Reviewerly/exHarmony huggingface dataset:
A Survey on Data Placement Strategy in Big Data Heterogeneous Environments

In the present computerized world the extent of the information is expanding at an arbitrary speed. Simultaneously, need to process and examine huge amounts of information likewise expanded. In a few challenge commercial enterprise and logical programs, there's a want to technique petabytes of information in productive way day by day. Hadoop is most popularly used in data intensive applications. The current Hadoop usage accept that computing are homogeneous in nature. The capacity of tremendous amount of information in Hadoop is done using Hadoop Distributed File System (HDFS). HDFS employments block placement arrangement to part a really expansive record into pieces and place them over the cluster in a distributed way. In the present era of social networking, we cannot imagine having a cluster of homogeneous nodes only. Information region has not been taken under consideration for propelling theoretical outline assignments, since it is expected that the most mappings are data-local. To bargain with this Hadoop has usefulness to duplicate the information square where mappers are running. This makes a part of execution corruption particularly on heterogeneous cluster due to I/O delay or organize congestions In this survey we first brief introduction to the Big data, Hadoop and MapReduce. Later several data placement strategies in heterogeneous environment are studied.
Human Image Complexity Analysis Using a Fuzzy Inference System

Image complexity has been estimated by computational algorithms that try to simulate the human criterion to determine complexity. Image complexity has been used to study the behavior of the human brain, as well as in the area of image processing. This paper presents a work to analyze image complexity. A fuzzy inference system using principal component analysis, PCA, is designed to model the human criterion. The PCA characteristics are obtained from the most used features reported in the literature; contrast, correlation, energy, homogeneity, frequency factor, edge density, compression ratio, number of regions, colorfulness, number of colors, and color harmony. The work was achieved with the data based RS1 and RS2. The results obtained by cross validation, demonstrate a correlation level of 0.8566 between the proposed method and the human criterion.
QuARS: A Pioneer Tool for NL Requirement Analysis

This paper summarizes the achievements of Stefania Gnesi's research activity in the area of the natural language requirements analysis and quality evaluation. The development of the QuARS tool has been the pivotal step of this research stream led by Stefania Gnesi at ISTI–CNR. A functional description of the QuARS tool is provided as well as a short report of its evolutions over a decade. The wide use of QuARS in several research and industrial contexts demonstrates the validity and the originality of Stefania's contribution in such an area of software engineering.
Active monitoring technology in studying the interaction of geophysical fields

The problem of studying the interaction of various geophysical (seismic, acoustic, meteorological) fields is considered in the context of prediction of geoecological risks generated by technogenic and natural phenomena. It is proved that meteorological -dependent processes of propagation of infrasound from explosions in the atmosphere can greatly enhance the ecological loading on the social and natural environments. Such a situation is observed in regions of open strip mines, test sites, and other areas where regular explosions are made. Therefore the active vibrational method is proposed and investigated by the authors for the sounding of adjacent lithosphere–atmosphere media by strictly repeating small-power vibrational seismic and acoustic oscillations that powerful seismic vibrators can radiate. It is proved that simultaneous remote recording of both types of oscillations is possible. The use of the approach of active monitoring of the natural environment makes it possible to achieve ecological purity, high repeatability, and precision accuracy of the measurement parameters being estimated. The results of these works are illustrated by data from numerous experiments and numerical calculations.
Allocating Virtual Machines exploring Type-2 Fuzzy Logic and Admissible Orders

Dynamic consolidation of virtual machines (VMs) is an effective way to improve resource utilization and power efficiency in cloud computing, directly affecting Quality of Service aspects. This paper presents Int-FLBCC, a new proposal with exploring a Type-2 Fuzzy Logic approach to address the uncertainties and inaccuracies in determining resource usage, aiming at energy savings with minimal performance degradation. Validation results in a simulated cloud computing environment showed improvements in energy efficiency of 8.83% with IQR_XY, 22.43% with MAD_XY. For fulfillment of Service Level Agreements (SLAs), the best values achieved were 9.06% with MAD_XY and 25% of THR_Lex1.
Detection of P-wave Onset in Seismic Signals using Wavelet Packet Transform

