Miracle-dz/UniFAR-FTUs huggingface dataset:
Constrained Estimation and Approximation Using Control, Optimization, and Spline Theory

Title of dissertation: Constrained Estimation and Approximation Using Control, Optimization, and Spline Theory Teresa M. Lebair, Doctor of Philosophy, 2016 Dissertation directed by: Professor Jinglai Shen Department of Mathematics and Statistics There has been an increasing interest in shape constrained estimation and approximation in the fields of applied mathematics and statistics. Applications from various areas of research such as biology, engineering, and economics have fueled this soaring attention. Due to the natural constrained optimization and optimal control formulations achieved by inequality constrained estimation problems, optimization and optimal control play an invaluable part in resolving computational and statistical performance matters in shape constrained estimation. Additionally, the favorable statistical, numerical, and analytical properties of spline functions grant splines an influential place in resolving these issues. Hence, the purpose of this research is to develop numerical and analytical techniques for general shape constrained estimation problems using optimization, optimal control, spline theory, and statistical tools. A number of topics in shape constrained estimation are examined. We first consider the computation and numerical analysis of smoothing splines subject to general dynamics and control constraints. Optimal control formulations and nonsmooth algorithms for computing such splines are established; we then verify the convergence of these algorithms. Second, we consider the asymptotic analysis of the nonparametric estimation of functions subject to general nonnegative derivative constraints in the supremum norm. A nonnegative derivative constrained B-spline estimator is proposed, and we demonstrate that this estimator achieves a critical uniform Lipschitz property. This property is then exploited to establish asymptotic bounds on the B-spline estimator bias, stochastic error, and risk in the supremum norm. Minimax lower bounds are then established for a variety of nonnegative derivative constrained function classes, using the same norm. For the first, second, and third order derivative constraints, these asymptotic lower bounds match the upper bounds on the constrained B-spline estimator risk, demonstrating that the nonnegative derivative constrained B-spline estimator performs optimally over suitable constrained Hölder classes, with respect to the supremum norm. Constrained Estimation and Approximation Using Control, Optimization, and Spline Theory
Usability Engineering als Ansatz zum Multimodalen Mensch-Maschine-Dialog

Multimodale Mensch-Maschine-Kommunikation soll die Benutzung von Software durch freie Verwendung von Kommunikationskanalen wie Sprache, Gestik etc. erleichtern. Diese Arbeit untersucht zuerst Einzelmodalitaten, um die Eigenschaften eines multimodalen Systems postulieren zu konnen. Gemas der Prinzipien des Usability Engineerings wird dann ein Prototyp aufgebaut, um Benutzertests durchzufuhren. Die Entwicklung dieses Systems, welches in einen Fahrsimulator integriert ist und die Bedienung von Komponenten wie Radio, Navigationssystem und Telefon ermoglicht, erfolgt zyklisch durch Tests und Verbesserungen. Die Resultate sind ein benutzeradaquates Design sowie Praxiserfahrungen mit den neuen Techniken.
Predicting Protein Secondary Structure Using Artificial Neural Networks and Information Theory

Large genome sequencing projects generate huge number of protein sequences in their primary structures that is difficult for conventional biological techniques to determine their corresponding 3D structures and then their functions. Protein secondary structure prediction is a prerequisite step in determining the 3D structure of a protein. In this thesis a method for prediction of protein secondary structure has been proposed and implemented together with other known accurate methods in this domain. The method has been discussed and presented in a comparative analysis progression to allow easy comparison and clear conclusions. A benchmark data set is exploited in training and testing the methods under the same hardware, platforms, and environments. The newly developed method utilizes the knowledge of the GORV information theory and the power of the neural network to classify a novel protein sequence in one of its three secondary structures classes. Nn-gorv-i is developed and implemented to predict proteins secondary structure using the biological information conserved in neighboring residues and related sequences. The method is further improved by a filtering mechanism for the searched sequences to its advanced version NN-GOR-V-II. The newly developed method is rigorously tested together with the other methods and observed reaches the above 80% level of accuracy. The accuracy and quality of prediction of the newly developed method is superior to all the six methods developed or examined in this research work or that reported in this domain. The Mathews Correlation Coefficients (MCC) proved that NN-GOR-V-IIsecondary structure predicted states are highly related to the observed secondary structure states. The NN-GOR-V-II method is further tested using five DSSP reduction schemes and found stable and reliable in its prediction ability. An additional blind test of sequences that have not been used in the training and testing procedures is conducted and the experimental results show that the NN-GOR-V-II prediction is of high accuracy, quality, and stability. The receiver operating characteristic (ROC) curve and the area under curve (AUC) are applied as novel procedures to assess a multi-class classifier with approximately 0.5 probability of one and only one class. The results of ROC and AUC prove that the NN-GOR-V-II successfully discriminates between two classes; coils and not-coils.
Simple Ad hoc Key Management (SAKM)

