Moussab/ORKG-training-evaluation-set huggingface dataset:
Research field: Science
Title: Macroeconomic Shock Synchronization in the East African Community
Abstract: <jats:p> The East African Community’s (EAC) economic integration has gained momentum recently, with the EAC countries aiming to adopt a single currency in 2015. This article evaluates empirically the readiness of the EAC countries for monetary union. First, structural similarity in terms of similarity of production and exports of the EAC countries is measured. Second, the symmetry of shocks is examined with structural vector auto-regression analysis (SVAR). The lack of macroeconomic convergence gives evidence against a hurried transition to a monetary union. Given the divergent macroeconomic outcomes, structural reforms, including closing infrastructure gaps and harmonizing macroeconomic policies that would raise synchronization of business cycles, need to be in place before moving to monetary union. </jats:p>
Extracted statement — Methodology: SVAR
Research field: Science
Title: Off-line English and Chinese Signature Identification Using Foreground and Background Features
Abstract: In the field of information security, the usage of biometrics is growing for user authentication. Automatic signature recognition and verification is one of the biometric techniques, which is only one of several used to verify the identity of individuals. In this paper, a foreground and background based technique is proposed for identification of scripts from bi-lingual (English/Roman and Chinese) off-line signatures. This system will identify whether a claimed signature belongs to the group of English signatures or Chinese signatures. The identification of signatures based on its script is a major contribution for multi-script signature verification. Two background information extraction techniques are used to produce the background components of the signature images. Gradient-based method was used to extract the features of the foreground as well as background components. Zernike Moment feature was also employed on signature samples. Support Vector Machine (SVM) is used as the classifier for signature identification in the proposed system. A database of 1120 (640 English+480 Chinese) signature samples were used for training and 560 (320 English+240 Chinese) signature samples were used for testing the proposed system. An encouraging identification accuracy of 97.70% was obtained using gradient feature from the experiment.
Extracted statement — Classifier: SVM
Research field: Science
Title: Off-line Signature Verification Based on Chain Code Histogram and Support Vector Machine
Abstract: In this paper, we present an approach based on chain code histogram features enhanced through Laplacian of Gaussian filter for off-line signature verification. In the proposed approach, the four-directional chain code histogram of each grid on the contour of the signature image is extracted. The Laplacian of Gaussian filter is used to enhance the extracted features of each signature sample. Thus, the extracted and enhanced features of all signature samples of the off-line signature dataset constitute the knowledge base. Subsequently, the Support Vector Machine (SVM) classifier is used as the verification tool. The SVM is trained with the randomly selected training sample's features including genuine and random forgeries and tested with the remaining untrained genuine along with the skilled forge sample features to classify the tested/questioned sample as genuine or forge. Similar to the real time scenario, in the proposed approach we have not considered the skilled fore sample to train the classifier. Extensive experimentations have been conducted to exhibit the performance of the proposed approach on the publicly available datasets namely, CEDAR, GPDS-100 and MUKOS, a regional language dataset. The state-of-art off-line signature verification methods are considered for comparative study to justify the feasibility of the proposed approach for off-line signature verification and to reveal its accuracy over the existing approaches.
Extracted statement — Classifier: SVM
Research field: Science
Title: Discriminative DCT: An Efficient and Accurate Approach for Off-line Signature Verification
Abstract: In this paper, we proposed to combine the transform based approach with dimensionality reduction technique for off-line signature verification. The proposed approach has four major phases: Preprocessing, Feature extraction, Feature reduction and Classification. In the feature extraction phase, Discrete Cosine Transform (DCT) is employed on the signature image to obtain the upper-left corner block of size mX n as a representative feature vector. These features are subjected to Linear Discriminant Analysis (LDA) for further reduction and representing the signature with optimal set of features. Thus obtained features from all the samples in the dataset form the knowledge base. The Support Vector Machine (SVM), a bilinear classifier is used for classification and the performance is measured through FAR/FRR metric. Experiments have been conducted on standard signature datasets namely CEDAR and GPDS-160, and MUKOS, a regional language (Kannada) dataset. The comparative study is also provided with the well known approaches to exhibit the performance of the proposed approach.
