Reviewerly/exHarmony huggingface dataset:
Subjective Ontology and the Individual’s Lifeworld

The author proposes an interpretation of the core role of subjective ontology in the formation of the individual’s lifeworld. It is shown that the actual implementation of the life journey obliges the person to think ontologically, explaining to oneself, basing on the accumulated experience, how the reality is arranged, and building a version of the causes and consequences of his own existence in it. Solving the tasks of self-development and constructing personal life stories, the individual relies upon some ultimate ontologemes (‘fate’, ‘fortuity’, ‘free will’), each of which generates different foci of interpretation of self-experience and defines the individual semantics of possible life events: if the ontology of free will dominates, then self-interpretation is built primarily as an explanation of human deeds; if the person believes in the predetermination of everything by fate, then life fulfillment would be in the focus of his/her consciousness; if everything is dominated by the fortuity, then life is perceived as a set of unpredictable incidents. The assimilated ultimate ontologemes stimulate the prevalence in self-interpretation of one of the mental processes (belief, thinking, intuition) and thus set three possible frames for the interpretation of the life path: fate — fulfillment — belief; fortuity — incident — intuition; free will — deed — thinking. The lifeworld is represented as a pyramid whose top is formed by subjective ontology that performs a system-forming function in relation to it, and its facets are its epistemological, axiological, praxeological and symbolic dimensions.
Bio-Inspired Hybrid Algorithm for Web Services Clustering

Web services clustering is the task of extracting and selecting the features from a collection of Web services and forming groups of closely related services.The implementation of novel and efficient algorithms for Web services clustering is relevant for the organization of service repositories on the Web.Counting with well-organized collections of Web services promotes the efficiency of Web service discovery, search, selection, substitution, and invocation.In recent years, methods inspired by nature using biological analogies have been adapted for clustering problems, among which genetic algorithms, evolutionary strategies, and algorithms that imitate the behavior of some animal species have been implemented.Computation inspired by nature aims at imitating the steps that nature has developed and adapting them to find a solution of a given problem.In this chapter, we investigate how biologically inspired clustering methods can be applied to clustering Web services and present a hybrid approach for Web services clustering using the Artificial Bee Colony (ABC) algorithm, K-means, and Consensus.This hybrid algorithm was implemented, and a series of experiments were conducted using three collections of Web services.Results of the experiments show that the solution approach is adequate and efficient to carry out the clustering of very large collections of Web services.
Beyond Winning and Losing: Modeling Human Motivations and Behaviors with Vector-Valued Inverse Reinforcement Learning

In recent years, reinforcement learning (RL) methods have been applied to model gameplay with great success, achieving super-human performance in various environments, such as Atari, Go and Poker. However, these studies mostly focus on winning the game and have largely ignored the rich and complex human motivations, which are essential for understanding humans’ diverse behavior. In this paper, we present a multi-motivation behavior model which investigates the multifaceted human motivations and learns the underlying value structure of the agents. Our approach extends inverse RL to vectored-valued rewards with Pareto optimality which significantly weakens the inverse RL assumption. Our model therefore incorporates a wider range of behavior that commonly appears in real-world environments. For practical assessment, our algorithm is tested on World of Warcraft datasets and demonstrates the improvement over existing methods.
Enhancing the Usefulness of Open Governmental Data with Linked Data Viewing Techniques

Open Governmental Data publishing has had mixed success. While many governmental bodies are publishing an increasing number of datasets online, the potential usefulness is rather low. This paper describes action research conducted within the context of the Dutch Cadastre’s open data platform. We start by observing contemporary (Dutch) Open Data platforms and observe that dataset reuse is not always realized. We introduce Linked Open Data, which promises to deliver solutions to the lack of Open Data reuse. In the process of implementing Linked Data in practice, we observe that users face a knowledge and skill and that contemporary Linked Open Data tooling is often unable to properly advertise the usefulness of datasets to potential users, thereby hampering reuse. We therefore develop four components for Linked Data viewing to enhance the current situation, making it easier to observe what a dataset is about and which potential use cases it could serve.
Activity Recognition Using RFID Phase Profiling in Smart Library

