nils-herrmann/clean_scirepeval huggingface dataset:
Multi label classification via multi target regression on data streams
Venue: Machine Learning

Multi label classification (MLC) tasks are encountered more and more frequently in machine learning applications. While MLC methods exist for the classical batch setting, only a few methods are available for streaming setting. In this paper, we propose a new methodology for MLC via multi target regression in a streaming setting. Moreover, we develop a streaming multi target regressor iSOUP Tree that uses this approach. We experimentally compare two variants of the iSOUP Tree method (building regression and model trees) as well as ensembles of iSOUP Trees with state of the art tree and ensemble methods for MLC on data streams. We evaluate these methods on a variety of measures of predictive performance (appropriate for the MLC task) The ensembles of iSOUP Trees perform significantly better on some of these measures, especially the ones based on label ranking, and are not significantly worse than the competitors on any of the remaining measures. We identify the thresholding problem for the task of MLC on data streams as a key issue that needs to be addressed in order to obtain even better results in terms of predictive performance.
Option Predictive Clustering Trees for Multi target Regression
Venue: DS

Decision trees are one of the most widely used predictive modelling methods primarily because they are readily interpretable and fast to learn. These nice properties come at the price of predictive performance. Moreover, the standard induction of decision trees suffers from myopia: A single split is chosen in each internal node which is selected in a greedy manner; hence, the resulting tree may be sub optimal. To address these issues, option trees have been proposed which can include several alternative splits in a new type of internal nodes called option nodes. Considering all of this, an option tree can be also regarded as a condensed representation of an ensemble. In this work, we propose to extend predictive clustering trees for multi target regression by considering option nodes, i.e. learn option predictive clustering trees (OPCTs) Multi target regression is concerned with learning predictive models for tasks with multiple continuous target variables. We evaluate the proposed OPCTs on 11 benchmark MTR datasets. The results reveal that OPCTs achieve statistically significantly better predictive performance than a single PCT. Next, the performance is competitive with that of bagging and random forests of PCTs. Finally, we demonstrate the potential of OPCTs for multifaceted interpretability and illustrate the potential of inclusion of domain knowledge in the tree learning process.
Modeling dynamical systems with data stream mining
Venue: Comput. Sci. Inf. Syst.

We address the task of modeling dynamical systems in discrete time using regression trees, model trees and option trees for on line regression. Some challenges that modeling dynamical systems pose to data mining approaches are described: these motivate the use of methods for mining data streams. The algorithm FIMT DD for mining data streams with regression or model trees is described, as well as the FIMT DD based algorithm ORTO, which learns option trees for regression. These methods are then compared on several case studies, i.e. tasks of learning models of dynamical systems from observed data. The experimental setup, including the datasets, and the experimental results are presented in detail. These demonstrate that option trees for regression work best among the considered approaches for learning models of dynamical systems from streaming data.
Endoscopic third ventriculostomy for treatment of adult hydrocephalus: long term follow up of 163 patients.
Venue: Neurosurgical focus

The efficacy of endoscopic third ventriculostomy (ETV) for the treatment of pediatric hydrocephalus has been extensively reported in the literature. However, ETV related long term outcome data are lacking for the adult hydrocephalus population. The objective of the present study was to assess the role of ETV as a primary or secondary treatment for hydrocephalus in adults.  The authors performed a retrospective chart review of all adult patients (age 18 years) with symptomatic hydrocephalus treated with ETV in Calgary, Canada, over a span of 20 years (1994 2014) Patients were dichotomized into a primary or secondary ETV cohort based on whether ETV was the initial treatment modality for the hydrocephalus or if other CSF diversion procedures had been previously attempted respectively. Primary outcomes were subjective patient reported clinical improvement within 12 weeks of surgery and the need for any CSF diversion procedures after the initial ETV during the span of the study. Categorical and actuarial data analysis was done to compare the outcomes of the primary versus secondary ETV cohorts.  A total of 163 adult patients with symptomatic hydrocephalus treated with ETV were identified and followed over an average of 98.6 months (range 0.1 230.4 months) All patients presented with signs of intracranial hypertension or other neurological symptoms. The primary ETV group consisted of 112 patients, and the secondary ETV consisted of 51 patients who presented with failed ventriculoperitoneal (VP) shunts. After the initial ETV procedure, clinical improvement was reported more frequently by patients in the primary cohort (87% relative to those in the secondary ETV cohort (65% p 0.001) Additionally, patients in the primary ETV group required fewer reoperations (p 0.001) with cumulative ETV survival time favoring this primary ETV cohort over the course of the follow up period (p 0.001) Fifteen patients required repeat ETV, with all but one experiencing successful relief of symptoms. Patients in the secondary ETV cohort also had a higher incidence of complications, with one occurring in 8 patients (16% compared with 2 in the primary ETV group (2% p 0.010) although most complications were minor.  ETV is an effective long term treatment for selected adult patients with hydrocephalus. The overall ETV success rate when it was the primary treatment modality for adult hydrocephalus was approximately 87% and 99% of patients experience symptomatic improvement after 2 ETVs. Patients in whom VP shunt surgery fails prior to an ETV have a 22% relative risk of ETV failure and an almost eightfold complication rate, although mostly minor, when compared with patients who undergo a primary ETV. Most ETV failures occur within the first 7 months of surgery in patients treated with primary ETV, but the time to failure is more prolonged in patients who present with failed previous shunts.
Clinical characteristics and late effects in CNS tumours of childhood: Do not forget long term follow up of the low grade tumours.
Venue: European journal of paediatric neurology EJPN official journal of the European Paediatric Neurology Society

