Reviewerly/exHarmony huggingface dataset:
Application of Cloud Computing Technology in Libraries

Cloud computing technology (ICT) has come up as a boon for libraries and is offering various opportunities and services for libraries to connect their services with clouds. ICT has forced the libraries and librarians to change its functioning and processing of the information retrieval system. In this digital era, libraries are moving in advanced level called cloud computing. In cloud computing, the libraries do not require the software, operating system and applications in the limits which will be provided by the service provider. Cloud computing offers a new importance in computing, it changes how we invent develop, scale, update, maintain and pay for applications and infrastructure on which they are run. This study tries to highlight basic knowledge about cloud computing, its deployment models, types, and benefits. Secondly, it also deals with cloud computing initiatives for libraries along and its benefits in LIS (Library Information Science) domain. Keywords: Cloud computing, infrastructure as a service (IaaS), platform as service (Paas), and software as a service (SaaS), deployment models, LIS Cite this Article Nitasha Gandotra, Nidhi Tyagi, Swati Tiwari. Application of Cloud Computing Technology in Libraries. Journal of Advancements in Library Sciences . 2019; 6(2): 16–23p.
Classification of Balanced Quadratic Functions

S-boxes, typically the only nonlinear part of a block cipher, are the heart of symmetric cryptographic primitives. They significantly impact the cryptographic strength and the implementation characteristics of an algorithm. Due to their simplicity, quadratic vectorial Boolean functions are preferred when efficient implementations for a variety of applications are of concern. Many characteristics of a function stay invariant under affine equivalence. So far, all 6-bit Boolean functions, 3- and 4-bit permutations have been classified up to affine equivalence. At FSE 2017, Bozoliv et al. presented the first classification of 5-bit quadratic permutations. In this work, we propose an adaptation of their work resulting in a highly efficient algorithm to classify n x m functions for n ≥ m. Our algorithm enables for the first time a complete classification of 6-bit quadratic permutations as well as all balanced quadratic functions for n ≤ 6. These functions can be valuable for new cryptographic algorithm designs with efficient multi-party computation or side-channel analysis resistance as goal. In addition, we provide a second tool for finding decompositions of length two. We demonstrate its use by decomposing existing higher degree S-boxes and constructing new S-boxes with good cryptographic and implementation properties.
FRAMING ANALYSIS IN MEDIA INFORMATION ABOUT THE CASE OF SETYA NOVANTO RELATED TO PROCUREMENT E-KTP (KOMPAS AND DETIK)

The recent case of the E-KTP procurement project has attracted a lot of public attention. In this study, the author focused on the object of research on the involvement of the PDI-P Party in this mega corruption case. The PDI-P party is President Jokowi's bearer party in the 2014 presidential election. In this case, Novanto stated that he was involved in this case and even became the main suspect in the E-KTP procurement project. Language tools used as a method in analyzing the object of this research are Framing Analysis. Basically, framing is a method for viewing media telling stories about events. News that contains framing does not mean that the news is false news. In framing a news, the information obtained by a journalist is processed by constructing the contents of the news and presenting it to the public. Some are highlighted and some are excluded. This depends on how journalists interpret an event, and how a fact from that information is written.Keywords: Framing Analysis, E-KTP Procurement Project
Оntology of tour of the virtual museum 3D panorama

The article is devoted to the systematic and ontological analysis of the research domain and the formation on its basis of information models of knowledge formalization and knowledge presentation in modern network environments of memory institutions. The ontological approach to the formation of the ontology of tour of a virtual museum 3D panorama, which provides interactive interaction and transdisciplinary perception of objects of educational and research activities, is considered.
DIFFERENT METHODOLOGY IN BIG DATA AND CLOUD COMPUTING RESOURCE MANAGEMENT FRAMEWORKS

Nowadays technical advancement is increased and digitizes our lives in an ultimate manner, which have led to the rapid growth of data. Such multidimensional datasets are precious due to the potential knowledge and developing decision-making insights from them. Analyzing this huge amount of data from multiple sources can help organizations to plan for the future and anticipate changing market trends and customer requirements. While the Hadoop framework is a popular platform for processing larger datasets, there are a number of other computing infrastructures, available to use in various application domains. The primary focus of the study is how to classify big data resource management systems in the context of a cloud computing environment. We identify some key features which characterize big data frameworks as well as their associated challenges and issues. We use various evaluation metrics from different aspects to identify usage scenarios of these platforms. The study came up with some interesting findings which are in contradiction with the available literature on the Internet.
Literary texts and artistic images in modern ground architecture

