Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Science
Title: LEPTO dipstick, a dipstick assay for detection of Leptospira-specific immunoglobulin M antibodies in human sera.
Abstract: We studied a dipstick assay for the detection of Leptospira-specific immunoglobulin M (IgM) antibodies in human serum samples. A high degree of concordance was observed between the results of the dipstick assay and an IgM enzyme-linked immunosorbent assay (ELISA). Application of the dipstick assay for the detection of acute leptospirosis enabled the accurate identification, early in the disease, of a high proportion of the cases of leptospirosis. Analysis of a second serum sample is recommended, in order to determine seroconversion or increased staining intensity. All serum samples from the patients who were confirmed to be positive for leptospirosis by either a positive microscopic agglutination test or a positive culture but were found to be negative by the dipstick assay were also judged to be negative by the IgM ELISA or revealed borderline titers by the IgM ELISA. Some cross-reactivity was observed for sera from patients with diseases other than leptospirosis, and this should be taken into account in the interpretation of test results. The dipstick assay is easy to perform, can be performed quickly, and requires no electricity or special equipment, and the assay components, a dipstick and a staining reagent, can be stored for a prolonged period without a loss of reactivity, even at elevated temperatures.
Extracted statement — Application: Leptospirosis
Research field: Science
Title: Computer understanding of document structure
Abstract: We describe a system which is capable of learning the presentation of document logical structure, exemplary as shown for business letters. Presenting a set of instances to the system, it clusters them into structural concepts and induces a concept hierarchy. This concept hierarchy is taken as a reference for classifying future input. The article introduces the sequence of learning steps and describes how the resulting concept hierarchy is applied to logical labeling, and reports the results.
Extracted statement — Application Domain: letters
Research field: Science
Title: Accurate eye center location and tracking using isophote curvature
Abstract: The ubiquitous application of eye tracking is precluded by the requirement of dedicated and expensive hardware, such as infrared high definition cameras. Therefore, systems based solely on appearance (i.e. not involving active infrared illumination) are being proposed in literature. However, although these systems are able to successfully locate eyes, their accuracy is significantly lower than commercial eye tracking devices. Our aim is to perform very accurate eye center location and tracking, using a simple Web cam. By means of a novel relevance mechanism, the proposed method makes use of isophote properties to gain invariance to linear lighting changes (contrast and brightness), to achieve rotational invariance and to keep low computational costs. In this paper we test our approach for accurate eye location and robustness to changes in illumination and pose, using the BioIDand the Yale Face B databases, respectively. We demonstrate that our system can achieve a considerable improvement in accuracy over state of the art techniques.
Extracted statement — Challenges: Lighting
Research field: Science
Title: Facility location in humanitarian relief
Abstract: In this study, we consider facility location decisions for a humanitarian relief chain responding to quick-onset disasters. In particular, we develop a model that determines the number and locations of distribution centres in a relief network and the amount of relief supplies to be stocked at each distribution centre to meet the needs of people affected by the disasters. Our model, which is a variant of the maximal covering location model, integrates facility location and inventory decisions, considers multiple item types, and captures budgetary constraints and capacity restrictions. We conduct computational experiments to illustrate how the proposed model works on a realistic problem. Results show the effects of pre- and post-disaster relief funding on relief system's performance, specifically on response time and the proportion of demand satisfied. Finally, we discuss the managerial implications of the proposed model.
Extracted statement — Decisions First-stage: Locations
Research field: Science
Title: Stochastic network design for disaster preparedness
Abstract: This article introduces a risk-averse stochastic modeling approach for a pre-disaster relief network design problem under uncertain demand and transportation capacities. The sizes and locations of the response facilities and the inventory levels of relief supplies at each facility are determined while guaranteeing a certain level of network reliability. A probabilistic constraint on the existence of a feasible flow is introduced to ensure that the demand for relief supplies across the network is satisfied with a specified high probability. Responsiveness is also accounted for by defining multiple regions in the network and introducing local probabilistic constraints on satisfying demand within each region. These local constraints ensure that each region is self-sufficient in terms of providing for its own needs with a large probability. In particular, the Gale–Hoffman inequalities are used to represent the conditions on the existence of a feasible network flow. The solution method rests on two pillars. A preprocessing algorithm is used to eliminate redundant Gale–Hoffman inequalities and then proposed models are formulated as computationally efficient mixed-integer linear programs by utilizing a method based on combinatorial patterns. Computational results for a case study and randomly generated problem instances demonstrate the effectiveness of the models and the solution method.
