Reviewerly/exHarmony huggingface dataset:
Research General Stop Words for: Big Data and Digital Aesthetic, Arts and Cultural Education: Hot Spots of Current Quantitative Research

This research was supported by a grant from the Federal Ministry of Education and Research (01JKD1711) to Benjamin Jorissen and Stephan Kroner
Hybrid Learning Approach Based Aspect Category Detection for Sentiment Summarization with Co-Occurrence Data

User-generated reviews are precious decision-making resources. Identifying the feature categories mentioned in a specified review phrase (e.g. food and service in restaurant reviews) is a significant task for analyzing sentiment and mining opinion. Most prior researchers hold hand-crafted characteristics and a classification algorithm to achieve the assignment given a predefined aspect category set. The key step to achieve better efficiency is feature engineering that consumes a great deal of human effort and can be volatile when the product domain changes. A hybrid learning method is suggested in this project to automatically learn helpful characteristics for the identification of aspect categories. Specifically, on a big collection of reviews with noisy labels, a Hybrid Aspect Analysis Algorithm is first suggested to achieve ongoing word depictions. We subsequently suggest generating deeper and hybrid characteristics through the stacked neural networks on the word vectors. Finally, a logistic regression classifier is trained to predict the aspect category with hybrid characteristics. The tests are conducted on a SemEval-2014 benchmark dataset. In this paper we achieves the state of the art results with the F1 score of 90.10% on the dataset. Overall, our approach to representation learning outperforms traditional hand-crafted characteristics and embedding algorithms with current words.
Modern Congresses as Scientific Communication: Domestic and Foreign Experience

Communication via large-scale international and national scientific forums is one of the effective technologies in the academic area. The paper explores the practices and outcomes of the largest domestic and foreign congresses held in 2018, such as the 24th World Congress of Philosophy held in Beijing, China, in August, the 25th World Congress of Political Science (Brisbane, Australia, July) and the 8th All-Russian Congress of Political Scientists (Moscow, Russia, December). The authors analyse the content of these events discussing the functions and role of academic congresses in the development of social-humanistic sciences. They strive to avoid bias in evalua­ti­ng the organization of congresses, assess the content and quality of Russian congresses against the backdrop of growing international political scientist community. The paper explores the similarities and differences between the global and Russian national congresses. The researchers conclude that the latter have reached a level largely comparable to the global level of world congres­ses by their content and functions over the past two decades. The obtained insights provide the basis for judgment on the desirability of expanding the agenda in the domestic political science by including issues of a post-digital cultural dialogue and deep transformation of the global order.
Wireless sensor network for landslide early warning and monitoring

In this chapter, a wireless sensor network (WSN) application aimed for monitoring and warning about imminent landslides is presented. It consists of multiple complex measurement nodes (MNs) connected in a radio communication network spread over the area to be monitored. Each node is able to measure two types of parameters: relative soil layers displacement and pore water pressure, each of them mounted at different soil depths. The sensors devoted to measure the above parameters are of original conception and feature very good metrological qualities, especially in terms of displacement sensing sensitivity, which is as low as 1 mm per year. The measurement node structure can be tailored according to the underground layer structure and can include several displacement and pore water pressure sensors that are connected together in a wired local network structure with serial communication. The WSN is coordinated by a gateway, which has the role of general administration of the network resources, data collection and storage and information transmission to the cloud, from where a central server accesses and downloads the data. The whole structure of such a network is detailed in this chapter, starting with sensors description, continuing with the network architecture, communication protocols, data structure, alarms generation, data analysis, power management and ending with the user interface and some results obtained by in-field deploying of the network.
ROSM - Robotic Oil Spill Mitigations

The overall aim of the ROSM project is the implementation of an innovative solution based on heterogeneous autonomous vehicles to tackle maritime pollution (in particular, oil spills). These solutions will be based on native microbial consortia with bioremediation capacity, and the adaptation of air and surface autonomous vehicles for in-situ release of autochthonous microorganisms (bioaugmentation) and nutrients (biostimulation). By doing so, these systems can be used as the first line of the responder to pollution incidents from several origins that may occur inside ports, around industrial and extraction facilities, or during transport activities, in a fast, efficient and low-cost way. The paper will address the development of a team of autonomous vehicles able to carry, as payload, native organisms to naturally degrade oil spills (avoiding the introduction of additional chemical or biological additives), the development of a multi-robot system able to provide a first line responses to oil spill incidents under unfavourable and harsh conditions with low human intervention, and then a decentralized cooperative planning with the ability to coordinate an efficient oil spill combat. Field tests have been performed in Leixões Harbour in Porto and Medas, Portugal, with a simulated oil spill and validated the decentralized coordinated task between the autonomous surface vehicle (ASV) ROAZ and the unmanned aerial vehicle (UAV).
Bell's Equality and Communication in Quantum Superposition

