Miracle-dz/UniFAR-FTUs huggingface dataset:
Fatigue Alters Landing Shock Attenuation During a Single-Leg Vertical Drop Jump

Background: Landings in fatigue conditions are considered to be one of the factors that cause noncontact anterior cruciate ligament (ACL) injury. Additionally, it is known that fatigue alters lower extremity landing strategies and decreases the ability to attenuate shock during landing. Purpose: To determine characteristics of knee kinematics and shock attenuation during the landing phase of a single-leg vertical drop jump in a fatigued condition. The hypothesis was that knee kinematics during the landing phase of a single-leg vertical drop jump would demonstrate a significant difference between before and after fatigue. Study Design: Controlled laboratory study. Methods: Thirty-four college females participated in this experiment. They were randomly assigned to either the fatigue (n = 17) or control group (n = 17). The fatigue group performed the single-leg vertical drop jump before and after the fatigue protocol, which was performed on a bike ergometer. Knee kinematics data were obtained from the 3-dimensional motion analysis system. The ratio of each variable (%) was calculated, comparing the pre- to postfatigue protocol. Unpaired t tests were used to compare changes in kinematic variables between the fatigue-induced group and control group. Results: Peak knee flexion angular velocity increased significantly in the fatigue group (106.1% ± 8.0%) in comparison with the control group (100.7% ± 6.6%) (P < .05). However, peak knee flexion angle and acceleration had no differences between each group. Peak knee adduction/abduction angle, velocity, and acceleration also had no differences between each group. Conclusion: Fatigue decreased the ability to attenuate shock by increasing angular velocity in the direction of knee flexion during single-leg drop jump landing. These findings indicate the need to evaluate the ability to attenuate shock by measuring knee flexion angular velocity when fatigue is considered. Clinical Relevance: Measuring knee angular velocity during landings might be an important evaluation parameter in the consideration of the knee injury prevention.
SABER OBJETIVADO E FORMAÇÃO DE PROFESSORES: REFLEXÕES PEDAGÓGICO-EPISTEMOLÓGICAS

RESUMO Este texto busca caracterizar saber objetivado tendo em vista a mobilização dessa noção no debate que trata da constituição dos saberes presentes no ensino e na formação de professores. Para tal, o estudo realiza um inventário sobre os modos diferentes de tratar os saberes presentes no ensino e na formação de professores, levando em consideração mapeamentos brasileiros recentes da pesquisa educacional sobre o tema. Na investigação dos saberes, dois tipos estão presentes: os saberes da ação e os saberes objetivados. Os primeiros constituindo paradigma dominante orientador das pesquisas sobre formação de professores, conforme indicação dos mapeamentos; e os saberes objetivados, sendo tratados por recentes estudos vindos da Universidade de Genebra, na Suíça, de modo a melhor caracterizar como historicamente são configurados os saberes presentes na formação de professores. Como conclusão, o texto argumenta sobre a importância da mobilização de bases teórico-metodológicas que tornem inteligíveis processos e dinâmicas de produção dos saberes profissionais dos professores, colocando no centro das discussões o saber, trazendo para o debate a epistemologia, tratada no campo da formação de professores, problematizando os saberes pedagógicos em termos de processos de objetivação, dando sentido às investigações que visam compreender como se constituem os saberes objetivados em sua articulação com os saberes da ação.
Learning contextual information for indoor semantic segmentation

Deep Convolutional Neural Networks(DCNNs) have recently shown great performance in many high-level vision tasks, such as image classification, object detection and more recently outdoor semantic segmentation. However, the convolutional layer only process the local regions in the image, ignoring the global context information. To overcome this poor localization property of Convolutional Neural Networks(CNNs), a new form of model that combine conditional random field(CRF) to CNNs is proposed. Hence, we train the CNNs to learn local pixel-wise information and then combine the CRF based on probabilistic graph model that are connected to global pixel. The experiment results on the public indoor NYUD v2 dataset demonstrate the proposed model outperform the existing state-of-the-art methods on a challenging 40 classes task, yielding a higher class average accuracy of 47.1% and pixel average accuracy of 66.4%.
The relationship between life satisfaction and academic achievement among trainee teachers / Azyyati Zakaria, Nurhazwani Abdul Halim and Noorshidah Mohamed Nordin

