varunr14/newsummary huggingface dataset:
Geographical masking is the conventional solution to protect the privacy of individuals involved in confidential spatial point datasets . The masking process displaces confidential locations to protect individual privacy while maintaining a fine level of spatial resolution . The adaptive form of this process aims to further minimize the displacement error by taking into account the underlying population density . We describe an alternative adaptive geomasking method referred to as Adaptive Areal Elimination . AAE creates areas of a minimum K anonymity and then original points are either randomly perturbed within the areas or aggregated to the median centers of the areas . In addition to the masked points K anonymized areas can be safely disclosed as well without increasing the risk of re identification . Using a burglary dataset from Vienna AAE is compared with an existing adaptive geographical mask the donut mask . The masking methods are evaluated for preserving a predefined K anonymity and the spatial characteristics of the original points . The spatial characteristics are assessed with four measures of spatial error displaced distance correlation coefficient of density surfaces hotspots divergence and clusters specificity . Masked points from point aggregation of AAE have the highest spatial error in all the measures but the displaced distance . In contrast masked points from the donut mask are displaced the least preserve the original spatial clusters better have the highest clusters specificity and correlation coefficient of density surfaces . However when the donut mask is adapted to achieve an actual K anonymity the random perturbation of AAE introduces less spatial error than the donut mask for all the measures of spatial error . 


Summary: An alternative adaptive geographical masking method referred to as Adaptive Areal Elimination AAE is proposed. K anonymized areas can be safely disclosed as well without increasing the risk of re identification. For an actual K anonymity random perturbation of AAE preserves the spatial characteristics better the other masks.
To determine initial velocities of enzyme catalyzed reactions without theoretical errors it is necessary to consider the use of the integrated Michaelis Menten equation . When the reaction product is an inhibitor this approach is particularly important . Nevertheless kinetic studies usually involved the evaluation of other inhibitors beyond the reaction product . The occurrence of these situations emphasizes the importance of extending the integrated Michaelis Menten equation assuming the simultaneous presence of more than one inhibitor because reaction product is always present . This methodology is illustrated with the reaction catalyzed by alkaline phosphatase inhibited by phosphate and urea . The approach is explained in a step by step manner using an Excel spreadsheet . Curve fitting by nonlinear regression was performed with the Solver add in . Discrimination of the kinetic models was carried out based on Akaike information criterion . This work presents a methodology that can be used to develop an automated process to discriminate in real time the inhibition type and kinetic constants as data are achieved by the spectrophotometer . 


Summary: A method for the study of enzyme kinetics in the presence of two inhibitors. Integrated Michaelis Menten equations were developed to carry out the analysis. This methodology is useful to discriminate the inhibition mechanisms. This method can be automated and applied in real time. An Excel template has been developed available in Appendix .
Background and Objective Viruses are infectious agents that replicate inside organisms and reveal a plethora of distinct characteristics . Viral infections spread in many ways but often have devastating consequences and represent a huge danger for public health . It is important to design statistical and computational techniques capable of handling the available data and highlighting the most important features . Methods This paper reviews the quantitative and qualitative behaviour of 22 infectious diseases caused by viruses . The information is compared and visualized by means of the multidimensional scaling technique . Results The results are robust to uncertainties in the data and revealed to be consistent with clinical practice . Conclusions The paper shows that the proposed methodology may represent a solid mathematical tool to tackle a larger number of virus and additional information about these infectious agents . 


Summary: We review the quantitative and qualitative behaviour of 22 infectious diseases caused by viruses. The information is compared and visualized by means of Multidimensional Scaling technique. The results are consistent with clinical practice. The proposed mathematical tools tackle a large number of virus and information.
An electrocardiogram measures the electric activity of the heart and has been widely used for detecting heart diseases due to its simplicity and non invasive nature . By analyzing the electrical signal of each heartbeat i.e . the combination of action impulse waveforms produced by different specialized cardiac tissues found in the heart it is possible to detect some of its abnormalities . In the last decades several works were developed to produce automatic ECG based heartbeat classification methods . In this work we survey the current state of the art methods of ECG based automated abnormalities heartbeat classification by presenting the ECG signal preprocessing the heartbeat segmentation techniques the feature description methods and the learning algorithms used . In addition we describe some of the databases used for evaluation of methods indicated by a well known standard developed by the Association for the Advancement of Medical Instrumentation and described in ANSI AAMI EC57 1998 2008 . Finally we discuss limitations and drawbacks of the methods in the literature presenting concluding remarks and future challenges and also we propose an evaluation process workflow to guide authors in future works . 


