Miracle-dz/UniFAR-FTUs huggingface dataset:
Talk with me: Student behaviours and pronoun use as indicators of discourse health across facilitation methods

Identifying which online behaviours and interactions are associated with students’ perception of being supported will enable a deeper understanding of how those activities contribute to student learning experiences. Features of student language, especially verbally immediate behaviours, are one of the aspects of student interactions in need of greater exploration within discourse-based online learning environments. As a result, the verbally immediate behaviour of pronoun usage is explored within online courses where the primary learning mechanism is student discourse. Student posting behaviours and features of their language, specifically their use of different classes of pronouns, are explored from the perspective of how supported students felt in their courses as well as how their behaviours and pronoun usage changed over time. Findings suggest that students who were taking instructor-facilitated courses felt more supported which was associated with higher levels of interaction and increased consistency in student behaviours from week to week within the term. Those enrolled in peer-facilitated courses, who felt less supported, used pronouns differently than those who experienced greater levels of support, suggesting the potential for pronoun-based analytics.
Decentralized Control of Multi-AGV Systems in Autonomous Warehousing Applications

In this paper, we present an algorithm for decentralized control of multiple automated guided vehicles performing transportation tasks within industrial and warehousing environments. By running on each vehicle in the system, the algorithm provides vehicles with capabilities for autonomous path planning and motion co-ordination. The path planning part of the algorithm implements a free-ranging motion scheme by determining the shortest feasible paths considering nonholonomic vehicle constraints. The motion co-ordination part of the algorithm ensures safe vehicle motions by reliable detection and resolution of different conflict situations with other vehicles in the shared workspace. Conflict resolution is based on a vehicle priority scheme and results in temporary stopping or removal of the lower priority vehicles taking part in the conflict. Removal action is always performed within the vehicle's private zone, i.e., the pre-allocated local region of the workspace surrounding the vehicle. By encoding information on the vehicle size and its kinematic constraints, the introduced private zone mechanism provides the necessary physical space required for successful execution of every removal action. We also analyze the stability of the presented algorithm and discuss its deadlock-free and livelock-free properties. Algorithm performance has been validated by simulation using ten vehicles and experimentally on two different setups-a laboratory setup comprising five Pioneer 3DX vehicles and by two state-of-the-art autonomous forklifts in industrial-like operating conditions.
EDUCATIONAL EVALUATION OF FEATURE-BASED MODELLING IN A PROBLEM SOLVING DOMAIN

Feature-Based Modelling is a machine learning based cognitive modelling methodology. An intelligent educational system has been implemented, for the purpose of evaluating the methodology, which helps students learn about the unification of terms from the Prolog programming language. The system has been used by Third Year Computer Science students at La Trobe University during September 1989. Students were randomly allocated to an Experimental condition, in which FBM modelling was used to select tasks, and give extra comments, or to a Control condition in which similar tasks and comments were given, but without FBM tailoring to the individual. Ratings of task appropriateness, and comment usefulness, were collected on-line as the students worked with the tutor; overall ratings were obtained by questionnaire at the end; and semester exam results were examined. Despite the fact that only a minority of students showed sufficient misunderstanding for FBM to have potential value, of the ten comparisons chat relate most directly to the aims of the Tutor, while in no case reaching significance, seven were in favour of the Tutor, and only two against. These preliminary results are very encouraging for the FBM principles of the Tutor.
On-Line New Event Detection using Single Pass Clustering

This paper discusses the implementation and evaluation of a new-event detection system. We focus on a strict on-line setting, in that the system must indicate whether the current document contains or does not contain discussion of a new event before looking at the next document. Our approach to the problem uses a single pass clustering algorithm and a novel thresholding model that incorporates the properties of events as a major component. A corpus containing newswire and transcribed broadcast news was analyzed using our system, and our results compared favorably to those of other systems. We develop an evaluation methodology based on a combination of techniques that allows us to infer the expected performance of our approach in the field, and to suggest avenues for future research that may lead to better performance.
Parametric Verification of Weighted Systems

This paper addresses the problem of parametric model checking for weighted transition systems. We consider transition systems labelled with linear equations over a set of parameters and we use them to provide semantics for a parametric version of weighted CTL where the until and next operators are themselves indexed with linear equations. The parameters change the model-checking problem into a problem of computing a linear system of inequalities that characterizes the parameters that guarantee the satisfiability. To address this problem, we use parametric dependency graphs (PDGs) and we propose a global update function that yields an assignment to each node in a PDG. For an iterative application of the function, we prove that a fixed point assignment to PDG nodes exists and the set of assignments constitutes a well-quasi ordering, thus ensuring that the fixed point assignment can be found after finitely many iterations. To demonstrate the utility of our technique, we have implemented a prototype tool that computes the constraints on parameters for model checking problems.
Bayesian Factor Analysis for Mixed Ordinal and Continuous Responses

