Miracle-dz/UniFAR-FTUs huggingface dataset:
QOE MODEL FOR MULTIMEDIA CONTENT DELIVERY FROM MCLOUD TO MOBILE DEVICES

Integration of mobile devices in the m-learning systems provides the learner with scaffolding outside the classroom, it allows them to easily store, record and deliver multimedia content in real-time. The process of delivering multimedia learning objects (rich text, video, images, audio, animation, etc.) from the mlearning systems to the users requires more computational resources than mobile devices can provide. Considering the existence of different kinds of mobile browsers, which have limited support for HTML plugins (Flash, Java and Silverlight), we face the challenge to deliver the adapted multimedia contents in the interactive m-learning system. Installation of add-ons to the mobile browsers is just a provisional solution that is not always possible on each mobile device. Generally, the m-learning systems presented on the mobile device that not provide rich multimedia information leads to a degraded learning experience. In order to provide users with multimedia content that is suitable for their mobile devices and according to their needs we introduce the mobile cloud computing environment as paradigm that is ideal to overcome these problems. The proposed interactive mCloud system should provide high scale collaboration and interaction between the professor and students, in direction of increasing the quality of learning. The main focus is the delivery of multimedia learning objects to the users depending on the student’s cognitive style and adapting the content in accordance with the context-aware network conditions. The main task within this paper is to design a QoE model for estimating the multidimensional metric based on multimedia content adaptive features.
Analysis of Data of Different Spatial Support: A Multivariate Process Approach

Dixon Hamil, K. PhD, Purdue University, December 2017. Analysis of Data of Different Spatial Support: A Multivariate Process Approach. Major Professor: Hao Zhang. Inherent to a spatial variable is the unit of support at which it is measured. In many studies, variables are observed at different support. For example, disease rates might be measured at an aggregated level while temperature is usually measured at specific points. It is still an interesting problem to study the relationship of variables having different support. However, it may be a different problem to statistically model the relationship of variables of different support, particularly when the supports do not have a hierarchical structure. Currently, cokriging, the use of one or more spatial variables to predict another variable, is applied to variables of the same support. In this work, I extend cokriging for use with variables of different support by constructing a nonparametric crosscovariance matrix. This method is flexible as it applies to any marginal spatial model and is suited to large datasets because it uses latent variables which can assist with dimension reduction. The proposed nonparametric method is demonstrated with two correlated variables which are measured at different spatial units. In addition, the method is implemented using two algorithms; one which yields an optimized matrix (Wang, 2011) and the other which produces an approximately optimized matrix but is computationally more efficient (Hu 2013). The results show that the method is appropriate for predicting data of different support and that it outperforms some competing methods with respect to predictive performance. Furthermore, as expected, the approximately optimized matrix does not perform as well as the alternative algorithm, but it performs better than the comparative methods.
Hashing via Efficient Addictive Kernel for Logistics Image Classification

In this paper, fast image search with efficient additive kernels and kernel locality-sensitive hashing has been proposed. As to hold the kernel functions, recent work has probed methods to create locality-sensitive hashing , which guarantee our approach’s linear time, however existing methods still do not solve the problem of locality-sensitive hashing (LSH) and indirectly sacrifice the loss in accuracy of search results in order to allow fast queries. To improve the search accuracy, we show how to apply explicit feature maps into the homogeneous kernels, which help in feature transformation and combine it with kernel locality-sensitive hashing. We prove our method on several large datasets, and illustrate that it improve the accuracy relative to commonly used methods and make the task of object classification, content-based retrieval more fast and accurate.
AUTOCORRELATION WEIGHTED SUM ENTROPY BASED SOFTWARE QUALITY MANAGEMENT FOR OPEN SOURCE APPLICATION

