Miracle-dz/UniFAR-FTUs huggingface dataset:
Face Animation Parameter Extraction in Real Video Environment

One of the challenging problems in machine vision and computer graphic is realistic face modeling and animation. Using markers on the face key points and tracking the movements of the markers is a low cost method to determine these parameters. Since MPEG4 standard has defined parameters for face modeling and animation, we place markers on the key points defined in MPEG-4 standard, and propose a system for accurate tracking of them. We examine the system in real movie capturing conditions. Various distortions such as change of lightening, Gaussian noise, blurring, compression, and temporal filtering are examined, and the system degradation in each case is reported.
A Pattern-based Transformation Approach to Parallelise Software Systems using a System Dependency Graph

Decades ago, the first multi-core processor machines were invented. Meanwhile, most computational devices contain two, four or more processors. Nevertheless, lots of software developers avoid concurrent programming because it requires cautious implementation to prevent race conditions and dead locks. However, without concurrency, the potential of multi-core processors cannot be exhausted. Therefore, a graph-based parallelisation approach for Java systems is demonstrated in this thesis. We start with a System Dependency Graph (SDG) which represents Java source code and is stored in a Neo4J graph database. As prototypes, we choose three patterns which can become parallelised. With pattern matching, we search for these patterns in the SDG. Then, the identified sub graphs are transformed so that they finally represent the parallel version of the original source code. In the evaluation, we test the pattern matching and transformation by means of several examples which all execute successfully. Also, we analyse the occurrences of the chosen prototype patterns in existing Java applications. We determine that two of the three prototypes exist multiple times in the examined source code. It is planned to generate Java source code from the transformed SDG in future work.
Stable and fast model-free walk with arms movement for humanoid robots

Controlling a humanoid robot with its typical many degrees of freedom is a complex task, and many methods have been proposed to solve the problem of humanoid locomotion. In this work, we generate a gait for a Hitec Robonova-I robot using a model-free approach, where fairly simple parameterized models, based on truncated Fourier series, are applied to generate joint angular trajectories. To find a parameter set that generates a fast and stable walk, optimization algorithms were used, specifically a genetic algorithm and particle swarm optimization. The optimization process was done in simulation first, and the learned walk was then adapted to the real robot. The simulated model of the Robonova-I was made using the USARSim simulator, and tests made to evaluate the resulting walks verified that the best walk obtained is faster than the ones publicly available for the Robonova-I. Later, to provide an additional validation, the same process was carried out for the simulated Nao from the RoboCup 3D Soccer Simulation League. Again, the resulting walk was fast and stable, overcoming the speed of the publicly available magma-AF base team.
Survey Paper on Spoofing Detection in Wireless Network

Spoofing Attack is one of the vulnerabilities in the wireless networks, which is a situation in which the intruder successfully pretends as legal one. These attacks will affect the network performance violating the network protocols. A spoofing attack is when a malicious party impersonates another device or user on a network in order to launch attacks against network hosts, steal data, spread malware, or bypass access controls While significant research effort has been dedicated to wireless localization over the past decades, most aspects of location security have been overlooked. In particular, adversaries can take advantage of security vulnerabilities of current location systems to launch location spoofing attacks, thus disguising their position in the network. The node can be prevented and verified by cryptography but conventional security approaches are cannot be executed always due to their tough prerequisites. In this paper we are proposing far-reaching information which will detect the IP spoofing attack and prevent it using received signal strength (RSS). Keywords—Wireless network, Spoofing attack, attack detection
Application of statistical physics in time series analysis

This dissertation covers the four major parts of my PhD research: i) Modeling instantaneous correlation ii) Quantifying time-lag correlation iii) Modeling time-lag correlation iv) Modeling and application of heteroskedasticity. For modeling instantaneous correlation, we study the limitations of random matrix theory (RMT) and investigate the impact of autocorrelations on the results of RMT. We propose autoregressive random matrix theory (ARRMT) which takes into account the impact of autocorrelations on the study of crosscorrelations in multiple time series. We illustrate the method using air pressure data for 95 US cities. For quantifying time-lag correlation, we propose time-lag random matrix theory (TLRMT) and find long-range magnitude crosscorrelations in financial, physiological and genomic data. For modeling time-lag correlation, we propose a global factor model (GFM) and build the relationship between the autocorrelation of the global factor and the time-lag crosscorrelation among individual time series. We apply the method to equity indices data for 48 countries and find that a single global factor can explain most of the time lag crosscorrelations among these indices. For modeling and application of heteroskedasticity, we propose a high frequency trading model using two fractionally intergrated autoregressive conditional heteroskedasticity (FIARCH) processes, and explained the fat-tailed distribution of returns and the long memory
A Multi-Source Data Aggregation and Multidimensional Analysis Model for Big Data

