nils-herrmann/clean_scirepeval huggingface dataset:
Technical and Fundamental Features' analysis for Stock Market Prediction with Data Mining Methods
Year: 2019

Of the most important concerns of market practitioners is future information of the companies which offer stocks. A reliable prediction of the company's financial status provides a situation for the investor to more confident investments and gaining more profits(Huang, 2012b) Accurately prediction of stocks' prices has a positive affects into the organizations financial stability (Asadi et al. 2012) Since financial market is complex and has non linear dynamic systems, its prediction is really challenging (Huang and Tsai, 2009) The steady and amazing progress of computer hardware technology in the past decades has led to large supplies of powerful and affordable computers, data collection equipment, and storage media. This technology provides a great boost to the database and information industry and makes a huge number of databases and information repositories available for transaction management, information retrieval, and data analysis. Data mining are defined as group of algorithms and methods designed to analyze data or to extract patterns in specific categories from data contributing greatly to business strategies, engineering, medical research, and financial areas (Klosgen and Zytkow, 1996) Prediction of stock prices, credit scores, and even bankruptcy potentials are examples of significant applicability of data mining in the field of finance. In this research we are using the potential tools of data mining area for the forecasting the stocks and shares' prices and future trends. However, there are different approaches in financial forecasting in general and stock market price forecasting in particular including using fundamental analysis, technical analysis, and news via econometric or machine learning algorithms (Atsalakis et al. 2011; Kar et al. 2014) while in the this thesis we will go through all of these methodologies. The structure of the thesis is consist of three papers of the author, published in the ISI journals about using technical and fundamental features for stock market prediction with different algorithms in the data mining as chapter 3 until chapter 5. The thesis exploits different types of financial data set and established three aspects of stock market forecasting via different combination of feature engineering in the finance dataset and machine learning models.
Application of IOT Technology in Heavy Oil Treatment Station
Year: 2015

IOT of oil/gas production based on field data acquisition,process control,parameter optimization,management and decision making is an inevitable trend for the building and development of information based oilfield. Heavy oil processing is a key link in the production. The physical property of heavy oil is complicated,which demands frequent control and regulation of processes. Therefore,it is very difficult to guarantee smooth and stable function of system by relying on conventional artificial management. Taking No. 1 Heavy Oil Processing Station of Fengcheng Oilfield as example,the application of IOT technology in heavy oil processing station is presented. The construction of IOT system of processing station is combined with the features of processing techniques of heavy oil and equipped with automatic acquisition. The system of central control room has been established and the production throughout the station is under centralized surveillance and the whole process is regulated and controlled in a unified manner.Assistant video monitors are used for major zones. The comprehensive application of production data is used for the support management of production. The successful application of IOT system has achieved the efficient management of heavy oil processing station.The construction approach of IOT for heavy oil processing station can serve as a reference for the construction in other oilfields.
Neural Comodulation of Independent Brain Processes Related to Multitasking
Venue: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Year: 2019

Distracted driving is regarded as an integrated task requiring different regions of the brain to receive sensory data, coordinate information, make decisions, and synchronize movements. In this paper, we applied an independent modulator analysis (IMA) method to temporally independent electroencephalography (EEG) components to understand how the human executive control system coordinates different brain regions to simultaneously perform multiple tasks with distractions presented in different modalities. The behavioral results showed that the reaction time (RT) in response to traffic events increased while multitasking. Moreover, the RT was longer when the distractor was presented in an auditory form versus a visual form. The IMA results showed that there were performance related IMs coordinating different brain regions during distracted driving. The component spectral fluctuations affected by the modulators were distinct between the single and dual task conditions. Specifically, more modulatory weight was projected to the occipital region to address the additional distracting stimulus in both visual and auditory modality in the dual task conditions. A comparison of modulatory weights between auditory and visual distractors showed that more modulatory weight was projected to the frontal region during the processing of the auditory distractor. This paper provides valuable insights into the temporal dynamics of attentional modulation during multitasking as well as an understanding of the underlying brain mechanisms that mediate the synchronization across brain regions and govern the allocation of attention in distracted driving.
Constrained R Cnn: A General Image Manipulation Detection Model
Venue: 2020 IEEE International Conference on Multimedia and Expo (ICME)
Year: 2020