Many situations exist in which a latent construct has both ordinal and continuous indicators. This presents a problem for the applied researcher because standard measurement models are not designed to accommodate mixed ordinal and continuous data. I address this problem by formulating a measurement model that is appropriate for such mixed multivariate responses. This model unifies standard normal theory factor analysis and item response theory models for ordinal data. I detail a Markov chain Monte Carlo algorithm for model fitting. I apply the model to cross-national data on political-economic risk and find that the model works well. Software for fitting this model is publicly available in the MCMCpack (Martin and Quinn 2004, “MCMCpack 0.4–8”) R package.
nsupervised seismic facies analysis using avelet transform and self-organizing maps

Unsupervised seismic facies analysis provides an effective way to estimate reservoir properties by combining different seismic attributes through pattern recognition algorithms. However, without consistent geological information, parameters such as the number of facies and even the input seismic attributes are usually chosen in an empirical way. In this context, we propose two new semiautomatic alternative methods. In the first one, we use the clustering of the Kohonen self-organizing maps SOMs as a new way to build seismic facies maps and to estimate the number of seismic facies. In the second method, we use wavelet transforms to identify seismic trace singularities in each geologically oriented segment, and then we build the seismic facies map using the clustering of the SOM. We tested both methods using synthetic and real seismic data from the Namorado deepwater giant oilfield in Campos Basin, offshore Brazil. The results confirm that we can estimate the appropriate number of seismic facies through the clustering of the SOM. We also showed that we can improve the seismic facies analysis by using trace singularities detected by the wavelet transform technique. This workflow presents the advantage of being less sensitive to horizon interpretation errors, thus resulting in an improved seismic facies analysis.
An adaptive multi-seed geometric active contour model for river recognition

This paper presents a novel multi-seed vector-valued framework for river recognition based on geometric active contours. There are four core components of this framework: vector-valued scanning algorithm, a geometric active contours model, low resolution segmentation with elevation data and high resolution optimization.The combined algorithm allows for a rapid evolution of the contour and a convergence to its final configuration with a small number of iterations. Compared with the conventional segmentation methods, our approach has the advantages of dealing effectively with complicated satellite images, automatically initializing a number of snakes based on color and texture features, accurately and rapidly identifying the target objects with elevation data.
Finding Top-k Covering Irreducible Contrast Sequence Rules for Disease Diagnosis

Diagnostic genes are usually used to distinguish different disease phenotypes. Most existing methods for diagnostic genes finding are based on either the individual or combinatorial discriminative power of gene(s). However, they both ignore the common expression trends among genes. In this paper, we devise a novel sequence rule, namely, top-k irreducible covering contrast sequence rules (TopkIRs for short), which helps to build a sample classifier of high accuracy. Furthermore, we propose an algorithm called MineTopkIRs to efficiently discover TopkIRs. Extensive experiments conducted on synthetic and real datasets show that MineTopkIRs is significantly faster than the previous methods and is of a higher classification accuracy. Additionally, many diagnostic genes discovered provide a new insight into disease diagnosis.
Computed tomography image denoising by learning to separate morphological diversity

Computed Tomography (CT) image denoising is a challenging topic because of the difficulty in modeling noise. In this paper, we propose an image decomposition approach to remove noise from low-dose CT images. We model the image as y = X1 + X2 where the main structures and noise are two superimposed layers. Total Variation (TV) minimization is used to learn two dictionaries to represent structure and noise respectively and sparse coding is used to separate x1 and x2. Finally, an iterative post-processing stage is introduced that uses image-adapted curvelet dictionaries to recover edges. Our results demonstrate that image separation is a viable alternative to the classic K-SVD denoising method.
Non-Negative Matrix Factorization with Constraints

Non-negative matrix factorization (NMF), as a useful decomposition method for multivariate data, has been widely used in pattern recognition, information retrieval and computer vision. NMF is an effective algorithm to find the latent structure of the data and leads to a parts-based representation. However, NMF is essentially an unsupervised method and can not make use of label information. In this paper, we propose a novel semi-supervised matrix decomposition method, called Constrained Non-negative Matrix Factorization, which takes the label information as additional constraints. Specifically, we require that the data points sharing the same label have the same coordinate in the new representation space. This way, the learned representations can have more discriminating power. We demonstrate the effectiveness of this novel algorithm through a set of evaluations on real world applications.
A Multipath TCP Based on Network Coding in Wireless Mesh Networks