With the rise of Internet applications such as search engines, social networks, and e-commerce, the amount of data in the Internet is rapidly expanding. There are a lot of data generated every moment, and the global information is also increasing. Therefore, the big data is driven by the Internet industry, and it is also a subversive technological innovation compared to the cloud computing and Internet of things. How to carry on the fast retrieval in the massive and different types of data? How to discover potential associations between different data? How to mining the potential value of the data? And how to create a multidimensional view of the data? These urgent problems need to be solved. In this paper, a multi-source data aggregation and multidimensional analysis model for big data (DAM_AM) is proposed. The model adopts the hierarchical structure and introduces data aggregation mechanism, multi-source processing mechanism, object and association mapping mechanism, and "walk" mechanism. Using these mechanisms, multi-source data is normalized to a coherent and consistent representation pattern. And then the fields that represent a class of entity in the representation pattern are aggregated into a set of fields. By mapping different field sets into different objects and associations and combining with the time dimension and space dimension, we can build a multifaceted visual model. Through the concrete case analysis and verification, it indicates that the DAM_AM model can analyze the data from multidimensional and multi-level, and shows the potential correlation between different data. The model not only has high computational efficiency and has high scalability, but also shows the analysis results clearly and intuitively.
Unicast Communication based on Context-aware Adaptive Routing in delay Tolerant Mobile Adhoc Network

: Applications of decentralized mobile systems are often characterized by network partitions. As a consequence delay tolerant networking research has received considerable attention in the recent years as a means to obviate to the gap between ad hoc network research and real applications. In this paper we present the design, implementation and evaluation of the Context-aware Adaptive Routing (CAR) protocol for delay tolerant unicast communication in intermittently connected mobile ad hoc networks. The protocol is based on the idea of exploiting nodes as carriers of messages among network partitions to achieve delivery. The choice of the best carrier is made using Kalman filter based prediction techniques.
Types of inverters and topologies for microgrid applications

Los inversores son los principales actuadores en el control de microrredes en AC, pues ellos gestionan los flujos de potencia de los generadores y los dispositivos de almacenamiento de energia. En general, existen tres tipos de inversores dependiendo de la estrategia de control: inversores alimentadores de red, inversores formadores de red e inversores soporte de red. Dichos inversores pueden ser implementados con diferentes topologias hardware, cada una de ellas con ventajas y desventajas. Este articulo presenta una sintesis de las topologias de inversores ampliamente usadas en microrredes de AC. Ademas, el articulo tambien describe la arquitectura y las principales estrategicas de control de los inversores.
Numerical methods for diffuse interface models of binary flows

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
Equalization Architectures for High Speed ADC-Based Serial I/O Receivers

The growth in worldwide network traffic due to the rise of cloud computing and wireless video consumption has required servers and routers to support increased serial I/O data rates over legacy channels with significant frequency-dependent attenuation. For these high-loss channel applications, ADC-based high-speed links are being considered due to their ability to enable powerful digital signal processing (DSP) algorithms for equalization and symbol detection. Relative to mixed-signal equalizers, digital implementations offer robustness to process, voltage and temperature (PVT) variations, are easier to re-configure, and can leverage CMOS technology scaling in a straight-forward manner. Despite these advantages, ADC-based receivers are generally more complex and have higher power consumption relative to mixedsignal receivers. The ensuing digital equalization can also consume a significant amount of power which is comparable to the ADC contribution. Novel techniques to reduce complexity and improve power efficiency, both for the ADC and the subsequent digital equalization, are necessary. This dissertation presents efficient modeling and implementation approaches for ADC-based serial I/O receivers. A statistical modeling framework is developed, which is able to capture ADC related errors, including quantization noise, INL/DNL errors and time interleaving mismatch errors. A novel 10GS/s hybrid ADC-based receiver, which combines both embedded and digital equalization, is then presented. Leveraging a time-interleaved asynchronous successive approximation ADC architecture, a new structure for 3-tap embedded FFE inside the ADC with low power/area overhead is used. In addition, a dynamically-enabled digital 4-tap FFE + 3-tap DFE equalizer architecture is introduced, which uses reliable symbol detection to
The Effects of Computer-assisted Pronunciation Readings on ESL Learners’ Use of Pausing, Stress, Intonation, and Overall Comprehensibility

