Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis of Tweets in Brazilian Portuguese with Convolutional Neural Networks

Sentiment analysis of texts posted on Twitter is a natural language processing task whose importance has grown along with the increase in the number of users of the platform and the interest of organizations on the opinions of their employees, customers and users.Although Brazil is the sixth country in the world with most active users of Tweeter and Portuguese is the seventh most spoken language in the world, with 221 million speakers (200 million of them living in Brazil), the number of articles that discuss sentiment analysis approaches for Brazilian Portuguese is a small fraction of those that focus on the English language. On the other hand, few works use deep learning for this task when compared with other machine learning and lexical based methods. In this context, the work described in this article addresses the problem using Convolutional Neural Networks (CNN). The paper presents the results of an experimental evaluation that shows that a CNN with a relatively simple architecture can perform much better than a previous approach that uses ensembles of other machine learning classifiers combined with text preprocessing heuristics
Reuse of program trees in genetic programming with a new fitness function in high-dimensional unbalanced classification

Genetic programming (GP) may also evolve biased classifiers when having the class imbalance issue. Class imbalance is a difficult but important issue, and high-dimensionality brings difficulty when addressing the class imbalance issue. This paper focuses on addressing the performance bias of GP in classification with high-dimensional unbalanced data, with the goal of increasing the accuracies of the majority class and the minority class, as well as saving the training time. In this paper, a new fitness function is developed to address the class unbalanced issue, and moreover, a strategy is proposed to reuse previous good GP trees when using multiple GP processes to build a multi-classifier system. Experimental results show the better performance of the proposed method.
Singularity Analysis of Volcanic Ages and Implications for Tectonic Setting in the Mesozoic, Great Xing’an Range, Northeast China

Frequency distribution of zircon U–Pb ages has been commonly utilized to interpret the age of a magmatic event. Anomalies in age peaks are related to plate movement caused by mantle convection during the formation of supercontinents and continent crust growth. In this paper, a singularity analysis method (frequency anomalies) is used to analyze a dataset (n = 823, discordance lower than 10%) of zircon U–Pb ages from the Great Xing’an Range (GXR), in order to characterize the causal relationship between age transitions and Pacific Plate subduction. The numberage plot result shows that there is a peak around at 125 Ma, and the log–log plot reveals that there are two transitional ages (knee points) at 125 Ma and 145 Ma. The age densities of the peak at 125 Ma and the transition at 145 Ma can both be fitted by power law functions, which indicate transitional ages have the characteristic of singularity. Combined with the subduction geological background in the late Mesozoic, the possible singularity mechanisms corresponding to the age peak at 125 Ma and the transition at 145 Ma are slab rollback and slab breakoff of the Pacific Plate, which is consistent with conclusions from geology and geochemistry. This result suggests that singularity analysis can be used as a new method to quantitatively characterize volcanic activities and tectonic setting in geological processes.
Interactive Graph Data Integration System With Spatial-Oriented Visualization and Feedback-Driven Provenance

This paper proposes new techniques in a visualization-enabled graph data quality assessment and alignment system leveraging feedback-driven provenance with goal of improving scalability, reducing execution time, and increasing relevance. The proposed system consists of following two components. First, graph data quality assessment with spatial-oriented visualization and feedback-driven provenance; in this section, we propose a new paradigm of feedback-driven provenance in order to streamline the collection of run-time provenance information based on user feedback with the goal of reducing execution time and improving relevance. We apply this idea in the context of graph data quality assessment and alignment, in which we propose a system that leverages user feedback on various components on the schema of a graph database during selection of samples of graphs to maintain provenance of representative nodes of samples. We utilize this provenance of representative nodes of samples to improve the effectiveness of future graph samples during quality assessment task. For the visualization component, we propose a solution based on the spatial-oriented approach with the goal of improving scalability, along with statistics and a visualization system based on the notion of heatmaps that involves utilizing quality information of dataset in order to assign to the spatial locations of various graph vertices on the screen varying degree of color intensity pixels. Second, graph data alignment leveraging spatial-oriented visualization and feedback-driven provenance: in this section, we propose a solution based feedback-driven provenance paradigm discussed earlier in context of graph databases by utilizing graph query logs as a feedback to select relevant data of neighborhoods of nodes that were matched during the process of graph alignment at run-time so as to improve its relevance and reduce execution time as well as a spatial-oriented solution to utilize the graph similarity measures in order to allocate spatial pixel positions to graph vertices as a part of a visual analytics tool with a focus on scalability allowing users to compare graphs visually.
Role and Application of RFID Technology in Internet of Things: Communication, Authentication, Risk, and Security Concerns

