Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Science
Title: Multi-Objective Approaches to Optimal Testing Resource Allocation in Modular Software Systems
Abstract: Software testing is an important issue in software engineering. As software systems become increasingly large and complex, the problem of how to optimally allocate the limited testing resource during the testing phase has become more important, and difficult. Traditional Optimal Testing Resource Allocation Problems (OTRAPs) involve seeking an optimal allocation of a limited amount of testing resource to a number of activities with respect to some objectives (e.g., reliability, or cost). We suggest solving OTRAPs with Multi-Objective Evolutionary Algorithms (MOEAs). Specifically, we formulate OTRAPs as two types of multi-objective problems. First, we consider the reliability of the system and the testing cost as two objectives. Second, the total testing resource consumed is also taken into account as the third objective. The advantages of MOEAs over state-of-the-art single objective approaches to OTRAPs will be shown through empirical studies. Our study has revealed that a well-known MOEA, namely Nondominated Sorting Genetic Algorithm II (NSGA-II), performs well on the first problem formulation, but fails on the second one. Hence, a Harmonic Distance Based Multi-Objective Evolutionary Algorithm (HaD-MOEA) is proposed and evaluated in this paper. Comprehensive experimental studies on both parallel-series, and star-structure modular software systems have shown the superiority of HaD-MOEA over NSGA-II for OTRAPs.
Extracted statement — Algorithm(s): HaD-MOEA
Research field: Science
Title: On the Relationship Between CO 2 Emissions and Economic Growth: The Mauritian Experience
Abstract: This paper analyses the relationship between GDP and carbon dioxide emissions for Mauritius and vice-versa in a historical perspective. Using rigorous econometrics analysis, our results suggest that the carbon dioxide emission trajectory is closely related to the GDP time path. We show that emissions elasticity on income has been increasing over time. By estimating the EKC for the period 1975-2009, we were unable to prove the existence of a reasonable turning point and thus no EKC “U” shape was obtained. Our results suggest that Mauritius could not curb its carbon dioxide emissions in the last three decades. Thus, as hypothesized, the cost of degradation associated with GDP grows over time and it suggests that the economic and human activities are having increasingly negative environmental impacts on the country as cpmpared to their economic prosperity.
Extracted statement — Shape of EKC: increasing
Research field: Science
Title: An econometric study of carbon dioxide (CO2) emissions, energy consumption, and economic growth of Pakistan
Abstract: Purpose – The purpose of this paper is to examine the relationship among environmental pollution, economic growth and energy consumption per capita in the case of Pakistan. The per capital carbon dioxide (CO2) emission is used as the environmental indicator, the commercial energy use per capita as the energy consumption indicator, and the per capita gross domestic product (GDP) as the economic indicator.Design/methodology/approach – The investigation is made on the basis of the environmental Kuznets curve (EKC), using time series data from 1971 to 2006, by applying different econometric tools like ADF Unit Root Johansen Co‐integration VECM and Granger causality tests.Findings – The Granger causality test shows that there is a long term relationship between these three indicators, with bidirectional causality between per capita CO2 emission and per capita energy consumption. A monotonically increasing curve between GDP and CO2 emission has been found for the sample period, rejecting the EKC relationship, i...
Extracted statement — Shape of EKC: increasing
Research field: Science
Title: Undifferentiated Sarcoma of the Liver in a 21-year-old Woman: Case Report
Abstract: A successful surgical case of malignant undifferentiated (embryonal) sarcoma of the liver (USL), a rare tumor normally found in children, is reported. The patient was a 21-year-old woman, complaining of epigastric pain and abdominal fullness. Chemical analyses of the blood and urine and complete blood counts revealed no significant changes, and serum alpha-fetoprotein levels were within normal limits. A physical examination demonstrated a film, slightly tender lesion at the liver's edge palpable 10 cm below the xiphoid process. CT scan and ultrasonography showed an oval mass, confined to the left lobe of the liver, which proved to be hypovascular on angiography. At laparotomy, a large, 18 x 15 x 13 cm tumor, found in the left hepatic lobe was resected. The lesion was dark red in color, encapsulated, smooth surfaced and of an elastic firm consistency. No metastasis was apparent. Histological examination resulted in a diagnosis of undifferentiated sarcoma of the liver. Three courses of adjuvant chemotherapy, including adriamycin, cis-diaminodichloroplatinum, vincristine and dacarbazine were administered following the surgery with no serious adverse effects. The patient remains well with no evidence of recurrence 12 months after her operation.