The Simple Ad hoc Key Management (SAKM) is a key management system that allows to the nodes of an ad hoc network to use asymmetric cryptography with zero configuration. It is intended to be applied to MANET routing protocols that provide security features that require the use of asymmetric cryptography (like SAODV).
Macro-texture perception through active and dynamic touch

The tactile mechanism of texture perception is getting well figured out, resulting in the emergence of texture rendering devices. It is worth noting that the textures in these studies have interior space gap in the millimeter level and below. This kind of textures is called “fine texture”. In contrast, the "Macro texture" has space gap in centimeters and above, and it is considered as the special texture with tiny texture pattern covered on curved surface. Compared with fine texture, macro texture contains richer surface information, including both roughness and curvature. This paper aims to study the mechanism of macro texture perception, and establish active and dynamic touch-based haptic perception models.
Cloud-Based Platforms and Infrastructures: Provisioning Physical and Virtual Networks

Cloud computing consists of three fundamental service models: infrastructure-as-a-service, platformas-a service and software-as-a-service. The technology “cloud computing” comprises four deployment models: public cloud, private cloud, hybrid cloud and community cloud. This chapter describes the six cloud service and deployment models, the association each of these services and models have with physical/virtual networks. Cloud service models are designed to power storage platforms, infrastructure solutions, provisioning and virtualization. Cloud computing services are developed to support shared network resources, provisioned between physical and virtual networks. These solutions are offered to organizations and consumers as utilities, to support dynamic, static, network and database provisioning processes. Vendors offer these resources to support day-to-day resource provisioning amid physical and virtual machines.
LETTERS AND CORRESPONDENCE

Letters and correspondence submitted for possible publication must be identified as such. Text length must not exceed 500 words and five bibliographic references. A single concise figure or table may be included if it is essential to support the communication. Letters not typed double-spaced will not be considered for publication. Letters not meeting these specifications will not be returned to authors. Letters to the Editor are utilized to communicate a single novel observation or finding. Correspondence is to be used to supplement or constructively comment on the contents of a publication in the journal and cannot exceed the restrictions for Letters to the Editor. The Editor reserves the right to shorten text, delete objectional comments, and make other changes to comply with the style of the journal. Permission for publication must be appended as a postscript. Submissions must be sent to Jay Umbreit, MD, PhD, Editor of Brief Reports/Letters to Editors, American Journal of Hematology, Winship Cancer Institute, Emory University, 1365-B Clifton Road, Suite B4100, Atlanta, GA 30322 to permit rapid consideration for publication.
Limited Preemptive Scheduling in Real-time Systems

