Reviewerly/exHarmony huggingface dataset:
Towards a Toolbox for Intercultural User Interface Design

In this paper, a method-mix (cultural dimensions, intercultural variables, user interface characteristics and human computer interaction (HCI) dimensions) for intercultural user interface design (IUID) is presented. Based on a hybrid approach covering cultural contexts in human–computer interaction (HCI) design using a model of culturally influenced HCI, this IUID method-mix represents the main constituent of a reasonable toolbox for IUID. The IUID method-mix is exemplified by application examples to demonstrate and discuss its benefit and limitations. The examples elucidate why and how cultural aspects play a role in HCI design and usability/UX engineering. Cultural influence on HCI is described using cultural variables for user interface design. The IUID method-mix serves to inspire HCI engineers in the requirement analysis phase as well as HCI designers in the design phase. The readers are sensitized to the challenges of intercultural usability/UX engineering and intercultural HCI design and will be equipped with relevant methodological knowledge needed to actively derive design recommendations for user interface design for and in their desired cultural contexts. Practitioners can prognosticate the ensuing effort and the expenditure for considering cultural context in intercultural user interface design.
Forensic Analysis of Heterogeneous Social Media Data

It is a challenge to aggregate and analyze data from heterogeneous social media sources not only for businesses and organizations but also for Law Enforcement Agencies. The latter’s core objectives are to monitor criminal and terrorist related activities and to identify the ”key players” in various networks. In this paper, a framework for homogenizing and exploiting data from multiple sources is presented. Moreover, as part of the framework, an ontology that reflects today’s social media perceptions is introduced. Data from multiple sources is transformed into a labeled property graph and stored in a graph database in a homogenized way based on the proposed ontology. The result is a cross-source analysis system where end-users can explore different scenarios and draw conclusions through a library of predefined query placeholders that focus on forensic investigation. The framework is evaluated on the Stormfront dataset, a radical right, web community. Finally, the benefits of applying the proposed framework to discover and visualize the relationships between the Stormfront profiles are presented.
Use of Ontologies in Chemical Kinetic Database CHEMCONNECT

CHEMCONNECT is an ontology cloud-based repository of experimental, theoretical and computational data for the experimental sciences domain that support the FAIR data principle, namely that data is findable, accessible, interoperable and re-usable. The design also promotes the good scientific practices of accountability, traceability and reproducibility. The key to meeting these design goals is the use of ontologies. The primary goals of using ontologies include not only capturing a domain specific knowledge base (with support of domain experts), but also to create a data/ontology driven software system for the data objects, data entry, the database and the graphical interface. The impetus within (combustion research) domain, which is the initial focus of CHEMCONNECT, of the knowledge base is formation and documentation of standard data reporting practices. The ontology is a software technical implementation of practices within the community. Storing and querying of specific instantiations of object data is done using a NOSQL database (Google datastore). This initial design of CHEMCONNECT is modelled for the chemical kinetics and combustion domain. Within this domain, the ontology defines templates of typical experimental devices producing data, algorithms and protocols manipulating data and the data forms that are encountered in this pipeline. These templates are then instantiated, with the aid of ontology driven cloud-based interface, to specific objects within the database. The knowledge base is key to uniting data input in various forms (including diverse labelling) to a common base for ease of search and comparison. The structure is not limited to this domain and will be expanded in future collaborative work. CHEMCONNECT is currently implemented with the Google App Engine at http:www.connectedsmartdata.info.
Development of a Gestational Diabetes Computer Interpretable Guideline using Semantic Web Technologies

The benefits of following Clinical Practice Guidelines (CPGs) in the daily practice of medicine have been widely studied, being a powerful method for standardization and improvement of medical care quality. However, applying these guidelines to promote evidence-based and up-to-date clinical practice is a known challenge due to the lack of digitalization of clinical guidelines. In order to overcome this issue, the use of Clinical Decision Support Systems (CDSS) has been promoted in clinical centres. Nevertheless, CPGs must be formalized in a computer interpretable way to be implemented within CDSS. Moreover, these systems are usually developed and implemented using local setups, and hence local terminologies, which causes lack of semantic interoperability. In this context, the implementation of Semantic Web Technologies (SWTs) to formalize the concepts used in guidelines promotes the interoperability and standardization of those systems. In this paper, an architecture that allows the formalization of CPGs into Computer Interpretable Guidelines (CIGs) supported by an ontology in the gestational diabetes domain is presented. This CIG has been implemented within a CDSS and a mobile application has been developed for guiding patients based on up-to-date evidence based clinical guidelines.
“New Literacy” As A Category Of Modern Western Education

