Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring Teachers' Learning from a Problem-Based Learning Approach to Professional Development in Science Education

In th is study we measured changes in science teachers' conceptua I science u ndersta nd ing (content knowledge) and pedagogical content knowledge (PCK) while participating in a problem-based learning (PBL) model of professional development. Teachers participated in a two-week long workshop followed by nine monthly meetings during one academic year that focused on deepening their content understanding and their teaching practices. We analyzed teachers' short writings and concept map depictions of their understanding, and found teachers significantly developed components of pedagogical content knowledge and clinical reasoning, such as knowledge of assessment. Gains on conceptual understanding of science, however, were limited to one group of teachers. We conclude that this PBL approach to professional development advanced components of science teachers' strategic pedagogical content knowledge and discuss the ongoing challenges in measuring changes in teachers'understanding in the context of an evolving PBL model of professional development.
Online Students' Expectations: Enhancing the Fit between Online Students and Course Design

Learner analysis and needs assessments are basic elements of all instructional design models and are of concern to those designing distance education courses. Mismatches between students' expectations and actual course features may impact learning. This investigation surveyed 249 geographically dispersed online students for the course features they valued and the course features included in their distance education courses. Results revealed general agreement between course features desired by students and those included in their current courses, however, there were mismatches; particularly concerning the use of instant messaging, team assignments, and creating biography pages. Implications for online course design are presented.
Reachability computation for hybrid systems with Ariadne

Abstract A riadne is an in-progress open environment to design algorithms for computing with hybrid automata, that relies on a rigorous computable analysis theory to represent geometric objects, in order to achieve provable approximation bounds along the computations. In this paper we discuss the problem of reachability analysis of hybrid automata to decide safety properties. We describe in details the algorithm used in A riadne to compute over-approximations of reachable sets. Then we show how it works on a simple example. Finally, we discuss the lower-approximation approach to the reachability problem and how to extend Ariadne to support it.
A hierarchical approach to ART-like clustering algorithm

We propose a hierarchical approach to ART-like clustering algorithm which is able to deal with data consisting of arbitrarily geometrical-shaped clusters. A combined hierarchical and ART-like clustering is suggested as a natural feasible solution to the two problems of determining the number of clusters and clustering data. A 2D artificial data set is tested to demonstrate the performance of the proposed algorithm.
Methyl [6‐(2‐chloro­benzo­yl)‐2‐oxo‐2,3‐dihydro‐1,3‐benzoxazol‐3‐yl]acetate

In the mol­ecular structure of the title compound, C17H12ClNO5, the 1,3-benzoxazole ring system makes a dihedral angle of 72.96 (7)° with the benzene ring of the 2-chloro­benzoyl group. In the crystal structure, two intra­molecular C—H⋯O hydrogen bonds influence the mol­ecular conformation.
Realization of a Scalable and Reliable Multicast Transport Protocol for Many‐to‐Many Sessions

In this paper, we present the Enhanced Communication Transport Protocol–Part 5 (ECTP‐5), which provides scalable and reliable multicast communication service for many‐to‐many applications by constructing high quality recovery trees from two‐layer logical trees and repairing the losses via unicast automatic repeat request–based error control. In order to realize the protocol, we developed feasible protocol architectures and building blocks including additional functions which deal with engineering details, such as membership dynamics and sender coordination. Experimental results show that ECTP‐5 scales well with various session sizes and packet loss rates in terms of control overhead and recovery latency.
Neural network control of a robotic manipulator arm for undersea applications

A lightweight, direct-drive undersea testbed manipulator arm was configured for integration and subsequent evaluation of neural network technologies. The author reports them initial results of an artificial neural network model used to control this undersea manipulator. An iterative trajectory generator for the manipulator (constrained to planar motion) using a backpropagation network is described. It provided the intermittent desired joint angles given the relative position information about the arm and the target. This work built upon the extended work of D. Sobajic and L. Pao, (1988). The author discusses a preliminary neural network architecture which learns the internal and controller model for the undersea manipulator arm. This control structure was inspired by the work of D. Nguyen and B. Widrow, (1990). Although this work is still underway, preliminary tests are encouraging, and are aimed at satisfying the adaptive capability necessary for operating in an unstructured ocean environment. <<ETX>>
Multiresolution image compression using image foveation and simulated depth of field for stereoscopic displays

