Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of novel strategies for template-based protein structure prediction

The most successful methods for predicting the structure of a protein from its sequence rely on identifying homologous sequences with a known structure and building a model from these structures. A key component of these homology modelling pipelines is a model combination method, responsible for combining homologous structures into a coherent whole. Presented in this thesis is poing2, a model combination method using physics-, knowledgeand template-based constraints to assemble proteins using information from known structures. By combining intrinsic bond length, angle and torsional constraints with longand short-range information extracted from template structures, poing2 assembles simplified protein models using molecular dynamics algorithms. Compared to the widely-used model combination tool MODELLER, poing2 is able to assemble models of approximately equal quality. When supplied only with poor quality templates or templates that do not cover the majority of the query sequence, poing2 significantly outperforms MODELLER. Additionally presented in this work is PhyreStorm, a tool for quickly and accurately aligning the three-dimensional structure of a query protein with the Protein Data Bank (PDB). The PhyreStorm web server provides comprehensive, current and rapid structural comparisons to the protein data bank, providing researchers with another tool from which a range of biological insights may be drawn. By partitioning the PDB into clusters of similar structures and performing an initial alignment to the representatives of each cluster, PhyreStorm is able to quickly determine which structures should be excluded from the alignment. For a benchmarking set of 100 proteins of diverse structure, PhyreStorm is capable of finding over 90 % of all highscoring structures in the PDB, and over 80 % of all structures of moderate alignment score. Declaration of originality This thesis, and the work described within this thesis, is entirely my own work unless explicitly specified otherwise.
PSO-Enabled Privacy Preservation of Data Clustering

Background/Objective: Privacy is the vital issue when sharing of the data comes into picture. The demand and scope for privacy is increasing day-by-day as data storage techniques have emerged from standalone database to distributed database and then progressed to parallel databases. K-means and Fuzzy C-means (FCM) are the frequently used clustering algorithms for standalone database, distributed database and parallel databases. The current paper highlights Particle Swam Optimization algorithm along with Fuzzy C-means clustering algorithm technique for preserving the privacy on distributed databases. Methods/Statistics Analysis: The experimentation is performed by means of the datasets accessible in the UCI machine-learning repository. The main benefit of the suggested technique is that, this technique will assess in terms of their privacy of cluster. Therefore, the technique plans to give improved visibility for the protected data. The technique is executed in the working platform of MATLAB and the effects will be examined to show the presentation of the suggested clustering technique. Findings: The performance of the proposed clustering technique based on privacy preserving is analyzed for accuracy and Database Different Ratio (DBDR) on six UCI medical related data sets namely Hugerian dataset, Cleveland data set, Reprocessed Hugerian data sets, Long Beach V.A data, BUPA and liver disorder data. Performance improvement observed in the range of 3%-6% on each of the six data sets compared to K-means algorithm. Application/Implementation: The main benefit of the suggested technique is that technique will have to assess in terms of their privacy of cluster. Therefore, the technique plans to give improved visibility for the protected data
Dynamic Replanning for Multi-UAV Persistent Surveillance

Swarms of autonomous vehicles are increasingly being considered for several applications, including weather monitoring, geographical surveys and extra-terrestrial exploration. The task of persistent surveillance is particularly relevant in situations where the target area needs to be continuously surveyed, minimizing the time between visitations to the same region. This distinction from one-time coverage does not allow a straightforward application of most exploration techniques to the problem, especially in presence of vehicle dynamic constraints. Furthermore, the stochastic nature of the environment coupled with unanticipated failures makes the mission management and coordination problem significantly challenging. In this research, we investigate techniques for high-level control, that are scalable, reliable, efficient, and robust to problem dynamics, and allow near real-time replanning in unscripted environments. We present a decentralized hybrid discrete-continuous hierarchical approach for combined control and mission planning, building upon our previous work on semi-heuristic multiple Unmanned Air Vehicle (UAV) control policies and enhancing it with a Probability Collectives (PC) based approach for dynamic mission replanning. PC combines bounded rationality game theory with statistical physics, using information theory. As such, it provides an efficient mechanism for learning in games and provides a single framework for treatment of both continuous and discrete variables/decisions. In this paper we focus on demonstrating the ability of the algorithm approach to plan and replan efficiently replan while maintaining performance.
Sistema de Reconhecimento Facial Baseado em Análise de Componentes Principais