Detecting the onset of P-waves in seismic signals is a crucial objective in the development of early warning systems for earthquake-prone regions. This work presents a novel time-frequency based method to efficiently detect and pick the onset of P-wave in seismic signals with low SNR (signal-to-noise ratio). The proposed technique rests on a combination of time-series modelling of seismic noise and wavelet packet transform (WPT), in which the core idea involves tracking the difference between energies of data and one-step ahead model predictions over a select set of wavelet packets (frequency bands). Auto-regressive integrated moving average (ARIMA) models are used for modelling seismic noise, while the packets are selected using the prevailing understanding of P-wave frequency content. The proposed method is superior to the existing methods in two respects, (i) accuracy of detection and picking since it zooms into the frequency bands of interest (corresponding to P-wave onset) and (ii) robustness in the sense of minimal false alarms due to outliers and other sources, especially from low SNR seismograms. The performance of proposed method is illustrated on a simplified simulated process and real-time seismic data sets acquired under low SNR conditions. A comparative study with a recently developed maximum normalized cross correlation method is also presented to demonstrate the superiority of the proposed method.
Demonstration of Packetized Display Protocol (PDP) to Overcome Speed and Resolution Limitations of Conventional Display Protocols

Traditional display protocols have limitations in terms of fixed refresh-rates, high bandwidth requirements, and control over the display of frames. This paper demonstrates the performance results of an alternative approach utilizing a packetized display protocol architecture incorporating dynamic refresh-rates, high-speed capabilities, to bridge performance gaps.
Deep feature based efficient regularised ensemble for engagement recognition

Over the years, open education in online environments, such as Massive Online Open Courses, has grown rapidly. While the trend is expected to bridge the educational gap among students, the new environment has also created new challenges such as the lack of feedback and difficulties in interaction. The authors propose an automated engagement recognition system to alleviate this problem, driven by the recent developments in computer vision and artificial neural networks. The authors' proposed system extracts deep features from a facial image and employs a combination of multiple regularised shallow networks to recognise engagement. They verified the system in a public data set. The proposed system has faster learning speed and better accuracy than single deep network based approaches do.
Communicability evaluation of video-exam in libras of the ENEM platform

The High School National Exam (ENEM) offers specialized assistance to support autonomy of candidates with disabilities. One of them is the video-exam in Brazilian Sign Language for Deaf or Hard of Hearing candidates, which past exams are available online for candidates study. This paper presents results from communicability evaluation and curriculum good practices inspection of video-exams in Libras. These techniques are part of the Phase 2 (Design and Develop) of a framework in validation. This framework combines three theoretical bases: Codesign, Cycle for Adoption of Assistive Technology and Human-Computer Interaction Life-Cycle. To validate our framework proposal, a solution is been under design, to help people who are D/HH to learn written language. Communicative breakdowns may influence in abandonment of use by candidates and/or identify recommendations for redesign of ENEM Platform's video-exams.
Laser Engraver Control System based on Reinforcement Adversarial Learning

In recent years, deep neural networks have demonstrated high efficiency in solving the problems associated with image processing and analysis. The latest studies in generative adversarial neural networks open up broad prospects for solving problems in image style transfer, cross-domain adaptation, and the generation of new data for a target probability distribution. The ability of generative networks to implement complex and weakly formalized transformations allows the information systems based on generative adversarial technologies to solve problems in machine learning in a manner similar to human creative thinking. This paper presents the results of an experimental study on the design, training and deployment of an intelligent control system for a CNC laser engraver based on the generative adversarial network. In order to train deep models, large amounts of data are required. Therefore, one of the main challenges in this experimental study is a lack of labelled training dataset. The proposed control system for the laser engraver based on the deep generative model allows decreasing the time of the manufacturing process.
Sensorial Network Framework Embedded in Ubiquitous Mobile Devices

: Today’s digital society is interconnected and networked, with modern smart devices ubiquitously built into and embedded within smart environments and other environments, where people (their users) typically live. It is very important to mention that sensorial awareness of an environment depends on one’s current location and equipment, as well as the equipment’s real-time capabilities. Personal sensorial information is considered to be the key factor for progress in the improvement of the productivity of everyday life and creation of a smart surrounding environment. This paper describes the design, implementation, and testing process of a new sensorial framework based on the current possibilities created by ubiquitous smart mobile devices with sensors, which involves computing power and battery power issues. The two parts of the proposed framework have been designed, implemented, and tested. The client part is represented by a front-end mobile application, and the back-end part is represented by a server-side application. The analysis of the data, captured during the testing phase, involves the analysis of the processing time, battery consumption, and transmitted data amount. This analysis reveals that Transmission Control Protocol (TCP) and user datagram protocol (UDP) protocols have a comparable performance, although TCP is preferable for use in local networks. In comparison to other solutions such as MobiSense or Feel the World framework, the final solution of the proposed and developed sensorial framework has two main capabilities, which are the security support and social networking possibilities. The advantage of the MobiSense platform is the existence of several real-world applications, whereas the proposed sensorial framework needs to be verified in the massive context of many users in real time.
Contextual‐based approach to reduce false positives

