Miracle-dz/UniFAR-FTUs huggingface dataset:
Specifying and Verifying Contract-driven Composite Web Services: a Model Checking Approach

As a promising computing paradigm in the new era of cross-enterprise e-applications, web services technology works as plugin mode to provide a value-added to applications using Service-Oriented Computing (SOC) and Service-Oriented Architecture (SOA). Verification is an important issue in this paradigm, which focuses on abstract business contracts and where services’ behaviors are generally classified in terms of compliance with / violation of their contracts. However, proposed approaches fail to describe in details both compliance and violation behaviors, how the system can distinguish between them, and how the system reacts after each violation. In this context, specifying and automatically generating verification properties are challenging key issues. This thesis proposes a novel approach towards verifying the compliance with contracts regulating the composition of web services. In this approach, properties against which the system is verified are generated automatically from the composition’s implementation. First, Business Process Execution Language (BPEL)that specifies actions within business processes with web services is extended to create custom activities, called labels. Those labels are used as means to represent the specifications and mark the points the developer aims to verify. A significant advantage of this labeling is the ability to target specific points in the design to be verified, which makes this verification very focused. Second, new translation rules from the extended BPEL into ISPL, the input language of the MCMAS model checker, are provided so that model checking the behavior of our contract-driven compositions is possible. The verification properties are expressed in the CTLC logic, which provides a powerful representation for modeling composition contracts using commitment-based multiagent interactions. A detailed case study with experimental results are also reported ins the thesis.
Automatic recognition method for quasi-circular oil depots in satellite remote sensing images

Oil depot is one type of typical martial targets and recognizing it is an important work for satellite image interpretation, which is typically implemented by manual operation of interpreters. To avoid hard work, an automatic recognition method for quasi-circular oil depots is presented. Firstly, the entropy method introduced by Kapur is used to get the optimum threshold, according to which, a binary image is obtained; then the white pixels in the binary image are clustered with the rule of nearest neighbor to generate some blobs whose area, posture ratio and rectangle tolerance are calculated at the same time. In the end, oil depots are recognized and their clusters are located by the knowledge that the shape of an oil depot is similar to a circle and some nearer oil depots are located. Results on the satellite images with high resolution are given to demonstrate the efficiency of our method.
The evolving knowledge base on leadership and teacher professional learning: a bibliometric analysis of the literature, 1960-2018

ABSTRACT This review of research used science mapping to analyze the knowledge base on leadership and teacher professional learning in K-12 schools. The review identified 793 Scopus-indexed documents related to this topic. Bibliographic data associated with these documents were examined using bibliometric analyses (e.g. author, journal and document citation and co-citation analysis). These analyses document intellectual progress in this field over time, identify key authors and documents, and institutions, and illuminate the intellectual structure of the field. Initially slow growth in the 1970s and 1980s was followed by markedly accelerating growth of research on this topic continuing to the present. We found three main research streams or Schools of Thought comprising this knowledge base: Teacher Leadership of Professional Learning Communities, Principal Leadership for Teacher Learning, Shared Leadership for Teacher Learning. The review identified a need for substantive reviews of research related to these Schools of Thought. Although the literature evidenced a geographical imbalance favoring Anglo-American-European scholarship, a trend of increasing contributions from other regions of the world was observed and should be encouraged. Scholars entering this field will wish to focus on the key authors and documents identified in this review.
The Design of Speech Recoding Devices for the Deaf: University of Manchester

The paper reviews the present status of speech recoding (frequency transposition) devices and concludes that convincing evidence for the superiority of recoding devices over, for example, selective amplification, does not yet exist. A number of fundamental questions requiring answers are then outlined, upon which the design of some ‘ideal’ recoding device appears to be contingent. Some interim design principles are, however, proposed and a description is given of a recoding device designed with these principles in mind. Finally, some initial, encouraging results with the device are reported, and various questions relating to the utility of the device, requiring further investigation, are indicated.
Creative capacity building : enhancing participatory design with rural Cambodian farmers: a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, Albany, New Zealand

