Reviewerly/exHarmony huggingface dataset:
Analysis of Big Data in Healthcare and Life Sciences Using Hive and Spark

Big data is a declaration used to recognize the database whose area is afar the potential of typical database software tools to store, organize and examine. Big data has shown a new path toward the mankind. With several theoretical and technological obstacles in health huge processing, it is onerous to transfer knowledge into fortunate and valuable applications. Meeting the challenge of handling big data in healthcare information construction procedure, this paper proposes a referential architecture on the Hive and Spark platform to overcome the problems in healthcare big data process. Hive is a noteworthy project as a result of it permits exposing the simplest components of Hadoop, specifically map reduce and knowledge storage. Spark may be a memory-based computing framework that features a higher ability of computing and fault tolerance, supports batch, interactive, iterative and flow calculations. Experiment results of data upload, data query and data analysis show that the performance of the proposed framework is greatly improved, and a brief summary of the performance and the differences between two methods of Hive and Spark is also discussed.
Detecting Multiple People with Low-Resolution Thermal Sensors in Smart Spaces

This paper presents a framework to accurately and non-intrusively detect the number of people in an environment. Our system setup uses only ambient sensors with low-resolution, using no multimedia resources or wearable sensors. This preserves user privacy in the environment, and requires no active participation by the users, causing no discomfort. Our experiments demonstrate that algorithm selection is very important, but with careful selection, we can obtain up to 100% accuracy. Also, we prove that sensor placement plays a crucial role on the system performance, where placing the sensor on the room ceiling yields to the best results.
Efficient Elliptic Curve Cryptoprocessor for enabling TLS protocol in low-cost reconfigurable SoCs

Low-cost Systems-on-Chip (SoC) are becoming very useful for building Internet of Things coordinators/gateways because of the embedded microprocessors allowing the execution of complex software for collecting and sending information to cloud servers. However, if this information has to be securely sent through the Internet, Transport Layer Security (TLS) handshake is required, thus implying heavy cryptographic operations. In this work, the design of an area-improved Elliptic Curve Cryptography (ECC) co-processor with great performance figures, is presented. The developed design requires less than 3500 LUTs when implemented in Zynq devices, while performing ECC scalar-point operation in around 400us operating at 50MHZ. This accelerator can free the main microprocessor from these operations, thus enabling the SoC to be securely connected to the Internet.
An Efficient Parameter Optimization Algorithm and Its Application to Image De-noising

Prevailing image enhancement algorithms deliver flexible tradeoff at different level between image quality and implementation complexity, which is usually achieved via adjusting multiple algorithm parameters, i.e. multiple parameter optimization. Traditional exhaustive search over the whole solution space can resolve this optimization problem, however suffering from high search complexity caused by huge amount of multi-parameter combinations. To resolve this problem, an Energy Efficiency Ratio Model (EERM) based algorithm is proposed which is inspired from gradient decent in deep learning. To verify the effectiveness of the proposed algorithm, it is then applied to image de-noising algorithm framework based on non-local means (NLM) plus iteration. The experiment result shows that the optimal parameter combination decided by our proposed algorithm can achieve the comparable quality to that of the exhaustive search based method. Specifically, 86.7% complexity reduction can be achieved with only 0.05dB quality degradation with proposed method.
Information Model of the Media Sphere – Semantic Network

Research into different aspects in media sphere is of urgent importance for modern society nowadays. The article discusses the structure, functions and properties of media sphere which are the parts of the social management framework. In the article, these are presented as a fragment of the semantic network. The totality of media which main function is to influence the mass audience is considered to be a system of media sphere. To study “media impact,” a cognitive model consisting of one target and fifteen control factors is developed. The model is formalized in the form of a weighted oriented graph. Congruence of values of correlation of the factors is carried out using the methods of mathematical statistics. A simulation experiment is conducted to investigate the systemic characteristics of “sustainability.” The author's algorithm of the software is built with the use of finite-difference equations. The results of the experiment show that the most important control factors of the cognitive model are such factors as “impact of power structures,” “power correction mechanisms,” “channels” and “methods” of media influence on mass consciousness. The semantic network, as an information model of the subject area to study, can be used to develop an automated analytical system of information flows. To achieve this, submissions, for example, to the publishing house are to be selected in accordance with the criteria given.
An HMM-based synthetic view generator to improve the efficiency of ensemble systems