: Software metrics is used to evaluate software systems quality and to improve the software reliability. Recently, few researches have been developed for enhancing the quality of open source software using Software metrics. But, the software quality management performance of existing works was not efficient while performing multiple software operations which affect the software reliability. To attain higher scalability rate with reduced service provisioning time while improving the reliability of software quality, a component model called Autocorrelation Weighted Sum Entropy (AWSE) technique is proposed. To minimize software quality degradation while performing multiple software operations, a service provisioning time entropy is considered. The AWSE technique initially measures autocorrelation function for consecutive versions of same application to find the relationship between a conventional versions and contemporary version. After that, AWSE technique computes autocorrelation for service provisioning time entropy that considers the effect of maintenance operations carried out both on contemporary versions and on the conventional versions. Then, AWSE technique measures average time entropy to obtain the time entropy of consecutive versions of same application. This in turn helps for reducing the service provisioning time and improving the scalability of software quality management. Finally, AWSE technique used Weighted Sum Entropy (WSE) model that considers the Mean Time between Failure (MTF) to improve the software reliability in a specified environment for a given amount of time and to reduce the cost of software quality testing. The AWSE technique conducts the experimental works on parameters such as scalability, service provisioning time and software reliability. The experimental result shows that the AWSE technique is able to improve the scalability and also reduces the service provisioning time of software quality management when compared to state-of-the-art-works.
Power-minimization and energy-reduction autonomous navigation of an omnidirectional Mecanum robot via the dynamic window approach local trajectory planning

To improve the energy efficiency of the Mecanum wheel, this article extends the dynamic window approach by adding a new energy-related criterion for minimizing the power consumption of autonomous mobile robots. The energy consumption of the Mecanum robot is first modeled by considering major factors. Then, the model is utilized in the extended dynamic window approach–based local trajectory planner to additionally evaluate the omnidirectional velocities of the robot. Based on the new trajectory planning objective that minimizes power consumption, energy-reduction autonomous navigation is proposed via the combinational cost objectives of low power consumption and high speed. Comprehensive experiments are performed in various autonomous navigation task scenarios, to validate the energy consumption model and to show the effectiveness of the proposed technique in minimizing the power consumption and reducing the energy consumption. It is observed that the technique effectively takes advantage of the Mecanum robot’s redundant maneuverability, can cope with any type of path and is able to fulfil online obstacle avoidance.
Attribute-Based, Usefully Secure Email

A secure system that cannot be used by real users to secure real-world processes is not really secure at all. While many believe that usability and security are diametrically opposed, a growing body of research from the field of Human-Computer Interaction and Security (HCISEC) refutes this assumption. All researchers in this field agree that focusing on aligning usability and security goals can enable the design of systems that will be more secure under actual usage. We bring to bear tools from the social sciences (economics, sociology, psychology, etc.) not only to help us better understand why deployed systems fail, but also to enable us to accurately characterize the problems that we must solve in order to build systems that will be secure in the real world. Trust, a critically important facet of any socio-technical secure system, is ripe for analysis using the tools provided for us by the social sciences. There are a variety of scopes in which issues of trust in secure systems can be studied. We have chosen to focus on how humans decide to trust new correspondents. Current secure email systems—such as S/MIME and PGP/MIME—are not expressive enough to capture the real ways that trust flows in these sorts of scenarios. To solve this problem, we begin by applying concepts from social science research to a variety of such cases from interesting application domains; primarily, crisis management in the North American power grid. We have examined transcripts of telephone calls made between grid management personnel during the August 2003 North American blackout and extracted several different classes of trust flows from these real-world scenarios. Combining this knowledge with some design patterns from HCISEC, we develop criteria for a system that will enable humans apply these same methods of trust-building in the digital world. We then present Attribute-Based, Usefully Secure Email (ABUSE) and not only show that it meets our criteria, but also provide empirical evidence that real users are helped by the system.
A feature-based approach to visualizing and mining simulation data

The physical and engineering sciences are increasingly concerned with the study of complex, large-scale evolutionary phenomena. Such studies are often based on analyzing data generated from numerical simulations at very fine spatial and temporal resolutions. The size of these simulation data significantly challenges our ability to explore and comprehend the generated data. In this thesis, the problem of developing a feature-based approach to visualizing and mining large-scale simulation data is investigated. The premise of this thesis is that for features in simulation data, a feature-based framework requires three essential components: feature detection, feature verification, and feature characterization. Feature detection is a process that automatically locates and extracts features of interest from the simulation data. Feature verification is a process that distinguishes actual features from spurious artifacts in the detection results. Feature characterization is a process that computes and quantifies the relevant properties of extracted features as determined by domain experts. To demonstrate the essential nature of each component, we have developed and analyzed algorithms for each component, and applied them to swirling features, or vortices, in computational fluid dynamics simulation data. Although the individual algorithms we have developed for these components may not useful for other types of features, the overall feature-based framework can be applied to other types of features from other types of simulations.
An Experimental Study on Competitive Coevolution of MLP Classifiers

