varunr14/newsummary huggingface dataset:
The paper describes the development of an algorithm for detecting and classifying MRI brain slices into normal and abnormal. The proposed technique relies on the prior knowledge that the two hemispheres of a healthy brain have approximately a bilateral symmetry. We use the modified grey level co occurrence matrix method to analyze and measure asymmetry between the two brain hemispheres. 21 co occurrence statistics are used to discriminate the images. The experimental results demonstrate the efficacy of our proposed algorithm in detecting brain abnormalities with high accuracy and low computational time. The dataset used in the experiment comprises 165 patients with 88 having different brain abnormalities whilst the remaining do not exhibit any detectable pathology. The algorithm was tested using a ten fold cross validation technique with 10 repetitions to avoid the result depending on the sample order. The maximum accuracy achieved for the brain tumors detection was 97.8 using a Multi Layer Perceptron Neural Network. 


Summary: Automated algorithm for detecting normality or abnormality in MRI brain scans. A new Modified Grey level Co occurrence Matrix MGLCM method is presented to extract second order statistical texture features for discriminating brain abnormality. MGLCM generates efficient texture feature that is used for measuring the symmetry of MRI brain scan than the tradition GLCM. An accuracy of 97.8 was achieved using MGLCM with 9 orientations and 1 distance.
Openflow a novel Software Defined Network technology is developing rapidly and has already been utilized in many fields . It facilitates decoupling between the control and forwarding plane enabling users to code the network functions easily and replace the traditional high cost network functions devices . The FlowTable of Openflow its base of operating the network packets consists of many flow entries and is stored in the Ternary Content Addressable Memories of the Openflow switch . When the FlowTable occupies the entire storage space of the Openflow switch and more flow entries are added the delete operation on the TCAMs increases and the latency and loss of packets deteriorates this is the primary issue . In order to solve this problem this paper proposes a distributed storage framework which stores the FlowTable in multiple Openflow switches equipped with small TCAMs . To conclude this paper simulates the algorithms used in the framework and builds a testbed . The experimental results prove the framework s feasibility and successful performance . 


Summary: SDN enabled network functions are limited by the switch TCAM capacity. We propose a distributed storage framework of FlowTable. Our framework can achieve high availability and failure resilience. There is a tradeoff between cost and high availability failure resilience. We achieve the storage capacity without inviting large communication overhead.
Data compression is a challenging process with important practical applications . Specialized techniques for lossy and lossless data compression have been the subject of numerous investigations during last several decades . Previously we studied the use of the pseudo distance technique in lossless compression of color mapped images and its parallel implementation . In this paper we present a new technique to improve compression gain of PDT . We also present a parallelized implementation of the new technique which results in substantial gains in compression time while providing the desired compression efficiency . We demonstrate that on non dithered images PDT2 outperforms PDT by 22.4 and PNG by 29.3 . On dithered images PDT2 achieves compression gains of 7.1 over PDT and 23.8 over PNG . We also show that the parallel implementation of PDT2 while compromising compression less than 0.3 achieves near linear speedup and utilization of Intel Hyper Threading technology on supported systems improves speedup on average 18 . 


Summary: A pseudo distance technique PDT2 for lossless compression of images is given. Parallelization of the technique is presented. The performance of the PDT2 in Hyper Threading is evaluated.
A nonlinear pull in behavior analysis of a cantilever nano actuator was carried out and an Euler Bernoulli beam model was used in the examination of the fringing field and the surface and Casimir force effects in this study. In general the analysis of an electrostatic device is difficult and usually complicated by nonlinear electrostatic forces and the Casimir force at the nanoscale. The nonlinear governing equation of a cantilever nano beam can be solved using a hybrid computational scheme comprising differential transformation and finite difference to overcome the nonlinear electrostatic coupling phenomenon. The feasibility of the method presented here as applied to the nonlinear electrostatic behavior of a cantilever nano actuator was analyzed. The numerical results for the pull in voltage were found to be in good agreement with previously published results. The analysis showed that the surface effects had significant influence on the dynamic characteristics of the cantilever nano actuator. 


