Miracle-dz/UniFAR-FTUs huggingface dataset:
The Use of CAS in the Simplification of Rational Expressions and Emerging Paper-and-Pencil Techniques.

n the last fe years, an area of research interest in mathematics ed cation has developed that deals ith the infl ence of CAS technology in st dents algebraic thin ing. homas, Monaghan, and Pierce 2004 , for e ample, have identified some cr cial estions hen considering the se of CAS in the learning of algebra o does the se of CAS infl ence st dent concept ali ation o does the ay st dents or on tas s by hand inform their or in a CAS environment and vice versa p. 166 . hese paramo nt estions and those arising from other recent st dies e.g., ieran Dri vers, 2006 itt ieran, 2009 G m n, ieran, Mart ne , 2010, 2011 have driven o r interest in this area. n partic lar, these st dies and others have s ggested the importance of the technical aspect in algebra learning in CAS environments. esearchers s ch as ieran and Dri vers 2006 have indicated that the se of CAS promotes concept al nderstanding if the technical aspect of algebra is ta en into acco nt these researchers have sho n specifically that technical and theoretical aspects of algebra co emerge in st dents thin ing. n this sense, and related ith the simplification of rational e pressions, G m n, ieran, and Mart ne 2010, 2011 have sho n the epistemic role of the se of CAS hen st dents confront their CAS or ith their paper and pencil or . hese st dies are related to the transformational activity of algebra ieran, 2004 a characteri ation of algebra in hich the importance of techni e ac ires relevance in the sense that, ithin transformational activity, concept al nderstanding can come ith techni e. G m n, ieran, and Mart ne 2010, 2011 have sho n that the se of CAS provo ed spontaneo s theoretical reflections in st dents, hich allo ed them to thin of ne techni es to simplify rational e pressions. he se of CAS promoted a change in the st dents techni e for simplifying rational e pressions hose denominator is a binomial from canceling literal components that ere repeated in both n merator and denominator to sing the polynomial division algorithm . his epistemic role played by the CAS occ rred in st dents hose initial techni e as cancelling literal components, b t for hom the notion of cancelling common factors and dividing polynomials as absent. ased on o r previo s st dies G m n, ieran, Mart ne , 2010, 2011 , one can therefore as the follo ing estion What is the role of CAS in st dents algebraic thin ing if they already have as initial techni es canceling literal components and the long division of polynomials for simplifying rational algebraic e pressions Does CAS promote other techni es and theories his paper ill deal ith this iss e.
Identification of the Electric Spark Electromagnetic Waveform Based on SVM

Electromagnetic wave of electrical spark is a potential cause to eletrical equipment failure. This research focused on identificating and comparative analyzing the different types of electromagnetic waveform generated by eletrical equipment failure based on SVM. After analyzing and extracting the features the electromagnetic waveform, a model was built to identificate the type of the elctromagnetic waveform. The collected standard electromagnetic waveforms were used as the imput of the train model and the model accuracy was improved by adjusting training parameters afer analyzing the results, When inputting an unknown type of electromagnetic waveform, SVM may predict the output of the network according to the recognition rule. Then the types of electromagnetic waveforms were identificated by using adjusted models. The result shows that the electromagnetic waveform can be effectively and feasibly identificated based on SVM, which provides a theoretical support on prediction method of gas explosion caused by electrical sparks. Keywords—SVM; features of electromagnetic waveform; waveform identification
Bioinformatics Strategies, Challenges, and Opportunities for Next Generation Sequencing-Based HLA Genotyping

The advent of next generation sequencing (NGS) has altered the face of genotyping the human leukocyte antigen (HLA) system in clinical, stem cell donor registry, and research contexts. NGS has led to a dramatically increased sequencing throughput at high accuracy, while being more time and cost efficient than precursor technologies. This has led to a broader and deeper profiling of the key genes in the human immunogenetic make-up. The rapid evolution of sequencing technologies is evidenced by the development of varied short-read sequencing platforms with differing read lengths and sequencing capacities to long-read sequencing platforms capable of profiling full genes without fragmentation. Concomitantly, there has been development of a diverse set of computational analyses and software tools developed to deal with the various strengths and limitations of the sequencing data generated by the different sequencing platforms. This review surveys the different modalities involved in generating NGS HLA profiling sequence data. It systematically describes various computational approaches that have been developed to achieve HLA genotyping to different degrees of resolution. At each stage, this review enumerates the drawbacks and advantages of each of the platforms and analysis approaches, thus providing a comprehensive picture of the current state of HLA genotyping technologies.
Research on Minimal Confusion Sets for Fine-Grained Visual Categorization