We present the development and implementation of a face recognition system using principal component analysis to construct a face model from a training set of face images. The computed principal component face model is applied in the feature extraction task. We tested the system in the task of correctly classifying 435 images from 102 people with a minimum distance classifier. These images have a great deal of variations concerning position, scale, expression and illumination, even among images of the same person. The proposed system was able to handle such variations and achieved a recognition rate of 97.70%.
Analysis and solution of different algorithmic problems

The goal of competitive programming is being able to find abstract solutions for some given algorithmic problems, and and also being able to code those ideas into an efficient and correct computer program. Performing this activity at a high level requires a bit of natural ability, (at least) hundreds of training hours, and a wide range of knowledge, obviously including many algorithms and data structures, some of them not trivial at all. This project constitutes a compilation of problems from several different relevant topics in competitive programming, with an explanation and analysis of their solution. Most of these problems were solved while training with the UPC programming teams, which have dominated their regional competition for more than one decade. The author hopes that this collection may eventually increase the interest of some readers towards competitive programming.
Random Controlled Pool base Differential Evolution algorithm (RCPDE)

This paper presents a novel random controlled pool base differential evolution algorithm (RCPDE) where powerful mutation strategy and control parameter pools have been used. The mutation strategy pool contains mutations strategies having diverse parameter values, whereas the control parameter pool contains varying nature pairs of control parameter values. It has also been observed that with the addition of rarely used control parameter values in these pools are highly beneficial to enhance the performance of the DE algorithm. The proposed mutation strategy and control parameter pools improve the solution quality and the convergence speed of DE algorithm. The simulation results of the proposed RCPDE algorithm shows significant performance as compared to other algorithms when tested over a set of multi-dimensional benchmark functions.
Motion video annotation and analysis: an overview

Digital storage of images and videos requires staggering amounts of storage. A mere hour long digitized video can occupy a staggering 30GB of storage. With the increasing dependence of a variety of applications on digital storage, the need for automatic and semiautomatic annotation and analysis tools for intelligent video data indexing and manipulation will be strongly felt. Manual annotation of this data will be practically infeasible and insipid. The paper presents highlights of some of the important problems to be solved for automating the process. Computer vision techniques with a human-in-the-loop approach can provide a powerful workbench of algorithms and techniques for the task. <<ETX>>
Partitioning-driven convergence in the design of random-logic blocks

The conventional methodology for computer-aided design of random-logic blocks requires several iterations of logic synthesis and layout tools, and a significant amount of manual intervention, before converging to an implementation that meets the design's constraints. This thesis proposes Nebula, a new design flow that uses netlist partitioning to achieve an optimal trade-off between two conflicting requirements for design convergence: accurate wire-load modeling in synthesis and merging incremental netlist optimizations into layout. The flow iterates between low-level synthesis, layout and re-partitioning optimizations. An experimental prototype, which emulates the Nebula flow, was developed to explore its viability and limitations. A partitioning scheme, implemented in the prototype system, accurately models global wires during synthesis, while allowing room for incremental optimizations in local logic. Experimental results show that in spite of overheads of optimizing a partitioned netlist, accurate wire-load modeling narrows the gap between post-synthesis and post-layout timing in the prototype system. This leads to a faster design convergence as opposed to the conventional methodology. A timing-driven repartitioning heuristic was implemented, which improves post-layout timing by reducing the contribution of global wire-loads along critical paths. Results show that relocating 1% of all gates in a design prunes out 80% of its critical paths. However, the scope of worst-case timing improvements is limited by the ability to merge relocations into the existing layout.
Calibrating a multi-camera system for 3D modelling

We describe a simple and novel way for calibrating the position and internal camera parameters of a camera viewing a scene with no prior knowledge of the camera being necessary. Only two views of a simple planar grid of spots are used to accurately determine the relative position of each camera in a multiple camera system. A multiple camera system is necessary for modelling dynamic objects (such as people). When the shape of the object is continually changing a large number of images must be taken simultaneously. The multiple camera system is also an important research tool allowing surface generation algorithms to be investigated under known accuracy in the camera positions. We have evaluated our algorithm's performance using simulations to determine the limits on the accuracy of our system and have demonstrated the performance in practice by producing 3D models from a four camera system.
Control Difuso para Sistemas de Interacción Hombre-Robot

