the intermediate nodes, while hiding the locations of sender and recipient as well as any path-related information. Our protocol is lightweight and scales with the number of hops a probe explores. We confirm its performance via real-world implementation and simulation experiments.