Miracle-dz/UniFAR-FTUs huggingface dataset:
Test Specifications for Highly Automated Driving Functions: Highway Pilot

Based on approach and experiences in the ongoing project PEGASUS a concept for deriving test specifications for highly automated driving functions is presented. PEGASUS seeks to establish criteria, methods and supporting tools for the homologation of highly automated vehicles. A level-3-system (highway pilot) is used to focus the research and demonstrate its results. It is proposed to formulate the test specification in terms of a catalogue of logical (abstract), parameterized scenarios. The pass/fail criterion relies on annotating the scenarios by criticality levels which must not be exceeded by the automation function. The talk also discusses ways of systematically deriving the test specification and how to evaluate the tests.
Learner Corpus and Academic Writing: Identifying the Error Patterns of Taiwanese EFL Students

The purpose of this study is to find practical implications for improving academic writing curriculum design by exploring lexical and grammatical errors produced by EFL learners in a vocational institution in Taiwan. To satisfy this purpose, a small learner corpus was compiled in this study. The data were obtained from 58 undergraduate students of a public vocational university from September 2012 to June 2013 in Taiwan. There are 112 essays in the corpus that include 34,426 tokens. Learner errors were annotated based on the error categories found in (Dagneaux et al, 1996). Transfer errors and intralingual errors were analyzed. Lexical Tutor and AntConc software were used to conduct the analyses. Lexical errors, including incorrect word choices, misspellings, and word insertions, deletions or replacements occurred primarily due to students’ insufficient mastery of vocabulary. The top three grammatical errors were verb forms, article errors, and preposition errors. Based on the research results, pedagogical implications that focus on teaching EAP writing to vocational university students in Taiwan are discussed.
Exploring the Phenomenon of Secondary Teachers Integrating the LMS Canvas in a Blended-Learning Course

Online learning has become fully ingrained within the educational environment and extensive LMS use in higher education settings is challenging secondary education institutions to keep pace with the growing trend to offer LMS resources to their teachers and students; however, schools that have chosen to implement an LMS face multiple challenges in motivating teachers and students to accept and integrate the new technology into their course curriculum. The purpose of this phenomenological study was to investigate teachers’ experiences integrating the LMS Canvas within a blended-learning course in a rural high school district located in the Southeastern United States. The study integrated the theoretical frameworks of the unified theory of acceptance and use of technology (UTAUT) and technological pedagogical content knowledge (TPACK) and draws primarily from a postpositivism framework. The study sought to understand teachers’ motivational and attitude factors for integrating the LMS Canvas into their blended-learning course and involved: distributing a questionnaire for descriptive purposes, conducting individual and focus group interviews, and evaluating course materials. Qualitative data analysis was conducted using NVivo, and coding was utilized to develop an interpretation of the phenomenon. Based on data analysis, four themes developed: (1) motivation and attitude, (2) training and technology support, (3) teaching effectiveness, and (4) student benefits, which along with their related categories, supported the central research question and subsequent subresearch questions. In the final analysis process, in which the essence of the phenomenon is formulated, a central concept for why teachers use Canvas was reduced to adaptability.
Human motion modelling for simulation testing of GNSS equipment

Pedestrian motion-induced dynamics along the line-of-sight (LOS) between a GNSS receiver and a satellite, may disrupt the nominal operation of GNSS carrier-tracking loops, by introducing cycle slips and/or false frequency locks. In combination with other factors, e.g. multipath interference, weak signal conditions or limited availability of GNSS signals, the receiver could provide a degraded navigation solution or even lose signal lock. This thesis researches firstly how pedestrian motion affects the operation of carrier phase lock loops (PLLs), used by some GNSS receivers, and frequency lock loops (FLLs), used by all GNSS receivers; and secondly, what is the best way to model pedestrian motion in order to simulate the error effects of pedestrian motion-induced dynamics on a GNSS antenna, via a simulated GNSS carrier phase lock loop (PLL). The thesis reviews the relevant literature on human biomechanical modelling, path-finding and inertial/GNSS navigation, to design a custom pedestrian motion model (PMM). The PMM validation is supported by motion capture (MoCap) experiments using an inertial/GNSS sensor held by, or attached, on a pedestrian. The thesis also describes an implementation of simulated GNSS carrier-tracking loops (SGCTLs) in Matlab, to assess the effect of human MoCap profiles and synthetic human motion profiles (from the PMM) on the performance of the SGCTLs. The testing results suggest that GNSS antenna motion dynamics due to typical pedestrian motion can induce excessive cycle slips due to dynamics stress on the simulated PLL and FLL. Therefore, antenna dynamics should be considered when designing GNSS tracking loops and navigation algorithms for pedestrian applications to allow the GNSS receiver track human motion-induced dynamics effectively. The thesis concludes with carrier-tracking bandwidth recommendations for GNSS receiver design, based on the presented evidence. Under good signal conditions (above 40dB-Hz), the minimum recommended bandwidths for PLLs and FLLs are 15Hz and 5Hz, respectively, in order to respond effectively to the dynamic stress induced by typical pedestrian movements. Finally, the results indicate that the PMM can represent the LOS dynamics stress on the SGPLL within an acceptable tolerance. Future work encompasses the analysis of the pedestrian motion effects on real GNSS receivers.
Integration of EBSCO Discovery Service widget into the learning spaces of LMS: A case study of Wawasan Open University