Fine-Grained Visual Categorization (FGVC) is challenging due to the large intra-class and small inter-class variance, and classes which are prone to be confused with one or more similar classes are called as confusing classes. Hierarchical visual data structures are widely used for image classification with a large number of classes so that the sophisticated classification problem can be processed from coarse to fine. However, hierarchical structures are accompanied with error propagation. In order to reduce the error propagation caused by the hierarchical structure, we propose the concept of minimal confusion set and its metrics. Minimal confusion set is the minimal set of classes that are highly confused with other classes in the set and much less confused with classes outside the set. Based on minimal confusion sets, a flat two-layer hierarchical structure can be formed, which reduces the error propagation. An effective mining method of minimal confusion set is also proposed, which can also be used for hierarchical learning. The mining method of minimal confusion set is based on confusion matrix, but it is different from the traditional methods using the confusion matrix obtained from test data. This method is independent on test data, and experiments on multiple datasets demonstrate the effectiveness of the method.
Multiscale Modeling of Dislocation-Based Crystal Plasticity within a Multiphysics Framework

Author(s): Huang, Yue | Advisor(s): Ghoniem, Nasr M | Abstract: Simulation-based design and design-by-analysis methods are important tools in the development of modern complex systems due to their impact on attaining shorter manufacturing and construction cycles and lower testing cost. The research in this thesis is devoted to the development of a multiphysics-multiscale FEM framework to provide precise analysis of complex energy conversion structural components with relatively high computational efficiency. The main focus is on applications where the incident heat flux on component surfaces is extreme. This motivation leads to four major contributions. Firstly, various widely-used multiphysics simulation strategies and algorithms are assessed, and recommendations on how to select a suitable multiphysics modeling strategy are made. Fully-coupled 3D CFD and heat transfer simulations are found to be necessary in forced-convection cooling in channels under single-sided heat load. Secondly, two multiscale methods for coupling heterogeneous constitutive models in coupled global-local domains are proposed for self-consistent structural analysis. The first is based on the Hu-Washizu variational principle that leads to accurate matching of all stress components across a global-local interface. This matching cannot be achieved by conventional sub-modeling methods. The second method couples the crystal plasticity framework with conventional continuum plasticity by matching the plastic slip at the coupling interface. These two methods have been shown to be accurate and numerically convergent. The superiority of the proposed approaches is demonstrated by comparison to three conventional sub-modeling methods from the literature. Thirdly, an advanced dislocation-based crystal plasticity model has been developed. The model is sensitive to the material microstructure, and can be readily incorporated into the multiscale framework. The model is validated by micro-indentation experiments, where the force-displacement curve, lattice rotations, and dislocation patterns obtained from experiment are quantitatively reproduced. The developed comprehensive multiphysics-multiscale modeling framework has been successfully implemented in the design of three real-life critical components for energy conversion in fusion energy power plants, demonstrating the practical feasibility of the framework.
Integrated fingerprint sensor and navigation device

PURPOSE: An integrated fingerprint sensor and a navigation device are provided to enable a user to operate the device through a nonconductive substrate by arranging the navigation device near the nonconductive substrate. CONSTITUTION: A nonconductive substrate(22) includes a first surface for a fingerprint and a second surface for applying a fingerprint sensing circuit(10). A navigation device is located near the nonconductive substrate and is operated by a user through the nonconductive device. The navigation device is electrically connected to the fingerprint sensing circuit. The fingerprint sensing circuit processes a signal received from the navigation device.
Research on spectral clustering infrared image segmentation algorithm based on improved sparse matrix

In the infrared image segmentation, spectral clustering needs to calculate the similarity matrix between pixel points, the amount of data is large and the calculation is time-consuming. To solve this problem, an improved spectral clustering infrared image segmentation algorithm based on improved sparse matrix is proposed. The algorithm combines the feature of the whole image with the relationship between pixels, and then convinces the network to extract the infrared image feature information through convolution, and uses the selected feature information to construct the sparse similarity matrix, and completes the segmentation by combining the spectral clustering method. Experimental results show that this algorithm can effectively reduce the computational complexity of spectral clustering and effectively improve the segmentation result of the target area of infrared images.
Segmented Animation, User-Control Strategy and Cognition

ABSTRACT The aim of this study was to investigate the effects of various user-control strategies of segmented animation in learning abstract contents or processes that are not naturally visual on the cognition of students. In particular, the study examined the effects of five different user-control strategies: linear user-control, random user-control, free user-control, program-control, and continuous user-control. The research design was quasi-experimental and the data obtained were statistically analysed using ANCOVA and ANOVA. The instruments involved were pre-test, post-test, and NASA-TLX cognitive load test. The sample size consisted of 265 semester-two students enrolled in the Diploma in Networking System. The results indicated significant differences in the post-test and cognitive load test outcomes. In conclusion, this study suggests that the use of user-control, either linear user-control or random user-control, were sufficient strategies for learning abstract contents in segmented animation form.
Analysis of BPMN collaboration diagrams using Petri nets

