Miracle-dz/UniFAR-FTUs huggingface dataset:
An approach to detecting failures automatically

Failure detection is a difficult and often expensive task. The principle of self-healing addresses this cost issue, but poses new research questions. This work focuses on detecting non-trivial failures that are hard to specify and difficult to detect within the context of self-healing software. Typical failures in this class would be problems arising at the component integration level. In this paper we discuss general requirements for failure detection in self-healing software, propose an approach to automatically map system level specifications to run-time checkable code-level assertions, and illustrate our technique through an example.
Extending The Utaut Model in M-Banking Adoption

The banks are motivated to leverage their m-banking activities with the advancement of mobile technologies and the high mobile penetration rate. Despite the availability of the m-banking services, many consumers still posses the attitude of ‘wait-and-see’ which resulted to unsatisfactory adoption rate. Hence, this paper aims to extend the Unified Theory of Acceptance and Use of Technology (UTAUT) model to better understand what drives Malaysian consumers to adopt m-banking services. The conceptual model also provide better strategic insights for commercial banks, service developers, mobile manufacturers, and others to yield higher acceptance of m-banking.
Load balancing in pre-processing of large-scale distributed sparse computation

In this paper, we study the sparse matrix-vector pr duct (SMVP) distribution on a large scale distribu ted system (LSDS). The framework is defined by three steps: pr e-processing, processing and post processing. We fo cus here only on the first step i.e. pre-processing. Our general goal is to detect, for a given sparse matrix, the best compression format i.e. which leads to the best performances for the SMVP o n the LSDS. Thus, we need to study different approa ches to distribute the data, essentially the sparse matrix. Three appr o ches are proposed. In the first one, the matrix A is partitioned into row blocks with the same number of rows. The second (re sp. third) approach consists in partitioning A into bl cks of contiguous (resp. non contiguous) rows with the same number of non-zero elements. These approaches result in solv ing some constrained scheduling problems of the NP-hard type . To solve them, we propose some multi-phase heuris tics. After presenting these approaches, we present for each on e a performance analysis. These analyses are then v alidated through a series of experimentations that permit to show thei r interest.
A constrained baum-welch algorithm for improved phoneme segmentation and efficient training

We describe an extension to the Baum-Welch algorithm for training Hidden Markov Models that uses explicit phoneme segmentation to constrain the forward and backward lattice. The HMMs trained with this algorithm can be shown to improve the accuracy of automatic phoneme segmentation. In addition, this algorithm is significantly more computationally efficient than the full BaumWelch algorithm, while producing models that achieve equivalent accuracy on a standard phoneme recognition task.
Design of compact imperfection-immune CNFET layouts for standard-cell-based logic synthesis

The quest for technologies with superior device characteristics has showcased carbon nanotube field effect transistors (CNFETs) into limelight. Among the several design aspects necessary for today's grail in CNFET technology, achieving functional immunity to carbon nanotube (CNT) manufacturing issues (such as mispositioned CNTs and metallic CNTs) is of paramount importance. In this work we present a new design technique to build compact layouts while ensuring 100% functional immunity to mispositioned CNTs. Then, as second contribution of this work, we have developed a CNFET design kit (DK) to realize a complete design flow from logic-to-GDSII traversing the conventional CMOS design flow. This flow enables a framework that allows accurate comparison between CMOS and CNFET-based circuits. This paper also presents simulation results to illustrate such analysis, namely, a CNFET-based inverter can achieve gains, with respect to the energy-delay product (EDP) metric, of more than 4times in delay, 2times in energy/cycle and significant area savings (more than 30%) when compared to a corresponding CMOS inverter benchmarked with an industrial 65 nm technology.
Incremental Reference Resolution: The Task, Metrics for Evaluation, and a Bayesian Filtering Model that is Sensitive to Disfluencies

In this paper we do two things: a) we discuss in general terms the task of incremental reference resolution (IRR), in particular resolution of exophoric reference, and specify metrics for measuring the performance of dialogue system components tackling this task, and b) we present a simple Bayesian filtering model of IRR that performs reasonably well just using words directly (no structure information and no hand-coded semantics): it picks the right referent out of 12 for around 50% of real-world dialogue utterances in our test corpus. It is also able to learn to interpret not only words but also hesitations, just as humans have shown to do in similar situations, namely as markers of references to hard-to-describe entities.
Analysis and Experiments of Compact Folded Substrate-Integrated Waveguide

