Reviewerly/exHarmony huggingface dataset:
29. The library in a changing world of scientific communication

The digital transformation has fundamentally changed the work and self-understanding of academic libraries over the course of the past two decades. Early on, library science experts attempted to address what impacts comprehensive digitalisation in research and academic instruction would have on libraries and to anticipate further new developments. There is a general consensus that the key challenges arise from the fact that academic research is now a digital process from the outset and throughout all phases of work. As a result, academic libraries now face new tasks, which include ensuring the accessibility of research data and implementing reliable solutions for storing and long-term archiving of digital data. Today’s libraries not only acquire and make available large volumes of digital information; use of library space has also increased enormously in recent years. Besides providing virtual books, information, and data, libraries can actively support situated learning by creating new kinds of spaces and buildings and thus contribute significantly to the ongoing development of academic cultures of learning and instruction. The traditionally close link between libraries’ role as reservoirs of knowledge and as spaces for instruction and learning continues to define their function within the post-Gutenberg galaxy, even as they develop new forms of serving academic communities.
An Improved Model to Increase Retrieval Time and Security by Data Fragmentation and Replication Process in Cloud

Cloud computing provides several features to users as well as to the organizations. Even though, there are some issues faced by the user while usingthe cloud. Security is a major concern that is always considered. Likewise Data replication is a significant technique to be consideredfor retrieval time. Replication helps to fetch the data from remote which is a high-time consuming process.To overcome the security issue along with data replication a novel approach is proposed in this paper.Dynamic fragmentation is utilized for the division of a file into fragments. Each cloud nodes has a different fragment to enhance the data security of the system. Blowfish technique is used for encrypt the files before storing in cloudthatdivides messages into 64 bits blocks then encrypts them separately. The result of experimental evaluation shows that this schemes increase the overall performance.
Review of paper “Objective extraction and analysis of statistical features of Dansgaard-Oeschger events” by J. Lohmann and P. Ditlevsen

This paper provides a very thorough statistical analysis of ice core records evidencing DO events in the last glacial interval (mostly the NGRIP d18O record, but some comparisons with the GRIP record are performed), as well potential external forcing factors such as among others global ice volume (as inferred from benthic d18O) and atmospheric CO2 from Antarctic ice cores.The focus is on first-order variability, and the proposed piecewise-linear fit is a suitable method to extract features such as stadial
SD Case Study: Transition to Software Design

The Address Book Application (ABA) case study is used to transition our thinking to software design. New software design models are introduced to describe the ABA. The new design models are IDEF0 function models, structure chart, structure diagram, and data-flow diagrams. The six characteristics for a good software design are then used to evaluate the ABA software design. To further explore software design concepts, a new case study is introduced using a top-down design approach. For this new case study, software design models are developed and then evaluated using the six characteristics for a good software design.
Jahrestagung des Aktionsbündnisses Patientensicherheit e.V. (APS) 9. und 10.05., Berlin

Code clone genealogy detection has been widely used in refactoring and maintenance of code in software systems. It identifies history of code clones in a source code repository. Existing code clone genealogy detection techniques are based on text similarity for extraction of different versions of program. However, existing proposals in literature for code clone genealogy detection fail to extract code clone genealogy on Git (a distributed version control system). To address this issue, we presented a new scheme, based on transitive closure computation that detects code clone genealogies in e-health system on Git. Our insight is to extract clone pairs from thousands of commits on a software system in Git by transitive closure computation. We implemented the proposed scheme using Hadoop and Neo4j on Open Medical Record System. The results prove efficacy of the proposed scheme using parameters such as transitive depth, ratio of similarity, and count of clones.
Supplementary material to &amp;quot;A solar optical hyperspectral library of rare earth-bearing minerals, rare earth oxides, copper-bearing minerals and Apliki mine surface samples&amp;quot;

RareEarth Mineral sample name, formula, size and provenance 2 Rare Earth Oxide Powders, Suppliers, Product Number and Lot Number 3 Copper minerals, Abbreviation, alteration, collection, collection locality 4 Copper bearing minerals listed with their sample name and collection and the area of sampling for EDX/WDX analyses and spectra retrieval 5 Apliki mine samples, including description, coordinates of sampling in March 2018 in the Republic of Cyprus and a field photo of the sample 10
The Difference Between Certified and Non-certified PE Teachers Performance Based on Range of Service Period