This paper investigates the effectiveness and efficiency of two competitive (predator-prey) evolutionary procedures for training multi-layer perceptron classifiers: Co-Adaptive Neural Network Training, and a modified version of Co-Evolutionary Neural Network Training. The study focused on how the performance of the two procedures varies as the size of the training set increases, and their ability to redress class imbalance problems of increasing severity. Compared to the customary backpropagation algorithm and a standard evolutionary algorithm, the two competitive procedures excelled in terms of quality of the solutions and execution speed. Co-Adaptive Neural Network Training excelled on class imbalance problems, and on classification problems of moderately large training sets. CoEvolutionary Neural Network Training performed best on the largest data sets. The size of the training set was the most problematic issue for the backpropagation algorithm and the standard evolutionary algorithm, respectively in terms of accuracy of the solutions and execution speed. Backpropagation and the evolutionary algorithm were also not competitive on the class imbalance problems, where data oversampling could only partially remedy their shortcomings.
Design Improvements for Frequently Misrecognized Letters

To enhance typeface legibility we studied how to improve the design of individual letters. Three different fonts were created, each containing several variations of the most frequently misrecognized letters. These variations were tested both with distance and short exposure methodologies. Creating variations within a typeface avoided confounds that occur when letters from different typefaces are compared against each other. The studies found that some variations were more legible than others despite the letters within a font having similar size, weight, and personality. The results showed that narrow letters benefit from being widened, and that x-height characters benefit from using more of the ascending and descending area.These findings can be used to improve the design of future typefaces.
Study of reconstruction ICA for feature extraction in images and signals

During the last years, neural networks have become a vehicular discipline in the field of machine learning. At the same time, classical machine learning methods have become easier to use due to the availability of higher computational power. The goal of this project is to reconstruct a classical machine learning algorithm used for feature and source extraction (ICA) using neural networks. This reconstruction could bypass some of the drawbacks presents when using ICA. We have studied how the reconstruction operates under different conditions and performed a comparison with the classical algorithm that we reconstructed.
Selecting User Influence on Twitter Data Using Skyline Query under MapReduce Framework

The aim of this research was to select and identify user influence on Twitter data. In identification stage, the method proposed in this study was matrix Twitter approach, sentiment analysis, and characterization of the opinion leader. The importan characteristics included external communication, accessibility, and innovation. Based on these characteristics and information from Twitter data through matrix Twitter and sentiment analysis, a algorithm of skyline query was constructed for the selection stage. Algorithm of skyline query selected user influence by comparing with other users according to values of each characteristic. Thus, user influence was indicated as user that was not influenced by other users in any combination of skyline objects. The use of MapReduce framework model in identification and selection stage, support whole operation where Twitter had big size data and rapid changes. The results in identification and selection of user influence exhibited that MapReduce framework minimized the execution time, whereas in parallel skyline query could reveal user influence on the data.
Design and evaluation of logic gates based on IG FinFET

The CMOS planar technology has been used in fabrication of integrated circuits in the last decades. However, short channel effects in the subthreshold operation region are becoming a critical restriction to the channel length reduction. With the use of FinFET devices, the scaling increases due to the reduction of short channel effects. The origin of the FinFET arises from the scaling limitations of planar devices, reducing the short-channel effects and continuing the scaling predicted by the Moore’s Law. A variation of the standard FinFET device is the independent-gate FinFET device (IG FinFET), in which two independently connected gates control an unique channel. In this work, the independentgate device was explored as a circuit element used for the implementation of different combinational logic networks. With independently connected gates, series/parallel arrays could be performed using a single device, but with direct impact in the electrical performance of logic gates. In this work, it is presented the electrical analysis in terms of signal delay propagation and energy consumption of compacted transistor networks. Different topologies derived from the independent-gate operation were tested through electrical simulations and the results demonstrate the existing trade-off between these two parameters. Also, an analytical delay expression was derived for logic networks which use IG FinFETs, deriving analytical expressions for the impact of reducing arrays of series transistors in logic networks. The analytical model for IG devices was tested in a logic data path and compared to SPICE simulation results, showing its utility for the timing analysis of digital circuits.
[Paper] A Low Noise and High Sensitivity Image Sensor with Imaging and Phase-Difference Detection AF in All Pixels

