Miracle-dz/UniFAR-FTUs huggingface dataset:
Atomic structure insight into crystallization of undercooled liquid metal Zr during isothermal relaxation processes

ABSTRACT The crystallization of undercooled liquid zirconium (Zr) was investigated via molecular dynamics simulations. The atomic structures were characterised and analysed by means of pair distribution function, angular distribution function, and the largest standard cluster analysis. At low temperature (T = 1000 K), it is found that the crystallization begins at the initial stage of relaxation and takes the pathway of undercooled liquid (UCL)→BCC→HCP instead of UCL→HCP. The HCP-type medium-range orders (MROs) are formed from the inside of the precursors formed by the BCC-type MROs, and growing at the cost of the precursors, in agreement with the Ostwald’s step rule. The number of MROs is found to play a key role in forming the crystal phases in this process. However, at high temperature (T = 1200 K) the time-scale to reach crystallization is two orders of magnitude bigger than that of T = 1000 K. No intermediate stage of crystallization is observed. The undercooled Zr melts are directly transformed into BCC crystal phases. The max size of BCC-MROs is intimately correlated with the crystallization.
Automatic Segmentation Method of Garment Figure Based on Convolutional Neural Network

Aiming to the problem that the rapid acquisition of clothing style map requires manual participation and takes a long time and heavy task, an automatic image segmentation method based on convolution neural network is proposed. Firstly, we denoised normalized and semantically annotated the sequence image in order to produce the data set. Then, we trained the convolution neural networks, which were improved with fusion multi-scale feature and residual connection, and obtained the optimized convolution neural network segmentation model. Finally, it loaded the pre-segmentation image into the optimized model to get the normalized mask pattern, and used the cubic spline interpolation methods to restore the resolution and result of the HD segmentation with the original mask. In this paper, the results of Photoshop segmentation are as the reference standard. The experimental results show that the accuracy of the method is close to the reference standard, and the batch segmentation can be realized automatically, which can solve the heavy problem of the target segmentation task in 3D reconstruction. Keywords-computer vision; image segmentation; convolutional neural networks; multi-scale feature fusion; residual connection
THE REPRESENTATION OF LOCAL CULTURE IN INDONESIAN EFL TEXTBOOKS: RATIONALES AND IMPLICATIONS

This article discusses the representation of local culture in EFL Junior High School textbooks in Indonesia. The two primary objectives of this paper are: 1) to reveal the primary reasons of incorporating local culture in EFL textbooks, and 2) to suggest further implications in responding to the incorporation of local culture in EFL textbooks. The discussion begins with the current EFL context in Indonesia with the emphasis on the considerable changes of the curriculum implementation in the Indonesian context, in which it attempts to reveal the representation of local culture implied within the 2013 curriculum. As its main arguments, this article argues that there are two predominant reasons for representing local culture in the EFL Junior High School textbooks in Indonesia which include promoting the sense of familiarity to Junior High School students in learning English as well as strengthening the aspects of national culture and identity portrayed in the textbooks. Furthermore, this article also discusses the future implications in responding to the representation of local culture in EFL Junior High School textbooks in Indonesia, including the needs to enhance the role of teachers as well as the importance of incorporating more diverse cultural representation in the textbooks.
The Relationship between Performance of Students in Internal Assessment and Final Summative Assessment in the Subject of Physiology in a North Indian Medical College

Background Assessments, if suitably conducted, can serve several goals and provide many advantages to all stakeholders-the medical students, the teaching faculty, the universities and the society as a whole. Assessment in medical education is mostly of two forms either formative or summative. Although it is generally assumed that the performance of a student in formative assessment will act as a predictor for performance in the summative examination not many studies are available support it. Objectives To explore whether performance in the continuous internal assessment, correlates with the final summative assessment in first year medical students in the subject of Physiology for the last three years. Materials and Method This study was conducted over a period of six months at SKIMS medical college, Srinagar. It was a observational record-based study on 280 students who took a thorough internal assessment and final examination in the subject of physiology in the batches 2014–15, 2015–16 and 2016–17. Results The internal assessment marks showed a positive correlation with marks obtained in final assessment or final university exams in all three batches, which is statistically highly significant (p<0.01). It was also observed that the percentage of final university marks was higher as compared to internal assessment marks in all batches. Conclusion The present study revealed a positive correlation between internal assessment and final university marks supporting the general assumption that the performance of a student in formative assessment will act as a predictor for performance in the final summative examination.
Modeling the impact of safety climate on process safety in a modern process industry: The case of the UAE’s oil-refining industry

