Computer Science and Enginnering

Multimedia transmission over wide-area networks currently only considers the server and network resource constraints and client device capabilities. It is also essential that the accessibility of the multimedia content for different users with diverse capabilities and disabilities be considered. As a first step in building an adaptation framework we need to automatically predict the accessibility requirements of a user, given the abilities the user possesses to access the multimedia information. In this paper we develop an adaptation technique to present the multimedia content to suit diverse disabled user groups by using an ability based classification approach. Using ability-based prioritization, appropriate alternate modalities and quality levels are chosen, to replace the inaccessible modalities. The adaptation process allows for refinements to cater to specific types and degrees of impairments. Our performance results illustrate the benefits of the ability-based adaptation approach.

Complex Networks can depict a clear image of real-world systems. A real-world scenario can be represented a graph with interconnected layers-called a multilayer network. Finding motifs can give an idea of the topology of complex systems and help understand the graphs' dynamics. Looking at motifs as atoms of the network is helpful to analyze the relationship between nodes and between layers. This work suggests a sub-graph enumeration approach to find and count the motifs in a multilayer network. The proposed work has many applications in graph mining, particularly to the structure and dynamics of complex networks.

Three competing models of cognitive aging (neural compensation, capacity limitations, neural inefficiency) were examined in relation to working memory for novel non-verbal material. To accomplish this goal young (n=25) and old (n=25) participants performed a delayed item recognition (DIR) task while being scanned with bold fMRI. The stimuli in the DIR task consisted of computergenerated closed-curve shapes with each shape presented only once in the testing conditions of each participant. This ensured that both the novelty and appearance of the shapes maximized visual demands and limited the extent of phonologic processing. Behaviorally, as expected, the old participants were slower and less accurate compared to the young participants. Spatial patterns of brain activation that corresponded to load-dependent (stimulus set size ranged from 1 to 3) fMRI signal during the three phases of the DIR task (memory set presentation, retention delay, probe presentation) were evaluated in both age groups. Support for neural compensation and capacity limitation was evident in retention delay and the probe phase, respectively. Data were inconsistent with the neural inefficiency model. The process specific support for the theories we examined is consistent with a large corpus of research showing that the substrates underlying the encoding, retention and probe phases are different. That is, cognitive aging theories can be specific to the neural networks/regions underlying the different phases of working memory. Delineating how these theories work in concert can increase knowledge of age-related effects on working memory.

Sixteen healthy young adults (ages 18-32) and 16 healthy older adults (ages 67-81) completed a delayed response task in which they saw the following visual sequence: memory stimuli (2 abstract shapes; 3,000 ms), a blank delay (5,000 ms), a probe stimulus of variable duration (one abstract shape; 125, 250, 500, 1,000, or 2,000 ms), and a mask (500 ms). Subjects decided whether the probe stimulus matched either of the memory stimuli; they were instructed to respond during the mask, placing greater emphasis on speed than accuracy. The authors used D. L. Hintzman & T. Curran's (1994) 3-parameter compound bounded exponential model of speed-accuracy tradeoff to describe changes in discriminability associated with total processing time. Group-level analysis revealed a higher rate parameter and a higher asymptote parameter for the young adult group, but no difference across groups in x-intercept. Proxy measures of cognitive reserve predicted the rate parameter value, particularly in older adults. Results suggest that in working memory, aging impairs both the maximum capacity for discriminability and the rate of information accumulation, but not the temporal threshold for discriminability.

Epidemiologic evidence suggests that cognitive reserve (CR) mitigates the effects of aging on cognitive function. The goal of this study was to see whether a common neural mechanism for CR could be demonstrated in brain imaging data acquired during the performance of 2 tasks with differing cognitive processing demands. Young and elder subjects were scanned with functional magnetic resonance imaging (fMRI) while performing a delayed item response task that used either letters (40 young, 18 old) or shapes (24 young, 21 old). Difficulty or load was manipulated by varying the number of stimuli that were presented for encoding. Load-dependent fMRI signal corresponding to each trial component (stimulus presentation, retention delay, and probe) and task (letter or shape) was regressed onto 2 putative CR variables. Canonical variates analysis was applied to the resulting maps of regression coefficients, separately for each trial component, to summarize the imaging data-CR relationships. There was a latent brain pattern noted in the stimulus presentation phase that manifested similar relationships between load-related encoding activation and CR variables across the letter and shape tasks in the young but not the elder age group. This spatial pattern could represent a general neural instantiation of CR that is affected by the aging process.

