Reviewerly/exHarmony huggingface dataset:
User-based Network Embedding for Collective Opinion Spammer Detection

Due to the huge commercial interests behind online reviews, a tremendousamount of spammers manufacture spam reviews for product reputation manipulation. To further enhance the influence of spam reviews, spammers often collaboratively post spam reviewers within a short period of time, the activities of whom are called collective opinion spam campaign. As the goals and members of the spam campaign activities change frequently, and some spammers also imitate normal purchases to conceal identity, which makes the spammer detection challenging. In this paper, we propose an unsupervised network embedding-based approach to jointly exploiting different types of relations, e.g., direct common behaviour relation and indirect co-reviewed relation to effectively represent the relevances of users for detecting the collective opinion spammers. The average improvements of our method over the state-of-the-art solutions on dataset AmazonCn and YelpHotel are [14.09%,12.04%] and [16.25%,12.78%] in terms of AP and AUC, respectively.
Um estudo dos usuários e usos do Arquivo Público Municipal de Vitória 

It presents the analysis of the users' profile and the uses of the fonds of the Arquivo Publico Municipal de Vitoria, through a documentary research based in 109 service forms of the institution. As a result, predominances were identified in relation to the purpose of the research; occupation and location of users; informational needs and consulted materials.Keywords: users; archives; municipal public archive; use.
Decoding Quantum Error Correction Codes With Local Variation

In this article, we investigate the role of local information in the decoding of the repetition and surface error correction codes for the protection of quantum states. Our key result is an improvement in resource efficiency when local information is taken into account during the decoding process: the code distance associated with a given logical error rate is reduced with a magnitude depending on the proximity of the physical error rate to the accuracy threshold of the code. We also briefly discuss an averaged approach with local information for table lookup and localized decoding schemes, an expected breakdown of these effects for large-scale systems, and the importance of this resource reduction in the near term.
Tissue Preservation Index (TPI) and Gelification Index (GI) with the Quality of Coal as well as the Connection to the Acid Water Mining

Tissue Preservation Index (TPI) and Gelification Index (GI) are products in coal related to the quality of coal and acid water mining. Research is conducted to know the relationship between the two by means of correlation using polynomial method and pressing method on the environmental diagram of the depositional environtment/facies. The correlation result is obtained that the higher the value of TPI, the value of Inherent Moisture and Volatile Matter is lower and the value of ash content and Fixed Carbon is higher. The higher the value of the GI, then the value of ash content and Fixed Carbon is higher and the value of Volatile Matter is getting lower. The lymnic deposition facies are areas that are located far from the sea and covered by a barrier and composed of land sediment with an active hydrological system. This is what causes minimal acidic water to occur because the sea sulfide does not intervene in the surrounding rocks. In addition, high GI value levels in the research area cause the oxidation process to be low and result in low rock acidity conditions.
WIP: An Effective Model for Leveraging Field Trips to Broaden Participation in STEM

Abstract Current and emerging research sites the need for early and sustained engagement in science and engineering learning to attract young learners to STEM careers. The STEM workforce size and demographics is a national concern in the U.S. and federal funding agencies such as NSF, NIH and the Department of Education have been focusing on solving this national problem. Attracting more underrepresented students to STEM fields has been recognized as a crucial to address the current and expanding needs of our national STEM workforce. Field trips are one vehicle to introduce youth to STEM topics. If designed and utilized effectively, these experiences can be leveraged to encourage pre, during, and post engagement in STEM learning. XX University has designed and implemented an effective and successful model of one-day field trips to a university campus. The model focuses on elementary and early middle school students, accompanied by their teacher and addition volunteers. Once on campus, the learning experience is led by University staff and undergraduate students who gain valuable experience interacting with children, communicating science and engineering concepts in addition to serving as role models for program participants. The model continues to evolve to maximize the impact of these one-day STEM experiences on all stakeholders. This paper will discuss the essential elements of one-day STEM field trips; the logistics, the daily program and schedule, and the process from start to finish. The paper will address the following research Questions: 1. What are the essential elements of training and organizational support to develop and sustain a comprehensive STEM outreach program built upon a one-day STEM field trip? 2. What interaction and materials: pre-, during- and post-field trips will maximize the “reach/impact” of these experiences on all stakeholders? 3. How far reaching can a one day experience be on student participation and interest? Can we assess continued discussion/impact beyond the experience and classroom? The paper will also share some of the current outcomes of the one-day field trip, as well as the analysis and visualization of these results.
LITERARY TEXT RECEPTION IN DEVELOPING TEACHING AND LEVEL OF MORAL DEVELOPMENT OF YOUNGER SCHOOL AGE STUDENTS