The novel folded substrate-integrated waveguide (FSIW) is 50% narrower than the substrate-integrated waveguide, but both have similar propagation characteristics. This paper derives the analytical formulas of propagation and cutoff characteristics of the FSIW, as well as the formulas to calculate the width and gap of the central metal septum. Very good agreements (<2% error) are observed among the results of the theoretical formulas, numerical simulations, and hardware experiments over wide frequency and parameter ranges.
Collaborative professional development for distributed teacher leadership towards school change

ABSTRACT Professional development that aims to build school change capacity requires spaces for collaborative action and reflection. These spaces should promote learning and foster skills for distributed leadership in managing school change. The present study analyses the case of the Seminar for Critical Citizenship (SCC) established by teachers of infant, primary, secondary and higher education to experiment with and share innovative practices. A focus group was formed first to identify which factors SCC participants perceived as influencing the development of this leadership for change, and second, to verify whether the SCC offers a space where the development of distributed leadership is promoted. We find that while it enables a network for collaboration, egalitarian dialogue and empowerment, certain tensions persist between theory and practice, and in attitudes towards innovation and school culture.
A Sober Look at Spectral Learning

Spectral learning recently generated lots of excitement in machine learning, largely because it is the first known method to produce consistent estimates (under suitable conditions) for several latent variable models. In contrast, maximum likelihood estimates may get trapped in local optima due to the non-convex nature of the likelihood function of latent variable models. In this paper, we do an empirical evaluation of spectral learning (SL) and expectation maximization (EM), which reveals an important gap between the theory and the practice. First, SL often leads to negative probabilities. Second, EM often yields better estimates than spectral learning and it does not seem to get stuck in local optima. We discuss how the rank of the model parameters and the amount of training data can yield negative probabilities. We also question the common belief that maximum likelihood estimators are necessarily inconsistent.
Effects of Imperfect Automation on Decision Making in a Simulated Command and Control Task

Objective: Effects of four types of automation support and two levels of automation reliability were examined. The objective was to examine the differential impact of information and decision automation and to investigate the costs of automation unreliability. Background: Research has shown that imperfect automation can lead to differential effects of stages and levels of automation on human performance. Method: Eighteen participants performed a “sensor to shooter” targeting simulation of command and control. Dependent variables included accuracy and response time of target engagement decisions, secondary task performance, and subjective ratings of mental workload, trust, and self-confidence. Results: Compared with manual performance, reliable automation significantly reduced decision times. Unreliable automation led to greater cost in decision-making accuracy under the higher automation reliability condition for three different forms of decision automation relative to information automation. At low automation reliability, however, there was a cost in performance for both information and decision automation. Conclusion: The results are consistent with a model of human-automation interaction that requires evaluation of the different stages of information processing to which automation support can be applied. Application: If fully reliable decision automation cannot be guaranteed, designers should provide users with information automation support or other tools that allow for inspection and analysis of raw data.
Support Vector Machines for Class Imbalance Rail Data Classification with Bootstrapping-based Over-Sampling and Under-Sampling

Abstract Support Vector Machines (SVMs) is a popular machine learning technique, which has proven to be very effective in solving many classical problems with balanced data sets in various application areas. However, this technique is also said to perform poorly when it is applied to the problem of learning from heavily imbalanced data sets where the majority classes significantly outnumber the minority classes. In this paper, we tackle the problem of learning from severely imbalanced Rail dataset via a new iterative support vector machine algorithm with bootstrapping-based over-sampling and under-sampling. We combine the good generalization ability of SVMs with the class distribution advantages of resampling techniques. Under-sampling and Over-sampling are commonly used methods for overcoming the class imbalance problem. In this work, we also address the influence of under-sampling and over-sampling techniques on rail data and show that achieving an optimal sampling rate yields a better SVM generalization capability. Experimental results show that the under-sampling outperforms over-sampling. The iterative SVM technique also shows a competitive generalization performance on the under-sampled rail data set, and that under-sampling can decrease the computational complexity of SVM algorithm.
Transmission delay in large scale ad hoc cognitive radio networks

There has been recent interest within the networking research area to understand the transmission delay in Cognitive Radio (CR) Networks with overlapping primary network and secondary network. In this paper, we investigate the scaling behavior of transmission delay in large scale ad hoc CR networks. We take different scenarios of CR networks into consideration and thus obtain a wind range of results. We first neglect propagation delay and study the ratio of transmission delay to distance, denoted by γ (λsAp and figure out its exact value in supercritical secondary network. In case of subcritical secondary network, we introduce a multi-cluster hop transmission process to get the lower bound of γ (λsAp. Then we take propagation delay into consideration to obtain further results. Finally, we use simulation results to verify our theoretical analysis. The results present the scaling behavior of transmission delay in CR networks and provide the design guidelines for large scale wireless networks.
On Exploiting Task Duplication in Parallel Program Scheduling

