Miracle-dz/UniFAR-FTUs huggingface dataset:
Soft Computing Techniques Based Image Classification using Support Vector Machine Performance

In this paper we compare different kernel had been developed for support vector machine based time series classification. Despite the better presentation of Support Vector Machine (SVM) on many concrete classification problems, the algorithm is not directly applicable to multi-dimensional routes having different measurements. Training support vector machines (SVM) with indefinite kernels has just fascinated consideration in the machine learning public. This is moderately due to the fact that many similarity functions that arise in practice are not symmetric positive semidefinite. In this paper, by spreading the Gaussian RBF kernel by Gaussian elastic metric kernel. Gaussian elastic metric kernel is extended version of Gaussian RBF. The extended version divided in two ways-time wrap distance and its real penalty. Experimental results on 17 datasets, time series data sets show that, in terms of classification accuracy, SVM with Gaussian elastic metric kernel is much superior to other kernels, and the ultramodern similarity measure methods. In this paper we used the indefinite resemblance function or distance directly without any conversion, and, hence, it always treats both training and test examples consistently. Finally, it achieves the highest accuracy of Gaussian elastic metric kernel among all methods that train SVM with kernels i.e. positive semi-definite (PSD) and Non-PSD, with a statistically significant evidence while also retaining sparsity of the support vector set.
Non-parametric and semi-parametric methods for parsimonious statistical learning with complex data

Sayan Dasgupta: Non-parametric and semi-parametric methods for parsimonious statistical learning with complex data (Under the direction of Michael R. Kosorok) In clinical research, non-parametric and semi-parametric methods are increasingly gathering importance as statistical tools to infer on accumulated data. They require fewer assumptions and their applicability is much wider than the corresponding parametric methods. Being robust, these methods are seen by some statisticians as leaving less room for improper use and misunderstanding. In this dissertation we study some of these nonparametric and semiparametric methods in statistical learning and their applications to various areas of biomedical research. In the first part of our dissertation, we study the application of temporal process regression in the study of medical adherence. Adherence refers to the act of conforming to the recommendations made by the provider with respect to timing, dosage, and frequency of medication taking. Here we assess the effect of drug adherence in the study of viral resistance to antiviral therapy for chronic Hepatitis C. We use Temporal Process Regression (Fine, Yan, and Kosorok 2004) to model adherence as a longitudinal predictor of SVR. We show that adherence has a significant effect on SVR and this analysis can serve as an archetype for more statistically efficient analyses of medical adherence in studies where the common theme till now has been to report summary statistics. In the second part of the dissertation, we develop an approach for feature elimination in support vector machines, based on recursive elimination of features. We present
Simulated annealing driven pattern search algorithms for global optimization

This dissertation is concerned with the unconstrained global optimization of nonlinear problems. These problems are not easy to solve because of the multiplicity of local and global minima. In this dissertation, we first study the pattern search method for local optimization. We study the pattern search method numerically and provide a modification to it. In particular, we design a new pattern search method for local optimization. The new pattern search improves the efficiency and reliability of the original pattern search method. We then designed two simulated annealing algorithms for global optimization based on the basic features of pattern search. The new methods are therefore hybrid. The first hybrid method is the hybrid of simulated annealing and pattern search. This method is denoted by MSA. The second hybrid method is a combination of MSA and the multi-level single linkage method. This method is denoted by SAPS. The performance of MSA and SAPS are reported through extensive experiments on 50 test problems. Results indicate that the new hybrids are efficient and reliable.
Design and Development of a Supervisory Control and Data Acquisition (SCADA) Laboratory

Supervisory control and data acquisition (SCADA) is a prevalent control system architecture that is commonly used for industrial automation. The SCADA systems play an integral role in large-scale processes for interfacing with transducers and machinery for real time control and data acquisition. The increasing demand to integrate SCADA systems with remote networks and Internet of Things (IoT) technologies has raised concerns for information security specialists. These systems are thought to have notable security vulnerabilities and may be subject to an increasing number of cyber threats. In this paper/project, several students from Sam Houston State University design and deploy a SCADA laboratory to better understand these systems and the inherent security threats that go with them. The details including system infrastructure, challenges faced during the establishment of the laboratory, student and faculty involvement, laboratory course objectives, student assessments, and the industry support is covered in the paper.
Target detection based on classification in shadow region of hyperspectral image

