Reviewerly/exHarmony huggingface dataset:
Social network information analysis for extraction of concept success rate within a geographic location

Social networks have been the main platform in the past decade for interaction and daily activities updates amongst civilians. The amount of shared data grows every second, as a result of wide information propagation. This strongly depends of society’s cultures, typical discussed subjects, and social behaviors relative to different geographic locations. This explosive availability of information serves as an opportunity for useful sectioning of areas. The purpose of our study is to develop a method for measuring users sentiment on a given concept in a geospatial area. To deploy the present study, it is crucial to analyze users’ shared content, and detect its dominant polarity. The key principle behind our method is to analyze people’s view on a concept, whether the community refers to it positively or negatively. The aim is to find an indicative ratio of business-related projects success. This paper focuses on developing a method that relies on certain fixed variables in entry of the proposed system, in order to select concept-related content and retrieve its polarity. To do that, we develop a sentiment analysis method to extract the expressed feelings, as well as a name entity disambiguation method, and a new method for detecting newly identified words. The system considers input variables such as the location, the concept, and the starting point of analysis. This enables an estimation of the concept’s dominance as well as its ratio of social acceptance, in other words an estimation of its promised success, within an urban internet-connected area, starting a point in time.
Addressing the Need to generalize choice of Interfaces for Cloud Engineering Process Automation: withr reference to IaaS - Storage

“Cloud Engineering processes, workflows, functions required heavy automations and automated solutions all the time. There are several choices of tools. techniques, languages targeting several interfaces offered by cloud solutions. Objective of this paper is limited to Study components along with interfaces involved in Storage services that can be applied to both cloud computing needs, Cloud-for-Storage or Storage-for-Cloud, then make generalized list which can be used for architecting automation addressing the Need to generalize choice of Interfaces for Cloud Engineering Process Automation or reusable cloud solutions.
Solving the Dependency Conflict of Java Components: A Comparative Empirical Analysis

Java components’ reference to the complex dependencies brought by third-party libraries may cause multiple versions of the libraries to exist. When this happens, the JVM will only load one of these versions, which may cause a runtime exception. In many cases, it is difficult to troubleshoot exactly what is going wrong, and sometimes it takes a lot of trouble to solve such problems, even for people with rich experience. This article discusses several common methods for resolving dependency conflicts, explains their principles, summarizes their advantages and disadvantages, and puts forward comments for empirical solution.
Using a metric based tuning of Converted Measurement Kalman Filter (CMKF) for realistic target tracking scenario

Converted measurement Kalman filter (CMKF), which is used in target tracking problems, converts polar measurements into Cartesian form to match Cartesian states. However, filter performance may degrade due to incorrect filter tuning of the biases and nonlinearities that arise due to the conversion. In this paper, the necessary converted measurement noise covariances have been derived in a 6 degree of freedom (6DOF) realistic target tracking scenario. In order to do so, the necessary frame conversions have also been accounted for to enable the estimation of the relative kinematics of the target. Thereafter, existing notions of robustness and sensitivity metrics have been suitably adapted to interpret the performance of CMKF in the 6DOF platform for various choices of tuning parameters and to predict the optimal tuning choice. The predicted performances and the optimal filter tuning choice have been validated in simulations in terms of the root mean squared errors (RMSE) for all measurements for 5000 Monte Carlo runs.
Innovative Solutions for Ensuring Information Security of Modern Enterprises

One of the key factors determining success of modern enterprises in the field of information security is their openness to innovations, work in promising development areas and their innovative activity. Enterprises of the information security market work exclusively in the aspect of innovative activities that ensure their sustainable economic development and strengthening of competitive positions in the market. The growing use of information technology leads to increased digital security risks. Cyber incidents lead to various types of losses for companies. To solve these problems, there are many technical and software products for ensuring information security of enterprises. Acquisition and development of innovative products in the field of information security will allow companies to build an effective protection system against threats and ensure successful operation and long-term development.
Knowledge Hiding in Decision Trees for Learning Analytics Applications

