Miracle-dz/UniFAR-FTUs huggingface dataset:
Transitioning between vocational and university education in Australia: the impact of the vocational education experience on becoming a university student

Abstract Research was undertaken to understand the experiences of students entering the University of Technology Sydney through vocational education pathways, with the aim of improving transition support for these students. A theme which emerged was the pivotal role played by the students’ vocational education teachers in facilitating their students’ self-belief that they had the capacity to succeed in their studies. This, together with increased academic skills and knowledge, provide students with the confidence and capabilities to continue on to university. The theoretical framework of institutional habitus and Gale and Parker’s conceptualisation of transition are utilised to analyse findings about the impact of the vocational educational learning environment upon students’ transition to university, and how the students experience and manage the transition between the different education sectors.
Real-time Flow Computations using an Image Based Depth Sensor and GPU Acceleration

This work presents a recently developed computational fluid dynamics (CFD) solver based on the Lattice Boltzmann Method (LBM) that is algorithmically optimized for CUDA, allowing for real-time user interaction via stylus input, or a Microsoft Kinect sensor. The linear operations and the local nature of the Lattice Boltzmann equations are well suited to massively parallel architecture and thus ideal for Graphical Processing Units (GPUs). The 2D LBM solver is capable of speeds of up to 820 million lattice updates per second (MLUPS), the equivalent of 2600 time steps per second at a resolution of 640x480 points.
Feature Selection Using Ensemble Based Ranking Against Artificial Contrasts

In contrast to typical variable selection methods such as CFS, tree-based ensemble methods can produce numerical importances of input variables of mixed type considering all variable interactions, not just one or two variables at a time. However, they do not indicate a cut-off point: how to set a threshold to the importance. This paper presents an efficient approach to doing this using artificial contrast variables. The result is a truly autonomous variable selection method in both multilevel classification and regression settings that can handle huge number of variables of mixed type with potentially non randomly missing values, resistant to noise both in input and response space, considers all variable interactions, and does not require a pre-set number of important variables.
Snap-together visualization: a user interface for coordinating visualizations via relational schemata

Multiple coordinated visualizations enable users to rapidly explore complex information. However, users often need unforeseen combinations of coordinated visualizations that are appropriate for their data. Snap-Together Visualization enables data users to rapidly and dynamically mix and match visualizations and coordinations to construct custom exploration interfaces without programming. Snap's conceptual model is based on the relational database model. Users load relations into visualizations then coordinate them based on the relational joins between them. Users can create different types of coordinations such as: brushing, drill down, overview and detail view, and synchronized scrolling. Visualization developers can make their independent visualizations snap-able with a simple API. Evaluation of Snap revealed benefits, cognitive issues, and usability concerns. Data savvy users were very capable and thrilled to rapidly construct powerful coordinated visualizations. A snapped overview and detail-view coordination improved user performance by 30-80%, depending on task.
Discovery of context-specific ranking functions for effective information retrieval using genetic programming

The Internet and corporate intranets have brought a lot of information. People usually resort to search engines to find required information. However, these systems tend to use only one fixed ranking strategy regardless of the contexts. This poses serious performance problems when characteristics of different users, queries, and text collections are taken into account. We argue that the ranking strategy should be context specific and we propose a , new systematic method that can automatically generate ranking strategies for different contexts based on genetic programming (GP). The new method was tested on TREC data and the results are very promising.
Statistical methods for diameter constrained reliability estimation in rare event scenarios

The object under study is a metric associated to each graph, called diameter constrained reliability. The exact evaluation of the diameter constrained reliability belongs to the class of NP-Hard problems, and becomes prohibitive in large graphs. In the literature, several estimation methods have been developed, inspired in statistics, combinatorics, algebra and other branches of knowledge. We are focused on the statistical evaluation of the diameter constrained reliability under rare event scenarios. Under these assumptions (highly reliable networks), Crude Monte Carlo method is not accurate. More sophisticated methods meet both accuracy and bounded relative error. We compare the performance of two variance reduction methods, to know, Approximate Zero Variance Importance Sampling (AZVIS) and Recursive Variance Reduction (RVR). These methods are compared to Crude Monte Carlo in terms of accuracy and computational effort. Numerical comparisons show the improvement in the global performance of these alternative statistical methods. The paper is closed with a discussion of novel hybrid methods to address network reliability analysis in robust networks, when failures represent a rare event.
SeaRum: A Cloud-Based Service for Association Rule Mining

