Reviewerly/exHarmony huggingface dataset:
CI/CD with Azure Kubernetes Service

Software development has been steadily moving toward a model of Continuous Integration and Continuous Delivery. A key enabler along the way has been the introduction of cloud-native applications. The Cloud Native Computing Foundation defines cloud-native as
Performance Evaluation of Parallel Heapsort Programs

Heapsort algorithm was found many decades ago, but no parallel heapsort algorithm had been presented. In our previous work, we challenged the development of a parallel heapsort algorithm. In this paper we improve our previous algorithm partially and analyze the performance in detail. As a more massive amount of data are sorted, or as more complex comparison is required for sorting, our parallel heapsort algorithm can sort the data more efficiently.
The effect of data standardization in cluster analysis

The application of multivariate techniques to experimental results requires a responsibility on behalf of the researcher to understand, evaluate and interpret their results, especially the ones that are more complex. In this work, the impact of three standardization techniques on the formation of clusters by the Kohonen neural network were studied. The techniques studied were logarithm (log10), generalized-log and improved min-max. The studies were performed using two databases consisting of 298 and 146 samples and containing the mass fractions of As, Na, K, La, Yb, Lu, U, Sc, Cr, Fe, Cs, Eu, Tn, Hf and Th, determined by neutron activation analysis. The results were evaluated using validation indices.
The Mediating Effect of Problem Focused Coping on the Relationship between Ego-Resiliency and Work Engagement of Teachers in Childcare Centers using the Structural Equation Model

The purpose of this study was to investigate the mediating effect of problem focused coping on the relationship between ego-resiliency and work engagement of teachers in childcare centers using the Structural Equation Model. The subjects were 251 childcare centers teachers. Questionnaires, which required self-reporting by childcare centers teachers, were used to investigate ego-resiliency, problem focused coping and work engagement. The results of our study were as follows. Structural Equation Modeling(SEM) revealed that first, the childcare center teacher’s ego-resiliency had an influence on problem focused coping. Second, problem focused coping had an influence on work engagement. Third, the childcare center teacher’s ego-resiliency had an indirect influence on work engagement through problem focused coping.BR The results suggest problem focused coping had more of an influence on work engagement of teachers in childcare centers than ego-resiliency. Therefore, to promote work engagement, training on problem focused coping strategies in stressful situation should be provided to teachers.
A Patient Outcome Prediction based on Random Forest

Since the research and development value of electronic health records (EHRs) which contains a large number of patient treatment data is very high and meaningful, EHRs has gained attention by researchers in recent years. EHRs has some characteristics such as temporality, sparsity, complexity, irregularity, noisiness and so on, which bring many challenges to direct study the medical data. Thus, an effective feature extraction, or phenotyping from patient EHRs is a key step before any further applications. In this paper, MIMIC-III intensive care database is selected for the experiments. To predict the patient's death outcome (namely death due to illness or still alive), we make full use of the visit records of patients and propose a prediction method that combines the medical concept representation model Med2Vec with random forest algorithm. Experimental results indicate that the proposed method is robust to parameter variations and noise. Besides, compared with other prediction methods, the performance metrics of the proposed method are very well. Finally, the effect of the Med2Vec model is superior to that obtained by raw data (i.e., no feature learning applied to EHRs data).
RTSLPS: Real time server load prediction system for the ever-changing cloud computing environment

Cloud Computing (CC) proposes a multi-tenant framework used by multiple concurrent users, each of which exhibits different and varied behavior. Such heterogeneity shapes a highly fluctuating load and creates new usage patterns overtime at the server level. Virtual Machines (VMs) interference also plays a big part in inducing changes at server load. Server load prediction is deemed crucial to ensure efficient resource usage. The execution of real-time interactive tasks constitutes an important part of CC. Thus, we propose, in this paper, a real-time server load prediction system based on incoming task classification and VM interference detection. The incoming task classification is used to capture the incoming workload trend and VM interference detection aims to capture the interference rate. Finally, load prediction considers server actual resources’ usage, VM interference rate, and incoming workload trend. We propose an improved version of Hoeffding Adaptive Tree (HAT), augmented by ensemble drift detectors. Results show that our Real-Time Server Load Prediction System (RTSLPS) was able to deliver great flexibility dealing with changes and very good accuracy with quick evaluation time and a small memory footprint.
ON THE ASSESSMENT OF STUDENTS' KNOWLEDGE ABOUT CLOUD COMPUTING

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 5972-5980ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.1449Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
Prototype of Pornographic Image Detection with YCbCr and Color Space (RGB) Methods of Computer Vision