Extracted statement — Classifier: SVM
Research field: Science
Title: A Pediatric Infection Screening System with a Radar Respiration Monitor for Rapid Detection of Seasonal Influenza among Outpatient Children
Abstract: Background: Seasonal influenza virus outbreaks cause annual epidemics, mostly during winter in temperate zone countries, especially resulting in increased morbidity and higher mortality in children. In order to conduct rapid screening for influenza in pediatric outpatient units, we developed a pediatric infection screening system with a radar respiration monitor. Methods: The system conducts influenza screening within 10 seconds based on vital signs (i.e., respiration rate monitored using a 24 GHz microwave radar; facial temperature, using a thermopile array; and heart rate, using a pulse photosensor). A support vector machine (SVM) classification method was used to discriminate influenza children from healthy children based on vital signs. To assess the classification performance of the screening system that uses the SVM, we conducted influenza screening for 70 children (i.e., 27 seasonal influenza patients (11 ± 2 years) at a pediatric clinic and 43 healthy control subjects (9 ± 4 years) at a pediatric dental clinic) in the winter of 2013-2014. Results: The screening system using the SVM identified 26 subjects with influenza (22 of the 27 influenza patients and 4 of the 43 healthy subjects). The system discriminated 44 subjects as healthy (5 of the 27 influenza patients and 39 of the 43 healthy subjects), with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 81.5%, 90.7%, 84.6%, and 88.6%, respectively. Conclusion: The SVM-based screening system achieved classification results for the outpatient children based on vital signs with comparatively high NPV within 10 seconds. At pediatric clinics and hospitals, our system seems potentially useful in the first screening step for infections in the future.
Extracted statement — Computational intelligence technologie: SVM
Research field: Science
Title: TASC: topology adaptive spatial clustering for sensor networks
Abstract: The ability to extract topological regularity out of large randomly deployed sensor networks holds the promise to maximally leverage correlation for data aggregation and also to assist with sensor localization and hierarchy creation. This paper focuses on extracting such regular structures from physical topology through the development of a distributed clustering scheme. The topology adaptive spatial clustering (TASC) algorithm presented here is a distributed algorithm that partitions the network into a set of locally isotropic, non-overlapping clusters without prior knowledge of the number of clusters, cluster size and node coordinates. This is achieved by deriving a set of weights that encode distance measurements, connectivity and density information within the locality of each node. The derived weights form the terrain for holding a coordinated leader election in which each node selects the node closer to the center of mass of its neighborhood to become its leader. The clustering algorithm also employs a dynamic density reachability criterion that groups nodes according to their neighborhood's density properties. Our simulation results show that the proposed algorithm can trace locally isotropic structures in non-isotropic network and cluster the network with respect to local density attributes. We also found out that TASC exhibits consistent behavior in the presence of moderate measurement noise levels
Extracted statement — Protocol: TASC
Research field: Science
Title: Terrain-based genetic algorithm (TBGA): modeling parameter space as terrain
Abstract: The Terrain-Based Genetic Algorithm (TBGA) is a self-tuning version of the traditional Cellular Genetic Algorithm (CGA). In a TBGA, various combinations of parameter values appear in different physical locations of the population, forming a sort of terrain in which individual solutions evolve. We compare the performance of the TBGA against that of the CGA on a known suite of problems. Our results indicate that the TBGA performs better than the CGA on the test suite, with less parameter tuning, when the CGA is set to parameter values thought in prior studies to be good. While we had hoped that good solutions would cluster around the best parameter settings, this was not observed. However, we were able to use the TBGA to automatically determine better parameter settings for the CGA. The resulting CGA produced even better results than were achieved by the TBGA which found those parameter settings.
Extracted statement — Name: TBGA
Research field: Science
Title: An Adaptive Data Dissemination Strategy for Wireless Sensor Networks
Abstract: Future large-scale sensor networks may comprise thousands of wirelessly connected sensor nodes that could provide an unimaginable opportunity to interact with physical phenomena in real time. However, the nodes are typically highly resource-constrained. Since the communication task is a significant power consumer, various attempts have been made to introduce energy-awareness at different levels within the communication stack. Clustering is one such attempt to control energy dissipation for sensor data dissemination in a multihop fashion. The Time-Controlled Clustering Algorithm (TCCA) is proposed to realize a network-wide energy reduction. A realistic energy dissipation model is derived probabilistically to quantify the sensor network's energy consumption using the proposed clustering algorithm. A discrete-event simulator is developed to verify the mathematical model and to further investigate TCCA in other scenarios. The simulator is also extended to include the rest of the communication stack to allow a comprehensive evaluation of the proposed algorithm.
Extracted statement — Protocol: TCCA
Research field: Science
Title: TEEN: a routing protocol for enhanced efficiency in wireless sensor networks
Abstract: Wireless sensor networks are expected to find wide applicability and increasing deployment in the near future. In this paper, we propose a formal classification of sensor networks, based on their mode of functioning, as proactive and reactive networks. Reactive networks, as opposed to passive data collecting proactive networks, respond immediately to changes in the relevant parameters of interest. We also introduce a new energy efficient protocol, TEEN (Threshold sensitive Energy Efficient sensor Network protocol) for reactive networks. We evaluate the performance of our protocol for a simple temperature sensing application. In terms of energy efficiency, our protocol has been observed to outperform existing conventional sensor network protocols.