Resumen En este trabajo se presenta el diseño de una ley de control difusa adaptable para mejorar el desempeño de los sistemas de interacción hombre-robot. Con el propósito de aproximar la dinámica desconocida del sistema robótico, se emplea el enfoque de sistemas difuso de tipo Mamdani. Además, una ley de adaptación es agregada al controlador para compensar incertidumbres debidas a la interacción hombre-robot. Los resultados teóricos obtenidos son ilustrados sobre un dispositivo háptico PHANToM Premium 1.0.
Teaching and Learning in the University: New Directions for Australian Higher Education

The purpose of this paper is to outline recent changes to higher education in Australia and assess the implications of those changes for teaching and learning in universities. Three main changes have been identified: 1. The meta-policy context that has transformed a binary system of higher education into a unified national system of universities. This has given rise to the concept of the comprehensive university accompanied by an economic rationalist approach to the funding of higher education. Issues identified are the relativities to be accorded to teaching and research in the new universities and the push for more efficient modes of teaching. 2. Institutional changes that have influenced the traditional character of universities. These changes include the politicisation of Vice-Chancellors whose peak body is now recognised by the government as the single voice of the university sector, the changing student composition of universities as a result of government policies relating to overseas students and students who have been traditionally underpresented in universities and the introduction of academic staff appraisal. An assessment is made of this broad range of changes on teaching and learning. 3. Changing conceptions of teaching are discussed with a particular emphasis on the work of Shulman and its implications for teaching in the university.
Image-based 3D scanning system using opacity hulls

We have built a system for acquiring and rendering high quality graphical models of objects that are typically difficult to scan with traditional 3D scanners. Our system employs an image-based representation and can handle fuzzy materials such as fur and feathers, and refractive materials like glass. The hardware setup of the system include two turntables, two plasmas displays, a fixed array of cameras and a rotating array of directional lights. Many viewpoints can be simulated by rotating the turntables. By an automatic process, we acquire opacity mattes using multi-background techniques. We introduce a new geometric representation based on the opacity mattes, called the opacity hull. It is an extension of the visual hull with view-dependent opacity, and it significantly improves the visual quality of the geometry and allows seamless blending of objects into new environments. Appearance models based on the surface reflectance fields are also captured by a hybrid sampling of the illumination environment. The opacity hull, coupled with the reflectance data, can then be used to render the object in novel lighting environments from arbitrary viewpoints photorealistically. This system is the first to acquire and render surface reflectance fields under arbitrary illumination from arbitrary viewpoints. Thesis Supervisor: Leonard McMillan Title: Associate Professor
Real-time UAV trajectory generation using feature points matching between video image sequences

Unmanned aerial vehicles (UAVs), equipped with navigation systems and video capability, are currently being deployed for intelligence, reconnaissance and surveillance mission. In this paper, we present a systematic approach for the generation of UAV trajectory using a video image matching system based on SURF (Speeded up Robust Feature) and Preemptive RANSAC (Random Sample Consensus). Video image matching to find matching points is one of the most important steps for the accurate generation of UAV trajectory (sequence of poses in 3D space). We used the SURF algorithm to find the matching points between video image sequences, and removed mismatching by using the Preemptive RANSAC which divides all matching points to outliers and inliers. The inliers are only used to determine the epipolar geometry for estimating the relative pose (rotation and translation) between image sequences. Experimental results from simulated video image sequences showed that our approach has a good potential to be applied to the automatic geo-localization of the UAVs system
Field programmable gate array (FPGA) implementation of novel complex PN-code-generator- based data scrambler and descrambler

A novel technique for the generation of complex and lengthy code sequences using low- length linear feedback shift registers (LFSRs) for data scrambling and descrambling is proposed. The scheme has been implemented using VHSIC hardware description language (VHDL) approach which allows the reconfigurability of the proposed system such that the length of the generated sequences can be changed as per the security requirements. In the present design consideration the power consumption and chip area requirements are small and the operating speed is high compared to conventional discrete I.C. design, which is a pre-requisite for any system designer. The design has been synthesised on device EP2S15F484C3 of Straitx II FPGA family, using Quarts Altera version 8.1. The simulation results have been found satisfactory and are in conformity with the theoretical observations.
Control Data on Cloud using Attribute-Based Encryption Technique