Abstract This study examines the psychometric properties of process safety and models the relationships between top management commitment to safety (Model 1), top management safety practices (Model 2), and supervisory safety behavior (Model 3). It is hypothesized that these determinants are positively related to process safety. Data were provided by 180 workers in an oil refining company in the UAE. The results show high reliability in the overall process safety score and dimensions (employees’ engagement in safety, employees’ safety performance, and safe working environment). Confirmatory factor analyses indicate that 12 items can be combined into a higher-order process safety factor model. The findings from the controlled models demonstrate that top management commitment to safety, top management safety practices, and supervisory safety behavior are positively and significantly related to process safety and its dimensions. By contrast, in the freely estimated model, top management commitment to safety and top management safety practices are not significantly related to process safety. Overall, process safety has very good psychometric properties, suggesting that it can be used for safety research and future research related to psychological–behavioral safety.
Implications of Princeling Connections for Business Groups and Internal Capital Markets: Evidence from Chinese Listed Firms

University of Wollongong Copyright Warning You may print or download ONE copy of this document for the purpose of your own research or study. The University does not authorise you to copy, communicate or otherwise make available electronically to any other person any copyright material contained on this site. You are reminded of the following: This work is copyright. Apart from any use permitted under the Copyright Act 1968, no part of this work may be reproduced by any process, nor may any other exclusive right be exercised, without the permission of the author. Copyright owners are entitled to take legal action against persons who infringe their copyright. A reproduction of material that is protected by copyright may be a copyright infringement. A court may impose penalties and award damages in relation to offences and infringements relating to copyright material. Higher penalties may apply, and higher damages may be awarded, for offences and infringements involving the conversion of material into digital or electronic form. Unless otherwise indicated, the views expressed in this thesis are those of the author and do not necessarily represent the views of the University of Wollongong.
Student Expression Recognition in Smart Education Environment based on Convolutional Neural Network

With the rapid construction of smart education and the continuous development of artificial intelligence technology, the advantages of assessment based on convolutional neural networks for teaching scenarios in smart learning environments are emerging. The practical applications tend to be more profound. We propose a convolutional neural network-based student expression recognition model and an assessment algorithm in the smart education environment to analyze the external learning characteristics of learners and realize the assessment and diagnosis of classroom learning effects. A student facial expression recognition dataset based on actual classroom scenarios is constructed, and teaching assessment is recognized by fusing classroom student expression states. Experiments are conducted on the publicly represented dataset and the self-built student expression recognition dataset to verify the effectiveness of the proposed student expression recognition model and the feasibility of the self-built student expression dataset. Finally, the proposed model achieved 89.31 % accuracy on the RAF -DB dataset and 80.59% on the self-built dataset. The results show that the proposed method performs well on both the public expression dataset and the self-built student expression dataset.
FM Fingerprint Database Online Construction and Calibration based on Propagation Model and PDR Fusion Indoor Positioning

The demand for indoor positioning-based services grows rapidly. Fingerprinting technology has been widely applied in indoor positioning since its high accuracy and low dependence on external devices. However, the main dilemma limiting its development is the time-consuming and laborious site survey in the offline stage. In this paper, we use FM radio signal as the navigation source, and present a completely online FM indoor fingerprint database construction and calibration method based on the propagation model. With the floorplan obtained only, FM data initially collected inside the building is utilized to estimate the model parameters and build the fingerprint database. In the stage of online calibration and positioning, PDR is used to calibrate the database and constrain the fingerprint matching range, then an EKF-based fusion method is developed to improve the positioning performance and ensure the accuracy of calibration. Considering that the little data collected at the initial walking stage may cause inaccurate estimation of model parameters, different calibration strategies are designed using the least square and characteristics of FM fingerprint distribution to increase the stability and accelerate the convergence speed of database error. Field experiments are conducted in the campus office building, and the results indicate the effectiveness of the proposed method.
Algorithmic Radar, a Novel Low-Power Architecture for High-Accuracy PMCW Radar

A novel architecture for high-accuracy PMCW radar is proposed that uses analog correlation in combination with a priority algorithm. The architecture applies Compressed Sensing and focuses on the radar targets, significantly reducing power consumption, without sacrificing the maximum speed or measurement time of individual targets. When using APAS codes, the architecture’s flexibility supports suppression of correlation artifacts.
Structural Characteristics and Chemical Composition of Cu2MgxZnl1-xSnS4Films for Solar Cells

