Reviewerly/exHarmony huggingface dataset:
A formal semantics for supporting the automated synthesis of choreography-based architectures

Nowadays, we are surrounded by a rapidly increasing number of software applications that provide different services in various domains. To fulfill the needs of this new reality, software systems are often built by reusing and integrating existing services distributed over the Internet, and thus promoting a reuse-based software production. Service Choreography is a decentralized service engineering approach to compose and coordinate existing services from a global perspective, in terms of peer-to-peer message exchanges. The current standard de-facto for modeling such choreographies are the BPMN2 Choreography Diagrams. However, BPMN2 specifications lack formal semantics which cause some misinterpretations by practitioners and researchers. Colored Petri Nets (CPN) have been used to model, analyse and simulate various types of systems, in particular distributed ones. Nonetheless, CPN is a formally proved notation with mathematical semantics and tool support. Following an approach similar to [2], this paper first proposes the definition of a rigorous mapping between BPMN2 Choreography Diagrams and CPN-based models. Then, a component-connector architectural style suitable for automated choreography realizability enforcement is proposed, where the devised CPN-based models are used to express the interaction behavior of the represented components and connectors, hence enabling automated reasoning.
Analysis of Facial Expression Recognition using Histogram of Oriented Gradient (HOG)

Recently various approaches for extracting facial features to recognize facial expression have recently gained importance in the field of human-robot interaction. Facial expressions like happy, anger, sad, surprise are contributing towards representing human behavior. This paper presents a most important technique i.e. Histogram of Oriented Gradient (HOG) for feature extraction. Classifiers like K-NN, SVM are proposed for recognition. MATLAB 2018(a) software, in Windows 10, 64-bit operating system is used for implementation.
Hyper switching memory utilization on hybrid main memory for improved task execution and reduced power consumption

The problem of lifetime maximization of PCM has been well studied. The arrival of non-volatile memory devices has replaced the traditional DRAM. Still the DRAM has many limitations on endurance and high power write operations. Similarly, number of designs has been discussed earlier to maximize the lifetime of PCM by catching the main memory at available DRAM. Still they could not achieve the performance on power consumption reduction and increasing memory utilization. To improve the performance in power consumption reduction and lifetime maximization, and categorical model is presented in this paper. The proposed method categorizes the processes according to their memory access activity. The categorized process has been allocated to respective part of hybrid memory which encourages maximum read and minimum write in PCM. The proposed method increases the lifetime of PCM than other methods.
An Intelligent Application for healthcare Recommendation using Fuzzy Logic

The concurrence of a flood of health awareness campaigns, technological advances, the increasing need for accurate information and a stark increase in stress levels across people from all walks of life has served as an impetus for the emergence of numerous health oriented diet applications which serve as a convenient, cost effective and time saving route for the adoption of a healthier lifestyle. Most of the prevalent online diet applications either use well established scientific formulae to generate a discrete output or they are directly affiliated with a professional dietician or nutritionist to help chart out a diet plan for the user, in which case the application is rarely free. The former method, while accurate, does not present much scope for customization whereas the latter method, though personalized, is not a cost-effective option. In both the above alternatives, the application is not intelligent as the generation of successive outputs is completely dependent on the user inputs. This paper proposes a novel fuzzy set approach to generate an intelligent personalised diet plan for the user in accordance with several parameters that would be input by the user. Type-1 Fuzzy Sets and the MinMax method are used for fuzzification purposes. The application generates the optimal calorie range intake based on the user's profile using Fuzzy Sets.
Low-cost similarity calculation on ontology fusion in knowledge bases

Ontology fusion in knowledge bases has become less easy, due to the massive capacity involved in the process of semantic similarity calculation. Many similarity calculation methods have been developed, although they are hardly united. This article contributes a low-cost similarity calculation method for ontology fusion, based on the inspiration of binary metrics, with the aim of reducing the size of similarity calculations both spatially and logically. By introducing the definitions of a heterogeneous ontology, entities of ontologies and rules of ontology fusion on the basis of concept fusion and relationship fusion, we put forward the algorithm of main traverse procedure and calculated to be the least cost in time and space in comparison with traditional methods. We adopted three experiments to testify the usability of our approach from the perspective of actual library resources, small datasets and large datasets. In Experiment 1, the bibliographic data from East China Normal University Library were used to show the feasibility and capability of our proposal and present the process of the algorithm. In both Experiments 2 and 3, our approach had at least 88% confidence in detecting accurate merging mappings and also decreased time cost. The test demonstrated a good fusion result. The problem of lower recalls caused by error analysis results from the conflict between the complex structures in ontologies and the recursive functions, which will be improved in the future.
Deadbeat vs. pole-free perfect control