Material from the thesis has been published as: Drain, A. , Shekar, A. , & Grigg, N. (2017). ‘Involve me and I’ll understand’: creative capacity building for participatory design with rural Cambodian farmers. CoDesign, 15(2), 110-127. https://doi-org.ezproxy. massey.ac. nz/10.1080/15710882.2017. 1399147 Drain, A. , Shekar, A. , & Grigg, N. (2018). Participatory design with people with disability in rural Cambodia: The creativity challenge. The Design Journal, 21(5), 685-706. https://doi-org.ezproxy. massey.ac. nz/10.1080/14606925.2018. 1488923 Drain, A. , Shekar, A. , & Grigg, N. (2018). Insights, solutions and empowerment: a framework for evaluating participatory design. CoDesign, 1-21. https://doi-org.ezproxy. massey.ac. nz/10.1080/15710882.2018. 1540641 Drain, A. , McCreery, M. , Shekar, A. , & Grigg, N. (2018). The collaborative design of a low-cost, accessible rice seeder for rural Cambodia: Trade-offs and challenges. IEEE Global Humanitarian Technology Conference Proceedings, 18-21 Oct. 2018. https://ieeexplore-ieee-org.ezproxy. massey.ac. nz/stamp/stamp.jsp? tp=&arnumber=8601663
Length-of-Stay Prediction Model of Appendicitis using Artificial Neural Networks and Decision Tree

For the efficient management of hospital sickbeds, it is important to predict the length of stay (LoS) of appendicitis patients. This study analyzed the patient data to find factors that show high positive correlation with LoS, build LoS prediction models using neural network and decision tree models, and compare their performance. In order to increase the prediction accuracy, we applied the ensemble techniques such as bagging and boosting. Experimental results show that decision tree model which was built with less number of variables shows prediction accuracy almost equal to that of neural network model, and that bagging is better than boosting. In conclusion, since the decision tree model which provides better explanation than neural network model can well predict the LoS of appendicitis patients and can also be used to select the input variables, it is recommended that hospitals make use of the decision tree techniques more actively.
Investigating Energy Efficiency of Physical and Virtual Machines in Cloud Computing

Power consumption of physical and virtual machines is a major challenge for small to larg-scale cloud computing data centers. Hundreds of thousands of physical and virtual machines run in cloud data center which consume huge amount of electricity. The energy efficiency and power optimization is an open research question. This project aims to present a study on the energy efficiency and power consumption of physical and virtual machine. The problem statement is addressed by running different workloads while scaling the frequencies of physical and virtual machines. The tools used in the project are Stress, NUMActl and CPUfrequtils. The power is measured through an external APC power distribution unit used in data centers. The results and experiments show that running different workloads on physical and virtual machine has different effect on the energy efficiency and power consumption. The study provides a good understanding of how different workloads effects the energy efficiency and power consumption of different servers.
Measuring Hearing Aid Benefit With the APHAB

American Journal of Audiology • Vol. 4 • 1059-0889/95/0403-0010 © American Speech-Language-Hearing Association P aul: The results shown by Ebinger, Holland, Holland, and Mueller (1995) in a poster presentation at the 1995 American Academy of Audiology meeting caught my eye. They reported on the use of the APHAB as a means to assess whether there were differences in benefit between Class D and K-AMP completely-in-the-canal (CIC) hearing aids. What interested me was that their subjects seemed to experience the same percentage of problems across the four APHAB listening situations in the aided condition as did subjects in our study (Paul, Talley, Pleasants, & Usrey, 1994) in which we used the PHAB to compare the benefit obtained with each of four different hearing aid gain and output selection approaches. As in the Ebinger et al. study, some of the aids we used were K-AMP, but most were Class D integrated receivers; however, all of our aids were ITEs. I constructed Figure 1 to show a comparison of the averaged unaided and aided frequency of problems for these two studies. The results for Ebinger et al. are averaged across the two types of hearing aid circuits used, whereas for our study, the results are averaged across the four hearing aid gain and output selection approaches. Figure 1 shows that the subject groups from these two studies appear to differ, on average, in the frequency of problems they experience across listening conditions without a hearing A forum in which two experts present their views on important and controversial issues of clinical or professional significance.
Fast treatment of pi-stacking using Density Functional Theory.

In this work, we concentrate on various unconventional Density Functional Theory approaches for calculations of electronic structure of molecules. In particular, we are motivated by gaining substantial speed-up of such calculations. Firstly, explore the Orbital-Free Density Functional Theory and show how one can compute the energies of dimers by performing large-scale optimization with more than a million variables. We also show that this approach does not give satisfactory results. Secondly, we investigate tight-binding methods and in particular show that Harris approximation gives good results when applied to calculations of energies of dimers. In the last section, we apply Harris approximation to molecular packing optimization,and show that the SPSA algorithm is capable of finding all minima of the energy surface.
Pseudo Temperature of Observed Data

A Boltzmann machine (BM) is a basic learning model forming a Markov random ﬁeld, and many approximate learning algorithms for it so far. In the present paper, a new strategy for approximate BM learnings is proposed by introducing a temperature of observed data which controls a smoothness of empirical distribution. By controlling the temperature, one can obtain better solutions to BM learning with an existing approximate learning algorithm
Extracting information from fuzzy models