The processes and outcomes of literary text reception are determined by the flows and methodical specificities of the traditional and innovative-developing teaching system. Increased intensity of literary text reception can be achieved in the models of developing teaching at a younger school age. Starting from the premises of the reception theory, the theory of innovative-developing teaching and the findings of tangent research, transferable theoretical foundations of literary reception and literary text reception microplans were developed in the models of developing classroom teaching (different complexity levels interactive teaching ‒ DCLIT and responsible teaching ‒ RT). During the one-year experimental-methodical programme, elementary school fifth grade students were adopting literary-artistic texts in the context of one innovative-developing model of classroom teaching (different complexity levels teaching ‒ E1 or responsible teaching – E2), and in the regular teaching of literature. On average, students in the experimental groups (E1 and E2) achieved generally statistically significantly higher scores during the final evaluation of the knowledge of moral qualities and knowledge of human personalities than the students in the control groups (K1 and K2). The obtained findings of the experimental-methodical research can be a significant contribution to the advancement of work on literary text in the teaching practice.
PERANCANGAN APLIKASI PEMBAYARAN IURAN KAS WARGA DI BANYU BIRU RESIDENCE BERBASIS ANDROID

Banyu Biru Residence is a cluster housing complex located on Al-Barokah road, rawa bereum village,lebak wangi village, kec. Sepatan Timur, kab. Tangerang. The housing cash contribution payment system stillrelies on the horror of using door to door or going home one by one and the recording system is still manual, sothis can take a long time and the recording system is not good. In today's digital era many activities are carried outusing or using technology as well as payment systems. With existing technology that can be applied in a residentialenvironment, it can be done by changing the traditional payment system to modern, by making a paymentapplication. The payment application was built using Android Studio which supports increased paymentawareness, facilitates management in managing and monitoring payment data, reminds citizens of payment ofdues, and introduces new residents.
Real-time Surgical Environment Enhancement for Robot-Assisted Minimally Invasive Surgery Based on Super-Resolution

In Robot-Assisted Minimally Invasive Surgery (RAMIS), a camera assistant is normally required to control the position and zooming ratio of the laparoscope, following the surgeon's instructions. However, moving the laparoscope frequently may lead to unstable and suboptimal views, while the adjustment of zooming ratio may interrupt the workflow of the surgical operation. To this end, we propose a multi-scale Generative Adversarial Network (GAN)-based video super-resolution method to construct a framework for automatic zooming ratio adjustment. It can provide automatic real-time zooming for high-quality visualization of the Region Of Interest (ROI) during the surgical operation. In the pipeline of the framework, the Kernel Correlation Filter (KCF) tracker is used for tracking the tips of the surgical tools, while the Semi-Global Block Matching (SGBM) based depth estimation and Recurrent Neural Network (RNN)-based context-awareness are developed to determine the upscaling ratio for zooming. The framework is validated with the JIGSAW dataset and Hamlyn Centre Laparoscopic/Endoscopic Video Datasets, with results demonstrating its practicability.
Data-driven parametrizations in numerical models using data assimilation and machine learning.

&amp;lt;p&amp;gt;Can we build a machine learning parametrization in a numerical model using sparse and noisy observations?&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In recent years, machine learning (ML) has been proposed to devise data-driven parametrizations of unresolved processes in dynamical numerical models. In most of the cases, ML is trained by coarse-graining high-resolution simulations to provide a dense, unnoisy target state (or even the tendency of the model).&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Our goal is to go beyond the use of high-resolution simulations and train ML-based parametrization using direct data. Furthermore, we intentionally place ourselves in the realistic scenario of noisy and sparse observations.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The algorithm proposed in this work derives from the algorithm presented by the same authors in https://arxiv.org/abs/2001.01520.The principle is to first apply data assimilation (DA) techniques to estimate the full state of the system from a non-parametrized model, referred hereafter as the physical model. The parametrization term to be estimated is viewed as a model error in the DA system. In a second step, ML is used to define the parametrization, e.g., a predictor of the model error given the state of the system. Finally, the ML system is incorporated within the physical model to produce a hybrid model, combining a physical core with a ML-based parametrization.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The approach is applied to dynamical systems from low to intermediate complexity. The DA component of the proposed approach relies on an ensemble Kalman filter/smoother while the parametrization is represented by a convolutional neural network. &amp;amp;#160;&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;We show that the hybrid model yields better performance than the physical model in terms of both short-term (forecast skill) and long-term (power spectrum, Lyapunov exponents) properties. Sensitivity to the noise and density of observation is also assessed.&amp;lt;/p&amp;gt;
Jugaad time: ecologies of everyday hacking in India