In the field of hyperspectral image (HSI) processing, shadow regions in HSIs are often ignored or simply treated as a category because of their low reflectivity and complex information. There have been some studies on shadow regions of HSIs but there are still few effective algorithms to detect the real substances under the shadow regions. In view of this problem and the good performance of convolution neural network (CNN) in HSI classification and target detection, this paper improved target detection method in shadow regions in HSI which combines the CNN and the adaptive coherence/cosine estimator (ACE) of the spectral derivative image. This method includes three main steps: firstly, shadow region would been determined by CNN model whose main parameters have been adjusted to optimize the network performance; secondly, the derivative data of hyperspectral image would be obtained by deriving the shadow region of hyperspectral image; finally, due to the prominent performance of ACE algorithm in target detection of HSI, this algorithm could be applied to detect the substances contained in the shadow regions. To assess the performance of the proposed method, one widely used HSI dataset is used in the experiments. The numbers of experiment results show that the proposed method can detect the substances under the shadow regions in HSIs and it also has promising prospect in the field of HSI data processing.
Dissemination of Missing Data Techniques in Medical, Biomedical andSocial Research

Among all state-of-the-art techniques in the statistical methodology, perhaps one of most readily available topic to the researchers in medical, biomedical and social research is the missing data. This is partly due to its natural containment of not only the theoretical development but also the computational algorithms. The efforts for producing software for public use accompanying the underlying methodology are often much more involved then the development of the method itself. Then why would the methodologists assume the responsibility to achieve this often rewardless task? Aside from achieving professional reputation, the underlying motivation is dissemination. Dissemination efforts as true scientific novelty merely aims to contribute validity of the results as well as to improve methods underlying the implemented methodology.
Robust Hybrid Systems for Control, Learning, and Optimization in Networked Dynamical Systems

Author(s): POVEDA, JORGE IVAN | Advisor(s): Teel, Andrew R. | Abstract: The deployment of advanced real-time control and optimization strategies in socially-integratedengineering systems could significantly improve our quality of life whilecreating jobs and economic opportunity. However, in cyber-physical systems such assmart grids, transportation networks, healthcare, and robotic systems, there still existseveral challenges that prevent the implementation of intelligent control strategies.These challenges include the existence of limited communication networks, dynamicand stochastic environments, multiple decision makers interacting with the system,and complex hybrid dynamics emerging from the feedback interconnection of physicalprocesses and computational devices.In this dissertation, we study the problem of designing robust control and optimizationalgorithms for cyber-physical systems using the framework of hybrid dynamicalsystems. We propose different theoretical frameworks for the design and analysis offeedback mechanisms that optimize the performance of dynamical systems without requiringan explicit characterization of their mathematical model, i.e., in a model-freeway. The closed-loop system that emerges of the interconnection of the plant with thefeedback mechanism describes, in general, a set-valued hybrid dynamical system. Thesetypes of systems combine continuous-time and discrete-time dynamics, and they usuallylack the uniqueness of solutions property. The framework of set-valued hybriddynamical systems allows us to study many complex dynamical systems that emerge indifferent engineering applications, such as networked multi-agent systems with switching graphs, non-smooth mechanical systems, dynamic pricing mechanisms in transportationsystems, autonomous robots with logic-based controllers, etc. We proposea step-by-step approach to the design of different types of discrete-time, continuous-time,hybrid, and stochastic controllers for different types of applications, extendingand generalizing different results in the literature in the area of extremum seeking control,sampled-data extremization, robust synchronization, and stochastic learning innetworked systems. Our theoretical results are illustrated via different simulations andnumerical examples.
Implementing the IEC Common Information Model for Distribution System Operators

TAMPERE UNIVERSITY OF TECHNOLOGY Master's Degree Programme in Electical Engineering Risto-Matti Keski-Keturi: Implementing the IEC Common Information Model for Distribution System Operators Masters Thesis, 91 pages, 2 appendix pages November 2011 Subject: Electric Energy Technology Examiner: Pekka Verho
Lecture 7: Space Complexity