Films of solid solutions of Cu2MgxZn1-xSnS4(x = 0.1; 0.2; 0.3; 0.4) were obtained from precursors with different zinc and magnesium concentrations ratios by spray pyrolysis. Their structural characteristics and chemical composition have been studied. X-ray diffractometry method showed that Mg atoms are successfully embedded in the material's crystal lattice. The resulting layers contain up to 9% magnesium and have a kesterite structure. This statement is confirmed by Raman spectroscopy. Analysis of the results shows an improvement in the structural characteristics of the layers with an increasing concentration of Mg atoms embedded in the crystal lattice of the material. Therefore, the films of solid solution Cu2MgxZn1-xSnS4are a promising material for the absorbing layers of the third-generation solar cells.
Lidar Simulation for Robotic Application Development: Modeling and Evaluation

Given the increase in scale and complexity of robotics, robot application development is challenging in the real world. It may be expensive, unsafe, or impractical to collect data, or test systems, in reality. Simulation provides an answer to these challenges. In simulation, data collection is relatively inexpensive, scenes can be procedurally generated, and state information is trivially available. Despite these benefits, the use of simulators is often limited to the early stages of application development. In this work, we take steps to close the gap between simulation and reality, for Lidar simulation. We adopt the perspective that the eventual purpose of a simulator is a tool for robot application development. Our framework for sensor simulation consists of three components. The first is sensor modeling, which describes how a sensor interacts with a scene. The second is scene generation, needed to construct simulated worlds corresponding to reality. The third is simulator evaluation, based on comparing real and simulated data. We formalize the intuition that application performance must be similar in simulation and reality, using an application-level loss. Our framework is broadly applicable to simulating sensors other than Lidars. We instantiate our framework for two domains. The first domain is planar Lidar simulation in indoor scenes. We construct a high-fidelity simulator using a parametric sensor model. We show how application development paths for our simulator are closer to reality, compared to a baseline. We also pose sensor modeling as a case of distribution regression, which leads to a novel application of a nonparametric method that adapts to trends in sensor data. The second domain is Lidar simulation in o -road scenes. Our approach is to build a library of terrain primitives, derived from real Lidar observations. These are shown to generalize, resulting in an expressive simulator for complex o -road scenes. For this domain as well, we quantitatively demonstrate that our simulator is better for application development, compared to a baseline. Our work suggests a generic approach to building useful simulators. We view them as predictive models, and perform thorough tests on real data. We evaluate them with an application-level loss, which supports their greater use in the development cycle.
RFID based implant for knee prosthesis 2.0

We report the design of a passive UHF RFID implant for the in-vivo long-term monitoring of knee prosthesis. The electronic part of the implant is made of an RFID IC with sensing functionalities connected to a miniaturized antenna robust to the presence of surrounding metal and bio-tissues. The RFID implant featuring a size of $17 {\mathrm {mm}} \times 13$ mm with a thickness of 3 mm is validated with the help of simulations for various bio-tissues environments in presence of metal, and by practical measurements in free-space.
Metaphoric Conceptualisation of “LOVE IS A PLANT” in Gĩkũyũ: A Cognitive-Semantics Perspective

Starting from the premise that a metaphor is a cognitive mechanism in which one experiential domain is partially mapped onto a different experiential domain, this paper examines the metaphoric conceptualisation of “LOVE IS A PLANT” in Gĩkũyũ from a cognitive-semantics perspective. In order to achieve this objective, the study adopted the fundamental tenets of the Conceptual Metaphor Theory (CMT). An interview schedule was administered to 48 speakers of Gĩkũyũ by the researcher assisted by two research assistants of different gender. The data collected were subjected to the Metaphor Identification Procedure Vrije Universiteit (MIPVU) to find out whether the lexical items collected were metaphorical or not. Using four annotators, including the researcher, the study identified nine plantosemic metaphors which play a pivotal role in the understanding of love in Gĩkũyũ. The study concludes that plantosemic metaphors are conceptual phenomena which are integral component of the Gĩkũyũ cultural milieu expressed in language. Further, the study also notes that gender is an important variable through which males and females conceptualise love in Gĩkũyũ.
Online Neuro-Adaptive Learning For Power System Dynamic State Estimation

With the increased penetration of Distributed Generators (DGs) in the contemporary Power System, having knowledge of rapid Real-Time electro-mechanical Dynamic States has become crucial to ensure the safety and reliability of the grid. In the conventional SCADA based Dynamic State Estimation (DSE) speed was limited by the slow sampling rates (2-4 Hz) so State Estimation was limited to static states such as Voltage and Angle at the buses. Fortunately, with the advent of PMU based synchro-phasor technology in tandem with WAMS, it has become possible to avail rapid real time measurements at the network nodes. In this paper, we have proposed a novel Machine Learning (Artiﬁcial Intelligence) based Real-Time Neuro-adaptive Algorithm for rotor angle and angular frequency estimation of synchronous generators, here proposed algorithm is based on reinforcement learning and adapts in real-time to achieve accurate state estimates. Applicability and accuracy of the proposed method is demonstrated under the inﬂuence of noise and faulty conditions. Simulation is carried out on Multi-Machine scenario (68 bus 16 generator NETS-NYPS model).
Robust digital image watermarking using distortion-compensated dither modulation

