Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Science
Title: A fast trilateral filterbased adaptive support weight method for stereo matching
Abstract: Adaptive support weight (ASW) methods represent the state of the art in local stereo matching, while the bilateral filter-based ASW method achieves outstanding performance. However, this method fails to resolve the ambiguity induced by nearby pixels at different disparities but with similar colors. In this paper, we introduce a novel trilateral filter (TF)-based ASW method that remedies such ambiguities by considering the possible disparity discontinuities through color discontinuity boundaries, i.e., the boundary strength between two pixels, which is measured by a local energy model. We also present a recursive TF-based ASW method whose computational complexity is O(N) for the cost aggregation step, and O(NLog2(N)) for boundary detection, where N denotes the input image size. This complexity is thus independent of the support window size. The recursive TF-based method is a nonlocal cost aggregation strategy. The experimental evaluation on the Middlebury benchmark shows that the proposed method, whose average error rate is 4.95%, outperforms other local methods in terms of accuracy. Equally, the average runtime of the proposed TF-based cost aggregation is roughly 260 ms on a 3.4-GHz Inter Core i7 CPU, which is comparable with state-of-the-art efficiency.
Extracted statement — Taxonomy stage: Step: ASW
Research field: Science
Title: A centralized energy-efficient routing protocol for wireless sensor networks
Abstract: Wireless sensor networks consist of small battery powered devices with limited energy resources. Once deployed, the small sensor nodes are usually inaccessible to the user, and thus replacement of the energy source is not feasible. Hence, energy efficiency is a key design issue that needs to be enhanced in order to improve the life span of the network. Several network layer protocols have been proposed to improve the effective lifetime of a network with a limited energy supply. In this article we propose a centralized routing protocol called base-station controlled dynamic clustering protocol (BCDCP), which distributes the energy dissipation evenly among all sensor nodes to improve network lifetime and average energy savings. The performance of BCDCP is then compared to clustering-based schemes such as low-energy adaptive clustering hierarchy (LEACH), LEACH-centralized (LEACH-C), and power-efficient gathering in sensor information systems (PEGASIS). Simulation results show that BCDCP reduces overall energy consumption and improves network lifetime over its comparatives.
Extracted statement — Protocol: BCDCP
Research field: Science
Title: Effect of Euler Number as a Feature in gender Recognition System from Offline HandwrittenSignature Using Neural Networks
Abstract: Recent growth of technology has also increased identification insecurity. Signature is a unique feature which is different for every other person, and each person can be identified using their own handwritten signature. Gender identification is one of key feature in case of human identification. In this paper, a feature based gender detection method has been proposed. The proposed framework takes handwritten signature as an input. Afterwards, several features are extracted from those images. The extracted features and their values are stored as data, which is further classified using Back Propagation Neural Network (BPNN). Gender classification is done using BPNN which is one of the most popular classifier. The proposed system is broken into two parts. In the first part, several features such as roundness, skewness, kurtosis, mean, standard deviation, area, Euler number, distribution density of black pixel, entropy, equi-diameter, connected component (cc) and perimeter were taken as feature. Then obtained features are divided into two categories. In the first category experimental feature set contains Euler number, whereas in the second category the obtained feature set excludes the same. BPNN is used to classify both types of feature sets to recognize the gender. Our study reports an improvement of 4.7% in gender classification system by the inclusion of Euler number as a feature.
Extracted statement — Classifier: BPNN
Research field: Science
Title: Reliability of Component Based systems- a Critical Survey
Abstract: Software reliability is defined as the probability of the failure free operation of a software system for a specified period of time in a specified environment. Day by day software applications are growing more complex and with more emphasis on reuse. Component Based Software (CBS) applications have emerged. The focus of this paper is to provide an overview for the state of the art of Component Based Systems reliability estimation. In this paper, we discussed various approaches in terms of their scope, model, methods, technique and validation scheme. This comparison provides insight into determining the direction of future CBS reliability research.
Extracted statement — Area of use: CBS
Research field: Science
Title: SVM-DSmT Combination for Off-Line Signature Verification
Abstract: We propose in this work a signature verification system based on decision combination of off-line signatures for managing conflict provided by the SVM classifiers. The system is basically divided into three modules: i) Radon Transform-SVM, ii) Ridgelet Transform-SVM and iii) PCR5 combination rule based on the generalized belief functions of Dezert-Smarandache theory. The proposed framework allows combining the normalized SVM outputs and uses an estimation technique based on the dissonant model of Appriou to compute the belief assignments. Decision making is performed through likelihood ratio. Experiments are conducted on the well known CEDAR database using false rejection and false acceptance criteria. The obtained results show that the proposed combination framework improves the verification accuracy compared to individual SVM classifiers.