Spatial contrast sensitivity varies considerably across the field of view, being highest at the fovea and dropping towards the periphery, in accordance with the changing density, type, and interconnection of retinal cells. This observation has enabled researchers to propose the use of multiple levels of detail for visual displays, attracting the name image foveation. These methods offer improved performance when transmitting images across low-bandwidth media by conveying only highly visually salient data in high resolution, or by conveying more visually salient data first and gradually augmenting with the periphery. For stereoscopic displays, the image foveation technique may be extended to exploit the additional acuity constraint of the human visual system caused by the focal system: limited depth of field. Images may be encoded at multiple resolutions laterally taking advantage of the space-variant nature of the retina (image foveation), and additionally contain blur simulating the limited depth of field phenomenon. Since optical blur has a smoothing effect, areas of the image inside the high-resolution fovea, but outside the depth of field may be compressed more effectively. The artificial simulation of depth of field is also believed to alleviate symptoms of virtual simulator sickness resulting from accommodation-convergence separation, and reduce diplopia.
Capacitive coupling and quantized feedback applied to conventional CMOS technology

An on-chip capacitor which is formed under a bonding pad is used to block the DC level of an external input signal. Quantized feedback using a self-triggered decision circuit is used to establish local DC voltage levels in the receiver and eliminate the "zero wander" effect. Measurement of a 0.5 /spl mu/m CMOS chip has demonstrated that: a BER (Bit Error Rate) test for a 2/sup 31/-1 sequence showed no errors at 600 Mb/s; the circuit detected a low frequency signal of 1 Kb/s indicating that coding is not required; and the input signal can be DC biased anywhere, limited only by the ESD diodes, between VDD and VSS without affecting the final recovered CMOS waveform.
The case for an open science in technology enhanced learning

In this paper, we make the case for an open science in technology enhanced learning (TEL). Open science means opening up the research process by making all of its outcomes, and the way in which these outcomes were achieved, publicly available on the World Wide Web. In our vision, the adoption of open science instruments provides a set of solid and sustainable ways to connect the disjoint communities in TEL. Furthermore, we envision that researchers in TEL would be able to reproduce the results from any paper using the instruments of open science. Therefore, we introduce the concept of open methodology, which stands for sharing the methodological details of the evaluation provided, and the tools used for data collection and analysis. We discuss the potential benefits, but also the issues of an open science, and conclude with a set of recommendations for implementing open science in TEL.
Real-time network security situation visualization and threat assessment based on semi-Markov process

To cope with a large amount of data in current sensed environments, decision aid tools should provide their understanding of situations in a time-efficient manner, so there is an increasing need for real-time network security situation awareness and threat assessment. In this study, the state transition model of vulnerability in the network based on semi-Markov process is proposed at first. Once events are triggered by an attacker’s action or system response, the current states of the vulnerabilities are known. Then we calculate the transition probabilities of the vulnerability from the current state to security failure state. Furthermore in order to improve accuracy of our algorithms, we adjust the probabilities that they exploit the vulnerability according to the attacker’s skill level. In the light of the preconditions and post-conditions of vulnerabilities in the network, attack graph is built to visualize security situation in real time. Subsequently, we predict attack path, recognize attack intention and estimate the impact through analysis of attack graph. These help administrators to insight into intrusion steps, determine security state and assess threat. Finally testing in a network shows that this method is reasonable and feasible, and can undertake tremendous analysis task to facilitate administrators’ work.
Finite Element Analysis Based on Volume CAD

Volume CAD, which is being developed at RIKEN, will be a common platform for storing and handling volume data in manufacturing processes. In VCAD, both geometry and volume data are represented as attributes of a set of cells in a simple voxel or octree data structure. In this paper, a method by which to conduct highly accurate structural analysis by directly using VCAD data is proposed. A degenerated hexahedral mesh representing the precise geometry of an object is generated from VCAD data, and finite element analysis is then conducted using the mesh. The proposed method is compared with the X-FEM, and the performances of these two methods are shown to be approximately the same. The robustness of the proposed method is demonstrated by presenting numerical examples of stamping tools.
Probabilistic data association methods in visual tracking of groups

Data association is a fundamental problem when tracking large numbers of moving targets. Most commonly employed methods of data association such as the JPDA estimator are combinatorial and therefore do not scale well to large numbers of targets. However, in many cases large numbers of targets form natural groups which can be efficiently tracked. We describe a method for defining groups based on the position and velocity of targets. This definition introduces a natural set of merging and splitting rules that are embedded into a Kalman filtering framework for tracking multiple groups. In cases where groups of different velocities cross, a general methodology for matching measurements to groups is introduced. This algorithm is based on a modified version of the PDA estimator. It is well suited to handle a high number of measurements and extends naturally to additional grouping constraints such as color or shape.
Design of a STT missile autopilot using functional inversion and LMI approach