Latest Internet and technology developments have brought in various changes in social interaction, specifically for the teenager to middle age people that excessively using social media. One of the negative effect of this interaction model change is the increase of wide spreading of contents that contain pornographic elements. The pornographic contents can be accessed from social media such as Facebook, Instagram, Path, Whatsapp and Youtube. The purpose of this study is to develop a system for identifying pornographic image content by designing a prototype of pornographic image detection using computer vision's YCbCr and color space (RGB) method by filtering the algorithm of previous researchers, measuring instruments to determine the accuracy of prototypes using the Confusion Matrix algorithm. The experiments using YCbCr produced an accuracy rate of 76%, precision value 82.142%, and recall value 76.666% from 50 random datasets of pornographic positive and non-pornographic images, while the RGB method produced an accuracy rate of 43.42%, precision value 44.23%, and recall value 50.17% from 146 random dataset of pornographic positive, non-pornographic and semi-pornographic images.
Active Matrix Completion for Algorithm Selection

The present work accommodates active matrix completion to generate cheap and informative incomplete algorithm selection datasets. Algorithm selection is being used to detect the best possible algorithm(s) for a given problem ($$\sim $$ instance). Although its success has been shown in varying problem domains, the performance of an algorithm selection technique heavily depends on the quality of the existing dataset. One critical and likely to be the most expensive part of an algorithm selection dataset is its performance data. Performance data involves the performance of a group of algorithms on a set of instance of a particular problem. Thus, matrix completion [1] has been studied to be able to perform algorithm selection when the performance data is incomplete. The focus of this study is to come up with a strategy to generate/sample low-cost, incomplete performance data that can lead to effective completion results. For this purpose, a number of matrix completion methods are utilized in the form of active matrix completion. The empirical analysis carried out on a set of algorithm selection datasets revealed significant gains in terms of the computation time, required to produce the relevant performance data.
Retrieving Articles and Image Labeling Based on Relevance of Keywords

When users input keywords into the search engine, a massive search results will be retrieved. However, it becomes difficult for the users to learn as it is unreadable with the excessive amount of results. This study establishes an information retrieval system for computer science related articles. It firstly collects articles by running a web crawler, and uses TF-IDF (Term Frequency-Inverse Document Frequency) method to extract keywords to acquire the focus of the article. And with the use of association rules and cosine similarity, the articles are classified by their relevance. Finally, according to users' feedbacks, the system provides appropriate resources to improve the motivation and willingness to learn. In addition, the pictures in the articles are also a basis for analyzing the articles. This study uses image semantic analysis to label the pictures so as to improve the accuracy in analyzing the articles.
SOME REMARKS ON MINIMAL ASYMPTOTIC BASES OF ORDER THREE

We study a question on minimal asymptotic bases asked by Nathanson [‘Minimal bases and powers of 2’, Acta Arith. 49 (1988), 525–532].
Mapping between CMMI-DEV v1.3 and ISO/IEC 90003:2014

Software development organizations require implementing models and standards to improve the quality of their software products. Currently, there are multiple models and standards, which make it difficult to determine which of them fits best the objectives of each organization. In this context, the present study pretends to address the lack of documentation about the correspondence of models and standards by presenting a mapping between a part of the model CMMI-DEV v1.3 and the standard ISO/IEC 90003:2014. The SFramework model was used to perform the mapping. As a result, it was determined that the level of relationship between the process areas of "Decision and supplier management" and "Creating a culture of excellence" of CMMI-DEV v1.3 and the processes of ISO/IEC 90003:2014 is 5,62%.
Spam Detection in Link Shortening Web Services Through Social Network Data Analysis

Twitter is one of the most popular social networking and micro-blogging Web sites in the world. TinyURL is a URL or link shortening web service used by Twitter. Recently, it is being exploited by spammers as a podium to transmit malicious information. TinyURL spam detection in Twitter is a challenging task. In this paper, an efficient scheme is proposed to detect spam in the TinyURL with particular focus on Twitter tweets. A set of features are first extracted from the tweets. This feature set is analyzed to select a reduced set of features. The reduced feature set is fed as input to train three classifiers, namely simple logistic regression, decision tree, and SVM. The classification results show that the SVM classifier has the highest accuracy in detecting spam in TinyURLs.
Time-resolved tomography of a driven adiabatic quantum simulation

