Reviewerly/exHarmony huggingface dataset:
Mining Approximate Frequent Itemsets Using Pattern Growth Approach

Approximate frequent itemsets (AFI) mining from noisy databases are computationally more expensive than traditional frequent itemset mining. This is because the AFI mining algorithms generate large number of candidate itemsets. This article proposes an algorithm to mine AFIs using pattern growth approach. The major contribution of the proposed approach is it mines core patterns and examines approximate conditions of candidate AFIs directly with single phase and two full scans of database. Related algorithms apply Apriori-based candidate generation and test approach and require multiple phases to obtain complete AFIs. First phase generates core patterns, and second phase examines approximate conditions of core patterns. Specifically, the article proposes novel techniques that how to map transactions on approximate FP-tree, and how to mine AFIs from the conditional patterns of approximate FP-tree. The approximate FP-tree maps transactions on shared branches when the transactions share a similar set of items. This reduces the size of databases and helps to efficiently compute the approximate conditions of candidate itemsets. We compare the performance of our algorithm with the state of the art AFI mining algorithms on benchmark databases. The experiments are analyzed by comparing the processing time of algorithms and scalability of algorithms on varying database size and transaction length. The results show pattern growth approach mines AFIs in less processing time than related Apriori-based algorithms.
Enhancing Security in Digital Data using various Function of S-box in Data Encryption Standard Method

Stage of networking is quintessential task in which security comes into play. Securing these networks which contains confidential digital data that needs to secured will be the agenda of cryptography. Many cryptographic algorithms increment their strengths over parameters like key size, increasing the rounds of iteration and finally using confusion box as S-box as it has best robustness. So, this paper mainly focusses over securing digital data with the help of S-box function over Data Encryption Standard (DES) algorithm. For this, a plain text and key will be given to this DES as it extracts 8x8(64) bit characters from the key and converting them into its corresponding ASCII value and are concatenating to form an 8 value by taking mod16. These will give to 8 S-box in order to generate its corresponding output to make even more secure and also shows dynamic DES gives much result than other crypto methods. The evaluation of this integrated s-box and DES shows much fruitful results over factors like non-linearity, Avalanche criterion, Balance, Robustness to linear cryptanalysis, Robustness to differential cryptanalysis.
Are we ready? Identifying the gap between academia and software industry in the context of agile methodologies

Nowadays, there is an exponential increase in the technology industry. However, there is not enough movement to promote changes in the Computer Science curricula. This study aims to analyze the alignment between Brazilian northeast academia and the global industry regarding developing skills in the context of agile methods. This research conducted an exploratory and quantitative survey with 161 participants represented by 65 students, 85 professionals from 10 countries, and 16 university professors. The preliminary results illustrate that academics believe that they are moderately aligned with the market. However, from the practitioner's viewpoint, it is unsatisfactory. This article reports relevant findings that can help the Brazilian academy align its practices with the needs of the global software industry.
Application of the Scrum Framework on Projects in IT Sector

The aim of this paper is to reveal, examine and study the most important specifics of the Scrum framework application from a theoretical and practical aspect.The first section explains the basics of the agile approach, i.e. agile values and principles, with the idea of creating a starting point for understanding the Scrum framework and the environment in which it originated.Furthermore, the focus shifts to defining the role, significance and structure of the Scrum framework, with special emphasis on presenting Scrum roles, Scrum events and Scrum artefacts as completely new categories in project management unique to the Scrum framework.Starting with the importance and responsibility of each Scrum team member, followed by the duration, proper sequence and purpose of Scrum events, as well as by the creation, updating and refinement of Scrum artefacts, the Scrum framework is presented to potential users so its application can be better, easier and simpler both within the projects in the IT sector and in other sectors where the application of the Scrum framework is suitable.The results of research on the application of Scrum framework and agile approach within projects regarding the development of new software solutions in the Republic of Serbia are presented at the end of the paper, together with the conclusions concerning the prospects for further development and wider application of the Scrum framework in projects, regardless of their type, complexity, or the field in which they are realised.
A Probabilistic Re-Intepretation of Confidence Scores in Multi-Exit Models

In this paper, we propose a new approach to train a deep neural network with multiple intermediate auxiliary classifiers, branching from it. These 'multi-exits' models can be used to reduce the inference time by performing early exit on the intermediate branches, if the confidence of the prediction is higher than a threshold. They rely on the assumption that not all the samples require the same amount of processing to yield a good prediction. In this paper, we propose a way to train jointly all the branches of a multi-exit model without hyper-parameters, by weighting the predictions from each branch with a trained confidence score. Each confidence score is an approximation of the real one produced by the branch, and it is calculated and regularized while training the rest of the model. We evaluate our proposal on a set of image classification benchmarks, using different neural models and early-exit stopping criteria.
The Problem of Matter of Education in the Digital Age

