Miracle-dz/UniFAR-FTUs huggingface dataset:
An Information Theoretic Framework for Camera and Lidar Sensor Data Fusion and its Applications in Autonomous Navigation of Vehicles.

This thesis develops an information theoretic framework for multi-modal sensor data fusion for robust autonomous navigation of vehicles. In particular we focus on the registration of 3D lidar and camera data, which are commonly used perception sensors in mobile robotics. This thesis presents a framework that allows the fusion of the two modalities, and uses this fused information to enhance state-of-the-art registration algorithms used in robotics applications. It is important to note that the time-aligned discrete signals (3D points and their reflectivity from lidar, and pixel location and color from camera) are generated by sampling the same physical scene, but in a different manner. Thus, although these signals look quite different at a high level (2D image from a camera looks entirely different than a 3D point cloud of the same scene from a lidar), since they are generated from the same physical scene, they are statistically dependent upon each other at the signal level. This thesis exploits this statistical dependence in an information theoretic framework to solve some of the common problems encountered in autonomous navigation tasks such as sensor calibration, scan registration and place recognition. In a general sense we consider these perception sensors as a source of information (i.e., sensor data), and the statistical dependence of this information (obtained from different modalities) is used to solve problems related to multi-modal sensor data registration.
Risk management for sovereign financing within a debt sustainability framework

The mix of instruments used to finance a sovereign is a key determinant of debt sustainability through its effect on funding costs and risks. We extend standard debt sustainability analysis to incorporate debt-financing decisions in the presence of macroeconomic, financial, and fiscal risks. We optimize the maturity of debt instruments to trade off borrowing costs with refinancing risk. Risk is quantified with a coherent measure of tail risk of financing needs, conditional Flow-at-Risk. A constraint on the pace of reduction of debt stocks is also imposed, and we model the effect of debt stocks on the yield curve through endogenous risk and term premia. On a simulated economy, we show that the cost-risk and flow-stock trade-offs embedded in issuance decisions are key determinants of the evolution of debt dynamics and are economically significant. Comparing three alternative optimizing strategies and some simple fixed-issuance rules, we also draw lessons on when and why optimizing matters the most. This depends on the risk tolerance level, the size, cost, and maturity of legacy debt, and the sensitivity of interest rates to debt. Our model quantifies thresholds for the minimum level of refinancing risks and the maximum pace of debt reduction that a sovereign could reach given its economic fundamentals. Going beyond those thresholds is only feasible through adjustments of gross financing needs, and an extension of the baseline model identifies the hot spots for these adjustments, computing their minimum size and optimal timing. Our findings inform policy decisions concerning both official sector borrowing and public finance, with a focus not only on minimizing interest payments but also on managing refinancing risks and increasing debt dynamics.
Flight to Safety from European Stock Markets

This paper investigates flight-to-safety from stocks to bonds in seven European markets. We use quantile regressions to identify flight-to-safety episodes. The simple risk-return trade-off on the stock markets is negative which is caused by flight-to-safety episodes: During normal periods, the risk-return trade-off is positive and during flight-to-safety episodes it is negative. The effects of flight-to-safety episodes on the risk-return trade-off are qualitatively similar for own country flight-to-safety episodes, for flight from own country stock market to the US bond market, and for US flight-to-safety. The strength of the trade-off is strongest for own country flight-to-safety episodes. The risk-return trade-off is not significantly influenced by recession periods or the recent sovereign debt crisis. The main results hold for flight to gold instead of to bonds.
Introducing Data Structures for Big Data

The homogeneous data structure ‘train’ and the heterogeneous data structure ‘atrain’ are the fundamental, very powerful dynamic and flexible data structures, being the first data structures introduced exclusively for big data. Thus ‘Data Structures for Big Data’ is to be regarded as a new subject in Big Data Science, not just as a new topic, considering the explosive momentum of the big data. Based upon the notion of the big data structures train and atrain, the author introduces the useful data structures for the programmers working with big data which are: homogeneous stacks ‘train stack’ and ‘rT-coach stack’, heterogeneous stacks ‘atrain stack’ and ‘rA-coach stack’, homogeneous queues ‘train queue’ and ‘rT-coach queue’, heterogeneous queues ‘atrain queue’ and ‘rA-coach queue’, homogeneous binary trees ‘train binary tree’ and ‘rT-coach binary tree’, heterogeneous binary trees ‘atrain binary tree’ and ‘rA-coach binary tree’, homogeneous trees ‘train tree’ and ‘rT-coach tree’, heterogeneous trees ‘atrain tree’ and ‘rA-coach tree’, to enrich the subject ‘Data Structures for Big Data’ for big data science.
Speech Recognition Based on a Hybrid Model of Hidden Markov Models and the Genetic Algorithm Neural Network

