varunr14/newsummary huggingface dataset:
Objectives The present work has the goal of developing a secure medical imaging information system based on a combined steganography and cryptography technique . It attempts to securely embed patient s confidential information into his her medical images . Methods The proposed information security scheme conceals coded Electronic Patient Records into medical images in order to protect the EPRs confidentiality without affecting the image quality and particularly the Region of Interest which is essential for diagnosis . The secret EPR data is converted into ciphertext using private symmetric encryption method . Since the Human Visual System is less sensitive to alterations in sharp regions compared to uniform regions a simple edge detection method has been introduced to identify and embed in edge pixels which will lead to an improved stego image quality . In order to increase the embedding capacity the algorithm embeds variable number of bits in edge pixels based on the strength of edges . Moreover to increase the efficiency two message coding mechanisms have been utilized to enhance the 1 steganography . The first one which is based on Hamming code is simple and fast while the other which is known as the Syndrome Trellis Code is more sophisticated as it attempts to find a stego image that is close to the cover image through minimizing the embedding impact . The proposed steganography algorithm embeds the secret data bits into the Region of Non Interest where due to its importance the ROI is preserved from modifications . Results The experimental results demonstrate that the proposed method can embed large amount of secret data without leaving a noticeable distortion in the output image . The effectiveness of the proposed algorithm is also proven using one of the efficient steganalysis techniques . Conclusion The proposed medical imaging information system proved to be capable of concealing EPR data and producing imperceptible stego images with minimal embedding distortions compared to other existing methods . In order to refrain from introducing any modifications to the ROI the proposed system only utilizes the Region of Non Interest in embedding the EPR data . 


Summary: A method for embedding patient s information into medical image is proposed. Two coding methods have been utilized to embed the EPR and improve imperceptibility. Cost optimization function is contributed to enhance the quality of the stego image. The proposed system is robust against textural feature steganalysis.
This paper presents a thorough study of gender classification methodologies performing on neutral expressive and partially occluded faces when they are used in all possible arrangements of training and testing roles . A comprehensive comparison of two representation approaches three types of features three classifiers and two performance measures is provided over single and cross database experiments . Experiments revealed some interesting findings which were supported by three non parametric statistical tests when training and test sets contain different types of faces local models using the 1 NN rule outperform global approaches even those using SVM classifiers however with the same type of faces even if the acquisition conditions are diverse the statistical tests could not reject the null hypothesis of equal performance of global SVMs and local 1 NNs . 


Summary: Study of gender recognition from neutral expressive and occluded faces Comparison of global local approaches grey level PCA LBP features and three classifiers Three statistical tests over two performance measures are employed to support the conclusions. Local models surpass global ones with different types of training and test faces. Global and local models perform equally with the same type of training and test faces.
The development of adequate mathematical models for blood glucose dynamics may improve early diagnosis and control of diabetes mellitus . We have developed a stochastic nonlinear second order differential equation to describe the response of blood glucose concentration to food intake using continuous glucose monitoring data . A variational Bayesian learning scheme was applied to define the number and values of the system s parameters by iterative optimisation of free energy . The model has the minimal order and number of parameters to successfully describe blood glucose dynamics in people with and without DM . The model accounts for the nonlinearity and stochasticity of the underlying glucose insulin dynamic process . Being data driven it takes full advantage of available CGM data and at the same time reflects the intrinsic characteristics of the glucose insulin system without detailed knowledge of the physiological mechanisms . We have shown that the dynamics of some postprandial blood glucose excursions can be described by a reduced model previously seen in the literature . A comprehensive analysis demonstrates that deterministic system parameters belong to different ranges for diabetes and controls . Implications for clinical practice are discussed . This is the first study introducing a continuous data driven nonlinear stochastic model capable of describing both DM and non DM profiles . 


Summary: Data driven model is presented for the response of glucose to food intake. Model describes blood glucose dynamics in people with and without diabetes. First study introducing a continuous data driven nonlinear stochastic model. Model s parameters belong to different ranges for diabetes and controls. Variational Bayesian learning approach was employed for model identification.
Robust high dimensional data processing has witnessed an exciting development in recent years. Theoretical results have shown that it is possible using convex programming to optimize data fit to a low rank component plus a sparse outlier component. This problem is also known as robust PCA and it has found application in many areas of computer vision. In image and video processing and face recognition the opportunity to process massive image databases is emerging as people upload photo and video data online in unprecedented volumes. However data quality and consistency is not controlled in any way and the massiveness of the data poses a serious computational challenge. In this paper we present t GRASTA or Transformed GRASTA Grassmannian robust adaptive subspace tracking algorithm . t GRASTA iteratively performs incremental gradient descent constrained to the Grassmann manifold of subspaces in order to simultaneously estimate three components of a decomposition of a collection of images a low rank subspace a sparse part of occlusions and foreground objects and a transformation such as rotation or translation of the image. We show that t GRASTA is 4 faster than state of the art algorithms has half the memory requirement and can achieve alignment for face images as well as jittered camera surveillance images. 