Purpose Recent advancement in technological development has encouraged distance learning institutions to be more productive and creative in effectively utilizing the Learning Management System (LMS). Among the many measures employed is the integration of federated search engine into the LMS which allows for a more productive and wider scope of information retrieval through the provisions of library resources and services. The purpose of this paper is to report one such case study in Wawasan Open University exploring the integration of federated search engine (EBSCO Discovery Service (EDS) widget) into the learning spaces of LMS. Widgets resemble apps that enable the integration of EDS functionality in providing access for students to retrieve library learning resources from the convenience of the LMS, excluding the need to log onto the library. Design/methodology/approach This paper presents a discussion that highlights the development and conjectural implementation of a framework on the integration of the EDS widget into the University’s LMS. Data collection includes meta-analysis data from the micro- and macro-level infrastructure that make up the framework, namely, end-user layer, system layer and data management layer. Findings Findings from this study addressed significant importance to the library in promoting effective search and utilization of information needs. The findings will also make clear recommendations in developing effective collaborations between the library and faculties. Although the implementation of this framework is still in a developmental stage, this study still provides pertinent information in validating the integration of EDS into the University’s LMS. Research limitations/implications While serious limitations are not anticipated, possible concerns do exist with programming algorithms in the integration of EDS into the LMS. These challenges will be reported in the paper as reference for future replications of study Practical implications One key implication is the increase in the usage of the library resources and the potential to reach a larger audience of remote library users. Originality/value The primary advantage is to minimize the need for multiple gateway login while ensuring the library to monitor relevant library databases activities throughout the system check of the LMS.
Effects of selection schemes in genetic programming for time series prediction

The problem of time series prediction provides a practical benchmark for testing the performance of evolutionary algorithms. In this paper, we compare various selection methods for genetic programming, an evolutionary computation with variable-size tree representations, with application to time series data. Selection is an important operator that controls the dynamics of evolutionary computation. A number of selection operators have been so far proposed and tested in evolutionary algorithms with fixed-size chromosomes. However, the effect of selection schemes remains relatively unexplored in evolutionary algorithms with variable-size representations. We analyze the evolutionary dynamics of genetic programming by means of the selection to response and the selection differential proposed in the breeder genetic algorithm (BGA). The empirical analysis using the laser time-series data suggests that hard selection is more preferable than soft selection. This seems due to the lack of heritability in genetic programming.
Utility and profit maximization in dynamic spectrum allocation .

OF THE DISSERTATION Utility and Profit maximization in Dynamic Spectrum Allocation by Joydeep Acharya Dissertation Director: Prof. Roy D. Yates Demand driven, short term allocation of spectrum will be important for future wireless systems. Engineering and economics will jointly determine optimal ways to operate such systems. In this thesis, we characterize two operating principles of dynamic spectrum access: decentralized commons and centralized property right. In decentralized commons, co-located devices sense spectrum for vacant bands to transmit. Assuming an OFDM based physical layer, this means that a device can transmit in non contiguous tones. We analyze how symbol timing synchronization can be achieved using cyclic prefix based algorithms. For different spectral occupancies of the transmitter and fading conditions, we identify scenarios where synchronization algorithms yield satisfactory results and scenarios where they do not. For the centralized property rights regime, we develop a two tiered spectrum allocation model where spectrum is first allocated to service providers (SPs) by a broker and then to customers by SPs. First we assume that the users transmit to the SPs in the uplink after spectrum allocation, who maximize the sum utility of the users. We derive optimal allocation for different system parameters. We introduce a spectrum price and use it to demonstrate several key results about spectrum allocation. The spectrum price proves to be the regulatory mechanism that brings about coordination amongst the SPs with minimal control messaging. Our approach thus strikes a balance between a total and no central coordination.
Context Aware Recommender System Algorithms: State of the Art and Focus on Factorization Based Methods

