Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Hierarchical Optimization for Humanoid Control

This thesis presents an online approach for controlling humanoid robots using hierarchical optimization. While our primary focus is to develop a fast and robust walking controller that is able to follow desired foot steps, full body manipulation capability is also achieved. The proposed hierarchical system consists of three levels: • a high level trajectory optimizer that generates nominal center of mass and swing foot trajectories, together with useful information such as a local value function approximation and a linear policy along the nominal trajectory; • a middle level receding-horizon controller that tracks the nominal plan and handles large disturbances by rapidly replanning for a short horizon; • a low level controller that computes joint level commands by solving full body inverse dynamics and kinematics using quadratic programming. Using just the high level and the low level controller, we achieved rough terrain walking and close to human walking speed and stride length in simulation. Walking and manipulation controllers were also developed for the Atlas robot based on the same architecture, and performed reliably during the DARPA Robotics Challenge. The full hierarchy with the middle level controller is implemented afterwards, and dynamic walking with strong perturbations is successfully demonstrated on the Atlas robot.
Design and Modeling of Specialized Architectures

Hardware acceleration in the form of customized datapath and control circuitry tuned to specific applications has gained popularity for its promise to utilize transistors more efficiently. However, architectural research in the area of specialization architectures is still in its preliminary stages. A major obstacle for such research has been the lack of an architecture-level infrastructure that analyzes and quantifies the benefits and trade-offs across different designs options. Existing accelerator design primarily relies on creating Register-Transfer Level (RTL) implementations, a tedious and time-consuming process, making early-stage, design space exploration for specialized architecture designs infeasible. This dissertation presents the case for early-stage, architecture-level design methodologies in specialized architecture design and modeling. Starting with workload characterization, the proposed ISA-independent workload characterization approach demonstrates its capability to capture application’s intrinsic characteristics without being biased due to micro-architecture and ISA artifacts. Moreover, to speed up the accelerator design process, this dissertation presents a new modeling methodology for quickly and accurately estimating accelerator power, performance, and area without RTL generation. Aladdin, as a working example of this methodology, is 100× faster than the existing RTL-based simulation, and yet maintains accuracy within 7% of RTL imple-
Fitness landscape-based analysis of nature-inspired algorithms

As the number of nature-inspired algorithms increases so does the need to characterise these algorithms. A rigorous process to characterise algorithms helps practitioners decide which algorithms may offer a good fit for their given problem. One approach is to relate the characteristics of a problem's associated fitness landscape with the performance of an algorithm. The aim of this thesis is to capitalise on the notion of fitness landscape characteristics as a technique for analysing algorithm performance, and to provide a novel algorithm- and problem-independent methodology that can be used to present the strengths and weaknesses of an algorithm. The methodology was tested by developing a portfolio of six nature-inspired algorithms commonly used to solve continuous optimisation problems. This portfolio includes the performance of these algorithms with parameters both “out of the box" and after they have been tuned using an automated tuning technique. Each of the algorithms shows a different “resilience" profile to the landscape characteristics, and responds differently to the tuning process. In order to provide a more practical way to utilise the portfolio an automated “ranking" methodology based on two machine learning techniques was developed. Using estimates of the fitness landscape characteristics on benchmark problems, the best algorithm to use is estimated, and compared with the actual performance of each algorithm. While results show that predicting algorithm performance is difficult, the results are promising, and show that this is an area worth exploring further. This methodology has significant advantages over the current practice of demonstrating novel algorithm performance on benchmark problems, most importantly offering a practical, generalised overview of the algorithm to a potential practitioner. Choosing to use a technique such as the one demonstrated here when presenting a novel algorithm could greatly ease the problem of algorithm selection.
Automated threshold detection for object segmentation in colour image

Object segmentation from single background colour image is important for motion analysis, object tracking, trajectory identification, and human gait analysis. It is a challenging job to extract an object from single background colour image because of the variations of colours and light intensity. Most common solution of the task is the uses of threshold strategy based on trial and error method. As the method is not automated, it is time consuming and sometimes a single threshold value does not work for a series of image frames of video data. In solving this issue, this paper presents an Automated Threshold Detection Algorithm, H(•). The algorithm is applied in segmenting human lower limbs from a series of image frames of human walking. The procedure starts with selection of optimal RGB channel. Then H(•) algorithm is applied for automated threshold detection to convert the image frames into grayscale image. In the next stage, Line Fill (LF) algorithm is applied for smoothing the edges of object and finally background is subtracted to extract the targeted object. Results of the applied procedure show that the algorithm is viable to extract object from single background color image and can be used in human gait analysis. © 2006-2016 Asian Research Publishing Network (ARPN).
Applications of Krylov Subspace and Balanced Truncation Model Order Reduction in Power Systems

