Miracle-dz/UniFAR-FTUs huggingface dataset:
Effective Test Data Generation using Genetic Algorithms

Software testing is an indispensible part of software development. It increases the confidence of programmer and user in the reliability and accuracy of software. However, it is a laborious and time-consuming task. Almost half of the software development resources spend on testing the software. Automatic software testing can substantially reduce the cost of development of software. Further exhaustive software testing is not feasible. Only the selective parts of the software are tested. Therefore design of a set of test cases is required in such a manner that it can find out as many faults as possible. We propose to improve software-testing efficiency with suitable optimization techniques. In this paper, the focus is on the use of genetic algorithms for generating the test data that can cover the most error-prone path; so that emphasis can be given on testing these paths firstly. Genetic algorithms are iterative techniques that apply simple operations repeatedly in the search for good solutions, or in this case, test data. By finding out the most error-prone path using this technique will help to reduce the software development cost and improve the testing efficiency.
A linear programming framework for logics of uncertainty

Several logics for reasoning under uncertainty distribute probability mass over sets in some sense. These include probabilistic logic, Dempster-Shafer theory, other logics based on belief functions, and second-order probabilistic logic. It is shown that these logics are instances of a certain type of linear programming model, typically with exponentially many variables, and how a single linear program package can implement these logics computationally if one plugs in a different column generation subroutine for each logic. <<ETX>>
Restriction endonuclease MvaI is a monomer that recognizes its target sequence asymmetrically

Restriction endonuclease MvaI recognizes the sequence CC/WGG (W stands for A or T, ‘/’ designates the cleavage site) and generates products with single nucleotide 5′-overhangs. The enzyme has been noted for its tolerance towards DNA modifications. Here, we report a biochemical characterization and crystal structures of MvaI in an apo-form and in a complex with target DNA at 1.5 Å resolution. Our results show that MvaI is a monomer and recognizes its pseudosymmetric target sequence asymmetrically. The enzyme consists of two lobes. The catalytic lobe anchors the active site residues Glu36, Asp50, Glu55 and Lys57 and contacts the bases from the minor grove side. The recognition lobe mediates all major grove interactions with the bases. The enzyme in the crystal is bound to the strand with T at the center of the recognition sequence. The crystal structure with calcium ions and DNA mimics the prereactive state. MvaI shows structural similarities to BcnI, which cleaves the related sequence CC/SGG and to MutH enzyme, which is a component of the DNA repair machinery, and nicks one DNA strand instead of making a double-strand break.
Medical student attitudes to mental health and psychiatry: the use of a patient-experience short film

Background: Medical student attitudes to mental illness are significantly influenced by their undergraduate educational experience. Medical education therefore has a key role to play in challenging the stigma associated with mental illness. We developed a short educational film aimed at challenging stigmatising attitudes to mental illness and explored its effects on undergraduate medical student attitudes. We hypothesised that levels of stigmatising attitudes in medical students would reduce after students viewed the educational film. Method: We used a validated scale (Mental Illness: Clinician Attitudes, MICA) to examine undergraduate medical student attitudes to mental illness at two time points - prior to (T1) and following (T2) viewing the short film. The film focused on patient experiences and was designed to highlight personal experiences of mental illness. Results: 92 students completed the MICA before the film and 73 students at both time points. Having a personal history of mental illness was associated with less stigmatising attitudes (t=2.4, df=87, p=0.019). Stigma scores were reduced following the film viewing (t=7.101, df=72, p<0.001). Discussion: This study suggests that patient experience films, used as educational tools, can challenge student perceptions of mental illness and lead to a reduction in stigmatising attitudes, at least in the short term. Future studies are required to examine the longer-term effects of such educational interventions in terms of student perceptions and attitudes towards mental health and psychiatry.
A MULTI-LEVEL CLASSIFICATION MODEL PERTAINING TO THE STUDENT’S ACADEMIC PERFORMANCE PREDICTION

The students’ performance monitoring and evaluation is an essential activity of an education system to keep track of the success and failure records of the students. The objective of this research is to provide the best classification model to predict the students’ academic performance. In this paper we propose a Multilevel Classification Model (MLCM) based on Decision Tree Algorithm for the predictions of the academic performance of the undergraduate engineering students. The multi-level classification model consists of two levels. In level one, the four classification models namely Decision Tree (J48), Lazy Learner (IBK), Neural Network (MLP) and Naïve Bayes Tree (NBT) were constructed, evaluated and compared. The decision tree classifier was selected for the model construction in this step. In level 2, the overall accuracy of the classification model as well as the accuracy of individual class was enhanced by eliminating the outliers from the original dataset and by constructing Multilevel Classification Model (MLCM) using filtered dataset.
D-HIP: A distributed key exchange scheme for HIP-based Internet of Things