Extracted statement — Site: Left
Research field: Science
Title: Embryonal sarcoma of the liver in an adult treated with preoperative chemotherapy, radiation therapy, and hepatic lobectomy
Abstract: A rare case of embryonal sarcoma of the liver in a 28‐year‐old man is reported. The patient was treated preoperatively with a combination of chemotherapy and radiation therapy. Complete surgical resection, 4.5 months after diagnosis, consisted of a left hepatic lobectomy. No viable tumor was found in the operative specimen. The patient was disease‐free 20 months postoperatively.
Extracted statement — Site: Left
Research field: Science
Title: Undifferentiated embryonal sarcoma of the liver
Abstract: IMAGING FINDINGS Case 1: Initial abdominal ultrasound scan demonstrated a large heterogeneous, echogenic mass within the liver displaying poor blood flow (Figure 1). A contrast-enhanced CT scan of the chest, abdomen and pelvis was then performed, revealing a well-defined, hypodense mass in the right lobe of the liver (Figure 2) measuring approximately 11.3 cm AP x 9.8 cm transverse x 9.2 cm in the sagittal plane. An arterial phase CT scan showed a hypodense mass with a hyperdense rim (Figure 3A) and a delayed venous phase scan showed the low-density mass with areas of increased density displaying the solid nature of the lesion (Figure 3A). These findings combined with biopsy confirmed undifferentiated embryonal sarcoma (UES). Case 2: An abdominal ultrasound scan initially revealed a large heterogeneous lesion in the center of the liver with a small amount of blood flow (Figure 4). Inconclusive ultrasound results warranted a CT scan of the chest, abdomen and pelvis with contrast, which showed a heterogeneous low-density lesion within the right lobe of the liver that extended to the left lobe (Figure 5). The mass measured approximately 12.3 AP x 12.3 transverse x 10.7 in the sagittal plane. Arterial-phase CT showed a well-defined hypodense mass with vessels coursing throughout (Figure 6A). Delayed venous phase demonstrated the solid consistency of the mass by showing continued filling in of the mass (Figure 6B). A PET scan was done to evaluate the extent of the disease. FDG-avid tissue was documented in the large lobulated hepatic mass (Figure 7A,7B).
Extracted statement — Site: Left
Research field: Science
Title: Undifferentiated (embryonal) sarcoma of liver in adult: a case report
Abstract: We report a case of undifferentiated (embryonal) sarcoma of the liver (UESL), which showed cystic formation in a 20-year-old man with no prior history of any hepatitis or liver cirrhosis. He was admitted with abdominal pain and a palpable epigastric mass. The physical examination findings were unremarkable except for a tenderness mass and the results of routine laboratory studies were all within normal limits. Abdominal ultrasound and computed tomography (CT) both showed a cystic mass in the left hepatic lobe. Subsequently, the patient underwent a tumor excision and another two times of hepatectomy because of tumor recurrence. Immunohistochemical study results showed that the tumor cells were positive for vimentin, alpha-1-antichymotrypsin (AACT) and desmin staining, and negative for alpha-fetoprotein (AFP), and eosinophilic hyaline globules in the cytoplasm of some giant cells were strongly positive for periodic acid-Schiff (PAS) staining. The pathological diagnosis was UESL. The patient is still alive with no tumor recurrence for four months.
Extracted statement — Site: Left
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 — ontology learning approach: automatic
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 — ontology learning approach: automatic
Research field: Semantic Web
Title: Medical Ontology Learning Based on Web Resources
Abstract: In order to deal with heterogeneous knowledge in the medical field, this paper proposes a method which can learn a heavy-weighted medical ontology based on medical glossaries and Web resources. Firstly, terms and taxonomic relations are extracted based on disease and drug glossaries and a light-weighted ontology is constructed, Secondly, non-taxonomic relations are automatically learned from Web resources with linguistic patterns, and the two ontologies (disease and drug) are expanded from light-weighted level towards heavy-weighted level, At last, the disease ontology and drug ontology are integrated to create a practical medical ontology. Experiment shows that this method can integrate and expand medical terms with taxonomic and different kinds of non-taxonomic relations. Our experiments show that the performance is promising.
