Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Guided Project-Based Learning Activities on Students’ Attitudes Toward Statistics in an Introductory Statistics Course

EFFECTS OF GUIDED PROJECT-BASED LEARNING ACTIVITIES ON STUDENTS’ ATTITUDES TOWARD STATSITICS IN AN INTRODUCTORY STATISTICS COURSE Timothy Jonathan Bayer Old Dominion University, 2016 Director: Dr. John M. Ritz This research sought to determine if the use of a guided project-based learning instructional approach improved students’ attitudes and academic performance in a college-level introductory statistics course at a community college. It also sought to determine if the guided project-based approach improved attitudes and academic performance more than a traditional lecture-based instructional approach. The research used a quasi-experimental Pre-test, Post-test approach. The independent variable was either the use of a guided project-based learning instructional approach or the use of a traditional lecture-based instructional approach. The dependent variables were student attitudes and final course grades. Students’ attitudes were measured using the six components of the Survey of Attitudes Toward Statistics (SATS-36). The statistical analysis was conducted using a Multivariate Analysis of Variance (MANOVA) followed by an Analysis of Variance (ANOVA). Significant differences were found in each analysis. Guided project-based learning was shown to improve the components of affect and value in students’ attitude toward statistics and academic performance. Students in the guided project-based instructional group (N = 83) performed better academically than students in the traditional lecture-based group (N = 58).
Automatically identifying facet roles from comparative structures to support biomedical text summarization

 Abstract Introduction; Background  Introduction Research Design and Methods; Materials and Methods, Methods  Method Preliminary Results; Early Results; Results; Result  Result Conclusion; Summary  Conclusion Table 13: Mapping of section names in scholarly articles Although section name variants in Table 13 may not be exhaustive and may not include every variation under which a particular section name appears, this mapping strategy provided a relatively good coverage for a large number of articles in the TREC Genomics collection. Table 14 indicates the results of the mapping process on the three TREC 2006 Genomics
Study of Hip Exoskeleton Technology for Elderly Stability During Walking

Walking Assistive Exoskeletons (WAEs) are wearable devices that can allow individuals with mobility impairments to maintain their autonomy. The growing elderly population has benefited from these devices by receiving assistance at joints where their muscle function has declined. Typically, the primary objective of WAEs has been to reduce the metabolic cost of walking, allowing users to walk for extended periods. However, this strategy does not address the growing concern that seniors are at an increased risk of falling and sustaining severe injuries. Gait and balance disorders are among the most common causes of falls in the elderly. As people age, it is increasingly important to investigate the musculoskeletal changes contributing to frontal plane instability, as mediolateral and postero-lateral falls are highly correlated with severe injuries. Specifically, the hip abductor and adductor muscles are essential in maintaining balance in the frontal plane, yet minimal research has been conducted on the effect of a hip abduction-adduction exoskeleton on elderly stability. This paper outlines gait biomechanics, muscle contributions for mediolateral stability, changes associated with ageing, and strategies to improve balance during elderly gait. Subsequently, a comparison of existing hip WAEs highlights the feasibility of applying this technology to improve elderly gait stability.
EMG based interfaces for human robot interaction in structured and dynamic environments