In this paper, we introduce a that is satisfied by any classical communication -- i.e., conveyed by a localized carrier traveling along well defined directions. In contrast, this equality is violated when the carrier is prepared in a quantum superposition of communication directions. This is phrased in terms of the success probability of a certain communication task, which results to be always constant and equal to 1/2 in the classical case. On the other hand, we develop two simple quantum schemes that deviate systematically from the classical value, thus violating the coherence equality. Such a violation can also be exploited as an operational way to witness spatial quantum superpositions without requiring the use of an interferometer, but only by means of spatially separated local measurements.
KAJIAN MACHINE LEARNING DENGAN KOMPARASI KLASIFIKASI PREDIKSI DATASET TENAGA KERJA NON-AKTIF

Comparative studies of machine learning are carried out with the aim of determining the best method base based on the ability to predict with true data. The study carried out on the labor dataset aims to extract information on the choice of agency employees to exit or not. The method used in the comparative study is K-Nearest Neighbors (KNN) from the basis of similarity, Naive Bayes (NB) from the probability base, and C4.5 from the basis of the decision tree. Application design and construction is done by receiving input labor data, the dataset is divided into training data and test data, training data for training and models while the test data is used when classifying by model. The classification process is carried out using supply training scenarios and cross validation of 14,999 data. The initial hypothesis C4.5 is the best method with an accuracy measure. Proof of the initial hypothesis will be true if the best accuracy majority is owned by the C4.5 method with supply trainning scenarios and cross validation. The results of the classification data analysis found that the C4.5 accuracy was superior in each parameter of the inventory training scenario data distribution and the k-fold parameter was 3. 5. 7, and 9 of the cross validation scenario so that the best method of non-active labor classification was C4.5.
Secure Data Retrieval for Decentralized Data Communication

Network Security involves the warrantation of ingress to data in a network.Network security mainly deals with authentication which involves username and password.The confidential information that is transmitted within the system has to be more secure.Nowadays there are crisis on the data loss.Decentralized systems are becoming outstanding solutions that allows wireless devices to communicate with each other and ingress the confidential information more steadily.There will be end to end security between source and destination pair.The data will be encrypted in the source node and decrypted in the receiver node with the aid of One Time Password, there will be a key manager to store the encrypted keys.In this system we will be implementing two algorithms i.e.AES and DES algorithms.It demonstrates how to apply the proposed mechanism to securely and efficiently manage the confidential data distributed in the decentralized network.
A modified wavelet-based method for detection of outliers in time series

As a multi-resolution analysis, wavelet transformation tool has been used to detect contingent outliers in time series data with no need to specify a model for the data. The objective of this article is to design an orthonormal wavelet system that optimizes the wavelet-based outlier detection procedure. In addition, we show that regardless of the selected base functions, the existing wavelet-based methods extract two adjacent suspicious observations so that probably one of them is an outlier. Therefore, we modify the wavelet-based outlier detection scheme by introducing a transformation matrix consisting of our designed wavelet filters that can be used to detect outlying observations without the above-mentioned ambiguity.  In a numerical example, a sample observation vector is analyzed using our scheme. At the same time, a robust statistical approach- modified z-score method- has been used to evaluate the capability of our desired wavelet-based procedure. The results were completely reliable and comparable.
Tracking of Intracavitary Instrument Markers in Coronary Angiography Images

In order to quickly and accurately obtain the marker points in coronary angiography images in interventional surgery for cardiovascular diseases, this paper proposes a method for tracking the marker points in coronary angiography images. Firstly, it is necessary for the operator to manually mark a small number of vascular center points to find the position of the guide line and the labeled points on the guide wire. So the vascular center line can be obtained by using the vascular center point of artificial labeling with Hessian matrix eigenvector. Then we design a filter to detect the guide wire by combining the characteristics of the guide wire and the center line of the vessel. Finally, the marker point is detected by filtering along the guide wire. In the following images, all the vascular center points in the image are obtained by the automatic vascular centerline acquisition algorithm, and the vascular centerline of the frame is obtained by Iterative Closest Point (ICP) registration of the point set with the adjacent frame centerline. Then the guide wire acquisition method of this frame image is consistent with the above algorithm. Finally, the marker of the frame is obtained by combining the positions of the adjacent frame marker and frame guide wire. The experimental results show that our method can quickly and accurately detect the location of marker points in coronary angiography image sequence with a small amount of manual assistance, and can be applied to the computer-aided diagnosis and treatment of cardiovascular diseases.
Sentra Main Peran untuk Meningkatkan Kemampuan Kosakata Anak di TK Dharma Wanita Kandangan Srengat Kota Blitar