Summary: Propose an online algorithm t GRASTA for the transformed robust PCA problem. Use a union of subspaces to approximate the nonlinear subspace learning process. Demonstrate the fully online mode of t GRASTA with videos with camera jitter.
This work provides a performance comparison of four different machine learning classifiers multinomial logistic regression with ridge estimators classifier k nearest neighbours support vector machine and na ve Bayes as applied to terahertz transient time domain sequences associated with pixelated images of different powder samples . The six substances considered although have similar optical properties their complex insertion loss at the THz part of the spectrum is significantly different because of differences in both their frequency dependent THz extinction coefficient as well as differences in their refractive index and scattering properties . As scattering can be unquantifiable in many spectroscopic experiments classification solely on differences in complex insertion loss can be inconclusive . The problem is addressed using two dimensional cross correlations between background and sample interferograms these ensure good noise suppression of the datasets and provide a range of statistical features that are subsequently used as inputs to the above classifiers . A cross validation procedure is adopted to assess the performance of the classifiers . Firstly the measurements related to samples that had thicknesses of 2mm were classified then samples at thicknesses of 4mm and after that 3mm were classified and the success rate and consistency of each classifier was recorded . In addition mixtures having thicknesses of 2 and 4mm as well as mixtures of 2 3 and 4mm were presented simultaneously to all classifiers . This approach provided further cross validation of the classification consistency of each algorithm . The results confirm the superiority in classification accuracy and robustness of the MLR and KNN algorithms which consistently outperformed the SVM and NB classifiers for the same number of feature vectors across all studies . The work establishes a general methodology for assessing the performance of other hyperspectral dataset classifiers on the basis of 2 D cross correlations in far infrared spectroscopy or other parts of the electromagnetic spectrum . It also advances the wider proliferation of automated THz imaging systems across new application areas e.g . biomedical imaging industrial processing and quality control where interpretation of hyperspectral images is still under development . 


Summary: A 2 D cross correlation based scheme is introduced for classification of THz signals. This work establishes a general way for assessing performance of other THz datasets. The proposed approach yields better performance than the existing method. The results confirm the superiority in classification accuracy of the MLR and KNN. It advances the wider proliferation of automated THz signals across new applications.
Graphical abstract Figure illustrates the role of person re identification in a typical surveillance scenario . An area monitored by multiple cameras is depicted by top view of a building floor plan and the relative placement of the cameras with respect to the building . Colored dots depict different people and numbers besides the dots are the IDs assigned to the people . As a person moves from one camera s FOV into another camera s FOV re identification is required to establish correspondence between disconnected tracks to accomplish multiple camera tracking . This paper explores the problem of person re identification and discusses the current solutions . Open issues and challenges of the problem are highlighted with a discussion on potential directions for further research . 


Summary: We present the problem of person re identification Re ID and associated challenges. A methodology based taxonomy and survey of current approaches is presented. We identify open and closed set re identification scenarios. Public datasets and current evaluation techniques for Re ID are discussed. Unaddressed issues like open set Re ID and long period Re ID are also discussed.
We propose a novel symmetry driven Bayesian framework to incorporate structural shape into conventional geometrical shape descriptor of an image indexing and retrieval . We use rotation and reflection symmetries for structural shape description . Symmetry detection on each shape image provides a qualitative and a quantitative categorization of the types and the degrees of symmetry level . The posterior shape similarity enhances the shape matching performance based on the symmetry structural discrimination . Experimental results show statistically significant improvement on retrieval accuracy over the state of the art methods on MPEG 7 data set . 