Preemptive and non-preemptive scheduling paradigms typically introduce undesirable side effects when scheduling real-time tasks, mainly in the form of preemption overheads and blocking, that potentially compromise timeliness guarantees. The high preemption overheads in preemptive real-time scheduling may imply high resource utilization, often requiring significant over-provisioning, e.g., pessimistic Worst Case Execution Time (WCET) approximations. Non-preemptive scheduling, on the other hand, can be infeasible even for tasksets with very low utilization, due to the blocking on higher priority tasks, e.g., when one or more tasks have WCETs greater than the shortest deadline. Limited preemptive scheduling facilitates the reduction of both preemption related overheads as well as blocking by deferring preemptions to favorable locations in the task code.In this thesis, we investigate the feasibility of limited preemptive scheduling of real-time tasks on uniprocessor and multiprocessor platforms. We derive schedulability tests for global limited preemptive scheduling under both Earliest Deadline First (EDF) and Fixed Priority Scheduling (FPS) paradigms. The tests are derived in the context of two major mechanisms for enforcing limited preemptions, viz., defer preemption for a specified duration (i.e., Floating Non-Preemptive Regions) and defer preemption to the next specified location in the task code (i.e., Fixed Preemption Points). Moreover, two major preemption approaches are considered, viz., wait for the lowest priority job to become preemptable (i.e., a Lazy Preemption Approach (LPA)) and preempt the first executing lower priority job that becomes preemptable (i.e., an Eager Preemption Approach (EPA)). Evaluations using synthetically generated tasksets indicate that adopting an eager preemption approach is beneficial in terms of schedulability in the context of global FPS. Further evaluations simulating different global limited preemptive scheduling algorithms expose runtime anomalies with respect to the observed number of preemptions, indicating that limited preemptive scheduling may not necessarily reduce the number of preemptions in multiprocessor systems. We then theoretically quantify the sub-optimality (the worst-case performance) of limited preemptive scheduling on uniprocessor and multiprocessor platforms using resource augmentation, e.g., processor speed-up factors to achieve optimality. Finally, we propose a sensitivity analysis based methodology to control the preemptive behavior of real-time tasks using processor speed-up, in order to satisfy multiple preemption behavior related constraints. The results presented in this thesis facilitate the analysis of limited preemptively scheduled real-time tasks on uniprocessor and multiprocessor platforms.
Head Pose Estimation Based on Robust Convolutional Neural Network

Abstract Head pose estimation plays an important role in face recognition. However, it faces vast challenges on account of the initialization, facial feature points’ location accuracy and so on. Inspired by the observation that head pose angles change smoothly and continuously, we present a method based on a robust convolutional neural network for head pose estimation. The proposed network architecture consists of three levels and each level has three convolutional neural networks. The first level is a global one; it predicts the head pose quickly as a preliminary estimation. The following two levels are local ones; they refine the estimation achieved from the previous level step by step. Higher and higher resolution image with different input regions are taken as input in our network. At last, a multi-level regression is employed to combine the estimations from each level. The whole process is conducted in a cascade way to improve the head pose estimation performance directly with three angles together. We perform large experiments on nine challenging benchmark datasets. The experimental results demonstrate that our method performs better than the compared methods.
Gendered Expression Online: Exploring Gendered Communication on Facebook and in a Collaborative Editing Task

Gendered Expression Online: Exploring Gendered Communication on Facebook and Examining the Role of Anonymity in an Editing Task By Christina Shane-Simpson Advisor: Dr. Kristen Gillespie-Lynch College students are increasingly using digital media, such as social network sites (SNSs) and collaborative editing tools (Wikipedia), as identity exploration tools, aligning or distancing themselves from their offline selves through the online affordances of anonymity and agentic choice. The opportunities for gender fluidity available online (Armentor-Cota, 2011) provide college students with opportunities to experiment with and manipulate varied identities in a safe space where consequences of confronting identity norms may be less severe (Turkle, 1996; Shaw, 1997). Similarly, restrictive offline gender differences may diminish in online spaces, favoring a more flexible and androgynous enactment of gender (Martin, Cook, & Andrews, 2016) in certain online spaces. Even so, research has identified a significant gender gap in collaborative digital spaces such as Wikipedia (Glott, Schmidt, & Ghosh, 2010; Hill & Shaw, 2013; Lam et al., 2011; Pande, 2011). The current research examined identity choices and gendered communicative patterns online using a popular SNS, Facebook, and a simulated collaborative editing environment. Study one explored gender variations in communicative patterns on Facebook, while study two explored gender expressions in a public, collaborative editing task. Although the studies found specific gendered communicative patterns on both Facebook and Wikipedia, the majority of the online
A New Artificial Intelligence Method for Prediction of Diabetes Type2