Life satisfaction is the way a person evaluates his or her life and how he or she feels about where it is going in the future. This study was conducted to investigate the level of high achievers’ life satisfaction, the domains of their life satisfaction and its relationship to their academic achievement. Based on the literature review, academic achievement is predicted to contribute significantly to high level of life satisfaction compared to other domains in life. Forty-five (45) high-achieving (those who obtained a CGPA of 3.50 to 3.99) students participated in this study. This study is adapted from a research of Diener et al. (1985) on Extended Satisfaction with Life Scale (ESWLS). Four domain factors, namely social, self, physical and family were studied. Both descriptive and inferential statistics were used in this study. The results showed that the high-achieving students showed a high level of life satisfaction in terms of family supports domains but moderate level of satisfaction on their appearance. Nevertheless, for various reasons, academic satisfaction is not a strong determinant of their life satisfaction.
VIBROTACTILE FEEDBACK FOR AN UNDERWATER TELEROBOT

As an alternative to force-feedback, vibrotactile feedback provides tactile information, with minimal cost and complexity. A prototype vibrotactile sensor and display was developed for a deep-sea manipulator. Vibrations were sensed with four strips of piezo-electric film embedded in a hard silicone rubber layer just beneath the jaw surface. An integrated vibration display was mounted onto the end of a modified master controller. The vibration display played the vibrations sensed at the jaw surface back to the human operator through two methods. The first shook a mass within the display providing a high frequency response. The second directly excited the fingertip pads through two paddles providing low frequency response. The system was tested underwater at the Monterey Bay Aquarium Research Institute (MBARI) both at sea while operators performed normal benthic tasks and in a test tank located onshore. For the tank, operators correctly identified the relative hardness of three blocks made of different materials -wood, aluminum and machine wax. For the first time MBARI operators were able to tactilely sense their deep-sea environment.
A Texture Based Approach to Reconstruction of Archaeological Finds

Reconstruction of archaeological finds from fragments, is a tedious task requiring many hours of work from the archaeologists and restoration personnel. In this paper we present a framework for the full reconstruction of the original objects using texture and surface design information on the sherd. The texture of a band outside the border of pieces is predicted by inpainting and texture synthesis methods. The confidence of this process is also defined. Feature values are derived from these original and predicted images of pieces. A combination of the feature and confidence values is used to generate an affinity measure of corresponding pieces. The optimization of total affinity gives the best assembly of the piece. Experimental results are presented on real and artificial data.
The Duties, Skills, and Knowledge of Software Architects

This paper focuses on the human aspects of architecting software-in particular, the duties, skills, and knowledge of software architects. We present the results of a survey of approximately 200 public sources of information aimed at professional software architects that we conducted in the summer of 2006. We summarize what those sources have to say about the duties, skills, and knowledge that competent architects must perform and have.
Adaptive Methods of Kalman Filtering for Personal Positioning Systems

Kalman filtering is very efficient for data fusion, in which the definition of the process and measurement noises (i.e. the matrices Q and R, respectively) greatly influences the filter performance. In recent years several studies reported that adjustments of Q and R can be helpful to reduce the errors of the estimations. In this paper, various methods for making adjustments to the matrices Q and R are introduced for the particular case of Personal Positioning Systems (PPS). The aim is to observe the improvements achieved in an extended Kalman filter when adaptive methods are applied, in other words to observe their influence on the user's path obtained. These adjustments are considered to be needed because environmental conditions in such systems are often not fixed. The methods to be analyzed are: (1) the weighted Kalman filter; (2) scaling matrix Q (3) adjustments of Q and R based on sequence-innovation; and (4) a combination of the method (2) and (3), i.e. Q is estimated by applying a scale factor and adjustments to R are realized in accordance with the method (3). Given that the filter may diverge, we use the 2 χ test to evaluate validity of the estimations, which is based on the analysis of the innovations. Individual components of the innovation vector are evaluated in order to correct or eliminate wrong information for data fusion. The PPS is based on the Dead Reckoning (DR) algorithm. The errors of the DR parameters are estimated with an Extended Kalman Filter (EKF), which combines the measurements of a GPS and an inertial measurement unit (IMU). The results show that each method allows us to obtain consistent Kalman filtering and they help to obtain better user’s trajectories, but additional techniques and/or technologies should be used.
The Sisal model of functional programming and its implementation

Programming a massively-parallel machine is a daunting task for any human programmer, and parallelization may even be impossible for any compiler. Instead, the functional programming paradigm may prove to be an ideal solution by providing an implicitly parallel interface to the programmer. We describe the Sisal (Stream and Iteration in a Single Assignment Language) project. Its goal is to provide a general-purpose user interface for a wide range of parallel processing platforms.
Effective Strategies to Catch Up in the Era of Globalization

OVERVIEW: Firms in emerging markets have traditionally followed one of two strategies to catch up with multinational enterprises (MNEs): developing customized products, services, or innovative business models or buying and absorbing technology from MNEs. In the era of globalization, these strategies are no longer effective. The new strategy to succeed is innovation-based differentiation, developing core technologies and advanced product offerings that are delivered at a low cost and with excellent customer service. Using this strategy, leading local firms are quickly catching up with MNEs in market development, technology development, or both.
Extracting surface textures and microstructures from multiple aerial images

