Miracle-dz/UniFAR-FTUs huggingface dataset:
Principles of Cyber-Physical Systems

A cyber-physical system consists of a collection of computing devices communicating with one another and interacting with the physical world via sensors and actuators in a feedback loop. Increasingly, such systems are everywhere, from smart buildings to medical devices to automobiles. This textbook offers a rigorous and comprehensive introduction to the principles of design, specification, modeling, and analysis of cyber-physical systems. The book draws on a diverse set of subdisciplines, including model-based design, concurrency theory, distributed algorithms, formal methods of specification and verification, control theory, real-time systems, and hybrid systems, explaining the core ideas from each that are relevant to system design and analysis.The book explains how formal models provide mathematical abstractions to manage the complexity of a system design. It covers both synchronous and asynchronous models for concurrent computation, continuous-time models for dynamical systems, and hybrid systems for integrating discrete and continuous evolution. The role of correctness requirements in the design of reliable systems is illustrated with a range of specification formalisms and the associated techniques for formal verification. The topics include safety and liveness requirements, temporal logic, model checking, deductive verification, stability analysis of linear systems, and real-time scheduling algorithms. Principles of modeling, specification, and analysis are illustrated by constructing solutions to representative design problems from distributed algorithms, network protocols, control design, and robotics.This book provides the rapidly expanding field of cyber-physical systems with a long-needed foundational text by an established authority. It is suitable for classroom use or as a reference for professionals.
Testing in top-down program development

A program design methodology is presented that advocates the synthesis of tests hand-in-hand with the design at every stage of program development and uses them for early detection of design flaws. It involves formal specifications of abstract programs and abstract data refinement that appear in the design. The main findings are: (1) formalization facilitates black-box and design-based functional testing; (2) abstract data testing allows a more natural selection of tests than concrete data testing; (3) black-box testing leads to significant structural coverage testing; and (4) the method can be combined with formal verification. <<ETX>>
Pridobivanje in razvoj znanstvenega in strokovnega človeškega kapitala v zdravstveni negi: študija primera

The aim of this paper was to model and apply a theoretical concept of acquisition and evolution of scientific and technical human capital of health care workers (graduate nurses). The research, structured as pilot study was carried out in the public health care organization named Orthopaedic Hospital of Valdoltra, Slovenia. We examined the entire population of 29 registered nurses who answered a questionnaire in summer 2005. In order to test the theoretical model (i.e. assumed relations) the correlation analysis was applied. Results: Respondents perceive the process of education and learning as upgrading of basic scientific and expert knowledge, linking it with practice. Also, research indicates that permanent education and learning process enables up-to-down delegation of new duties/work obligation to health care workers, accelerating acquisition and development of new personal and organizational knowledge. Conclusions: The »activation« of health care workers in the process of permanent education and learning discloses a positive correlation between the process of permanent education and learning and the indicators of the successful acquisition and evolution of scientific and technical human capital. The research confirms public health care workers as permanent creators of intellectual capital.
An intelligent fault diagnosis framework dealing with arbitrary length inputs under different working conditions

As fault diagnosis for motor drive systems enters the era of big data, intelligent fault diagnosis methods exhibit excellent performance because of their learning capabilities. However, the existing methods are strict with the signal size, which reduces the performance of these methods for modern condition monitoring. Besides, most existing intelligent methods have a great limitation: the training data and testing data are under the same working conditions. To overcome these limitations, we propose a novel three-layer model inspired by a recurrent neural network (RNN) and transfer learning, which has the ability to process variable size sequences under different working conditions. In the first layer, the input unit is extended to ensure that there is adequate dimension to store the information of sequences. In the second layer, the main information of the whole sequence is stored, transmitted, transformed and output by gates. In the final layer, softmax is employed to classify the health conditions based on the output of the RNN with a long short-term memory cell. The classification loss based on the whole framework and the domain loss using kernel method are proposed to train the model. Furthermore, a bearing dataset is adopted to verify the effectiveness of the proposed method. The experimental results show that the proposed method can not only break the limitations of existing methods, but also achieve a superior performance compared with related methods.
I/O-Efficient Refinement of Triangulated Terrains

This project investigates the use of triangulations (TINs) to represent and compute on very large terrains in Geographic Information Systems (GIS). While grid-based terrains are highly researched, both in theory and in practice, the exploration of triangulated terrains has been mostly theoretical. We study the problem of simplifying a terrain into a TIN that approximates it within a specified error threshold. Our main contribution is an I/O-efficient terrain simplification algorithm that converts grids into TINs. We present an implementation of our algorithm and analyze its performance and scalability to large datasets. We port the algorithm into the open source GIS package GRASS for use and experimentation by the GIS community. This is the first grid simplification system that incorporates theoretical and practical applications on triangulated terrains while maintaining scalability. Finally, we show how to extend our algorithm to simplify arbitrary point-data, and explain how this will be useful for processing LIDAR data.
A System-Wide, Collaborative, Purposeful, and Sustainable Distributed Leadership Plan Utilizing Teacher Leaders to Facilitate Professional Learning Communities.