This research was conducted in junior high schools (SMP) in Deli Serdang Regency, North Sumatra Province.This research has been conducted for 2 months, namely October to November 2018.The population in this study were all physical education teachers who taught in the SMP throughout Deli Serdang District.The sample in this study were 12 physical education teachers who would be sampled to distribute the questionnaire.While the respondent representatives for the interview took 3 physical education teachers, 3 school principals, and 1 supervisor.This research is a mix methods research, which is a research step by combining two forms of approaches in research, namely qualitative and quantitative.The conclusions in this study are that classically the results of the percentage of teachers who were certified were 84.44% which was in the Very Good category.Based on data, the overall percentage for teachers who have not been certified 75.97% means that the performance of teachers who have not been certified is good.
Vehicle Collision Detection During Night Time

”Vehicle collision detection during night time” is an attempt to find an improved solution for vehicle detection problem, during night time. Night time vehicle detection is a challenging area since, the contrast between back ground and foreground is very less due to non uniform lighting, reflections, multiple light sources etc. This algorithm is a blend of night time image enhancement technique and fusion technique for fusing multiple features based on their weight. Image enhancement is an important aspect of the algorithm as it helps in improving the contrast between foreground and back ground as well as improving the lighting, so that it becomes easier to distinguish vehicle and other illuminated objects such as street lights. For improved reliability of the algorithm, set of complementary features - LBP (local binary pattern), HOG (Histogram of Oriented Gradients) and a CNN based feature - are used in the algorithm and are fused together using weighted fusion technique. For classification purpose, individual SVMs are trained for each feature and another SVM is trained for final rank based vehicle detection. Accurate region of interest in computed during detection phase, and is used for vehicle detection by trained SVM classifiers. Tail light detection is used for efficient region proposals. The algorithm is improved by leveraging the region proposals and high accuracy detection of Faster RCNN [10]. The algorithm detects multiple vehicles in single image frame and at possible multiple locations and distances within the frame.
Exploiting Chi Square Method for Sentiment Analysis of Product Reviews

Sentiment analysis is an extension of data mining which employs natural language processing and information extraction task to recognize people's opinion towards entities such as products, services, issues, organizations, individuals, events, topics, and their attributes. It gives the summarized opinion of a writer or speaker. It has received lot of attention due to increasing number of posts/tweets on social sites. The proposed system is meant to classify a given text of review into positive, negative, or the neutral category. Primary objective of this article is to provide a method of exploiting permutation and combination and chi values for sentiment analysis of product reviews. Publicly available freely dictionary SentiWordNet 3.0 has been used for review classification. The proposed system is domain independent and context aware. Another objective of the proposed system is to identify the feature specific intensity with which reviewer has expressed his opinion. Effectiveness of the proposed system has been verified through performance matrix and compared with other research work.
DV-DVFS: Merging Data variety and DVFS Technique to Manage the Energy Consumption of Big Data Processing

Abstract Data variety is one of the most important features of Big Data. Data variety is the result of aggregating data from multiple sources and uneven distribution of data. This feature of Big Data causes high variation in consumption of processing resources such as CPU consumption. In this paper, we used Dynamic Voltage and Frequency Scaling (DVFS) to reduce the energy consumption of computation. To this goal, we consider a deadline as our constraint and before applying the DVFS technique to computer nodes, we estimate the processing time and the frequency needed to meet the deadline. We have used a set of data sets and applications in the evaluation phase. The experimental results show that our proposed approach surpasses the other scenarios in processing real datasets. Based on the experimental results in this paper, DV-DVFS can achieve up to 15% improvement in energy consumption.
Photos: Proceedings of the 5th International Symposium on Current Progress in Mathematics and Sciences (ISCPMS2019)

First Page
Convolution Index based Unsupervised Label Procedure for Efficient Medical Image Exploration

Medical imaging is a forceful idea of various medicinal ideas i.e. malignant growth and other related infections, present days; various kinds of therapeutic pictures are caught and saved in computerized position in medicinal research focuses. Confronting this kind of huge volume of picture information with various sorts of picture modalities, it is critical to execute effective content based image retrieval (CBIR) for restorative research focuses. Picture mark ordering is another actualized strategy for medicinal picture recovery. Traditionally various kinds of CBIR methodologies are proposed to give unsatisfied therapeutic picture recovery results. So that in this paper, propose a Convolution Index based Unsupervised Label (CIUL) way to deal with recover marks of pictures utilizing AI wording. We characterize AI as matrix convex optimization with cluster-based matrix representation which can be utilized to improve the productivity in picture recovery framework.
OPTIMALISASI POTENSI KARYA MAHASISWA MATA KULIAH GRAFIKA DKV MELALUI GALERI ONLINE