Business Process Modelling Notation (BPMN) is a popular process modelling notation. BPMN allows the user to model interactions between processes with so called collaboration diagrams (CD). However these BPMN collaboration diagrams do not have a formal deﬁnition of soundness. We investigate methods for checking the soundness of BPMN collaboration diagrams. We use Petri nets as a formal semantical framework for BPMN collaboration diagrams because soundness can be well deﬁned for Petri nets. In order to execute an analysis of a BPMN collaboration diagram using Peti nets, we use a case study. The case study contains a BPMN CD modelling the settlement process in the ﬁnancial sector. This collaboration diagram is considered not sound, since it contains a dead end and indeed the corresponding Petri net is not sound. Starting from this Petri net we identify the problem and we propose ﬁve different solutions. Each one improving on the previous one. So, although we do not give a generic way to guarantee the soundness of a collaboration diagram using Petri nets, we present an analysis of the collaboration diagram of the settlement process and propose a Petri net solution for the soundness problem in the settlement process.
Blood Pressure Assessment with Differential Pulse Transit Time and Deep Learning: A Proof of Concept

Background: Modern clinical environments are laden with technology devices continuously gathering physiological data from patients. This is especially true in critical care environments, where life-saving decisions may have to be made on the basis of signals from monitoring devices. Hemodynamic monitoring is essential in dialysis, surgery, and in critically ill patients. For the most severe patients, blood pressure is normally assessed through a catheter, which is an invasive procedure that may result in adverse effects. Blood pressure can also be monitored noninvasively through different methods and these data can be used for the continuous assessment of pressure using machine learning methods. Previous studies have found pulse transit time to be related to blood pressure. In this short paper, we propose to study the feasibility of implementing a data-driven model based on restricted Boltzmann machine artificial neural networks, delivering a first proof of concept for the validity and viability of a method for blood pressure prediction based on these models. Summary and Key Messages: For the most severe patients (e.g., dialysis, surgery, and the critically ill), blood pressure is normally assessed through invasive catheters. Alternatively, noninvasive methods have also been developed for its monitorization. Data obtained from noninvasive measurements can be used for the continuous assessment of pressure using machine learning methods. In this study, a restricted Boltzmann machine artificial neural network is used to present a first proof of concept for the validity and viability of a method for blood pressure prediction.
Simultaneous Three-Dimensional Mapping and Geolocation of Road Surface

This paper presents a technique for localizing road surface images acquired by a downward-facing monocular camera on a vehicle with sparse low-accuracy Global Positioning System (GPS) readings. Images are collected by reading vehicle speed through on-board diagnostics (OBD) such that distance between two neighboring images is constant. The images are then stitched to create the road surface of arbitrary length. Lastly, the three-dimensional (3-D) road surface is created and globally corrected by using the GPS and the elevation map as well as the Adaptive Extended Kalman Filter (AEKF). The advantage of this technique is the possible deployment of a sparse low-accuracy GPS due to the use of the adaptive version of Extended Kalman Filter (EKF). The proposed technique was used for localization of local roads and highways of 6.9 km total length in Blacksburg, VA. The results of the localization show the reconstructed 3-D differ from the satellite imagery data only by 7.97%.
Live Migration for OpenCL FPGA Accelerators

FPGAs are currently being deployed at a large scale across data-centres for various applications because of their performance and power benefits. In particular, cloud service operators are now offering FPGAs as a Service. However, to completely integrate FPGAs in a data-centre environment like standard software systems, support for fault tolerance and task migration is essential. In this paper, we propose a live migration technique for FPGA accelerators to provide support for fault tolerance, system maintenance, and resource management. Our technique allows migration of OpenCL accelerators not only within a single FPGA but also across FPGAs with zero downtime. It achieves this by overlapping the computation with datamovements transparently from the user for OpenCL kernels. Moreover, distributed check-pointing mechanisms can be employed to recover from unknown faults with minimal loss of completed work. Altogether it enables system updates such as changing the static FPGA configuration or upgrading the OS without an interruption of service.
Security and Privacy of Smart Cities: A Survey, Research Issues and Challenges