The goal of this study is to quantify the importance of the input features in a fuzzy solution of two real-world problems. The first analysis definer the most effective ElectroCardioGraphic (ECG) measures for an automatic fuzzy recognition of cardiac arrhythmic beats. The second problem aims to assess the role played by duration, amplitude and pitch features of vocalic nuclei for the automatic recognition of prosodic stress in spoken American English. A similar analysis is performed for both problems by means of statistical decision trees under the form of the C4.5 algorithm. In general, decision trees and fuzzy systems seem to exploit the same or related input features for the analysis, especially if the input space dimension is low.
Social network site use and university adjustment

Abstract Previous research suggests that students often use social network sites (SNS) as academic distractors, though little is known about the association between students’ use of individual SNS and university adjustment. The present study examined academic outcomes associated with using Tumblr, Facebook, Twitter, Pinterest, and Instagram. University students (N = 422; Mage = 21.24) completed self-report measures assessing SNS use, procrastination, academic engagement, and university adjustment. For each SNS, participants reported how often they used the site in general, in addition to how often they used the site to procrastinate on academic tasks, and their frequency of using the site for non-academic purposes during class. Results indicated that Tumblr, Facebook, and Pinterest were linked with university maladjustment, whereas Twitter and Instagram were associated with some benefits to adjustment. The findings suggest that using SNS to procrastinate, or using SNS during class, play an important role in understanding students’ adjustment to university.
Wavelet packets-based digital watermarking for image authentication

The rapid expansion of the Internet and the overall development of digital technologies in the past years have sharply increased the availability of digital media. Digital contents can be reproduced without loss of quality, but they may also be easily modified, and sometimes, imperceptibly. In many contexts, any alteration of image, video or audio data must be detected. Therefore, some work needs to be done to develop security systems to protect the content of digital data. Watermarking is accepted as a plausible candidate for such an application as it allows for the invisible insertion of information in a host by its imperceptible modification. This thesis is concerned with the protection of information contained in digital images. A novel, semi-fragile watermarking technique for the authentication of images is developed. Image protection is achieved by the insertion of a secret author's identification key in an image's wavelet packet (WP) decomposition. Rounding the mean of selected regions of WP coefficients embeds the binary key. To take maximum advantage of the host image's characteristics in the embedding process, an optimal quantization protocol is formulated. The image's verification is done without the use of the original unmarked image. The detection of unauthorized frequency or spatial tampering with the image is performed by a combined interband/intraband verification protocol. This new technique can detect malicious tampering with images, but stays unaffected by high quality JPEG compression.
A 1-bit compressive sensing approach for SAR imaging based on approximated observation

Compressive sensing (CS) theory has achieved significant success in the field of synthetic aperture radar (SAR) imaging. Recent studies have shown that SAR imaging for sparse scene can also be successfully performed with 1-bit quantized data. Existing reconstruction algorithms always involve large matrix-vector multiplications which make them much more time and memory consuming than traditional matched filtering (MF) -based focusing methods because the latter can be effectively implemented by FFT. In this paper, a novel CS approach named BCS-AO for SAR imaging with 1-bit quantized data is proposed. It adopts the approximated SAR observation model deduced from the inverse of MFbased methods and is solved by an iterative thresholding algorithm. The BCS-AO can handle large-scaled data because it uses MF-based fast solver and its inverse to approximate the large matrix-vector multiplications. Both the simulated and real data are processed to test the performance of the novel algorithm. The results demonstrate that BCS-AO can perform sparse SAR imaging effectively with 1-bit quantized data for large scale applications.
New Doubly Periodic Solutions of Nonlinear Evolution Equations via Weierstrass Elliptic Function Expansion Algorithm

A Weierstrass elliptic function expansion method and its algorithm are developed in this paper. The method changes the problem solving nonlinear evolution equations into another one solving the correspondingsystem of nonlinear algebraic equations. With the aid of symbolic computation (e.g. Maple), the method is applied to the combined KdV-mKdV equation and (2+1)-dimensional coupled Davey-Stewartson equation. As a consequence, many new types of doubly periodic solutions are obtained in terms of the Weierstrass elliptic function. Jacobi elliptic function solutions and solitary wave solutions are also given as simple limits of doubly periodic solutions.
Automated yield enhancements implementation on full 28nm chip: challenges and statistics