Are sequential visual arrays represented as separate images or as a combined image in visual short-term memory (VSTM)? Proponents of the integration account suggest that an image of the first array is gradually formed and integrated with an image of the second to produce a combined representation. This view is evidenced by successful performance in an empty-cell detection task. In this task, on a 4 ¥ 4 square matrix, 7 locations are occupied on a first array, followed by a variable interval, and then by 8 other occupied locations on a second array. Subjects' success in identifying the remaining empty cell has been taken as evidence for integration. In this study, we show that success in this task can be better accounted for by a convert-and-compare process than by an integration process. We conclude that VSTM only supports limited integration across sequential arrays.

Two dot arrays, each containing a different set of six randomly selected locations from a 5 × 5 matrix, were presented briefly and were separated by an interstimulus interval (ISI) of 0, 200, 500, or 1,500 msec. Subjects were asked to remember these locations and to report whether a probe dot matched the locations in memory. To find out whether the subjects had formed an integrated representation of the two arrays, the probe dot was accompanied by matrix elements from the first array, from the second array, or from both arrays. Memory for Array 1 was significantly impaired when the retrieval context was drawn from Array 2, and vice versa, suggesting that the two arrays were represented separately. This effect was observed only at an ISI of 500 msec or longer. We propose that as Array 1 is better consolidated, its representation becomes more separated from that of Array 2.

The capacity of visual short-term memory (VSTM) for a single visual display has been investigated in past research, but VSTM for multiple sequential arrays has been explored only recently. In this study, we investigate the capacity of VSTM across two sequential arrays separated by a variable stimulus onset asynchrony (SOA). VSTM for spatial locations (Experiment 1), colors (Experiments 2–4), orientations (Experiments 3 and 4), and conjunction of color and orientation (Experiment 4) were tested, with the SOA across the two sequential arrays varying from 100 to 1,500 msec. We find that VSTM for the trailing array is much better than VSTM for the leading array, but when averaged across the two arrays VSTM has a constant capacity independent of the SOA. We suggest that multiple displays compete for retention in VSTM and that separating information into two temporally discrete groups does not enhance the overall capacity of VSTM.

Road infrastructure in India is developing at a very fast pace. A good pavement is needed for the safe, comfortable and economical movement of traffic. The thickness of road depends on geotechnical properties of subgrade soil and traffic intensity. The soil having more liquid limit (LL), lower Maximum dry density (MDD) and higher Optimum moisture content (OMC) is suitable for subgrade of road. The black cotton soilis not suitable for subgrade due to its swelling characteristics and causing number of damages to the structures particularly light buildings and pavements. Thus, worldwide these soils are considered to be problematic soils and pose several challenges for engineers.This paper investigates the effect of using a new stabilization product, RBI Grade-81 on black cotton soil to improve the properties of subgrade soil.Atterberg’s limit, Compaction, California Bearing Ratio (CBR), Unconfined Compressive Strength (UCS) and X-ray diffractiontests were carried out on the samples of ...

The Graphics Processing Unit (GPU) has become an integral part of mainstream computing. The advancement and evaluation of GPU has lead to a significant performance improvement of many algorithms used in day today life. Being powerful Graphics engine, GPU’s are also powerful programmable processors featuring wide range of benefits over memory and arithmetic calculation. Along with GPU’s, advancement of Sensor Technology has led to many innovative Applications in recent years. The introduction of Microsoft Kinect has opened a plethora of application in Augmented Reality (AR) domain. Ornamental Dressing Room is an AR application meant for Jewellery. Aim of the application is to develop an Augmented Reality (AR) system that gives a visualization of ornaments worn on a human body. The User who intended to purchase ornaments, will face the Kinect Sensor, the application does the rest by identifying the control point at the neck and augments the selected jewelry over the neck. This project is aimed at comparing the results of applying parallel programming concepts for algorithms used in Ornament Dressing Room with GPU over CPU.