Vocabulary is the basic of sentence formation, purpose of sentences will not be formed if there are no words. The richer a person's vocabulary, easier for him/her to arrange a sentence to communicate both orally and in written. This study aims to determine the effect of the application of the role playing center to increase vocabulary for children aged 4-5 years. The population and sample in this study were boys aged 4-5 years with low value criteria on the vocabulary development scale. This study used an experimental design research method. There are two groups, namely the experimental group and the control group. The analysis in this study used the Mann-Whitney U test and the Wilcoxon test.U mann-Whitney analysis obtained the average increase in scores in the experimental group (Mean Ranks on observations after treatment was 8.00and before intervention was given to 5,166) higher than the control group (Mean Ranks on observations after treatment was 3.00) This shows that the experimental group after being treated or treat in the form of a role playing center, the subject experienced an increase in vocabulary skills better than the control group where p &lt;0.005. The Z score difference is 2,611 while the Wilcoxon p &lt;0.005 difference Z score is 2,023. These show there are differences in vocabulary in the experimental group after being given treatment while the control group did not experience an increase where p&gt; 0.005 and Z score 1.761
An Approach Towards Bilingual Sentiment Analysis

&lt;p&gt;In the existing An Approach towards Bilingual Sentiment Analysis, Sentiment analysis is an emerging area of research. Over the past few decades increase in user generated content provides important aspect for researchers, companies and even government organization.&lt;/p&gt; &lt;p&gt;With the increase in online user generated content sentiment becomes a big challenge and brings promising opportunity in field of sentiment analysis.&lt;/p&gt; &lt;p&gt;User content varies tremendously since a user may.&lt;/p&gt; &lt;ul&gt; &lt;li&gt;Use single language&lt;/li&gt; &lt;li&gt;Use mixed language&lt;/li&gt; &lt;li&gt;May use mixed languages (Hindi + English)&lt;/li&gt; &lt;li&gt;May use abbreviations instead of full words&lt;/li&gt; &lt;/ul&gt;
Smart Insole and Smartwatch System with Big Data Analytics to Improve Balance Training and Walking Ability

BACKGROUND Applying wearable motion sensors to capture balance/gait performance and provide the corresponding biofeedback/reminder have been proved effective in improving users’ balance/gait [1-5]. ...
Landscaping Language Technologies using Topic Modeling and Graph Analysis: Overview of the Spanish Contribution

This work has been carried out in the framework of the Spanish State Plan for Natural Language Technologies. The work of J. Arenas-Garcia has also been partly funded by MINECO projects TEC2014-52289-R and TEC2017-83838-R.
Structural economic vulnerability and public revenue performance

This paper investigates the impact of developing countries' structural economic vulnerability (EVI) on their public revenue performance. Relying on a sample of 126 countries over the period 1996-2013, the empirical analysis suggests that EVI exerts a negative and significant impact on total public revenue. However, this negative impact reflects differentiated impact of the EVI's components (namely, the degree of exposure to shocks and the size and frequency of the shocks) on public revenue performance: exposure to shocks influences positively public revenue, while the size and frequency of shocks exerts a negative and significant impact on public revenue. These results apply also to LDCs. In this context, governments in developing countries and the international community should cooperate to mitigate developing countries' structural economic vulnerability. This would help increase their public revenue performance, which is needed to address development challenges, and reduce in the long-run, their dependence on international development assistance.
Adapting NMT to caption translation inWikimedia Commons for low-resource languages

This paper presents a successful domain adaptation of a general neural machine translation (NMT) system using a bilingual corpus created with captions for images inWikimedia Commons for the Spanish-Basque and English-Irish pairs.
A Contextual Data Offloading Service With Privacy Support