The aim of the article is to philosophically address and expand the contemporary discourse of the increasingly digitalized education by examining the problem of matter of education and its conceptualizations in this context. With the digital transformation of education a significant change occurs not only in terms of what is considered to be a matter of education, but also in the very principles of interaction between the subjects of education. In order to do this, in the first section several examples of conceptual justifications of applying digital technologies in education are examined with the task of demonstrating how in such justifications the activity of education is reduced to the process of learning and what risks such instrumental interpretation engenders. Then, these issues are analyzed and contextualized utilizing the material of the philosophical discussion of the 20th century - the critique of the progressive education in the analytic philosophy of education. By outlining the relevance of the arguments of discussion on manner/matter to realities of digital education, the issue of the dominance of the logic of learning in current education is analyzed. In the last section several adjustments to the contemporary discourse on digitalization of education are proposed, aimed at finding a balance between individual and social dimensions of the problem of matter of education, given the current tendency of mass personalization in education. Particularly, the importance of the idea of education as a common good in the contemporary educational discourse is claimed.
Promotion of Purbalingga Tourism Object Using Augmented Reality Location Based Service &amp; Virtual Reality 360° Based on Android

One of the technologies that can be used as an attraction in Purbalingga Regency is Augmented Reality (AR) merging virtual objects with real objects, where those objects can be seen in real-time. And using the Location-Based Service (LBS) method, this method is a service with the main parameters being the position and location of the user. LBS Location Based Service will be enhanced with Google Maps and GPS (Global Positioning System) to find its user position and information about several locations to be visited. Virtual Reality (VR) brings users can interact in a virtual world environment simulated by a computer, so that users feel they are inside. The appearance of the Image object using 3600 panoramic so that it can display images indefinitely and not cut and can look around the image. Application creation is done by Multimedia Development Life Cycle (MDLC) and Unity3D. The application has been successfully operated on the Android operating system with minimum specifications of 2GB RAM and KitKat version 4.4 OS. The results of the study have been conducted through a questionnaire to 15 respondents with 15 questions earned an The total value of the frequency of each question gets a score of 1,009 or a percentage of eligibility of 89,68% starting that the application is well worth using. Based on the black-box test, the application has been running properly and can display Location Based Service and 3600 panoramic and its information.
Regularity Normalization: Neuroscience-Inspired Unsupervised Attention across Neural Network Layers

Inspired by the adaptation phenomenon of neuronal firing, we propose the regularity normalization (RN) as an unsupervised attention mechanism (UAM) which computes the statistical regularity in the implicit space of neural networks under the Minimum Description Length (MDL) principle. Treating the neural network optimization process as a partially observable model selection problem, the regularity normalization constrains the implicit space by a normalization factor, the universal code length. We compute this universal code incrementally across neural network layers and demonstrate the flexibility to include data priors such as top-down attention and other oracle information. Empirically, our approach outperforms existing normalization methods in tackling limited, imbalanced and non-stationary input distribution in image classification, classic control, procedurally-generated reinforcement learning, generative modeling, handwriting generation and question answering tasks with various neural network architectures. Lastly, the unsupervised attention mechanisms is a useful probing tool for neural networks by tracking the dependency and critical learning stages across layers and recurrent time steps of deep networks.
A Three-Tier Authentication Scheme for Kerberized Hadoop Environment

Abstract Apache Hadoop answers the quest of handling Bigdata for most organizations. It offers distributed storage and data analysis via Hadoop Distributed File System (HDFS) and Map-Reduce frameworks. Hadoop depends on third-party security providers like Kerberos for its security requirements. Kerberos by itself comes with many security loopholes like Single point of Failure (SoF), Dictionary Attacks, Time Synchronization and Insider Attacks. This paper suggests a solution that aims to eradicate the security issues in the Hadoop Cluster with a focus on Dictionary Attacks and Single Point of Failure. The scheme roots on Secure Remote Password Protocol, Blockchain Technology and Threshold Cryptography. Practical Byzantine Fault Tolerance mechanism (PBFT) is deployed at the blockchain as the consensus mechanism. The proposed scheme outperforms many of the existing schemes in terms of computational overhead and storage requirements without compromising the security level offered by the system. Riverbed Modeller (AE) Simulation results strengthen the aforesaid claims.
COVID-19 Information Sharing with Blockchain