Simultaneous localization and mapping, usually called as SLAM is the technique
used for construction of maps in new environment which digital machines
experience. The SLAM technique is also used for updating the maps which already
exist. When a digital machine, like a mobile robot visits the location for which map
already exists, the new changes in environment are recognized and the changes are
given as the feedback to system which is controlling the machine. The control
system takes the feedback values for the input and after carrying out the
verifications and mathematical calculations, decides whether to treat the feedback
input is a advice to update the map's content or as an error. Simultaneous
localization and mapping technique comes with many steps which have to be
carried out accordingly. The main job of SLAM is to check the position of the robot
every now and then, with aid of mobile robot's environment where it is standing at
any point of time. By following this step the robot can have the information where it
is in the world.

We propose SiCaGCN, a learning system to predict the similarity of a given software code to a set of codes that are permitted to run on a computational resource, such as a supercomputer or a cloud server. This code characterization allows us to detect abusive codes. Our system relies on a structural analysis of the control-flow graph of the software codes and two different graph similarity measures: Graph Edit Distance (GED) and a singular values based metric. SiCaGCN combines elements of Graph Convolutional Neural Networks (GCN), Capsule networks, attention mechanism, and neural tensor networks. Our experimental results include a study of the trade-offs between the two similarity metrics and two variations of our learning networks, with and without the use of capsules. Our main findings are that the use of capsules reduces mean square error significantly for both similarity metrics. Use of capsules reduces the runtime to calculate the GED while increases the runtime of singular values calculation.

Now-a-days many erogenous personal data may be sent through internet that may be tapped. Different techniques have been used to ensure that secret messages would reach their intended recipient without being tampered with along the way. A Data Concealing Method Characterized by DNA coding proposes a data hiding method build on DNA coding using word document or PDF as transporter. Firstly, we encode the plaintext by DNA coding to a DNA sequence. Secondly, we encrypt the DNA sequence by another equallength DNA sequence generated by Chebyshev maps, and attach the result to a primer DNA sequence. After circularly shifting the whole sequence for finite times, we embed them into the Word document by modifying the forecolour of the characters. Each character in the Word document can be embedded one character, i.e. 6-bit DNA coding. The initial values and parameter of the Chebyshev maps and shifting times are all served as keys, and the plaintext can be extracted successfully from the host document. This technique ensures the plaintext to be embedded into the characters, and can also skip the non-character contents in the Word document, such as image and object, the same to the extracting process. Thus the method is well suited to some data hiding applications, such as fragile watermarking, secret communication, and online content distribution systems. This paper is a modification of a paper titled "A novel data hiding method based on deoxyribonucleic acid coding" [1] aimed at increasing the security of the data hiding method proposed in it.

Due to the morphological characteristics and other biological aspects in histopathological images, the computerized diagnosis of colon cancer in histopathology images has gained popularity. The images acquired using the histopathology microscope may differ for greater visibility by magnifications. This causes a change in morphological traits leading to intra and inter-observer variability. An automatic colon cancer diagnosis system for various magnification is therefore crucial. This work proposes a magnification independent segmentation approach based on the connected component area and double density dual tree DWT (discrete wavelet transform) coefficients are derived from the segmented region. The derived features are reduced further shortened with fuzzy c-means. Further, with the aid of artificial neural network (ANN) optimized with salp swarm optimization (SSO), images are classified into normal and abnormal ones. This magnification independent proposed framework is evaluated across four different datasets (two realtime datasets and two public datasets) with different magnifications and the outcomes of all datasets were substantial when compared with the existing techniques. The proposed framework has shown strong concordance for cancer detection and can assist pathologists with a second opinion.

Automated grading of colon biopsy images across all magnifications is challenging because of tailored segmentation and dependent features on each magnification. This work presents a novel approach of robust magnification-independent colon cancer grading framework to distinguish colon biopsy images into four classes: normal, well, moderate, and poor. The contribution of this research is to develop a magnification invariant hybrid feature set comprising cartoon feature, Gabor wavelet, wavelet moments, HSV histogram, color auto-correlogram, color moments, and morphological features that can be used to characterize different grades. Besides, the classifier is modeled as a multiclass structure with six binary class Bayesian optimized random forest (BO-RF) classifiers. This study uses four datasets (two collected from Indian hospitals-Ishita Pathology Center (IPC) of 4X, 10X, and 40X and Aster Medcity (AMC) of 10X, 20X, and 40X-two benchmark datasets-gland segmentation (GlaS) of 20X and IMEDIATREAT of 10X) comprising multiple microscope magnifications. Experimental results demonstrate that the proposed method outperforms the other methods used for colon cancer grading in terms of accuracy (97.