Dynamic representations of power systems usually result in the order of hundreds or even thousands of buses. Therefore, reduction of these dynamic representations is convenient. Two applications of model order reduction in power systems are discussed in this thesis. First, Krylov subspace-based method is applied to the IEEE-123 Node Test Feeder in the context of distributionlevel power systems simulation. Second, a Balanced Truncation-based model reduction is implemented in the 3-Machine 9-Bus system for designing a power system controller in the context of generationand transmission-level power systems. First, for the IEEE-123 Node Test Feeder, a two-sided Arnoldi algorithm is proposed to compute the basis of the Krylov subspace-based model reduction. The two-sided Arnoldi algorithm was found to decrease the deviation between the reduced and full-order model. Second, for the 3-Machine 9-Bus, a linear quadratic regulator (LQR) controller is designed based on the reduced model. The selection method of Q and R matrices is adopted from [1]. The resulting controller is shown to damp the oscillations of the open-loop system.
ON USAGE OF MACHINE LEARNING FOR NATURAL LANGUAGE PROCESSING TASKS AS ILLUSTRATED BY EDUCATIONAL CONTENT MINING

In this paper, we review most popular approaches to a variety of natural language processing (NLP) tasks, primarily those, which involve machine learning: from classics to state-of-the-art technologies. Most modern approaches can be separated into three rough categories: ones based on distributional hypothesis, those extracting information from graph-like structures (such as ontologies) and the ones that look for lexico-syntactic patterns in text documents. We focus mainly on the former of the three. Before the analysis can even begin, one of the important steps in preparation stage of NLP is the task of representing words and documents as numeric vectors. There exists a variety of approaches from the most simplistic Bag-of-Words to sophisticated machine learning methods, such as word embedding. Today, in the task of information retrieval the best quality for both English and Russian languages is achieved by approaches based on word embedding algorithms, trained on carefully picked text corpora in conjunction with deep syntactic and semantic analysis using various deep neural networks. A big variety of different machine learning algorithms is being applied for NLP tasks such as Part-of-Speech-tagging, text summarization, named entity recognition, document classification, topic and relation extraction and natural language question answering. We also review possibilities of applying these approaches and methods to educational content analysis, and propose the novel approach to utilizing NLP and machine learning capabilities in analyzing and synthesizing educational content in a form of a decision support systems.
Simulated annealing with improved reheating and learning for the post enrolment course timetabling problem

Abstract In this paper, we utilise a two-stage approach for addressing the post enrolment course timetabling (PE-CTT) problem. We attempt to find a feasible solution in the first stage. The solution is further improved in terms of soft constraint violations in the second stage. We present an enhanced variant of the Simulated Annealing with Reheating (SAR) algorithm, which we term Simulated Annealing with Improved Reheating and Learning (SAIRL). We propose a reinforcement learning-based methodology to obtain a suitable neighbourhood structure for the search to operate effectively. We incorporate the average cost changes into the reheating temperature function. The proposed enhancements are tested on three widely studied benchmark data-sets. Our algorithm eliminates the need for tuning parameters in conventional SA as well as neighbourhood structure composition in SAR. The results are highly competitive with SAR and other state of the art methods. In addition, SAIRL is scalable when the runtime is extended. The algorithm achieves new best results for 6 instances and new mean results for 14 instances.
Data storage device

The invention discloses a data storage device, which comprises a plurality of data storages, an accommodating part and a circuit board arranged in the accommodating part, wherein the circuit board is provided with a plurality of rows of connector slots for connecting the data storages respectively; the connector slots in two adjacent rows are arranged in a relatively staggered manner; a plurality of partitions are arranged in the accommodating part for forming a plurality of mounting units corresponding to the plurality of rows of connector slots respectively; the two sides of each partition have a plurality of sliding rails which are arranged in a relatively staggered manner; and the pair of sliding rails corresponding to each mounting unit may be provided with a data storage. In the data storage device, as the sliding rails on the two sides of the partitions and the connector slots in two adjacent rows are arranged in a staggered manner, the thickness of the partitions is reduced and the available space of the data storage device can be saved.
Cross-Lingual Voice Conversion With Non-Parallel Data