A typical goal of a quantum simulation is to find the energy levels and eigenstates of a given Hamiltonian. This can be realized by adiabatically varying the system control parameters to steer an initial eigenstate into the eigenstate of the target Hamiltonian. Such an adiabatic quantum simulation is demonstrated by directly implementing a controllable and smoothly varying Hamiltonian in the rotating frame of two superconducting qubits, including longitudinal and transverse fields and iswap-type two-qubit interactions. The evolution of each eigenstate is tracked using time-resolved state tomography. The energy gaps between instantaneous eigenstates are chosen such that, depending on the energy transition rate, either diabatic or adiabatic passages are observed in the measured energies and correlators. Errors in the obtained energy values induced by finite ${T}_{1}$ and ${T}_{2}$ times of the qubits are mitigated by extrapolation to short protocol times.
Querying Encrypted Data in Graph Databases

Encryption is an effective way to protect sensitive data in a database from various attacks. Querying encrypted data, however, becomes a challenge. Either the data should be decrypted before the querying, leaving it vulnerable to server-side attacks, or one has to apply computationally expensive methods for querying encrypted data. In this paper, we present a flexible mechanism for the execution of queries over encrypted graph databases. Data privacy is protected at the server side, through the use of multi-layered encryption and encryption layer adjustment, conducted dynamically during the execution of queries. The proposed scheme reveals less information to the adversary than in the case of static adjustment done prior to execution. We report on the implementation of the scheme as applied to a subset of Cypher graph queries (graph traversal queries) directed at a Neo4j graph database. The experimental results show the efficiency of query execution for various types of query on encrypted graph data stores.
Feature Selection and Clustering of Documents Using Random Feature Set Generation Technique

In this digital world, information exists in abundance everywhere. Document clustering and instances involving pattern mining requires a subset of relevant features for analysis. Feature sets, which constitute one or more words, from the content of a document play a vital role in document clustering. A filter-based method for feature selection algorithm called random feature set generation (RFG) is proposed in this paper for document clustering. In feature selection, the selected features are checked for its quality using a quality metric and the best quality terms are used as the basis for document clustering ignoring the worst terms as well as frequently occurring common terms from the corpus. Exhaustive experimentation for identification of feature sets is impossible due to its increased demand for computational effort. The advantage of RFG approach lies in selecting “good” quality terms for document clustering. The feature sets thus obtained are used for document clustering using K-means and X-means clustering algorithms. Experimental results have shown “good” quality terms filtered using random feature set generation (RFG) do not rely on monotonicity assumptions, and it shows positive correlation than the feature sets obtained using sequential forward feature set generation.
A fuzzy algorithm for text classification in data science

It is a big challenge to describe the accuracy of similar features in text document based on user requirements. Data classification is biggest issue in huge data sets. Fuzzy Relevance Feature Discovery Algorithm (FRFDA) is used to relevance feature discovery and it also classifies words in to different categories and updates that word weights in particular pattern. The testing result proves that, the proposed FRFDA is better than existing manual and automation methods. The data set Reuters-21578 shows that the proposed model significantly outperforms quicker and obtains better extracted quality than other methods.
A Study of Bitcoin Stock Market Prediction: Methods, Techniques and Tools

Bitcoin is a kind of Cryptocurrency and now is one of type of investment in the stock market. Stock markets are influenced by many risk of factor. And bitcoin is one kind of cryptocurrency that keep rising in recent few years, and sometimes fall without knowing influence behind it, on stock market . Because it’s fluctuations, there’s a need Autamoted tool to prediction of bitcoin on stock market. This research study learn how to make bitcoin stock market prediction, method to apply on predictiom and also technique and tools that used from previous research paper, many authors, web-sources and other available sourcs. For the last section we discussed and make conclussions to provide good method on future works.
A Four-Valued Hybrid Logic with Non-dual Modal Operators

Hybrid logics are an extension of modal logics where it is possible to refer to a specific state, thus allowing the description of what happens at specific states, equalities and transitions between them. This makes hybrid logics very desirable to work with relational structures. However, as the amount of information grows, it becomes increasingly more common to find inconsistencies. Information collected about a particular hybrid structure is not an exception. Rather than discarding all the data congregated, working with a paraconsistent type of logic allows us to keep it and still make sensible inferences. In this paper we introduce a four-valued semantics for hybrid logic, where contradictions are allowed both at the level of propositional variables and accessibility relations. A distinguishing feature of this new logic is the fact that the classical equivalence between modal operators will be broken. A sound and complete tableau system is also presented.
Analysis of a M/M/k system with exponential setup times and reserves servers