We present a surface texture and microstructure extraction system to provide added realism in visualization and virtual reality applications. The system uses multiple images, 3-D models and camera information, in addition to knowledge about man-made structures, to cope with problems such as perspective distortion, data deficiency, and corruption caused by shadows and occlusions. Combined with the Ascender site modeling system and scene rendering algorithms, the system is typically useful for urban site model refinement and visualization.
The validity and reliability of a problem-based learning implementation questionnaire

Purpose: The aim of this paper is to provide evidence for the validity and reliability of a questionnaire for assessing the implementation of problem-based learning (PBL). This questionnaire was developed to assess the quality of PBL implementation from the perspective of medical school graduates. Methods: A confirmatory factor analysis was conducted to assess the validity of the questionnaire. The analysis was based on a survey of 225 graduates of a problem-based medical school in Indonesia. Results: The results showed that the confirmatory factor analysis model had a good fit to the data. Further, the values of the standardized loading estimates, the squared inter-construct correlations, the average variances extracted, and the composite reliabilities all provided evidence of construct validity. Conclusion: The PBL implementation questionnaire was found to be valid and reliable, making it suitable for evaluation purposes.
Evaluating the density parameter in density peak based clustering

The density peak based clustering algorithm is a simple yet effective clustering approach. This algorithm firstly calculates the local density of each data and the distance to the nearest neighbor with higher density. Based on the assumption that cluster centers are density peaks and they are relatively far from each other, this algorithm isolates the candidates of cluster centers from the non-center data. After the cluster centers are identified, the other data are assigned labels equaling to those of their nearest neighbors with higher density. In this way the clustering can be accomplished efficiently and clusters of arbitrary shapes can be obtained. The key of the density peak based clustering algorithm lies in the density calculation method. In this paper we study the influence of the data amount used in density calculation on the clustering results of the density peak based algorithm. As a result, we arrive at some conclusions on the selection of the data amount, which can be useful in applying this algorithm in real tasks.
Achievable Outage Rates In Cognitive Radio Networks under Imperfect Spectrum Sensing

In this paper, we aim at deriving the outage rates achieved by the primary user due to spectrum sensing in a cognitive radio network, that we call sensing-induced pri- mary outage rates. To reach this goal, in the first step, in- stead of classical spectrum sensing techniques that evaluate sensing performance only based on correct detection of the presence of the primary user's signal, we propose a modified framework that also takes into account the correct detection of the absence of primary user's signal for spectrum sens- ing performance evaluation. In a second step, we derive the information rates achieved by the coexistence of a primary and a cognitive network. In the last step, assuming slow- fading sensing channels, we derive the sensing-induced pri- mary outage rates, i.e., outage rates achieved by the primary network in the presence of a CR with imperfect spectrum sensing, characterized by a given miss-detection probability. Numerical results show that the proposed spectrum sens- ing outperforms conventional spectrum sensing techniques in terms of primary signal outage rates and total achiev- able throughputs, without any increase in the cognitive radio complexity.
View-based detection and analysis of periodic motion

We describe a technique that detects periodic motion. Assuming a static camera, we first segment moving objects from the background. By tracking objects of interest, we compute the object's self-similarity as it evolves in time. For periodic motion, the self-similarity metric is periodic, and is Fourier analyzed to detect and characterize periodicity. Examples on real image sequences are given.
Image mosaics based on pseudo-Zernike moments

The traditional feature-based algorithm was found to be sensitive to rotations and noise. In this paper, an automatic image mosaics technique was proposed by using the pseudo-Zernike moments defined on the feature point's neighborhood. Firstly using the Harris corner detector gain the feature points, compute the pseudo-Zernike moments defined on these feature point's neighborhood, through comparing the Euclidean distance of these pseudo-Zernike moments to extract the initial feature points pair, then eliminate spurious feature points pair by geometric transform model obtained from RANSAC method, finally transform the input image with the correct mapping model for image fusion and complete image stitching. Experimental results demonstrate that the proposed algorithm is robust to translation, rotation, noise and slight scaling.
Introduction 1.1 What Is Machine Learning? 1.2 Examples of Machine Learning Applications 1.2.1 Learning Associations 1.2.2 Classification 1.2.3 Regression