We present a systematic approach for the autopilot design of skid-to-turn (STT) missiles. First, the nonlinear model of a STT missile is partially linearized via functional inversion techniques and then, the additional set-point tracking controller can be designed by the well-known LMI approach. The stabilization conditions are given in terms of LMIs.
Encyclopedic dictionary of the sciences of language

The "Encyclopaedic Dictionary of the Sciences of Language" is a wide-ranging and thorough study of language and its functions. Eminent French theorists Oswald Ducrot and Tzvetan Todorov cover such major trends in the development of modern linguistics as general grammars and Saussurianism, as well as methodological and descriptive concepts. Combining the breadth of a dictionary (800 terms are listed in an alphabetical index) with the depth of an encyclopaedia (50 articles provide sustained discussions of specific topics), this is a definitive reference for the study of linguistics, semiotics, poetics and literary theory. Todorov is also author of "The Fantastic: A Structural Approach to a Literary Genre".
Stereo camera calibration

We present all the components needed for a fully-fledged stereo vision system, ranging from object detection through camera calibration to depth perception. We propose an efficient, automatic and practical method to calibrate cameras for use in 3D machine vision metrology. We develop an automated stereo calibration system that only requires a series of views of a manufactured calibration object in unknown positions. The system is tested against real and synthetic data, and we investigate the robustness of the proposed method compared to standard calibration practice. All the aspects of 3D stereo reconstruction is dealt with and we present the necessary algorithms to perform epipolar rectification on images as well as solving the correspondence and triangulation problems. It was found that the system performs well even in the presence of noise, and calibration is easy and requires no specialist knowledge. i Stellenbosch University http://scholar.sun.ac.za
A Statistical Perspective of Neural Networks for Imbalanced Data Problems

It has been an interesting challenge to find a good classifier for imbalanced data, since it is pervasive but a difficult problem to solve. However, classifiers developed with the assumption of well-balanced class distributions show poor classification performance for the imbalanced data. Among many approaches to the imbalanced data problems, the algorithmic level approach is attractive because it can be applied to the other approaches such as data level or ensemble approaches. Especially, the error back-propagation algorithm using the target node method, which can change the amount of weight-updating with regards to the target node of each class, attains good performances in the imbalanced data problems. In this paper, we analyze the relationship between two optimal outputs of neural network classifier trained with the target node method. Also, the optimal relationship is compared with those of the other error function methods such as mean-squared error and the n-th order extension of cross-entropy error. The analyses are verified through simulations on a thyroid data set.
A multimodal traffic responsive strategy using particle swarm optimization

Abstract The paper describes a model predictive control strategy for multimodal urban traffic regulation for private vehicle (PV) traffic and public transport (PT). By acting on the traffic lights, the strategy aims to achieve two objectives. First to make public transport vehicles respect their timetable and, second, to improve the global traffic conditions. Based on a complex model, the strategy determines the optimal green splits and the offsets of the traffic lights. The optimization problem is multiobjective and resolved by the particle swarm optimization algorithm. Two criteria are proposed for private vehicle traffic and public transport. One of the originalities of this work is that the two criteria are expressed globally and are related since the PT progression model depends on the PV traffic. Two PSO algorithm versions are used: the original algorithm and a modified version expected to be more efficient. The efficiency of the strategy is evaluated on a large virtual urban network.
Radio resource allocation based on greedy algorithm and successive interference cancellation in Device-to-Device (D2D) communication

Device-to-Device (D2D) communication underlaying cellular networks can improve resource utilization and thus increase total throughput. However, resource allocation and interference coordination between cellular networks and D2D system will become critical and need to be properly handled. According to some papers, which just allowed a set of cellular user equipment (UE) and a pair of D2D UEs to coexist in a cellular channel, the growth of resource utilization is not sufficient. In this paper, we propose a method of radio resource allocation based on greedy algorithm and successive interference cancellation (SIC), which allows the coexistence of a cellular UE and three pairs of D2D UEs in a channel. The greedy algorithm is applied to maximize the performance of D2D links and then the SIC technique is used to cope with the interference from D2D UEs to cellular UEs. Simulation results show that the proposed method can achieve substantial gains in terms of resource utilization and total cell throughput.
Experimental Confirmation of an HF Channel Model