Extracted statement — Offline
Database: CEDAR
Research field: Science
Title: Advanced models of cellular genetic algorithms evaluated on SAT
Abstract: Cellular genetic algorithms (cGAs) are mainly characterized by their spatially decentralized population, in which individuals can only interact with their neighbors. In this work, we study the behavior of a large number of different cGAs when solving the well-known 3-SAT problem. These cellular algorithms differ in the policy of individuals update and the population shape, since these two features affect the balance between exploration and exploitation of the algorithm. We study in this work both synchronous and asynchronous cGAs, having static and dynamically adaptive shapes for the population. Our main conclusion is that the proposed adaptive cGAs outperform other more traditional genetic algorithms for a well known benchmark of 3-SAT.
Extracted statement — Name: CGA
Research field: Science
Title: Chromosomal abnormalities in Philadelphia chromosome negative metaphases appearing during imatinib mesylate therapy in patients with newly diagnosed chronic myeloid leukemia in chronic phase
Abstract: The development of chromosomal abnormalities (CAs) in the Philadelphia chromosome (Ph)-negative metaphases during imatinib (IM) therapy in patients with newly diagnosed chronic myecloid leukemia (CML) has been reported only anecdotally. We assessed the frequency and significance of this phenomenon among 258 patients with newly diagnosed CML in chronic phase receiving IM. After a median follow-up of 37 months, 21 (9%) patients developed 23 CAs in Ph-negative cells; excluding -Y, this incidence was 5%. Sixteen (70%) of all CAs were observed in 2 or more metaphases. The median time from start of IM to the appearance of CAs was 18 months. The most common CAs were -Y and + 8 in 9 and 3 patients, respectively. CAs were less frequent in young patients (P = .02) and those treated with high-dose IM (P = .03). In all but 3 patients, CAs were transient and disappeared after a median of 5 months. One patient developed acute myeloid leukemia (associated with - 7). At last follow-up, 3 patients died from transplantation-related complications, myocardial infarction, and progressive disease and 2 lost cytogenetic response. CAs occur in Ph-negative cells in a small percentage of patients with newly diagnosed CML treated with IM. In rare instances, these could reflect the emergence of a new malignant clone.
Extracted statement — Disease: CML
Research field: Science
Title: Investigating the impacts of energy consumption, real GDP, tourism and trade on CO 2 emissions by accounting for cross-sectional dependence: a panel study of OECD countries
Abstract: The objective of this study is to analyse the long-run dynamic relationship of carbon dioxide emissions, real gross domestic product (GDP), the square of real GDP, energy consumption, trade and tourism under an Environmental Kuznets Curve (EKC) model for the Organization for Economic Co-operation and Development (OECD) member countries. Since we find the presence of cross-sectional dependence within the panel time-series data, we apply second-generation unit root tests, cointegration test and causality test which can deal with cross-sectional dependence problems. The cross-sectionally augmented Dickey-Fuller (CADF) and the cross-sectionally augmented Im-Pesaran-Shin (CIPS) unit root tests indicate that the analysed variables become stationary at their first differences. The Lagrange multiplier bootstrap panel cointegration test shows the existence of a long-run relationship between the analysed variables. The dynamic ordinary least squares (DOLS) estimation technique indicates that energy consumption and tourism contribute to the levels of gas emissions, while increases in trade lead to environmental improvements. In addition, the EKC hypothesis cannot be supported as the sign of coefficients on GDP and GDP2 is negative and positive, respectively. Moreover, the Dumitrescu–Hurlin causality tests exploit a variety of causal relationship between the analysed variables. The OECD countries are suggested to invest in improving energy efficiency, regulate necessary environmental protection policies for tourism sector in specific and promote trading activities through several types of encouragement act.
Extracted statement — Methodology: DOLS
Research field: Science
Title: Robust Discriminative Response Map Fitting with Constrained Local Models
Abstract: We present a novel discriminative regression based approach for the Constrained Local Models (CLMs) framework, referred to as the Discriminative Response Map Fitting (DRMF) method, which shows impressive performance in the generic face fitting scenario. The motivation behind this approach is that, unlike the holistic texture based features used in the discriminative AAM approaches, the response map can be represented by a small set of parameters and these parameters can be very efficiently used for reconstructing unseen response maps. Furthermore, we show that by adopting very simple off-the-shelf regression techniques, it is possible to learn robust functions from response maps to the shape parameters updates. The experiments, conducted on Multi-PIE, XM2VTS and LFPW database, show that the proposed DRMF method outperforms state-of-the-art algorithms for the task of generic face fitting. Moreover, the DRMF method is computationally very efficient and is real-time capable. The current MATLAB implementation takes 1 second per image. To facilitate future comparisons, we release the MATLAB code and the pre-trained models for research purposes.