Large volumes of data are being produced by various modern applications at an ever increasing rate. These applications range from wireless sensors networks to social networks. The automatic analysis of such huge data volume is a challenging task since a large amount of interesting knowledge can be extracted. Association rule mining is an exploratory data analysis method able to discover interesting and hidden correlations among data. Since this data mining process is characterized by computationally intensive tasks, efficient distributed approaches are needed to increase its scalability. This paper proposes a novel cloud-based service, named SEARUM, to efficiently mine association rules on a distributed computing model. SEARUM consists of a series of distributed MapReduce jobs run in the cloud. Each job performs a different step in the association rule mining process. As a case study, the proposed approach has been applied to the network data scenario. The experimental validation, performed on two real network datasets, shows the effectiveness and the efficiency of SEARUM in mining association rules on a distributed computing model.
Hite: Histogram-based traffic estimation on real-time trajectory

Traffic estimation in real-time is able to help users to choose a better router timely, and also saves the travel time and energy consumption. Several methods have been investigated to estimate the traffic condition based on large amounts of trajectory data. However, these methods need to be maintained with tremendous overhead if the traffic varies frequently. We build a system Hite, a distributed stream processing platform for real-time traffic estimation. In Hite a histogram-based model is proposed to forecast the traffic condition and can be incremental updated in real-time. Experimental results demonstrate that the performance for traffic estimation by Hite is better than existing method ARIMA and ES methods.
mRNA Structural Constraints on EBNA1 Synthesis Impact on In Vivo Antigen Presentation and Early Priming of CD8+ T Cells

Recent studies have shown that virally encoded mRNA sequences of genome maintenance proteins from herpesviruses contain clusters of unusual structural elements, G-quadruplexes, which modulate viral protein synthesis. Destabilization of these G-quadruplexes can override the inhibitory effect on self-synthesis of these proteins. Here we show that the purine-rich repetitive mRNA sequence of Epstein-Barr virus encoded nuclear antigen 1 (EBNA1) comprising G-quadruplex structures, limits both the presentation of MHC class I-restricted CD8+ T cell epitopes by CD11c+ dendritic cells in draining lymph nodes and early priming of antigen-specific CD8+ T-cells. Destabilization of the G-quadruplex structures through codon-modification significantly enhanced in vivo antigen presentation and activation of virus-specific T cells. Ex vivo imaging of draining lymph nodes by confocal microscopy revealed enhanced antigen-specific T-cell trafficking and APC-CD8+ T-cell interactions in mice primed with viral vectors encoding a codon-modified EBNA1 protein. More importantly, these antigen-specific T cells displayed enhanced expression of the T-box transcription factor and superior polyfunctionality consistent with the qualitative impact of translation efficiency. These results provide an important insight into how viruses exploit mRNA structure to down regulate synthesis of their viral maintenance proteins and delay priming of antigen-specific T cells, thereby establishing a successful latent infection in vivo. Furthermore, targeting EBNA1 mRNA rather than protein by small molecules or antisense oligonucleotides will enhance EBNA1 synthesis and the early priming of effector T cells, to establish a more rapid immune response and prevent persistent infection.
Spatio-Temporal Detection and Isolation: Results on PETS 2005 Datasets

Recently developed statistical methods for background subtraction have made increasingly complicated environments amenable to automated analysis. Here we illustrate results for spatio-temporal background modeling, anomaly detection, shape description, and object localization on relevant parts of the PETS2005 data set. The results are analyzed both to distinguish between difficulties caused by different challenges within the data set, especially dropped frames, recovery time from camera motions, and what can be extracted with very weak assumptions about object appearance.
Stem injection of 15N–NH4NO3 into mature Sitka spruce (Picea sitchensis)