The article focuses on the image of ground that had various meanings in the human culture from ancient times. On the one hand, it was a place of living that provided people with all means necessary for their physical survival and had an exclusively material value. On the other hand the ground was a part of myth; it was a subject of philosophical (or would-be philosophical) reflections. Step by step, humanity learned to perceive ground from artistic and aesthetic perspectives: as a place that sometimes could be beautiful by itself and sometimes needed to be decorated by people. In the modernity, the ground is more often viewed not only as a surface or a place for artistic work but as an environment or even a material for the latter. Inhabitants tend to decorate not only its aboveground but also its underground surroundings. This tendency has resulted in introduction of the modern high-tech underground buildings and the phenomenon of ecological ground architecture formation. In field of the visual design, the new approach to ground as a material for artistic activity emerged. The article presents a hypothesis that historical traditions contributed to the modern land architecture to a lesser degree. In the past the ground architecture was often awkward, pragmatic, and artistically inconsistent. It was a result of some specific materials, technological and climate conditions which people faced. At the same time, in folklore and fiction literature the aboveground and underground environments, including houses, were often depicted as artistically attractive. Therefore, for modern artists, the popular verbal and visual images of underground buildings serve as a rich source for inspiration in their work on the real-life projects in the field of ground architecture. The names and design features of some projects attest this idea. For instance, the modular “Hobbit House” created by the Green Magic Homes company makes an appeal to the literary works by J. R. R. Tolkien. The links between the modern ground architecture, literary texts and artistic images demonstrate that in the modern world not only the real life influences art, but virtual imaginative worlds begin to form the space of the reality itself.
A Bayesian Approach to the Data Description Problem

In this paper, we address the problem of data description using a Bayesian framework. The goal of data description is to draw a boundary around objects of a certain class of interest to discriminate that class from the rest of the feature space. Data description is also known as one-class learning and has a wide range of applications. The proposed approach uses a Bayesian framework to precisely compute the class boundary and therefore can utilize domain information in form of prior knowledge in the framework. It can also operate in the kernel space and therefore recognize arbitrary boundary shapes. Moreover, the proposed method can utilize unlabeled data in order to improve accuracy of discrimination. We evaluate our method using various real-world datasets and compare it with other state of the art approaches of data description. Experiments show promising results and improved performance over other data description and one-class learning algorithms.
Intelligent Synchronous E-Learning Management System Based on Multi-Agents of Linked Data, Ontology, and Semantic Service.

E-learning is a method of distance learning using electronic media, where each learner and instructor have interacted via learning content present on the web. So, e-learning playing a major role in the learning and teaching activities at institutions of higher education worldwide. Web technology for the construction of synchronous e-learning has been successfully developed to provide automatic educational content. The most important developments in this area are the semantic web that provides distributed information with a specific meaning precisely so that e-learning becomes an efficient relevant task that grows in new dynamically changing at the same time with the learning requirements and needs of the learner. This paper has contributed to this goal by establishing a system known as Intelligent Synchronous E-Learning Management System (ISEMS) that based on multi-agent such as semantic service, ontology, and linked data. Each agent represents the basis for organizing content resources, sequence learning, and adaptation. ISEMS shows how the automatic generates of hypertext structures from distributed metadata is utilized from the semantic web resource description formats. The description is based primarily on the content, context, and content structure of the learning material then provides flexible and personalized access to these educational materials.
AN ANALYSIS OF FLRA TO EFL LEARNERS AT IKIP SILIWANGI

This study is aimed to know the scale on the level of anxiety on Foreign Language in general and in reading on EFL learners especially to English department learner of 2015 in IKIP Siliwangi Bandung. The method that is used in this research is descriptive qualitative. The data were gathered using an online form by using a Likert scale questions of FLRAS (Foreign Language Reading Anxiety Scale) and FLCAS (Foreign Language Classroom Anxiety Scales). The data that have been collected were then analyzed using IBM SPSS Statistics 22 that consisted of descriptive statistics (e.g. means, standard deviations, percentages, etc.). The data used to measure the level of FL anxiety in general and in reading. From the results, more than 66% of the EFL learners have a medium-level in FLRAS, and 76% of the participant has a level of mildly anxious in FLCAS toward English as a foreign language.
Java Websockets Development

This chapter covers how to use Websockets in Java to create real-time applications supporting millions of concurrent users. Websockets provide a bidirectional persistent connection to a HTTP web server so that the time taken to create HTTP connections can be reduced. HTTP 1.1 also allows long-lived connections through the keep-alive header, but the payload in each request is significantly bigger than Websockets.
Deep transfer learning for fine-grained categorization on micro datasets