One of the main obstacles in obtaining high performance from message-passing multicomputer systems is the inevitable communication overhead which is incurred when tasks executing on different processors exchange data. Given a task graph, duplication-based scheduling can mitigate this overhead by allocating some of the tasks redundantly on more than one processor. In this paper, we focus on the problem of using duplication in static scheduling of task graphs on parallel and distributed systems. We discuss five previously proposed algorithms and examine their merits and demerits. We describe some of the essential principles for exploiting duplication in a more useful manner and, based on these principles, propose an algorithm which outperforms the previous algorithms. The proposed algorithm generates optimal solutions for a number of task graphs. The algorithm assumes an unbounded number of processors. For scheduling on a bounded number of processors, we propose a second algorithm which controls the degree of duplication according to the number of available processors. The proposed algorithms are analytically and experimentally evaluated and are also compared with the previous algorithms.
Low Frequency Component-Based Watermarking Scheme using

A robust watermark should be able to withstand most image manipulations such as cropping, additive noise and JPEG type compressions. It should also be able to hold telltale signs of attacks that are unknown to the user. Both of these aims can be fulfilled by embedding two watermarks, one fragile and one robust in different domains where watermarks are not vulnerable to the aforementioned manipulations. It is a well known fact that watermarking of DC components of discrete cosine transform (DCT) stand against JPEG compression. We build on this concept by inserting a watermark in the transform domain to further enhance the robustness. We use a 2D Datamatrix as the watermark as this has error correction capabilities and we show that this can be used to insert data imperceptibly into the host image. A second Datamatrix watermark is embedded in the low pass component of any wavelet decomposition at a specific level only known to the author. This improves the resistance of the watermarking scheme to attack. Our experimental results indicate that these invisible watermarks can carry significant information and are robust to many image manipulations. Keyword: Datamatrix, Low frequency components, Transform domain, Error correction, Wavelet decomposition, Watermark attacks, DC components, Robustness, Image watermarking.
Soft User Behavior Modeling in Virtual Environments

A framework for online user behavior soft modeling is presented in this work. Behavior models of users in dynamic virtual environments has been described in the literature in terms of timed transition automata which can be compiled in a planning domain. The extended notion of soft timed transition automata is proposed in order to recognize a larger class of user histories. The notion of deviation from the user model allows to assess and evaluate in real time the dynamic behavior of users acting in virtual environments, such as e-learning and e-business platforms. The timed automata model allows to describe virtually infinite sequences of user actions subject to temporal constraints, while soft measures allows to assess recognition of behaviors by evaluating the amount of temporal deviation, additional or omitted actions contained in an observed behavior. The proposed model allow the partial recognition of user history also when the observed actions only partially meets the given behavior model constraints. This approach is more realistic for real time user support systems, with respect to standard boolean model recognition, when more than one user model is potentially available the amount of deviation from the models can be used as guide to generate the system support by anticipation, projection and other known techniques. Experiments based on logs from an e-learning platforms and plan compilation of the soft timed automaton show the expressivity of the proposed model.
Automated detection and grading of hard exudates from retinal fundus images

Diabetic retinopathy is the major cause of blindness and the appearance of hard exudates is one of its earliest signs. In this study, an automated algorithm to detect and grade the severity of hard exudates is proposed. The detection process is based on top-down image segmentation and local thresholding by a combination of edge detection and region growing. Using features of the fovea and their geometric relations with other retinal structures, a method for the fovea localisation is proposed. Grading of hard exudates was performed using a polar coordinate system centred at the fovea. The results of hard exudate detection process were validated based on clinician hand-labelled data (ground truth) with an overall sensitivity of 93.2%. The superior performance of this technique suggests that it could be used for a computer-aided mass screening of retinal diseases.
CogALex-V Shared Task: Mach5 – A traditional DSM approach to semantic relatedness

This contribution provides a strong baseline result for the CogALex-V shared task using a traditional “count”-type DSM (placed in rank 2 out of 7 in subtask 1 and rank 3 out of 6 in subtask 2). Parameter tuning experiments reveal some surprising effects and suggest that the use of random word pairs as negative examples may be problematic, guiding the parameter optimization in an undesirable direction.
High Resolution 3D Shape Texture from Multiple Videos