Stem injection techniques can be used to introduce 15N into trees to overcome a low variation in natural abundance and label biomass with a distinct 15N signature, but have tended to target small and young trees, of a variety of species, with little replication. We injected 98 atom% 15N ammonium nitrate (NH4NO3) solution into 13 mature, 9- to 13-m tall edge-profile Sitka spruce trees in order to produce a large quantity of labelled litter, examining the distribution of the isotope throughout the canopy after felling in terms of both total abundance of 15N and relative distribution of the isotope throughout individual trees. Using a simple mass balance of the canopy alone, based on observed total needle biomass and modelled branch biomass, all of the isotope injected was accounted for, evenly split between needles and branches, but with a high degree of variability both within individual trees, and among trees. Both 15N abundance and relative within-canopy distribution were biased towards the upper and middle crown in foliage. Recovery of the label in branches was much more variable than in needles, possibly due to differences in nitrogen allocation for both growth and storage, which differ seasonally between foliage and woody biomass.
Performance Analysis of a QoS Guaranteed Dynamic Channel Reservation for Handoff Prioritization in Cellular Mobile Networks

The handoff prioritization schemes in channel allocation are often carefully scheduled for it is more serious to drop an ongoing call than blocking a new call. In order to let the number of reserved channel for handoff prioritization be compatible with the real time handoff traffic variation in a cluster and seek the balance between the new call blocking probability and the handoff failure probability in cellular mobile networks, in this paper, a dynamic channel reservation scheme (DCRS) adopted with quality of service (QoS) guarantee is investigated. A closed-form expression is also derived to calculate the number of reservation channel to adapt the handoff traffic variations. The system performance is improved more than 15% compared with the guard channel scheme (GCS).
Acute Stress Symptoms, Dissociation, and Depression Among Rescue Personnel 24 Hours after the Bet-Yehoshua Train Crash: The Effects of Exposure to Dead Bodies

Abstract Introduction: The immediate impact of exposure to severe wounds, dead bodies, and immediate threat to life has been understudied. Most studies focus on the acute stress disorder and/or post-traumatic stress disorder phases in order to assess rescue personnel's symptomatology, and tend to neglect the immediate exposure to elements of the disaster. Hypothesis: Rescue personnel who had a history of previous exposure to dead bodies would exhibit higher levels of acute stress symptoms, dissociation, and depressive symptoms within the 24 hours following a traumatic event. Methods: Twenty-three rescue personnel participated in the search and excavation of dead and mutilated bodies following the Bet-Yehoshua train crash in Israel.The rescue personnel group was divided based on previous exposure to dead bodies. Each participant completed a demographic questionnaire, which included a question on perceived threat to life, the impact of event scale revised, the dissociative experience scale, and the center of epidemiologic studies depression questionnaire. Student's t-tests, along with multivariate analysis of covariance (MANCOVA) were conducted in order to learn which factors are related to psychiatric symptomatology following the immediate exposure to such stressors. Results: Among rescue personnel, those with previous exposure to dead bodies did not differ in their levels of acute stress symptoms, dissociation, and depressive symptoms from those who were not previously exposed to dead bodies. Conclusions: These results may suggest the possibility that the impact of exposure to dead bodies does not emerge in the acute stress reactions (ASR) phase (up to 24 hours after the event), but later when people have time to process the trauma. Another possibility is that the rescue coping mechanisms of detachment may serve as a buffer for the horrific sights encountered during the ASR period.
Text Enhanced Recommendation System Model Based on Yelp Reviews