Specially designed HF ionospheric propagation measurements were made and analyzed to confirm the validity and bandwidth limitations of a proposed stationary HF ionospheric channel model. In the model, the input (transmitted) signal feeds an ideal delay line and is delivered at several taps with adjustable delays, one for each resolvable ionospheric modal component. Each delayed signal is modulated in amplitude and phase by a baseband tap-gain function, and the delayed and modulated signals are summed (with additive noise) to form the output (received) signal. Statistical specifications for the tap-gain functions involved three hypotheses: 1) that each tap-gain function is a complexGaussian process that produces Rayleigh fading, 2) that the tapgain functions are independent, and 3) that each tap-gain function has a spectrum that in general is the sum of two Gaussian functions of frequency, one for each magnetoionic component. Statistical tests were performed on daytime and nighttime measurements confirming the validity of the three hypotheses, and thereby the validity of the model. For practical applications, the model can be considered valid over a bandwidth equal to about one fourth of the reciprocal of the effective (weighted) time spreads on the ionospheric modal components. The model should be useful both in theoretical analyses of communication system performance and for channel simulator designs.
A Multi-Agent MicroBlog Behavior based User Preference Profile Construction Approach

Nowadays, the user-centric application based web 2.0 has replaced the web 1.0. The users gain and provide information by interactive network applications. As a result, traditional approaches that only extract and analyze users’ local document operating behavior and network browsing behavior to build the users’ preference profile cannot fully reflect their interests. Therefore this paper proposed a preference analysis and indicating approach based on the users’ communication information from MicroBlog, such as reading, forwarding and @ behavior, and using the improved PersonalRank method to analyze the importance of a user to other users in the network and based on the users’ communication behavior to update the weight of the items in the user preference. Simulation result shows that our proposed method outperforms the ontology model, TREC model, and the category model in terms of 11SPR value.
Human Face Recognition

scale invariant feature transform) being a feature extraction algorithm was initially used for object recognition, pattern recognition. But from past few years this algorithm is frequently being used for human face recognition based on feature extraction and matching. The twist here is that between feature extraction and its recognition I have used fuzzy clustering to create clusters so that we can localize the areas for feature point detection. As this recognition is based on a GUI system whose recognition rate is almost 99% - 100% for face recognition and feature detection.
A low power, 10-bit CMOS D/A converter for high speed applications

In this paper, the realization of a fully binary 10-bit current steering CMOS DAC is presented. Both the measured INL and DNL are smaller than 0.2 LSB. Better than 60 dB SFDR is achieved for all output signals up to a 30 MS/s Nyquist frequency. For a 1 MHz signal, the chip achieves better than 60 dB SFDR for all update rates up to 800 MS/s. The presented DAC core occupies 0.23 mm/sup 2/. The digital power consumption is only 1 mW for a 30 MS/s Nyquist operation. Based on a fundamental theoretical INL- and DNL-yield analysis, the presented design explores the limits towards the binary and the low-power edges of the design space.
Pulsed Magnetic Induction for Underground Wireless Communication

Abstract Magnetic induction (MI) communication has been an emerging communication technology to alternate electromagnetic communication in underground environment. However, the path loss of MI communication is too high, and limits its transmission range. In order to increase the transmission range, the direct way is to raise the transmitting power. However, in underground environment, the power supply cannot be large because of the limited size of apparatuses. In this paper, we use pulsed power to solve this problem and use a capacitor connected with receiver coil to form a resonant circuit. These above ideas are all analysed theoretically and compared with traditional MI communication. The results show that this method can improve the transmission range. Some key factors that can influence the transmission effect are also found in this paper.
BPGM: A big graph mining tool

The design and implementation of a scalable parallel mining system target for big graph analysis has proven to be challenging. In this study, we propose a parallel data mining system for analyzing big graph data generated on a Bulk Synchronous Parallel (BSP) computing model named BSP-based Parallel Graph Mining (BPGM). This system has four sets of parallel graph mining algorithms programmed in the BSP parallel model and a well-designed workflow engine optimized for cloud computing to invoke these algorithms. Experimental results show that the graph mining algorithm components in BPGM are efficient and have better performance than big cloud-based parallel data miner and BC-BSP.
Image restoration-based template matching with application to motion estimation

In this paper, we investigate the relationship between the image restoration and the template matching problems. A new formulation of template matching is presented. According to this formulation the relationship between image restoration and template matching is captured by the observation that the restored template can be used to ascertain its location. As a consequence, the duality between image restoration and template matching is established. An alternative approach is also presented to provide the interpretation of template matching as a special case of blurred image restoration. This approach is based on the observation that template matching can be viewed as image restoration of the degraded version of an unknown image blurred by a linear point spread function -- the template. It is shown that, unlike image restoration, all second order statistics required for the implementation of linear minimum mean-square error (LMMSE) template matching can be easily estimated. The linear minimum mean-square error (LMMSE) template matching is subsequently applied to block-matching motion estimation from degraded image sequences. Our experimental results indicate: First, that LMMSE template matching is superior to traditional block-matching. Second, that there exist certain applications, for example multichannel image sequence restoration, that greatly benefit from LMMSE-based template matching.