The high rate of false positive alerts generated by the intrusion detection system (IDS), raises a crucial problem in the face of the security operator to differentiate between true attacks and failed ones. In order to solve this problem, several approaches have been developed relying on contextual information such as applications, services, network location, and vulnerabilities. The change of the context can be an effective factor to reduce false positive rate. However, most approaches in the literature have not dealt with this factor. Therefore, the authors propose non-monotonic ontology contextual-based approach (NOC-IDS), which represents a set of helpful contextual information in flexible format and dynamic reasoning. NOC-IDS aims in general to filter false positive alerts and to figure out relevant alerts, and helping the security operator to analyse relevant ones. NOC-IDS is defined by the description logic that provides non-monotonic reasoning. They illustrate the effectiveness and the powerfulness of the authors’ approach using the well-known DARPA 2000 data set.
Initializing k-Means Efficiently: Benefits for Exploratory Cluster Analysis

Data analysis is a highly exploratory task, where various algorithms with different parameters are executed until a solid result is achieved. This is especially evident for cluster analyses, where the number of clusters must be provided prior to the execution of the clustering algorithm. Since this number is rarely known in advance, the algorithm is typically executed several times with varying parameters. Hence, the duration of the exploratory analysis heavily dependends on the runtime of each execution of the clustering algorithm. While previous work shows that the initialization of clustering algorithms is crucial for fast and solid results, it solely focuses on a single execution of the clustering algorithm and thereby neglects previous executions. We propose Delta Initialization as an initialization strategy for k-Means in such an exploratory setting. The core idea of this new algorithm is to exploit the clustering results of previous executions in order to enhance the initialization of subsequent executions. We show that this algorithm is well suited for exploratory cluster analysis as considerable speedups can be achieved while additionally achieving superior clustering results compared to state-of-the-art initialization strategies.
Identification of Non-linear Dynamic System

The behaviour of non-linear dynamic systems is studied. In this paper, the authors investigate the modelling and prediction abilities of a Recurrent Neural Network, Long Short Term Memory and Gated Recurrent Unit networks. The the input data sets has a chaotic nature. The effectiveness of all networks in modelling the several chaotic attractors is studied. And a comparison of their prediction quality is made.
CALVADOS: A Tool for the Semantic Analysis and Digestion of Web Contents

Web users these days are confronted with an abundance of information. While this is clearly beneficial in general, there is a risk of "information overload". To this end, there is an increasing need of filtering, classifying and/or summarizing Web contents automatically. In order to help consumers in efficiently deriving the semantics from Web contents, we have developed the CALVADOS (Content AnaLytics ViA Digestion Of Semantics) system. To this end, CALVADOS raises contents to the entity-level and digests its inherent semantics. In this demo, we present how entity-level analytics can be employed to automatically classify the main topic of a Web content and reveal the semantic building blocks associated with the corresponding document.
Accuracy of super-resolution for hyperspectral ocean observations

Super-resolution, a class of techniques used to reconstruct a high-resolution image from one or more low-resolution observations, is a possible route to utilize the full remote imaging capabilities of small satellites and unmanned aerial vehicles. Here, we test two frequently-used variants, Robust Super-Resolution (RSR) and Projection onto Convex Sets (POCS), to see how accurately each technique reconstructs images from a small satellite. The two techniques are chosen because each utilizes a different kind of prior knowledge. RSR utilizes knowledge about the scene, while POCS utilizes knowledge about the imaging process. The algorithms are run on three bands of two hyperspectral images: one lab-acquired image of a wooden block and one simulated image of a remote sensing ocean scene. The superresolution reconstructions of the simulated image are evaluated by calculating the brightness error and spectral angle with respect to the original scene. Both super-resolution algorithms improve both metrics relative to the raw, registered data. RSR achieves more improvement overall, but POCS operates faster.
Automatic Extraction of Building Regions by using Color Clustering