The Internet of Things (IoT) is a very encouraging and fast-growing area that brings together the benefits of wireless systems, sensor networks, actuators, etc.A wide range of IoT applications have been targeted and several aspects of this field have been identified to address specific issues, as well as technologies and standards developed in various domains such as in radio frequency identification(RFID), sensors, and mobile telephony, to name a few. This article aims to talk specifically about the RFID technology and its accompanying communication, authentication, risk, and security concerns while applied to the IoT field. An important part of this work is indeed focused on security aspects that derive from the use of RFID in IoT, especially in IoT networks. The results of our research work highlighted an excellent integration of RFID in the field of Internet of things, particularly in healthcare systems.
Mob Programming: A Systematic Literature Review

Here, we present a systematic literature review of mob programming. Mob programming is a new software development approach, a whole team (more than two people) works together on a code at the same time, in the same space, and on the same computer. The objective is to determine the current reported knowledge of mob programming. We searched two digital libraries, but only ten papers were found. These papers were analyzed and the benefits and risks of mob programming as well as the future research directions are discussed.
Professional Development of Librarians due to the Necessity of Qualitative Changes of the Branch

The purpose of the paper is to research the vocational education needs of Ukrainian universities’ librarians in order to acquire / update competencies under the circumstances of rapid technological, social and economic change. The results were obtained through an online survey conducted by The Section of the University Libraries of the Ukrainian Library Association. The online questionnaire consisted of six logically related sections designed to find out the following: demographic characteristics, the attitude to the need for continuous vocational education, the knowledge level, skills and abilities based on self-examination, effective education forms, motivating and restraining factors, peculiarities of information perception, etc. The findings demonstrated that librarians are cognizant of the necessity for continuing professional development in order to implement changes. Another important finding is that there is a demand for professional development programs. The findings are important because they specify the range of topics, skills, abilities that librarians feel the necessity for, as well as the priorities in choosing the convenient educational forms. The study is also intended to contribute towards the development of appropriate curricula, resources, and services for advanced training.
PENINGKATAN KETERAMPILAN MEMBACA CEPAT DENGAN TEKNIK SKIMMING

&#x0D; &#x0D; &#x0D; &#x0D; A B S T R A C T&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; This research is a CAR designed to improve speed reading skills with skimming techniques of second semester students in Indonesian Language and Literature Education Study Program FKIP UTP. The research design uses the Kemmis and Taggart models which consist of planning, action, observation, and reflection. The data collection technique of this research is in the form of observation and interviews. The level of success of this study is the reading speed of students reached 350 kpm with the percentage of reading comprehension of 70%. Based on the results, the average speed of student reading on pre-cycle is 153 kpm, in cycle I is 258 kpm, and in cycle II is 351 kpm. Furthermore, for the pre-cycle reading comprehension level is 37%, in the first cycle is 64%, and in the second cycle is 73%. Besides that, it was proven that positive changes occurred based on observations and interviews.&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;
Editorial for Special Issue “Polymetallic Metallogenic System”

In the last century, following the development of Earth System Science, the metallogenic system has become an important topic in the study of mineral deposits [...]
Axiological aspect in the context of teaching philosoph

In what way should philosophical subjects be taught from the perspective of values?In essence, there is a possibility of doing it in three distinct ways: The first one is via maintaining a certain ideological position and choosing a style of education, methods, and approaches through the prism of the subject in question.The second possibility is determined by the approach to teaching stemming from a neutral evaluative aspect, or by giving a neutrally charged portfolio.The third option is to utilize an apophatic grasp on the problem in question achieved through the negative ostracization of certain ideological positions.In this case, the differential criterion would be represented by the social consequences of historically existing models asserting concrete opinions.This paper constitutes a discussion on the process of reconciling the possibilities above.
A Proposed Experimental Method for the Preparation of Rammed Earth Material

To build with raw earth, several methods can be applied.The most common of them are: adobe, compressed earth blocks and rammed earth.However, not all earth types are suitable for these construction methods.Indeed, for each construction technique, there are requirements to fulfilled, particularly regarding grain-size distribution and plasticity proprieties of the earth chosen to be used.This paper presents a case study on a clayey earth.The aim is to check its suitability for rammed earth technique.an experimental method has therefore been adopted to achieve this purpose.As a first step of this approach, the earth has been identified by analyzing its grain-size and its plasticity.Then, and based on the results of these analysis, which show that the material does not satisfy the requirements so that it can be used in rammed earth, texture modification by adding sand and gravel has been carried out.As a result, clay activity and shrinkage-swelling potential has significantly decreased.In the second step, several compaction tests bas been carried out, which showed a decrease in the optimum compaction moisture content and an increase in the maximum dry density of the material after sand was added.These improvements are even greater after adding gravel.The quantities of sand and gravel added to raw earth are determined in order to increase the compactness of the material thus formulated.In the final step, and to assess the relevance of the stabilizations applied, uniaxial compression tests are carried out on dry specimens made from the materials formulated.The results show slight but beneficial impact on dry compressive strength.
Realization of Person Tracking and Gesture Recognition with a Quadrotor System