Σε αυτή την διδακτορική διατριβή επικεντρωνόμαστε σε διεπαφές ηλεκτρομυογραφικών σημάτων οι οποίες μπορούν να χρησιμοποιηθούν για εφαρμογές αλληλεπίδρασης ανθρώπου ρομποτικών συστημάτων, τόσο σε δομημένα όσο και σε δυναμικά περιβάλλοντα. Αρχικά παρουσιάζουμε μια σειρά από προηγμένα σχήματα μηχανικής μάθησης για διεπαφές ηλεκτρομυογραφικών σημάτων, τα οποία συνδυάζουν έναν ταξινομητή με έναν παλινδρομητή, προκειμένου να κατακερματίσουν τον χώρο δράσης του ρομπότ, προσφέροντας καλύτερα αποτελέσματα αποκωδικοποίησης της ανθρώπινης κίνησης με μοντέλα εκπαιδευμένα για συγκεκριμένες διεργασίες. Όσον αφορά τις εφαρμογές αλληλεπίδρασης ανθρώπου ρομπότ, επικεντρωνόμαστε κυρίως στη έννοια και τις διαφορετικές χρήσεις του ανθρωπομορφισμού των ρομποτικών συστημάτων. Αρχικά διακρίνουμε τις διαφορετικές έννοιες του ανθρωπομορφισμού και εισάγουμε την έννοια του λειτουργικού ανθρωπομορφισμού για σχήματα αντιστοίχησης της ανθρώπινης κίνησης σε ανθρωπομορφική ρομποτική κίνηση, τηρώντας παράλληλα συγκεκριμένους περιορισμούς που θέτει ο χρήστης. Στην συνέχεια προτείνουμε μια ολοκληρωμένη μεθοδολογία για την ποσοτικοποίηση του ανθρωπομορφισμού των ρομποτικών χεριών, βασισμένη σε μεθόδους θεωρίας συνόλων και υπολογιστικής γεωμετρίας. Η συγκεκριμένη μεθοδολογία παρέχει ένα κατανοητό μετρικό του ανθρωπομορφισμού το οποίο κυμαίνεται από 0 (μη-ανθρωπομορφικά ρομποτικά συστήματα) σε 1 (ανθρωπομορφικά ρομποτικά συστήματα) και μπορεί να χρησιμοποιηθεί για διαφορετικά είδη ρομπότ. Τέλος, αναπτύσσουμε μια σειρά από ρομποτικά χέρια, ανοιχτού υλικού και κώδικα, τα οποία είναι ελαφριά, χαμηλού κόστους, εύκολα συναρμολογούμενα, υποϋπενεργούμενα και εγγενώς υποχωρητικά. Τα συγκεκριμένα χέρια μπορούν να χρησιμοποιηθούν τόσο για μελέτες ηλεκτρομυογραφικού ελέγχου (ακόμη και για οικονομικά μυοηλεκτρικά προσθετικά χέρια), όσο και για εφαρμογές αλληλεπίδρασης ανθρώπου ρομποτικών συστημάτων (για μελέτες τηλεχειρισμού ρομποτικών συστημάτων βραχίονα – χεριού), για την αρπαγή πληθώρας καθημερινών αντικειμένων σε δυναμικά περιβάλλοντα (ακόμη και υπό συνθήκες αβεβαιότητας σχετικά με τη θέση και το σχήμα των αντικειμένων). Προκειμένου να αποδείξουμε την αποδοτικότητα και λειτουργικότητα των προτεινόμενων μεθοδολογιών, εκτελέσαμε σειρά πειραμάτων με διαφορετικά ρομποτικά συστήματα, τόσο σε δυναμικά όσο και σε δομημένα περιβάλλοντα.
Description of flower colors for image based plant species classification

Apart from shape, color is the most visually prominent and perceivable feature of a flower. To use color as a feature for fine-grained plant species classification based on flower images, its descriptor has to be discriminative, compact, and robust against photometric variations. Therefore, we studied state-of-the-art color description methods and evaluated their discriminative power in an image classification pipeline. Experiments have been performed on three flower image datasets possessing large photometric and geometric varieties. We found that implicit photometric invariance by pooling 11 basic colors from patches around local features allows for robust color description outperforming explicitly photometric invariant descriptors in most cases.
Electrochemical biosensing based on graphene for detection of the SARS-Co V-2 Nucleocapsid Protein

Monitoring and controlling infection is required in order to prevent the progression of the coronavirus severe acute respiratory syndrome 2(SARS-Co- V-2). To accomplish this goal, the development and implementation of sensitive, quick and accurate diagnostic methods are essential. Electrochemical sensors have exposed large application possibilities in biological detection due to the advantages of high sensitivity, short time-consuming and specificity. Here, we report the improvement of a sensitive electrochemical sensor capable of detecting the presence of the SARS-CoV-2 virus using graphene-modified interdigitated working electrodes functionalized with antibodies targeting the SARS-CoV-2 nucleocapsid protein (N protein).
Ocean‐Atmosphere Trajectories of Extended Drought in Southwestern North America