With recent advances of information and communication technology, smart city has been emerged as a new paradigm to dynamically optimize the resources in cities and provide better facilities and quality of life for the citizens. Smart cities involve a variety of components, including ubiquitous sensing devices, heterogeneous networks, large-scale databases, and powerful data centers to collect, transfer, store, and intelligently process real-time information. Smart cities can offer new applications and services for augmenting the daily life of citizens on making decisions, energy consumption, transportation, health-care, and education. Despite the potential vision of smart cities, security and privacy issues remain to be carefully addressed. This paper delineates a comprehensive survey of security and privacy issues of smart cities, and presents a basis for categorizing the present and future developments within this area. It also presents a thematic taxonomy of security and privacy issues of smart cities to highlight the security requirements for designing a secure smart city, identify the existing security and privacy solutions, and present open research issues and challenges of security and privacy in smart cities.
Biomechanical alterations of gait on overweight subjects

Introduction: Obesity is a major global public health issue and approximately half of the population is classified as overweight. Many studies have investigated gait modification in obese people; however, no research has been carried out in overweight subject ́s gait influence. Methods: The goal of this study was to identify biomechanical changes on gait in overweight subjects by the application of principal component analysis (PCA). Two groups of adults participated of this study: overweight (n = 9; 25.0 < Body Mass Index < 29.9 kg/m2), and normal‐weight (n = 15; 20.0 < Body Mass Index < 24.9 kg/m2. Three‐dimensional kinematics of the lower limb and ground reaction forces (GRF) were recorded during gait. PCA was performed on data. Results: PCA evidenced statistical differences at the first principal component (PC) of the sagittal ankle movement, anteroposterior and mediolateral GRF, as well as at the third PC of the sagittal hip movement and mediolateral GRF. Overweight subjects walked with reduced ankle plantarflexion at toe-off and reduced hip flexion at the heel strike and at mid swing, as well as a reduced hip extension during push off. Additionally, increased anteroposterior and mediolateral GRF on overweight subjects were observed. Conclusion: overweight individuals might adjust their gait characteristics in response to their increased weight to reduce overload lower limbs joints. These gait modifications pointed to a possible increase in the risk of musculoskeletal pathology in this population.
Detecting microcalcification clusters in digital mammograms: Study for inclusion into computer aided diagnostic prompting system

Detecting Microcalcification Clusters in Digital Mammograms: Study for Inclusion into Computer Aided Diagnostic Prompting System By Gopalkrishna Veni Emma Regentova, Ph.D., Examination Committee Chair Associate Professor of Electrical Engineering University of Nevada, Las Vegas Among signs of breast cancer encountered in digital mammograms radiologists point to microcalcification clusters (MCCs). Their detection is a challenging problem from both medical and image processing point of views. This work presents two concurrent methods for MCC detection, and studies their possible inclusion to a computer aided diagnostic prompting system. One considers Wavelet Domain Hidden Markov Tree (WHMT) for modeling microcalcification edges. The model is used for differentiation between MC and non-MC edges based on the weighted maximum likelihood (WML) values. The classification of objects is carried out using spatial filters. The second method employs SUSAN edge detector in the spatial domain for mammogram segmentation. Classification of objects as calcifications is carried out using another set of spatial filters and Feedforward Neural Network (NN). A same distance filter is employed in both methods to find true clusters. The analysis of two methods is performed on 54 image regions from the mammograms selected randomly from DDSM database, including benign and cancerous cases as well as cases which can be classified as “hard cases” from both radiologists and the computer perspectives. WHMT/WML is able to detect 98.15%
Machine learned boundary definitions for an expert's tracing assistant in image processing

OF DISSERTATION MACHINE LEARNED BOUNDARY DEFINITIONS FOR AN EXPERT'S TRACING ASSISTANT IN IMAGE PROCESSING Most image processing work addressing boundary definition tasks embeds the assumption that an edge in an image corresponds to the boundary of interest in the world. In straightforward imagery this is true, however it is not always the case. There are images in which edges are indistinct or obscure, and these images can only be segmented by a human expert. The work in this dissertation addresses the range of imagery between the two extremes of those straightforward images and those requiring human guidance to appropriately segment. By freeing systems of a priori edge definitions and building in a mechanism to learn the boundary definitions needed, systems can do better and be more broadly applicable. This dissertation presents the construction of such a boundary-learning system and demonstrates the validity of this premise on real data. A framework was created for the task in which expert-provided boundary exemplars are used to create training data, which in turn are used by a neural network to learn the task and replicate the expertÕs boundary tracing behavior. This is the framework for the ExpertÕs Tracing Assistant (ETA) system.
A video quality improvement technique for a fast combined watermark embedding method

: The use of watermarks to embed user information is expected to deter the unauthorized use of video content in VOD (Video On-Demand) streaming services. This requires that watermark embedding and video encoding for each video must be ﬁ nished so rapidly as to not create excessive VOD delay between the user ’ s video request and video download to the user. This paper describes a watermark embedding and encoding process for a fast combined watermark embedding method, and proposes a new encoding technique that minimizes video quality degradation.