This week we will talk about space complexity. We started at poly-time (P) to investigate time complexity. Linear time (O(n)) is the smallest granularity we looked at which lets us read the whole input (we may talk about sublinear time later). However, space seems more robust than time. An input of size n can be read (and processed) in logarithmic space. For example, a TM can simulate a RAM model with pointers of size O(log n). So the starting point of space complexity is logarithmic instead of linear. We try to research on some more delicate structures inside P. Definition 1 (Space complexity for languages: SPACE(·)) A language L ⊂ {0, 1} * is in SPACE(S(n)) if there is a Turing machine M that accepts it. M has two tapes: • Input tape, length n, read-only. • Work tape, length S(n), read-write. and two halting states: q accept and q reject. Note the above TM doesn't have an output tape because it doesn't need it. Read Page 79 in the text [1] for more details. An example of a real-world computation device is a router. It processes information that is much larger than it stores in its local cache. Similar to time complexity, we have some interesting classes.
The Potential of Recall and Precision as Interface Design Parameters for Information Retrieval Systems Situated in Everyday Environments

this paper, we investigate ways for a tighter integration of IR and HCI in new urban contexts, as HCI expands its reach outside the workplace towards environments where efficiency and performance no longer constitute the backbone of interaction requirements. In particular, we propose to use Recall and Precision as design parameters to describe the information settings and performance of situated interfaces acting as retrieval systems in these environments. To explore this notion, we follow an inductive design research process by which different prototypes are designed, developed, and evaluated. Our experience shows that Recall and Precision, as design parameters, help to reflect the information requirements onto the interface design, and contribute to adapting IR to the contemporary challenges it faces, although more work is needed to consolidate its role vis-a-vis the growing ubiquity of computer technologies.
Ranking Domain Names Using Various Rating Methods

This paper deals with the ranking of domain names, which is considered important because it is associated with their selling price. For this purpose, four well-known rating methods were used, the Massey method, the Colley method, the Keener method and finally, a method based on finite Markov chains and therefore, called Markov method. Although Massey's, Colley's and Keener's methods have their origin in sports teams rating, they can be modified and successfully applied for domain names ranking. Our effort to correctly rank domain names is based on search volume of the keyword of each domain and therefore, we used Google trends. We have also considered other factors, such as Alexa rank and keyword popularity. Information was collected via Internet and implementation of the models took place using computing tools. Our study is directly related to the global online information and for this reason allows us to do a more sophisticated rating model.
A New Design of DC/DC Converter to Improve the Efficiency of Photovoltaic System

In a photovoltaic system, its power output is heavily reliant to the irradiance instantaneously and this make the efficiency of DC/DC converter using in the photovoltaic system reduces, especially during low irradiance period. This paper proposes a new DC/DC converter to improve the efficiency of a PV system. The main idea of the new DC/DC converter is developed a DC/DC converter with variable capacity to meet the variation in the output power of the photovoltaic system. The designed converter is combined from multi-small DC/DC converters to obtain a high enough capacity and these small DC/DC converters are able to switch on/off to change the percentage of power flowing through such as the power loss in small DC/DC converter is reduced. This designed converter is verified via experiment shoots. The results indicate that the efficiency of proposed converter is higher than that with a conventional DC/DC converter.
Real-Time Outlier (Anomaly) Detection Over Data Streams

Outlier detection has become an increasing challenging data-mining task in modern applications due to the fact that the data may come in the form of streams rather than batches. In this thesis, we will present two online algorithms to detect outliers over data streams, based on the sliding window approach, to address all the challenges when processing data over streams. Both algorithms keep statistical summaries of previous windowed data in memory that help with the decisions of future data and do not require a secondary memory. Proved by different datasets, our proposed methods can detect outliers accurately over data streams, where data distribution may change over time.
I An intrqduction to snapshot I algorithms in distributed computing

Recording on-the-fly global states of distributed executions is an important paradigm when one is interested in analysing, testing, or verifying properties associated with these executions. Since Chandy and Lamport's seminal paper on this topic, this problem is called the snapshot problem. Unfortunately, the lack of both a globally shared memory and a global clock in a distributed system, added to the fact that transfer delays in these systems are finite but unpredictable, makes this problem non-trivial. This paper first discusses issues which have to be addressed to compute distributed snapshots in a consistent way. Then several algorithms which determine on-the-fly such snapshots are presented for several types of networks (according to the properties of their communication channels, namely, FIFO, non-FIFO, and causal delivery). Columbus, OH 43210, USA
An Advanced Scheme to Support Stronger Security By Encrypting The File With Differential Privilege Keys