Cloud computing is revolutionary computing paradigm which enables flexible on-demand and low-cost usage of computing resources. But this Advantages contain different security problem such as . Badly configured server can lead to unauthorized access. Ciphertext–policy attribute based encryption(CP-ABE)is a promising cryptographic primitive for fine-grained access control of shared data. A user is able to decrypt a ciphertext if and only if his attributes satisfy the ciphertext access structure . In this paper we use A secure server plus application . this application provides a protected foundation for hosting your Web applications, and Web server configuration which plays a critical role in your Web application's security. Badly configured server can lead to unauthorized access. This system provides bidirectional encryption of communications between a client and server, which protects against eavesdropping and tampering with and/or forging the contents of the communication. As compared to existing schemes, our proposed scheme provide security for website account and sensitive information.
Devising a Language Certificate for Primary School Teachers of English Elaboración de una certificación lingüística para profesores de inglés en la escuela primaria

This paper sets out to examine how the Common European Framework of Reference can be employed as a useful tool for the purpose of devising a language certificate meant to assess the competence needed for effective teaching at the primary school level. To this end, the B1 level descriptors of the CEF have been re-written so as to make them correspond, as closely as possible, to the abilities actually displayed in the context of primary language teaching and they have also been referred to three different contexts of use: classroom management, professional self-development and language awareness. A tentative draft of one of the parts of the certificate meant to assess oral comprehension will be shown as the final step of a process which started with the definition of the profile of the foreign language teacher.
Long Term Electrical Load Forecasting: An Empirical Study across Techniques and Domains

Objective: The rapid development of human population, buildings, industries and technology has caused electric consumption to grow rapidly. So it is crucial to have accurate load forecasts for resource planning, rate cases, designing rate structures, financial planning etc. Long-term load forecastingc fuzzy logic model; wavelet networks and genetic algorithm/programming are explained in detail. Findings: The results from the comparative study show that the ANN model is a superior technique for long term electric load forecasting due to its ability to give satisfactory results where the availability of past data is low. It also gives lower error than the other models discussed. Application/Improvement: The findings will help the researchers to go for hybrid models for improving the error in long term forecasting. It can be applied for finding out the appropriate methodologies of long term forecasting of cities, states, countries or islands with respect to different economic scenarios.
A Layered Iterative Load Balancing Algorithm for Distributed Virtual Environment: A Layered Iterative Load Balancing Algorithm for Distributed Virtual Environment

To support large-scale users to share a virtual environment,multi-server architecture is applied to the virtual environment system and each server handles a partition of the virtual environment.The unpredictable movements and interactions of avatars may cause some servers to be overloaded.Existing load balancing algorithms try to redistribute load among servers and incur too much overhead,which may reduce the interactivity of system. In this paper,a layered iterative dynamic load balancing algorithm is proposed.Setting the overloaded region as the center,the algorithm chooses a limited number of neighboring regions as the load adjusting goals and iteratively spreads the overload outward from the center.The load balancing state can be achieved through iterations.Based on the virtual environment wherein avatars are scattered under skewed and clustered distribution,the algorithm is built and comparison with the existing load balancing algorithms are made.The results show that this algorithm can adjust load efficiently and incur less overhead.
Performance of Combined Amplitude and Phase-Modulated Communication Systems

The performance of two types of digital phase-and amplitude-modulated systems is investigated for the high signal-to-noise ratio region. Approximate expressions for the probability of error and channel capacity of the more optimum of these two systems are compared with corresponding expressions for probability of error and channel capacity for a digital phase-modulated system. It is shown that the phase- and amplitude-modulated systems show a definite power advantage over the phase-only system when the information content per transmitted symbol must be greater than 3 bits. From a channel capacity standpoint, the phase- and amplitude-modulated systems make more efficient use of the channel for signal-to-noise ratios greater than 11 db. The more optimum of the two phase and amplitude systems has only a 3-db advantage over the less optimum and is considerably more difficult to instrument.
Knowledge Transfer Activation Analysis: Knowledge Trade Perspective

This article empirically analyzes a model of knowledge transfer activation based on trade in knowledge market perspective. We aim to provide insights into trade in the knowledge market through an empirical analysis. Using a research model with a theoretical basis on knowledge sharing culture and trade in knowledge market, we conducted an empirical study using data on enterprises' knowledge management systems (KMSs). The results revealed that 62.6% of the variance in knowledge transfer activation was explained by the items of knowledge organization, knowledge strategy, KMS, and knowledge reward through knowledge sharing culture and trade in knowledge market. This suggests that horizontal organization and a strategy that pursues an organization's objectives positively impact knowledge sharing culture, while knowledge technology (KMS) and reward positively affect the trade in knowledge market. We therefore conclude that trade in knowledge market is an important factor to activate knowledge transfer. This article contributes to lay the foundation for future work on empirical study of knowledge transfer based on trade in knowledge market.