We examine the problem of retrieving high resolution textures of objects observed in multiple videos under small object deformations. In the monocular case, the data redundancy necessary to reconstruct a high-resolution image stems from temporal accumulation. This has been vastly explored and is known as image super-resolution. On the other hand, a handful of methods have considered the texture of a static 3D object observed from several cameras, where the data redundancy is obtained through the different viewpoints. We introduce a unified framework to leverage both possibilities for the estimation of an object's high resolution texture. This framework uniformly deals with any related geometric variability introduced by the acquisition chain or by the evolution over time. To this goal we use 2D warps for all viewpoints and all temporal frames and a linear image formation model from texture to image space. Despite its simplicity, the method is able to successfully handle different views over space and time. As shown experimentally, it demonstrates the interest of temporal information to improve the texture quality. Additionally, we also show that our method outperforms state of the art multi-view super-resolution methods existing for the static case.
Spherical collapse model with non-clustering dark energy

We investigate a spherical overdensity model for the non-clustering dark energy (DE) with the constant equation of state, ωde in a flat universe. In this case, the exact solution for the evolution of the scale factor is obtained for general ωde. We also obtain the exact (when ωde = −1/3) and the approximate (when ωde≠−1/3) solutions for the ratio of the overdensity radius to its value at the turnaround epoch (y≡R/Rta) for general cosmological parameters. Also the exact and approximate solutions of the overdensity at the turnaround epoch ζ = (ρcluster/ρm)|z = zta are obtained for general ωde. Thus, we are able to obtain the non-linear overdensity Δ≡1+δNL = ζ(x/y)3 at any epoch for the given DE model. The non-linear overdensity at the virial epoch Δvir = ζ(xvir/yvir)3 is obtained by using the virial theorem and the energy conservation. The non-linear overdensity of every DE model converges to that of the Einstein de Sitter universe ΔvirEdS = 18π2((1/2π)+(3/4))2 147 when zvir increases. We find that the observed quantities at high redshifts are insensitive to the different ωde models. The low-redshift cluster (zvir ~ 0.04, i.e., zta ~ 0.7) shows the most model dependent feature and it should be a suitable object for testing DE models. Also as ΩM0 increases, the model dependence of the observed quantities decreases. The error in the approximate solutions is at most 2% for a wide range of the parameter space. Even though the analytic forms of y and ζ are obtained for the constant ωde, they can be generalized to the slowly varying ωde. Thus, these analytic forms of the scale factor, y, and ζ provide a very accurate and useful tool for measuring the properties of DE.
Current modeling and simulation of dual-material surrounding-gate MOSFET with asymmetric halo

The dual-material gate and asymmetrical halo structure is used in surrounding gate MOSFET to improve the performance. By treating the device as three surrounding-gate MOSFETs connecting in series and maintaining current continuity, a comprehensive drain current model is developed for it. It is concluded that the device also exhibits increased current drivability and improved hot carrier reliability. The derived analytical model is verified with numerical simulation.
On configuring a case-based reasoning software project prediction system

This paper explores some of the practical issues associated with the use of case-based reasoning (CBR) or estimation by analogy for software project effort prediction. We note that different research teams have reported widely differing results with this technology. Whilst we accept that underlying characteristics of the datasets being used play a major role we also argue that configuring a CBR system can also have an impact. We examine the impact of the choice of number of analogies when making predictions. Our analysis is based on project effort data derived from two sources. The first data has been collected by a Canadian software house. The other data was obtained by means of simulation. The results show that using a larger number of analogies generated better predictions. On the other hand, the nearest neighbour is observed to be the better predictor for smaller training sets. We also found that the underlying distribution of the dataset does have a significant impact on the choice of the number of analogies. It was difficult to identify patterns for datasets with complex distribution properties. We believe that a considerable amount of work is required to understand the relationship between the properties of the dataset and configuring a CBR system.
Thermal Analysis of Radial-Flux Electrical Machines With a High Power Density

A lumped-parameter-based thermal analysis applicable to radial-flux electrical machines with a high power density is presented. The modeling strategies using T-equivalent lumped-parameter blocks as well as conventionally defined thermal resistances are discussed. Special attention is paid to the modeling of the convective heat transfer in the air gap of radial-flux electrical machines at different rotational speeds. A brief overview of the evaluation of different loss components is given. The performance of the developed thermal model was verified by comparing the calculated temperature values with the measurements in three different applications.