Nowadays there is a wide range of digital information available to educational institutions regarding learners, including performance records, educational resources, student attendance, feedback on the course material, evaluations of courses and social network data. Although collecting, using, and sharing educational data do offer substantial potential, the privacy-sensitivity of the data raises legitimate privacy concerns. The sharing of data among education organizations has become an increasingly common procedure. However, any organization will most likely try to keep some patterns hidden if it must share its datasets with others. This chapter focuses on preserving the privacy of sensitive patterns when inducing decision trees and demonstrates the application of a heuristic to an educational data set. The employed heuristic hiding method allows the sanitized raw data to be readily available for public use and, thus, is preferable over other heuristic solutions, like output perturbation or cryptographic techniques, which limit the usability of the data.
Web Services Security Using Semantic Technology

For the present growing distributed Web Services (WS) communication, security is of significant concern in the context of dynamically changing entities. It is a challenge to ensure the security goals such as authentication, authorisation and policy bounded exchange that exists between dynamic web-based applications. As the WS are enhanced to next level Semantic Web Services (SWS), the syntactic based security approach are to be improvised using additional domain knowledge called semantics between the entities (web services). Therefore, the incorporation of semantics will upgrade the quality of security management and provide solutions to the security challenges. The service semantics extracted as additional knowledge describes the web services with which one can check the certainty of entity authentication. The appropriate use of the semantic in policy representation and evaluation against a request is more suitable for challengeable security management of present web information system. Overall, the chapter discusses the incorporation of semantics in all attempts of seeking security solutions that lead to enrichment of interoperability between web services in a dynamic instant.
Visualization of Individual Variation of Multiple Annotators Working on Training Datasets for Machine Learning

Quality of training datasets is essential for the quality of machine learning. Machine learning projects often invite multiple workers for these annotation tasks for training dataset creation. It is important to observe on what types of contents multiple workers make different annotations, or which workers often make abnormal annotations, to guarantee the quality of training datasets. This paper presents a tool for the visualization of abnormality of annotations by multiple workers. The tool generates a matrix of abnormality of annotations for each of the images by each of the workers and displays as a heatmap. This paper introduces an example using a training dataset where estimated ages are annotated to 7,748 pictures of human faces by eight workers.
Natural ontologies with elastic matching for elicited knowledge comparison

Natural Ontologies are presented in this work as a useful tool to model the way in which concepts are organized inside the human mind. In order to be compared, ontologies are represented as matrices and an elastic matching technique is used. For this purpose, a distance measure called Modern Fréchet is proposed, which is an approximation to the NP-Complete problem of elastic matching between matrices. An applied case of study is presented in which human knowledge is compared among different groups of people in the Computer Science domain.
Benchmark on Face Image Retrieval From Learning Attributes

Choosing the most desirable image from many images can be a hard task for a user. In this study, we developed an image selection system based on 4 ways of calculating similarity. We conducted an experiment to validate the usability of the system and decide the best method for recommendation. It was confirmed that our system significantly helps users and Principal Component Analysis is a good way to calculate characteristics from many images.
Hybrid Convolutional Neural Network Ensemble for Activity Recognition in Mobile Phones

The increasing importance of human activity recognition in Ambient Assisted Living systems (AAL) has brought a tremendous growth in research efforts in the field. Amongst these, the identification of activities of daily living using sensors available in mobile devices has emerged as one of the most interesting goals for AAL systems. With the development of deep learning algorithms in recent years, there has been increasing interest in hybrid models, which are able to perform as well as traditional machine learning models and yet entail the self-learning capabilities of deep learning at the same time. Using the time series data obtained from the MotionSense dataset, this paper introduces the concept of converting time series signals to images. The paper then proposes a novel approach for activity recognition by fusing a variety of traditional machine learning models with a deep convolutional neural network via a majority voting ensemble. The proposed method produced better results than the traditional machine learning models.
Modeling and Comparing Robot Behaviors for Anomaly Detection

Detection of anomalies and faults is a key element for long-term robot autonomy, because, together with subsequent diagnosis and recovery, allows to reach the required levels of robustness and persistency. The aim of my PhD thesis is to develop new techniques to model and quantitatively compare observed robot behaviors with nominal ones. My goal is to propose approaches for detecting anomalous behaviors of robot systems involved in complex long-term autonomy scenarios, both online, while robots are operating, and offline, after robots have completed a run of their tasks.
Availability Model of Two-Zone Physical Security System considering Cyber Attacks and Software Update