Abstract One of the most active areas of research in semi-supervised learning has been to study methods for constructing good ensembles of classifiers. Ensemble systems are techniques that create multiple models and then combine them to produce improved results. These systems usually produce more accurate solutions than a single model would. Specially, multi-view ensemble systems improve the accuracy of text classification because they optimize the functions to exploit different views of the same input data. However, despite being more promising than the single-view approaches, document datasets often have no natural multiple views available. This study proposes an algorithm to generate a synthetic view from a standard text dataset. The model generates a new view from the standard bag-of-words approach using an algorithm based on hidden Markov models (HMMs). To show the effectiveness of the proposed HMM-based synthetic view generation method, it has been integrated in a co-training ensemble system and tested with four text corpora: Reuters, 20 Newsgroup, TREC Genomics and OHSUMED. The results obtained are promising, showing a significant increase in the efficiency of the ensemble system compared to a single-view approach.
Decision Support System in Determining Smart TV using MOORA

Smart TV, also known as a TV connected to the internet, is a traditional television device integrated with an internet connection and interactive features. This TV allows users to hear music and videos in realtime, surf the internet, and view photos. Smart TV is a convergence of computer technology, television, and set-top boxes. In addition to the traditional functions of television sets and set-top boxes provided through traditional broadcast media. It provides Internet, online interactive media, over-the-top content (OTT), and streaming media-on-demand, and home network access. Choosing a Smart TV is not easy because of the many brands and types on the market. Several criteria must be met in choosing a Smart TV. The MOORA method is one of the methods of decision support systems that can help a person choose the best Smart TV according to the weight of the preferences set by the user. Users can emphasize a weight on specific criteria to determine the focus of the search. By applying the MOORA method, users can get the best Smart TV recommendations according to their choice.
Design of Short Messaging Service based Public Address System for Industrial Architectures over Wireless Networks

This paper deals with a web-based public address system for conveying information for different institutions. An architecture for a Short Messaging Service (SMS) based public address (PA) system that can be programmed by an authorized mobile phone or device is proposed. This PA system will facilitate information transfer from heads of offices, managers, directors, and deans of the institutions to its constituents or unit area as well as enable postings of information with proper authentication and validation remotely. The system supports high priority messaging, allowing the conveyance of critical and time sensitive information. The mobility management support for the PA system will be based on the Hierarchical Mobile Internet Protocol version 6 (HMIPv6), hence, allowing for a seamless connectivity to the system. The major advantage of this proposed PA system as compared with the traditional electronic displays and bulletin boards is seamless mobility wherein the display devices can be programmed remotely by heads of the institutions and business organization. It also allows faster communication and immediate actions concerning the different institutions, organizations, and businesses, thus, ensuring high productivity.
A Markov Network Model for Natural Language Semantic Matching

We address the problem of designing practical methods for natural language semantic matching. While current deep neural networks can achieve high accuracy on benchmark datasets, their model structures are typically rigidly defined and as a result often encounter generalization issues and cannot easily adapt to various training data limitations. In this paper, we propose a Markov Network Model that leverages linguistic independence assumptions to decompose the problem into subproblems that can be solved independently for both generalization and the ability to adapt to various amounts of training data. The proposed framework is orthogonal to deep neural networks, since they can be integrated to the framework in the form of potential functions on the cliques corresponding to smaller sub-problems. Experiments on diverse real-world datasets show our method outperforms current state-of-the-art both in terms of accuracy, generalization and the ability to perform under various training data limitations.
Design and Prototype Development of Advanced Disaster Visualization: An Intelligent, Interactive Information System for Disaster Safety Data Code Operation

Recent disasters have caused a great deal of personal and social damage.In addition to the work of preventing disasters by examining subsequent damage, it is important to manage disasters in real time.Therefore, a variety of measures are prepared to utilize empirical disaster information to manage ongoing disasters.However, the current system available to disaster managers is insufficient.In this study, we discuss the necessity of a visualization system for safety data code operation in rapid disaster situation management.This system should address safety operations according to the type of disaster, so those involved in situation management can easily grasp appropriate data.The purpose of this project is to establish a visualization system that can reduce the damage of future disasters by utilizing planned rapid situation management.Through this study, we propose a prototype of an information visualization system that assesses and provides task analysis and requirements for the responding disaster officer.It will be possible to establish such a practical disaster safety information system through the use of practical trials, an operation plan, and an improvement plan.
Minimal Perimeter Parallelograms Inscribed in a Pair of Confocal Ellipses

