Miracle-dz/UniFAR-FTUs huggingface dataset:
POCP: A package for polynomial optimal control problems

POCP is a new Matlab package running jointly with GloptiPoly 3 and, optionally, YALMIP. It is aimed at nonlinear optimal control problems for which all the problem data are polynomial, and provides an approximation of the optimal value as well as some control policy. Thanks to a user-friendly interface, POCP reformulates such control problems as generalized problems of moments, in turn converted by GloptiPoly 3 into a hierarchy of semidefinite programming problems whose associated sequence of optimal values converges to the optimal value of the polynomial optimal control problem. In this paper we describe the basic features of POCP and illustrate them with some numerical examples.
Profiling of Selected MicroRNAs in Proliferative Eutopic Endometrium of Women with Ovarian Endometriosis

It has been well documented that aberrant expression of selected microRNAs (miRNAs) might contribute to the pathogenesis of disease. The aim of the present study is to compare miRNA expression by the most comprehensive locked-nucleic acid (LNA) miRNA microarray in eutopic endometrium of patients with endometriosis and control. In the study we recruited 21 patients with endometriosis and 25 were disease-free women. The miRNA expression profiles were determined using the LNA miRNA microarray and validated for selected molecules by real-time PCR. We identified 1198 human miRNAs significantly differentially altered in endometriosis versus control samples using false discovery rate of <5%. However only 136 miRNAs showed differential regulation by fold change of at least 1.3. By the use of selected statistical analysis we obtained 45 potential pathways that might play a role in the pathogenesis of endometriosis. We also found that natural killer cell mediated cytotoxicity pathway was found to be inhibited which is consistent with previous studies. There are several pathways that may be potentially dysregulated, due to abnormal miRNA expression, in eutopic endometrium of patients with endometriosis and in this way contribute to its pathogenesis.
Model checking concurrent linux device drivers

The Slam toolkit demonstrates that predicate abstraction enables automated verification of real world Windows device drivers. Our predicate abstraction-based tool DDVerifyenables the automated verification of Linux device drivers and provides an accurate model of the relevant parts of the kernel. We report on benchmarks based on Linux device drivers, confirming the results that Slam established for the Windows world. Furthermore, we take predicate abstraction one step further and introduce a technique to verify concurrent software with shared memory
Supervised learning classification for dross prediction in ductile iron casting production

Foundry is one of the key axes in society because it provides with important pieces to other industries. However, several defects may appear in castings. In particular, Dross is defect that is a type of non-metallic, elongated and filamentary inclusion. Unfortunately, the methods to detect Dross have to be performed once the production has already finished using quality controls that incur in a subsequent cost increment. Given this context, we propose the first machine-learning-based method able to foresee Dross in iron castings, modelling the foundry production parameters as input. Our results have shown that this method obtains good accuracy results when tested with real data from a heavy-section casting foundry.
‘Technically wrong leh’

There has been much discussion on the use of particles (also referred to as ‘discourse particles’ or ‘pragmatic particles’) as a key feature of Singapore English. Wong (2004) has pointed out that particles are essential in the Singapore speech community, and in order to pass as a functional member of the speech community a speaker needs to have a proficient knowledge of the meanings and functions of particles in spoken (and increasingly in texted) discourse. There is no doubt that the use of particles in Singapore Colloquial English is prevalent in the language use of speakers in Singapore, as attested by the numerous studies conducted on the topic since the 1970s (including Tongue, 1974; Richards & Tay, 1981; Kwan–Terry, 1978; Platt & Weber, 1980; Platt, Weber & Ho, 1983; Platt & Ho, 1989; Gupta, 1992; Wong, 2004, 2005; Wee, 2002, 2003, 2004, 2010; Liemgruber, 2016; Botha, 2018). Despite the fact that particles have been studied extensively, very few studies have investigated which particular particles can be considered prototypical in Singapore English in general, as well as more specifically in the vernacular speech of Singaporeans. Not only that, there appears still to be a lot we do not know about the functions and uses of many of these particles, specifically in the vernacular speech of Singaporeans.
3D object retrieval with bag-of-region-words