With advances in computer technology, we currently have the ability to store and process large amounts of data, as well as to access it from physically distant locations over a computer network. Most data acquisition devices are digital now and record reliable data. Think, for example, of a supermarket chain that has hundreds of stores all over a country selling thousands of goods to millions of customers. The point of sale terminals record the details of each transaction: date, customer identification code, goods bought and their amount, total money spent, and so forth. This typically amounts to gigabytes of data every day. This stored data becomes useful only when it is analyzed and turned into information that we can make use of, for example, to make predictions. We do not know exactly which people are likely to buy a particular product, or which author to suggest to people who enjoy reading Hem-ingway. If we knew, we would not need any analysis of the data; we would just go ahead and write down the code. But because we do not, we can only collect data and hope to extract the answers to these and similar questions from data. We do believe that there is a process that explains the data we observe. Though we do not know the details of the process underlying the generation of data—for example, consumer behavior—we know that it is not completely random. People do not go to supermarkets and buy things at random. When they buy beer, they buy chips; they buy ice cream in summer and spices for Glühwein in winter. There are certain patterns in the data. We may not be able to identify the process completely, but we believe 2 1 Introduction we can construct a good and useful approximation. That approximation may not explain everything, but may still be able to account for some part of the data. We believe that though identifying the complete process may not be possible, we can still detect certain patterns or regularities. This is the niche of machine learning. Such patterns may help us understand the process, or we can use those patterns to make predictions: Assuming that the future, at least the near future, will not be much different from the past when the sample data was collected, the future predictions can also be expected to be right. Application of machine learning methods to large …
The Multi-streamed Solid-State Drive

This paper makes a case for the multi-streamed solid-state drive (SSD). It offers an intuitive storage interface for the host system to inform the SSD about the expected lifetime of data being written. We show through experimentation with a real multi-streamed SSD prototype that the worst-case update throughput of a Cassandra NoSQL DB system can be improved by nearly 56%. We discuss powerful use cases of the proposed SSD interface.
Agile Software Development with Distributed Teams: Senior Management Support

Agile teams advocates face-to-face conversation, frequent communication and close collaboration between team members in order to work together as one team. In distributed software development, however, separation through space, time and culture affects the ability of the members of a distributed team to work together as one team. Through a Grounded Theory study that involved 55 participants from 38 different software companies in the USA, India and Australia, we investigate key concerns of distributed teams in Agile software development. We found that distributed teams need significant support from senior management in terms of organisational culture, human resource management, financial sponsorship, infrastructure and technology, and customer liaison. In this paper, we discuss the importance of senior management support for Agile software development with distributed teams.
The AAT/WFI survey of the Monoceros Ring and Canis Major Dwarf galaxy: II. from l = (280 - 025)deg

This paper concludes a systematic search for evidence of the Monoceros Ring and Canis Major dwarf galaxy around the Galactic Plane. Presented here are the results for the Galactic longitude range of l = (280 - 025)deg. Testing the claim that the Monoceros Ring encircles the entire Galaxy, this survey attempts to document the position of the Monoceros Ring with increasing Galactic longitude. Additionally, with the discovery of the purported Canis Major dwarf galaxy, searching for more evidence of its interaction is imperative to tracing its path through the Galaxy and understanding its role in the evolution of the Milky Way. Two new detections of the Monoceros Ring have been found at (l, b) = (280,+15)deg and (300,+10)deg. Interestingly, in general there seem to be more detections above the Plane than below it; in this survey around 2/3 of the firm Monoceros Ring detections are in the North. This coincides with the Northern detections appearing to be qualitatively denser and broader than their Southern counterparts. The maximum of the Galactic Warp in the South is also probed in this survey. It is found that these fields do not resemble those in the Canis Major region suggesting that the Warp does not change the shape of the CMD as is witnessed around Canis Major. The origins and morphology of the Monoceros Ring is still elusive primarily due to its enormous extent on the sky. Continued probing of the Galactic Outer Disc is needed before a consensus can be reached on its nature.
Concurrency control in distributed database system

Concurrency manipulates the control of concurrent transaction execution. Distributed database management system enforce concurrency manipulate to make sure serializability and isolation of transaction. Lots of research has been done on this area and a number of algorithms have been purposed. In this article, we are comparing few algorithms for preserving the ACID property (atomicity, consistency, isolation, and durability) of transactions in DDBMS.
A 20GS/s low-power BiCMOS comparator using an active inductor load

A 20GS/s BiCMOS latched comparator for highspeed, low-power Analog-to-Digital Converter (ADC) is proposed. The resister load of conventional current-steering comparator is replaced by an active inductor load made by a MOS transistor and a resister. This solution extends the bandwidth of the circuit without excessive power dissipation. Implemented in 0. 35μm SiGe BiCMOS technology, this comparator only occupies a die area of 97×67 μm2 with a power dissipation of 31 mW from a 3.3-V power supply. Operating with an input frequency of 2 GHz, the circuit can oversample up to 20 GS/s with 4 bits of resolution; while operating at Nyquist, the comparator can sample up to 20 GS/s with 3 bits resolution. This design achieves a considerable trade-off between power, speed and resolution.