Extracted statement — Protocol: TEEN
Research field: Science
Title: UHEED - An Unequal Clustering Algorithm for Wireless Sensor Networks
Abstract: Prolonging the lifetime of wireless sensor networks has always been a determining factor when designing and deploying such networks. Clustering is one technique that can be used to extend the lifetime of sensor networks by grouping sensors together. However, there exists the hot spot problem which causes an unbalanced energy consumption in equally formed clusters. In this paper, we propose UHEED, an unequal clustering algorithm which mitigates this problem and which leads to a more uniform residual energy in the network and improves the network lifetime. Furthermore, from the simulation results presented, we were able to deduce the most appropriate unequal cluster size to be used.
Extracted statement — Protocol: UHEED
Research field: Science
Title: An investigation of cointegration and causality between energy consumption and economic growth
Abstract: This paper reexamines the causality between energy consumption and economic growth with both bivariate and multivariate models by applying the recently developed methods of cointegration and Hsiao`s version of the Granger causality to transformed U.S. data for the period 1947-1990. The Phillips-Perron (PP) tests reveal that the original series are not stationary and, therefore, a first differencing is performed to secure stationarity. The study finds no causal linkages between energy consumption and economic growth. Energy and gross national product (GNP) each live a life of its own. The results of this article are consistent with some of the past studies that find no relationship between energy and GNP but are contrary to some other studies that find GNP unidirectionally causes energy consumption. Both the bivariate and trivariate models produce the similar results. We also find that there is no causal relationship between energy consumption and industrial production. The United States is basically a service-oriented economy and changes in energy consumption can cause little or no changes in GNP. In other words, an implementation of energy conservation policy may not impair economic growth. 27 refs., 5 tabs.
Extracted statement — Countries: USA
Research field: Science
Title: Nuclear energy consumption and economic growth in the US: an empirical note
Abstract: Abstract This empirical note examines the relationship between nuclear energy consumption growth and real gross domestic product (GDP) growth within a neoclassical production function framework for the US using annual data from 1957 to 2006. The Toda-Yamamoto (1995) test for long-run Granger-causality reveals the absence of Granger-causality between nuclear energy consumption growth and real GDP growth which supports the neutrality hypothesis within the energy consumption-economic growth literature.
Extracted statement — Country: USA
Research field: Science
Title: On biomass energy consumption and real Output in the U
Abstract: Abstract This empirical note utilizes US annual data from 1949 to 2007 to examine the causal relationship between biomass energy consumption and real gross domestic product (GDP) within a multivariate framework. Toda-Yamamoto causality tests reveal unidirectional causality from biomass energy consumption to real GDP supportive of the growth hypothesis.
Extracted statement — Country: USA
Research field: Science
Title: Real Exchange Rate Volatility and U.S. Bilateral Trade: A VAR Approach
Abstract: This paper uses VAR models to investigate the impact of real exchange rate volatility on U.S. bilateral imports from the United Kingdom, France, Germany, Japan and Canada. The VAR systems include U.S. and foreign macro variables, and are estimated separately for each country. The major results suggest that the effect of volatility on imports is weak, although permanent shocks to volatility do have a negative impact on this measure of trade, and those effects are relatively more important over the flexible rate period. Copyright 1989 by MIT Press.
Extracted statement — Countries and Estimation technique used: VAR
Research field: Science
Title: Does Exchange Rate Volatility Depress Trade Flows? Evidence from Error- Correction Models
Abstract: This paper examines the impact of exchange rate volatility on the trade flows of the G-7 countries in the context of a multivariate error-correction model. The error-correction models do not show any sign of parameter instability. The results indicate that the exchange rate volatility has a significant negative impact on the volume of exports in each of the G-7 countries. Assuming market participants are risk averse, these results imply that exchange rate uncertainty causes them to reduce their activities, change prices, or shift sources of demand and supply in order to minimize their exposure to the effects of exchange rate volatility. This, in turn, can change the distribution of output across many sectors in these countries. It is quite possible that the surprisingly weak relationship between trade flows and exchange rate volatility reported in several previous studies are due to insufficient attention to the stochastic properties of the relevant time series. Copyright 1993 by MIT Press.