View-based method becomes an essential approach to 3D object retrieval in recent years. In the view-based 3D object retrieval framework, each object is described by a set of views and representative features are extracted from these views to match the objects in database. In this paper, we propose a novel 3D multi-view representation method, Bag-of-Region-Words (BoRW). It first gridly selects points in each view and extracts local SIFT features. Each local feature is encoded into a visual word with a trained visual vocabulary. Then each view is split into several regions, and each region is represented by a bag-of-visual-words feature vector. All the obtained regions are further grouped into clusters based on the bag-of-visual-words feature, and one feature is selected from each cluster with corresponding weight. In this way, each object is described by a set of BoRW. The Earth Movers Distance is employed to estimate the distance between two BoRW feature vectors. Experimental results show that the proposed method can achieve better retrieval performance than existing methods.
Strathprints Institutional Repository (2013) Power-optimised Multi-radio Network under Varying Throughput Constraints for Rural Broadband Access. In: 21st European Signal Processing Conference, Power-optimised Multi-radio Network under Varying Throughput Constraints for Rural Broadband Access

Strathprints is designed to allow users to access the research output of the University of Strathclyde. Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (http://strathprints.strath.ac.uk/) and the content of this paper for research or study, educational, or not-for-profit purposes without prior permission or charge. ABSTRACT The use of complementary radio access technologies within a network allows the advantages of each technology to be combined to overcome individual limitations. In this paper we show how 5 GHz and " TV White Space " overlay networks can be combined to provide fixed wireless access coverage within a rural environment. By creating a model of the whole network we derive the optimum assignment of stations between the two overlay networks to maximise the capacity of individual stations given a desired individual station data rate. Through simulation we show how the power consumption of a base station can be minimised by dynamically adjusting station assignments based on network data rate requirements changing over the course of a day.
On-Line Prediction of Nonstationary Variable-Bit-Rate Video Traffic

In this paper, we propose a model-based bandwidth prediction scheme for variable-bit-rate (VBR) video traffic with regular group of pictures (GOP) pattern. Multiplicative ARIMA (autoregressive integrated moving-average) process called GOP ARIMA (ARIMA for GOP) is used as a base stochastic model, which consists of two key ingredients: prediction and model validity check. For traffic prediction, we deploy a Kalman filter over GOP ARIMA model, and confidence interval analysis for validity determination. The GOP ARIMA mPodel explicitly models inter and intra-GOP frame size correlations and the Kalman filter-based prediction maintains ? state? across the prediction rounds. Synergy of the two successfully addresses a number of challenging issues, such as a unified framework for frame type dependent prediction, accurate prediction, and robustness against noise. With few exceptions, a single video session consists of several scenes whose bandwidth process may exhibit different stochastic nature, which hinders recursive adjustment of parameters in Kalman filter, because its stochastic model structure is fixed at its deployment. To effectively address this issue, the proposed prediction scheme harbors a statistical hypothesis test in the prediction framework. By formulating the confidence interval of a prediction in terms of Kalman filter components, it not only predicts the frame size but also determines validity of the stochastic model. Based upon the results of the model validity check, the proposed prediction scheme updates the structures of the underlying GOP ARIMA model. We perform a comprehensive performance study using publicly available MPEG-2 and MPEG-4 traces. We compare the prediction accuracy of four different prediction schemes. In all traces, the proposed model yields superior prediction accuracy than the other prediction schemes. We show that confidence interval analysis effectively detects the structural changes in the sample sequence and that properly updating the model results in more accurate prediction. However, model update requires a certain length of observation period, e.g., 60 frames (2 s). Due to this learning overhead, the advantage of model update becomes less significant when scene length is short. Through queueing simulation, we examine the effect of prediction accuracy over user perceivable QoS. The proposed bandwidth prediction scheme allocates less 50% of the queue(buffer) compared to the other bandwidth prediction schemes, but still yields better packet loss behavior.
Analysis of evolutionary algorithms: from computational complexity analysis to algorithm engineering

Analyzing the computational complexity of evolutionary algorithms has become an accepted and important branch in evolutionary computation theory. This is usually done by analyzing the (expected) optimization time measured by means of the number of function evaluations and describing its growth as a function of a measure for the size of the search space. Most often asymptotic results describing only the order of growth are derived. This corresponds to classical analysis of (randomized) algorithms in algorithmics. Recently, the emerging field of algorithm engineering has demonstrated that for practical purposes this analysis can be too coarse and more details of the algorithm and its implementation have to be taken into account in order to obtain results that are valid in practice. Using a very recent analysis of a simple evolutionary algorithm as starting point it is shown that the same holds for evolutionary algorithms. Considering this example it is demonstrated that counting function evaluations more precisely can lead to results contradicting actual run times. Motivated by these limitations of computational complexity analysis an algorithm engineering-like approach is presented.
Mindfulness and its relevance for sports coaches adopting nonlinear pedagogy

