Reviewerly/exHarmony huggingface dataset:
The goal-setting of knowledge-intensive industries: decision support based on cognitive maps

The paper describes the features of the stage of goal-setting of the development of knowledge-intensive industries, which is associated with the identification of technological areas of advanced development. The decision support scheme is based on the regular use of the principles of e-expertise to solve the problems of analyzing the acceptability of a system of indicators of development programs and the formation of priorities and tasks of managing the creation of a scientific and technical reserve. Addition of basic multicriteria analysis technics with cognitive modelling helps to analyze a strategy of one industry in conjunction with other related direction of science-technical development. The paper consists of three parts: discussion of particularities of goal-setting tasks on the illustrative example, description of decision support scheme combining multicriteria evaluation method with an evaluation of influences on cognitive-map-based-model, an illustrative example description.
Insight into Adaptive Differential Evolution Variants with Unconventional Randomization Schemes

The focus of this work is the deeper insight into arising serious research questions connected with the growing popularity of combining metaheuristic algorithms and chaotic sequences showing quasi-periodic patterns. This paper reports an analysis of population dynamics by linking three elements like distribution of the results, population diversity, and differences between strategies of Differential Evolution (DE). Experiments utilize two frequently studied self-adaptive DE versions, which are simpler jDE and SHADE, further an original DE variant for comparisons, and totally ten chaos-driven quasi-random schemes for the indices selection in the DE. All important performance characteristics and population diversity are recorded and analyzed for the CEC 2015 benchmark set in 30D.
An Augmented Reality-Based Application Relying on the Use of Tangible User Interfaces for English Teaching to Children Between 10 and 12 Years

One of the main challenges associated to the foreign language teaching-learning process is lack of motivation from students. In the literature it is reported that it is possible to promote motivation towards such process through the realization of educational activities on familiar environments created by using animations and digital sounds. In this paper, the process that was followed for the implementation of an Augmented Reality-based application to support the development of the listening comprehension skill in children between 10- and 12-years learning English as foreign language is presented, considering educational content provided by certification institutions and technological elements. A preliminary assessment of interface prototypes by driving an experiment using the Wizard of Oz technique was also addressed.
Requirements Engineering for Large-Scale Big Data Applications

As the use of smartphone proliferates, and human interaction through social media is intensified around the globe, the amount of data available to process is greater than ever before. As consequence, the design and implementation of systems capable of handling such vast amounts of data in acceptable timescales has moved to the forefront of academic and industry-based research. This research represents a unique contribution to the field of software engineering for Big DataBig data in the form of an investigation of the big data architectures of three well-known real-world companies: Facebook, Twitter and Netflix. The purpose of this investigation is to gather significant non-functional requirements for real-world big dataBig data systems, with an aim to addressing these requirements in the design of our own unique reference architecture for big data processing in the cloud: MC-BDP (Multi-Cloud Big Data Processing). MC-BDP represents an evolution of the PaaS-BDP (Platform as a Service for Big Data Processing)Big data architectural pattern, previously developed by the authors. However, its presentation is not within the scope of this study. The scope of this comparative study is limited to the examination of academic papers, technical blogs, presentations, source code and documentation officially published by the companies under investigation. Ten non-functional requirements are identified and discussed in the context of these companies’ architectures: batchBatch data, streamStream data, late and out-of-order data, processing guaranteesProcessing guarantees , integration and extensibility, distribution and scalabilityScalability , cloud support and elasticity, fault tolerance, flow controlFlow control and flexibility and technology agnosticismTechnology agnosticism . They are followed by the conclusion and considerations for future work.
Feature Engineering Meets Deep Learning: A Case Study on Table Detection in Documents

Traditional computer vision approaches heavily relied on hand-crafted features for tasks such as visual object detection and recognition. The recent success of deep learning in automatically extracting representative and powerful features from images has brought a paradigm shift in this area. As a side effect, decades of research into hand-crafted features is considered outdated. In this paper, we present an approach for table detection in which we leverage a deep learning based table detection model with hand-crafted features from a classical table detection method. We demonstrate that by using a suitable encoding of hand-crafted features, the deep learning model is able to perform better at the detection task. Experiments on publicly available UNLV dataset show that the presented method achieves an accuracy comparable with the state-of-the-art deep learning methods without the need of extensive hyper-parameter tuning.
Aspects of Quantum Sensing for Aerospace Systems