Context Aware Recommender Systems (CARS) have become an important research area since its introduction in 2001 by (Herlocker and Konstan, 2001) and (Adomavicius and Tuzhilin, 2001). According to the classification of Adomavicius et al. (Adomavicius and Tuzhilin, 2011), there are three main categories of CARS algorithms: pre-filtering, post-filtering, and contextual modeling ones. Surprisingly, until the year of 2010, almost no CARS modeling algorithms were proposed, even though contextual modeling recommender systems can theoretically accept more dimensions as contextual variables (Karatzoglou et al., 2010). Starting from 2010, many contextual modeling CARS algorithms were proposed, most of them are built on factorization models. In this paper, we first present a state of the art of domain independent CARS algorithms listed following a chronological order. Then we study factorization models used for the Context Aware Recommendation task and suggest some possible research directions for developing more performing contextual modeling CARS algorithms.
Neural Networks: A Comprehensive Foundation

From the Publisher: This book represents the most comprehensive treatment available of neural networks from an engineering perspective. Thorough, well-organized, and completely up to date, it examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks. Written in a concise and fluid manner, by a foremost engineering textbook author, to make the material more accessible, this book is ideal for professional engineers and graduate students entering this exciting field. Computer experiments, problems, worked examples, a bibliography, photographs, and illustrations reinforce key concepts.
Design and Characterization of a Secure Automatic Dependent Surveillance-Broadcast Prototype

Abstract : During sensitive military operations, such as air operations in theater, the broadcasting of ADS-B messages poses a serious security concern, but the small message payload of ADS-B transmissions renders encryption standards such as AES unsuitable. Format-preserving encryption (FPE), a technique already in use on small message sizes such as credit card numbers, provides a suitable implementation of strong symmetric key encryption for the military context. This research proposes and details a Secure ADS-B (SADS-B) system which utilizes a bump-in-the-wire approach for encryption and decryption of ADS-B messages using FPE, going on to then characterize the prototype's performance using the following metrics: detection and error rates, operational latency, and utilization of resources on the underlying field programmable gate array (FPGA) hardware. Findings include sub-millisecond operational latency, full data rate throughput capability for ADS-B, and approximately 50% utilization of the available FPGA resources. Overall, findings suggest that a layered security system such as SADS-B is suitable for adding confidentiality to ADS-B.
Key Based Artificial Fingerprint Generation for Privacy Protection

With the widespread use of biometrics recognition systems, it is of paramount importance to protect the privacy of biometrics. In this paper, we propose to protect the fingerprint privacy by the artificial fingerprint, which is generated based on three pieces of information, i) the original minutiae positions; ii) the artificial fingerprint orientation; and iii) the artificial minutiae polarities. To make it real-look alike and diverse, we propose to generate the artificial fingerprint orientation by a model taking both the global and local fingerprint orientation into account. Its parameters can be easily guided by an user specific key with simple constraints. The artificial minutiae polarities are generated from the same key, where a block based and a function based approach are proposed for the minutiae polarities generation. These information are properly integrated to form a real-look alike artificial fingerprint. It is difficult for the attacker to distinguish such a fingerprint from the real fingerprints. If it is stolen, the complete fingerprint minutiae feature will not be compromised, and we can generate a different artificial fingerprint using another key. Experimental results show that the artificial fingerprint can be recognized accurately.
Control design implementation for Sawyer motors used in manufacturing systems

In this paper, hardware integration and control design for a dual-axis linear stepper (Sawyer) motor are addressed. In particular, the Sawyer motor used in the Yaskawa/MotoMan manufacturing system which is utilized in various applications such as assembly, packaging, sorting, and probing, is considered. These motors are equipped with four optical sensors with a position resolution of 0.25 microns. We develop a detailed model of the motor for control validation and provide a comparison of two control designs, namely, a PD (or PID) and a robust adaptive nonlinear controller. To achieve high performance, a number of practical issues (such as delay/latency, finite sampling time, sensor noise, commutation rate, etc.) need to be considered. Effects of these factors are outlined and experimentally demonstrated. Both the considered controllers utilize knowledge of motor position and velocity in all axes. Current measurements are not required. Either numerical differentiation or a dynamic observer can be used to construct the velocity signals from the measured position data. The designed nonlinear controller provides practical stabilization of position tracking errors and achieves better overall performance. Adaptations are utilized so that no knowledge of the electromechanical system parameters is required. The proposed nonlinear controller is robust to load torques, friction, cogging forces, and other disturbances satisfying certain bounds. Furthermore, the controller corrects for the unintended yaw and achieves synchrony of the motor and rotor teeth. We have also observed that if the rotational motion is not corrected for, the performance is very poor for both controllers. This is also true in the case of delay/latency and higher rates of commutation.
Utilizing educational technology in computer science and programming courses : theory and practice