COVID-19 is a disease caused by a novel coronavirus originated in Wuhan, China. The virus rapidly spread over more than 200 countries around the world and caused deaths of more than 690.000 of people. To prevent rapid spreading of this disease, the information sharing related to the findings about the COVID-19 disease must be fast and secure between countries. Since the COVID-19 related health data such as the symptoms and private patient records are confidential, such information requires privacy protection. The blockchain and smart contracts are well-suited solutions for speed, privacy, and security needs of dissemination the COVID-19 related information. Blockchain based e-health solutions have been discussed for years. However, a pandemic is more important than the regular health problems. Thus, this study proposes how critical pandemic related information should be shared between the participating countries and can be accessed by health data actors such as researchers, doctors, laboratory staff, authorized institutions of different countries as well as the World Health Organization.
The Mediating Roles of GDP on the Relationship Between Income Inequality and Life Expectancy

This study examined the mediation effect of gross domestic product (GDP) on the relationship between life expectancy and income inequality based on data from 23 Organisation for Economic Co-operation and Development (OECD) countries for 2004 through 2014. To form a theoretical framework, Preston curve and Kuznets curve were employed. The study set up a mediation model with life expectancy as an outcome variable, GDP as a mediator variable, and three variables characterising income inequality as predictor variables: Gini index, income share held by highest 20%, and poverty headcount ratio at US$1.90 a day. The study found that GDP clearly mediates the effects of the predictor variables on life expectancy, although the magnitudes of the effects vary. This study takes an important initial step in exploring the mediation effect of GDP on the relationship between life expectancy and income inequality.
Intersemiotic translation from fairy tale to sculpture: An exploration of secondary narrativity

We present a cognitive semiotic case study of the narrative potential of the statue Den lille Havfrue (‘the little mermaid’) by Edvard Eriksen in Copenhagen. On the basis of theoretical analysis and a survey in which 20 European and 19 Chinese participants replied to questions concerning this statue we argue that it, and similar statues, may be considered as products of intersemiotic translation, but only if we dispense with any requirements of “equivalence” between source and target, since statues are necessarily semiotically highly reduced. While the source narratives constitute cases of primary narrativity, with narrations providing the audiences with stories, statues may partake only of secondary narrativity, where a prior story is needed for the statue to be understood as narration. In our study, this was reflected by correlations between reported prior knowledge and narrative (and possibly even non-narrative) interpretations of Den lille Havfrue. Finally, we relate the discussion to present-day cultural and political “settings”, where many statues, including Den lille Havfrue, have become part of a global anti-racism narrative.
Educational research on mathematics differential equation to simulate the model of children's mental health prevention and control system

Abstract In order to solve the practicality of the infant mental health tracking system, the system is simulated based on mathematical differential equations. The SSH framework technology of the mode-view-controller (MVC) design model analyses and designs the model of the prevention and control system of children's mental health. In this design pattern, the Struts framework is used to realise the separation of the view layer, the business layer and the control layer, and the Hibernate framework is used to realise the data persistence layer, thereby obtaining a system solution with strong scalability, low coupling and easy maintenance.
A hybrid model for prediction of software effort based on team size

Most of the software development organisations frequently use an appreciable amount of resources to estimate the effort in the beginning of the development process. In most of the cases, inaccurate estimates tend to wastage of these resources. Very few generalised models have been found in the literature. These models have been developed using the prototype dataset of the organisation. The project management team of an organisation tries to predict the effort needed for the development of software using various mathematical techniques. These techniques are mostly based on statistical methods (viz. simple linear regression (SLR), multi linear regression, support vector machine, cascade correlation neural network (CCNN) etc.) and some probability-based models. They use historical data of similar projects. The work presented in this article envisages the use of Support Vector Regression (SVR) and constructive cost model (COCOMO), where SVR can be used for both linear and non-linear models and COCOMO can be used as a regression model. The proposed hybrid model has been tested on the International Software Benchmarking Standards Group dataset. The data has been grouped according to the size of man power. It has been found that the proposed model yields better results than the SVR or SLR for each group of data in general.
Is Bitcoin Price Driven by Macro-financial Factors and Liquidity? A Global Consumer Survey Empirical Study