The article explores the concepts of “new literacy”, “multimodal literacy” and “media literacy”, which are widespread in theoretical and applied studies conducted by Western teachers. The paper also looks at practical media technologies, which are the main tool for working with texts in the Western secondary education. The philosophy of using the methodology of new digital literacy in modern education is based on developing media literacy among school students as a socially-significant phenomenon and increases their chances of successful performance at school. Within the framework of Western studies, students are considered to be viewers rather than readers. In this connection, there is a necessity for new multimodal technologies intended to develop students’ reading culture. Multimedia technologies are viewed as computer technologies that provide integration of text, sound, photos, videos, pictures into a single unit. The new trend in developing multimedia literacy in the West is the use of interactive software. Web 2.0 tools allow modern school students to interpret texts they study in a multimedia environment more widely. Multimodal technologies fulfill the task set in the 21st century: studying with the help of the media and studying how to work with the media. According to Western studies, education with the help of multimodal media broadens the concept of text in general and the concept of literary text in particular and includes all types of messages in this concept: verbal, audial or visual (or all of them at once) in order to produce and transfer ideas from one person to another.
Applying Feature Selection to Rule Evolution for Dynamic Flexible Job Shop Scheduling

Dynamic flexible job shop scheduling is an optimization problem concerned with job assignment in dynamic production environments where future job arrivals are unknown. Job scheduling systems employ a pair of rules: a routing rule which assigns a machine to process an operation and a sequencing rule which determines the order of operation processing. Since hand-crafted rules can be time and effort-consuming, many papers employ genetic programming to generate optimum rule trees from a set of terminals and operators. Since the terminal set can be large, the search space can be huge and inefficient to explore. Feature selection techniques can reduce the terminal set size without discarding important information and they have shown to be effective for improving rule generation for dynamic job shop scheduling. In this paper, we extend a niching-based feature selection technique to fit the requirements of dynamic flexible job shop scheduling. The results show that our method can generate rules that achieves significantly better performance compared to ones generated from the full feature set.
A Survey on Refinement in Formal Methods and Software Engineering

In software engineering, formal methods allow the design, modelling and verification of hardware and software systems.Formal methods introduce preciseness, remove ambiguity in specifications, and support the verification of requirements and design properties.Methods and approaches are needed to manage the formal models and handle their complexity.Refinement has been carried out for system artefacts ranging from modelling and design levels like architectures, and state machines to implementation and programming levels like source code.Refinement is a significant way for building complicated systems starting from simple ones by adding features gradually.Refinement has to be understood carefully in the context of formal specification and verification.This article provides a survey on some refinement techniques and methods and in the context of formal methods and software engineering.We believe that this survey sheds a light on the research direction in regards to the refinement of formal methods.This survey also helps formal methods practitioners and users in observing and understanding the advantages and limitations of refinements methods and techniques of various studied formal methods.Accordingly, they can decide which formal method is to be used in modelling systems via refinement or which formal method is to be extended with new concepts and notions to support refinement.
Run-time Detection and Mitigation of Power-Noise Viruses

Power-noise viruses can be used as denial-of-service attacks by causing voltage emergencies in multi-core microprocessors that may lead to data corruptions and system crashes. In this paper, we present a run-time system for detecting and mitigating power-noise viruses. We present voltage noise data from a power-noise virus and benchmarks collected from an Arm multi-core processor, and we observe that the frequency of voltage emergencies is dramatically increasing during the execution of power-noise attacks. Based on this observation, we propose a regression model that allows for a run-time estimation of the severity of voltage emergencies by monitoring the frequency of voltage emergencies and the operating frequency of the microprocessor. For mitigating the problem, during the execution of critical tasks that require protection, we propose a system which periodically evaluates the severity of voltage emergencies and adapts its operating frequency in order to honour a predefined severity constraint. We demonstrate the efficacy of the proposed run-time system.
An Index for Sequencing Reads Based on the Colored de Bruijn Graph

In this article, we show how to transform a colored de Bruijn graph (dBG) into a practical index for processing massive sets of sequencing reads. Similar to previous works, we encode an instance of a colored dBG of the set using BOSS and a color matrix C. To reduce the space requirements, we devise an algorithm that produces a smaller and more sparse version of C. The novelties in this algorithm are (i) an incomplete coloring of the graph and (ii) a greedy coloring approach that tries to reuse the same colors for different strings when possible. We also propose two algorithms that work on top of the index; one is for reconstructing reads, and the other is for contig assembly. Experimental results show that our data structure uses about half the space of the plain representation of the set (1 Byte per DNA symbol) and that more than 99% of the reads can be reconstructed just from the index.
Knowledge Management and Its Impact on Organizational Performance in the Private Sector in India