To investigate clinical characteristics and late effects of CNS tumours in childhood with a special focus on low grade tumours, especially low grade astrocytoma and glioneuronal tumours.  A retrospective population based study was performed at Uppsala University Children's Hospital, a tertiary referral centre for children with CNS tumours. Patients were identified from the National Brain Tumour Registry and the National Epilepsy Surgery Registry. Hospital medical records were analysed for patients with a follow up of =5 years after diagnosis. A re evaluation of the neuro pathological diagnosis was performed.  A total of 193 patients (age 0 17.99 years) during a twelve year period (1995 2006) were included; 149 survived =5 years. Three larger subgroups could be identified: astrocytic, embryonal and glioneuronal tumours. A supratentorial location was found in 52% Medical late effects were mainly neurological and endocrinological, affecting 81% and 26% of surviving patients. Cognitive late effects were a frequent finding in the whole group but also in low grade astrocytoma and glioneuronal tumours (53% and 67% Thirty per cent had some kind of pedagogic support in school.  Late effects are common in long term survivors of CNS tumours in childhood. Low grade astrocytoma and glioneuronal tumours are no exception, and the findings support the need for long term follow up.
Assemblytics: a web analytics tool for the detection of variants from an assembly
Venue: Bioinform.

Assemblytics is a web app for detecting and analyzing variants from a de novo genome assembly aligned to a reference genome. It incorporates a unique anchor filtering approach to increase robustness to repetitive elements, and identifies six classes of variants based on their distinct alignment signatures. Assemblytics can be applied both to comparing aberrant genomes, such as human cancers, to a reference, or to identify differences between related species. Multiple interactive visualizations enable in depth explorations of the genomic distributions of variants. AVAILABILITY AND IMPLEMENTATION http:/assemblytics.com, https:/github.com/marianattestad/assemblytics CONTACT mnattest@cshl.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Web analytics and the functional differentiation of journalism cultures: individual, organizational and platform specific influences on newswork

A key development in journalism over the past decade has been the rise of web analytics for real time measurement of how audiences respond to news content. While this has led to concerns that analytics may contribute to a dumbing down of news, our understanding of the impact analytics have on journalism culture is still limited. This study explores individual, organizational and platform specific influences on ways in which journalists and newsrooms access, use, interpret and apply analytics in their daily work. It argues that, increasingly, analytics are beginning to shape and reshape journalistic roles, values, norms and practices across different types of newsrooms. These developments are contributing to processes of functional differentiation in the journalistic field, which is found particularly in emerging practices of day and platform parting. The study argues that this differentiation has important consequences for scholars wanting to study news production, distribution and content in the digital age.
An ontology based data integration approach for web analytics in e commerce
Venue: Expert Syst. Appl.

