Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Science
Title: Identification of critical success factors 261 to achieve high green supply chain management performances in Indian automobile industry”
Abstract: Green supply chain management (GSCM) has been receiving the spotlight in last few years. The study aims to identify critical success factors (CSFs) to achieve high GSCM performances from three perspectives i.e., environmental, social and economic performance. CSFs to achieve high GSCM performances relevant to Indian automobile industry have been identified and categorised according to three perspectives from the literature review and experts' opinions. Conceptual models also have been put forward. This paper may play vital role to understand CSFs to achieve GSCM performances in Indian automobile industry and help the supply chain managers to understand how they may improve environmental, social and economic performance.
Extracted statement — has research problem: Supply chain management
Research field: Science
Title: Critical success factors of green supply chain management for achieving sustainability in Indian automobile industry
Abstract: The aim of this study was to identify and analyse the key success factors behind successful achievement of environment sustainability in Indian automobile industry supply chains. Here, critical success factors (CSFs) and performance measures of green supply chain management (GSCM) have been identified through extensive literature review and discussions with experts from Indian automobile industry. Based on the literature review, a questionnaire was designed and 123 final responses were considered. Six CSFs to implement GSCM for achieving sustainability and four expected performance measures of GSCM practices implementation were extracted using factor analysis. interpretive ranking process (IRP) modelling approach is employed to examine the contextual relationships among CSFs and to rank them with respect to performance measures. The developed IRP model shows that the CSF ‘Competitiveness’ is the most important CSF for achieving sustainability in Indian automobile industry through GSCM practices. This study is one of the few that have considered the environmental sustainability practices in the automobile industry in India and their implications on sectoral economy. The results of this study may help the mangers/SC practitioners/Governments/Customers in making strategic and tactical decisions regarding successful implementation of GSCM practices in Indian automobile industry with a sustainability focus. The developed framework provides a comprehensive perspective for assessing the synergistic impact of CSFs on GSCM performances and can act as ready reckoner for the practitioners. As there is very limited work presented in literature using IRP, this piece of work would provide a better understanding of this relatively new ranking methodology.
Extracted statement — has research problem: Supply chain management
Research field: Science
Title: Critical success factors of customer involvement in greening the supply chain: an empirical study
Abstract: The role of customers and their involvement in green supply chain management (GSCM) has been recognised as an important research area. This paper is an attempt to explore factors influencing involvement of customers towards greening the supply chain (SC). Twenty-five critical success factors (CSFs) of customer involvement in GSCM have been identified from literature review and through extensive discussions with senior and middle level SC professionals. Interviews and questionnaire-based survey have been used to indicate the significance of these CSFs. A total of 478 valid responses were received to rate these CSFs on a five-point Likert scale (ranging from unimportant to most important). Statistical analysis has been carried out to establish the reliability and to test the validity of the questionnaires. Subsequent factor analysis has identified seven major components covering 79.24% of total variance. This paper may help to establish the importance of customer role in promoting green concept in SCs and to develop an understanding of factors influencing customer involvement – key input towards creating ‘greening pull system’ (GPSYS). This understanding may further help in framing the policies and strategies to green the SC.
Extracted statement — has research problem: Supply chain management
Research field: Science
Title: Maritime situation awareness capabilities from satellite and terrestrial sensor systems
Abstract: Maritime situation awareness is supported by a combination of satellite, airborne, and terrestrial sensor systems. This paper presents several solutions to process that sensor data into information that supports operator decisions. Examples are vessel detection algorithms based on multispectral image techniques in combination with background subtraction, feature extraction techniques that estimate the vessel length to support vessel classification, and data fusion techniques to combine image based information, detections from coastal radar, and reports from cooperative systems such as (satellite) AIS. Other processing solutions include persistent tracking techniques that go beyond kinematic tracking, and include environmental information from navigation charts, and if available, ELINT reports. And finally rule-based and statistical solutions for the behavioural analysis of anomalous vessels. With that, trends and future work will be presented.