In this project a Phonetic Posteriorgram (PPG) based Voice Conversion system is implemented. The main goal is to perform and evaluate conversions of singing voice. The cross-gender and cross-lingual scenarios are considered. Additionally, the use of spectral envelope based MFCC and pseudo-singing dataset for ASR training are proposed in order to improve the performance of the system in the singing context.
Performance Evaluation of Energy Detection in Spectrum Sensing on the Cognitive Radio Networks

Cognitive radio technology is a modernistic technology by which the idle licensed spectrum can be used by an unlicensed user which are called cognitive radio CR or secondary users. The CR technology was found to overcome the spectrum incompetence and inefficiency usage troubles. The major motivation of CR technology is current heavily underutilized of spectrum. Surly the primary user PU had all rights to use his spectrum band so CR mustn’t interfere it on that band. Here we have to discuss about energy detection which is one of spectrum sensing techniques in CR functions and how to obtain the best available spectrum, to detect the spectrum hole, to estimate the optimum threshold voltage that produce the minimum Probability of false alarm and finally to enhance energy detection sensing algorithm by using MATLAB simulation.
Sensorless Field Oriented Control of PMSM drives based on Full Order SMO

This document deals with a sensorless field oriented control (FOC) for permanent magnet synchronous motor (PMSM) drives by using a full order sliding mode observer (FOSMO). We have constructed a FO-SMO to estimate the position and velocity. The Lyapunov theorem is used to verify the FO-SMO stability. The experimental findings indicate the robustness of SM observer under parametric variations. Keywords— Digital signal processor, FO-SMO, PMSM, Sensorless, Sign function.
Bayesian Computation with Application to Spatial Models and Neuroimaging

Analysis of Neuroimaging data has experienced great strides over the last few decades. Two key aspects of Neuroimaging data are its high-dimensionality and complex spatiotemporal autocorrelation. Classical approaches are somewhat limited in dealing with these two issues, as a result, Bayesian approaches are being utilized more frequently due to their flexibility. Despite their flexibility, there are several challenges for Bayesian approaches with respect to the required computation. First, the need for an efficient posterior computation method is paramount. Second, even in conjugate models, statistical accuracy in Bayesian computation may be hard to achieve. Since accuracy is of primary concern when studying the human brain, a careful and innovative exploration of Bayesian models and computation is necessary. In this dissertation, we address some of these issues by looking at various Bayesian computational algorithms in terms of both accuracy and speed in the context of Neuroimaging data. The algorithms we study are the Hamiltonian Monte Carlo (HMC), Variational Bayes (VB), and integrated nested Laplace approximation (INLA) algorithms. HMC is a MCMC method that’s particularly powerful for sampling in high-dimensional space with highly correlated parameters. It’s robust and accurate, yet not as fast as some approximate Bayesian methods, for example, Variational Bayes (VB). However, since there is no theoretical guarantee that the resulting posterior derived from VB is accurate, its performance has to be analyzed on a case-by-case basis. INLA is another extremely fast method based on numerical integration with Laplace approximations but, like VB, there are no generally applicable theoretical guarantees of accuracy.
A Multi-level Fuzzy Evaluation Method for Smart Distribution Network Based on Entropy Weight

Smart distribution network is considered as the future trend of distribution network. In order to comprehensive evaluate smart distribution construction level and give guidance to the practice of smart distribution construction, a multi-level fuzzy evaluation method based on entropy weight is proposed. Firstly, focus on both the conventional characteristics of distribution network and new characteristics of smart distribution network such as self-healing and interaction, a multi-level evaluation index system which contains power supply capability, power quality, economy, reliability and interaction is established. Then, a combination weighting method based on Delphi method and entropy weight method is put forward, which take into account not only the importance of the evaluation index in the experts’ subjective view, but also the objective and different information from the index values. Thirdly, a multi-level evaluation method based on fuzzy theory is put forward. Lastly, an example is conducted based on the statistical data of some cites’ distribution network and the evaluation method is proved effective and rational.
Crafting Organizational Information Security Policies