Current technology trends indicate that quantum computers are on the threshold of broad adoption and usability across many industries. At present a number of quantum computers have been realized in hardware, typically with only a few qubits and with performance limitations including short coherence times, difficult coupling to input/output processing, and strong susceptibility to noise in the ambient environment. However, the separate but closely related field of quantum sensing shows much more near-term promise in respect of its applicability to broad classes of systems and missions, including many types of aerospace systems, sensors, and the processing and evaluation of their data. This paper reviews the basic methodology and types of quantum sensing, some history of related measurements and proof-of-concept experiments, and assesses the potential applicability of quantum sensing techniques to future aerospace sensors and systems. Performance metrics using published models and demonstration experiments show distinct performance advantages in several cases and are used to compare near-term quantum sensing to more conventional systems. In addition, the application of quantum communication to networked systems is discussed, along with the potential for novel data fusion techniques using quantum-inspired protocols and methods.
Protecting Facebook Password: Indonesian Users’ Motivation

Facebook is one of the social networking services that have users around 1.86 billion active users spread all over the world. To be able to enjoy various services from Facebook, a user is required to have a Facebook account. In the registration process to create a new Facebook account, the user is prompted to create a password to protect his account. The password policy service applied by Facebook requires all users to create and use a password for their Facebook account in accordance with the policies. This study aims to analyze user behavior with case study of Facebook account by using 12 construct variables adapted from Protection Motivation Theory (PMT). Data collected from Facebook users of 300 respondents. Data analysis method used is Structural Equation Modeling (SEM) analysis. From the research result, the factors that influence Facebook users’ intention to protect their account are perceived vulnerability, fear, response efficacy, response cost, subjective norm, prior experience with safety hazard, threat susceptibility and personal responsibility. Meanwhile, they ignore the threat sverity, coping self-efficacy and security support from others while protecting their Facebook account.
An Application for the Acquisition and Understanding of Expert Knowledge in a Maintenance Department

The knowledge of experienced and competent workers, or expert knowledge, so called, is an important element in the decision-making process in a maintenance department. Motivated by the need for collecting and storing a unique expert knowledge of maintenance, the Maintenance Business Processes Assignment Model, namely, the Eknow-Maintenance Model, was developed, by using the Group Method of Data Handling (GMDH). The Eknow-Maintenance Model includes: Expert Maintenance Identification (E1), Acceptance of Expert Maintenance using FAHP (E2), Expert Knowledge Acquisition (E3), Expert Knowledge Representation (E4) and Expert Knowledge Understanding using the GMDH method (E5). This model is implemented in the web-application.
Image Registration of Infrared and Visible Image Based on Geometric Constraint

Abstract With the wide application of infrared sensors and visible sensors, the registration of two heterogeneous images has caused widespread concern. Multi-angle comprehensive analysis of multi-sensor imaging systems can obtain more accurate image information. However, the main challenge of image registration is the detection of feature points and the acquisition of the correct feature point matching pairs. A Geometric constraint method is proposed to obtain the correct feature matching pairs. Three feature point matching pairs are randomly selected in the two images, and the ratio of the corresponding line segment lengths is obtained according to the coordinates of the three feature points to find the most three similar feature point matching pairs. Compared with the RANSAC algorithm, the method can obtain the correct feature point matching pair better. The algorithm runs on the DSP platform for about 30s, the error is 1 pixel, and the correct rate reaches 80%.
Investigating the Effect of the Cloud Computing on Education Internet of Things (Eiot)

Internet of things (IoT) has been applied widely in various industry, especially for manufacture industry, energy industry, transportation, and education. Its technology has brought transformations in various fields, educational technology. Education IoT (EIoT) is the innovation framework in the educational field, EIoT can be described as the interactive framework where numerous information is connected and synchronized by applying cloud computing, third-party technologies, gateway, and in a data communication. This study surveyed the EIoT-related studies from 2012 to 2019. It investigates the disciplinary distribution of emerging the EIoT-related topics. The findings of this study show that cloud computing, big data, e-learning, online services, and machine learning are the key issues driving researches, which are associated with EIoT. Recently, the application of cloud computing in the EIoT has attracted considerable attention from various researchers and professionals. Education as a Service (EaaS) is considered as one of the cloud-computing type. Additionally, this study investigates the present EaaS for achieving the goal of EIoT using the cloud machine learning module. The results demonstrated that the cloud-computing solution offers the appropriate tools needed for the users to analyze their learning profile data and build the intelligent educational strategy.
Benchmarking BNT Inference Engines using an Early Warning System