Jugaad Time explores how the socio-technological relationships of people and cities coevolve in the digital age, specifically through the use of the mobile phone. Jugaad is a common Hindi and Punja...
CURRENT TRENDS IN THE FUNCTIONING OF THE HIGHER EDUCATION SYSTEM WITHIN THE FRAMEWORK OF AN INNOVATIVE ECONOMY

The proposed article considers the need to recognize the economic utility and social value of human resources, the development of which requires investment in comparison with other types of economic resources. The urgency of solving the problem of functioning of higher education system is dictated by the need to apply new approaches to the management of an educational institution. Successful companies focus their efforts on creating and developing a management system for educational institutions designed to work effectively with intellectual resources that are becoming less used today. All these circumstances explain the emergence of a special interest of scientists and practitioners in the management of intellectual capital and the effectiveness of the knowledge process. In this regard, there is a need for constant updating and change, which means that knowledge is always innovative. At the same time, the most interesting are the ways of transforming universities into a new type of organization focused on meeting the needs of the market, making a profit, developing innovative technologies, supporting high-tech research projects and managing them. Close interaction of business corporations with the University community, forms a diversified financial base, develops contacts with business, society and the state, and encourages the widespread use of initiative and support for innovation in the scientific and educational environment. The new conceptual approach has enriched the practice with progressive methods and technologies for managing people in a complex innovative environment. The priorities were flexible forms of labor use, continuous improvement of the quality of human resources, new approaches to organizing and stimulating labor, and addressing cultural and ethical factors of labor productivity and quality of life. The main components of scientific novelty are the regulation and mutual influence of the functioning infrastructure and the applied methodology of higher education.
The generalization of the hyperbolic secant distribution and the logistic distribution in the single distribution with variable kurtosis

The generalization V of the logistic distribution is proposed and investigated. For a random variable having a generalized logistic distribution of type V, the characteristic function is found, the generating function of moments is formed, and the expression of dispersion is obtained. The dependence of the kurtosis coefficient of the generalized logistic distribution on the power parameter is found and investigated. The interval of values of the coefficient of kurtosis of the generalized logistic distribution is determined. It is found that the coefficient of kurtosis depends only on the power parameter.
Modular Multi Target Tracking Using LSTM Networks.

The process of association and tracking of sensor detections is a key element in providing situational awareness. When the targets in the scenario are dense and exhibit high maneuverability, Multi-Target Tracking (MTT) becomes a challenging task. The conventional techniques to solve such NP-hard combinatorial optimization problem involves multiple complex models and requires tedious tuning of parameters, failing to provide an acceptable performance within the computational constraints. This paper proposes a model free end-to-end approach for airborne target tracking system using sensor measurements, integrating all the key elements of multi target tracking -- association, prediction and filtering using deep learning with memory. The challenging task of association is performed using the Bi-Directional Long short-term memory (LSTM) whereas filtering and prediction are done using LSTM models. The proposed modular blocks can be independently trained and used in multitude of tracking applications including non co-operative (e.g., radar) and co-operative sensors (e.g., AIS, IFF, ADS-B). Such modular blocks also enhances the interpretability of the deep learning application. It is shown that performance of the proposed technique outperforms conventional state of the art technique Joint Probabilistic Data Association with Interacting Multiple Model (JPDA-IMM) filter.
Cybercrime Regulation through Laws and Strategies: a Glimpse into the Indian Experience

Cybercrimes in India is increasing at an alarming rate. Though various legal provisions under the conventional criminal laws including form the Indian Penal Code, could be used to regulate the cybercrimes, yet the changing nature of these crimes necessitated adoption of a new law framework. Thus, the Indian Information Technology Act was enacted in 2000 but seldom could regulate cybercrimes since it focused on promoting and facilitating e-commerce and e-governance. This Act underwent amendment in 2008 to accommodate provisions essential to regulate cybercrimes as well as protect data and privacy on cyber space. In addition to the law, other strategies were designed and adopted to better regulate cyber offences including announcing cyber security policies, constituting institutions to take care of certain concerns including those relating to critical infrastructure information, etc. This paper aims to provide an overview of the approach adopted in India primarily the legal approach adopted to regulate cybercrimes. Additionally, other strategies adopted by India is also reviewed in brief. Despite these, since cybercrimes are technical as well as dynamic in nature, there is a need to constantly review and revise nation’s strategies, which is also one of the objects of this paper.
Effective Segmented Face Recognition (SFR) for IoT