In this paper an overview of various state feedback-based control strategies is presented. Five different scenarios are considered in this extensive research study. It is shown, that the widely known deadbeat control problem can be understood and solved in different manners. Moreover, the so-called pole-free perfect control instance turned out to have, despite similarities in the terms of pole placement problem, interesting peculiarities worth of further investigation. Simulation examples made in Matlab/Simulink environment are presented here in order to illustrate the differences between mentioned control strategies.
Histopathological image classification with deep convolutional neural networks

In the last few years, deep learning approaches have been applied successfully in different modalities of medical imaging problems and achieved state-of-the-art accuracy. Due to the huge volume and variety of imaging modalities, it remains a large open research area. However, in this paper, we have applied Inception Residual Recurrent Convolutional Neural Network (IRRCNN) model for histopathological image classification where a new publicly available dataset named KIMIA Path960 is used. This database contains 960 histopathological images with 20 different classes (different types of tissue collected from 400 Whole Slide Images). In this implementation, we have evaluated the model with non-overlapping patches size of 64×64 pixels and the variant of samples are generated from each patch with different data augmentation techniques including rotation, shear, zooming, and horizontal and vertical flipping. The experimental results are compared against Linear Binary Pattern (LBP), bag-of-visual words (BoVW), and deep learning method with AlexNet and VGG16 networks. The IRRCNN model shows around 98.79% testing accuracy for augmented patch-level evaluation which is around 2.29% and 4% superior performance compared to Support Vector Machine with histogram intersection kernel (IKSVM) with BoVW and VGG16 methods respectively. Additionally, this evaluation also demonstrates that the deep feature representation-based method outperforms compared to a traditional feature-based method including LBP and BoVW for the histopathological image classification problem.
Image Enhancement

This chapter describes a novel method to enhance degraded nighttime images by dehazing and color correction method. In the first part of this chapter, the authors focus on filtering process for low illumination images. Secondly, they propose an efficient dehazing model for removing haziness Thirdly, a color correction method proposed for color consistency approach. Removing nighttime haze technique is an important and necessary procedure to avoid ill-condition visibility of human eyes. Scattering and color distortion are two major problems of distortion in case of hazy image. To increase the visibility of the scene, the authors compute the preprocessing using WLS filter. Then the airlight component for the non-uniform illumination presents in nighttime scenes is improved by using a modified well-known dark-channel prior algorithm for removing nighttime haze, and then it uses α-automatic color equalization as post-processing for color correction over the entire image for getting a better enhanced output image free from haze with improved color constancy.
A Probabilistic Graphical Model-Based Approach for the Label Ranking Problem

The goal of the Label Ranking (LR) Problem is to learn preference models that predict the preferred ranking of class labels for a given unlabelled instance. Different well-known machine learning algorithms have been adapted to deal with the LR problem. In particular, fine-tuned instance-based algorithms have exhibited a remarkable performance, specially when the model is trained with complete rankings, while model-based algorithms (e.g. decision trees) have been proved to be more robust when some data is missing, that is, the model is trained with incomplete rankings. Probabilistic Graphical Models (PGMs, e.g. Bayesian networks) have not been considered to deal with this problem because of the difficulty to model permutations in that framework. In this paper, we propose a Hidden Naive Bayes classifier (HNB) to cope with the LR problem. By introducing the hidden variable we can design a hybrid Bayesian network in which several types of distributions can be combined, in particular, the Mallows distribution, which is a well-known distribution to deal with permutations. The experimental evaluation shows that the HNB classifier is competitive in accuracy when compared with Label Ranking (decision) Trees, being, moreover, considerably faster.
A Framework for Recommender Systems Using Improved Collaborative Filtering

In the last few years, most of e-commerce, social media networks, and digital libraries websites require a recommender systems in their websites. These systems have the ability to suggest items to users that may meet their interests. Recommender systems rely on a collaborative filtering techniques (CF) are the most widely used and successful technique in this field. However, most of the recommender systems suffer from the missing value problem that leads to inaccurate prediction and leads to poor recommendation quality. To overcome this issue, this paper introduces an improved collaborative filtering framework. The proposed framework averages the distance between the active user and the selected reliable users based on the shared ratings given to some items in common. Then, makes a list of users who shared a specific number of items in common with the active user. After that, it predicts the unknown ratings based on novel mathematical formulas. Furthermore, it creates a list of nearest neighbors to the active user and then recommends a list of items. In this paper, the dataset proposed by literature was used to test the proposed framework. The experimental results show that the proposed work improved CF technique and outperform other literature in case of which user should be selected and how much is accurate the predicted rating. In this context, we adopted the Mean Absolute Error (MEA) metric that widely used to measure the accuracy of the improved CF.
An Edge Detection Approach for Fractal Image Processing