Diabetes is a chronic illness without a conclusive cure, and is the most common cause of amputations, blindness, and chronic kidney failure, and an important risk factor in heart problems. The only hope for these patients is through proper care. The main difficulty, regarding this dangerous and destructive illness, is not detecting it in time, and generally, a weakness in detection. Hence, implementation of a method that can help in the detection of this illness is an important step toward the prevention and control of this illness, especially in the early stages. In this article, using adaptive neural fuzzy inference system (ANFIS), we have attempted to predict this illness. The speed and the validity of the suggested algorithm is more than the other smart methods used. The method proposed in this article, with a 10% validity increase during training and a 5% validity increase during experimentation has a better performance than previous smart methods
Muscle Confessions Of An Unlikely Bodybuilder

Thank you for downloading muscle confessions of an unlikely bodybuilder. Maybe you have knowledge that, people have look hundreds times for their favorite books like this muscle confessions of an unlikely bodybuilder, but end up in malicious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they cope with some infectious bugs inside their laptop. muscle confessions of an unlikely bodybuilder is available in our digital library an online access to it is set as public so you can get it instantly. Our book servers hosts in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the muscle confessions of an unlikely bodybuilder is universally compatible with any devices to read.
Innovation Journey – Navigating Unknown Waters

Product design and creation can been regarded as an ‘innovation journey’, biased by unforeseen setbacks along the road. In fact, real and sustainable innovation success should rather be viewed as 'by-products along the journey' than as end result. If one takes a closer look to the contingencies during such a journey, retrospective attributions of success to certain approaches or persons will prove to be misleading; such rash “attributions reinforced top managers’ belief that managing innovation is fundamentally a control problem when it should be viewed as one of orchestrating a highly complex, uncertain and probabilistic process
Peringkasan Sentimen Esktraktif di Twitter Menggunakan Hybrid TF-IDF dan Cosine Similarity

The using of Twitter by selebrities has become a new trend of impression management strategy . Mining public reaction in social media is a good strategy to obtain feedbacks, but extracting it are not trivial matter. Reads hundred of tweets while determine their sentiment polarity are time consuming . Extractive sentiment summarization machine are needed to address this issue. Previous research generally do not include sentiment information contained in a tweet as weight factor, as a results only general topics of discussion are extracted. This research aimed to do an extractive sentiment summarization on both positive and negative sentiment mentioning Indonesian selebrity, Agnes Monica , by combining SentiStrength, Hybrid TF-IDF, and Cosine Similarity. SentiStrength is used to obtain sentiment strength score and classify tweet as a positive, negative or neutral. The summarization of posisitve and negative sentiment can be done by rank tweets using Hybrid TF-IDF summarization and sentiment strength score as additional weight then removing similar tweet by using Cosine Similarity. The test results showed that the combination of SentiStrength, Hybrid TF-IDF, and Cosine Similarity perform better than using Hybrid TF-IDF only, given an average 60 % accuracy and 62% f-measure . This is due to the addition of sentiment score as a weight factor in sentiment summ­ari­zation.
Yaw Controller in Sliding Mode for Underwater Autonomous Vehicle

Autonomous Underwater Vehicles (AUV) have become a new tool for researchers for their applications in different research areas. The control system is responsible for ensure the autonomous behavior of this kind of vehicle. Currently, unconventional strategies are used to control AUV's, because of the advantages they offer. The present article presents the methodology design for the slider and the results it offers for the autonomous underwater vehicle HRC-AUV developed by the Robotics and Automation Group Perception (GARP). Control of direction in sliding mode is designed from the model 3 DOF nonlinear dynamics defining HRC-AUV in the horizontal plane. This design simulation is evaluated by using a solution to reduce the undesirable effect of the chattering.
The Car Plate Chinese Character Feature Extraction Based on Wavelet