Summary: We use rotation and reflection symmetries for structural shape description. The posterior shape similarity enhances the shape matching performance. Test results on public dataset show increased retrieval accuracy. Any other previous or future shape representation can be combined with our method.
Recently Universum data that does not belong to any class of the training data has been applied for training better classifiers. In this paper we address a novel boosting algorithm called AdaBoost that can improve the classification performance of AdaBoost with Universum data. AdaBoost chooses a function by minimizing the loss for labeled data and Universum data. The cost function is minimized by a greedy stagewise functional gradient procedure. Each training stage of AdaBoost is fast and efficient. The standard AdaBoost weights labeled samples during training iterations while AdaBoost gives an explicit weighting scheme for Universum samples as well. In addition this paper describes the practical conditions for the effectiveness of Universum learning. These conditions are based on the analysis of the distribution of ensemble predictions over training samples. Experiments on handwritten digits classification and gender classification problems are presented. As exhibited by our experimental results the proposed method can obtain superior performances over the standard AdaBoost by selecting proper Universum data. 


Summary: We address a novel boosting algorithm by taking advantage of Universum data. A greedy stagewise functional gradient procedure is taken to derive the method. Explicit weighting schemes for labeled and Universum samples are provided. Practical conditions to verify effectiveness of Universum learning are described. This algorithm obtains superior performances over AdaBoost with Universum data.
The purpose of this study was to evaluate the use of fractional order modeling in asthma . To this end three FrOr models were compared with traditional parameters and an integer order model . We investigated which model would best fit the data the correlation with traditional lung function tests and the contribution to the diagnostic of airway obstruction . The data consisted of forced oscillation measurements obtained from healthy and asthmatic volunteers with mild moderate and severe obstructions . The first part of this study showed that a FrOr was the model that best fit the data . The correlation analysis resulted in reasonable to very good associations between FrOr parameters and spirometry . The closest associations were observed between parameters related to peripheral airway obstruction showing a clear relationship between the FrOr models and lung mechanics . Receiver operator analysis showed that FrOr parameters presented a high potential to contribute to the detection of the mild obstruction in a clinical setting . The accuracy observed in these parameters was higher than that observed in traditional FO parameters and that obtained from the InOr model . Patients with moderate and severe obstruction were identified with high accuracy . In conclusion the results obtained are in close agreement with asthma pathology and provide evidence that FO measurement associated with FrOr models is a non invasive simple and radiation free method for the detection of biomechanical abnormalities in asthma . 


Summary: Our aim was to evaluate the use of fractional order FrOr modeling in asthma. We compared FrOr models with traditional parameters and an integer order model FrOr parameters best fit the data showing good associations with spirometry. They also showed a high accuracy in the detection of the mild obstruction in asthma FrOr models provide meaningful information in asthmatic patients.
Today smart mobile devices are very commonly used due to their powerful hardware and useful features . According to an eMarketer report in 2014 there were 1.76 billion smartphone users in the world it is predicted that this number will rise by 15.9 to 2.04 billion in 2015 . It is thought that these devices can be used successfully in biomedical applications . A wireless blood pressure measuring device used together with a smart mobile device was developed in this study . By means of an interface developed for smart mobile devices with Android and iOS operating systems a smart mobile device was used both as an indicator and as a control device . The cuff communicating with this device through Bluetooth was designed to measure blood pressure via the arm . A digital filter was used on the cuff instead of the traditional analog signal processing and filtering circuit . The newly developed blood pressure measuring device was tested on 18 patients and 20 healthy individuals of different ages under a physician s supervision . When the test results were compared with the measurements made using a sphygmomanometer it was shown that an average 93.52 accuracy in sick individuals and 94.53 accuracy in healthy individuals could be achieved with the new device . 


Summary: A wireless blood pressure measuring device used together with a smart mobile device was developed. The smart mobile device was used as an indicator and a control device. The cuff communicating with this device through Bluetooth was designed to measure blood pressure on the arm. Digital filter was used on the cuff instead of classical analog signal processing and filtering circuit. The test results showed that higher accuracy could be achieved with the device developed.
This paper proposes a weighted scheme for elastic graph matching hand posture recognition. Visual features scattered on the elastic graph are assigned corresponding weights according to their relative ability to discriminate between gestures. The weights values are determined using adaptive boosting. A dictionary representing the variability of each gesture class is expressed in the form of a bunch graph. The positions of the nodes in the bunch graph are determined using three techniques manually semi automatically and automatically. Experimental results also show that the semi automatic annotation method is efficient and accurate in terms of three performance measures assignment cost accuracy and transformation error. In terms of the recognition accuracy our results show that the hierarchical weighting on features has more significant discriminative power than the classic method uniform weighting . The hierarchical elastic graph matching WEGM approach was used to classify a lexicon of ten hand postures and it was found that the poses were recognized with a recognition accuracy of 97.08 on average. Using the weighted scheme computing cycles can be decreased by only computing the features for those nodes whose weight is relatively high and ignoring the remaining nodes. It was found that only 30 of the nodes need to be computed to obtain a recognition accuracy of over 90 . 