Face recognition technology becoming pervasive in the fields of computer vision, image processing, and pattern recognition.However, face recognition accuracy rates will decrease if training is done on disguised images with covered objects on a face area.This paper aims to propose a state-of-the-art face recognition methodology which could be applied in Internet of Things (IoT) devices as an input source; then face segmentation and training process will be executed in the cloud via internet; the recognition result will be sent to the connected applications which determines a safety check for personal or public security.This paper focuses on implementation of face segmentation and training process for IoT.Face extraction from the background and disguised part is applied by Fully Convolutional Networks (FCN), and then deep convolution neural network is employed for face training and testing process.This algorithm has been experimented on a challengeable face dataset.The proposed face recognition system is applied to IoT services which have multiple applications, such as, personal home security and public library space management.Compared to recognition without face segmentation, the results of proposed methodology indicate a better accuracy regarding recognition rate.
Low-Resource Adaptation of Neural NLP Models

Real-world applications of natural language processing (NLP) are challenging. NLP models rely heavily on supervised machine learning and require large amounts of annotated data. These resources are often based on language data available in large quantities, such as English newswire. However, in real-world applications of NLP, the textual resources vary across several dimensions, such as language, dialect, topic, and genre. It is challenging to find annotated data of sufficient amount and quality. The objective of this thesis is to investigate methods for dealing with such low-resource scenarios in information extraction and natural language understanding. To this end, we study distant supervision and sequential transfer learning in various low-resource settings. We develop and adapt neural NLP models to explore a number of research questions concerning NLP tasks with minimal or no training data.
Two-Stage Batch Algorithm for Nonlinear Static Parameter Estimation

A two-stage batch estimation algorithm for solving a class of nonlinear, static parameter estimation problems that appear in aerospace engineering applications is proposed. It is shown how these problems can be recast into a form suitable for the proposed two-stage estimation process. In the first stage, linear least squares is used to obtain a subset of the unknown parameters (set 1), while a residual sampling procedure is used for selecting initial values for the rest of the parameters (set 2). In the second stage, depending on the uniqueness of the local minimum, either only the parameters in the second set need to be re-estimated, or all the parameters will have to be re-estimated simultaneously, by a nonlinear constrained optimization. The estimates from the first stage are used as initial conditions for the second stage optimizer. It is shown that this approach alleviates the sensitivity to initial conditions and minimizes the likelihood of converging to an incorrect local minimum of the nonlinear cost function. An error bound analysis is presented to show that the first stage can be solved in such a way that the total cost function will be driven to the optimal cost, and the difference has an upper bound. Two tutorial examples are used to show how to implement this estimator and compare its performance to other similar nonlinear estimators. Finally, the estimator is used on a 5-hole Pitot tube calibration problem using flight test data collected from a small Unmanned Aerial Vehicle (UAV) which cannot be easily solved with single-stage methods.
Impact of Bit coin in Developing countries Economy

This paper examines the role of Bit coin (crypto currency) in Developing countries Economy. Bit coin is a new type of digital currency that has increases the importance of traction like as an economic instrument (no direct link established). Although Bit coin’s constantly increases, as compared with different areas of financial system now Bit coin in an emerging stage and there were not much of scholarly articles on bit coin topic. This paper is an outcome of examining study on the awareness and role of Bit coin in the economy and its impact on society with a special emphasis on Asia and also one of the first studies to examine Bit coin’s use as a complement to developing markets currencies. The overall reaction from the different group member of sample is mixed towards different variables. This study also focuses on both sides issues of bit coin particularly in interest of financial institutions and economists with a prospective transformation. This study also discusses and highlights an application of advance technology and revolution with a digital currency. Key Terms: Bit coin, Digital currencies, Cryptography, Developing Countries Economy
Error correction algorithm developed for special-purpose computing devices based on polynomial modular codes

Abstract Special-purpose computing devices (SPCD) are used to solve tasks that require either real-time processing of a large amount of data, including digital processing of audio/video signals and OFDM systems, or high accuracy, like friend-or-foe identification for satellite communications systems. To achieve this goal, it is proposed to use parallel processing at various levels. Parallel computing increases circuit costs though, which incites SPCD to fail. The most promising solution to this contradiction lies in the use of position-independent redundant polynomial modular codes (PMC). These codes aim to increase the speed of data processing through the parallelization of computation at the level of arithmetic operations. What is more, thanks to independent and parallel processing of residues, they have the potential to detect and correct errors caused by the failures to occur during computation and during transmission over communication lines due to interference. Therefore, it is crucial to develop an error correction algorithm for special-purpose computing devices based on polynomial modular codes.