Extracted statement — Decisions First-stage: Locations
Research field: Science
Title: Bi-objective stochastic programming models for determining depot locations in disaster relief operations
Abstract: This paper presents two-stage bi-objective stochastic programming models for disaster relief operations. We consider a problem that occurs in the aftermath of a natural disaster: a transportation system for supplying disaster victims with relief goods must be established. We propose bi-objective optimization models with a monetary objective and humanitarian objective. Uncertainty in the accessibility of the road network is modeled by a discrete set of scenarios. The key features of our model are the determination of locations for intermediate depots and acquisition of vehicles. Several model variants are considered. First, the operating budget can be fixed at the first stage for all possible scenarios or determined for each scenario at the second stage. Second, the assignment of vehicles to a depot can be either fixed or free. Third, we compare a heterogeneous vehicle fleet to a homogeneous fleet. We study the impact of the variants on the solutions. The set of Pareto-optimal solutions is computed by applying the adaptive Epsilon-constraint method. We solve the deterministic equivalents of the two-stage stochastic programs using the MIP-solver CPLEX.
Extracted statement — Decisions First-stage: Locations
Research field: Science
Title: Design Patterns in Software Maintenance: An Experiment Replication at University of Alabama
Abstract: Design patterns are widely used within the software engineer community. Researchers claim that design patterns improve software quality. In this paper, we describe two experiments, using graduate student participants, to study whether design patterns improve the software quality, specifically maintainability and understandability. We replicated a controlled experiment to compare the maintainability of two implementations of an application, one using a design pattern and the other using a simpler alternative. The maintenance tasks in this replication experiment required the participants to answer questions about a Java program and then modify that program. Prior to the replication, we performed a preliminary exercise to investigate whether design patterns improve the understandability of software designs. We gave the participants the graphical design of the systems that would be used in the replication study. The participant received either the version of the design containing the design pattern or the version containing the simpler alternative. We asked the participants a series of questions to see how well they understood the given design. The results of two experiments revealed that the design patterns did not improve either the maintainability or the understandability of the software. We found that there was no significant correlation between the maintainability and the understandability of the software even though the participants had received the design of the systems before they performed the maintenance tasks.
Extracted statement — Quality attribute: Maintainability
Research field: Science
Title: Impact of the visitor pattern on program comprehension and maintenance
Abstract: In the software engineering literature, many works claim that the use of design patterns improves the comprehensibility of programs and, more generally, their maintainability. Yet, little work attempted to study the impact of design patterns on the developers' tasks of program comprehension and modification. We design and perform an experiment to collect data on the impact of the Visitor pattern on comprehension and modification tasks with class diagrams. We use an eye-tracker to register saccades and fixations, the latter representing the focus of the developers' attention. Collected data show that the Visitor pattern plays a role in maintenance tasks: class diagrams with its canonical representation requires less efforts from developers.
Extracted statement — Quality attribute: Maintainability
Research field: Science
Title: Omya Hustadmarmor optimizes its supply chain for delivering calcium carbonate slurry to European paper manufacturers
Abstract: The Norwegian company Omya Hustadmarmor supplies calcium carbonate slurry to European paper manufacturers from a single processing plant, using chemical tank ships of various sizes to transport its products. Transportation costs are lower for large ships than for small ships, but their use increases planning complexity and creates problems in production. In 2001, the company faced overwhelming operational challenges and sought operations-research-based planning support. The CEO, Sturla Steinsvik, contacted More Research Molde, which conducted a project that led to the development of a decision-support system (DSS) for maritime inventory routing. The core of the DSS is an optimization model that is solved through a metaheuristic-based algorithm. The system helps planners to make stronger, faster decisions and has increased predictability and flexibility throughout the supply chain. It has saved production and transportation costs close to US$7 million a year. We project additional direct savings of nearly US$4 million a year as the company adds even larger ships to the fleet as a result of the project. In addition, the company has avoided investments of US$35 million by increasing capacity utilization. Finally, the project has had a positive environmental effect by reducing overall oil consumption by more than 10 percent.