The study proposes a comprehensive model comprising of various relationships between antecedents to effective Knowledge Management (KM) and organizational performance. A review of literature besides a focus group discussion and a personal interview were used to design an instrument and propose seven hypotheses. Data was collected from 127 managers working in private sector organizations in India. To test the hypotheses, Structural Equation Modelling (SEM) analysis through Partial Least Squares (PLS) was used. The results indicate that although all the hypotheses had the desired positive sign, five out of them were significant. This paper presents empirical evidence of the role of KM planning and design (KMPD), KM implementation and evaluation (KMIE), Technology in KM (TKM), Culture in KM (CKM), Leadership in KM (LKM) and Structure in KM (SKM) in enhancing organizational performance. Further, improvements in organizational performance leads to improvements in financial performance.
Ontological Integration of Semantics and Domain Knowledge in Energy Scenario Co-simulation

The transition of the power system to more decentralized power plants and intelligent devices in a smart grid leads to a significant rise in complexity. For testing new technologies before their implementation in the field co-simulation is an important approach, which allows to couple diverse simulation models from different domains. In the planning and evaluation of co-simulation scenarios experts from different domains have to collaborate. To assist the stakeholder in this process, we propose to integrate on the one hand semantics of simulation models and exchanged data and on the other hand domain knowledge in the planning, execution, and evaluation of interdisciplinary co-simulation based on ontologies. This approach aims to allow the high-level planning of simulation and the seamless integration of its information to simulation scenario specification, execution and evaluation. Thus, our approach intents to improve the usability of large-scale interdisciplinary co-simulation scenarios.
Polynomial-Delay Enumeration of Maximal Common Subsequences

A Maximal Common Subsequence (MCS) between two strings X and Y is an inclusion-maximal subsequence of both X and Y. MCSs are a natural generalization of the classical concept of Longest Common Subsequence (LCS), which can be seen as a longest MCS. We study the problem of efficiently listing all the distinct MCSs between two strings. As discussed in the paper, this problem is algorithmically challenging as the same MCS cannot be listed multiple times: for example, dynamic programming [Fraser et al., CPM 1998] incurs in an exponential waste of time, and a recent algorithm for finding an MCS [Sakai, CPM 2018] does not seem to immediately extend to listing. We follow an alternative and novel graph-based approach, proposing the first output-sensitive algorithm for this problem: it takes polynomial time in n per MCS found, where $$n = \max \{ |X|, |Y|\}$$ , with polynomial preprocessing time and space.
Unsupervised Dimension Reduction Using Supervised Orthogonal Discriminant Projection for Clustering

This paper proposes a novel unsupervised dimensionality reduction method for clustering by combining supervised orthogonal discriminant projection (SODP) and K-means selective clustering ensemble, called SODP-KSCE. The novel algorithm, operating in an iterative manner, adaptively optimizes the clustering results and learns a subspace with optimal separation. To enhance the stability of K-means, SODP-KSCE adopts ensemble learning. Moreover, a negentropy increment (NI) index is introduced to measure the clustering performance. The K means clustering ensemble algorithm is performed in the low-dimensional subspaces to generate pseudo class labels for unlabeled data, which are then adopted to guide the dimension reduction process of SODP in the original space. Experimental results on multiple data sets indicate the effectiveness of SODPKSCE.
Productivity and Economic Growth by Using Business Management Based on the E-marketing: Evidence from Developed and Developing Countries, 2000-2018

The paper presents a comparative analysis of the growth rates decomposition in 20 countries for the period from 2000 to 2018. In the research the authors used regression modeling based on the aggregate production function presented as multiplicative power Cobb-Douglas function. Having performed the regression analysis, the authors obtained numerical estimates of the model parameters for all analyzed countries: GDP elasticities of the considered countries by extensive factors - capital and labor, as well as by the impact of the intensive factor - TFP growth rates that present the dynamics of the resulting variable (GDP) at an acceptable level of significance. The chosen countries have been divided into clusters depending on the impact level of the analyzed factors onto the economic growth rate. The authors established the significant limitation of the neoclassical approach to analyzing the causes of economic growth due to using production functions with an aggregated result and the idea of the constant product specialization of a country. It was proposed to take into account the production structure of national economies when analyzing the pace and scale of economic development.
Video steganography with steganalysis

This paper presents steganographic algorithm, for hiding secret text in raw video files. The implementing and extraction method are described and empirical tests are performed to evaluate the security of the proposed algorithm. The steganographic analysis includes files comparison, histogram analysis, Peak to signal noise ratio and Chi-square analysis.
HoT: Unleash Web Views with Higher-order Templates