Extracted statement — has research problem: Vessel detection
Research field: Science
Title: NEAR REAL-TIME AUTOMATIC MARINE VESSEL DETECTION ON OPTICAL SATELLITE IMAGES
Abstract: Abstract. Vessel monitoring and surveillance is important for maritime safety and security, environment protection and border control. Ship monitoring systems based on Synthetic-aperture Radar (SAR) satellite images are operational. On SAR images the ships made of metal with sharp edges appear as bright dots and edges, therefore they can be well distinguished from the water. Since the radar is independent from the sun light and can acquire images also by cloudy weather and rain, it provides a reliable service. Vessel detection from spaceborne optical images (VDSOI) can extend the SAR based systems by providing more frequent revisit times and overcoming some drawbacks of the SAR images (e.g. lower spatial resolution, difficult human interpretation). Optical satellite images (OSI) can have a higher spatial resolution thus enabling the detection of smaller vessels and enhancing the vessel type classification. The human interpretation of an optical image is also easier than as of SAR image. In this paper I present a rapid automatic vessel detection method which uses pattern recognition methods, originally developed in the computer vision field. In the first step I train a binary classifier from image samples of vessels and background. The classifier uses simple features which can be calculated very fast. For the detection the classifier is slided along the image in various directions and scales. The detector has a cascade structure which rejects most of the background in the early stages which leads to faster execution. The detections are grouped together to avoid multiple detections. Finally the position, size(i.e. length and width) and heading of the vessels is extracted from the contours of the vessel. The presented method is parallelized, thus it runs fast (in minutes for 16000 × 16000 pixels image) on a multicore computer, enabling near real-time applications, e.g. one hour from image acquisition to end user.
Extracted statement — has research problem: Vessel detection
Research field: Science
Title: Texture-based vessel classifier for electro-optical satellite imagery
Abstract: Satellite imagery provides a valuable source of information for maritime surveillance. The vast majority of the research regarding satellite imagery for maritime surveillance focuses on vessel detection and image enhancement, whilst vessel classification remains a largely unexplored research topic. This paper presents a vessel classifier for spaceborne electro-optical imagery based on a feature representative across all satellite imagery, texture. Local Binary Patterns were selected to represent vessels for their high distinctivity and low computational complexity. Considering vessels characteristic super-structure, the extracted vessel signatures are sub-divided in three sections bow, middle and stern. A hierarchical decision-level classification is proposed, analysing first each vessel section individually and then combining the results in the second stage. The proposed approach is evaluated with the electro-optical satellite image dataset presented in [1]. Experimental results reveal an accuracy of 85.64% across four vessel categories.
Extracted statement — has research problem: Vessel detection
Research field: Science and Technology Studies
Title: Self-citation is the hallmark of productive authors, of any gender
Abstract: It was recently reported that men self-cite >50% more often than women across a wide variety of disciplines in the bibliographic database JSTOR. Here, we replicate this finding in a sample of 1.6 million papers from Author-ity, a version of PubMed with computationally disambiguated author names. More importantly, we show that the gender effect largely disappears when accounting for prior publication count in a multidimensional statistical model. Gender has the weakest effect on the probability of self-citation among an extensive set of features tested, including byline position, affiliation, ethnicity, collaboration size, time lag, subject-matter novelty, reference/citation counts, publication type, language, and venue. We find that self-citation is the hallmark of productive authors, of any gender, who cite their novel journal publications early and in similar venues, and more often cross citation-barriers such as language and indexing. As a result, papers by authors with short, disrupted, or diverse careers miss out on the initial boost in visibility gained from self-citations. Our data further suggest that this disproportionately affects women because of attrition and not because of disciplinary under-specialization.
Extracted statement — has research problem: self-citation
Research field: Semantic Web
Title: Discovery of ontologies from knowledge bases
Abstract: Current approaches to building knowledge-based systems propose the development of an ontology as a precursor to building the problem-solver. This paper outlines an attempt to do the reverse and discover interesting ontologies from systems built without the ontology being explicit. In particular the paper considers large classification knowledge bases used for the interpretation of medical chemical pathology results and built using Ripple-Down Rules (RDR). The rule conclusions in these knowledge bases provide free-text interpretations of the results rather than explicit classes. The goal is to discover implicit ontological relationships between these interpretations as the system evolves. RDR allows for incremental development and the goal is that the ontology emerges as the system evolves. The results suggest that approach has potential, but further investigation is required before strong claims can be made.
Extracted statement — has research problem:  discover implicit ontological relationships
Research field: Semantic Web
Title: Using an Ontology Learning System for Trend Analysis and Detection
Abstract: The aim of ontology learning is to generate domain models (semi-) automatically. We apply an ontology learning system to create domain ontologies from scratch in a monthly interval and use the resulting data to detect and analyze trends in the domain. In contrast to traditional trend analysis on the level of single terms, the application of semantic technologies allows for a more abstract and integrated view of the domain. A Web frontend displays the resulting ontologies, and a number of analyses are performed on the data collected. This frontend can be used to detect trends and evolution in a domain, and dissect them on an aggregated, as well as a fine-grained-level.