The article analyzes the process of functioning of physical security systems in the conditions of component failures, cyberattacks and software updates. A zonal model of physical security systems, which includes motion detection and access control subsystems, have been built. Updating the software functions of each zone, attacks on the physical component of the motion detection subsystem, and cyber attacks on the access control system have been investigated. To model software updates, a multi-fragment model, including 9 states of each fragment and program update states has been created. To study the behavior of the model while changing the input parameters, Matlab scripts have been developed. Simulation results allow to evaluate the availability function minimum value, availability function value in stationary mode, time interval for the transition of the availability function to the stationary mode.
Phonemes: An Explanatory Study Applied to Identify a Speaker

Speaker Identification (SI) is a process of identifying a speaker automatically via a machine using the speaker’s voice. In SI, one speaker’s voice is compared with n- number of speakers’ templates within the reference database to find the best match among the potential speakers. Speakers are capable of changing their voice, though, such as their accent, which makes is more challenging to identify who is talking. In this paper, we extracted phonemes from a speaker’s voice recording and investigated the associated frequencies and amplitudes to be assist in identifying the person who is speaking. This paper demonstrates the importance of phonemes in both speech and voice recognition systems. The results demonstrate that we can use phonemes to help the machine identify a particular speaker, however, phonemes get better accuracy in speech recognition than speaker identification.
Mining International Political Norms from the GDELT Database

Researchers have long been interested in the role that norms can play in governing agent actions in multi-agent systems. Much work has been done on formalising normative concepts from human society and adapting them for the government of open software systems, and on the simulation of normative processes in human and artificial societies. However, there has been comparatively little work on applying normative MAS mechanisms to understanding the norms in human society. This work investigates this issue in the context of international politics. Using the GDELT dataset, containing machine-encoded records of international events extracted from news reports, we extracted bilateral sequences of inter-country events and applied a Bayesian norm mining mechanism to identify norms that best explained the observed behaviour. A statistical evaluation showed that the normative model fitted the data significantly better than a probabilistic discrete event model.
Programming teaching methodological foundation improvement

Abstract Annotation. In the paper an analysis of various programming languages previously used for educational process are carried out. Criteria are proposed to be followed for choosing a language in programming courses that will result in the improvements in the study. The basic methodological approaches to teaching programming in the course “Languages and programming methods” are described. The requirements for training tasks are developed, which will gain the interest in programming. Ways of further improvements to the teaching process are discussed.
Assessing the Ability of Neural Machine Translation Models to Perform Syntactic Rewriting

We describe work in progress for evaluating performance of sequence-to-sequence neural networks on the task of syntax-based reordering for rules applicable to simultaneous machine translation. We train models that attempt to rewrite English sentences using rules that are commonly used by human interpreters. We examine the performance of these models to determine which forms of rewriting are more difficult for them to learn and which architectures are the best at learning them.
Towards Multiple Tethered Aerial Vehicles

Here we consider a multi-agent extension of the original problem analyzed in Chap. 4, by looking at a system composed by two underactuated flying vehicles lying on a vertical plane that are connected to the ground and to each other through two generic links, as depicted in Fig. 7.1. One can notice the similarity with a classic two-link Cartesian robot where the end of the chain represents the end-effector, while the aerial vehicles are the actuated joints of the robot. For this singular system, never studied before according to our best knowledge, we aim to extend part of the results found for the single tethered case. In particular, we want to control not only the elevation but also the internal force of the two links. Moreover we want to obtain the tracking of the output of interest along any desired time-varying trajectory, instead of just achieving regulation to constant values. For this goal we shall show that also in this case the elevations and internal force along the links are differential flat/feedback outputs. Following the analysis of Chap. 4, we will design a state feedback linearizing controller for the precise tracking of the output of interest. Finally, we investigate which is the minimal set of sensors needed to estimate the full state of the system. Based on such sensory setup we will design a nonlinear observer based on the HGO in order to obtain the sough estimation of the state. We remark that this topic is still a work in progress that will be further developed in the future.
Movement computation and analysis for entrainment in the rhythms of Bharatanatyam