Summary: The HEGM method for classifying hand postures with a hit rate of 97.08 on average over uniform and complex backgrounds. This method allows computing only features corresponding to highly discriminative nodes thus decreasing computing time. A semi automatic technique to annotate bunch graphs is described which is efficient and leads to faster graph creation.
This paper proposes an unsupervised variational segmentation approach of color texture images . To improve the description ability the compact multi scale structure tensor total variation flow and color information are integrated to extract color texture information . Since heterogeneous image object and nonlinear variation exist in color texture image it is not appropriate to use one single multiple constant in the Chan and Vese model to describe each phase . Therefore a multiphase successive active contour model based on the multivariable Gaussian distribution is presented to describe each phase . As geodesic active contour has a stronger ability in capturing boundary . To inherit the advantages of edge based model and region based model we incorporate the GAC into the MSACM to enhance the detection ability for concave edge . Although multiphase optimization of our proposed MSACM is a NP hard problem we can discretely and approximately solve it by a multilayer graph method . In addition to segment the color texture image automatically an adaptive iteration convergence criterion is designed by incorporating the local Kullback Leibler distance and global phase label so that we can control the segmentation process converges . Comparing to state of the art unsupervised segmentation methods on a substantial of color texture images our approach achieves a significantly better performance on capture ability of homogeneous region smooth boundary and accuracy . 


Summary: A new color texture feature is constructed. Multiphase successive active contour model MSACM is proposed. MSACM can be discretely optimized by multilayer graph. The combined energy of local and global is iteratively optimized. Performances are assessed through the qualitative and quantitative comparisons.
The present work attempts to build a bio cryptographic system that combines transformed minutiae pairwise feature and user generated password fuzzy vault . The fingerprint fuzzy vault is based on a new minutiae pairwise structure which overcomes the fingerprint feature publication while the secret binary vault code is generated according to the fingerprint fuzzy vault result . The authentication process involves two stages fuzzy vault matching and secret vault code validation . Our minutiae pairwise transformation produces different templates thus resolving the problem of cross matching attacks in fingerprint fuzzy vault . So the original fingerprint template can not be recreated because it is protected by the key generated from the user password . In addition the proposed bio cryptographic system ensures an acceptable security level for user authentication . 


Summary: A new fingerprint feature representation. The proposed fingerprint fuzzy vault scheme has three layers of security. The hardened fuzzy vault scheme produces different templates. The hardened fuzzy vault scheme can resolve the problem of cross matching attacks. The results show better user authentication performance with FRR 6.31 at FAR 0 .
This paper presents a new heuristic algorithm for reduct selection based on credible index in the rough set theory applications . This algorithm is efficient and effective in selecting the decision rules particularly the problem to be solved in a large scale . This algorithm is capable to derive the rules with multi outcomes and identify the most significant features simultaneously which is unique and useful in solving predictive medical problems . The end results of the proposed approach are a set of decision rules that illustrates the causes for solitary pulmonary nodule and results of the long term treatment . 


Summary: Discover certain regularities at the primitive concept level. Improve the efficiency of the discovery process and express the user s preference. Develop high quality attribute oriented and rule based models. Find the causes for solitary pulmonary nodule and results of the long term treatment.
Human Nonverbal Communication Computing aims to investigate how people exploit nonverbal aspects of their communication to coordinate their activities and social relationships . Nonverbal behavior plays important roles in message production and processing relational communication social interaction and networks deception and impression management and emotional expression . This is a fundamental yet challenging research topic . To effectively analyze Nonverbal Communication Computing motion analysis methods have been widely investigated and employed . In this paper we introduce the concept and applications of Nonverbal Communication Computing and also review some of the motion analysis methods employed in this area . They include face tracking expression recognition body reconstruction and group activity analysis . In addition we also discuss some open problems and the future directions of this area . 


Summary: Introduce the concept of nonverbal communication computing and it use cases Review motion analysis techniques used for nonverbal communication computing Discuss future directions of this area