Extracted statement — mode: Maritime
Research field: Science
Title: New Directions for Traditional Lessons”: Can Handheld Game Consoles Enhance Mental Mathematics Skills?
Abstract: This paper reports on a pilot study that compared the use of commercial off-the-shelf (COTS) handheld game consoles (HGCs) with traditional teaching methods to develop the automaticity of mathematical calculations and self-concept towards mathematics for year 4 students in two metropolitan schools. One class conducted daily sessions using the HGCs and the Dr Kawashima’s Brain Training software to enhance their mental maths skills while the comparison class engaged in mental maths lessons using more traditional classroom approaches. Students were assessed using standardised tests at the beginning and completion of the term and findings indicated that students who undertook the Brain Training pilot study using the HGCs showed significant improvement in both the speed and accuracy of their mathematical calculations and selfconcept compared to students in the control school. An exploration of the intervention, discussion of methodology and the implications of the use of HGCs in the primary classroom are presented.
Extracted statement — Topic: Mathematics
Research field: Science
Title: A Study on Exploiting Commercial Digital Games into School Context
Abstract: Digital game-based learning is a research field within the context of technology-enhanced learning that has attracted significant research interest. Commercial off-the-shelf digital games have the potential to provide concrete learning experiences and allow for drawing links between abstract concepts and real-world situations. The aim of this paper is to provide evidence for the effect of a general-purpose commercial digital game (namely, the “Sims 2-Open for Business”) on the achievement of standard curriculum Mathematics educational objectives as well as general educational objectives as defined by standard taxonomies. Furthermore, students’ opinions about their participation in the proposed game-supported educational scenario and potential changes in their attitudes toward math teaching and learning in junior high school are investigated. The results of the conducted research showed that: (i) students engaged in the game-supported educational activities achieved the same results with those who did not, with regard to the subject matter educational objectives, (ii) digital gamesupported educational activities resulted in better achievement of the general educational objectives, and (iii) no significant differences were observed with regard to students’ attitudes towards math teaching and learning.
Extracted statement — Topic: Mathematics
Research field: Science
Title: User-Priority guided min min scheduling algorithm for load balancing in cloud computing
Abstract: Cloud computing is emerging as a new paradigm of large-scale distributed computing. In order to utilize the power of cloud computing completely, we need an efficient task scheduling algorithm. The traditional Min-Min algorithm is a simple, efficient algorithm that produces a better schedule that minimizes the total completion time of tasks than other algorithms in the literature [7]. However the biggest drawback of it is load imbalanced, which is one of the central issues for cloud providers. In this paper, an improved load balanced algorithm is introduced on the ground of Min-Min algorithm in order to reduce the makespan and increase the resource utilization (LBIMM). At the same time, Cloud providers offer computer resources to users on a pay-per-use base. In order to accommodate the demands of different users, they may offer different levels of quality for services. Then the cost per resource unit depends on the services selected by the user. In return, the user receives guarantees regarding the provided resources. To observe the promised guarantees, user-priority was considered in our proposed PA-LBIMM so that user's demand could be satisfied more completely. At last, the introduced algorithm is simulated using Matlab toolbox. The simulation results show that the improved algorithm can lead to significant performance gain and achieve over 20% improvement on both VIP user satisfaction and resource utilization ratio.
Extracted statement — Tools used for simulation: Matlab
Research field: Science
Title: Translating the Concept of Goal Setting into Practice: What ‘else’ Does It Require than a Goal Setting Tool?: 
Abstract: This conceptual paper reviews the current status of goal setting in the area of technology enhanced learning and education. Besides a brief literature review, three current projects on goal setting are discussed. The paper shows that the main barriers for goal setting applications in education are not related to the technology, the available data or analytical methods, but rather the human factor. The most important bottlenecks are the lack of students goal setting skills and abilities, and the current curriculum design, which, especially in the observed higher education institutions, provides little support for goal setting interventions.