Bayesian Networks (BN) are systematic probabilistic reasoning models for visualization and inspection of interrelated data due to their ability to capture uncertain knowledge in an intuitive and efficient manner. They can be applied for various tasks such as decision making, reliability analysis, reasoning, etc. There are numerous toolboxes that support and simplify the usage of Bayesian Networks such as Bayes Net Toolbox (BNT) which is one of the most academically used and comprehensive BN toolbox. Inference algorithms supported by BNT differ in terms of performance. Therefore it is crucial to establish a criterion for selecting appropriate inference algorithms. The goal of this paper is to establish a benchmark of inference algorithms supported by BNT on a 49 node sized Early Warning System model.
Automatic Annotation of Text Classification Data Set in Specific Field Using Named Entity Recognition

Natural language processing has set off another development boom in the field of artificial intelligence. Intelligent question answering, machine translation, and dialogue systems are all important research contents in the field of natural language processing. Among them, text mining is an important branch in the field of natural language processing, including text classification, named entity recognition and other technologies. In actual research and engineering projects, obtaining a large number of reliable text data sets is the guarantee for these tasks. In the industrial field, the production of text data sets is usually done by manual sorting and labeling, but manual labeling consumes a lot of manpower and time, making the text data set not ideal owing to the different subjective thoughts of each person. Consequently, it can not meet the needs of the actual industrial field. This paper proposes an automatic annotation method based on the number of named entities for text datasets, which can quickly obtain a large number of text datasets in the field of terrorism, and ensure the smooth progress of research and engineering projects.
An Explicit Relationship Between Sequential Patterns and Their Concise Representations

Mining sequential patterns in a sequence database (SDB) is an important and useful data mining task. Most existing algorithms for performing this task directly mine the set $$ {\mathcal{F}\mathcal{S}} $$ of all frequent sequences in an SDB. However, these algorithms often exhibit poor performance on large SDBs due to the enormous search space and cardinality of $$ {\mathcal{F}\mathcal{S}} $$. In addition, constraint-based mining algorithms relying on this approach must read an SDB again when a constraint is changed by the user. To address this issue, this paper proposes a novel approach for generating $$ {\mathcal{F}\mathcal{S}} $$ from the two sets of frequent closed sequences $$ \left( {{\mathcal{F}\mathcal{C}\mathcal{S}}} \right) $$ and frequent generator sequences $$ ({\mathcal{F}\mathcal{G}\mathcal{S}}) $$, which are concise representations of $$ {\mathcal{F}\mathcal{S}} $$. The proposed approach is based on a novel explicit relationship between $$ {\mathcal{F}\mathcal{S}} $$ and these two sets. This relationship is the theoretical basis for a novel efficient algorithm named GFS-CR that directly enumerates $$ {\mathcal{F}\mathcal{S}} $$ from $$ {\mathcal{F}\mathcal{C}\mathcal{S}} $$ and $$ {\mathcal{F}\mathcal{G}\mathcal{S}} $$ rather than mining them from an SDB. Experimental results show that GFS-CR outperforms state-of-the-art algorithms in terms of runtime and scalability.
Representing and Querying Norm States Using Temporal Ontology-Based Data Access

The prominent role of normative primitives for regulating interactions in enterprise systems makes their representation and inspection a crucial task. In particular, querying the states of norms is essential to assess the accountability and compliance of interacting parties, as well as to reconstruct the historical evolution of their mutual contractual relationships. The main issue, however, is that the temporal data stored during the operation of the enterprise system are typically at a low level of abstraction, and do not directly refer to norms and their states. To overcome this problem, we propose the QUEN framework for querying norm states on top of legacy relational databases. QUEN builds on the temporal extension of the well-established framework of Ontology-Based Data Access (OBDA) to semantically represent normative primitives and their temporal states, and to map them to the underlying legacy data. The distinctive feature of QUEN is that mappings are not arbitrarily expressed by the modeler, but are instead automatically synthesized starting from a specification that explicitly indicates how the constitutive components of a normative primitive, as well as its lifecycle, can be reconstructed from the legacy data.
Tracking Changing Human Emotions from Facial Image Sequence by Landmark Triangulation: An Incircle-Circumcircle Duo Approach

Nasir, Md Dutta, Paramartha Nandi, AvishekIntelligent recognition of human emotions from face images is a challenging proposition in the field of affective computing which becomes even more difficult when one has to deal with characterizing the nature of transition of human emotion from a relevant sequence of face images. In the present scope, we considered a triangulation mechanism derived from the landmark points of the face images. Resulting in a number of triangle formulations which are found to be sensitive to different emotions like anger, disgust, fear, happiness, sadness, and surprise. Accordingly a pair of circles, viz, incircle and circumcircle corresponding to these triangles are taken into account and geometric features arising out of such pair are utilized for classification of different emotional transitions from various face image sequences. Results of the proposed method obtained by application on various benchmark image databases are found to be quite impressive and encouraging compared to existing state-of-the-art technique.
Comparison of Estimation Methods by Different Photovoltaic Software and Performance Evaluation