Nonlinear pedagogy is an approach for structuring skills acquisition that is underpinned by dynamical systems theory and ecological psychology approach of appreciating motor behavior. Given that nonlinear pedagogy calls for a different set of coaching strategies that departs from the traditional prescriptive approach, a heightened level of mindfulness—underpinned by attention/awareness and acceptance (Bishop et al., Mindfulness: a proposed operational definition. Clinical Psychology: Science and Practice 2004; 11(3): 230–241)—on coaches’ part may be particularly pertinent for facilitating this creativity-based and learner-centered approach. In this paper, the relevance of the mindfulness construct is discussed with reference to the need for coaches implementing nonlinear pedagogy to (a) develop sensitivity towards the dynamics of the movement system, (b) be open about impending variability and creativity in learner’s behavior, and (c) be accepting towards learners for who they are. The case for how coaches with heightened mindfulness might be better apt to accomplish the above is put forth. Some suggestions for future work in this area are also suggested. In line with the complex systems perspective in skills acquisition, perhaps it is not too far-fetched to consider a coach’s level of mindfulness during the coaching session as an important control parameter within a larger movement system that has a role in determining learner’s success in skill acquisition.
3-D Nonequilibrium Green's Function Simulation of Nonperturbative Scattering From Discrete Dopants in the Source and Drain of a Silicon Nanowire Transistor

As these As transistors are scaled to nanometer dimensions, the discreteness of the dopants becomes increasingly important. Transistors of 3 times 3 nm2 cross section contain, on average, approximately one dopant atom per nanometer of length, making any self-averaging impossible. The individual random dopants act as localized scatterers whose distribution, and therefore, impact on the electron transport, varies from device to device. This is complemented by electrostatic variation in the potential that controls the threshold voltage and the dominant current paths. The current density is greatly influenced by resonances associated with the attractive potential of the donors and screening effects. In this paper, for the first time, a full 3-D nonequilibrium Green's function (NEGF) simulation in the effective mass approximation has been used to study the influence of individual discrete donors in the source/drain on the I-V characteristics of a narrow n-channel Si nanowire transistor. We have compared devices with microscopically different configuration of dopants. The simulated variations in the I-V curves are analyzed with reference to the behavior of the transmission coefficients. We have highlighted the importance of resonance states when solving the NEGF and Poisson equations self-consistently.
E-government services challenges and opportunities for developing countries: The case of Libya

Implementation of e-government in the developing countries has always been a challenging problem due to the technical, infrastructure, cultural and social issues. This paper aims to explore the challenges of implementing e-government as well as opportunities available in developing countries, particularly Libya. This was achieved by investigating electronic government (e-government) implementation problems with an emphasis on analysing data gathered from conducting a literature review and primary data collection generated based on this theoretical study. More importantly, the paper sought to understand the current status of the e-government in Libya, which could aid policy makers in pursuing the development of the public sector's organisations. In this paper, implementation problems are identified, and recommendations are provided for implementing a successful e-government in Libya, which may also be applicable to other developing countries after some customisation.
Predicting Obesity from Grocery Data: A Conceptual Process Framework

In upholding the Islamic way of life, effort to seek for moderation can be in the form of obesity prevention. Obesity is becoming the future burden of nations and actions have been taken to curb the problem of obesity. Most nations predict obesity based on the national past trend using data from population-based health surveys which are costly. Alternative method now points to data analytics which have created a productivity growth wave in business organization through its predictive and prescriptive capabilities. Exploratory works on the use of data analytics have extended into areas such as healthcare in the effort to improve the quality of life. To offer alternative methods of obesity prediction, we present our early work on applying data analytics in predicting obesity based on dietary pattern derived from the retailing transactional data of grocery shopping instead of using health-related data collected from periodic population–based survey. We described the framework development and later proposed a process framework for predicting obesity from grocery data. The first part of the framework describes the process of deriving dietary intake patterns from grocery data and the second phase describes the obesity prediction process that utilizes data mining tools. This paper also reviews the data mining techniques that had been applied in obesity prediction in previous researches.
Predict Edges in Fliker Social Network Using Data Mining Method

Using social networking services is becoming more popular day by day. The websites of the social networks like face-book currently are among the most popular internet services just after giant portals such as Yahoo, MSN and search engines like Google. One of the main problems in analyzing these networks is the prediction of relationships between people in the network. The purpose of this paper is to forecast the friendship of a person with a new person using existing data on Flickr website accurately. In this paper, we achieved about 90% percent correct prediction with regards to the results which are obtained by using data mining methods.
A tensorial approach to computational continuum mechanics using object-oriented techniques