A semantic approach to represent and consolidate web analytic data is proposed.An OWL ontology for web analytics in e commerce is designed and proposed.The proposed approach is validated with tracking data of 15 real world e shops.Obtained semantized data successfully train advanced data mining algorithms.We provide actual e shops with tools to enhance their commercial activities. Web analytics has emerged as one of the most important activities in e commerce, since it allows companies and e merchants to track the behavior of customers when visiting their web sites. There exist a series of tools for web analytics that are used not only for tracking and measuring web traffic, but also for analyzing the commercial activity. However, most of these tools focus on low level web attributes and metrics, making other sophisticated functionalities and analyses only available for commercial (non free) versions.In this context, the SME Ecompass European initiative aims at providing e commerce SMEs with accessible tools for high level web analytics. These software facilities should use different sources of data coming from digital footprints allocated in e shops, to fuse them together in a coherent way, and to make them available for advanced data mining procedures. This motivated us to propose in this work an ontology based approach to collect, integrate and store web analytics data, from many sources of popular and commercial digital footprints. As article's main impact, we obtain enriched and semantically annotated data that is used to properly train an intelligent system, involving data mining procedures, for the analysis of customer behavior in real e commerce sites. In concrete, for the validation of our semantic approach, we have captured and integrated data from Google Analytics and Piwik digital footprints allocated in 15 e shops of different commercial sectors and countries (UK, Spain, Greece and Germany) throughout several months of activity. The obtained results show different perspectives in customer's behavior analysis that go one step beyond the most popular web analytics tools in the current market.
Diagnostic evaluation of the Community Earth System Model in simulating mineral dust emission with insight into large scale dust storm mobilization in the Middle East and North Africa (MENA)

Large amounts of mineral dust are injected into the atmosphere during dust storms, which are common in the Middle East and North Africa (MENA) where most of the global dust hotspots are located. In this work, we present simulations of dust emission using the Community Earth System Model Version 1.2.2 (CESM 1.2.2) and evaluate how well it captures the spatio temporal characteristics of dust emission in the MENA region with a focus on large scale dust storm mobilization. We explicitly focus our analysis on the model's two major input parameters that affect the vertical mass flux of dust surface winds and the soil erodibility factor. We analyze dust emissions in simulations with both prognostic CESM winds and with CESM winds that are nudged towards ERA Interim reanalysis values. Simulations with three existing erodibility maps and a new observation based erodibility map are also conducted. We compare the simulated results with MODIS satellite data, MACC reanalysis data, AERONET station data, and CALIPSO 3 d aerosol profile data. The dust emission simulated by CESM, when driven by nudged reanalysis winds, compares reasonably well with observations on daily to monthly time scales despite CESM being a global General Circulation Model. However, considerable bias exists around known high dust source locations in northwest/northeast Africa and over the Arabian Peninsula where recurring large scale dust storms are common. The new observation based erodibility map, which can represent anthropogenic dust sources that are not directly represented by existing erodibility maps, shows improved performance in terms of the simulated dust optical depth (DOD) and aerosol optical depth (AOD) compared to existing erodibility maps although the performance of different erodibility maps varies by region.
CONSCIOUS STUDY OF IMPACT OF DUST STORM ON AVIATION AND AIRPORT MANAGEMENT

This paper assessed the ecotoxicity of dust storm on aviation and airport management risk in the various Arab countries environment. Here we are going to discuss the various aspects of dust storm and its effect on aviation. The study includes occasion's occurs of dust storm where many challenges has been faced by aviation at the time of flying as well as in airport also. In airport it is very difficult to manage take off and landing if there is an environment of dust storm. In this paper we are going to discuss issues related to dust storm in the field of aviation and airport management. We are also going to discuss the existing study solution provided by researchers which are having some limitations which leads us to have conscious study of impact of dust storm on aviation and airport management. This paper also provides solution ahead as per our study and survey. The basic objective of this paper is to provide information about the concept of dust storm as well as its impact on aviation and airport management. The another major objective is to draw attention on a series of potential problems that might be associated with the flight of aircrafts in dust loaded areas as well as to propose some means to minimize these problems At last, the basic prevention, detection and recovery ways of dust storm on aviation and airport also included in this study paper.
CityPulse: A Platform Prototype for Smart City Social Data Mining

Cities experience radical shifts from conventional areas of fragmented services and interactions, to whole of service and end to end providers, while their citizens are empowered primarily via social networking applications with geotagging capabilities. This work is motivated by the fact that the exploitation of a (smart) city's social networking and collective awareness can lead to improvements in the citizens' daily life and assist city's crowd wise policy and decision making. This challenging objective requires appropriate platforms which will not only offer analytics of the city's social networking data threads but also aggregation and visualization of these data for revealing and highlighting latent information in terms of the city's emerging topics and trends. The proposed CityPulse is a modular platform for offering smart city services based on social data analysis in the context of a city. CityPulse is based on the main principle that a carefully designed backend system supports appropriate data storage, aggregation and analysis methodologies, while the derived results are exposed through Web service interfaces to ensure interoperability with various smart city applications that serve the needs of various city stakeholders. Here, we indicatively describe a generic mobile front end interface that demonstrates the functionalities that can be implemented based on CityPulse results derived by geolocated social data mining. We also demonstrate the results of CityPulse's application on an representative smart city case study which indicate that it can effectively capture and summarize social media user activities within the city and deliver useful latent information to interested city communities in an comprehensive, flexible manner.
A cross layer framework for sensor data aggregation for IoT applications in smart cities
Venue: 2016 IEEE International Smart Cities Conference (ISC2)