Graphic reproduction courses or printing methods are one of the courses that always exist in every college of Visual Communication Design in Indonesia, with this course students are expected to understand how the Final Artwork (FA) printing process is correct, the result is the quality of the design work Finally, students have met print-ready standards in accordance with the graphic industry standard so that it has the potential to be offered to the public at large as ready-to-use works, but unfortunately the student's work has only ended up in storage. This study aims to optimize the potential that exists in the final design work to be more widely useful. This study uses a qualitative method with a descriptive approach aimed at analyzing the quality of student design assignments in the Gafika Reproduction Method course, and creating an online gallery as a means of channeling existing design potential
Introduction of Luo’s Theory - Timbre Evaluation of Musical Instruments

Luo’s theory is the first approach to an ancient problem: timbre evaluation of musical instruments and other acoustic sources. The invention of a series of timbre indexes based on rigorous mathematical definitions makes the quantitative analysis of artistic sense possible, which will benefit both instrument manufacturers and music player. A Matlab application is presented at the end of the paper.
Decision Support System for Employees Enrollment using AHP and Promethee Methods

PT Sumber Berkat Anugerah is one of the companies engaged in spare parts, vehicle accessories, home audio, and others. The stages of acceptance of prospective employees in this company are still manual, which causes problems, namely the time in the selection process for the recruitment of employees is quite long and the occurrence of human error during data storage. Based on the problems that occur, the HRD department requires a reception decision support system for new prospective employees who are expected to be able to correct the problems that occur and produce better and more accurate calculations. This system is designed and built using the AHP method which has advantages in determining priority weights and Promethee methods that have advantages in the alternative rating process. The result of this system is the rank of prospective employees sorted from highest to lowest. The system has been built and tested by comparing the results of manual calculations with the results of calculations generated by the system. Usability testing on the system uses the USE Questionnaire which can be concluded that the results of overall system user satisfaction reached 85.2%.
Manajemen Mutu Terpadu

Education quality management is important for consumers in considering to include their children in educational institutions and therefore through good human resources are expected to guarantee the quality of education.along with the development and development of technology, the use of media in the scope of education has also changed, the most important aspects in integrated quality management such as human resource management must also be updated, renewed, what is meant here is the potential and expertise in all midwives.by developing a literacy study and existing data I developed it into an article which uses observation and interview techniques to get results that many human resources lack in responding to technological advances especially the use of IT, and maintaining the quality of education is still good, human resources must developed in accordance with the times. there are several ways of developing human resources such as workshops, training, and giving seminars, forms of development for teachers such as MGMP for secondary teachers and KKG for SD / MI / SDLB teachers, so that the goals of education are achieved
UI Automation

Interacting with several desktop and web applications is an essential part of our daily work routine. We use these applications to accomplish tasks like logging timesheets in a time tracking system, storing, organizing, and retrieving documents in a document management system, or tracking projects in a project management software. To interact with these various applications, we use their user interface (UI). Similarly, UiPath StudioX UI automation interacts with the user interfaces to automate desired tasks by replicating the user interaction like checking boxes, selecting dropdowns, entering data, and clicking buttons.
Blockchain-based Big Data Analytics Approach for Smart Cities

The wireless communication is the fastest-growing field of studies to enable humans to communicate with each other. Introducing a new blockchain architecture with big data analytics can be an enticing platform towards increasing connectivity performance throughout the smart cities. Blockchains can build a convenient mechanism allowing two devices to effectively transmit currency as well as data, to each other with a secure and reliable agreement. Smart cities provide an opportunity to connect people and places that use emerging technologies that help to improve urban planning and development. Smart cities can enhance public infrastructure and people's living experience. Presently, high-speed, intelligent, effective with several innovations, like low energy consumption, etc., seem to be accessible to interact together in the modern environment. By using the blockchain-based big data approach the physical devices are allowed to communicate securely with other physical devices in heterogeneous environments. This approach builds a new blockchain-based computing structure throughout the IoT technology configuration. This approach can examine blockchain technology to the underlying technology or maintains the IoT authentication reliable. This strengthens blockchain and cloud to develop an enabling IoT pervasive environment for secure communication among the physical objects.
Graph-based model and algorithm for minimising big data movement in a cloud environment