Through collaboration between one rural southeastern university and a local rural school system of high-poverty in southeast Georgia (a pseudonym, Justice County School System [JCSS]), a mixed method case study analysis was conducted to examine a system-wide professional learning initiative. The goal of this initiative was to provide professional learning that was collaborative, purposeful, and sustainable. The professional learning initiative is a semi-structured plan developed based on distributed leadership to share the responsibilities of administration by utilizing teacher leaders to facilitate system-wide professional learning. In addition, school personnel perceptions of their changes in professional practices as a result of this collaborative PLC work were explored to determine effectiveness of the professional learning communities. The outcome of this study resulted in the presentation of a replicable or modifiable plan that was formalized with evidence-based practices that could be disseminated to other districts and schools exploring similar professional learning opportunities. Georgia certification mandates require that districts and schools possess accountability measures that ensure the professional growth of all school personnel through PLCs. The mode in which JCSS approached professional learning could advance other professional learning initiatives or in many cases launch these initiatives. JCSS should serve as a model system with a proven record of using an innovative professional learning approach that distributed the responsibilities among both administrators and staff, specifically teacher leaders to effectively improve teachers’ classroom practices. Institutions of higher education and local school systems need to implement collaborative, purposeful and sustainable professional learning with fidelity by distributing leadership efforts. INTRODUCTION School leaders are continually charged with adhering to federal and state mandates to lead district and school improvement initiatives in an effort to improve teaching and learning. For reasons related to school improvement, school leaders are working diligently to identify sound professional learning to keep pace with these mandates. Embracing school improvement to achieve organizational change is a constant challenge for school leaders. These professional learning demands add to the current pressures of school leaders’ responsibilities when challenged with the overwhelming tasks required to maintain daily operations. As Walker
Condition Assessment of Aged Ester-Based Nanofluid Through Physicochemical and Spectroscopic Measurement

Vegetable oils (VOs) are based on natural ester oil that is considered as an alternative to mineral oil for transformer applications. The dispersion of nanofillers into the VO is expected to improve the physicochemical and electrical performances. Surface-modified insulating nanoparticle is dispersed into the VO to prepare the VO-based nanofluid (NF). An open-beaker oxidative aging study is carried out at 115 °C for VO and NF at three distinct aging durations such as 100, 300, and 500 h. Alterations in the molecular level of aged VO and NF are studied through the various spectroscopic and physicochemical analyses such as nuclear magnetic resonance, Fourier transform infrared, UV-vis absorbance, fluorescence, color, acid number, interfacial tension, and flash point. The fluorescence-based analysis is carried out for VO and NF at fresh and aged condition to monitor the comparative aging degradation. Oxidative aging of VO and NF results in the formation of flammable and harmful gases, which are analyzed using dissolved gas analysis technique. A comparative analysis of the electrical properties such as ac breakdown voltage, dielectric constant, and dielectric dissipation factor for fresh and aged sample of VO and NF is studied.
Online Computation of Up-To-Date Summaries in the Swiss Feed Database

This thesis develops the up-to-date summary, an automatic approach to aggregate histories of nutrient measurements in the Swiss Feed Database. Since measurements are taken irregularly and are sparse in the time, a simple aggregation over the entire history is not representative of the real world state. We fight this challenge by detecting trends in history of measurements with a set of data fitting functions: uniform fitting function, linear regression and kernel regression. The experimental evaluation proves the scalability of our approach to aggregate the measurements of good and bad quality data. Further, this thesis contributes to the development of the Feed Database with the integration of the up-to-date summaries into web application and with the import of the raw temporal data.
A Temperature-dependent Power-aware Partitioning for Three-dimensional Field Programmable Gate Arrays