Existing fine-grained categorization models require extra manual annotation in addition to the image category labels. To solve this problem, we propose a novel deep transfer learning model, which transfers the learned representations from large-scale labelled fine-grained datasets to micro fine-grained datasets. Firstly, we introduce a cohesion domain to measure the degree of correlation between source domain and target domain. Secondly, select the transferrable feature that are suitable for the target domain based on the correlation. Finally, we make most of perspective-class labels for auxiliary learning, and learn all the attributes through joint learning to extract more feature representations. The experiments show that our model not only achieves high categorization accuracy but also economizes training time effectively, it also verifies the conclusion that the inter-domain feature transition can accelerate learning and optimization.
Java GraphQL Development

GraphQL is a query language for APIs and a runtime for fulfilling those queries with your existing data. GraphQL was developed by Facebook in 2012 and then publicly released in 2015. There has been lot of development recently around GraphQL, especially since tools like AWS Cloud AppSync and frameworks like Prisma came about. GraphQL’s primary purpose is to handle issues with RESTful API design and make HTTP communication faster.
Telemonitoring ADL platform based on non-intrusive and privacy-friendly sensors for the care of the elderly in smart homes

During the last years, several sensor-based monitoring systems have been developed to detect in real time frequent problems in older people, such as falls and nocturia. Some devices can also measure different variables of the environment (e.g. temperature, pollution, etc.) to generate alarms and thus help the user's welfare. All these devices generate numerous sensitive data related to the health and behavior of user/patients. The presence of some of these sensors in homes can mean a vulnerability of the user’s privacy. In this article we propose a telemonitoring ADL platform based on non-intrusive sensors for the care of the elderly, restricted by a user-centered protocol that guarantees their privacy and facilitates their acceptance by the user.
Modelling the Volatility of the Price of Bitcoin

This study assessed the volatility and the Value at Risk (VaR) of daily returns of Bitcoins by conducting a comparative study in the forecast performance of symmetric and asymmetric GARCH models based on three different error distributions. The models employed are the SGARCH and TGARCH which were validated based on AIC, MAE and MSE measures. The results indicated that the SGARCHGED (1,1) with generalised error distribution term was identified as the best fitted GARCH model. Though, this best fitted model based on information loss (AIC) did not provide the best out-of-sample forecast, the differences was insignificant. Thus, the study clearly demonstrates that it is reliable to use the best fitted model for volatility forecasting. Also, to further validate the performance of the best fitted model, it was subjected to a historical back-test using Value at Risk (VaR). Though, it was evident from the study that no model was superior, it was indicated that an average loss of 1.2% is expected to be exceeded only 1% of the time. Moreover, volatility forecast from the back testing was relatively high during the first quarter of 2018 but begun decreasing steadily with time.
Improving Students’ Skills Of Writing Scientific Articles Through ‘One Semester One Book’ Writing Program

Writing scientific articles for higher education students, especially undergraduate ones will be difficult without habituation, training, and program with product oriented in form of book. If it runs naturally and lecturers only give assignments to write scientific articles without education or literacy education, they tend to make plagiarism. This study aims at improving the students’ skills of writing scientific articles through ‘One Semester One Book’ writing program. Classroom activity is designed as the pillar of literacy namely reading, writing, and arithmetic in accordance with the integrated language learning approach by combining four language skills which is oriented by product of articles or book. The results of the study show that there are improvements of the students’ scientific articles writing skills achieved by 89 students in some aspects. They cover the progress of: first, skills of determining the research problems from 35 students (39%) to 85 students (95%); second, skills of determining the theme and tittle of the scientific articles from 24 students (26%) to 89 (100%); third, skills of determining the word choice appropriately from 25 students (28%) to 80 students (89%); fourth, skills of using correct spelling based on General Indonesian Spelling Guidelines (PUEBI) rules from 35 students (39%) to 75 students (84%); fifth, skills of applying citation method from 25 students (28%) to 80 students (89%); sixth, improvement of the number of the students who do not commit plagiarism from 25 students (28%) to 85 students (95%); and seventh, skills of writing contemporary issues about education or religion from 34 students (38%) to 84 students (94%). The research findings reveal that the ‘One Semester One Book’ writing program is strategic in improving the students’ skills of writing scientific articles.
Digitale Erschließung der (kurialen) Quellen in der Slowakei