It is a challenge that network coding achieve wide use in wireless mesh networks. Network coding must be compatible or friendly with TCP in practice. We propose a mechanism that naturally add network coding to current network systems. In our scheme, the destination feedback the degree of freedom of encoding block, and delays of packet delivery minimize reordering and timeouts. Using multipath routing, the source side can send more encode packets and the transmission rate becomes more faster, as a result, the network achieves higher throughput compared to TCP in the presence of lossy wireless links.
An Inter-Networking Mechanism with Stepwise Synchronization for Wireless Sensor Networks

To realize the ambient information society, multiple wireless networks deployed in the region and devices carried by users are required to cooperate with each other. Since duty cycles and operational frequencies are different among networks, we need a mechanism to allow networks to efficiently exchange messages. For this purpose, we propose a novel inter-networking mechanism where two networks are synchronized with each other in a moderate manner, which we call stepwise synchronization. With our proposal, to bridge the gap between intrinsic operational frequencies, nodes near the border of networks adjust their operational frequencies in a stepwise fashion based on the pulse-coupled oscillator model as a fundamental theory of synchronization. Through simulation experiments, we show that the communication delay and the energy consumption of border nodes are reduced, which enables wireless sensor networks to communicate longer with each other.
Attenuation of Krüppel-Like Factor 4 Facilitates Carcinogenesis by Inducing G1/S Phase Arrest in Clear Cell Renal Cell Carcinoma

Krüppel-like factor 4 (KLF4) is a transcription factor with diverse functions in various cancer types; however, the function of KLF4 in clear cell renal cell carcinoma (ccRCC) carcinogenesis remains unknown. In this study, we initially examined KLF4 expression by using a cohort of surgically removed ccRCC specimens and cell lines. Results indicated that the transcription and translation of KLF4 were lower in ccRCC tissues than in patient-matched normal tissues. Furthermore, the KLF4 expression was significantly downregulated in the five ccRCC cell lines at protein and mRNA levels compared with that in normal renal proximal tubular epithelial cell lines (HKC). KLF4 downregulation was significantly correlated with tumor stage and tumor diameter. Promoter hypermethylation may contribute to its low expression. In addition, in vitro studies indicated that the KLF4 overexpression significantly inhibited proliferation in human ccRCC cell lines 786-O and ACHN. Moreover, the KLF4 overexpression arrested the cell cycle progress at the G1/S phase transition by upregulating p21WAF1/CIP1 expression and downregulating cyclin D1 expression, KLF4 knockdown in HKC cells did the opposite. In vivo studies confirmed the anti-proliferative effect of KLF4. Our results suggested that KLF4 had an important function in suppressing the growth of ccRCC.
Addressing the data-association problem for multiple sound source localization using DOA estimates

In this paper, we consider the data association problem that arises when localizing multiple sound sources using direction of arrival (DOA) estimates from multiple microphone arrays. In such a scenario, the association of the DOAs across the arrays that correspond to the same source is unknown and must be found for accurate localization. We present an association algorithm that finds the correct DOA association to the sources based on features extracted for each source that we propose. Our method results in high association and localization accuracy in scenarios with missed detections, reverberation, and noise and outperforms other recently proposed methods.
Effects of Primary Blast Overpressure on Retina and Optic Tract in Rats

Blast has been the leading cause of injury, particularly traumatic brain injury and visual system injury, in combat operations in Iraq and Afghanistan. We determined the effect of shock tube-generated primary blast on retinal electrophysiology and on retinal and brain optic tract histopathology in a rat model. The amplitude of a- and b-waves on the electroretinogram (ERG) for both right and left eyes were measured prior to a battlefield simulation Friedlander-type blast wave and on 1, 7, and 14 days thereafter. Histopathologic findings of the right and left retina and the right and left optic tracts (2.8 mm postoptic chiasm) were evaluated 14 days after the blast. For two experiments in which the right eye was oriented to the blast, the amplitude of ERG a- and b-waves at 7 days post blast on the right side but not on the left side was diminished compared to that of sham animals (P = 0.005–0.01) Histopathologic injury scores at 14 days post blast for the right retina but not the left retina were higher than for sham animals (P = 0.01), and histopathologic injury scores at 14 days for both optic tracts were markedly higher than for shams (P < 0.0001). Exposure of one eye to a blast wave, comparable to that causing human injury, produced injury to the retina as determined by ERG and histopathology, and to both postchiasmatic optic tracts as determined by histopathology. This model may be useful for analyzing the effect of therapeutic interventions on retinal damage due to primary blast waves.
Prognostic models for stable coronary artery disease based on electronic health record cohort of 102 023 patients