Summary: Surveys the feature description methods and the learning algorithms employed. Also surveys the ECG signal preprocessing and the heartbeat segmentation techniques. Description of databases used for methods evaluation indicated by the AAMI standard. Discussion of limitations and drawbacks of the methods in the literature. Concluding remarks and future challenges are also pointed out.
Background and objective Injury of knee joint cartilage may result in pathological vibrations between the articular surfaces during extension and flexion motions . The aim of this paper is to analyze and quantify vibroarthrographic signal irregularity associated with articular cartilage degeneration and injury in the patellofemoral joint . Methods The symbolic entropy approximate entropy fuzzy entropy and the mean standard deviation and root mean squared values of the envelope amplitude were utilized to quantify the signal fluctuations associated with articular cartilage pathology of the patellofemoral joint . The quadratic discriminant analysis generalized logistic regression analysis and support vector machine methods were used to perform signal pattern classifications . Results The experimental results showed that the patients with cartilage pathology possess larger SyEn and ApEn but smaller FuzzyEn over the statistical significance level of the Wilcoxon rank sum test than the healthy subjects . The mean standard deviation and RMS values computed from the amplitude difference between the upper and lower signal envelopes are also consistently and significantly larger for the group of CP patients than for the HS group . The SVM based on the entropy and envelope amplitude features can provide superior classification performance as compared with QDA and GLRA with an overall accuracy of 0.8356 sensitivity of 0.9444 specificity of 0.8 Matthews correlation coefficient of 0.6599 and an area of 0.9212 under the receiver operating characteristic curve . Conclusions The SyEn ApEn and FuzzyEn features can provide useful information about pathological VAG signal irregularity based on different entropy metrics . The statistical parameters of signal envelope amplitude can be used to characterize the temporal fluctuations related to the cartilage pathology . 


Summary: Symbolic entropy approximate entropy and fuzzy entropy are used to measure the irregularity of knee vibroarthrographic signals. Mean standard deviation and root mean squared value parameters of the signal envelope amplitude are computed to characterize the signal fluctuations. Gender dependent statistics of entropy and envelope amplitude parameters are discussed.
We study the impact of settlement sizes on network connectivity in a spatial setting . First we develop a model of geometric urban networks that posits a positive relationship between connectivity and size . Empirical evidence is then presented validating the model prediction that local links exhibit super linear scaling with the exponent greater than 1 while long range connections scale linearly with the unit exponent . The scaling exponents thus suggest that the impact of population size on connectivity is stronger within cities than between cities . We next combine the geometric framework with a computational model of interacting agents to generate a realistic settlement distribution and urban networks from the bottom up . Calibrated simulation results demonstrate the consistency between hierarchical rank size distribution and scale free connectivity . Finally coupling the spatial network with a tipping diffusion model allows us to consolidate the evolution of network connectivity city sizes and social practices in a unified computational framework . 


Summary: A theoretical model of urban networks is developed linking connectivity to city sizes. The theoretical model predicts inter city links scale linearly while same city connections exhibit super linear scaling. The data for the U.S. New England and South Korea support the theoretical model s predictions. Agent based simulations are conducted to identify the conditions that preserve the diversity of norms in scale free networks.
One of the greatest challenges while working on image segmentation algorithms is a comprehensive measure to evaluate their accuracy . Although there are some measures for doing this task but they can consider only one aspect of segmentation in evaluation process . The performance of evaluation measures can be improved using a combination of single measures . However combination of single measures does not always lead to an appropriate criterion . Besides its effectiveness the efficiency of the new measure should be considered . In this paper a new and combined evaluation measure based on genetic programming has been sought . Because of the nature of evolutionary approaches the proposed approach allows nonlinear and linear combinations of other single evaluation measures and can search within many and different combinations of basic operators to find a good enough one . We have also proposed a new fitness function to make GP enable to search within search space effectively and efficiently . To test the method Berkeley and Weizmann datasets besides several different experiments have been used . Experimental results demonstrate that the GP based approach is suitable for effective combination of single evaluation measures . 