In the library, recognizing the activity of the reader can better uncover the reading habit of the reader and make book management more convenient. In this study, we present the design and implementation of a reading activity recognition approach based on passive RFID tags. By collecting and analyzing the phase profiling distribution feature, our approach can trace the reader's trajectory, recognize which book is picked up, and detect the book misplacement. We give a detailed analysis of the factors that can affect phase profiling in theory and combine these factors with relevant activities. The proposed approach recognizes the activities based on the amplitude of the variation of phase profiling, so that the activities can be inferred in real time through the phase monitoring of tags. We then implement our approach with off-the-shelf RFID equipment, and the experiments show that our approach can achieve high accuracy and efficiency in activity recognition in a real-world situation. We conclude our work and further discuss the necessity of a personalized book recommendation system in future libraries.
Reachability Analysis for Termination and Confluence of Rewriting

In term rewriting, reachability analysis is concerned with the problem of deciding whether or not one term is reachable from another by rewriting. Reachability analysis has several applications in termination and confluence analysis of rewrite systems. We give a unified view on reachability analysis for rewriting with and without conditions by means of what we call reachability constraints. Moreover, we provide several techniques that fit into this general framework and can be efficiently implemented. Our experiments show that these techniques increase the power of existing termination and confluence tools.
Big data: is clinical practice changing?

The widespread diffusion of the digital culture and technology, involving both individual and population, together with the fast pacing digital globalization process (far surpassing 'political' globalization), is radically changing the world social landscape, including medicine and clinical research. The most significant change in clinical research is the ever more frequent acceptance of observational data, both through the use of registry of rare or common conditions and the implementation of capillary networks recording the daily clinical practice (Electronic Health Recording system). By becoming 'observational' clinical practice should change significantly: (i) record of different data (epidemiological, clinical, and administrative) in inter-operational database, producing a dynamic map of the health demands, either met or not, that allows a reconfiguration of the health systems capable to adapt to the shifting clinical needs. Implicate the larger group possible of patients and healthy individuals, who, through smartphone technology, could participate in primary and secondary prevention projects and epidemiological analyses. (ii) Support scientific research by integrating it with the clinical practice as instrument of good government that is scientific evidence-based Public Health System: the Learning Health System. The road will be long and gruelling. A first negative by-product is the proliferation of cybercrime throughout digital medicine.
The Rewrite Engines Competitions: A RECtrospective

Term rewriting is a simple, yet expressive model of computation, which finds direct applications in specification and programming languages (many of which embody rewrite rules, pattern matching, and abstract data types), but also indirect applications, e.g., to express the semantics of data types or concurrent processes, to specify program transformations, to perform computer-aided verification, etc. The Rewrite Engines Competition (REC) was created under the aegis of the Workshop on Rewriting Logic and its Applications (WRLA) to serve three main goals: (i) being a forum in which tool developers and potential users of term rewrite engines can share experience; (ii) bringing together the various language features and implementation techniques used for term rewriting; and (iii) comparing the available term rewriting languages and tools in their common features. The present article provides a retrospective overview of the four editions of the Rewrite Engines Competition (2006, 2008, 2010, and 2018) and traces their evolution over time.
Human Action Recognition on Real Time and Offline Data

Human action recognition is an important area of research in the field of computer vision due to its extensive applications like security surveillance; content based video retrieval and annotation, human computer interaction, human fall detection, video summarization, robotics, etc.The surveillance system deals with the monitoring and analysing the human behaviour and activities.The main aim of the smart surveillance system is to recognize anomalous behaviour in given scene and provide real time intimation to relevant person.We have designed and tested Smart Surveillance System for College Corridor Scene (3S2CS).The system recognises the anomalous behaviour and an intimation is provided in the form of Firebase Cloud Messaging (FCM) alert on the android mobile phone to the authorised user.This paper mainly discuss the methodologies used for the human action recognition.The basic step is to provide video as an input.These videos are further divided into number of frames.The videos are used for training and for each video, Scale Invariant Feature Transform (SIFT) is applied for extracting features and developing feature vectors.The actions are classified using K Nearest Neighbour (KNN) and Support Vector Machine (SVM) classifier.Two standard offline datasets considered for testing are Weizmann and UTD-MHAD.For real time scenario we have created dataset in college campus called as College Corridor dataset.It contains student activities like falling, fighting, walking, running, sitting and other general actions.If falling or fighting action is detected, the notification is sent to the authorized user who has installed ActionDetector android application and registered a device for the same.Action recognition accuracy is 92.91% using SVM and 90.83% using KNN.
Designing IT Blue Print Academic System on Higher Education with Togaf