Aims The population with stable coronary artery disease (SCAD) is growing but validated models to guide their clinical management are lacking. We developed and validated prognostic models for all-cause mortality and non-fatal myocardial infarction (MI) or coronary death in SCAD. Methods and results Models were developed in a linked electronic health records cohort of 102 023 SCAD patients from the CALIBER programme, with mean follow-up of 4.4 (SD 2.8) years during which 20 817 deaths and 8856 coronary outcomes were observed. The Kaplan–Meier 5-year risk was 20.6% (95% CI, 20.3, 20.9) for mortality and 9.7% (95% CI, 9.4, 9.9) for non-fatal MI or coronary death. The predictors in the models were age, sex, CAD diagnosis, deprivation, smoking, hypertension, diabetes, lipids, heart failure, peripheral arterial disease, atrial fibrillation, stroke, chronic kidney disease, chronic pulmonary disease, liver disease, cancer, depression, anxiety, heart rate, creatinine, white cell count, and haemoglobin. The models had good calibration and discrimination in internal (external) validation with C-index 0.811 (0.735) for all-cause mortality and 0.778 (0.718) for non-fatal MI or coronary death. Using these models to identify patients at high risk (defined by guidelines as 3% annual mortality) and support a management decision associated with hazard ratio 0.8 could save an additional 13–16 life years or 15–18 coronary event-free years per 1000 patients screened, compared with models with just age, sex, and deprivation. Conclusion These validated prognostic models could be used in clinical practice to support risk stratification as recommended in clinical guidelines.
CMOS quadrature voltage controlled oscillator uses in measuring blood glucose applications

This letter proposes a LC-resonance CMOS quadrature voltage controlled oscillator (QVCO) uses in wireless communications and measuring blood glucose applications fabricated in the 0.18 µm UMC CMOS process and describes the circuit design, working principle and measured results of the QVCO. The QVCO circuit is composed of a LC resonance, PMOS cross-coupled, bias resister, and DC block of capacitor in QVCO. A quadrature voltage controlled oscillator with four output ports. At the supply voltage of 1.3 V, the dc consumption is 6.89mW. At the output power of the QVCO has tuning range which is from 2.12GHz to 2.41 GHz.
Content Semantic Similarity Boosted Collaborative Filtering

Collaborative filtering (CF) is one of the most promising techniques in recommender systems, providing personalized recommendations to users based on their previously expressed preferences in the form of ratings and those of other similar users. In practice, a large number of ratings from similar users are not available, due to the sparsity inherent to rating data. Consequently, recommendation quality can be poor. In this paper, we present an effective content semantic similarity boosted CF framework (CSSCF) for combining content meaning and collaboration. Our approach uses a content semantic similarity based rater (CSSR) to enhance existing user data, and then provides personalized suggestions through collaborative filtering. The new model is however more robust to data sparsity, because missing ratings are rated using the CSSR in advance. Experiments demonstrate that the proposed method gives better recommendations than pure collaborative filter.
Universal space-time codes from demultiplexed trellis codes

In broadcast scenarios or in the absence of accurate channel probability distribution information, code design for consistent channel-by-channel performance, rather than average performance over a channel distribution, may be desirable. Root and Varaiya's compound channel theorem for linear Gaussian channels promises the existence of universal codes that operate reliably whenever the channel mutual information (MI) is above the transmitted rate. This paper presents two-dimensional trellis codes that provide such universal performance over the compound linear vector Gaussian channel when demultiplexed over two, three, and four transmit antennas. The presented trellis codes, found by exhaustive search, guarantee consistent performance on every matrix channel that supports the information transmission rate with an MI gap that is similar to the capacity gap of a well-designed additive white Gaussian noise (AWGN)-specific code on the AWGN channel. As a result of their channel-by-channel consistency, the universal trellis codes presented here also deliver comparable, or, in some cases, superior, frame-error rate and bit-error rate performance under quasi-static Rayleigh fading, as compared with trellis codes of similar complexity that are designed specifically for the quasi-static Rayleigh-fading scenario
The Integration of user knowledge to learn a specialized decision tree from a real-life data: an empirical and computational study

Decision trees are the most applicable technique of data mining, because of its power and its simplicity of interpretation. However, learning decision trees from medium to large dataset are different from learning from small dataset, especially when data contain instance that are semantically independent, this lead to lose in accuracy. In our approach, we build patterns according to some criteria with the help of the users‟ knowledge. We use knowledge to filter and reduce the database and remove the data; this is considered as a noise. Learning from the filtered data can generate more accurate and small decision tree. In our experimentation, we show the difference in accuracy between learning over the entire data and filtered data.