In this paper, the design of a quadrotor vehicle having a person-tracking and observation system, which uses human gesture recognition, is described.The system has three operating functions, namely, object tracking, human gesture recognition, and fixed-point cruising.The tracking-learning-detection (TLD) algorithm was used to enable the autonomous tracking of the object from images.An extended Kalman filter (EKF) provides an estimate of the current position of the quadrotor vehicle, and a fuzzy-proportional integral derivative (PID) controller provides position error compensation.The principle of the human gesture recognition system is as follows.A background model is first built from images using a Gaussian mixture model (GMM) to detect the foreground image.A nonlinear support vector machine (SVM) is then employed to recognize changes of gesture and establish interactivity between the vehicle and the user.The coordinates of the vehicle are marked using a GPS for fixed-point cruising.The coordinates and parameters of the points are set so that the quadrotor vehicle can follow them during cruising.Lastly, all of the functions are incorporated into the person-tracking and gesture-recognition system in the quadrotor.The experimental results show the feasibility of the above-mentioned methods, which can help us easily recognize the various gestures in this study.
Corruption and Its Effects on Sustainable Economic Performance

&lt;p&gt;The study examines corruption and its effects on achieving sustainable economic performance in Africa with a data set from 2002-2017. The Hausman Test for determining the appropriate model selection between Random and Fixed effects was employed with the fixed effects model of estimation chosen to be the appropriate method of estimation indicating that the degree of relationship and significant between corruption and sustainable economic performance in negative. The R² explains that 95% of variations in sustainable economic performance in the estimation of prime independent variables. Aside corruption having a negative and insignificant impact on sustainable economic performance, an increase in human development and labour resulted in a positive and significant relations on sustainable economic performance, with the rest of the explanatory variables having a poor and negative affiliation with sustainable economic performance. The above therefore follows the empirical, conventional and theoretical perspective that corruption declines growth and sustainability both domestically and globally.&lt;/p&gt;
Improvement accuracy of recognition isolated Balinese characters with Deep Convolution Neural Network

The numbers of Balinese script and the low quality of palm leaf manuscripts provide a challenge for testing and evaluation for character recognition. The aim of high accuracy for character recognition of Balinese script,we implementation Deep Convolution Neural Network using SmallerVGG (Visual Geometry Group) Architectur for character recognition on palm leaf manuscripts. We evaluated the performance that methods and we get accuracy 87,23% .
Feature Intertwiner for Object Detection.

A well-trained model should classify objects with a unanimous score for every category. This requires the high-level semantic features should be as much alike as possible among samples. To achive this, previous works focus on re-designing the loss or proposing new regularization constraints. In this paper, we provide a new perspective. For each category, it is assumed that there are two feature sets: one with reliable information and the other with less reliable source. We argue that the reliable set could guide the feature learning of the less reliable set during training - in spirit of student mimicking teacher behavior and thus pushing towards a more compact class centroid in the feature space. Such a scheme also benefits the reliable set since samples become closer within the same category - implying that it is easier for the classifier to identify. We refer to this mutual learning process as feature intertwiner and embed it into object detection. It is well-known that objects of low resolution are more difficult to detect due to the loss of detailed information during network forward pass (e.g., RoI operation). We thus regard objects of high resolution as the reliable set and objects of low resolution as the less reliable set. Specifically, an intertwiner is designed to minimize the distribution divergence between two sets. The choice of generating an effective feature representation for the reliable set is further investigated, where we introduce the optimal transport (OT) theory into the framework. Samples in the less reliable set are better aligned with aid of OT metric. Incorporated with such a plug-and-play intertwiner, we achieve an evident improvement over previous state-of-the-arts.
The asymptotic behavior of automorphism groups of function fields over finite fields