Purpose: Currently, various simulation programs around the world are being developed in connection with the analysis of photovoltaic systems. Accurate interpretation of simulation programs requires understanding of the differences and characteristics between the programs. Therefore, this study selects solar power analysis program used in Korea and analyzes its characteristics. Method: In this study, PVsyst, Solar Pro, and SAM are selected as the photovoltaic simulation program widely used in Korea, and the characteristics and differences of the program are analyzed. Although the analysis algorithms vary depending on the analysis method, this study aims to campare the solar radiation model of slope which is closely related to photovoltaic power generation, photovoltaic module modeling, and temperature model of solar cell which is closely related to power generation efficiency. In addition, the program analyzed the accuracy of the actual generation amount and the predicted value by analyzing the difference. Result: Each program differed in the accuracy implementation depending on the understanding of the system. In order to derive an accurate estimated value of the photovoltaic system, it is necessary to select a program suitable for the purpose of the estimation considering the characteristics of each program.
Knowledge graph using resource description framework and connectionist theory

Abstract Interest in Knowledge Graph has peeked these years. The use of RDF triples for the construction of knowledge graph has been limited by the fact that the vector embedding of RDF to make it machine readable has been constraining factor. This article presents the use of weighted RDF as a vector embedding of RDF that could be used with Bayesian networks in Graph Neural Networks. The vector embedding helps in representing the weights on RDF that could be obtained using Bayes theorem of assessing probability. The resulting weighted RDF could be used in many applications. The use of it in a Clinical Decision Support System is given as a simple illustration together with the calculation of the weights using Bayes theorem. The weighted RDF in Graph Neural Network will represent the knowledge graph using RDF and connectionist theory.
Novel Cost Function based Motion-planning Method for Robotic Manipulators

In this paper an offline motion-planning algorithm is presented for robotic manipulators.In this method to solve the path-planning task, the Transition-based Rapidly-exploring Random Tree (T-RRT) algorithm was applied, that requires a cost-function over the search space.The goal of this cost-function is to keep distance between the actual position and configurations that cause collisions.The paper presents two possible cost function generation methods.The first one is based on multidimensional Gauss functions and the second solution uses fuzzy function-approximation to determine the cost all over the configuration space.At last, the trajectory generator method will be introduced that calculates the desired shape of the function of joint variables and their derivatives.The algorithm is universal, the only restriction is that the manipulator and the environment are modelled by their bounding polyhedra.To demonstrate the presented approach, simulation were performed in MATLAB Simulink environment using the Mitsubishi RV-2F-Q robotic manipulator.
Top Position Sensitive Ordinal Relation Preserving Bitwise Weight for Image Retrieval

In recent years, binary coding methods have become increasingly popular for tasks of searching approximate nearest neighbors (ANNs). High-dimensional data can be quantized into binary codes to give an efficient similarity approximation via a Hamming distance. However, most of existing schemes consider the importance of each binary bit as the same and treat training samples at different positions equally, which causes many data pairs to share the same Hamming distance and a larger retrieval loss at the top position. To handle these problems, we propose a novel method dubbed by the top-position-sensitive ordinal-relation-preserving bitwise weight (TORBW) method. The core idea is to penalize data points without preserving an ordinal relation at the top position of a ranking list more than those at the bottom and assign different weight values to their binary bits according to the distribution of query data. Specifically, we design an iterative optimization mechanism to simultaneously learn binary codes and bitwise weights, which makes their learning processes related to each other. When the iterative procedure converges, the binary codes and bitwise weights are effectively adapted to each other. To reduce the training complexity, we relax the discrete constraints of both the binary codes and the indicator function. Furthermore, we pretrain a tensor ordinal graph to decrease the time consumption of computing a relative similarity relationship among data points. Experimental results on three large-scale ANN search benchmark datasets, i.e., SIFT1M, GIST1M, and Cifar10, show that the proposed TORBW method can achieve superior performance over state-of-the-art approaches.
Implementation of Essence Practice into Project Management System Redmine

In modern software engineering different software development processes serve as guidelines for solving standard set of problems in projects. But in the last 20 years the quantity of different software development processes and tendency of their authors to keep the distance from existing processes resulted in a great confusion in terms, rules and applications for project managers, software engineers and scientists. Considering that the SEMAT initiative set its goal to find the common ground for different development processes for business and academic representatives there is still no case of implementing their results to the existing software development systems. In this paper we present a solution for the implementation of SEMAT initiative to the Redmine software project management system.