In this paper, we propose a robust feature extraction based digital image watermarking method using Distortion- Compensated Dither Modulation (DC-DM). Our proposed local watermarking method provides stronger robustness and better flexibility than traditional global watermarking methods. We improve robustness by introducing feature extraction and DC-DM method. To extract the robust feature points, we propose a DAISY-based Robust Feature Extraction (DRFE) method by employing the DAISY descriptor and applying the entropy calculation based filtering. The experimental results show that the proposed method achieves satisfactory robustness under the premise of ensuring watermark imperceptibility quality compared to other existing methods.
Exploring Bitslicing Architectures for Enabling FHE-Assisted Machine Learning

Homomorphic encryption (HE) is the ultimate tool for performing secure computations even in untrusted environments. Application of HE for deep learning (DL) inference is an active area of research, given the fact that DL models are often deployed in untrusted environments (e.g., third-party servers) yet inferring on private data. However, existing HE libraries [somewhat (SWHE), leveled (LHE) or fully homomorphic (FHE)] suffer from extensive computational and memory overhead. Few performance optimized high-speed homomorphic libraries are either suffering from certain approximation issues leading to decryption errors or proven to be insecure according to recent published attacks. In this article, we propose architectural tricks to achieve performance speedup for encrypted DL inference developed with exact HE schemes without any approximation or decryption error in homomorphic computations. The main idea is to apply quantization and suitable data packing in the form of bitslicing to reduce the costly noise handling operation, Bootstrapping while achieving a functionally correct and highly parallel DL pipeline with a moderate memory footprint. Experimental evaluation on the MNIST dataset shows a significant ( $37\times$ ) speedup over the nonbitsliced versions of the same architecture. Low memory bandwidths (700 MB) of our design pipelines further highlight their promise toward scaling over larger gamut of Edge-AI analytics use cases.
Co-creation with vulnerable consumers – an action research case study of designing a pictorial language for logistics

Within the context of Universal Design and User Centered Design, vulnerable consumers, especially immigrants, still represent a fringe group when it comes to co-creation processes. Therefore, the aim of our study was to find ways to integrate immigrants into a co-creation process, namely the development and testing phase of a pictorial language for warehouse jobs. Within an action research case study consisting of a four-part storyline settled in a warehouse context, we identified and specified four key success factors that contribute to a successful co-creation project with vulnerable consumers: (1) integration at the very beginning of the co-creation project, (2) design of a suitable toolbox with service design, creative and varied methods, a storyline concept, use of easy language, additional icons and possibility of parallel use, (3) availability of network partners with access to vulnerable consumers as well as (4) the innovative setting of a living lab.
Determination of seasonal variation in reference evapotranspiration using regression analysis

In the study, variability in reference evapotranspiration (ET 0 ) from Southeastern Anatolian Project (GAP) area was investigated with linear regression method. For the purpose, seasonal ET 0 time series were formed from monthly reference evapotranspiration (ET 0 ). The ET 0 data sets of three sites showed a statistically significance decreasing trend while there was upward trend in some seasons of two sites. But, v ariation in all seasonal ET 0 time series of Kilis site was not detected.
Automated managed cloud-platforms based on energy policies

Delivering environmentally friendly services has become an important issue in Cloud Computing due to awareness provided by governments and environmental conservation organisations about the impact of electricity usage on carbon footprints. Cloud providers and cloud consumers (organisations/ enterprises) have their own defined \(green\) \(policies\) to control energy consumption at their data centers. At service management level, \(green\) \(policies\) can be mapped as \(energy\) \(management\) \(policies\) or \(management\) \(policies\). Focusing at cloud consumer's side, \(management\) \(policies\) are described by business managers which can change regularly. The continuous changing is based on the nature of the technical environment, changes in regulation; and business requirements. Therefore, there is a gap between the level of describing and implementing \(management\) \(policies\) in the cloud environment. This thesis provides a method to bridge that gap by (a) defining a specification for formulating \(management\) \(policies\) into executable form for an infrastructure-as-a-service (IaaS) cloud model; (b) designing a framework to execute the described \(management\) \(policies\) automatically; (c) proposing a modelling and analysis method to identify the potential \(energy\) \(management\) \(policy\) that would save energy-cost. Each aspect covered in the thesis is evaluated with a help of an Energy Management Case Study for a private cloud scenario.