Over the past decades, templates views have been the most used approach to build dynamic HTML pages. Simply put, a template engine (such as JSP, Handlebars, Thymleaf, and others) generates HTML by merging templates with given data models. Yet, this process may turn impractical for large data sets that postpone the HTML resolution until all data become available to the engine. This behavior results in poor user experience preventing the browser to render the end user-interface. In this paper we introduced the concept of higher-order templates (HoT) provided in Java implementation of HtmlFlow, which allows HTML to be resolved on demand as data becomes available. This lets the user-interface to be rendered incrementally by the browser in line with the availability of the data. Finally we also show some advantages of HtmlFlow over state of the art front-end frameworks such as ReactJS.
A Multi-Scale Neural Network for Traffic Sign Detection Based on Pyramid Feature Maps

In the actual traffic environment, it is highly susceptible to interference from external factors such as illumination intensity and deformation of traffic signs, resulting in low detection rate. Particularly, traditional neural network only relies on features of top layer for recognition, which leads to a consequence that spatial information and edge pixels are easily lost. Aiming at the disadvantages, we combine the high-resolution pixels of shallow convolutional layers with the semantically strong features of deep convolutional layers, and propose an end-to-end feature pyramid framework to construct the complete semantic information about the target. In the optimization phase, ROI Align, soft-NMS and an improved weighted cross entropy loss function are applied to deal with the problem of the limited target pixels and unbalanced distribution of categories. Experiments on GTSDB dataset illustrate the performance of the proposed algorithm has achieved significant improvements with good generalization and stability.
A Sequential Heteroscedastic Probabilistic Neural Network for Online Classification

In this paper, a novel online classification algorithm called sequential heteroscedastic probabilistic neural network (SHPNN) is proposed. This algorithm is based on Probabilistic Neural Networks (PNNs). One of the advantages of the proposed algorithm is that it can increase the number of its hidden node kernels adaptively to match the complexity of the data. The performance of this network is analyzed for a number of standard datasets. The results suggest that the accuracy of this algorithm is on par with other state of the art online classification algorithms while being significantly faster in majority of cases.
The Improved Joint Bayesian Method for Person Re-identification Across Different Camera

Due to the view point, illumination, personal gait and other background situation, person re-identification across cameras has been a challenging task in video surveillance area. In order to address the problem, a novel method called Joint Bayesian across different cameras for person re-identification (JBR) is proposed. Motivated by the superior measurement ability of Joint Bayesian, a set of Joint Bayesian matrices is obtained by learning with different camera pairs. With the global Joint Bayesian matrix, the proposed method combines the characteristics of multi-camera shooting and person re-identification. Then this method can improve the calculation precision of the similarity between two individuals by learning the transition between two cameras. For investigating the proposed method, it is implemented on two compare large-scale re-ID datasets, the Market-1501 and DukeMTMC-reID. The RANK-1 accuracy significantly increases about 3% and 4%, and the maximum a posterior (MAP) improves about 1% and 4%, respectively.
Segmentation of kidney lesions with attention model based on Deeplab

We participate this challenge by developing a hierarchical framework.We build the model from two fully convolutional networks:(1) a simple Unet model to normalize the input iamges, (2) a segmentaion network which is an attention model based on Deeplab model.Two models are connected in tandem and trained end-to-end.To ensure a better results, we use the preprocess method proposed by nnUnet in our experiments.
Pengembangan E-Learning Menggunakan Portal Pembelajaran Mahasiswa pada Mata Kuliah Konsep Dasar IPA 2 di Era Disruption

In the world of education today must also be changed according to the development of science and technology for example in the E-learning education. Because with E-learning the delivery of information content is not only an educational goal, but with media E-learning, learning activities can be done anywhere at any time without being limited by space and time. But in the reality, on the courses of science basic concept 2, students conduct learning activities only in classrooms with considerable time and with the same activities. So that students tend to be bored. Based on these explanations, the purpose of this study was to develop E-learning (valid, practice and effective) using student learning portals in the science basic concepts 2 in the disruption era. The type model of development research in this development research is the modification of the Borg &amp; Gall and Lee &amp; Owen models with the stages of research and data collection, planning, product development, design validation, design revision, product testing, product revision, and usage testing . Based on the results of product testing, E-learning media using student learning portals on the courses of science basic consep 2 in the disruption era according to substance experts obtained a percentage validity score of 88%, 89% media experts, and 86% linguists with valid categories. Based on students' practicality response, this product obtained a score of 80% based on the user's practicality score of 90%. While the effectiveness of this product reaches 80% in the effective category. Based on these data it can be concluded that E-learning media is valid, practical, and effective. So that E-learning media using the student learning portal in the courses science basic concept 2 in the disruption era is feasible to use.