Summary: Nano actuator is considered to incorporate the influence of the surface the fringing field and the Casimir force effects using an Euler Bernoulli beam model. Nonlinear governing equation is solved using the hybrid computational scheme comprising the differential transformation and the finite difference methods. The results indicate that for a constant detachment length the beam thickness and surface effect are linked and it can be seen that the influence of surface effects decreases with increasing beam thickness.
Critical infrastructures require protection systems that are both flexible and efficient . Flexibility is essential to capture the multi organizational and state based nature of these systems efficiency is necessary to cope with limitations of hardware resources . To meet these requirements we consider a classical protection environment featuring subjects that attempt to access the protected objects . We approach the problem of specifying the access privileges held by each subject . Our protection model associates a password system with each object the password system features a password for each access privilege defined for this object . A subject can access the object if it holds a key matching one of the passwords in the password system and the access privilege corresponding to this password permits to accomplish the access . Password systems are implemented as hierarchical bidimensional one way chains . Trade offs are possible between the memory requirements for storage of a password system and the processing time necessary to validate a key . 


Summary: A significant protection problem is to specify the access privileges held by each active subject on the protected objects. We associate a password system with each object featuring a password for each access privilege. Access privileges are partitioned into privilege levels for distribution and revocation. Password systems are implemented as hierarchical bidimensional one way chains.
This paper proposes a face recognition system based on a steerable pyramid transform SPT and local binary pattern LBP for e Health secured login. In an e Health framework patients are sometimes unable to identify themselves by traditional login modalities such as username and password. Automatic face recognition can replace the conventional login modalities if the recognition system is robust. In the proposed system SPT can decompose a face image into several subbands of different scales and orientations and LBP can encode the subbands in binary texture pattern. Therefore SPT LBP scheme represents a face image in a robust way that includes multiple information sources from different scales and orientations. The proposed system is evaluated on the facial recognition technology FERET database. According to the results the proposed system achieves 99.28 recognition in fb set 80.17 in dup I set and 79.54 in dup II set. 


Summary: A metric learning model directly calibrating measured numerics of feature data. A 2D semi metric model by relaxing one degree of freedom. Simple and intuitively natural models solvable by convex quadratic programming.
Issues related to space time adaptivity for a class of nonlinear and time dependent problems are discussed . The dG methods are adopted for the time integration and the a posteriori error control is based on the appropriate dual problem in space time . One key ingredient is to decouple the error generation in space and time with a hierarchical decomposition of the discrete space of dual solutions . The main idea put forward in the paper is to increase the computational efficiency of the adaptive scheme by avoiding recursive adaptations of the whole time mesh rather the space mesh and the time step defining each finite space time slab are defined in a truly sequential fashion . The proposed adaptive strategy is applied to the coupled consolidation problem in geomechanics involving large deformations . Its performance is investigated with the aid of a numerical example in 2D . 1 . Compute based on that was computed for the previous time slab . Then solve for the enhanced dual solution from the decoupled dual problem whereby it is noted that has been replaced by the background dual solution as the load for the current space time slab . Compute the error contributions SOL FEM etc . Check the stopping criterion in If TOL then exit and take a new time step . Refine the space mesh the time interval or both If FEM FEM FEM then refine in space and time uniformly . Else if FEM FEM then refine in space Else if FEM FEM then refine in time 


Summary: A novel approach for space time adaptive finite element analysis is presented. Global error control is obtained by the technique of solving a dual problem. Space and time errors are estimated using a hierarchical decomposition of the dual. Recursive adaptations of the whole space time mesh is avoided. The coupled consolidation problem in geomechanics is considered as an application.
The use of computational accelerators such as NVIDIA GPUs and Intel Xeon Phi processors is now widespread in the high performance computing community with many applications delivering impressive performance gains . However programming these systems for high performance performance portability and software maintainability has been a challenge . In this paper we discuss experiences porting applications to the Titan system . Titan which began planning in 2009 and was deployed for general use in 2013 was the first multi petaflop system based on accelerator hardware . To ready applications for accelerated computing a preparedness effort was undertaken prior to delivery of Titan . In this paper we report experiences and lessons learned from this process and describe how users are currently making use of computational accelerators on Titan . 