In this era of deep sub-micron technologies for integrated circuits (ICs), applications demand aggressive decrement in the desired power budget, thus putting many challenges to the chip designers. Power becomes an ever-increasing concern due to the growing design complexity and the shrinking process technology. Designing for low power consumption demands power-efficient devices and good design practices to leverage the architectural features without compromising performance. It becomes necessary to understand the power consumption early in the design cycle to meet the desired power budget. Power estimation at an early stage of electronic design automation (EDA) flow is essential in order to handle the design issues much earlier. Three-dimensional (3D) field-programmable gate array (FPGA) is one of the promising technologies which can lead to the reduction in delay, area and power. On the other side, 3D integration suffers from heat dissipation between the layers due the power consumed by various resources. Thermal issues are cited as critical concern in 3D integration which results in degradation of device performance. There is an absolute necessity to develop algorithms and software tools to exploit the advantages of the third dimension, and to solve the complex tasks associated with them. This dissertation focuses on a temperature-dependent power-aware partitioning methodology for 3D FPGA based on the necessary metrics estimations. Following this, an application study for 3D FPGA is discussed as well by surveying a big data processing platform, features and recommendations. The main objective of the later discussion is to show the future prospects of 3D FPGA in real-time commercial world.
First experimental demonstration of GMTI improvement through antenna switching

Up to now, theoretical investigations have shown that switching between parts of the transmit and/or receive aperture in a pulse-to-pulse cycle improves the GMTI performance of a two-channel radar system. This paper intends to verify experimentally the GMTI capability of the switching concept for the first time. Accordingly, three different GMTI modes were implemented in the PAMIR system including two switching modes. An experiment was conducted using six cooperative vehicles following a predefined scenario to compare the GMTI performance and in particular the positioning accuracy of the different GMTI modes. The results of this experiment are presented in this paper.
Dark Count Resilient Time Estimators for Time-of-Flight PET

A better timing resolution for positron emission tomography (PET) detectors adds time-of-flight information to increase the image contrast. The timing resolution can be improved by timestamping multiple photons from a scintillation event and combining the information using the mean or the Gauss–Markov estimator. To circumvent the performance degradation incurred by these estimators at higher dark count rates (DCRs), two dark count resilient algorithms were studied independently in the literature: the filtered Gauss–Markov and the artificial neural networks. This paper presents parametric simulations that compare them under different DCR conditions. We studied two different types of scintillator-based digital silicon photomultipliers: a typical <inline-formula> <tex-math notation="LaTeX">$1\times 1\times 10$ </tex-math></inline-formula> mm<sup>3</sup> LYSO:Ce crystal paired with a 180 ps single photon timing resolution (SPTR) photodetector and a fast <inline-formula> <tex-math notation="LaTeX">$1\times 1\times 3$ </tex-math></inline-formula> mm<sup>3</sup> LYSO:Ce:rCa crystal paired with a 35 ps SPTR photodetector. For a single photon avalanche diode with DCR over 1 Hz/<inline-formula> <tex-math notation="LaTeX">$\mu {\mathrm{ m}}^{2}$ </tex-math></inline-formula>, simulations show a significant coincidence timing resolution (CTR) gain when using the filtered Gauss–Markov estimator, improving the CTR of the typical detector by 10% and the fast detector by 33% over the unfiltered Gauss–Markov estimator. In all evaluated cases, the filtered Gauss–Markov estimator yields the lowest CTR.
AC 2007-3069: ADAPTING A POST-SECONDARY STEM INSTRUCTIONAL MODEL TO K-5 MATHEMATICS INSTRUCTION

If Science, Technology, Engineering, and Mathematics (STEM) education is to be improved, knowledge about teaching and learning for STEM disciplines should be a fundamental consideration in curriculum design, beginning at the earliest grades. This study examines the efficacy of a mathematics project that adapts a post-secondary instructional model developed by Richard M. Felder and Elizabeth Brent to the lesson design of an elementary mathematics curriculum that includes materials for K-5 classrooms and for K-5 teacher development. The purpose of this paper is to describe how the Felder/Brent instructional model for post-secondary engineering students adapts to an elementary environment and to share preliminary findings in student mathematical achievement and program implementation.
DEVELOPMENT OF DIGITAL AND MIXED SIGNAL STANDARD CELLS FOR A 0.25µm CMOS PROCESS

The competitiveness of the semiconductor market makes the tradeoffs between performance, cost and time to market very crucial. The current scenario is such that the non-recurring expenses involved in the design process, like design and tooling costs, dwarf the actual cost of producing the chip; termed as the recurring expenses. Unfortunately, high non-recurring costs can make it impossible for many smaller companies to remain profitable. The standard cell library used in higher-level synthesis and placement steps is one of the significant cost incurring steps of the design flow. The proposed thesis develops a 32-cell library for the TSMC quarter micron process using a unique and cost effective design flow, where only non-commercial tools available in the open domain are used. The custom design flow used is discussed in detail. An overview of other similar CAD resources available in the open domain is also presented. Such a flow, although time intensive, is a cost effective alternative for smaller ventures.
Scaling Down Dimensions and Feature Extraction in Document Repository Classification