With research showing the benefits of pronunciation instruction aimed at suprasegmentals (Derwing, Munro, & Wiebe, 1997, 1998; Derwing & Rossiter, 2003; Hahn, 2004; McNerney and Mendelsohn, 1992), more materials are needed to provide learners opportunities for self-directed practice. A 13-week experimental study was performed with 75 ESL learners divided into control and treatment groups. The treatment group was exposed to 11 weeks of self-directed computer-assisted practice using Cued Pronunciation Readings (CPRs). In the quasi-experimental pre-test/post-test design, speech perception and production samples were collected at Time 1 (week one of the study) and Time 2 (week 13). Researchers analyzed the treatment’s effect on the learners’ perception and production of key suprasegmental features (pausing, word stress, and sentence-final intonation), and the learners’ level of perceived comprehensibility. Results from the statistical tests revealed that the treatment had a significant effect on learners’ perception of pausing and word stress and controlled production of stress, even with limited time spent practicing CPRs in a self-directed environment.
Light-field Image Super-resolution Using Convolutional Neural Network

Light-field cameras can capture 2D spatial and 2D angular information in a single shot. Nevertheless, light-field cameras usually have a trade-off between the spatial and angular resolution in a restricted sensor resolution. The low spatial resolution of light-field cameras limits the application of light-field cameras. In this paper, we present a novel light-field super-resolution method based on convolutional neural network (CNN). With low-resolution light-field multiview images as input, we directly learn the mapping between low-resolution images and high-resolution images by developing an end-to-end CNN. Experimental results demonstrate that in terms of visual effects and evaluation metrics, the reconstruction results of the proposed methods is superior to those of state-of-the-art methods. The proposed approach takes advantage of useful information among the multiple views of light fields for super-resolution reconstruction.
Adaptive maximization of lossless compression of satellite images

Presents an adaptive algorithm for maximizing the lossless compression of remote sensing satellite images based on image contexture information at cost-effective and reasonable performance rates. This approach involves image remapping, feature-based image segmentation to determine regions of similar entropy, and high-order arithmetic coding to obtain significant improvements over the use of conventional compression techniques alone. Image remapping is used to transform the original image into a lower entropy state. Several techniques were tested on satellite images including differential pulse code modulation, bi-linear interpolation, and block-based linear predictive coding. The results of these experiments are discussed and trade-offs between computation requirements and entropy reductions are used to identify the optimum approach for a variety of satellite images. Further entropy reduction can be achieved by segmenting the image based on local entropy properties then applying a coding technique which maximizes compression for the region. Experimental results are presented showing the effect of different coding techniques for regions of different entropy. A rule-base is developed through which the technique giving the best compression is selected. <<ETX>>
Facilitating Autonomic Computing Using Reflection

Supervisory Committee Dr. Hausi A. Muller, Department of Computer Science, University of Victoria Supervisor Dr. Margaret-Anne Storey, Department of Computer Science, University of Victoria Departmental Member Dr. Kin Li, Department of Computer Science, University of Victoria Outside Member Abstract Continuous evolution is a key trait of software-intensive systems. Many research projects investigate mechanisms to adapt software systems effectively in order to ease evolution. By observing its internal state and surrounding context continuously using feedback loops, an adaptive system is potentially able to analyze its effectiveness by evaluating quality criteria and then self-tune to improve its operations.Continuous evolution is a key trait of software-intensive systems. Many research projects investigate mechanisms to adapt software systems effectively in order to ease evolution. By observing its internal state and surrounding context continuously using feedback loops, an adaptive system is potentially able to analyze its effectiveness by evaluating quality criteria and then self-tune to improve its operations. To be able to observe and possibly orchestrate continuous evolution of software systems in a complex and changing environment, we need to push monitoring and control of evolving systems to unprecedented levels. This thesis proposes implementing monitoring and evolution in adaptive systems using autonomic elements that rely on the reflective capabilities of the language in which the system is implemented. Such monitoring will allow a system to detect anomalous internal behaviour, and infer that changes to the operating context or environment have occurred.
How Things Become Popular

This work discusses the mechanisms of popularity generation on the Internet. What we propose here is a model that replicates the statistical distribution profile of popularity. It is a probabilistic model of the number of individuals who read, hear or see, and then replicate a message, and parameterizes an individual’s preference for either new or older messages. Messages can gain in popularity according to a process of paying attention and the resulting popularity distribution has a stretched lognormal configuration. The stretch depends on the degree of attention paid to new messages versus that paid to older messages. We considered three sets of data to test the fit of the model: the American singers/songwriters listed on Wikipedia, videos on YouTube belonging to two different categories, and the number of visits to Wikipedia pages on music albums and film categories. Our main results adjust, with good approximation, to this experimental data. In each of the three case studies, the fit produced by the model is better adjusted to the data than the lognormal standard function.
VOCABULARY DEVELOPMENT OF KINDERGARTEN STUDENTS OF APPLE TREE PRE-SCHOOL SAMARINDA BY USING FLASHCARDS