Developing Cloud is registering innovation, giving measured administration and cash putting something aside for individual Users and associations. Information proprietors are exchanging nearby delicate information to payable cloud . before Sending information to cloud it is scrambled for protection. Here presenting information seek over encoded information against plaintext keyword look. Existing work concentrated on single catchphrase inquiry or Boolean watchword look. Present works concentrated on multi keyword seek over scrambled information without protection a break is testing problem. In request to conquer this issue presenting secure k-nearest neighbour method for protection and Locality delicate hashing based secure record Algorithm which gives quick comparability look over cipher text information. In the long run these procedures are successful and effective regarding correspondence and calculation overhead.
Unobtrusive Measurement of Blood Pressure During Lifestyle Interventions

Hypertension is one of the most prevalent chronic diseases worldwide. Early diagnosis of this condition can prevent the incidence of stroke and also, cardiovascular diseases (CVDs) such as myocardial infarction and heart failure. Lifestyle interventions, such as intermittent fasting (IF), aim to lower blood pressure (BP) levels and increase the health of patients with cardiometabolic conditions. However, for monitoring BP, we still rely on a cuff that slows the flow of blood, which is both uncomfortable and makes continuous monitoring implausible. Recent research has shown that BP can be estimated using comfortable sensors such as the photoplethysmography (PPG) and the electrocardiography (ECG). Features that can be used for the estimation of BP are systolic upstroke time (SUT) and diastolic time (DT) extracted from the PPG signal, and pulse arrival and transit time (PAT/PTT) derived from the combination of ECG and PPG signals. In this paper we present: (1) a study design to collect continuous physiological signals, before and after a 7-days intermittent fasting (IF) intervention from both cardiometabolic and non-hypertensive patients using wearable devices and (2) initial results for predicting continuous blood pressure from the PPG and ECG signals using statistical and machine learning methods.
An Adaptive Controller for Very Flexible Aircraft

Recently, a high-order linearized generic Very Flexible Aircraft (VFA) model that captures both flight dynamics and body elastic effects has been released to the public domain. To solve the control challenge for such state inaccessible plant, a new output-feedback adaptive controller for a general multiple-input-multipleoutput (MIMO) plant is developed in this paper. The proposed controller is able to fly the VFA model under two types of model uncertainties, i.e. plant uncertainties and actuator anomalies, which commonly exist for a long endurance flight. The first step towards the solution is the development of a reduced-order model. Based on the model, a baseline linear LQG controller is applied for the flight under nominal conditions. Then an adaptive controller is designed and added to the baseline for the additional robustness against uncertainties. Simulation results show the adaptive controller provides improved transient response and tracking stability compared with the baseline controller when subject to a large magnitude of the uncertainty.
Migration Jobs in Cloud Computing

Cloud computing is the delivery of computing services over the internet. Cloud services allow individuals and other businesses organization to use data that are managed by third parties or another person at remote locations. In cloud computing, Process and virtual machine migration are two migration techniques. Process migration is a technique in which an active process is moved from one machine to another of possibly different architecture and Virtual Machine (VM) is an emulation of a particular computer system. Virtual machine migration is a useful tool for migrating OS instances across multiple physical machines. Both techniques are used to load balancing, fault management, lowlevel system maintenance, better communication and reduce energy consumption. This paper presents the various virtual machine and process migration techniques.
Short‐distance visible light communication with simple clock data recovery for audio applications

As light emitting diodes (LEDs) of fast response time are wide‐spreading in a variety of applications such as displays, lighting, traffic signals, and automobiles, many researches on transferring the data with the visible lights of LEDs have been conducted. This paper describes such a visible light communication system that supports the audio applications at the short distance up to 80 cm and the data bandwidth of 32 kHz. Especially, a simple clock data recovery (CDR) is used to achieve the optimum sampling and compensate for the frequency offset between transmitter and receiver. The proposed CDR obtains three samples per symbol and then adjusts the sampling point properly with the pattern of three samples over the frequency offset range of ±1.5% without changing the operating frequency of a receiver side. In the prototype system, a transmitter is composed of audio encoder, field programmable gate array, LED driver, and LEDs and a receiver consists of photodiode, comparator, field programmable gate array, and audio decoder. At the distance of 50 cm and the data rate of 32 kbps, the measured bit error rates are 0.101 and less than 10−8 without and with CDR, respectively.