An organizational information security policy (InfoSec policy) is a directiongiving instrument for information security within an organization that seeks to communicate an organization’s posture in protecting its information assets. Researchers and practitioners alike agree that an InfoSec policy has a foundational role in securing an organization’s information assets. In an era where information is a precious resource and information security breaches are ever more prevalent, developing such a policy has become even more crucial for organizations. The importance of an InfoSec policy has resulted in scholarly research on the policy’s contents and structure, and on the means to promote employee compliance to the set policies. In regards to policy development, research has privileged abstractions – abstract methods and procedures policy development should follow. By emphasizing such abstractions, research has paid less attention to how policies are crafted in practice. Therefore, the purpose of this dissertation, which consists of a compendium of articles, is to increase our understanding of the crafting of InfoSec policies. Theoretically, the dissertation draws on practice theory, which takes orderly social and materially mediated doings and sayings (“practices”) as an arena for studying organizational phenomena. Empirically, the dissertation includes three qualitative studies: two ethnographic studies on InfoSec policy crafting and one case study on the implications of the crafting to policy compliance. Empirical material includes participant and non-participant observation, documentary sources, and semistructured interviews. The dissertation contributes to the literature on information security management. The primary contribution of this dissertation is the conceptualization of InfoSec policy crafting as emerging in the lived contradictions between the international information security best practices and the local organizational practices. More broadly, the dissertation contributes to research on InfoSec policy development by positing that to understand policy crafting requires deep engagement with the actors who participate in the policy crafting and with the field where the policy is crafted. Further, the dissertation contributes to discussions on policy compliance by suggesting that compliance should be considered as partly emerging from and through the practices of the policy crafting and as relational to them. The potential for developing the policy as a joint engagement with different organizational members should not be underestimated. The argument developed in this dissertation is that both organizations and research should place more emphasis on the practical accomplishment of InfoSec policy crafting. InfoSec policy development is not about following a rote procedure, but is a practical, joined, and skilled accomplishment – a craft. Policy crafting influences what is included in and excluded from the policy and how the policy will be complied with.
An Analysis of Assessment of Financial Distress in Textile Sector of Pakistan (2012-2018)

As business expand in modern global economy, cutthroat competition also becomes a reality. It becomes more important for businesses not only to diversify but also to break through with their respective products on different markets through cost cutting techniques and technologies. For survival and carrying on business activities, an organization should have to manage their finances on priority matters that need utmost care and attention. Therefore, company’s financial health and position can be monitored through their financial statement. Broad based expansion in private sector loans during Jul-Feb FY2018 makes financing requirement increased for working capital due to increase in input prices of cotton and coal. Large amount of loans are a burden on textile firms. More use of loans may results into the problem of financial distress that may lead to future bankruptcy.The objective of the study is to analyze the financial distress prediction in textile sector of Pakistan during the period 2012 to 2018. Dependent variable is financial distress prediction and independent variables are WC/TD, RE/TA, EBT/Equity, CF/TD, S/TA, interest rates, exchange rates, cotton prices and export prices. Researcher took secondary data of 120 textile-listed firms for the period of seven years from 2012 to 2018 derived from annual reports.Regression analysis, ANOVA and correlation were performed to check whether independent variables have significant relation with financial distress prediction. Results revealed that WC/TD, interest rates, cotton prices and export prices were found significant.RE/TA, EBT/Equity, CF/TD, and S/TA and exchanges rates have negative and insignificant relation with financial distress prediction. Keywords: Financial distress, bankruptcy prediction, Fulmer model, Pakistan Stock Exchange, Financial ratios, Macroeconomic indicators DOI : 10.7176/EJBM/11-19-01 Publication date :July 31 st 2019
Online Business Sustainability and Challenges to Meet the Demands of Online Consumers

Digital entrepreneurship the trend of entrepreneurship, where entrepreneurs are able to sell their items, as the online space seems to be this unique entrepreneurial space for entrepreneurs. Common perceptions seem to be that very little goes into selling items online, where barriers are expected to be low, with low cost equipment and not much time needed. Even brick and mortar shops are selling items online to sustain their business in this ever growing business space. This research delves into what goes on behind the scene for these online entrepreneurs. It also strives to understand consumer behaviours, where it looks at the different platforms that consumers use most and why these platforms are more trusted. This ethnographic case study looks at the digital business from multiple different angles where consumers, entrepreneurs, instructors, along with an online paying platform staff were given open ended surveys, interviewed online and face to face. The purpose of this research is to evaluate the effort entrepreneurs put in, and how much they are getting back, evaluating consumer’s perspectives, and assessing whether entrepreneur practice in the online platform is relevant to what is needed by the consumers. The research implements a micro and macro analysis approach. The micro analysis evaluates different components from the two sets of participants, which are the online shoppers and the online entrepreneurs, with their own separate themes whereas the instructor’s answers and the third party customer service answers were used as a macro analysis. © 2019 Published by Future Academy www.FutureAcademy. org.UK