Abstract: This present study aimed to investigate 14 kindergarten students of Apple Tree pre-school Samarinda with various ability toward their English vocabularies development by flashcards. A Class Action Research was applied in this study. The data was collected through observation checklist, sequence of cycles and interview transcript. Then, building on the analysis of the collected data, it further discusses the vocabulary development of YL and provides suggestions for TEYL. This study revealed that; (1) most of the students developed their English vocabularies gradually by flashcards. (2) These result indicated that TEYL especially kindergarten students by using flashcards could give significant vocabularies development in learning process. Flashcards is one of the simplest and effective teaching materials for teaching YL vocabulary due to the fact that flashcards are categorized based on themes with full colored pictures which attractive for YL. As this study showed the students were engaged with the topics given since the teachers used flashcards to teach English vocabulary. It was difficult to make engagement with YL in English teaching and learning because YL have different mood, self-motivation, and self-confidence which influenced to the willingness in grasping the lesson. Finally, through this based-picture learning, the students indicated that their progress in vocabulary development although this phenomena was commonly happened in TEFL for YL that lead to teaching method done by English teachers who are required to do more innovation toward their teaching method, to develop sufficient knowledge and to use proper teaching media.
FIN-CLARIN: A Framework for Depositing and Disseminating Language Resources for R&D

Proceedings of the NODALIDA 2011 Workshop Visibility and Availability of LT Resources. Editors: Sjur Norstebo Moshagen and Per Langgard. NEALT Proceedings Series, Vol. 13 (2011), 3–6. © 2011 The editors and contributors. Published by Northern European Association for Language Technology (NEALT) http://omilia.uio. no/nealt . Electronically published at Tartu University Library (Estonia) http://hdl.handle. net/10062/16975
Cryptography & Network Security

A textbook for beginners in security. In this new first edition, well-known author Behrouz Forouzan uses his accessible writing style and visual approach to simplify the difficult concepts of cryptography and network security. This edition also provides a website that includes Powerpoint files as well as instructor and students solutions manuals. Forouzan presents difficult security topics from the ground up. A gentle introduction to the fundamentals of number theory is provided in the opening chapters, paving the way for the student to move on to more complex security and cryptography topics. Difficult math concepts are organized in appendices at the end of each chapter so that students can first learn the principles, then apply the technical background. Hundreds of examples, as well as fully coded programs, round out a practical, hands-on approach which encourages students to test the material they are learning. Table of contents Cryptography and Network Security1 IntroductionPart 1 Symmetric-Key Encipherment2 Mathematics of Cryptography3 Traditional Symmetric-Key Ciphers4 Mathematics of Cryptography5 Introduction to Modern Symmetric-Key Ciphers6 Data Encryption Standard (DES)7 Advanced Encryption Standard (AES)8 Encipherment Using Modern Symmetric-Key CiphersPart 2 Asymmetric-Key Encipherment9 Mathematics of Cryptography10 Asymmetric-Key CryptographyPart 3 Integrity, Authentication, and Key Management11 Message Integrity and Message Authentication12 Cryptographic Hash Functions13 Digital Signature14 Entity Authentication15 Key ManagementPart 4 Network Security16 Security at the Application Layer: PGP and S/MIME17 Security at the Transport Layer: SSL and TLS18 Security at the Network Layer: IPSec
Energy-efficient approximate computation in Topaz

The increasing prominence of energy consumption as a first-order concern in contemporary computing systems has motivated the design of energy-efficient approximate computing platforms. These computing platforms feature energy-efficient computing mechanisms such as components that may occasionally produce incorrect results. We present Topaz, a new task-based language for computations that execute on approximate computing platforms that may occasionally produce arbitrarily inaccurate results. The Topaz implementation maps approximate tasks onto the approximate machine and integrates the approximate results into the main computation, deploying a novel outlier detection and reliable re-execution mechanism to prevent unacceptably inaccurate results from corrupting the overall computation. Because Topaz can work effectively with a very broad range of approximate hardware designs, it provides hardware developers with substantial freedom in the designs that they produce. In particular, Topaz does not impose the need for any specific restrictive reliability or accuracy guarantees. Experimental results from our set of benchmark applications demonstrate the effectiveness of Topaz in vastly improving the quality of the generated output while only incurring 0.2% to 3% energy overheard.