&#x0D; &#x0D; &#x0D; This research aims to utilize macro-financial and liquidity elements as the factors that may affect the price of Bitcoin as the largest cryptocurrency in terms of market capitalization. The macro-financial factors analyzed in this study were foreign exchange, stock market index, interest rates, and gold, while liquidity ratio is the internal factor. This study applied a fixed-effect model (FEM) and Generalized Method of Moments (GMM) on gathered weekly data from 1 January 2017 to 29 December 2019 from 18 countries with the total of 2,826 observations. The analysis revealed that US Dollar amplifies Bitcoin trading; an increase in interest rate will decrease investors’ intention to invest in Bitcoin as a speculative asset, and gold could replace Bitcoin as a substitute asset. Moreover, Bitcoin was found to be highly liquid, which attracts many investors, while the stock market index proved to be insignificant.&#x0D; &#x0D; &#x0D;
3D corrective nose reconstruction from a single image

Abstract There is a steadily growing range of applications that can benefit from facial reconstruction techniques, leading to an increasing demand for reconstruction of high-quality 3D face models. While it is an important expressive part of the human face, the nose has received less attention than other expressive regions in the face reconstruction literature. When applying existing reconstruction methods to facial images, the reconstructed nose models are often inconsistent with the desired shape and expression. In this paper, we propose a coarse-to-fine 3D nose reconstruction and correction pipeline to build a nose model from a single image, where 3D and 2D nose curve correspondences are adaptively updated and refined. We first correct the reconstruction result coarsely using constraints of 3D-2D sparse landmark correspondences, and then heuristically update a dense 3D-2D curve correspondence based on the coarsely corrected result. A final refinement step is performed to correct the shape based on the updated 3D-2D dense curve constraints. Experimental results show the advantages of our method for 3D nose reconstruction over existing methods.
Reduction of prediction error sensitivity to parameters in Kalman filter

The desensitized Kalman filter Karlgaard and Shen (2013)[1] is a practical and intuitive robust filtering method. However, a thorough analysis of its stability and impact of assumptions is missing. This paper expands the theory of desensitized Kalman filtering by proposing a stochastic approach to reduce estimation error sensitivity to parameters. The novel approach leads to the exact desensitized Kalman filter that does not neglect the gain sensitivity to a parameter. The suboptimal form equivalent to the original desensitized Kalman filter in a special form is proposed. The stability analysis and the definition of stability conditions are possible due to the proposed form that can be interpreted as the Kalman filter with correlated process and measurement noise with time-variant statistics. Furthermore, adaptive normalization of objectives is introduced, which improves the desensitizing performance.
Privacy Personas for IoT-Based Health Research: A Privacy Calculus Approach

The reliance on data donation from citizens as a driver for research, known as citizen science, has accelerated during the Sars-Cov-2 pandemic. An important enabler of this is Internet of Things (IoT) devices, such as mobile phones and wearable devices, that allow continuous data collection and convenient sharing. However, potentially sensitive health data raises privacy and security concerns for citizens, which research institutions and industries must consider. In e-commerce or social network studies of citizen science, a privacy calculus related to user perceptions is commonly developed, capturing the information disclosure intent of the participants. In this study, we develop a privacy calculus model adapted for IoT-based health research using citizen science for user engagement and data collection. Based on an online survey with 85 participants, we make use of the privacy calculus to analyse the respondents' perceptions. The emerging privacy personas are clustered and compared with previous research, resulting in three distinct personas which can be used by designers and technologists who are responsible for developing suitable forms of data collection. These are the 1) Citizen Science Optimist, the 2) Selective Data Donor, and the 3) Health Data Controller. Together with our privacy calculus for citizen science based digital health research, the three privacy personas are the main contributions of this study.
Spectral Clustering of Mixed-Type Data

Cluster analysis seeks to assign objects with similar characteristics into groups called clusters so that objects within a group are similar to each other and dissimilar to objects in other groups. Spectral clustering has been shown to perform well in different scenarios on continuous data: it can detect convex and non-convex clusters, and can detect overlapping clusters. However, the constraint on continuous data can be limiting in real applications where data are often of mixed-type, i.e., data that contains both continuous and categorical features. This paper looks at extending spectral clustering to mixed-type data. The new method replaces the Euclidean-based similarity distance used in conventional spectral clustering with different dissimilarity measures for continuous and categorical variables. A global dissimilarity measure is than computed using a weighted sum, and a Gaussian kernel is used to convert the dissimilarity matrix into a similarity matrix. The new method includes an automatic tuning of the variable weight and kernel parameter. The performance of spectral clustering in different scenarios is compared with that of two state-of-the-art mixed-type data clustering methods, k-prototypes and KAMILA, using several simulated and real data sets.