Extracted statement — Countries and Estimation technique used: VAR
Research field: Science
Title: How Would Monetary Policy Matter in the Proposed African Monetary Unions? Evidence from Output and Prices
Abstract: We analyze the effects of monetary policy on economic activity in the proposed African monetary unions. Findings broadly show that: (1) but for financial efficiency in the EAMZ, monetary policy variables affect output neither in the short-run nor in the long-term and; (2) with the exception of financial size that impacts inflation in the EAMZ in the short-term, monetary policy variables generally have no effect on prices in the short-run. The WAMZ may not use policy instruments to offset adverse shocks to output by pursuing either an expansionary or a contractionary policy, while the EAMZ can do with the ‘financial allocation efficiency’ instrument. Policy implications are discussed.
Extracted statement — Methodology: VAR
Research field: Science
Title: Efficient mining of weighted association rules (WAR)
Abstract: In this paper, we extend the tradition association rule problem by allowing a weight to be associated with each item in a transaction, to re ect interest/intensity of the item within the transaction. This provides us in turn with an opportunity to associate a weight parameter with each item in the resulting association rule. We call it weighted association rule (WAR). WAR not only improves the con dence of the rules, but also provides a mechanism to do more effective target marketing by identifying or segmenting customers based on their potential degree of loyalty or volume of purchases. Our approach mines WARs by rst ignoring the weight and nding the frequent itemsets (via a traditional frequent itemset discovery algorithm), and is followed by introducing the weight during the rule generation. It is shown by experimental results that our approach not only results in shorter average execution times, but also produces higher quality results than the generalization of previous known methods on quantitative association rules.
Extracted statement — Algorithm name: WAR
Research field: Science
Title: A Fuzzy-set based Semantic Similarity Matching Algorithm for Web Service
Abstract: A critical step in the process of reusing existing WSDL-specified services for building web-based applications is the discovery of potentially relevant services. However, the category-based service discovery, such as UDDI, is clearly insufficient. Semantic Web Services, augmenting Web service descriptions using Semantic Web technology, were introduced to facilitate the publication, discovery, and execution of Web services at the semantic level. Semantic matchmaker enhances the capability of UDDI service registries in the Semantic Web Services architecture by applying some matching algorithms between advertisements and requests described in OWL-S to recognize various degrees of matching for Web services. Based on Semantic Web Service framework, semantic matchmaker, specification matching and probabilistic matching approach, this paper proposes a fuzzy-set based semantic similarity matching algorithm for Web Service to support a more automated and veracity service discovery process in the Semantic Web Service Framework.
Extracted statement — Specification Languages: WSDL
Research field: Science
Title: H-Mine: Fast and space-preserving frequent pattern mining in large databases
Abstract: In this study, we propose a simple and novel data structure using hyper-links, H-struct, and a new mining algorithm, H-mine, which takes advantage of this data structure and dynamically adjusts links in the mining process. A distinct feature of this method is that it has a very limited and precisely predictable main memory cost and runs very quickly in memory-based settings. Moreover, it can be scaled up to very large databases using database partitioning. When the data set becomes dense, (conditional) FP-trees can be constructed dynamically as part of the mining process. Our study shows that H-mine has an excellent performance for various kinds of data, outperforms currently available algorithms in different settings, and is highly scalable to mining large databases. This study also proposes a new data mining methodology, space-preserving mining, which may have a major impact on the future development of efficient and scalable data mining methods. †Decreased
Extracted statement — Algorithm name: H-mine
Research field: Science
Title: Comparative analyses of seven algorithms for copy number variant identification from single nucleotide polymorphism arrays
Abstract: Determination of copy number variants (CNVs) inferred in genome wide single nucleotide polymorphism arrays has shown increasing utility in genetic variant disease associations. Several CNV detection methods are available, but differences in CNV call thresholds and characteristics exist. We evaluated the relative performance of seven methods: circular binary segmentation, CNVFinder, cnvPartition, gain and loss of DNA, Nexus algorithms, PennCNV and QuantiSNP. Tested data included real and simulated Illumina HumHap 550 data from the Singapore cohort study of the risk factors for Myopia (SCORM) and simulated data from Affymetrix 6.0 and platform-independent distributions. The normalized singleton ratio (NSR) is proposed as a metric for parameter optimization before enacting full analysis. We used 10 SCORM samples for optimizing parameter settings for each method and then evaluated method performance at optimal parameters using 100 SCORM samples. The statistical power, false positive rates, and receiver operating characteristic (ROC) curve residuals were evaluated by simulation studies. Optimal parameters, as determined by NSR and ROC curve residuals, were consistent across datasets. QuantiSNP outperformed other methods based on ROC curve residuals over most datasets. Nexus Rank and SNPRank have low specificity and high power. Nexus Rank calls oversized CNVs. PennCNV detects one of the fewest numbers of CNVs.
Extracted statement — Algorithm: cnvFinder