The purpose of this paper is to investigate the asymptotic behavior of automorphism groups of function fields when genus tends to infinity. Motivated by applications in coding and cryptography, we consider the maximum size of abelian subgroups of the automorphism group $\textrm {Aut}(F/{\mathbb F}_q)$ in terms of genus ${g_F}$ for a function field $F$ over a finite field ${\mathbb F}_q$. Although the whole group $\textrm {Aut}(F/{\mathbb F}_q)$ could have size $\Omega ({g_F}^4)$, the maximum size $m_F$ of abelian subgroups of the automorphism group $\textrm {Aut}(F/{\mathbb F}_q)$ is upper bounded by $4g_F+4$ for $g_F\ge 2$. In the present paper, we study the asymptotic behavior of $m_F$ by defining $M_q=\limsup _{{g_F}\rightarrow \infty }\frac {m_F \cdot \log _q m_F}{{g_F}}$, where $F$ runs through all function fields over ${\mathbb F}_q$. We show that $M_q$ lies between $2$ and $3$ (resp., $4$) for odd characteristic (resp., even characteristic). This means that $m_F$ grows much more slowly than genus does asymptotically. The second part of this paper is to study the maximum size $b_F$ of subgroups of $\textrm {Aut}(F/{\mathbb F}_q)$ whose order is coprime to $q$. The Hurwitz bound gives an upper bound $b_F\le 84(g_F-1)$ for every function field $F/{\mathbb F}_q$ of genus $g_F\ge 2$. We investigate the asymptotic behavior of $b_F$ by defining ${B_q}=\limsup _{{g_F}\rightarrow \infty }\frac {b_F}{{g_F}}$, where $F$ runs through all function fields over ${\mathbb F}_q$. Although the Hurwitz bound shows ${B_q}\le 84$, there are no lower bounds on $B_q$ in the literature. One does not even know whether ${B_q}=0$. For the first time, we show that ${B_q}\ge 2/3$ by explicitly constructing some towers of function fields in this paper.
A Formal Methodology for Easing Development and Maintenance of Entity Services in Service Oriented Software-Defined Internet of Things

Internet of Things (IoT) systems are usually built with entity services, which are those abstracting functionalities of sensing and executing devices in the physical space. As requirements of sensing or controlling the physical space can be varied with different systems, entity services are supposed to be easily adapted to meet such dynamicity. To ease updating and modification of entity services, although a software-defined network approach has been applied in building IoT systems, entity services developed with the same software architecture as traditional services on the Internet have an inherited problem in adaptability. In order to solve the problem, we abstract the functionalities of an entity service in social, cyber, and physical spaces into application model, sense-execute model, and physical model, respectively, and propose a physical model driven software architecture (PMDA) for guiding design of entity services. To ease development of entity services, we also propose a formal development method of entity services (fDES) to transform the abstracted models of PMDA into implementable software modules. Besides, to reduce maintenance cost of entity services when adapting them to different requirements from the social space, we propose a formal maintenance method of entity service (fMES). The correctness of fDES and fMES is verified by a case study, and their effectiveness in reducing cost of developing and maintaining IoT systems composed of large-scale frequently changed entity services is proved by analysis.
Extended Trigger Terms for Extracting Adverse Drug Reactions in Social Media Texts

Adverse Drug Reaction (ADR) is a disorder caused by taking medications.Studies have addressed extracting ADRs from social networks where users express their opinion regarding a specific medication.Extracting entities mainly depends on specific terms called trigger terms that may occur before or after ADRs.However, these terms should be extended, especially when examining multiple representation of N-gram.This study aims to propose an extension of trigger terms based on the multiple representation of N-gram.Two benchmark datasets are used in the experiments and three classifiers, namely, support vector machine, Naïve Bayes and linear regression, are trained on the proposed extension.Furthermore, two document representations have been utilized including Term Frequency Inverse Document Frequency (TFIDF) and Count Vector (CV).Results show that the proposed extended trigger terms outperform the baseline by achieving 88% and 69% of F1-scores for the first and second datasets, respectively.This finding implies the effectiveness of the proposed extended trigger terms in terms of detecting new ADRs.
Bitcoin and Future of Cryptocurrency

There are diverse variables that impact the future usage of bitcoin, from issues related to security to concerns pertaining to garnering acceptance from the market and customers. In this article, few predictive statements have been analysed regarding the future of bitcoin, such as (1) the growth of share of digital purchases, (2) customer acceptance for blockchain innovation in electronic settlements, payment and banking system, (3) the emergence of bitcoin and also various other cryptocurrencies as niche cash, and (4) the implications of bitcoin or some other cryptocurrency as a specific niche cash in nations with specifically weak currency, and (5) the possibilities of regulative dangers of bitcoin usage in these nations.
Blockchain for Healthcare: Privacy Preserving Medical Record

Blockchain technology is considered as a promising in most of the fields like Healthcare, Banking sector, Education industry, digital currency, insurance industry, etc.In today's world it is treated as a most secure and reliable technology to use for the security purpose and eliminate the need for trusted third party.The main focus of this approach is the use of blockchain in managing and securing patient data from malicious activity.The physiological data of a person are sensitive .If the patient have any embarrassing sickness, with the use of private blockchain , aim is to make the patient data secure by applying Secure Hash Algorithm for the generation of hash values and Paillier algorithm for Reencrypt same information of patient data that is divided in number of different servers.This will increases the difficulty of hacker to hack or access the data.This approach maintains the security parameters i.e.