In this paper, we introduce a useful natural language model for improving recommendation systems using collaborative filtering algorithms on ordinal ratings data. Since their inception, recommendation systems have evolved from simple user-business-rating matrices to complex systems that can consume multiple dimensions. Using Yelp’s competition data set, we explore extending these dimensions to include natural language by leveraging a dual neural network architecture to produce a new and improved star rating system which offers potential improvements to collaborative filtering based recommendation systems.
New Message-Passing Decoding Algorithm of LDPC Codes by Partitioning Check Nodes

In this paper, we propose a new sequential message-passing decoding algorithm of low-density parity-check (LDPC) codes by partitioning check nodes. This new decoding algorithm shows better bit error rate(BER) performance than that of the conventional message-passing decoding algorithm, especially for small number of iterations. Analytical results tell us that as the number of partitioned subsets of check nodes increases, the BER performance becomes better. We also derive the recursive equations for mean values of messages at variable nodes by using density evolution with Gaussian approximation. Simulation results also confirm the analytical results.
Modeling and Control of Variable Speed Wind Turbine Generators for Frequency Regulation

Wind turbine generators (WTG) can participate in system frequency regulation via virtual inertial controllers (VIC). In the presence of frequency disturbances, VIC temporarily regulates the WTG power output forcing it to release/absorb kinetic energy into/from the grid. With increasing penetration of WTGs in power systems, grid operators require these generators to provide frequency regulation services; however, kinetic energy release/absorption can destabilize WTGs. Hence, to address these issues, a new large-perturbation nonlinear WTG model is proposed in this paper, based on the WTG internal response that is used to tune typical VICs. Novel worst case and optimal VIC tuning approaches are also proposed and discussed based on the developed WTG nonlinear model. Several simulations are presented to test and validate the proposed model and VIC tuning techniques, demonstrating their adequate performance and advantages.
Multi-Label Classification with Cutset Networks

In this work, we tackle the problem of Multi-Label Classification (MLC) by using Cutset Networks (CNets), weighted probabilistic model trees, recently proposed as tractable probabilistic models for discrete distributions. We employ CNets to perform Most Probable Explanation (MPE) inference exactly and efficiently and we improve a state-of-the-art structure learning algorithm for CNets by explicitly taking advantage of label dependencies. We achieve this by forcing the tree inner nodes to represent only feature variables and by exploiting structural heuristics while learning the leaf models. A thorough experimental evaluation on ten real-world datasets shows how the proposed approach improves several metrics for MLC, proving it to be competitive with problem transformation methods like classifier chains.
A Case Study in Dynamic Belief Networks: Monitoring Walking, Fall Prediction and Detection

The task is to monitor walking patterns and give early warning of falls using foot switch and mercury trigger sensors. We describe a dynamic belief network model for fall diagnosis which, given evidence from sensor observations, outputs beliefs about the current walking status and makes predictions regarding future falls. The model represents possible sensor error and is parametrised to allow customisation to the individual being monitored. 2
Functional determinants in transit sequences: import and partial maturation by vascular plant chloroplasts of the ribulose-1,5- bisphosphate carboxylase small subunit of Chlamydomonas

The precursor of the ribulose-1,5-bisphosphate carboxylase small subunit and other proteins from Chlamydomonas reinhardtii are efficiently transported into chloroplasts isolated from spinach and pea. Thus, similar determinants specify precursor-chloroplast interactions in the alga and vascular plants. Removal of all or part of its transit sequence was found to block import of the algal small subunit into isolated chloroplasts. Comparison of available sequences revealed a nine amino acid segment conserved in the transit sequences of all small subunit precursors. A protease in the vascular plant chloroplasts recognized this region in the Chlamydomonas precursor and produced an intermediate form of the small subunit. We propose that processing of the small subunit precursor involves at least two proteolytic events; only one of these has been evolutionarily conserved.
Constructive memory for situated design agents