The car plate recognition system is an indispensable part of intelligent traffic system. In car plate recognition system, one of the most difficult problems is the feature and recognition of car plate Chinese character. The multiresolution property of wavelet is applied to extract car plate Chinese character feature extraction, and a method that directly extracts feature from gray scale image is proposed. The method firstly extracts two kinds of features: one is wavelet moments in different multiresolution, and the other is the zoning density of wavelet-decomposed image. Then, two kinds of features are combined and a feature selection algorithm based recognition rate is presented to choose better features. Finally, the selected features are as the input of BP neural network, which is adopted to recognize car plate Chinese character. As a result, the proposed method avoids the binary operation used in some traditional Chinese character feature extractions that will seriously destroy the Chinese character structure. Furthermore, the extracted features can describe the local and global property of the character. Compared with some other feature extraction methods, the proposed method can achieve better recognition performance.
Graphical Simulation Algorithm of 3D TS'UN for Chinese Landscape Painting

With conclusion of shape and distribution characteristics of TS'UN texture strokes in Chinese Landscape Painting,a sketch-based fractal construction algorithm was proposed to build 3D rock model,and then the baseline of TS'UN texture strokes was generated based on the principle curvature direction of model.The brush model and stylized brush-style model were added to complete the rendering of 3D TS'UN.The results indicate that the algorithm realizes the 3D rendering of TS'UN effect in Chinese Landscape Painting successfully.
Matrix-based Filtering and Load-balancing Algorithm for Efficient Similarity Join Query Processing in Distributed Computing Environment

하둡 맵리듀스와 같은 분산 컴퓨팅 플랫폼이 개발됨에 따라, 기존 단일 컴퓨터 상에서 수행되는 질의 처 리 기법을 분산 컴퓨팅 환경에서 효율적으로 수행하는 것이 필요하다. 특히, 주어진 두 데이터 집합에서 유사도가 높은 모든 데이터 쌍을 탐색하는 유사 조인 질의를 분산 컴퓨팅 환경에서 수행하려는 연구가 있어 왔다. 그러나 분산 병렬 환경에서의 기존 유사 조인 질의처리 기법은 데이터 전송 비용만을 고려하기 때문 에 클러스터 간에 비균등 연산 부하 분산의 문제점이 존재한다. 본 논문에서는 분산 컴퓨팅 환경에서 효율 적인 유사 조인 처리를 위한 행렬 기반 부하 분산 알고리즘을 제안한다. 제안하는 알고리즘은 클러스터의 균등 부하 분산을 위해 행렬을 이용하여 예상되는 연산 부하를 측정하고 이에 따라 파티션을 생성한다. 아 울러, 클러스터에서 질의 처리에 사용되지 않는 데이터를 필터링함으로서 연산 부하를 감소시킨다. 마지막 으로 성능 평가를 통해 제안하는 알고리즘이 기존 기법에 비해 질의 처리 성능 측면에서 우수함을 보인다.
Modelling Wireless Propagation for Indoor Localization

This paper presents a ray-tracing technique to model the multi-path fading effect in indoor spaces. Random set P of points on all surfaces inside a given hypothetical indoor space are chosen. Each pi ∈ P is considered to be a point from which the transmitted signal reflects just before reaching the receiver. The received signal is the vector sum of various reflections that arrive at the receiver. The received signal strength (RSS) is then computed from the signal envelope. This technique provides RSS statistics that are similar to the models of signal propagation developed after extensive measurements in multi-path environments. In addition, this technique captures the spatial correlation of signal impairment. For example, path loss computed with this technique shows that co-moving receivers experience correlated signal fades while those moving in different spaces see un-correlated fading. The technique presented here is a low cost, first principle approach to simulate channel impairments due to multi-path effect and interference. It benefits any wireless simulation study that needs the signal-space mapping and context such as indoor localization. This randomized ray-tracing technique does not compete with or replace other, more accurate ray-tracing techniques that use either brute force or geometric optics to obtain site-specific signal-to-space mapping. Journal of Cyber Security, Vol. 4, 279–304. doi: 10.13052/jcsm2245-1439.443 c © 2016 River Publishers. All rights reserved.