Extracted statement — has research problem: an ontology learning system to create domain ontologies from scratch
Research field: Semantic Web
Title: Building accurate semantic taxonomies from monolingual MRDs
Abstract: This paper presents a method that conbines a set of unsupervised algorithms in order to accurately build large taxonomies from any machine-readable dictionary (MRD). Our aim is to profit from conventional MRDs, with no explicit semantic coding. We propose a system that 1) performs fully automatic extraction of taxonomic links from MRD entries and 2) ranks the extracted relations in a way that selective manual refinement is allowed. Tested accuracy can reach around 100% depending on the degree of coverage selected, showing that taxonomy building is not limited to structured dictionaries such as LDOCE.
Extracted statement — has research problem: automatic extraction of taxonomic links from MRD entries
Research field: Semantic Web
Title: Automatic Subject Indexing with Knowledge Graphs
Abstract: Automatic subject indexing has been a longstanding goal of digital curators to facilitate effective retrieval access to large collections of both online and offline information resources. Controlled vocabularies are often used for this purpose, as they standardise annotation practices and help users to navigate online resources through following interlinked topical concepts. However, to this date, the assignment of suitable text annotations from a controlled vocabulary is still largely done manually, or at most (semi-)automatically, even though effective machine learning tools are already in place. This is because existing procedures require a sufficient amount of training data and they have to be adapted to each vocabulary, language and application domain anew. In this paper, we argue that there is a third solution to subject indexing which harnesses cross-domain knowledge graphs. Our KINDEX approach fuses distributed knowledge graph information from different sources. Experimental evaluation shows that the approach achieves good accuracy scores by exploiting correspondence links of publicly available knowledge graphs.
Extracted statement — has research problem: automatic subject indexing
Research field: Semantic Web
Title: ShEx-Lite: Automatic Generation of Domain Object Models from a Shape Expressions Subset Language
Abstract: Shape Expressions (ShEx) was defined as a human-readable and concise language to describe and validate RDF. In the last years, the usage of ShEx has grown and more functionalities are being demanded. One such functionality is to ensure interoperability between ShEx schemas and domain models in programming languages. In this paper, we present ShEx-Lite, a tabular based subset of ShEx that allows to generate domain object models in different object-oriented languages. Although the current system generates Java and Python, it offers a public interface so anyone can implement code generation in other programming languages. The system has been employed in a workflow where the shape expressions are used both to define constraints over an ontology and to generate domain objects that will be part of a clean architecture style.
Extracted statement — has research problem: Code Generation
Research field: Semantic Web
Title: AUTOMATING DATA ACQUISITION INTO ONTOLOGIES FROM PHARMACOGENETICS RELATIONAL DATA SOURCES USING DECLARATIVE OBJECT DEFINITIONS AND XML
Abstract: Ontologies are useful for organizing large numbers of concepts having complex relationships, such as the breadth of genetic and clinical knowledge in pharmacogenomics. But because ontologies change and knowledge evolves, it is time consuming to maintain stable mappings to external data sources that are in relational format. We propose a method for interfacing ontology models with data acquisition from external relational data sources. This method uses a declarative interface between the ontology and the data source, and this interface is modeled in the ontology and implemented using XML schema. Data is imported from the relational source into the ontology using XML, and data integrity is checked by validating the XML submission with an XML schema. We have implemented this approach in PharmGKB (http://www.pharmgkb.org/), a pharmacogenetics knowledge base. Our goals were to (1) import genetic sequence data, collected in relational format, into the pharmacogenetics ontology, and (2) automate the process of updating the links between the ontology and data acquisition when the ontology changes. We tested our approach by linking PharmGKB with data acquisition from a relational model of genetic sequence information. The ontology subsequently evolved, and we were able to rapidly update our interface with the external data and continue acquiring the data. Similar approaches may be helpful for integrating other heterogeneous information sources in order make the diversity of pharmacogenetics data amenable to computational analysis.
Extracted statement — has research problem: interfacing ontology models with data acquisition from external relational data sources
Research field: Semantic Web
Title: Automated Learning of Social Ontologies
Abstract: Learned social ontologies can be viewed as products of a social fermentation process, i.e. a process between users who belong in communities of common interests (CoI), in open, collaborative, and communicative environments. In such a setting, social fermentation ensures the automatic encapsulation of agreement and trust of shared knowledge that participating stakeholders provide during an ontology learning task. This chapter discusses the requirements for the automated learning of social ontologies and presents a working method and results of preliminary work. Furthermore, due to its importance for the exploitation of the learned ontologies, it introduces a model for representing the interlinking of agreement, trust and the learned domain conceptualizations that are extracted from social content. The motivation behind this work is an effort towards supporting the design of methods for learning ontologies from social content i.e. methods that aim to learn not only domain conceptualizations but also the degree that agents (software and human) may trust these conceptualizations or not.