Summary: Lessons learned are given for moving applications to the GPU based Titan system. A carefully managed readiness effort is essential to preparing for new hardware. Applications typically require code restructuring to port to accelerators. Exposing more parallelism and minimizing data traffic are common porting themes. Performance gains of 2X 7X have been realized for application codes on Titan.
An important issue to be addressed when data are to be published is data privacy . In this paper the problem of data privacy based on a prominent privacy model Anonymous is addressed . Our scenario is that when a new dataset is to be released there may be at the same time datasets that were released elsewhere . A problem arises because some attackers might obtain multiple versions of the same dataset and compare them with the newly released dataset . Although the privacy of all of the datasets has been well preserved individually such a comparison can lead to a privacy breach which is a so called incremental privacy breach . To address this problem effectively we first study the characteristics of the effects of multiple dataset releases with a theoretical approach . It has been found that a privacy breach that is subjected to an increment occurs when there is overlap between any parts of the new dataset with any parts of an existing dataset . Based on our proposed studies a polynomial time algorithm is proposed . This algorithm needs to consider only one previous version of the dataset and it can also skip computing the overlapping partitions . Thus the computational complexity of the proposed algorithm is reduced from to only pn where p is the number of partitions n is the number of tuples and m is the number of released datasets . At the same time the privacy of all of the released datasets as well as the optimal solution can be always guaranteed . In addition experiment results that illustrate the efficiency of our algorithm on real world datasets are presented . 


Summary: An efficient algorithm is developed to prevent incremental privacy breach. Only the most recent previously released data is required for privacy preservation. The solution can always be guaranteed the optimal result.
In level set methods for structural topology and shape optimization the level set function gradients at the design interface need to be controlled in order to ensure stability of the optimization process . One popular way to do this is to enforce the level set function to be a signed distance function by periodically using initialization schemes which is commonly known as re initialization . However such re initialization schemes are time consuming as additional partial differential equations need to be solved in every iteration step . Furthermore the use of re initialization brings some undesirable problems for example it may move the zero level set away from the expected position . This paper presents a level set method with distance suppression scheme for structural topology and shape optimization . An energy functional is introduced into the level set equation to maintain the level set function to close to a signed distance function near the structural boundaries meanwhile forcing the level set function to be a constant at locations far away from the structural boundaries . As a result the present method not only can avoid the need for re initialization but also can simplify the setting of the initial level set function . The validity of the proposed method is tested on the mean compliance minimization problem and the compliant mechanisms synthesis problem . Different aspects of the proposed method are demonstrated on a number of benchmarks from the literature of structural optimization . 


Summary: A level set method with distance suppression scheme is developed. An energy functional is developed and built into the level set equation. The need for re initialization can be eliminated. The initialization of the level set function can be simplified.
In this paper we present two novel interference management stratagems for coexisting one primary user and multiple secondary users by exploiting the unused spatial directions at PU . The cognitive stations sense their environment to determine the users they are interfering with and adapt to it by designing the corresponding precoders using interference alignment in order to avoid causing performance degradation to nearby PU and SUs . The first proposed approach judiciously designs the set of precoders based on an improved version of minimum weighted leakage interference algorithm . However there are still leftover interference signals in SUs desired signal space due to the limited iterative times in the first algorithm . To tackle this problem another scheme combining the first one and power allocation method at the secondary stations is developed . Numerical results validate the effectiveness of the proposed algorithms . 


Summary: The cognitive interference alignment feasibility condition is investigated. A new convex optimization problem of the CIA problem is then obtained. An efficient algorithm is then proposed to solve the convex problem based on matrix distance. An improved algorithm considering power allocation is derived. The convergences of our algorithms are analyzed.
Spectrum allocation scheme in cognitive radio networks becomes complex when multiple CR users concomitantly need to be allocated new and suitable bands once the primary user returns . Most existing schemes focus on the gain of individual users ignoring the effect of an allocation on other users and rely on the periodic sensing and transmission cycle which reduces spectrum utilization . This paper introduces a scheme that exploits collaboration among users to detect PU s return which relieves active CR users from the sensing task and thereby improves spectrum utilization . It defines a Capacity of Service metric based on the optimal sensing parameters which measures the suitability of a band for each contending user and takes into consideration the impact of allocating a particular band on other band seeking users . The proposed scheme significantly improves capacity of service reduces interference loss and collision and hence enhances dynamic spectrum access capabilities . 