A novel method that uses GrabCut for automatically extracting only regions in buildings which exist from a scenery image is proposed. As for the proposed method, the fact that the background region of a scenery image in which no buildings exist tends to be found in the upper and lower parts of the image is utilized, and the initial seed of GrabCut is automatically generated. To verify the effectiveness of this approach, with 37 images included in ZuBuD as evaluation objects, the accuracy of the proposed method was compared with that of GrabCut (which uses an initial seed generated on the basis of ideal manual input). According to the result of that comparative evaluation, it was confirmed that the extraction precision of the proposed method for building areas is 1.7% lower than that of the comparative method; in other words, there is room for improvement in regard to the proposed method. However, that difference in performance is not statistically significant; accordingly, the proposed method is considered to be effective in terms of enabling automation while maintaining a certain performance.
Conv-MCD: A Plug-and-Play Multi-task Module for Medical Image Segmentation

For the task of medical image segmentation, fully convolutional network (FCN) based architectures have been extensively used with various modifications. A rising trend in these architectures is to employ joint-learning of the target region with an auxiliary task, a method commonly known as multi-task learning. These approaches help impose smoothness and shape priors, which vanilla FCN approaches do not necessarily incorporate. In this paper, we propose a novel plug-and-play module, which we term as Conv-MCD, which exploits structural information in two ways - (i) using the contour map and (ii) using the distance map, both of which can be obtained from ground truth segmentation maps with no additional annotation costs. The key benefit of our module is the ease of its addition to any state-of-the-art architecture, resulting in a significant improvement in performance with a minimal increase in parameters. To substantiate the above claim, we conduct extensive experiments using 4 state-of-the-art architectures across various evaluation metrics, and report a significant increase in performance in relation to the base networks. In addition to the aforementioned experiments, we also perform ablative studies and visualization of feature maps to further elucidate our approach.
Ontology-Based Approach to Organizing the Support for the Analysis of Argumentation in Popular Science Discourse

The paper presents an approach to modeling and analyzing the argumentation found in popular science literature. Argumentation is modeled using an argumentation ontology based on the AIF base ontology expanded by the means for modeling the target audience and allowing for a more detailed description of the arguments’ content. In terms of this ontology, the authors give the descriptions of argumentation schemes, arguments’ structure and elements, as well as of the network of arguments and their constituent parts extracted from the texts under study. To analyze argumentation, a software system is being developed. It provides tools for modeling and identifying the structure of argumentation on a corpus of texts relating to popular science discourse. In addition, the system can examine the extracted argumentation with the purpose of revealing and analyzing argumentative strategies and rhetorical methods used in scientific and popular science texts. The paper describes the specific features of the proposed argumentation ontology and presents the architecture and functionality of the software system designed for argumentation analysis.
Region Based Convolutional Neural Network for Human-Elephant Conflict Management System

Human elephant conflict occurs due to migration of elephants from their habitat to human living areas in search of food and water. In order to reduce the Human-Elephant Conflict, a real time prototype is built to migrate the elephant to human living areas is minimized by generating honey bee sound and tiger growl sound to which the elephant's dislikes. Four object detection algorithms such as SSD mobilenet v2 model, SSDlite mobilenet v2 model, SSD inception v2 model, and Fast R-CNN inception v2 are considered. SSDlite mobilenet v2 model produced the best results with precision = 0.854 AP, recall = 0.718 AR, f1-score = 0.780, prediction time = 34.49ms for a frame rate = 31.15fps. Real time implementation is carried out using Raspberry Pi 3 with SSDlite mobilenet v2 architecture.
EAGLE—A Scalable Query Processing Engine for Linked Sensor Data

Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio-temporal correlations. Most semantic approaches do not have spatio-temporal support. Some of them have attempted to provide full spatio-temporal support, but have poor performance for complex spatio-temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio-temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio-temporal computing in the linked sensor data context.
Moon Base: A Serious Game for Education

To increase the interest of young people in the technological and scientific aspects of space research, the European Space Education Resources Office (ESERO) has decided to develop a set of games with space themes. Our group was selected to design and implement a game in virtual reality. In this paper, we describe the main design principles, interaction and navigation techniques, and technological details of the final game titled Moon Base.