Extracted statement — has research problem: learning ontologies from social content
Research field: Semantic Web
Title: DBpedia Archivo - A Web-Scale Interface for Ontology Archiving under Consumer-oriented Aspects
Abstract: Abstract While thousands of ontologies exist on the web, a unified system for handling online ontologies – in particular with respect to discovery, versioning, access, quality-control, mappings – has not yet surfaced and users of ontologies struggle with many challenges. In this paper, we present an online ontology interface and augmented archive called DBpedia Archivo, that discovers, crawls, versions and archives ontologies on the DBpedia Databus. Based on this versioned crawl, different features, quality measures and, if possible, fixes are deployed to handle and stabilize the changes in the found ontologies at web-scale. A comparison to existing approaches and ontology repositories is given .
Extracted statement — has research problem: Unified System for handling online Ontologies
Research field: Social and Behavioral Sciences
Title: Bullying Victimization among In-School Adolescents in Ghana: Analysis of Prevalence and Correlates from the Global School-Based Health Survey
Abstract: (1) Background: Although bullying victimization is a phenomenon that is increasingly being recognized as a public health and mental health concern in many countries, research attention on this aspect of youth violence in low- and middle-income countries, especially sub-Saharan Africa, is minimal. The current study examined the national prevalence of bullying victimization and its correlates among in-school adolescents in Ghana. (2) Methods: A sample of 1342 in-school adolescents in Ghana (55.2% males; 44.8% females) aged 12–18 was drawn from the 2012 Global School-based Health Survey (GSHS) for the analysis. Self-reported bullying victimization “during the last 30 days, on how many days were you bullied?” was used as the central criterion variable. Three-level analyses using descriptive, Pearson chi-square, and binary logistic regression were performed. Results of the regression analysis were presented as adjusted odds ratios (aOR) at 95% confidence intervals (CIs), with a statistical significance pegged at p < 0.05. (3) Results: Bullying victimization was prevalent among 41.3% of the in-school adolescents. Pattern of results indicates that adolescents in SHS 3 [aOR = 0.34, 95% CI = 0.25, 0.47] and SHS 4 [aOR = 0.30, 95% CI = 0.21, 0.44] were less likely to be victims of bullying. Adolescents who had sustained injury [aOR = 2.11, 95% CI = 1.63, 2.73] were more likely to be bullied compared to those who had not sustained any injury. The odds of bullying victimization were higher among adolescents who had engaged in physical fight [aOR = 1.90, 95% CI = 1.42, 2.25] and those who had been physically attacked [aOR = 1.73, 95% CI = 1.32, 2.27]. Similarly, adolescents who felt lonely were more likely to report being bullied [aOR = 1.50, 95% CI = 1.08, 2.08] as against those who did not feel lonely. Additionally, adolescents with a history of suicide attempts were more likely to be bullied [aOR = 1.63, 95% CI = 1.11, 2.38] and those who used marijuana had higher odds of bullying victimization [aOR = 3.36, 95% CI = 1.10, 10.24]. (4) Conclusions: Current findings require the need for policy makers and school authorities in Ghana to design and implement policies and anti-bullying interventions (e.g., Social Emotional Learning (SEL), Emotive Behavioral Education (REBE), Marijuana Cessation Therapy (MCT)) focused on addressing behavioral issues, mental health and substance abuse among in-school adolescents.
Extracted statement — has research problem: bullying
Research field: Social and Behavioral Sciences
Title: Patterns and Correlates for Bullying among Young Adolescents in Ghana
Abstract: Bullying is relatively common and is considered to be a public health problem among adolescents worldwide. The present study examined the risk factors associated with bullying behavior among adolescents in a lower-middle-income country setting. Data on 6235 adolescents aged 11–16 years, derived from the Republic of Ghana’s contribution to the Global School-based Health Survey, were analyzed using bivariate and multinomial logistic regression analysis. A high prevalence of bullying was found among Ghanaian adolescents. Alcohol-related health compromising behaviors (alcohol use, alcohol misuse and getting into trouble as a result of alcohol) increased the risk of being bullied. In addition, substance use, being physically attacked, being seriously injured, hunger and truancy were also found to increase the risk of being bullied. However, having understanding parents and having classmates who were kind and helpful reduced the likelihood of being bullied. These findings suggest that school-based intervention programs aimed at reducing rates of peer victimization should simultaneously target multiple risk behaviors. Teachers can also reduce peer victimization by introducing programs that enhance adolescents’ acceptance of each other in the classroom.
Extracted statement — has research problem: bullying