Properties of parallelograms inscribed in a pair of confocal ellipses with minimal perimeters are derived from those describing parallelograms inscribed in confocal ellipses with maximal and not minimal perimeters.
Global informational society: questions and perspectives (the problem of information systems as ideal models of interpersonal communication)

The modern world is firmly connected with the phenomenon of globalization. In fact, globalization means a new stage in the historical development of society, where the economic, political and cultural interaction of the countries of the modern world is becoming more intense and profound. The method of temporal analysis and the method of personalistic and ideal-typical reconstruction, a tool that is adequate to the author’s interpretation of inter-personal understanding in the era of globalization as a process of constituting the temporal and ethical dominance of individuals and collectivities that formalize the integrity of the cultural epoch, will become the immediate, applied methodological basis. The article deals with the phenomenon of social understanding, as important interaction between individuals in the global modern society through the analysis of information systems and inter-machine communication. The authors are convinced that as a result of all these processes we can speak about principal new level of individual relationship. In our opinion, information communication systems created by people are the ideal communication model for the future society. The article is devoted to for the inter-personal understanding in the future society based on information systems.
Path Planning through Tight Spaces for Payload Transportation using Multiple Mobile Manipulators

In this paper, the problem of path planning through tight spaces, for the task of spatial payload transportation, using a formation of mobile manipulators is addressed. Due to the high dimensional configuration space of the system, efficient and geometrically stable path planning through tight spaces is challenging. We resolve this by planning the path for the system in two phases. First, an obstacle-free trajectory in R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> for the payload being transported is determined using RRT. Next, near-energy optimal and quasi-statically stable paths are planned for the formation of robots along this trajectory using non-linear multi-objective optimization. We validate the proposed approach in simulation experiments and compare different multi-objective optimization algorithms to find energy optimal and geometrically stable robot path plans.
Prins of boswachter? Interpretatie van subjectanaforen in het nederlands als tweede taal

This study aims to investigate offline processing of subject anaphora in Dutch. An important parameter in this study is the difference between pro drop and non-pro drop languages. An experiment has been run to investigate whether 27 Czechs that speak Dutch as a second language are able to interpret the referent belonging to a subject anaphora as well as 27 native Dutch speakers. These anaphors occurred in short Dutch stories with or without a topic shift, a cognitively demanding process where anaphors point to different referents in the story. One can expect this to lead to misinterpretation, especially in the case of groups of language users, whose cognitive capacities are already somehow compromised, such as the elderly or non-native speakers of a language. For the purposes of hierarchisation of subject anaphors, the Givenness Hierarchy has been used. To be able to determine the type of anaphora, the algorithm derived from the Centering Theory has been used. The study shows that Czechs fail to interpret the right referent of subject anaphora in the stories without the topic shift significantly more than the native Dutch control group. This can be explained by the interference between pro drop and non-pro drop languages. A pronoun signals a topic shift in Czech, while in Dutch, pronouns express topic continuity. Czechs often also incorrectly interpret the anaphora in the stories with a topic shift, although here the difference with the control group was not significant. This could be explained by the low number of parameters which have been considered during the experimental design.
Visualization of Learning Outcome Structure for Self-Learning

Abstract The success and quality of an educational or training system is assessed by its match to the competences it claims to develop. In this research, we propose a conceptual model of contextualized competence (or learning outcome), being a learner’s capability with respect to some subject matter. We also suggest a controlled vocabulary of capability verbs, based upon Merrill’s theory insight that a particular capability should be associated with a particular type of subject matter. At this stage, the model is deployed as a subject matter graph without contextualization in the domain of programming fundamentals. The implemented tool associates learning resources (mainly html links) with the domain knowledge structures, offering graph creation and visualization, resource suggestions, and learning paths. Future work will include learning outcomes and related context, and will evaluate user satisfaction and accessibility.
Relativizzazione e restrittività: alcuni criteri distintivi in russo

This work discusses issues related to the syntax and semantics of relative clauses in contemporary Russian. More precisely, the study explores some differences between restrictive and non-restrictive relative clauses on the basis of data drawn from the Russian National Corpus and, additionally, on the basis of native speakers’ judgments. It will show the occurrences of the relative introducers čto and kotoryj investigating the nature of their possible antecedents (animacy feature), the Case-marking strategy and the Resumption phenomenon, which has been tested in colloquial language. My observations appear to support the hypothesis on a relationship, in Russian, between the Resumption strategy (that only čto -relatives license) and the non-restrictive interpretation of the relative clause, if island-constraints are not present.
Determination of Breast Cancer Using Neural Networks