A new method for speech recognition based on a hybrid model of hidden Markov models(HMM) and the genetic algorithm neural network(GA-BP) is presented.The HMM is employed to compute the Viterbi output score.Then the score is used as the input of the GA-BP network to acquire the classification information.Finally,the sampled data are trained and tested by the Matlab software.And the result of recognition is made by the recognition information.The recognition experiment shows that the model has higher performance than the hidden Markov model in speech recognition,because of the dynamic time series,the greatly strengthened modeling ability of HMM,and the greatly strengthened classification ability of the GA-BP network.
Introducing Active Index to the Microbloggers' Ranking

The number of registered and active users in microblogging is growing increasingly high at a staggering rate.At present,because of its locating of targeted users and the need of the website′s developing,monotonous index is widely used as the foundation of microbloggers′ ranking.However,this type of ranking is hard to reflect the microbloggers′ real activity in the service,since the differences between microblogging and other SNS(Social Networking Services). This paper focuses on two types of users′ distributional feature and message propagation in Sina microblogging and Twitter.The model of how to compute the active index of microbloggers is also given.According to the experiments and analyzes,active index is capable of reflecting the characteristics of active microbloggers,which is suitable for the measurement of active microbloggers′ ranking.
Automated code compliance checking in the construction domain using semantic natural language processing and logic-based reasoning

Construction projects must comply with various regulations. The manual process of checking the compliance with regulations is costly, time consuming, and error prone. With the advancement in computing technology, there have been many research efforts in automating the compliance checking process, and many software development efforts led by industry bodies/associations, software companies, and/or government organizations to develop automated compliance checking (ACC) systems. However, two main gaps in the existing ACC efforts are: (1) manual effort is needed for extracting requirements from regulatory documents and encoding these requirements in a computer-processable rule format; and (2) there is a lack of a semantic representation for supporting automated compliance reasoning that is non-proprietary, non-hidden, and userunderstandable and testable. To address these gaps, this thesis proposes a new ACC method that: (1) utilizes semantic natural language processing (NLP) techniques to automatically extract regulatory information from building codes and design information from building information models (BIMs); and (2) utilizes a semantic logic-based representation to represent and reason about the extracted regulatory information and design information for compliance checking. The proposed method is composed of four main models/algorithms that are combined in one computational framework: (1) semantic, rule-based algorithms that leverage NLP techniques to automatically extract regulatory information from building codes and represent the extracted information into semantic tuples, (2) semantic, rule-based algorithms that leverage NLP techniques to automatically transform the extracted regulatory information into logic rules to prepare for automated reasoning, (3) semantic, rule-based information extraction and information transformation algorithms to automatically extract design information from BIMs and transform the extracted information into logic facts to prepare for automated reasoning, and (4) a logic-based information representation and compliance reasoning schema to represent regulatory and design information for enabling the automated compliance reasoning process. To test the proposed method, a building information model test case was developed based on the Duplex Apartment Project from buildingSMARTalliance of the National Institute of Building Sciences. The test case was checked for compliance with a randomly selected chapter, Chapter 19, of the International Building Code 2009. Comparing to a manually developed gold standard, 87.6% precision and 98.7% recall in noncompliance detection were achieved, on the testing data.
Paths of economic development: A global evidence for the mediating role of human capital

ABSTRACT This paper revisits the roles of institutions and human capital in the development process by using structural equation modeling with a latent construct. Two models are constructed by using the data of 143 countries with 14 publicly available indicators; non-mediated (Model A) and mediated one (Model B). A path between institutional quality and economic development is identified in Model A and found as significant. When human capital is added into the Model B as a mediator, the direct relationship between institutional quality and economic development which is confirmed in Model A becomes insignificant. This evidence contributes to the debate by explaining the roles of institutions and human capital in the development process, based on the existing level of institutional quality that determines conditions on decisions for starting or sustaining the development process. In other words, (a) improving institutions in addition to human capital is needed for the countries with low level of institutional quality to start development process and; (b) on the other hand, since the human capital develops immunity on the quality of the institutions, human capital plays a more basic role to sustain the development process for the countries with a high level of institutional quality.
Towards Effective Troubleshooting With Data Truncation