Research in the field of fractal image processing (FIP) has increased in the current era. Edge detection of fractal images can be considered as an important domain of research in FIP. Detecting edges in different fractal images accurate manner is a challenging problem in FIP. Several methods have introduced by different researchers to detect the edges of images. However, no method works suitably under all conditions. In this chapter, an edge detection method is proposed to detect the edges of gray scale and color fractal images. This method focuses on the quantitative combination of Canny, LoG, and Sobel (CLS) edge detection operators. The output of the proposed method is produced using matrix laboratory (MATLAB) R2015b and compared with the edge detection operators such as Sobel, Prewitt, Roberts, LoG, Canny, and mathematical morphological operator. The experimental outputs show that the proposed method performs better as compared to other traditional methods.
Targeting VHMS mineralization at Erayinia in the Eastern Goldfields Superterrane using lithogeochemistry, soil chemistry and HyLogger data

Exploration for volcanic-hosted massive sulfide (VHMS) deposits in the Archaean Yilgarn Craton of Western Australia is hampered by a combination of a paucity of outcrop, deep weathering, and saline groundwaters in geologically complex greenstone belts. We present a detailed account of the geology at Erayinia NW (Murrin Domain) in the southern part of the Kurnalpi Terrane, ~150 km SE of Kalgoorlie and 4 km NW of the King VHMS deposit (Edjudina Domain). We have used a combination of logging, petrography, lithogeochemical, pXRF, hyperspectral and soil datasets to help identify VHMS proximal signatures for future drill testing. Diamond drilling at Erayinia NW has identified widespread hydrothermal alteration and minor base metal mineralization across a strike length of at least 3 km. A lower package of calc-alkaline metabasalt, is overlain by intermediate volcanic and associated volcaniclastic rocks, a mixed sequence of argillaceous sedimentary rocks interbedded with FI to FII affinity dacitic rocks, and calc-alkaline metabasalt. SHRIMP UPb zircon geochronology has constrained the age of the host stratigraphy to 2685 ± 5 Ma. Hydrothermal alteration (quartz-sericite-pyrite) is most prevalent in the 50-75 m thick felsic dominated sequence interbedded with black shales, with local zones of chloritization associated with minor ZnPb mineralization. Massive pyrite was intercepted in the two southernmost holes located approximately 300 m apart. Black shales and hydrothermally altered felsic rocks contain elevated Ag, As, Bi, Cd, Cu, Hg, In, Mo, Pb, Sb, Te, Tl associated with increasing Zn concentrations. Broad halos of Sb, Tl, Eu/Eu* and normative corundum define the stratigraphy of interest. Hydrothermally altered felsic rocks are characterized by higher abundances of white mica and lesser chlorite than surrounding strata. Increased quantities of Mg-rich chlorite, muscovitic (Al-rich) white mica, and Fe‑carbonate (siderite) occur proximal to FeZn mineralization. Significant enrichments in trace elements (Ag, As, Bi, Cd, In, Mo, Ni, Sb, Sn, Te, Tl; plus Alteration Index and K2O; and lower Al, Ba, Sr) in black shales towards drillholes EC154D and EC157D suggest this area is most proximal to VHMS mineralization. Soil metal concentrations vary significantly according to underlying regolith type (e.g. colluvium, alluvium, playa lake). Geochemical data levelled by regolith type reveal several bulls-eye multi-element geochemical anomalies (e.g. Zn, Pb, Cu, As, Bi, Cd, Mo, Sn, Tl, W) over the King deposit (2.15 Mt. at 3.47 wt% Zn), King North occurrence (e.g. 1 m at 6.7 wt% Zn + Pb), with new targets identified at Erayinia NW and elsewhere for future drill testing.
ALICA 2.0 - Domain-Independent Teamwork

We present a new version of ALICA - “A Language for Interactive Cooperative Agents”. The ALICA framework is a highly reactive multi-agent framework and comprises three components for working with multi-agent plans: a specification language, an execution engine, and a graphical modelling tool. The framework automatically coordinates teams, allocates tasks to team members, and compensates execution failures in a fully distributed manner. In a major redesign, we extended the description language and re-implemented the execution engine and graphical modelling tool. As a result, the second version of ALICA encompasses fewer dependencies, is domain independent, and adaptable to different environments.
Comparative Analysis of Different Classifiers on Crisis-Related Tweets: An Elaborate Study