Mobile and context-aware applications are now a reality thanks to the increased capabilities of mobile devices. In the last twenty years, researchers had proposed several software infrastructures to help the development of context-aware applications. We verified that most of them do not store contextual data history and that few of these infrastructures take into account the privacy of contextual data. This article presents a service named COP (Contextual data Offloading service with Privacy support) to mitigate these problems. Its foundations are: (i) a context model; (ii) a privacy policies; and (iii) a list of synchronization policies. The COP aims at storing and processing the contextual data generated from several mobile devices, using the computational power of the cloud. We have implemented one experiment evaluated the impact of contextual filter processing in the mobile device and the remote environment. In this experiment, we measured the processing time and the energy consumption of COP approach. The analysis detected that the migration of data from mobile device to a remote environment is advantageous.
Testing of PPP Solution Aided with Enhanced Algorithms of Integer Ambiguity Resolution

PPP Algorithms take advantage of the low noise associated to carrier phase measurements of the GNSS signals to provide an accuracy in the range of centimeters. However, the use of carrier phase measurements requires the proper estimation of ambiguities. The fact that some combinations of ambiguities are an integer multiple of the wavelength allows to improve the time needed to properly estimate them and the accuracy of the resulting estimation. This can be done through the use of Integer Ambiguity Resolution (IAR) algorithms, which have become during the last few years one of the major advancements in precise positioning. Algorithms of IAR have an impact both on the generation of precise orbits and clocks for the GNSS satellites and on the use of these precise products for the positioning at user level. In the first case, algorithms are mostly focused on the computation of double differences to remove the impact of satellite and station biases, and on the fixing of those ambiguities that are close enough to an integer. The selection of the combinations of frequencies and observables is a key element in the effective execution of these algorithms. In this sense, the use of wide-lane combinations as first step to the complete ambiguity resolution has shown to be the optimal choice. Apart from the significant improvements observed in the estimated orbits and clocks, the IAR algorithms have been widely applied at user level for improving the positioning solution (PPP). At user level, the provision of phase biases allows to fix integer ambiguities by performing single differences and applying searching methods. Integer Ambiguity Resolution works best for PPP algorithms when ionospheric corrections are used, allowing for both a shorter convergence time and a better accuracy of the estimated delays. The use of IAR algorithms with accurate ionospheric corrections has shown very promising results in terms of convergence time and positioning accuracy. Our magicPPP service provides phase biases and ionospheric corrections to the algorithm, which implements IAR to obtain very fast convergence and accuracy in the centimetre level. In this work, the results of a dedicated experimentation campaign will be presented, including static and kinematic PPP scenarios with different environmental conditions and receiver types, based on our magicPPP system (www.magicgnss.gmv.com). These scenarios show the improvement in performance associated to enhanced IAR algorithms and their combination with ionospheric corrections.
Internet of Things of Trees - Conversational Objects via SMS Protocols

The paper describes a `work in progress' to develop a system to enable users to engage with the historical and environmental story behind veteran trees in Hampstead Heath in the spirit of the Internet of Things. Unlike other `Internet of Trees' projects, this study focuses on story telling rather than sensor networks. Building on previous work, conversational agents (`chatbots') are used as proxies for the trees to enable a two-way narrative exchange between the user and the `tree'. Two interaction pathways are proposed (direct SMS and web-based geofencing) and the technical development of both approaches is described, as well as ethnographic studies undertaken on Hampstead Heath to elicit engaging content for the chatbot. An initial deployment of the SMSbased interaction at Tate Exchange, a project space within Tate Modern, London is discussed and a preliminary evaluation presented.
Fourier Optics Coprocessor for Image Processing and Convolutional Neural Network

Convolution Neural Networks (CNN) are artificial networks able to extract features from large dataset by spatial filtering. Here we propose an optical coprocessor able to perform large image Altering and convolutions, outperforming current architectures.
Doing Research: An Emerging Task of Academic Librarians for University Development in Vietnam

As Vietnamese universities have gradually shifted their investment into research development, the roles and duties of librarians have also changed. Academic librarians not only provide research support but also can contribute to the research performance of their university. This study explores the research participation of librarians at Vietnamese universities and the perceptions of library directors and librarians about librarians doing research. The authors reviewed 1,350 articles in two major Vietnamese library journals to identify academic librarians and followed up with questionnaires and interviews with 36 librarians from 14 universities and 17 directors from 17 universities. While most librarians feel "encouraged" to do research, only five universities have research as a "planned activity" of the library, and only two designated research as a "duty." Becoming aware of the benefits of research and the factors that prevent librarians from doing research, the authors propose some recommendations for improving the research participation of academic librarians in Vietnam.
The World of Cryptocurrency and its Challenges in India

This paper identified the understanding of cryptocurrency. The paper analyzes the awareness, presence and challenges of crypto currency in India.