The process of fixing bug is bug triage or bug assortment. The aim of this, to correctly assign a developer to a new bug. Triaging these incoming reports manually is error-prone and time consuming.Software companies pay most of their cost in dealing with these bugs. For software repositories traditional software analysis is not completely suitable for the large-scale and complex data.To reduce time and cost of bug triaging,present an automatic approach to predict a developer with relevant experience to solve the new coming report. In proposed approach explain data reduction on bug data set which will reduce the scale of the data as well as increase the quality of the data.And also give domain specific bugs with their solution by developers. For implementing this use instance selection and feature selection for reducing bug of data. And Top-K pruning algorithms for tackling domain specific task
Classification of Microblog Sentiment Based on Nave Bayesian

Based on the twice sentiment feature extraction approach,this paper uses syntactic dependency as the first extraction method and semantic lexicon as the second.A sentiment classifier based on nave Bayesian is constructed in order to classify the inclination of emotions from the collected hot topic data in Chinese microblog and hotel remarks.The experiments mainly compare the classification performance of different combination groups including emoticons,punctuation, extraction methods based on semantic lexicon feature and those based on twice sentiment feature to find out better pretreatment methods for sentiment classification of microblog text. Besides,the experiments also compare and analyze the sentiment classification results between microblog text and hotel remarks to seek out the reasons for influencing the classification performance of microblog sentiment.The results indicate that the twice sentiment feature extraction gain the higher F1.And the performance of "emoticons + punctuation + twice sentiment feature extraction + BOOL" is the best pretreatment method.Meanwhile,it also shows the reason why the classifier based on nave Bayesian obtains higher classification performance in hotel remarks is probably that the topic in microblog is various.
Study of Partial shading Effects on Photovoltaic Arrays with Comprehensive Simulator for Global MPPT control

The partial shading is one of the major problems that exist in the photovoltaic farm installations. This is due to the presence of multi peaks in the power curves characteristics where the conventional methods of maximum power point tracking were not able to solve the problem. To overcome this shortcoming, in the present contribution we suggest a simulator based on a tracker algorithm which is able to track the global maximum GP in three cases , namely uniform irradiance ,sudden irradiance , and the partial shade conditions. The obtained results are compared to the data reported in the literature and showed good agreement. Thus, our simulator has been extended to include not only uniform irradiance case, but also the other mentioned cases.
TEMPERATURE MEASUREMENT OF LIQUID BASED ON SOFTWARE OF FLUENT

By comparative method of theory and simulation,the application of Fluent is introuduced,two examples simply is specialized.The software of Fluent is a simulated software of liquid engineering.In this two examples,the temperature of liquid was measured by Fluent.In the end,the theoretical results of the examples and the mathematic models of Matlab are compared with the simulation outcomes of the software.The accuracy of Fluent is tested.
Booker T. Washington in Biographical Perspective

IN the current vogue of black history Booker T. Washington has been a figure to ignore rather than to grapple with, an anomaly, an embarrassment. This is partly because his methods were too compromising and unheroic to win him a place in the black pantheon, but it is also because he was so complex and enigmatic that historians do not know what to make of him. We have lost the thread we used to believe would guide us through his labyrinth. When his rich private collection of papers was opened to scholars two decades ago, historians had to abandon the simpler picture of Washington presented in his autobiography. They generally seized upon the concept that Washington was a symbol of his age in race relations, a representative figure whose actions and philosophy were pragmatically adjusted to the demands of an era of sharply worsening race relations. He was the type of Negro leader that the age of Jim Crow would throw to the top. There is something to be said for this view, and certainly Washington was delicately attuned to his age. From the biographical perspective, however, Washington seems thoroughly consistent throughout a life that spanned from the slavery era into the twentieth century. In the period of his leadership after I895 he followed the lessons he had learned at Hampton Institute in the seventies and practiced at Tuskegee in the eighties. In his mature years Washington's life became extremely complex. There was first of all the public image, that of a race leader who told his people to accommodate themselves to the realities of white power, and whose own personal success illustrated that such a course could be personally rewarding. In the Atlanta Address in I895, the year the old militant leader Frederick Douglass died, Washington stated the formula: "In all things that are purely social we can be as separate as the fingers, yet one as the hand in all things essential to mutual progress. " Put down your buckets where they are, make peace and common cause with your white neighbor, seek a white patron, but also improve yourself slowly through education and property, through "severe and constant struggle rather than . artificial forcing. "1 A few years later Washington's success story, Up from Slavery, a worldwide best seller, further buttressed the accommodation
In-Memory Computing and Big Data Analysis with User-Defined Functions