Recently, deep learning based models have exhibited remarkable performance for image manipulation detection. However, most of them suffer from poor universality of handcrafted or predetermined features. Meanwhile, they only focus on manipulation localization and overlook manipulation classification. To address these issues, we propose a coarse to fine architecture named Constrained R CNN for complete and accurate image forensics. First, the learnable manipulation feature extractor learns a unified feature representation directly from data. Second, the attention region proposal network effectively discriminates manipulated regions for the next manipulation classification and coarse localization. Then, the skip structure fuses low level and high level information to refine the global manipulation features. Finally, the coarse localization information guides the model to further learn the finer local features and segment out the tampered region. Experimental results show that our model achieves state of the art performance. Especially, the F1 score is increased by 28.4% 73.2% 13.3% on the NIST16, COVERAGE, and Columbia dataset.
COMPREHENSIVE STUDY OF ANT COLONIES FOR THE TRAVELING SALESMAN PROBLEM AND COMPARE EFFICIENCY AND PERFORMANCE WITH BASIC ALGORITHM
Year: 2012

The travelling salesman problem (TSP) is an NP hard problem in combinatorial optimization studied in operations research and theoretical computer science. Given a list of cities and their pair wise distances, the task is to find the shortest possible tour that visits each city exactly once. It is used as a benchmark for many optimization methods. Even though the problem is computationally difficult, a large number of heuristics and exact methods are known, so that some instances with tens of thousands of cities can be solved. The TSP has several applications even in its purest formulation, such as planning, logistics, and the manufacture of microchips. The ant colony optimization is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs. In ACO, a set of software agents called artificial ants search for good solutions to a give optimization problem. The traveling salesman problem (TSP) is the problem of finding a shortest closed tour which visits all the cities in a given set. In this article we will restrict attention to TSPs in which cities are on a plane and a path (edge) exists between each pair of cities (i.e. the TSP graph is completely connected) Ant colony optimization is a metaheuristic in which a colony of artificial ants cooperates in finding good solutions to difficult discrete optimization problems.
Motif based embedding for graph clustering
Year: 2016

Community detection in complex networks is a fundamental problem that has been extensively studied owing to its wide range of applications. However, because community detection methods typically rely on the relations between vertices in networks, they may fail to discover higher order graph substructures, called the network motifs. In this paper, we propose a novel embedding method for graph clustering that considers higher order relationships involving multiple vertices. We show that our embedding method, which we call motif based embedding, is more effective in detecting communities than existing graph embedding methods, spectral embedding and force directed embedding, both theoretically and experimentally.
Mediating Effects of Academic Self efficacy on the Relationship Between Academic Stress and the Academic Burnout of Adolescents in Korea
Year: 2014

This study investigate the mediating effects of academic self efficacy on the relationship between academic stress and academic burnout of Korean adolescents. A survey was conducted using a convenient sample drawn from 850 students from nine junior high schools in Kunggi do Province. Data were analyzed using SAS package program. The results show that many students were dissatisfied with their own academic performance. There was a significant positive relationship between academic stress and academic burnout. In particular, academic performance and studying had a significant interaction effect on academic stress. Academic self efficacy had negative effects on academic stress and academic burnout. Academic self efficacy partly mediated the relationship between academic stress and academic burnout. These results suggest that improving academic self efficacy is crucial for preventing and reducing symptoms of academic stress and academic burnout. In this regard, continuous social support and efforts are needed to develop instructional strategies and educational programs that can improve the academic self efficacy of adolescents.
Young carer awareness, identification and referral.
Venue: British journal of community nursing
Year: 2017

Young carers often provide care because they have always done so for the people they care about, and because they believe that the care recipient could not manage without them. For many young carers, looking after their own health, combining caring with schoolwork, getting access to training or having time off from carer duties can be a major challenge Department of Health [DH] 2008 This paper presents evidence from a literature review that builds a substantial body of knowledge to suggest that community nursing teams must develop supportive approaches towards increasing an awareness of young carers' needs. Identification of young carers by community nurses will encourage referral to appropriate services and agencies. The aim of this study was to appraise, for the Queen's Nursing Institute, the published evidence base to explore young carers' needs and how community nurses could support young carers' needs in England. Databases were systematically searched. Title and abstract reviews found 606 potential studies  mental health and wellbeing; education needs and resilience; and development of coping strategies. Full text review resulted in 26 publications that met the study's inclusion criteria. The results of this work show that there is a scarcity of publications around the community nursing needs of young carers. However, studies consistently report young carers are hidden from view and have a significant requirement for support and information. Therefore, effective ways of delivering community nursing support and information to young carers needs to be developed by service providers as a matter of priority, and implemented to give the support that young carers need.
The Effect of Social Media Effectiveness on MSMEs Marketing
Venue: 2020 2nd International Conference on Cybernetics and Intelligent System (ICORIS)
Year: 2020