Multiyear droughts are a common occurrence in southwestern North America (SWNA), but it is unclear what causes these persistent dry periods. The ocean‐atmosphere conditions coinciding with droughts have traditionally been studied using correlation and composite methods, which suggest that cool conditions in the tropical Pacific are associated with SWNA droughts and warm conditions are associated with wet periods in SWNA. Nevertheless, the extent to which multiyear droughts are truly consistent with this paradigm remains unknown. This is, in part, because the temporal trajectory of ocean‐atmosphere conditions during these dry periods have not been sufficiently characterized. Here we examine the continuum of ocean‐atmosphere trajectories before, during, and after multiyear droughts in SWNA using observation‐based data and an ensemble of climate model simulations from the Community Earth System Model. An examination of sea surface temperature patterns at the beginning, middle, and end of SWNA droughts shows that an El Niño event tends to precede SWNA droughts, a cool tropical Pacific occurs during droughts, and central Pacific El Niño events end droughts. However, moderate El Niño events can occur in the middle of persistent droughts, so a warm tropical Pacific does not always end these dry periods. These findings are important for drought predictability and emphasize the need to improve simulations of the magnitude, life cycle, and frequency of occurrence of El Niño events.
Paleoenvironmental shifts spanning the last ~6000 years and recent anthropogenic controls inferred from a high-altitude temperate lake: Anchar Lake, NW Himalaya

Integrating multiproxy results (geochemistry, mineral magnetism, grain size, and C/N ratio variability supported by 14C AMS dating), obtained from a 1.4-m sediment core retrieved from high-altitude Anchar Lake, Kashmir Valley, NW Himalaya, we present a 6000-years record of paleoenvironmental and paleolimnological shifts. Phase 1 (6000–4700 cal. yr BP) revealed a wetter climate with a significant terrestrial input corresponding to the gradual strengthening of the westerlies. Phase 2 (4700–3900 cal. yr BP) reflects an overall improved westerly precipitation and autochthonous sources of organic matter (OM). Magnetic parameters also indicate higher lake levels and reducing conditions during this phase. Phase 2 was followed by a gradual diminishing pattern of the westerlies as also represented by phase 3 (3900–2500 cal. yr BP) and phase 4 (2500–1600 cal. yr BP) indicating moderate precipitation conditions, catchment stability, and temperate and/or cold-dry climatic conditions. Phase 5 (1600–500 cal. yr BP) revealed the prevalence of moderately cold/dry and further subdued westerly precipitation. Phase 6 (500 cal. yr BP to present) is represented by reduced westerly precipitation, shrinking lake margins, and significant terrestrial/anthropogenic controls over the lake basin. Mineral magnetic parameters indicate reducing lake bottom water conditions and eutrophication during this phase due to anthropogenic activities. These paleoenvironmental shifts reveal near synchronous changes (within dating uncertainties) with other regional paleoclimate records close to the present Anchar Lake location and reflect the gradual late-Holocene diminishment of the amount of winter/early summer moisture provided by the mid-latitude westerlies.
Optimally selecting and combining assessment and assessor types for information retrieval evaluation

Current test collection construction methodologies for Information Retrieval evaluation generally rely on large numbers of document relevance assessments, obtained from experts at great cost. Recently, the use of inexpensive crowd workers has been proposed instead. However, while crowd workers are inexpensive, their assessments are also generally highly inaccurate, rendering their collective assessments far less useful than those obtained from experts in the traditional manner. Our thesis is that instead of using either experts or crowd workers, one can obtain the advantages of both—inexpensive and accurate assessments—by optimally combining them. Another related problem in Information Retrieval evaluation is asking right kind of question to the assessors for the collection relevance judgments. Traditional methods of collecting relevance judgments are based on collecting binary or graded nominal judgments, but such judgments are limited by factors such as inter-assessor disagreement and the arbitrariness of grades. Previous research has shown that it is easier for assessors to make pairwise preference judgments. However, unless the preferences collected are largely transitive, it is not clear how to combine them in order to obtain document relevance scores. Another difficulty is that the number of pairs that need to be assessed is quadratic in the number of documents. We show how to combine a linear number of pairwise preference judgments from multiple assessors to compute relevance scores for every document. We propose a general Bayesian framework for leveraging disparate categories of workers on the cost-accuracy scale, asking the right worker the right kind of question at the right time in order to obtain accurate assessments most costeffectively for test collection construction. Experiments with Mechanical Turks and expert assessors show promising results for our framework.
Machine Learning and Radio Planning in the Design and Optimization of Wireless Networks