Design is situated. “Situatedness” in designing entails the explicit consideration of the state of the environment, the knowledge and experiences of the designer, and the interactions between the designer and the environment during designing. Central to the notion of situatedness is the notion of design situation and constructive memory. A design situation models a particular state of interaction between a design agent and the environment at a particular point in time. Memory construction occurs whenever a design agent uses past experiences and knowledge within the current design environment in a situated manner. This paper is concerned with the development of an agent-based computational design tool that takes into consideration the notion of situatedness in designing. A key element of this tool is a constructive memory system that supports the dynamic nature of designing. Memories of past experiences are constructed as required by the current situation, and past experiences are refined for future utility according to the current interactions between the agent and the environment. This latter case of knowledge improvement is illustrated through a series of experiments that demonstrates the effect of grounding on the operating modes and responses of a constructive memory system.
FAKTOR-FAKTOR YANG MEMPENGARUHI PERAWAT DALAM PENERAPAN 6 SKP (SASARAN KESELAMATAN PASIEN) PADA AKREDITASI JCI (JOINT COMMISSION INTERNATIONAL) DI RUANG RAWAT INAP RUMAH SAKIT PANTI WALUYA MALANG

Perbaikan demi perbaikan dalam mutu pelayanan kesehatan harus dilakukan untuk mendapatkan status terakreditasi tingkat paripurna. Fokus akreditasi JCI adalah keselamatan pasien yang tertuang dalam standart 6 Sasaran Keselamatan Pasien. Sebagian besar standart 6 Sasaran Keselamatan Pasien diterapkan oleh perawat, khususnya perawat di ruang rawat inap yang dituntut untuk selalu berinteraksi dengan pasien. Tujuan penelitian ini adalah untuk mengetahui faktor-faktor yang mempengaruhi perawat dalam penerapan 6 SKP. Serta faktor yang paling dominan yang mempengaruhi penerapan 6 SKP (Sasaran Keselamatan Pasien) Pada Akreditasi JCI (Joint Commission International) Di Ruang Rawat Inap RS. Panti Waluya Malang. Desain penelitian yaitu deskriptif analitik dengan pendekatan cross sectional. Intrument penelitian untuk variabel independen menggunakan kuisioner dan variabel dependen menggunakan checklist. Populasinya adalah semua perawat pelaksana di ruang rawat inap dewasa sebanyak 186 orang. Sampel sebanyak 124 orang ditentukan dengan teknik proporsional simple random sampling. Analisis data menggunakan uji Chi Square (p<0,05). Berdasarkan hasil analisa Bivariat, faktor yang berpengaruh terhadap 6 SKP yaitu jumlah tanggungan (p=0,018), lama bekerja (p=0,018), pengetahuan perawat (p=0,002), motivasi perawat (p=0,019), supervisi (p=0,001), dan pengaruh organisasi (p=0,029). Berdasarkan hasil analisa multivariat didapatkan faktor yang paling dominan adalah Pengetahuan dengan (OR=17,299), sehingga dapat diasumsikan bahwa seseorang yang memiliki pengetahuan baik cenderung lebih baik dalam melakukan penerapan 6 SKP dibandingkan dengan perawat yang memiliki pengetahuan rendah. Direkomendasikan bagi pihak rumah sakit agar melakukan peningkatan pengetahuan dan ketrampilan kinerja perawat dengan mengadakan pelatihan secara rutin dan berkesinambungan, khususnya pelatihan tentang 6 Sasaran Keselamatan Pasien.
A Multi-Agent Based Decentralized Algorithm for Social Network Community Mining

Research has shown that many social networks come into being hierarchically based on some basic building blocks called communities, within which the social interactions are very intensive, but between which they are very weak. Network community mining algorithms aim at efficiently and effectively discovering all such communities from a given network. Many related methods have been proposed and applied to different areas including social network analysis, gene network analysis and web clustering engine. Most of the existing methods for mining communities are centralized. In this paper, we present a multi-agent based decentralized algorithm, in which a group of autonomous agents work together to mine a network through a proposed self-aggregation and self-organization mechanism. Thanks to its decentralized feature, our method is potentially suitable for dealing with distributed networks, whose global structures are hard to obtain due to their geographical distributions, decentralized controls or huge sizes. The effectiveness of our method has been tested against different benchmark networks.