Extracted statement — Application Domain: Medical
Research field: Semantic Web
Title: Image based mammographie ontology learning
Abstract: Understanding the content of an image is one of the challenges in the image processing field. Recently, the Content Based Image Retrieval (CBIR) and especially Semantic Content Based Image Retrieval (SCBIR) are the main goal of many research works. In medical field, understanding the content of an image is very helpful in the automatic decision making. In fact, analyzing the semantic information in an image support can assist the doctor to make the adequate diagnosis. This paper presents a new method for mammographic ontology learning from a set of mammographic images. The approach is based on four main modules: (1) the mammography segmentation, (2) the features extraction (3) the local ontology modeling and (4) the global ontology construction basing on merging the local ones. The first module allows detecting the pathological regions in the represented breast. The second module consists on extracting the most important features from the pathological zones. The third module allows modeling a local ontology by representing the pertinent entities (conceptual entities) as well as their correspondent features (shape, size, form, etc.) discovered in the previous step. The last module consists on merging the local ontologies extracted from a set of mammographies in order to obtain a global and exhaustive one. Our approach attempts to fully describe the semantic content of mammographic images in order to perform the domain knowledge modeling.
Extracted statement — Application Domain: Medical
Research field: Work, Economy and Organizations
Title: Challenge: Processing web texts for classifying job offers
Abstract: Today the Web represents a rich source of labour market data for both public and private operators, as a growing number of job offers are advertised through Web portals and services. In this paper we apply and compare several techniques, namely explicit-rules, machine learning, and LDA-based algorithms to classify a real dataset of Web job offers collected from 12 heterogeneous sources against a standard classification system of occupations.
Extracted statement — method: explicit-rules
Research field: Cultural History
Title: The Role of Smart City Characteristics in the Plans of Fifteen Cities
Abstract: ABSTRACT This paper identifies the characteristics of smart cities as they emerge from the recent literature. It then examines whether and in what way these characteristics are present in the smart city plans of 15 cities: Amsterdam, Barcelona, London, PlanIT Valley, Stockholm, Cyberjaya, Singapore, King Abdullah Economic City, Masdar, Skolkovo, Songdo, Chicago, New York, Rio de Janeiro, and Konza. The results are presented with respect to each smart city characteristic. As expected, most strategies emphasize the role of information and communication technologies in improving the functionality of urban systems and advancing knowledge transfer and innovation networks. However, this research yields other interesting findings that may not yet have been documented across multiple case studies; for example, most smart city strategies fail to incorporate bottom-up approaches, are poorly adapted to accommodate the local needs of their area, and consider issues of privacy and security inadequately.
Extracted statement — proposes: smart city characteristic
Research field: Databases/Information Systems
Title: A fast method based on multiple clustering for name disambiguation in bibliographic citations
Abstract: Name ambiguity in the context of bibliographic citation affects the quality of services in digital libraries. Previous methods are not widely applied in practice because of their high computational complexity and their strong dependency on excessive attributes, such as institutional affiliation, research area, address, etc., which are difficult to obtain in practice. To solve this problem, we propose a novel coarse‐to‐fine framework for name disambiguation which sequentially employs 3 common and easily accessible attributes (i.e., coauthor name, article title, and publication venue). Our proposed framework is based on multiple clustering and consists of 3 steps: (a) clustering articles by coauthorship and obtaining rough clusters, that is fragments; (b) clustering fragments obtained in step 1 by title information and getting bigger fragments; (c) and clustering fragments obtained in step 2 by the latent relations among venues. Experimental results on a Digital Bibliography and Library Project (DBLP) data set show that our method outperforms the existing state‐of‐the‐art methods by 2.4% to 22.7% on the average pairwise F1 score and is 10 to 100 times faster in terms of execution time.
Extracted statement — Performance metric: Pairwise F1
Research field: Databases/Information Systems
Title: A Real-time Heuristic-based Unsupervised Method for Name Disambiguation in Digital Libraries
Abstract: This paper addresses the problem of name disambiguation in the context of digital libraries that administer bibliographic citations. The problem occurs when multiple authors share a common name or when multiple name variations for an author appear in citation records. Name disambiguation is not a trivial task, and most digital libraries do not provide an ecient way to accurately identify the citation records for an author. Furthermore, lack of complete meta-data information in digital libraries hinders the development of a generic algorithm that can be applicable to any dataset. We propose a heuristic-based, unsupervised and adaptive method that also examines users’ interactions in order to include users’ feedback in the disambiguation process. Moreover, the method exploits important features associated with author and citation records, such as co-authors, aliation, publication title, venue, etc., creating a multilayered hierarchical clustering algorithm which transforms itself according to the available information, and forms clusters of unambiguous records. Our experiments on a set of researchers’ names considered to be highly ambiguous produced high precision and recall results, and decisively armed the viability of our algorithm.