Breast cancer is the second leading causes of disease in women. In practice effective ways to prevent breast cancer is still researched for a better solution, since the cause is not yet organised completely. Early detection is an effective way to diagnose and manage breast cancer can give better chance of full recovery. This work focuses on detection of breast cancer in digital mammograms. In this paper various preprocessing methods, segmentation process and classifier were discussed. In our work, Support Vector Machine is used to show accuracy on the breast cancer image. The accuracy test we had undergone using three SVM techniques.
A Memory Bandwidth-efficient Architecture for Lossless Compression Using Multiple DPCM Golomb-rice Algorithm

In this paper, we propose a lossless compression architecture with less memory bandwidth. For this purpose, the compression ratio is improved with the prediction function using multiple differential pulse-code modulation algorithm which performs the variable sign code Golomb-rice encoding algorithm with entropy coding. For performance evaluation, several experiments were performed on 4k image sequences on the proposed architecture. The results show that it achieved an average compression ratio of 1.67 on the benchmarks.
Collaborative Filtering Recommender Algorithm Based on Ontology and Singular Value Decomposition

Aiming at the recommendation accuracy of user-item rating matrix sparse generation in e-commerce, we propose a Collaborative Filtering (CF) recommender algorithm based on ontology and Singular Value Decomposition (SVD). Firstly, we build a hierarchy tree based on the ontology concept, and calculate the semantic similarity between items. Then, fill the partial missing values in the user-item sparse matrix according to the semantic similarity, and adjust the threshold so that the intrinsic attribute features of the original matrix are not destroyed. Finally, based on the padding score matrix, the SVD is used to predict and fill the missing values in the sparse matrix again. We test the method taking MovieLens as dataset and compare the experimental results with traditional methods. The result showed that the proposed method effectively mitigates the sparseness of CF recommender and improving its accuracy.
Data analysis in price police marketing research in construction

Abstract The article deals with the data analysis marketing approach to pricing in construction in the segment of luxury housing at the present stage. The authors consider three approaches to setting the price of luxury housing in construction, dwell on the marketing aspect of determining the price. The problem of perceived quality and its relation to price is described at length. The authors demonstrate that psychological pricing is one of the common approaches to price determination in this sector of construction industry. The article presents the results of survey aimed at young Russian consumers. The distinctive features of this segment and their requirements to justify the price are described in detail.
Combination of Feature-based and Instance-based methods for Domain Adaptation in Sentiment Classification

Methods of domain adaptation can be roughly divided into two categories: feature-based and instance-based. In summary, both methods are a kind of weighted-learning, but feature-based gives weight to features and instance-based gives weight to instance. Generally, feature-based is more effective than instance-based. However, these two methods can be combined to improve the accuracy of only the feature-based method. In this paper, we do it using the neural network model, where we use the feature-based method as SVD and instance-based method proposed in our previous work. In the experiment for the Amazon dataset, we confirmed the effectiveness of the proposed method.
Towards the use of Hyperknowledge in Educational Domain through Mutimedia Trails

There has been many efforts in the research community to create reusable learning objects with rich metadata specification. Even with this effort, there is still a gap in the document representation of learning objects and its contents semantics. This gap makes difficult to create individual plans made to explore the learning curve and necessities of each student when consuming digital learning objects. In this paper we present a novel approach based on the hyperknowledge conceptual model to improve the understanding, reasoning and visualization of the professors about the learning process of his or her students. This approach helps professors to make queries that identify student behaviors over time. It also allows recommendation systems to be developed exploiting good user practices and past behavior patterns to suggest better ways for students to browse educational content.
Fabio Magro, Arnaldo Soldani: Il sonetto italiano. Dalle originia oggi. Roma: Carocci, 2017

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Classification of Dementia MRI Images using Blended Biogeography Based Optimization

This research paper investigates the performance of Blended Biogeography Based Optimization in classification of brain MRI images into two categories: Demented and NonDemented. Using Biogeography based optimization for solving optimization and feature selection problems are so common. But using this technique to solve classification problem will be ingenious. For this analysis, MRI brain images of 52 demented subjects and 65 non-demented subjects were downloaded from OASIS website. To justify the performance of Blended Biogeography based optimization, the popular benchmark meta-heuristic algorithms such as Particle Swarm Optimization and Harmony Search algorithms are used. The performance of these classifiers is analyzed for two cases: with and without statistical features. It is observed that Blended Biogeography Based Optimizations with statistical features provides highest accuracy of 82.05% while the other tested techniques provide the accuracy less than 71%.