In this article the principles of the field operation and manipulation (FOAM) C++ class library for continuum mechanics are outlined. Our intention is to make it as easy as possible to develop reliable and efficient computational continuum-mechanics codes: this is achieved by making the top-level syntax of the code as close as possible to conventional mathematical notation for tensors and partial differential equations. Object-orientation techniques enable the creation of data types that closely mimic those of continuum mechanics, and the operator overloading possible in C++ allows normal mathematical symbols to be used for the basic operations. As an example, the implementation of various types of turbulence modeling in a FOAM computational-fluid-dynamics code is discussed, and calculations performed on a standard test case, that of flow around a square prism, are presented. To demonstrate the flexibility of the FOAM library, codes for solving structures and magnetohydrodynamics are also presented with a.
60 GHz VCO with wideband tuning range fabricated on VLSI SOI CMOS technology

A 60 GHz cross-coupled differential LC CMOS VCO is presented in this paper, which is optimized for a large frequency tuning range using conventional MOSFET varactors. The MMIC is fabricated on digital 90 nm SOI technology and requires a circuit area of less than 0.1 mm/sup 2/ including the 50 /spl Omega/ output buffers. Within a frequency control range from 52.3 GHz to 60.6 GHz, a supply voltage of 1.5 V and a supply current of 15 mA, the circuit dividers a very constant output power of -6.8 /spl plusmn/ 0.2 dBm and yields a phase noise between -85 to -92 dBc/Hz at 1 MHz frequency offset.
Identifying trust in social networks with stubborn agents, with application to market decisions

No man is an island: the opinions that shape human economic decisions reflect the fabric of trust that exists between one agent and its social network. In this work we discuss how stubborn agents in a network expose the relative trust that exists among the agents, by analyzing the system equations that are used in popular opinion diffusion models. We propose methods to measure the agents beliefs from their actions on social media. Preliminary results with simulations and real data highlight the interesting insights that can be gained by interpreting the social network interactions under this lens. We also discuss how the perception of personal utility in an economic transaction can be inferred by identifying the relative trust and controlling the stubborn agents influence, advertising through them products so that the social network perceives them more favorably.
A contour-based surface modeling approach for scattered human body data

Aiming at the characteristics of human body structure, a surface modeling method for 3D scattered human body data is presented in this paper. Firstly, a series of horizontal cross sections are applied to sample the 3D scattered point cloud data. Secondly, for each cross section, the sample points are clustered into several point sets. And then, cubic splines are employed to approximate the above point sets to build several curves for circle sampling to obtain the triangle vertexes. Finally, the vertexes are employed to build the triangular meshes by adjacent contour lines triangulation method, and then the 3D human surface model is reconstructed. The experiment shows that our method is valid and feasible.
ORIGEN: Automatic Extraction of Offset-Revealing Instructions for Cross-Version Memory Analysis

Semantic gap is a prominent problem in raw memory analysis, especially in Virtual Machine Introspection (VMI) and memory forensics. For COTS software, common memory forensics and VMI tools rely on the so-called "data structure profiles" -- a mapping between the semantic variables and their relative offsets within the structure in the binary. Construction of such profiles requires the expert knowledge about the internal working of a specified software version. At most time, it requires considerable manual efforts, which often turns out to be a cumbersome process. In this paper, we propose a notion named "cross-version memory analysis", wherein our goal is to alleviate the process of profile construction for new versions of a software by transferring the knowledge from the model that has already been trained on its old version. To this end, we first identify such Offset Revealing Instructions (ORI) in a given software and then leverage the code search techniques to label ORIs in an unknown version of the same software. With labeled ORIs, we can localize the profile for the new version. We provide a proof-of-concept implementation called ORIGEN. The efficacy and efficiency of ORIGEN have been empirically verified by a number of softwares. The experimental results show that by conducting the ORI search within Windows XP SP0 and Linux 3.5. 0, we can successfully recover the data structure profiles for Windows XP SP2, Vista, Win 7, and Linux 2.6. 32, 3.8. 0, 3.13. 0, respectively. The systematical evaluation on 40 versions of OpenSSH demonstrates ORIGEN can achieve a precision of more than 90%. As a case study, we integrate ORIGEN into a VMI tool to automatically extract semantic information required for VMI. We develop two plugins to the Volatility memory forensic framework, one for OpenSSH session key extraction, the other for encrypted filesystem key extraction. Both of them can achieve the cross-version analysis by ORIGEN.