Since Slovakia was part of the Kingdom of Hungary until 1918, Slovakian historians are not the only ones who do research on the history and the source material of this territory. The sources of the Roman Curia concerning this territory were published within different projects, some of which are older (Monumenta Vaticana historiam Regni Hungariae illustrantia), others more recent (Monumenta Vaticana Slovaciae). Sources from different local archives appeared in various other editions. At the moment a complex historical portal www.PamMap.sk is being designed, in which the sources of the Roman Curia, among others, will be prepared multilingually with images and metadata for a broader community.
Deep Learning-based Sequential Recommender Systems: Concepts, Algorithms, and Evaluations

In the field of sequential recommendation, deep learning methods havereceived a lot of attention in the past few years and surpassed traditionalmodels such as Markov chain-based and factorization-based ones. However,DL-based methods also have some critical drawbacks, such as insufficientmodeling of user representation and ignoring to distinguish the different typesof interactions (i.e., user behavior) among users and items. In this view, thissurvey focuses on DL-based sequential recommender systems by taking theaforementioned issues into consideration. Specifically, we illustrate theconcept of sequential recommendation, propose a categorization of existingalgorithms in terms of three types of behavioral sequence, summarize the keyfactors affecting the performance of DL-based models, and conduct correspondingevaluations to demonstrate the effects of these factors. We conclude thissurvey by systematically outlining future directions and challenges in thisfield.
Container-based microservice architecture for local IoT services

Abstract. Edge services are needed to save networking and computational resources on higher tiers, enable operation during network problems, and to help limiting private data propagation to higher tiers if the function needing it can be handled locally. MEC at access network level provides most of these features but cannot help when access network is down. Local services, in addition, help alleviating the MEC load and limit the data propagation even more, on local level. This thesis focuses on the local IoT service provisioning. Local service provisioning is subject to several requirements, related to resource/energy-efficiency, performance and reliability.This thesis introduces a novel way to design and implement a Docker container-based micro-service system for gadget-free future IoT (Internet of Things) network. It introduces a use case scenario and proposes few possible required micro-services as of solution to the scenario. Some of these services deployed on different virtual platforms along with software components that can process sensor data providing storage capacity to make decisions based on their algorithm and business logic while few other services deployed with gateway components to connect rest of the devices to the system of solution. It also includes a state-of-the-art study for design, implementation, and evaluation as a Proof-of-Concept (PoC) based on container-based microservices with Docker. The used IoT devices are Raspberry Pi embedded computers along with an Ubuntu machine with a rich set of features and interfaces, capable of running virtualized services.This thesis evaluates the solution based on practical implementation. In addition, the thesis also discusses the benefits and drawbacks of the system with respect to the empirical solution. The output of the thesis shows that the virtualized microservices could be efficiently utilized at the local and resource constrained IoT using Dockers. This validates that the approach taken in this thesis is feasible for providing such services and functionalities to the micro
Intellectual Capital Disclosure by Listed Companies in Jordan: A Comparative Inter-sector Analysis

This study examines the level and quality of the disclosures of intellectual capital (IC) by listed companies in Jordan as a case for emerging economies, especially those from Arab nations. Using content analysis, 2016’s annual reports of all 215 listed companies on the Amman Stock Exchange (ASE) were analyzed and compared for the main sectors: financial, service and manufacturing. The key findings indicate a low disclosure level of IC. The most disclosed components of IC were the items of internal capital. Financial companies were found to disclose the most items, followed by manufacturing firms and finally the service sector. This research has implications for policy makers and standard setters regarding mechanisms to boost the practice of IC disclosure in annual reports. This in turn enhances the quality of disclosed information as well as facilitating a better assessment of firm value. This research fills a void in the comparative study of IC disclosure practices among main sectors in the ASE, Jordan.
OBSTACLE DETECTION AND AVOIDANCE FOR AUTONOMOUS ELECTRIC VEHICLE USING ARDUINO

Now a days Autonomous electric vehicle are the current research trends in the automobile industry and future vehicle used by humans.However, the commercial realization of autonomous vehicles remains a significant challenge.My project put forward an obstacle avoider robotics vehicle that uses and sharp distance IR sensor for this purpose the system uses an ATmega2560 Arduino microcontroller to achieve this functionality.The first part of my project is to design a system in such a way that it avoids any kind of obstacle that comes in its path.The vehicle achieves this functionality with the help of sharp distance IR sensors, ATmega2560 microcontroller, and motors.The system allows the vehicle to guide itself during obstacle occurred.The obstacle detection is done using Sharp distance IR sensors.After detecting the obstacle and this signal is passed to the ATmega2560 microcontroller on receiving the signals it guides the vehicle to moves in a different direction by actuating the motors through the motor driver.