This paper shares the details of the Yield Enhancements that were done at 28nm full chip level sharing the complexity involved in implementing such a flow and then the verification challenges involved , e.g., at mask data preparation. We discuss and present the algorithm used to measure the efficiency of the tool, explaining why we used this algorithm while sharing some alternate algorithms possible. We also share the detailed statistics regarding run time, machine resource, data size, polygon counts etc. We also present good techniques used by us for efficient flow management involved in large complex 28nm chips.
Policy learning in collaborative environmental governance processes

ABSTRACT Collaborative governance processes have become a popular mechanism for addressing complex environmental problems. Their success is premised, in part, on the assumption that they promote learning among diverse participants, who are then better equipped to develop creative, consensus-oriented environmental management actions. Significant gaps remain, however, in our understanding of how collaborative governance processes foster learning and what impact increased learning has on policymaking outputs. To investigate these relationships, this study provides one of the first empirical applications of Heikkila and Gerlak's collective learning framework. Key framework concepts are operationalized via interview data and existing literature and then measured via survey data collected from participants in a collaborative environmental governance process in Colorado, U.S. Findings indicate that both internal and exogenous contextual factors affect how much an individual learns within a collective context. Additionally, participants who report more learning also more strongly agree that the process produced favorable outputs and outcomes. These findings advance theories of learning in collaborative contexts and inform process design to maximize learning.
Random Sampling applied to Rapid Disk Analysis

This document is a Research Project report for the Master education in System and Network Engineering at the University of Amsterdam, The Netherlands. The main focus of the study is on how random sampling can contribute to rapid forensics investigation of hard disk drives. Forensically investigating several terabytes of data, bit per bit, requires large computing resources. Not only is it inherently long to extract all the data of a hard drive, but any automation tool aimed at parsing, organizing, and encoding all the data for human readability, also demands high amount of memory and computation capabilities. The ever-increasing capacity of digital storage devices, along with new forensics and law-enforcement challenges, raise new concerns: time has become a critical investigation factor. This requires techniques of rapid analysis for digital storage devices, and also balances a trade-off between time and certainty. This document presents a thorough modeling and evaluation of random sampling applied to data analysis on hard disk drives. The influence of most parameters in play are determined, based on general observations on modern digital storage mechanisms, and the precision and scalability of the technique are evaluated. Instructor Prof. C. de Laat University of Amsterdam, The Netherlands Supervisors Dr. E. van Eijk, Dr. Z. Geradts Netherlands Forensics Institute, The Hague, The Netherlands
Book Review: Reducing Crime: The Effectiveness of Criminal Justice Interventions

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Using Network Application Behavior to Predict Performance

Today’s continuously growing Internet requires users and network applications to have knowledge of network metrics. This knowledge is critical for decision making during the usage of network applications. This thesis studies application related network metrics. The major approach in this work is to examine the traffic between a simulated user and network applications. We use the historical data collected from previous usage of network applications to make predictions for future usage of those applications. We also use the historical data obtained from a given application to make predictions about another application. Prediction mechanisms require us to make parameter choices so that certain weights can be placed on historical data versus current data. We study these different choices and use the values from our best experimental results. From these studies we conclude that our data prediction is quite accurate and remains stable over a range of parameter choices. The use of shared routing paths between users and network applications are explored in the performance prediction of applications. Only some servers at the same locations show similar prediction results. The network applications studied are also varied, including web, streaming, DNS, etc. We see whether sharing information obtained from different applications can be used to make predictions of application performance. However, we observe limited success in predictions across applications.
Site‐specific production of TGF‐β in oral mucosal and cutaneous wounds

Wound healing in the oral mucosa is clinically distinguished by rapid healing and lack of scar formation compared with dermal wounds. Mechanisms of favorable mucosal healing are yet to be elucidated. Utilizing a murine model of equivalent‐size mucosal and skin wounds, we verified the rapid reepithelializaton and reduction in scarring of oral wounds reported in humans. Collagen fibrillar structure in oral wounds rapidly approached the size of normal collagen fibrils, while the collagen ultrastructure in skin remained immature through the later phases of healing. To determine whether the transforming growth factor‐β (TGF‐β) contributes to the lack of scar formation in oral mucosa, we compared the expression and production in oral and skin wounds. The RNase protection assay demonstrated significantly lower levels of TGF‐β1 expression in oral wounds compared with dermal wounds, and no changes were observed in the expression levels of TGF‐β2 or TGF‐β3. ELISA analysis confirmed that oral wounds contained lower levels of TGF‐β1 levels compared with dermal wounds, along with a significant increase in the ratio of TGF‐β3 to ‐β1. These findings showed reduced scarring in oral wounds at the ultrastructural level, and provide evidence that site‐specific differences in TGF‐β production contributes to the superior healing of oral wounds.