A critical issue in modern server farm system architecture is to know how to efficiently allocate the number of servers to manage unpredictable demand patterns, in order to get the best performance without wasting energy. Although it is demanded to shut down servers when they become inactive to save energy, the high configuration cost (in terms of setup time and energy consumption) required to restart the server can affect performance. The problem is even more complicated since today's servers offer several states of sleep or standby that allow the setup cost to be balanced by the power consumed while the server is "in sleep". In this paper, we will consider the M/M/k queuing system with setup costs: M/M/k/Setup. Servers are obviously shut down when there is no work to be done, but turning on a server that has been shut down will incur setup costs. The setup cost takes the shape of a delay and a power penalty, event that idle servers are shut down to save on running expenses. Since servers in setup mode consume a lot of energy, the number of servers that can be in setup at any given time is often limited. In the configuration model, a maximum of one server can be setup at any time. The proposed mechanism and its performance parameters are described by a a mathematical model. In addition, the effect of the proposed mechanism on energy consumption behaviour is assessed. The discussion on the results shows the role of the consideration of reserves in the system.
Frequent Item Set, Sequential Pattern Mining and Sequence Prediction: Structures and Algorithms

The very basic objective of data mining and data analytics has been discovering hidden complex patterns in massive scale of data that cannot be processed by conventional statistical methodologies in the raw form. This leads to introduction of our field of research: mining of item sets and patterns from various forms of data. While talking about the versatility of data, in transaction databases, which mainly are used for worldwide E-commerce, product recommendation systems, ranking and indexing systems, search result, text analysis and recommendation system, it is a typical job for us to find the frequent, unopted and interesting item sets from the huge scale of transactions done by the customers or benefactors. This brings us to the study of frequent item set mining and recent developments of the same as high utility, fuzzy and uncertain item set mining. Where in several other kinds of researches like E-learning, bioinformatics, etc. One of the most important progressive trends is finding out patterns from sequential databases. This leads us to the adjacent topic: sequential pattern mining algorithms. The paper aims to illustrate and explicate the gradual developments on the mining of sequential patterns and transactional item sets, identify the functionalities and limitations of various algorithms and also enlighten about the recent studies and advancements in the field of research towards pattern mining and sequence prediction—which is the key point research sector of consumer behaviour analysis, stock market prediction, product tendency analysis, Genetic disease prediction, evolutionary computational biology, cognition analytics and many other most cultivated research field of today’s multidisciplinary world of science.
On Computational Hardness of Multidimensional Subtraction Games†

We study algorithmic complexity of solving subtraction games in a~fixed dimension with a finite difference set. We prove that there exists a game in this class such that any algorithm solving the game runs in exponential time. Also we prove an existence of a game in this class such that solving the game is PSPACE-hard. The results are based on the construction introduced by Larsson and Wastlund. It relates subtraction games and cellular automata.
Capturing Anomalies of Cassandra Performance with Increase in Data Volume: A NoSQL Analytical Approach

NoSQL database technology has been doing rounds since the early 1990s, but it was the exponential growth of internet and the rise of web applications that lead to a dynamic surge in the popularity of NoSQL databases. The BigTable research by Google (2006) and the Dynamo research by Amazon (2007) paved the way for databases which could develop with agility and operate at any scale. Cassandra and MongoDB have emerged as the two most widely used NoSQL database and hence either of the two is preferred depending on the data problem user is attempting to solve. This paper describes the underlying principles as well as the differences between both the databases. We focus on showing the anomaly in performance of Cassandra as the data volume increases and at the same time we compare its performance with that of MongoDB. We establish how important factor is data volume in choosing either of the databases for an application. Extensive experiments have been carried out to scale the performance in terms of anomaly similarities, and the future scope is pinpointed.
Artificial Intelligence and Common Sense: The Shady Future of AI

Tirumala, Sreenivas SremathArtificial Intelligence (AI) revolution has recorded more impact than any other revolutions in human history. AI systems have been more far ahead with the recent advances particularity with deep learning that attained state-of-the-art results in almost all Machine Learning (ML) tasks. However, AI systems are venerable but are highly vulnerable which demands timely human intervention. Further, AI systems can easily be tweaked and misguided to produce misleading results that are far from the reality. It is high time to address this susceptibility of AI since reliance on AI systems is keep growing exponentially. AI lacks the key aspect of human intelligence—common sense that guides humans to take better action and decision based on consequences and makes humans more adaptable. This missing aspect of AI inspired the researchers to work toward Artificial general Intelligence (AGI). AGI research involves developing AI systems with human-like consequential and conscious learning. This paper presents the theoretical and practical vulnerability of AI through literature, examples, experiments. The literature and examples concentrates on famous and popular AI systems like deep learning, Google Translate, visual cognition, etc. The experiments are carried out using two datasets; a gene expression dataset for prediction and image dataset for object detection and scene recognition. The experiment results reassert the weakness of AI and the requirement of AGI.