Program visualization (PV) is potentially a useful method for teaching programming basics to novice programmers. However, there are very few studies on the effects of PV. We have developed a PV tool called ViLLE at the University of Turku. In this paper, multiple studies on the effects of the tool are presented. In addition, new qualitative data about students’ feedback of using the tool is presented. Both, the results of our studies and the feedback indicate that ViLLE can be used effectively in teaching basic programming concepts to novice programmers.
Extensive Write Caching

This article describes the effect of extensive write caching on file system read latencies for a Unix workload. Extensive write caching reduces the number of disk writes and minimizes disk read/write contention. A reduced disk read/write contention leads to better read response times. If disk writes are delayed longer, more data will be lost in case there is a system failure. To prevent this from happening, this article also describes a fault tolerant distributed cache for the Pegasus File Server to recover data that has not yet been writen to disk. New data is replicated to multiple independent nodes to protect it against failures, and parts of the system are powered by a UPS to protect data against power outages.
The Nurses’ Role in Providing Comfort During Childbirth Using Ambulation and Hydrotherapy

Providing care and comfort to women during childbirth is an important role. While medical interventions are available, all are associated with potential risks. Using nonpharmacologic means to support the normal process of birth, nurses may reduce the use of medical procedures and their potential complications. The purpose of this article is to discuss the use of two techniques that promote physiologic childbirth, ambulation and hydrotherapy, and the nurses’ role in providing comfort using these techniques in this important life event. Use of these techniques may delay or minimize the use of medical interventions while supporting the normal physiology of labor and birth.
The Contribution of Business Intelligence Use and Business Intelligence System Infrastructure Flexibility to Organizational Dynamic Capability.

There is growing use of business intelligence (BI) for better decision-making in many organizations. However, there is a lack of empirical studies on BI in academic research. This research investigates BI from an organizational dynamic capability perspective. Organizational dynamic capability is the ability to sense and respond to market opportunities and threats with speed, and BI can be of great use in that sensing role of organizational dynamic capability. Drawing on the dynamic capabilities framework, and literature business intelligence, and IS infrastructure flexibility, we hypothesize that BI use and IS (BIS for the purposes of this paper) infrastructure flexibility are major sources of organizational dynamic capability. A research model was developed to define the relationship of BI and BIS infrastructure flexibility with organizational dynamic capability. Survey data were collected and used to assess the model. The hypotheseses that BI and BIS infrastructure flexibility are significant sources of organizational dynamic capability are supported by the results. This research also empirically examines the weight and significance of BI in the business context of an organization. The results of the research also demonstrate that information technology and systems are of have strategic value for organizations, as they are significant sources of organizational dynamic capabilities and competitive advantages.
Video Object Segmentation

The detection and segmentation of foreground objects in videos is a fundamental problem in computer vision research and a key component for a wide array of applications. Ranging from higher-level vision problems such as semantic scene understanding and video summarization, to low-level video post-production and editing tools, video segmentation encompass the entire spectrum of video related tasks. This diverse set of applications yields different objectives and impose different requirements in terms of quality, efficiency, and manual effort necessary. This thesis investigates novel video object segmentation techniques, spanning different types of end applications. First, we study the problem of reducing or eliminating human effort to enable unsupervised segmentation of videos by proposing approaches to roughly estimate the primary or ”salient” object. Next, we explore methods that operate in a semi-automatic fashion, i.e. with minimal human supervision, and methods that enable user control and interaction. Finally, we introduce a new dataset and evaluation methodology to enable a deeper understanding of the results and to point towards promising avenue for future research. The first part of the thesis addresses the problem of discovering salient objects in still images. Motivated by psychological and neurobiological studies, we tackle the problem from complementary perspectives. On one hand, we combine, in a single-high dimensional Gaussian filtering framework, color contrast and spatial color distribution. On the other hand, we exploit spectral clustering properties to model common rules of photographic composition. These two approaches are orthogonal to each other and instrumental to unsupervised video object segmentation. The second part of the thesis explores semi-automatic video segmentation techniques with different type of annotations and therefore different levels of human supervision and suitable applications. We demonstrate robustness to challenging situations such as occlusions, by estimating the maximum-a-posteriory of a fully connected graphical model built over object proposals. We leverage the discriminative power of fully convolutional networks trained on static images and initialized with precise segmentation masks or bounding-boxes. The third part of the thesis is related to interactive segmentation techniques. In this domain, responsiveness is crucial to enable user interaction. Therefore we