Summary: A scheme is proposed to allocate bands among multiple cognitive radio users. Suitability Throughput Gain defines the suitability of a band for contending users. The scheme takes into account the impact of allocating a band to a user on others. It offers higher user satisfaction less collision and better spectrum utilization.
In this paper we present a set of server selection schemes that includes all crucial components of resources available at each relevant node of the network to reach at the server selection decision and at the same time attempts to maximise the system wide resource utilisation . First in the series a server centric scheme is proposed that formulates the problem as distributed constraint optimisation problem and solves it using Fast max sum algorithm . Though it does not scale well in large network scenario yet it provides an estimate of the optimal values of performance metrics achievable under a specific scenario . Next a client centric scheme is proposed that outperforms the existing ones but does not guaranty to attain optimal Nash Equilibrium . This motivates us to design another client centric scheme that can avoid the price of anarchy by attaining superior NE of mix strategies . 


Summary: An efficient server centric server selection scheme is proposed for small scenario. A client centric scheme is proposed that outperforms the existing ones. The client centric scheme does not guaranty optimal Nash Equilibrium NE . A Mixed strategy MS based solution is proposed that can attain more efficient NE. The MS based scheme performs better than existing and previously proposed schemes.
Cooperative spectrum sensing is a process of achieving spatial diversity gain to make global decision for cognitive radio networks . However accuracy of global decision effects owing to the presence of malicious users nodes during cooperative sensing . In this work an extended generalized extreme studentized deviate method is proposed to eliminate malicious nodes such as random nodes and selfish nodes in the network . The random nodes are carried off based on sample covariance of each node decisions on different frames . Then the algorithm checks the normality of updated soft data using Shapiro Wilk test and estimates the expected number of malicious users in cooperative sensing . These are the two essential input parameters required for classical GESD test to eliminate significant selfish nodes accurately . Simulation results reveal that the proposed algorithm can eliminate both random and frequent spectrum sensing data falsification attacks in cooperative sensing and outperforms the existing algorithms . 


Summary: Cooperative sensing with most frequent data falsification SSDF attacks. Proposed an extended generalized extreme studentized deviate EGESD method. It can eliminate both random and selfish attacks in cooperative sensing. It estimates two important input parameters required for GESD test. It is reliable and has low misdetection probability compared to existing algorithms.
A fundamental challenge in the design of Wireless Sensor Network is the proper utilization of resources that are scarce . The critical challenge is to maximize the bandwidth utilization in data gathering and forwarding from sensor nodes to the sink . The main design objective is to utilize the available bandwidth efficiently . The proposed Bandwidth Efficient Cluster based Data Aggregation algorithm presents the solution for the effective data gathering with in network aggregation . It considers the network with heterogeneous nodes in terms of energy and mobile sink to aggregate the data packets . The optimal approach is achieved by intra and inter cluster aggregation on the randomly distributed nodes with the variable data generation rate . The proposed algorithm uses the correlation of data within the packet for applying the aggregation function on the data generated by nodes . BECDA shows significant improvement in PDR and throughput as compared to the state of the art solutions . 


Summary: Cluster head is elected according to the highest energy among the CM number of neighbor nodes with one hop connectivity. Network uses random distribution of heterogeneous nodes with mobile sink. Packets are generated at variable rate by each node and aggregated at CH and then at sink. It uses the perfectly compressible aggregation function on data generated by nodes. The performance is measured in terms of PDR and throughput to show the effective utilization of bandwidth.
Signals associated with eye blinks are orders of magnitude larger than electric potentials generated on the scalp because of cortical activity . These and other such non cortical biological artifacts spread across the scalp and contaminate the Electroencephalogram . We present here a novel approach for efficient detection and effective suppression of these artifacts using single channel EEG data by combining Ensemble Empirical Mode Decomposition along with Principal Component Analysis . We present a methodology for ocular artifact suppression by performing EEMD on the contaminated EEG data segment to get the intrinsic mode functions and subsequent elimination of artifacts by automatic selection of particular principal components which capture ocular artifact features after using PCA on IMFs . 


Summary: A novel methodology for ocular artifact suppression in EEG data using EEMD with PCA. The proposed method eliminates the ocular artifacts from the measured EEG without using reference electrooculogram channel. The proposed method exhibits effective suppression of ocular artifact with low distortion compared to wavelet approach.