This paper describes the incorporation of Machine Learning in the process of designing and optimization of a Long Term Evolution network in an urban parish of Quito city performed with the help of a radio planning tool. Potential subscribers behavior within this area was extracted from an online survey. Two Machine Learning methods were applied to this data: K-Nearest Neighbors and K-Means Clustering. The former was used to identified the dissatisfied users whilst the latter to classify network users into adequate categories that were included in the design. Details of the environment were also taken account, by adding several clutter maps that include geographical information, roads, buildings and traffic patterns with the aid of a Radio Planning software that provides the propagation models and the means required for the accurate modelling, and eventually the optimization of the deployed network.
PROTOTYPING MUSICAL EXPERIMENTS FOR TANGISENSE, A TANGIBLE AND TRACEABLE TABLE

We describe two musical experiments that are designed for the interaction with a new tangible interface named by us Tangisense, based on a set of antennas and RFIDs. These experiments (classification, game of) use different kind of time schedule, and are now simulated using Max-Msp and Java programs and common computer-human interfaces. They are developed in such a way that they can be ported on a specific tangible interface using RFID tags in its heart. Details about this portage are given. These experiments will in the future serve as user-centered applications of this interactive table, be it for musical practice or sonic interaction design.
Detection of random noise and anatomy of continuous seismic waveforms in dense array data near Anza California

We develop a methodology to separate continuous seismic waveforms into random noise (RN), not random noise (NRN) produced by earthquakes, wind, traffic and other sources of ground motions, and an undetermined mixture of signals. The analysis is applied to continuous records from a dense seismic array on the San Jacinto fault zone. To detect RN signals, we cut hourly waveforms into non-overlapping 1 s time windows and apply cross-correlations to separate RN candidates from outliers. The cross-correlation coefficients between different RN candidates fall into a tight range (i.e. 0.09–0.35), while cross-correlation coefficients of RN candidates with NRN signals (e.g. seismic or air-traffic events) are lower. The amplitude spectra of RN candidates have a well-defined level, while the amplitude spectra of other signals deviate from that level. Using these properties, we examine the amplitude spectra of moving time windows and cross-correlation coefficients with RN templates in each hour. The hourly RN is quasi-stationary and the results cluster tightly in the parameter space of cross-correlation coefficients and L2 norm deviations from the mean spectra of RN candidates. Time windows with parameters in this tight cluster are identified as RN, windows that deviate significantly from the RN cluster are identified as NRN and windows with values in between are identified as mixed signals. Several iterations on each hourly data are used to update and stabilize the selection of RN templates and mean noise spectra. For the days examined, the relative fractions of RN, NRN and mixed signals in local day (night) times are about 26 (42 per cent), 40 (33 per cent) and 34 per cent (25 per cent), respectively.
Continuous production of porous hollow fiber mixed matrix membranes for gas separation

v Table of contents . vi List of Tables . xi List of Figures . xii Abbreviations . xviii Symbols.xix Dedication . xxi Acknowledgments. xxii Forewords . xxiii General introduction . 1 Membrane technology . 1 Porous structure polymer membrane . 5 Cellular foam structure . 6 Stretching method . 10 Thesis objectives and research contributions . 11 1 Chapter 1. 15 1.
A Survey on Dynamic and Public Auditing with Fair Arbitration for Cloud Data

Cloud clients no more extended physically have their information, so how to guarantee the trustworthiness of their outsourced information turns into a testing assignment. As of late proposed plans, for example, "provable information ownership" and "verifications of retrievability" are intended to address this issue, however they are intended to review static document information and thusly absence of information elements bolster. Besides, danger models in these plans more often than not accept a fair information proprietor and concentrate on recognizing an untrustworthy cloud specialist organization in spite of the way that customers may likewise make trouble. This paper proposes an open evaluating plan with information progression support and decency mediation of potential debate. Specifically, we outline a list switcher to kill the constraint of list utilization in label calculation in current plots and accomplish proficient treatment of information progression. To address the decency issue so that no gathering can get out of hand without being distinguished, we additionally amplify existing danger models and receive signature trade thought to configuration reasonable mediation conventions, so that any conceivable question can be genuinely settled. The security examination demonstrates our plan is provably secure, and the execution assessment exhibits the overhead of information elements and question discretion are sensible.
Substorm‐Ring Current Coupling: A Comparison of Isolated and Compound Substorms