UPDATED---May 20, 2020. Computational video analysis methods based on direct synchronization between the dance-beat and the music-beat consider many natural movements irregular and non-dancelike. We will give examples, through performances as well as through mathematical and computational analysis, to show that such "singularities" can be "smoothly continued" by improvisations afforded through the rich structure of rhythm in Carnatic music and Bharatanatyam dance.
A consistently oriented basis for eigenanalysis

Abstract Repeated application of eigen-centric methods to an evolving dataset reveals that eigenvectors calculated by well-established computer implementations are not stable along an evolving sequence. This is because the sign of any one eigenvector may point along either the positive or negative direction of its associated eigenaxis, and for any one eigenfunction call, the sign does not matter when calculating a solution. This work reports a model-free algorithm that creates a consistently oriented basis of eigenvectors. The algorithm postprocesses any well-established eigen call and is therefore agnostic to the particular implementation of the latter. Once consistently oriented, directional statistics can be applied to the eigenvectors in order to track their motion and summarize their dispersion. When a consistently oriented eigensystem is applied to methods of machine learning, the time series of training weights becomes interpretable in the context of the machine-learning model. Ordinary linear regression is used to demonstrate such interpretability. A reference implementation of the algorithm reported herein has been written in Python and is freely available, both as source code and through the Python package.
Formalization and Evolution of Learning Path in Embedded Systems

Embedded systems can be attributed to one of knowledge-intensive areas – it has constant updating of labor market requirements for specialists and technologies used in the embedded systems design domain. This article describes the personal learning path formalization for future specialists in embedded systems engineering. The research proposes a method of educational programs forming on-base of embedded systems design features and perspective requirements for them. For this purpose, design and learning paths were defined and formalized using OMG Essence standard language. Created models have been compared, and actual specialists' competencies were formed. This approach allows for solving the problem of education separation from today's challenges. Also, it provides a tool for holistic and efficient personal learning path management by stakeholders' needs. The proposed approach for learning path formalization was applied to the formation of e-workshop for subject "Embedded systems" and showed its effectiveness.
Identification of Potential Forensic Artifacts in Cloud Storage Application

Cloud storage services possess the majority portion of the cloud market. Its exponential growth represents the continuous storage of public and personal information to the remote location over the cloud for on-the-go availability. However, cloud storage services also attract criminals due to low-cost on- demand resources. In the case of SaaS applications, the cloud service providers control all the underlying hardware and execution environment of the application. Forensic investigators must depend upon the cooperation of CSP for evidence identification and collection. This is the reason that forensics of cloud storage application become an obstacle as well as an opportunity for forensic researchers and examiners. In this paper, we focus on the identification of potential forensic artifacts for a single SaaS application: Dropbox
A Process Framework to Migrate Legacy Application to Cloud

An enterprise has a challenge in keeping pace with the quickly evolving technology. The biggest challenge comes in terms of legacy application migration to technologies like cloud. Legacy application migration should be well thought out at the very start (i.e., pre-migration and supported by migration framework). In this chapter, the author proposes a legacy application migration framework with a focus on pre-migration area. A robust technical and business analysis of existing/legacy applications, based on the enterprise's focus parameters, during pre-migration sets the migration path for subsequent area of framework. The proposed pre-migration mathematical assessment helps an enterprise to understand a legacy application's current state and also helps in unearthing the information with respect to candidate application and helps in taking well-informed decisions like GO or NO-GO w.r.t legacy application migration. Considering application migration a journey, it is important that it reaches its destination, so pre-migration is an important area of the migration journey.
Rethinking Higher Education Through Sustainable Development Goals (SDGs): A Russian Perspective

This article aims at providing a 0.0 measurement and assessment of how Plekhanov Russian University of Economics, Russia perceives the importance of their impact on society, education and various stakeholders. Based on and using The University Impact Rankings 2019 by Sustainable Development Goals: quality education methodology, the author is making a starting point in a comprehensive analysis to explore Plekhanov university impact rankings for individual SDGs. He starts with the SDG 4 (Quality Education) as it seems the most relevant measurement and assessment tool at the moment. The present study seeks to fill the gap in research regarding the manner of implementation at mentioned university regarding the SDG goals and describes how the objectives of internationalization processes are determined by the higher education regulations and policies inside the HEIs.