STIE XYZ is one of the universities of economics that has not been optimal in the use of information technology.IT implementation is applied when there are urgent and sudden needs.So it is not planned carefully according to the development needs of this educational institution.Therefore many patchy applications are created.As the consequence, it affects the quality of service for all stake holder include students, lectures and all staffs.This study aims to provide solutions to existing problems by designing an output or blueprint that can be used as a foundation for the development of a better academic system in support of business process STIE XYZ.The blueprint is designed by using framework TOGAF and ADM framework.The solution is built by creating artifacts of each core architecture in TOGAF (business, data, application and technology architecture).This research focuses on new admissions services, study plan cards, lectures, study cards, graduation, and finance.The results show some recommendations to build based on each architecture.
Application of Economic Load Distribution of Power System Based on BAS-PSO

Beetle Antennae Search (BAS) is a h euristic algorithm proposed in 2017. BAS is the bionic optimization algorithm. The principle comes from the fact that Tianniu finds food by sensing the direction of flight by sensing the strength of the smell emitted by the left and right. The BAS algorithm has better optimization speed and precision when solving low-dimensional problems. However, it is easy to fall into local optimum when solving high-dimensional problems. For the purpose of the better search ability of BAS, we h ave combined particle swarm optimization (PSO) algorithm and proposed a new hybrid BAS and PSO algorithm (BAS-PSO).In BASPSO, First use the standard PSO for particle position update and velocity update.Particle learning ontology information and group optimal individual information for evolution, then the particles are seen as individual beetles to use BAS. Each beetle individual performs a local search and continues to cycle until it finds the optimal value.In this paper, the design simulation experiment is applied to the problem of economic load dispatching (ELD) allocation.By comparing the optimization results of BAS-PSO with the three types of algorithms, the results suggest that the BAS-PSO algorithm is better in the ELD optimization problem.
Features and Advantages of Using Websites in Teaching Mathematics (Interactive Educational Platform UCHI.ru)

The number of electronic educational resources is multiplying day by day. Though such resources can be used in teaching children, teachers are not aware of their effectiveness thus far. So as to include websites in educational process purposefully and methodologically profoundly, it is important for the teacher to have cognizance of educational tasks that can be solved with the help of electronic resources, methodological functions they perform, and types of training activities they support and initiate. The paper is aimed to reveal the effectiveness of the interactive educational platform UCHI.ru while teaching math to primary school students. The leading methods applied in the research are as follows: testing, observation and analysis of educational process aimed to teach Mathematics to children aged between eight and nine through the use of UCHI.ru interactive educational platform to specify the dynamics of mathematical skills development in primary school students. The paper proves the validity of primary school students’ mathematical training using UCHI.ru interactive educational platform; reveals features in teaching Mathematics to children aged 8-9 with the use of an educational website; identifies possible problem areas in teaching Mathematics using UCHI.ru interactive educational platform. UCHI.ru interactive educational platform, proposed in the paper, improves learners’ motivation, facilitates their search competence formation and identifies gaps in basic knowledge of “Mathematics” as a subject for each student in class. UCHI.ru interactive educational platform can be employed by primary school teachers as supplementary didactic material.
Experimental Quantum-enhanced Cryptographic Remote Control

The Internet of Things (IoT), as a cutting-edge integrated cross-technology, promises to informationize people's daily lives, while being threatened by continuous challenges of eavesdropping and tampering. The emerging quantum cryptography, harnessing the random nature of quantum mechanics, may also enable unconditionally secure control network, beyond the applications in secure communications. Here, we present a quantum-enhanced cryptographic remote control scheme that combines quantum randomness and one-time pad algorithm for delivering commands remotely. We experimentally demonstrate this on an unmanned aircraft vehicle (UAV) control system. We precharge quantum random numbers (QRN) into controller and controlee before launching UAV, instead of distributing QRN like standard quantum communication during flight. We statistically verify the randomness of both quantum keys and the converted ciphertexts to check the security capability. All commands in the air are found to be completely chaotic after encryption, and only matched keys on UAV can decipher those commands precisely. In addition, the controlee does not response to the commands that are not or incorrectly encrypted, showing the immunity against interference and decoy. Our work adds true randomness and quantum enhancement into the realm of secure control algorithm in a straightforward and practical fashion, providing a promoted solution for the security of artificial intelligence and IoT.
Artificial intelligence for software engineering

Artificial intelligence is predicted to impact many industries (including the software industry), changing how we produce, manufacture, and deliver. The rise of artificial intelligence may significantly transform the practice of software engineering, helping us build better software faster.
Expecting the Unexpected in Participatory Design