In this study a comprehensive evaluation of two supervised feature selection methods for dimensionality reduction is performed - Latent Semantic Indexing (LSI) and Principal Component Analysis (PCA). This is gauged against unsupervised techniques like fuzzy feature clustering using hard fuzzy C-means (FCM) . The main objective of the study is to estimate the relative efficiency of two supervised techniques against unsupervised fuzzy techniques while reducing the feature space. It is found that clustering using FCM leads to better accuracy in classifying documents in the face of evolutionary algorithms like LSI and PCA. Results show that the clustering of features improves the accuracy of document classification.
Forms with a Meaning. Exercise “4x4”, developing ways to let students experience the potentiality of design’s communicative feature

The aim of this paper is to present an exercise that the author developed for the students of Industrial Design courses in order to let them face the issue of the “meaning of forms”. The purpose of the exercise is to let students understand that, when they are designing a product, they end up giving it a form and this form will always communicate a meaning. This connection is not obvious in an education – such as that of the Faculty of Design at Politecnico di Milano - based on a functional approach where great attention is given to a user’s basic needs, technical and manufacturing aspects, thus the form results simply from meeting such requirements. What typically happens is that students are concentrated in responding to all the technical and usability requirements, and loose their attention on the formal aspects. In particular, they are not even aware that they are communicating a meaning with the form of their products. Thus the aim of the exercise is to introduce a different kind of learning experience into the classical design studio; this experience should let students understand that they have the chance and the responsibility of giving meaning to the products they design.
Emotion Detection in Textual Information by Semantic Role Labeling and Web Mining Techniques

Automatic emotion detection in textual information is critical for the development of intelligent interfaces in many interactive multimedia applications. In the literature, existing approaches based on keyword spotting or statistic natural language process techniques, have limited success rate in free text emotion sensing applications. In this paper, we describe a system, developed in the framework of the National ChiNan University and LORIA collaboration, that associates semantic labeling and web mining techniques, to detect several basic emotions. A common sense knowledgebase – ConceptNet – is also used in order to retrieve some additional contextual information that can be used to retrieve appropriate background images for the presentation. Our objective is to adapt a multimedia presentation by detecting emotions contained in the textual information.
Theoretical sensitivity of ERS-1 SAR backscatter over forest

A theoretical microwave backscatter model was used to simulate the sensitivity of ERS-1 SAR data over temperate forest to SAR system, forest stand, and environmental parameters. Of particular interest were the simulations of the temperature dependence of the backscattering coefficient under consideration of the partial freezing of the soil and plant water at temperatures below 0/spl deg/C and of leaf and needle biomass variations as they occur due to the seasonal development and as a consequence of stress and disease. <<ETX>>
Attack-Resistant Digital Reputation and Privacy Assessment in Social Media

Web 2.0 has been growing rapidly in the past decade, and leading to surging popularity of online social media. There are over 2.1 billion people that are using social media, which is 28% of the global population. Social media has become one of the most complex computing and communication systems in the planet. Social media attracts large amount of people to create, share and exchange information, interests, ideas, pictures, videos, and etc. in the virtual communities. In social media, people can interact with acquaintances and strangers, and thus privacy and security should be considered seriously. From the privacy perspective, one of the severe type of privacy breach is related to online social networks, such as Facebook, Linkedin, Google+, and Twitter. Online social network users are often not aware of the size and the nature of the audience viewing their profiles, and therefore they may reveal more information than what is appropriate to be viewed publicly. Due to the lack of privacy awareness, online social network users can suffer a number of privacy related threats. In this dissertation, a quantitative online social network privacy risk analysis framework – TAPE is proposed. Inspired by the reliability analysis of a wireless sensor network, the binary decision diagram tool is employed to calculate online social network privacy level. The privacy awareness and privacy trust metrics are proposed to evaluate online social network users’ intention of privacy protection. To our best knowledge, TAPE framework is the first work that take both privacy awareness and privacy trust into consideration. Based on the TAPE framework, we also propose an unfriending strategy in terms of privacy protection, which outperforms other existing unfriending strategies. The detail of this framework is introduced in Chapter 2. From the security perspective, online product/service review system is one of the most vulnerable systems in social media. Since there are enormous profits of online markets and the customers’ purchasing decision is relying on the product/service review, it is highly possible that firms and retailers at the online marketplace may create fake reviews to mislead customers. In this dissertation, a novel angle of fake review detection is introduced, which is called Equal Rating Opportunity (ERO) principle. Based on ERO principle, ERO analysis is proposed. ERO analysis can be implemented with limited cost. It is a new direction of fake review detection. Based on real data testing, ERO analysis is able detect new perspectives of fake review, which cannot be detected by other approaches, while giving a relatively low false alarm rate. The ERO principle and ERO analysis is presented in Chapter 3.