Extracted statement — contains: Methods
Research field: Science
Title: A Technology-enhanced Smart Learning Environment based on the Combination of Knowledge Graphs and Learning Paths: 
Abstract: In our position paper on a technology-enhanced smart learning environment, we propose the innovative combination of a knowledge graph representing what one has to learn and a learning path defining in which order things are going to be learned. In this way, we aim to identify students’ weak spots or knowledge gaps in order to individually assist them in reaching their goals. Based on the performance of different learning paths, one might further identify the characteristics of a learning system that leads to successful students. In addition, by studying assessments and the different ways a particular problem can be solved, new methods for a multi-dimensional classification of assessments can be developed. The theoretical findings on learning paths in combination with the classification of assessments will inform the design and development of a smart learning environment. By combining a knowledge graph with different learning paths and the corresponding practical assessments we enable the creation of a smart learning tool. While the proposed approach can be applied to different educational domains and should lead to more effective learning environments fostering deep learning in schools as well as in professional settings, in this paper we focus on the domain of mathematics in primary and high schools as the main use case.
Extracted statement — contains: Methods
Research field: Science
Title: Measuring the predictability of life outcomes with a scientific mass collaboration
Abstract: <jats:p>How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.</jats:p>
Extracted statement — contains: Methods
Research field: Science
Title: Treatment of metronidazole-refractory Clostridium difficile enteritis with vancomycin
Abstract: BACKGROUND Clostridium difficile infection of the colon is a common and well-described clinical entity. Clostridium difficile enteritis of the small bowel is believed to be less common and has been described sparsely in the literature. METHODS Case report and literature review. RESULTS We describe a patient who had undergone total proctocolectomy with ileal pouch-anal anastomosis who was treated with broad-spectrum antibiotics and contracted C. difficile refractory to metronidazole. The enteritis resolved quickly after initiation of combined oral vancomycin and metronidazole. A literature review found that eight of the fifteen previously reported cases of C. difficile-associated small-bowel enteritis resulted in death. CONCLUSIONS It is important for physicians who treat acolonic patients to be aware of C. difficile enteritis of the small bowel so that it can be suspected, diagnosed, and treated.
Extracted statement — Treatment: metronidazole
Research field: Science
Title: Falcon 2.0: An Entity and Relation Linking Tool over Wikidata
Abstract: The Natural Language Processing (NLP) community has significantly contributed to the solutions for entity and relation recognition from a natural language text, and possibly linking them to proper matches in Knowledge Graphs (KGs). Considering Wikidata as the background KG, there are still limited tools to link knowledge within the text to Wikidata. In this paper, we present Falcon 2.0, the first joint entity and relation linking tool over Wikidata. It receives a short natural language text in the English language and outputs a ranked list of entities and relations annotated with the proper candidates in Wikidata. The candidates are represented by their Internationalized Resource Identifier (IRI) in Wikidata. Falcon 2.0 resorts to the English language model for the recognition task (e.g., N-Gram tiling and N-Gram splitting), and then an optimization approach for the linking task. We have empirically studied the performance of Falcon 2.0 on Wikidata and concluded that it outperforms all the existing baselines. Falcon 2.0 is open source and can be reused by the community; all the required instructions of Falcon 2.0 are well-documented at our GitHub repository (https://github.com/SDM-TIB/falcon2.0). We also demonstrate an online API, which can be run without any technical expertise. Falcon 2.0 and its background knowledge bases are available as resources at https://labs.tib.eu/falcon/falcon2/.
Extracted statement — contains: Model
Research field: Science
Title: multimodal speech emotion recognition using audio and text
Abstract: Speech emotion recognition is a challenging task, and extensive reliance has been placed on models that use audio features in building well-performing classifiers. In this paper, we propose a novel deep dual recurrent encoder model that utilizes text data and audio signals simultaneously to obtain a better understanding of speech data. As emotional dialogue is composed of sound and spoken content, our model encodes the information from audio and text sequences using dual recurrent neural networks (RNNs) and then combines the information from these sources to predict the emotion class. This architecture analyzes speech data from the signal level to the language level, and it thus utilizes the information within the data more comprehensively than models that focus on audio features. Extensive experiments are conducted to investigate the efficacy and properties of the proposed model. Our proposed model outperforms previous state-of-the-art methods in assigning data to one of four emotion categories (i.e., angry, happy, sad and neutral) when the model is applied to the IEMOCAP dataset, as reflected by accuracies ranging from 68.8% to 71.8%.
Extracted statement — contains: Model