Participatory Design (PD) provides unique benefits in designing technology with and for specific target audiences. However, it can also be an intensive and difficult process, with unexpected situations which can arise at any stage. In this Special Interest Group (SIG), we propose that PD researchers may exchange "war stories'' about their unexpected and difficult experiences with PD. This will facilitate reflective discussions and the identification of possible solutions, and enable future PD research to plan for similar situations, thereby making difficulties a little less unexpected.
Using Text Mining to Identify Trends in Oropharyngeal Dysphagia Research: A Proof of Concept

Objectives: Meta-research can provide valuable insights into patterns in research.Text mining as a specific method to carry out meta-research has been less explored by the speechlanguage pathologist community.The purpose of this article is to delineate the history of research trends in the area of oropharyngeal dysphagia and its evolution across the past five decades.It also aims to identify hidden patterns in the research field using a combination of text mining and bibliometric-scientometric techniques.Methods: We utilized quantitative and qualitative approaches through text mining techniques in the background of scientometric and bibliometric analyses.Abstracts of the articles were text mined from the Scopus database and were subjected to hierarchical cluster analysis and co-occurrence networks analyses.The frequency of published research across journals as well as amount of research articles published overtime were calculated.Results: A total of 1,526 articles were published in the area of oropharyngeal dysphagia across 60 journals in the Scopus database.The evolution of research themes has been described.Conclusion: The present study summarizes the research that has been carried out from 1970 till present in the area of oropharyngeal dysphagia using a text mining technique.Dysphagia research has evolved to be truly multi-disciplinary, as contribution from various professionals could be observed.The research area of oropharyngeal dysphagia continues to pose new challenges and offers wider prospects for further research.
Segmentation of Scanned Images of Newspapers and Magazines

In this chapter, we present a method for the segmentation of scanned images of newspapers and magazines. For further Mixed Raster Content (MRC) compression, we classify blocks of an image into background, or picture areas, or text regions. The method is relatively simple and fast, because it is intended for implementation in firmware. Textural features are calculated for image blocks. We train three one-to-rest classifiers by the AdaBoost technique on a publicly available dataset. The output of the classifiers can be treated as a posteriori probability. We smooth these probabilities among adjacent blocks. After smoothing, a voting procedure sets the class for each block. We argue that the Dual Leave-Group-of-Sources-Out cross-validation scheme is beneficial for the tuning of algorithm parameters. We discuss the advantages and shortcomings of several segmentation quality metrics.
Significance of Image Compression and Its Upshots - A Survey

In the recent years, digital imaging and multimedia are comprising a large growth. It comes to practice that huge amount of image has been utilizing and it probably demand the image compression methods. Image compression is mainly used for reduce the storage size and transmission cost of an image. Based on the quality requirement, it is classified as either lossy or lossless. In this paper, we explore the significance of image compression and the upshot of the survey conducted from the image compression literature. Additionally, we review the various evaluation metrics for image compression such as Compression Ratio, Bit per Pixel, Mean Square Error, Peak Signal to Noise Ratio and Structural Similarity Index.
Interpretive Structural Modeling in the Adoption of IoT Services

This study aims to use ISM to identify the enablers affecting the acceptance of IoT services.For this purpose, this study conducted an ISM analysis and a MICMAC analysis, extracted the enablers from Internet of Things -An Action Plan for Europe published by the EU for the research, and conducted interviews and surveys.The study found that it would be preferentially necessary to prepare the base for successful IoT services through international cooperation and the security of objective data.In addition, it turned out that it would be necessary to make efforts to spread and develop IoT services by conducting R&D and implementing projects through public-private partnerships and the organization of a consultative group.Lastly, since information security and standardization are the desired objects of the IoT industry, it was found that both the government and the industrial world should focus on them.This study has significance in that it can provide practical implications for the effective acceptance of IoT services.
Identification of Alteration Minerals from Unstable Reflectance Spectra Using a Deep Learning Method

Hydrothermal alteration minerals, which are important as indicators in the exploration of ore deposits, exhibit diagnostic absorption peaks in the short-wavelength infrared region. We propose an approach for the identification of alteration minerals that uses a deep learning method and compare it with conventional identification methods which use numerical calculation. Inexpensive spectrometers often tend to show errors in the wavelength direction, even after wavelength calibration, which causes erroneous mineral identification. In this study, deep learning is applied to extract features from reflectance spectra to remove such errors. Two typical deep learning methods—a convolutional neural network and a multi-layer perceptron—were applied to spectral reflectance data, with and without hull quotient processing, and their accuracy rates and f-values were evaluated. There was an improvement in mineral identification accuracy when hull quotient processing was applied to the learning data.