Extracted statement — Methods: DRMF
Research field: Science
Title: An Energy-Aware Distributed Unequal Clustering Protocol for Wireless Sensor Networks
Abstract: Due to the imbalance of energy consumption of nodes in wireless sensor networks (WSNs), some local nodes die prematurely, which causes the network partitions and then shortens the lifetime of the network. The phenomenon is called “hot spot” or “energy hole” problem. For this problem, an energy-aware distributed unequal clustering protocol (EADUC) in multihop heterogeneous WSNs is proposed. Compared with the previous protocols, the cluster heads obtained by EADUC can achieve balanced energy, good distribution, and seamless coverage for all the nodes. Moreover, the complexity of time and control message is low. Simulation experiments show that EADUC can prolong the lifetime of the network significantly.
Extracted statement — Protocol: EADUC
Research field: Science
Title: An energy aware fuzzy unequal clustering algorithm for wireless sensor networks
Abstract: In order to gather information more efficiently, wireless sensor networks (WSNs) are partitioned into clusters. The most of the proposed clustering algorithms do not consider the location of the base station. This situation causes hot spots problem in multi-hop WSNs. Unequal clustering mechanisms, which are designed by considering the base station location, solve this problem. In this paper, we introduce a fuzzy unequal clustering algorithm (EAUCF) which aims to prolong the lifetime of WSNs. EAUCF adjusts the cluster-head radius considering the residual energy and the distance to the base station parameters of the sensor nodes. This helps decreasing the intra-cluster work of the sensor nodes which are closer to the base station or have lower battery level. We utilize fuzzy logic for handling the uncertainties in cluster-head radius estimation. We compare our algorithm with some popular algorithms in literature, namely LEACH, CHEF and EEUC, according to First Node Dies (FND), Half of the Nodes Alive (HNA) and energy-efficiency metrics. Our simulation results show that EAUCF performs better than the other algorithms in most of the cases. Therefore, EAUCF is a stable and energy-efficient clustering algorithm to be utilized in any real time WSN application.
Extracted statement — Protocol: EAUCF
Research field: Science
Title: An evaluation of the viability of a single monetary zone in ECOWAS
Abstract: Currency convertibility and monetary integration activities of the Economic Community of West African States (ECOWAS) are directed at addressing the problems of multiple currencies and exchange rate changes that are perceived as stumbling blocks to regional integration. A real exchange rate (RER) variability model shows that ECOWAS is closer to a monetary union now than before. As expected, the implementation of structural adjustment programmes (SAPs) by various governments in the subregion has brought about a reasonable level of convergence. However, wide differences still exist between RER shocks facing CFA zone and non-CFA zone West African countries. Further convergence in economic policy and alternatives to dependence on revenues from taxes on international transactions are required for a stable region-wide monetary union in West Africa.
Extracted statement — Countries: ECOWAS
Research field: Science
Title: Monetary union in West Africa: who might gain, who might lose, and why?
Abstract: We develop a model in which governments' financing needs exceed the socially optimal level because public resources are diverted to serve the narrow interests of the group in power. From a social welfare perspective, this results in undue pressure on the central bank to extract seigniorage. Monetary policy also suffers from an expansive bias, owing to the authorities' inability to precommit to price stability. Such a conjecture about the fiscal-monetary policy mix appears quite relevant in Africa, with deep implications for the incentives of fiscally heterogeneous countries to form a currency union. We calibrate the model to data for West Africa and use it to assess proposed ECOWAS monetary unions. Fiscal heterogeneity indeed appears critical in shaping regional currency blocs that would be mutually beneficial for all their members. In particular, Nigeria's membership in the configurations currently envisaged would not be in the interests of other ECOWAS countries unless it were accompanied by effective containment on Nigeria's financing needs.
Extracted statement — Countries: ECOWAS
Research field: Science
Title: West African Single Currency and Competitiveness
Abstract: This paper compares different nominal anchors to promote internal and external competitiveness in the case of a fixed exchange rate regime for the future single regional currency of the Economic Community of the West African States (ECOWAS). We use counterfactual analyses and estimate a model of dependent economy for small commodity exporting countries. We consider four foreign anchor currencies: the US dollar, the euro, the yen and the yuan. Our simulations show little support for a dominant peg in the ECOWAS area if they pursue several goals: maximizing the export revenues, minimizing their variability, stabilizing them and minimizing the real exchange rate misalignments from the fundamental value.