Substorms are a highly variable process, which can occur as an isolated event or as part of a sequence of multiple substorms (compound substorms). In this study we identify how the low‐energy population of the ring current and subsequent energization varies for isolated substorms compared to the first substorm of a compound event. Using observations of H+ and O+ ions (1 eV to 50 keV) from the Helium Oxygen Proton Electron instrument onboard Van Allen Probe A, we determine the energy content of the ring current in L‐MLT space. We observe that the ring current energy content is significantly enhanced during compound substorms as compared to isolated substorms by ∼20–30%. Furthermore, we observe a significantly larger magnitude of energization (by ∼40–50%) following the onset of compound substorms relative to isolated substorms. Analysis suggests that the differences predominantly arise due to a sustained enhancement in dayside driving associated with compound substorms compared to isolated substorms. The strong solar wind driving prior to onset results in important differences in the time history of the magnetosphere, generating significantly different ring current conditions and responses to substorms. The observations reveal information about the substorm injected population and the transport of the plasma in the inner magnetosphere.
Storage and flux of carbon in live trees, snags, and logs in the Chugach and Tongass national forests

Barrett, T.M. 2014. Storage and flux of carbon in live trees, snags, and logs in the Chugach and Tongass National Forests. Gen. Tech. Rep. PNW-GTR-889. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. 44 p. Carbon storage and flux estimates for the two national forests in Alaska are provided using inventory data from permanent plots established in 1995–2003 and remeasured in 2004–2010. Estimates of change are reported separately for growth, sapling recruitment, harvest, mortality, snag recruitment, salvage, snag falldown, and decay. Although overall aboveground carbon mass in live trees did not change in the Tongass National Forest, the Chugach National Forest showed a 4.5 percent increase. For the Tongass National Forest, results differed substantially for managed and unmanaged forest: managed lands had higher per-acre rates of sequestration through growth and recruitment, and carbon stores per acre that were higher for decomposing downed wood, and lower for live trees and snags. The species composition of carbon stores is changing on managed lands, with a carbon mass loss for yellow-cedar but increases for red alder and Sitka spruce. On unmanaged lands, the Chugach National forest had carbon mass increases in Sitka spruce and white spruce, and the Tongass National Forest had increases in western redcedar and red alder.
Farmer Friendly Smart App for Pomegranate Disease Identification

Pomegranate is a fruit with a good yield that grows in several Asian countries and is the most profitable one. However, due to a variety of factors, the plants become affected by a wide range of illnesses, resulting in the full destruction of the plant and a drastically reduced yield. Preventing decreases in agricultural production is possible with the early detection of plant diseases. Pomegranate leaf diseases are extremely tough to keep track on manually. As a result, pomegranate plant diseases are detected using Deep Learning (DL). Automating the disease detection system for pomegranates using leaf images is the goal of this study. Image gathering, processing, classification, and deployment are all part of the disease detection system process. Pomegranate leaf health and disease images are built using Mendeley data. The raw image is then processed further. Two DL models, AlexNet and VGG-16, are employed for classification. Accuracy and loss metrics are used to identify the optimal model. The metrics analysis shows that AlexNet is efficient in detecting leaf disease. A mobile app utilizing the AlexNet approach is then created to assist farmers in the detection of pomegranate disease without the assistance of specialists.
Electric buses’ sustainability effects, noise, energy use, and costs

Abstract Electric buses are growing in numbers in Sweden, which contributes to the development of a fossil fuel free society and a reduction of emissions. Earlier studies of bus systems have identified a need to further investigate societal costs, total cost of ownership, energy use on a yearly basis to account for seasonal variations, and noise during acceleration. Addressing those needs was the purpose of this study. Investigations were made in five cities in Sweden that have recently implemented different electric buses in their respective public transport system. Based on results from these investigations and earlier studies, new and developed models where designed and applied on electric buses on route 1 in Karlskrona, as a representative example. It was found that there were significant savings in societal costs and total cost of ownership when compared to diesel and biogas powered buses, mainly due to decreased noise, no emissions in the use phase, and decreased energy use.