This study was conducted to assess and compare the effectiveness of small and medium sized food enterprise (MSMEs) advertisements in Palembang on advertising services on Youtube, Facebook, and Instagram. This assessment and comparison is carried out to determine the extent of the effectiveness of advertising promotions and to find out which social media has the highest level of effectiveness in advertising services. Using random sampling, it was found that 60 research samples looked at Micro Small Medium Enterprises (MSMEs) promotional advertising services on Youtube, Facebook, and Instagram. The research stages started from testing the reliability and validity of the questionnaire, determining the scale range of the study in assessing the effectiveness, tabulating the data by looking for the average value of each dimension in the empathy, persuasion, interest, and communication (EPIC) model, then calculating the EPIC rate to determine the effectiveness value of advertisements promoted on social media. The result is that advertisements on the three social media are very effective, seen from the EPIC rate which is on a very effective scale range. EPIC rate for Instagram is 3.3, Facebook is 3.26, and Youtube is 3.28. The highest EPIC rate of 3.3 on Instagram means that Instagram is more effective than Youtube, which is in second place, and the last is Facebook. MSMEs in Palembang are right to choose these three social media as promotional media.
Parallel point defect identification in molecular dynamics simulations without post processing: A compute and dump style for LAMMPS
Venue: Comput. Phys. Commun.
Year: 2020

Two classes for the molecular dynamics program LAMMPS one compute style and one dump style are presented that are designed to identify, count, and output point defects in cascade damage and related molecular dynamics simulations. The calculations are done in parallel across multiple MPI processes, and can be done at the same time as the original simulation. This drastically reduces storage requirements by eliminating the need to post process every atom in the system. Non cubic lattices, free surfaces, and large voids can be eliminated from the output by suitable choices of the reference lattice. The classes are derived from LAMMPS's Dump and Compute classes, and pose no additional overhead to LAMMPS if they are not used.  Compute style "frenkel" and dump style "frenkel"   GPLv2  C+  Identify and count point defects (interstitials and vacancies) in metals created by high energy cascade damage, in parallel, without post processing, relative to an arbitrary defect free crystal lattice.  Defects are identified and counted in parallel, across an arbitrary number of processors, through LAMMPS's domain decomposition techniques  Defects are counted according to the occupancy of the Wigner Seitz cell of each lattice site, but can be visualized by either Wigner Seitz cell occupancy or by Lindemann sphere analysis. Results are then shared across all processes by message passing.  This implementation is likely restricted to orthorhombic domains. It is also inadvisable to use this software in the presence of grain boundaries. It is possible (with care) to use it with free surfaces.  S. Plimpton, Fast Parallel Algorithms for Short Range Molecular Dynamics,  (1995) 1 19. doi: https:/doi.org/10.1006/jcph.1995.1039
Medidas de contencion de la violencia de genero durante la pandemia de COVID 19
Venue: Gaceta Sanitaria
Year: 2020

Resumen Las crisis, las emergencias y las epocas de disturbios se han relacionado con un aumento de la violencia interpersonal, incluida la violencia hacia las mujeres. A raiz de la declaracion de estado de alarma y confinamiento se han puesto en marcha nuevas medidas encaminadas a paliar el posible impacto de la violencia de genero (Plan de contingencia contra la violencia de genero ante la crisis del coronavirus o Real Decreto Ley de medidas urgentes) Se revisan en este documento las medidas de contencion adoptadas hasta el momento por el gobierno de Espana y las comunidades autonomas, y las iniciativas formuladas en diferentes paises. Ante la ausencia de medidas economicas concretas planteadas hasta la fecha y el escenario de incertidumbre economica concluimos que no es posible prevenir la violencia de genero de manera integral sin considerar el aumento del desempleo, la temporalidad y la inestabilidad laboral, la dependencia economica o la sobrecarga de tareas reproductivas, entre otros elementos que la facilitan.  Crises, emergencies and times of unrest have been linked to increased interpersonal violence, including violence against women. Following the declaration of alarm status and quarantine, different measures have been implemented to mitigate the possible effect of gender violence (Contingency Plan against Gender Based Violence in Coronavirus Crisis or Royal Decree Law on Emergency Measures) This document reviews the measures adopted so far by the government of Spain, the autonomous governments and the initiatives formulated in different countries. In the absence of concrete economic measures to date, and the scenario of economic uncertainty, we conclude that it is not possible to prevent gender based violence in a comprehensive way, without considering the increase in unemployment, temporary and instability employment, economic dependency or the overload of household chores and reproductive tasks, among other elements that facilitate it.
On the Partially Overlapped Channel Assignment on Wireless Mesh Network Backbone: A Game Theoretic Approach
Venue: IEEE Journal on Selected Areas in Communications
Year: 2012