In this paper, we describe a device structure and optical design for a CMOS image sensor with the phase-difference detection photodiodes (PD) for an autofocus (AF) function. This image sensor has a pixel separated into two PDs by a PN junction. All the effective pixels function as both the imaging and the phase-difference detection AF (PDAF). We have realized a low dark random noise (=1.8eat 1PD, 2.5eat 1pixel) and high sensitivity (=78,000e-/lx sec at 1green pixel) image sensor with the imaging and the PDAF functions in all the effective pixels. Introduction Recently, image sensors with the PDAF function at image plane has been developed [1], [2]. These realize the AF function by arranging exclusive pixels to detect the phase-difference. In these image sensors, a pair of partially light shielded PD is arranged in a part of pixel array. The focusing speed is extremely fast in comparison with conventional contrast detection AF. The sensitivity, however, is degraded by the light shielding structure and interpolation processing by neighboring pixels is necessary for image generation [2]. Therefore, the number of pixels for AF function is limited to avoid the deterioration of image quality. In this work, we developed an image sensor with the imaging and the PDAF functions in all the effective pixels without exclusive pixel for AF function. All the effective pixels have two PDs each to detect phase-difference in one pixel without partially light shielding structure. Therefore, one pixel works as one AF point, and the sum of two PD outputs equals one pixel output. Fig. 1: Principle of the PDAF function Principle Fig. 1 shows the principle of the PDAF function. Exit pupil of an optical lens and a PD of the image sensor are in optically conjugate relation by on-chip micro-lens (ML). Therefore, each pixel by separating into two PDs has the pupil split function for the PDAF function. A light flux which passes through a right half of the optical lens is led to the PD-A (the PD at left si of the optica side). Defocusing between th provided fro image-B (th The directi image-B is and the back These mea lens is drive defocusing a
FACIAL RECONSTRUCTION FROM SKULL USING FREE FORM DEFORMATION

The purpose of facial reconstruction is to estimate the facial outlook from a discovered skull with the intention to provide a positive effect for deceased identification. In this paper, we tried to develop a novel method for facial reconstruction through the use of Free Form Deformation. The target face was obtained by deforming the craniometric landmarks of known skull into unknown skull. Forcing soft tissue on the known skull to the unknown skull with correspondent deformation gives the desired shape of the soft tissue for the unknown skull. Modified from Rhine’s landmarks, the 33 craniometric landmarks were used in this work. For the deformation process, the application of Free Form Deformation was employed. The resulting face from this scheme shows promising forensic facial reconstruction.
Statistical analysis of Amazon EC2 cloud pricing models

In this paper, we conduct statistical analyses for two Amazon cloud pricing models: on demand and spot. On demand cloud instances are charged a fixed price and can only be terminated by the user, with very high availability. On the other hand, spot instances are charged a dynamic price determined by a market‐driven model and can be revoked by the provider when the spot price becomes higher than the user‐defined price, having possibly low availability. Our analysis for on‐demand instances resulted in multiple linear regression equations that represent the influence of characteristics of the processor and RAM memory in the composition of the price of different types of instances available on the Amazon EC2 provider. In order to analyze the Amazon spot pricing, we used time‐smoothed moving averages by 12‐hour periods, aiming to provide a price‐availability trade‐off to the user. Our experiments with spot price histories from September to November 2016 show that the user's bid can be set at 30% of the on‐demand price, with an availability above of 90%, depending on instance type.
Neural signal processing techniques to analyse motor functions in EEG-based brain-computer interfaces

iii List of Figures . xv List of Tables . xvii List of Abbreviations . xviii
Corner Detection-based Road Sign Detection for Low-cost Sensor Fusion-based Precise Localization System

To overcome the limitations of satellite navigation and inertial navigation, low-cost, precise localization systems that utilize road facilities as landmarks are being developed. Road signs are regarded as important landmarks because they are installed on all roads, and they can be seen even during congestion, unlike road surface markings. In this paper, we have proposed a method to detect road signs with various aspect ratios by identifying corners and combining such elements. By verifying each step to maximize detection performance and by tracking the bottom corners, the corners at close range can be used. The proposed method is applied to the images acquired on the highway, and high detection performance and real-time operation can be confirmed.
ESKA: A Highly Reliable Authentication Protocol Based-on One-way Key Chain for WSN Broadcast

Abstract The authentication of broadcast sources is one of the most important research issues in the area of network security. To confirm that the control information received is from the genuine management nodes, the message broadcasted from management nodes must be verifiable for the working nodes. In this paper, a highly reliable WSN broadcast authentication protocol based-on one-way key chain, namely, the ESKA, is presented. For the purpose of comparison, a group of simulations are conducted. The results have demonstrated that compared with μTESLA, the presented method is of lower authentication delay and higher authentication rate in large broadcast data traffic transmission, meanwhile reducing considerable communication overheads in those applications.