Problems of data migrations from one data storage to another are always existed, and in era of big data, such problems becoming more crucial. When data started to grow, there is no chance of termination of such process, and in such situation, the important point is not only to just store the data, but also to process on the different levels of granularity. Current work is focused on today's situation of complex data analysis. It mainly concerning data exportation issues and challenges that must be faced by academia and industry in order to solve related problem. Publication sates problematic, provides review of related literature, proposes ideas of solution, that should be implemented, and provides methodology that need to be followed in order to conduct relevant, scientific value adding research.
Graphic Storytelling And Visual Narrative

Thank you for reading graphic storytelling and visual narrative. As you may know, people have search hundreds times for their chosen novels like this graphic storytelling and visual narrative, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they juggled with some malicious bugs inside their laptop. graphic storytelling and visual narrative is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the graphic storytelling and visual narrative is universally compatible with any devices to read.
Rotation invariant histogram features for object detection and tracking in aerial imagery

ROTATION INVARIANT HISTOGRAM FEATURES FOR OBJECT DETECTION AND TRACKING IN AERIAL IMAGERY Name: Mathew, Alex University of Dayton Advisor: Dr. Vijayan K. Asari Object detection and tracking in imagery captured by aerial systems are becoming increasingly important in computer vision research. In aerial imagery, objects can appear in any orientation, varying sizes and in different lighting conditions. Due to the exponential growth in sensor technology, increasing size and resolution of aerial imagery are becoming a challenge for real-time computation. A rotation invariant feature extraction technique for detecting and tracking objects in aerial imagery is presented in this dissertation. Rotation invariance in the feature representation is addressed by considering concentric circular regions centered at visually salient locations of the object. The intensity distribution characteristics of the object region are used to represent an object effectively. A set of intensity-based features is derived from intensity histograms of the circular regions and they are inherently rotation invariant. An integral histogram computation approach is used to compute these features efficiently. To improve the representational strength of the feature set for rotation and illumination-invariant object detection, a gradient-based feature set is derived from normalized gradient orientation histograms of concentric regions. Rotation invariance is achieved by considering the magnitude of the Discrete Fourier Transform (DFT) of the gradient orientation histograms. A iii novel computational framework called Integral DFT is presented for fast and efficient extraction of gradient-based features in large imagery. A part-based model, which relies on a representation of an object as an aggregation of significant parts, using the gradient-based features is also presented in this dissertation. Integrating the features of significant parts gives robustness to partial occlusions and slight deformations, thus leading to a better object representation. The effectiveness of the intensity-based feature is demonstrated in tracking objects in Wide Area Motion Imagery (WAMI) data. The object detection capability of the gradient-based feature extraction technique is evaluated on three different types of targets in low altitude aerial imagery. It is observed that the speed of computation is several times faster than state-of-the-art methods while maintaining comparable detection accuracies. Research work is continuing to automatically identify significant parts of an object to build the part-based model. Another direction of this research is to use the gradient-based rotation invariant features for scene matching.
Cloud Computing: Issues in Data Mobility and Security

Cloud Computing is a generic term for anything that involves delivering hosted services over the internet, based on a pay-as-you-go approach. Cloud Computing offers numerous benefits and, therefore, many large enterprises have embraced the cloud technologies and infrastructures. Vendors are also developing tools and applications to fulfill the demand and tap into the growing market. Like any new technology and paradigm, there are also numerous issues and concerns, including security and availability of data. This paper explores some of the security issues surrounding data location, mobility and availability as well as issues relating to the security of data at rest. The aim is to provide some useful background information for enterprises preparing to take advantage of Cloud Computing paradigm. Keywordscloud computing; enterprise computing; cloud security; data privacy; data security
Robust Perception of an Interaction Partner Using Depth Information

—Social interactive robots require sophisticated perception abilities to behave and interact in a natural way. The proper perception of their human interaction partners plays a crucial role. The reduction of the false positive rate for human detection is very important for increasing the natural interaction abilities. This paper presents a combined method using RGB data as well as depth information to ﬁnd humans in the robot’s surrounding. To track a person over time a Kalman ﬁlter is applied, which also reduces the processing time. Furthermore, a head pose estimation on the basis of Support Vector Machines is integrated, which can be used to perceive nonverbal expressive cues like nodding. The proposed method is tested in various experiments.