Many Internet of Things (IoT) applications like smart parking, waste management, and traffic congestion management, are being developed for smart cities. These applications make use of billions of sensors which in turn generates a huge amount of data that comes under the category of Big Data. For IoT/Smart city applications to make use of these data efficiently there needs to be a proper framework through which the required sensor could be easily searched and made use of. The existing Extract Transformation Loading (ETL) tools and other search mechanisms for sensors assume there exist registries where the sensors can be searched for the desired criteria through ontologies or other suitable techniques. However, there has not been enough contribution to efficiently retrieve the sensor data and to make it available in the required format for the registries to search for. In this paper, we analyze a distributed cross layer commit protocol (CLCP) for data aggregations and its support for query based search for IoT application.
Women's participation in self help groups as a pathway to women's empowerment: A case of Nepal

Non governmental organizations (NGOs) and the government of Nepal have made some effort to reduce poverty in Nepal by creating women's affiliation groups, some of which are micro credit organizations. Using capabilities as defined by Amartya Sen (Development as freedom,  2000) which includes employment opportunities, women's ownership in productive resources such as land and/or homes, educational opportunities, and women's participation in decision making in the family, this study evaluated the extent to which women's ethnic group or caste affiliation affected a woman's likelihood of being empowered by participation in these groups. We analyzed a sample of 8,973 women which was taken from the 2011 Nepal Demographic and Health Survey. Previous research has demonstrated that participation in gender based groups is correlated with higher economic status. This study adds to the literature on women's affiliation groups by investigating the impact of structural factors, such as caste and ethnicity, on women's self help group participation (women's groups and credit groups)
A Primer on Alternative Risk Premia

The concept of alternative risk premia can be viewed as an extension of the factor investing approach. Factor investing is a term that is generally dedicated to long only equity risk factors. A typical example is the value equity strategy. Alternative risk premia designate non traditional risk premia other than long exposure to equities and bonds. They may concern equities, rates, credit, currencies or commodities and correspond to long/short portfolios. For instance, the value strategy can be extended to credit, currencies and commodities. This paper provides an overview of the different alternative risk premia to be found in the academic and professional spheres. Using a database of commercial indices, we estimate the generic cumulative returns of 59 alternative risk premia in order to analyze their risk, diversification power and payoff function. From this, it is clear that the term "alternative risk premia" encompasses two different types of risk factor: skewness risk premia and market anomalies. We then reconsider portfolio allocation in light of this framework. Indeed, we show that skewness aggregation is considerably more complex than volatility aggregation, and we illustrate that the volatility risk measure is less appropriate and pertinent when managing a portfolio with these risk premia. The development of alternative risk premia shall also affect the risk/return analysis of non linear strategies, e.g. hedge fund strategies. In particular, using alternative risk factors instead of traditional risk factors leads to an extension of the alternative beta framework. Therefore, we apply the previously estimated risk premia to a universe of hedge fund indices. To that end, we develop a model selection based on the lasso regression to identify the most pertinent risk premia for each hedge fund strategy. It appears that many traditional risk factors, with the exception of long equity and credit exposure on developed markets, vanish when we include alternative risk premia.
Facial expression classification using Support Vector Machine based on bidirectional Local Binary Pattern Histogram feature descriptor
Venue: 2016 17th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)

In this paper, two class emotion detection and multi class facial expression classification using Support Vector Machine (SVM) is presented. Facial feature vectors in dual form are obtained using Local Binary Pattern (LBP) Histogram by tracing the bins in clockwise and anticlockwise direction. The Histogram feature descriptors are calculated from LBP images in dual form which are then concatenated to obtain features of complete face image. The proposed algorithm is tested using standard Japanese Female Facial Expression Database and Taiwanese facial Expression Database and results are verified using locally developed Indian face database of students. The proposed algorithm significantly outperforms the classical LBP based algorithms.