Summary: A genetic programming GP approach is proposed to design a combined evaluation measure for image segmentation algorithms. The proposed approach improved the performance of image segmentation evaluation. A new fitness evaluation scheme is proposed for GP. A new evaluation measure based on intra region similarity and inter region disparity is proposed in this paper.
Confronted with the explosive growth of web images the web image annotation has become a critical research issue for image search and index. Sparse feature selection plays an important role in improving the efficiency and performance of web image annotation. Meanwhile it is beneficial to developing an effective mechanism to leverage the unlabeled training data for large scale web image annotation. In this paper we propose a novel sparse feature selection framework for web image annotation namely sparse Feature Selection based on Graph Laplacian FSLG 2. FSLG applies the l2 1 2 matrix norm into the sparse feature selection algorithm to select the most sparse and discriminative features. Additional graph Laplacian based semi supervised learning is used to exploit both labeled and unlabeled data for enhancing the annotation performance. An efficient iterative algorithm is designed to optimize the objective function. Extensive experiments on two web image datasets are performed and the results illustrate that our method is promising for large scale web image annotation. 


Summary: Spare feature selection method based on l 2 1 2 matix norm is proposed. Graph Laplacian based semi supervised learning is exploited. A effective algorithm for optimizing the objective function is introduced. The convergence of the algorithm is proven. Experiments demonstrate that the method is suitable for web image annotation.
Hair segmentation is challenging due to the diverse appearance irregular region boundary and the influence of complex background . To deal with this problem we propose a novel data driven method named Isomorphic Manifold Inference . The IMI method assumes the coarse probability map and the binary segmentation map as a couple of isomorphic manifolds and tries to learn hair specific priors from manually labeled training images . For an input image firstly the method calculates a coarse probability map . Then it exploits regression techniques to obtain the relationship between the coarse probability map of the test image and those of training images . Finally this relationship i.e . a coefficient set is transferred to the binary segmentation maps and a soft segmentation of the test image will be achieved by a linear combination of those binary maps . Further we employ this soft segmentation as a shape cue and integrate it with color and texture cues into a unified segmentation framework . A better segmentation is achieved by the Graph Cuts optimization . Extensive experiments are conducted to validate effectiveness of the IMI method compare contributions of different cues and investigate the generalization of IMI method . The results strongly encourage our method . 


Summary: Similar coarse hair probability maps should correspond to similar segmentations. Data driven Isomorphic Manifold Inference is proposed to exploit the shape priors. We integrate the inferred shape color and texture into a unified framework. Besides hair segmentation we also validate our IMI on horse class segmentation. Experiments on hair and horse databases show impressive performances.
Automatic pain recognition from videos is a vital clinical application and owing to its spontaneous nature poses interesting challenges to automatic facial expression recognition research . Previous pain vs no pain systems have highlighted two major challenges ground truth is provided for the sequence but the presence or absence of the target expression for a given frame is unknown and the time point and the duration of the pain expression event in each video are unknown . To address these issues we propose a novel framework where each sequence is represented as a bag containing multiple segments and multiple instance learning is employed to handle this weakly labeled data in the form of sequence level ground truth . These segments are generated via multiple clustering of a sequence or running a multi scale temporal scanning window and are represented using a state of the art Bag of Words representation . This work extends the idea of detecting facial expressions through concept frames to concept segments and argues through extensive experiments that algorithms such as MIL are needed to reap the benefits of such representation . The key advantages of our approach are joint detection and localization of painful frames using only sequence level ground truth incorporation of temporal dynamics by representing the data not as individual frames but as segments and extraction of multiple segments which is well suited to signals with uncertain temporal location and duration in the video . Extensive experiments on UNBC McMaster Shoulder Pain dataset highlight the effectiveness of the approach by achieving competitive results on both tasks of pain classification and localization in videos . We also empirically evaluate the contributions of different components of MS MIL . The paper also includes the visualization of discriminative facial patches important for pain detection as discovered by our algorithm and relates them to Action Units that have been associated with pain expression . We conclude the paper by demonstrating that MS MIL yields a significant improvement on another spontaneous facial expression dataset the FEEDTUM dataset . 


Summary: Target problem of pain classification and localization using weakly labeled pain videos Algorithm combines multiple instance learning with multiple segment representation. Rigorous experiments show that our algorithm achieves state of the art performance. Empirically evaluate the contributions of different components of our algorithm Visualize discriminative facial patches as learned by our algorithm
Multipath interference of light is the cause of important errors in Time of Flight ToF depth estimation. This paper proposes an algorithm that removes multipath distortion from a single depth map obtained by a ToF camera. Our approach does not require information about the scene apart from ToF measurements. The method is based on fitting ToF measurements with a radiometric model. Model inputs are depth values free from multipath interference whereas model outputs consist of synthesized ToF measurements. We propose an iterative optimization algorithm that obtains model parameters that best reproduce ToF measurements recovering the depth of the scene without distortion. We show results with both synthetic and real scenes captured by commercial ToF sensors. In all cases our algorithm accurately corrects the multipath distortion obtaining depth maps that are very close to ground truth data. 