Extracted statement — Countries: ECOWAS
Research field: Science
Title: Analysis of convergence criteria in a proposed monetary union: a study of the economic community of West African States
Abstract: This study examines the processes of the monetary union of the Economic Community of West African States (ECOWAS). It takes a critical look at the convergence criteria and the various conditions under which they are to be met. Using the panel least square technique an estimate of the beta convergence was made for the period 2000-2008. The findings show that nearly all the explanatory variables have indirect effects on the income growth rate and that there tends to be convergence in income over time. The speed of adjustment estimated is 0.2% per year and the half-life is -346.92. Thus the economies can make up for half of the distance that separates them from their stationary state. From the findings, it was concluded that a well integrated economy could further the achievement of steady growth in these countries in the long run.
Extracted statement — Countries: ECOWAS
Research field: Science
Title: An energy-efficient unequal clustering mechanism for wireless sensor networks
Abstract: Clustering provides an effective way for prolonging the lifetime of a wireless sensor network. Current clustering algorithms usually utilize two techniques, selecting cluster heads with more residual energy and rotating cluster heads periodically, to distribute the energy consumption among nodes in each cluster and extend the network lifetime. However, they rarely consider the hot spots problem in multihop wireless sensor networks. When cluster heads cooperate with each other to forward their data to the base station, the cluster heads closer to the base station are burdened with heavy relay traffic and tend to die early, leaving areas of the network uncovered and causing network partition. To address the problem, we propose an energy-efficient unequal clustering (EEUC) mechanism for periodical data gathering in wireless sensor networks. It partitions the nodes into clusters of unequal size, and clusters closer to the base station have smaller sizes than those farther away from the base station. Thus cluster heads closer to the base station can preserve some energy for the inter-cluster data forwarding. We also propose an energy-aware multihop routing protocol for the inter-cluster communication. Simulation results show that our unequal clustering mechanism balances the energy consumption well among all sensor nodes and achieves an obvious improvement on the network lifetime
Extracted statement — Protocol: EEUC
Research field: Science
Title: A Generalizable, Data-Driven Approach to Predict Daily Risk of Clostridium difficile Infection at Two Large Academic Health Centers
Abstract: OBJECTIVE An estimated 293,300 healthcare-associated cases of Clostridium difficile infection (CDI) occur annually in the United States. To date, research has focused on developing risk prediction models for CDI that work well across institutions. However, this one-size-fits-all approach ignores important hospital-specific factors. We focus on a generalizable method for building facility-specific models. We demonstrate the applicability of the approach using electronic health records (EHR) from the University of Michigan Hospitals (UM) and the Massachusetts General Hospital (MGH). METHODS We utilized EHR data from 191,014 adult admissions to UM and 65,718 adult admissions to MGH. We extracted patient demographics, admission details, patient history, and daily hospitalization details, resulting in 4,836 features from patients at UM and 1,837 from patients at MGH. We used L2 regularized logistic regression to learn the models, and we measured the discriminative performance of the models on held-out data from each hospital. RESULTS Using the UM and MGH test data, the models achieved area under the receiver operating characteristic curve (AUROC) values of 0.82 (95% confidence interval [CI], 0.80–0.84) and 0.75 ( 95% CI, 0.73–0.78), respectively. Some predictive factors were shared between the 2 models, but many of the top predictive factors differed between facilities. CONCLUSION A data-driven approach to building models for estimating daily patient risk for CDI was used to build institution-specific models at 2 large hospitals with different patient populations and EHR systems. In contrast to traditional approaches that focus on developing models that apply across hospitals, our generalizable approach yields risk-stratification models tailored to an institution. These hospital-specific models allow for earlier and more accurate identification of high-risk patients and better targeting of infection prevention strategies. Infect Control Hosp Epidemiol 2018;39:425–433
Extracted statement — Features: EHR
Research field: Science
Title: Automated Detection of Postoperative Surgical Site Infections Using Supervised Methods with Electronic Health Record Data
Abstract: The National Surgical Quality Improvement Project (NSQIP) is widely recognized as “the best in the nation” surgical quality improvement resource in the United States. In particular, it rigorously defines postoperative morbidity outcomes, including surgical adverse events occurring within 30 days of surgery. Due to its manual yet expensive construction process, the NSQIP registry is of exceptionally high quality, but its high cost remains a significant bottleneck to NSQIP’s wider dissemination. In this work, we propose an automated surgical adverse events detection tool, aimed at accelerating the process of extracting postoperative outcomes from medical charts. As a prototype system, we combined local EHR data with the NSQIP gold standard outcomes and developed machine learned models to retrospectively detect Surgical Site Infections (SSI), a particular family of adverse events that NSQIP extracts. The built models have high specificity (from 0.788 to 0.988) as well as very high negative predictive values (>0.98), reliably eliminating the vast majority of patients without SSI, thereby significantly reducing the NSQIP extractors’ burden.
Extracted statement — Features: EHR