The Wireless Mesh Network (WMN) has already been recognized as a promising broadband access network technology from both academic and commercial perspective. In order to improve the performance of WMNs, extensive research efforts have been dedicated towards finding means to increase the number of simultaneous transmissions in the network while avoiding signal interference among radios. In case of WMNs based on IEEE 802.11 b/g standards, most recent research works have relied upon the usage of orthogonal channels for solving the Channel Assignment (CA) problem. In this paper, we explore the possibility of exploiting Partially Overlapped Channels (POCs) by introducing a novel game theoretic distributed CA algorithm. Our proposed algorithm outperforms both the conventional orthogonal channel approach and the recent heuristic CA algorithms using POC. The proposed algorithm is shown to achieve near optimal performance in the average case. In addition, the upper bound Price of Anarchy for Multi Radio Multi Channel (MRMC) networks is derived to evaluate the effectiveness of the proposed approach.
Measuring human capital: a systematic analysis of 195 countries and territories, 1990 2016
Venue: The Lancet
Year: 2018

Human capital is recognised as the level of education and health in a population and is considered an important determinant of economic growth. The World Bank has called for measurement and annual reporting of human capital to track and motivate investments in health and education and enhance productivity. We aim to provide a new comprehensive measure of human capital across countries globally.  We generated a period measure of expected human capital, defined for each birth cohort as the expected years lived from age 20 to 64 years and adjusted for educational attainment, learning or education quality, and functional health status using rates specific to each time period, age, and sex for 195 countries from 1990 to 2016. We estimated educational attainment using 2522 censuses and household surveys; we based learning estimates on 1894 tests among school aged children; and we based functional health status on the prevalence of seven health conditions, which were taken from the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) Mortality rates specific to location, age, and sex were also taken from GBD 2016.  In 2016, Finland had the highest level of expected human capital of 28*4 health, education, and learning adjusted expected years lived between age 20 and 64 years (95% uncertainty interval 27*5 29*2) Niger had the lowest expected human capital of less than 1*6 years (0*98 2*6) In 2016, 44 countries had already achieved more than 20 years of expected human capital; 68 countries had expected human capital of less than 10 years. Of 195 countries, the ten most populous countries in 2016 for expected human capital were ranked: China at 44, India at 158, USA at 27, Indonesia at 131, Brazil at 71, Pakistan at 164, Nigeria at 171, Bangladesh at 161, Russia at 49, and Mexico at 104. Assessment of change in expected human capital from 1990 to 2016 shows marked variation from less than 2 years of progress in 18 countries to more than 5 years of progress in 35 countries. Larger improvements in expected human capital appear to be associated with faster economic growth. The top quartile of countries in terms of absolute change in human capital from 1990 to 2016 had a median annualised growth in gross domestic product of 2*60% (IQR 1*85 3*69) compared with 1*45% (0*18 2*19) for countries in the bottom quartile.  Countries vary widely in the rate of human capital formation. Monitoring the production of human capital can facilitate a mechanism to hold governments and donors accountable for investments in health and education. Funding Institute for Health Metrics and Evaluation.
The impact of inter generational change on the attitudes of working class South Asian Muslim parents on the education of their daughters
Year: 2010

This paper presents the findings of ethnographic research into inter generational attitudinal change of parents towards the education of young British Muslim women. Based on in depth interviews with parents of different generations, given social class and ethnicity, there is a universal belief in the importance of education for young Muslim women per se, with economic and cultural factors significant in shaping this sentiment. A range of important differences in attitudes towards Islamic schooling and mainstream education, and questions relating to marriage, however, were found. There are complex issues of identity and religion among Muslims in relation to educational issues, but there has been a move towards Islamisation among both generations; the first generations through a form of cultural traditionalism and the second generations through Islamic conservatism. Although this finding is based on a study of a relatively small and isolated working class Muslim community in a declining post industrial town in the West Midlands, it is argued that this Islamisation places both particular risks and opportunities in relation to young Muslim women in education in such isolated and disaffected communities which have a wider conceptual, theoretical and policy impact.