Twitter is a popular micro-blogging platform that has obtained a lot of reputation in the last few years and offer a diverse source of real-time information about different events, often during mass crises. During any crisis, it is necessary to filter through a huge amount of tweets rapidly to extract incident related information. Different machine learning (ML) algorithms have been used to classify crisis related tweets from non crisis-related ones, and has great importance in constructing an emergency management framework. These algorithms rely heavily on datasets used, and also different hyper-parameters which need to be tuned to provide better performance. Hence, this paper focuses on: (1) different Natural Language Processing (NLP) techniques to make tweets suitable for applying ML algorithms, (2) hyper-parameter tuning of neural networks when used as classifiers on short messages, tweets, (3) comparative analysis of different state-of-the-art ML algorithms (classifiers) which can be applied to categorize crisis-related tweets with a higher accuracy. The experiments have been done on six different crisis related datasets, each approximately consisting of 10,000 tweets. Analysis have shown that Support Vector Machines and Logistic Regression have performed significantly well than Naive Bayes and Neural Networks (NN) with a very high accuracy of 96% (variations seen with different dataset though). With proper hyper-parameter tuning, NN have also showed promising results.
Dynamic color descriptor based Frenet-Serret to classify stress zones from pixel variations recorded in photoelasticity videos

We proposed a Frenet-Serret descriptor to classify stress categories based on color dynamics of pixels stored in photoelasticity videos. A collection of image compression models of disc and ring with a monotonic incremental load was generated. For each pixel, a temporal curve was created using color changes each frame. A descriptor histogram with Frenet Serret parameters was used to train a neuronal network; it classified in four types of stress zones (concentrated, high, medium, low). With the proposed method, a dynamic differentiation was possible in the field of stress without considering traditional digital photoelastic procedures.
Show me how to win

We present an approach for robot learning from demonstration and communication applied to simple board games like Connect Four. In such games, a visual representation of a winning condition on the board can be converted to an extensive form representation that can then support computation of a winning strategy. We present a robot that can learn simple games from responses to visual questions based on synthesized images, or to verbal questions. We illustrate how reliance on both modalities leads to more efficient learning.
Integrating Open Technologies in the Virtual Mobility Learning Hub

The global economy with technological developments, mobility incrementation and new financial and working models affects the higher education at its core. Not only the education system, policies, or delivery models are influenced, but also students' and staff mobility. A new era of online and mobile learning, of massive open online courses (MOOCs), created virtual mobilities or internships. Virtual Mobility (VM) has a great potential to contribute to the internationalization, innovation and inclusion in higher education. This paper presents the development of the Open Virtual Mobility Learning Hub, a European-funded project, as an integration of several interactive technologies in an open source learning environment based on Moodle. The Open Virtual Mobility Learning Hub is a collaborative online and mobile environment for promoting and assessing VM Skills of educators and students in the European Higher Education Area. The OpenVM Learning Hub includes several interactive tools in components, services and technologies. This paper analyses different open-source software and tools, essential for the OpenVM Learning hub, as its development is based on open education principles.
Taking the Subjectivity out of UX Evaluation with Emotiv EPOC+

Changes in the contemporary business environment are shifting the focus of designers and developers of Information Systems (IS) from a narrow focus on system usability to an overall user experience (UX). The subjective nature of UX meant that the majority of UX evaluation methods are subjective. However, advancements in new technologies now make it possible to evaluate UX objectively by collecting physiological data unobtrusively. In this study we explored the use of the Emotiv EPOC+ headset to collect electro-encephalography (EEG) patterns to 'examine' brain activities to determine the presence or absence of the emotion of anger, as well as 'performance metrics' on engagement, excitement, interest, focus, relaxation, and stress, in order to evaluate UX. The results showed that the Emotiv EPOC+ headset provides a low-cost means of evaluating UX objectively.
Towards a game-independent model and data-structures in digital board games

The increasing number of options of digital board games is exciting not only from an entertainment perspective but also from an academic prospect. It enables, for example, the application of modern computational techniques and algorithms for extracting and analyzing data from a variety of games - which range from classics like Chess and Go to modern board games with complex rules like Settlers of Catan and Terra Mystica. It is intuitive that different digital board games require distinct representation schemes and data structures to save, for example, the status or snapshot of a particular game at a specific moment. The choice for a representation model and data-structures is a crucial design decision that affects the selection of an algorithmic solution as well as the suitableness for artificial intelligence agents. This survey focuses on the different schemes and data structures used to represent game states, physical components, players, actions and rules for digital board games that have been reported in the academic literature. This study aims to lay the groundwork for the development of a game-independent computational framework which includes a generic game representation to facilitate and promote the application of computational techniques such as, for example, artificial intelligence and machine learning to this domain.
Retrieving videogame moments with natural language queries

Search engines for books can usually tell us which specific pages in a book mention the concepts we seek. A similar ability to search within the contents of games, locating specific moments in their spaces of interactivity, is not yet available. This limits players' ability to find deeply relevant games and game scholars' ability to find moments that advance their arguments. Drawing on computer vision and natural language processing, our work introduces the ability to search within a space of game moments using natural language queries. We describe and evaluate a prototype system which is capable of retrieving moments from two contemporary, narrative-driven games by semantic matching on both the auditory and visual content of scenes.