Extracted statement — Method: Unsupervised and Adaptive
Research field: Earth Sciences
Title: Application of remote sensing methods to hydrology and water resources
Abstract: Abstract A brief review of research in remote sensing of water resources indicates that there are many positive results, and some techniques have been applied operationally. Currently, remote sensing data are being used operationally in precipitation estimates, soil moisture measurements for irrigation scheduling, snow water equivalent and snow cover extent assessments, seasonal and short term snowmelt runoff forecasts, and surface water inventories. In the next decade other operational applications are likely using remote measurements of land cover, sediment loads, erosion, groundwater, and areal inputs to hydrological models. Many research challenges remain, and significant progress is expected in areas like albedo measurements, energy budgets, and evapotranspiration estimation. The research in remote sensing and water resources also has much relevance for related studies of climate change and global habitability.
Extracted statement — Outcomes:  Snow water equivalent
Research field: Engineering
Title: Design and Development of a Flexible Strain Sensor for Textile Structures Based on a Conductive Polymer Composite
Abstract: The aim of this work is to develop a smart flexible sensor adapted to textile structures, able to measure their strain deformations. The sensors are “smart” because of their capacity to adapt to the specific mechanical properties of textile structures that are lightweight, highly flexible, stretchable, elastic, etc. Because of these properties, textile structures are continuously in movement and easily deformed, even under very low stresses. It is therefore important that the integration of a sensor does not modify their general behavior. The material used for the sensor is based on a thermoplastic elastomer (Evoprene)/carbon black nanoparticle composite, and presents general mechanical properties strongly compatible with the textile substrate. Two preparation techniques are investigated: the conventional melt-mixing process, and the solvent process which is found to be more adapted for this particular application. The preparation procedure is fully described, namely the optimization of the process in terms of filler concentration in which the percolation theory aspects have to be considered. The sensor is then integrated on a thin, lightweight Nylon fabric, and the electromechanical characterization is performed to demonstrate the adaptability and the correct functioning of the sensor as a strain gauge on the fabric. A normalized relative resistance is defined in order to characterize the electrical response of the sensor. Finally, the influence of environmental factors, such as temperature and atmospheric humidity, on the sensor performance is investigated. The results show that the sensor's electrical resistance is particularly affected by humidity. This behavior is discussed in terms of the sensitivity of the carbon black filler particles to the presence of water.
Extracted statement — keywords: carbon black
Research field: Engineering
Title: A Highly Sensitive Nonenzymatic Glucose Biosensor Based on the Regulatory Effect of Glucose on Electrochemical Behaviors of Colloidal Silver Nanoparticles on MoS2
Abstract: A novel and highly sensitive nonenzymatic glucose biosensor was developed by nucleating colloidal silver nanoparticles (AgNPs) on MoS2. The facile fabrication method, high reproducibility (97.5%) and stability indicates a promising capability for large-scale manufacturing. Additionally, the excellent sensitivity (9044.6 μA·mM−1·cm−2), low detection limit (0.03 μM), appropriate linear range of 0.1–1000 μM, and high selectivity suggests that this biosensor has a great potential to be applied for noninvasive glucose detection in human body fluids, such as sweat and saliva.
Extracted statement — keywords: colloidal silver nanoparticle
Research field: Information Systems, Process and Knowledge Management
Title: SoMeSci- A 5 Star Open Data Gold Standard Knowledge Graph of Software Mentions in Scientific Articles
Abstract: Knowledge about software used in scientific investigations is important for several reasons, for instance, to enable an understanding of provenance and methods involved in data handling. However, software is usually not formally cited, but rather mentioned informally within the scholarly description of the investigation, raising the need for automatic information extraction and disambiguation. Given the lack of reliable ground truth data, we present SoMeSci-Software Mentions in Science-a gold standard knowledge graph of software mentions in scientific articles. It contains high quality annotations (IRR: K=.82) of 3756 software mentions in 1367 PubMed Central articles. Besides the plain mention of the software, we also provide relation labels for additional information, such as the version, the developer, a URL or citations. Moreover, we distinguish between different types, such as application, plugin or programming environment, as well as different types of mentions, such as usage or creation. To the best of our knowledge, SoMeSci is the most comprehensive corpus about software mentions in scientific articles, providing training samples for Named Entity Recognition, Relation Extraction, Entity Disambiguation, and Entity Linking. Finally, we sketch potential use cases and provide baseline results.
Extracted statement — data source: PubMed Central