Summary: Multipath Interference MpI is a non systematic error in Time of Flight ToF imaging. MpI causes severe distortions in ToF measurements. We propose a ray tracing model that renders realistic ToF images including MpI. We use nonlinear optimization to find the scene that best renders the input image. We present results with real and synthetic datasets. Error is reduced in all cases.
Background and objective In clinical examinations and brain computer interface research a short electroencephalogram measurement time is ideal . The use of event related potentials relies on both estimation accuracy and processing time . We tested a particle filter that uses a large number of particles to construct a probability distribution . Methods We constructed a simple model for recording EEG comprising three components ERPs approximated via a trend model background waves constructed via an autoregressive model and noise . We evaluated the performance of the particle filter based on mean squared error P300 peak amplitude and latency . We then compared our filter with the Kalman filter and a conventional simple averaging method . To confirm the efficacy of the filter we used it to estimate ERP elicited by a P300 BCI speller . Results A 400 particle filter produced the best MSE . We found that the merit of the filter increased when the original waveform already had a low signal to noise ratio . We calculated the amount of averaging necessary after applying a particle filter that produced a result equivalent to that associated with conventional averaging and determined that the particle filter yielded a maximum 42.8 reduction in measurement time . The particle filter performed better than both the Kalman filter and conventional averaging for a low SNR in terms of both MSE and P300 peak amplitude and latency . For EEG data produced by the P300 speller we were able to use our filter to obtain ERP waveforms that were stable compared with averages produced by a conventional averaging method irrespective of the amount of averaging . Conclusions We confirmed that particle filters are efficacious in reducing the measurement time required during simulations with a low SNR . Additionally particle filters can perform robust ERP estimation for EEG data produced via a P300 speller . 


Summary: We constructed a model describing event related potential ERP by trend model. A 400 particle filter produced the best mean square error in the ERP estimation. The filter reduced an amount of average by 42.8 compared with simple averaging. The filter could estimate P300 robustly by application to EEG in P300 speller. Real time processing is realized in any computer with an appropriate particle number.
Recent advances in public sector open data and online mapping software are opening up new possibilities for interactive mapping in research applications . Increasingly there are opportunities to develop advanced interactive platforms with exploratory and analytical functionality . This paper reviews tools and workflows for the production of online research mapping platforms alongside a classification of the interactive functionality that can be achieved . A series of mapping case studies from government academia and research institutes are reviewed . The conclusions are that online cartography s technical hurdles are falling due to open data releases open source software and cloud services innovations . The data exploration functionality of these new tools is powerful and complements the emerging fields of big data and open GIS . International data perspectives are also increasingly feasible . Analytical functionality for web mapping is currently less developed but promising examples can be seen in areas such as urban analytics . For more presentational research communication applications there has been progress in story driven mapping drawing on data journalism approaches that are capable of connecting with very large audiences . 


Summary: Interactive online mapping tools are reviewed with workflows for site production. The mapping case studies illustrate best practice alongside the methods used. All the case study sites received tens of thousands of visitors. Exploratory interactive mapping functionality is increasingly extensive. Analytical functionality is more limited but there are signs of innovation.
Automatically focusing and seeing occluded moving object in cluttered and complex scene is a significant challenging task for many computer vision applications . In this paper we present a novel synthetic aperture imaging approach to solve this problem . The unique characteristics of this work include the following To the best of our knowledge this work is the first to simultaneously solve camera array auto focusing and occluded moving object imaging problem . A unified framework is designed to achieve seamless interaction between the focusing and imaging modules . In the focusing module a local and global constraint based optimization algorithm is presented to dynamically estimate the focus plane of the moving object . In the imaging module a novel visibility analysis based active synthetic aperture imaging approach is proposed to remove the occluder and significantly improve the quality of occluded object imaging . An active camera array system has been set up and evaluated in challenging indoor and outdoor scenes . Extensive experimental results with qualitative and quantitative analyses demonstrate the superiority of the proposed approach compared with state of the art approaches . 


Summary: This is a novel work to solve camera array auto focusing and occluded object imaging simultaneously. A unified framework is proposed to achieve seamless interaction between focusing and imaging. An optimization algorithm is presented to dynamically estimate the focus plane. A visibility analysis is proposed to remove the occluder and improve the imaging quality.