In this paper, we discuss load balancing and data placement strategies in cloud environments. The main goal in data placement strategies is to improve the overall performance through the reduction of data movements among the participating datacentres. Load balancing and efficient data placement on cloud systems are critical problems that are difficult to simultaneously cope with. In this context, we propose a threshold-based load balancing algorithm, which first balances the load between datacentres, and afterwards minimises the overhead of data exchanges. It is divided into three phases. First, the dependencies between the datasets are identified. Second, the load threshold of each datacentre is estimated based on the processing speed and the storage capacity. Third, the load balancing between the datacentres is managed through the threshold parameters. Our experimental results show that our approach can efficiently reduce the frequency of data movement and keep a good load balancing between the datacentres.
Applying Word Co-Occurrence Graph in Enhancing LDA Model for Topic Discovering in Large-Scaled Text Corpus

Topic modeling, such as LDA is considered as a useful tool for the statistical analysis of text document collections and other text-based data. Recently, topic modeling becomes an attractive researching field due to its wide applications. However, there are remained disadvantages of traditional topic modeling like as LDA due the shortcoming of bag-of-words (BOW) model as well as low-performance in handle large text corpus. Therefore, in this paper, we present a novel approach of topic model, called LDA-GOW, which is the combination of word co-occurrence, also called: graph-of-words (GOW) model and traditional LDA topic discovering model. The LDA-GOW topic model not only enable to extract more informative topics from text but also be able to leverage the topic discovering process from large-scaled text corpus. We test our proposed model in comparing with the traditional LDA topic model, within several standardized datasets, include: WebKB, Reuters-R8 and annotated scientific documents which are collected from ACM digital library to demonstrate the effectiveness of our proposed model. For overall experiments, our proposed LDA-GOW model gains approximately 70.86% in accuracy.
EMOTION MINING IN ENGLISH TEXT USING BAYES &amp; SVM APPROACH

Emotions have captivated researchers for years, as is obvious in the huge body of research work related to emotion in area of mental characteristics, language, socialism, and interaction. Human emotion manifests itself in the form of facial expressions, speech utterances, writings, and in gestures and actions. As a result, technical research in emotion has been pursued along several proportions and has drawn upon research from various areas. This paper results in the chore of emotion gratitude by attempting to robotically learn emotions from text. In this paper, a new technique to mine the emotions from an English text has been introduced. We have used 10 categories from which we can extract the motions. Proposed system use bays and SVM approach to perform the given task. The accuracy of the proposed system is better as compared to the existing system.
Authentication of Communicating Node Key Generation Based on User with Mutual Removal Concepts

The present conventions for making pair wise mysteries between hubs in a remote system, so these insider facts are secured from a secret agent, yet with constrained system nearness. The main present a fundamental mystery understanding convention for single-bounce systems, where mysteries are developed utilizing traffic traded between the hubs, and we demonstrate that under standard hypothetical suppositions, our convention is data hypothetically secure. Secondly, proposing a secrecy understanding convention for subjective, multi-jump arranges that expand on the fundamental convention yet in addition contains configuration highlights for utilizing extra sources that multi-bounce offers, for mystery. At long last, assess our conventions, and give exploratory proof that it is practical to make a huge number of mystery bits every second, in reasonable remote setups.
Product Recommendation based on Sybil and Trusted Votes in Social Networks

Social Networks is a platform which is easily accessible by normal users worldwide. Online Social Networks facilitates users online to get registered with ease of speed and create their own accounts to communicate with the social world for information gathering. This platform allows everyone to get registered online irrespective of their social behaviour. Users here are creating duplicate accounts that is creating Sybil in the network. By this Sybil online Social Networks are suffering for different kinds of Sybil attacks online. In social networks user’s feedback and preferences play an important role in suggesting friends online or recommending products online. When collecting the feedback or preferences of any product online both Sybil user’s and real user’s data is considered as we are not differentiating the Sybil user or real user. From this products, recommended online will not have an efficient rating which would divert the buyers online. To over this problem we propose Sybil Community Detection Algorithm (SCD) and TrustRank Algorithm that bifurcates real user votes and Sybil users votes to fetch the efficient products online thus build secure online environment.