Host Identity Protocol (HIP) emerges as the most suitable identification protocol for the Internet of Things. HIP not only provides identifier/locator split but also a key agreement procedure named HIP Base Exchange, which allows secure connections between HIP peers. However, the heterogeneous and decentralized nature of IoT architecture, coupling resource-constrained networks with powerful Internet, impedes the use of HIP on small devices due to its computationally expensive cryptographic operations. In this paper, we propose a distributed lightweight key exchange protocol designed to reduce the requirements of HIP Base Exchange, in order to be supported by resource-constrained nodes.
Why teachers find it difficult to include students with EBD in mainstream classes

ABSTRACT In Sweden, teachers in mainstream schools show frustration and insecurity about how to organise education for inclusion and diversity. This article contributes to the understanding of how they articulate their view of the advantages and disadvantages of including students with EBD in mainstream classes. To study teachers’ understanding, an approach of discourse theory which takes inspiration from Laclau and Mouffe (1985. Hegemony and Socialist Strategy. London: Verso) was applied. The empirical material consisted of 6 focus group interviews and 37 individual interviews based on stimulus texts. According to the results, the prevailing discourses focused on the disadvantages of it. However, they were articulated differently and filled with meaning mainly by three recurring nodal points: (1) problems, (2) dilemmas and (3) impossibility. The advantages of including students with EBD in mainstream classes were only to be found in the antagonistic discourses. They were articulated in different ways but were overpowered by others and therefore failed to fix the meaning. The overall conclusion is that teachers base their understanding on both their experiences and on the policy of the Educational Act, but the pragmatic discourse of the disadvantages was hegemonic to the ideological antagonistic discourse of the advantages.
A mathematical approach to bone tissue engineering

Tissue engineering is becoming consolidated in the biomedical field as one of the most promising strategies in tissue repair and regenerative medicine. Within this discipline, bone tissue engineering involves the use of cell-loaded porous biomaterials, i.e. bioscaffolds, to promote bone tissue regeneration in bone defects or diseases such as osteoporosis, although it has not yet been incorporated into daily clinical practice. The overall success of a particular bone tissue engineering application depends strongly on scaffold design parameters, which do away with long and expensive clinical protocols. Computer simulation is a useful tool that may reduce animal experiments and help to identify optimal patient-specific designs after concise model validation. In this paper, we present a novel mathematical approach to bone regeneration within scaffolds, based on a multiscale framework. Results are presented over an actual scaffold microstructure, showing the potential of computer simulation, and how it can aid in the task of making bone tissue engineering a reality in clinical practice.
ERP requirements engineering practice: lessons learned

Standard off-the-shelf requirements engineering processes have become a key to conceptualizing any integrated, corporate-wide solution based on packaged enterprise resource planning software. A generic RE model offers defined processes, suggests process stakeholders, specifies steps to accomplish tasks, indicates task dependencies, and provides standard tool support for ERP RE. Essentially, any off-the-shelf RE process is about composition and reconciliation: you start with a general set of business process and data requirements, then explore standard ERP functionality to see how closely it matches your organization's process and data needs.
An integer tone mapping operation for HDR images in OpenEXR with denormalized numbers

We propose an integer tone mapping operator (TMO) for high dynamic range (HDR) images expressed in a floating-point data format. Two purposes are achieved by the proposed TMO. The first purpose is to implement a TMO with less memory space. The second purpose is to give an important step to realize a fixed-point TMO. The proposed TMO is available for HDR images in the OpenEXR format. The OpenEXR format has two numerical representations (the normalized number and the denormalized number) which are not in other HDR formats such as RGBE. These two numerical representations cause a problem in applying an integer TMO. The proposed method enables us to avoid the problem by using the intermediate format. Moreover, the exponent part and the mantissa part are processed separately as two integer numbers. As a result, an integer TMO with less numerical range is achieved by our method. The experimental results show that the proposed method can generate high-quality low dynamic range (LDR) images with less memory space.
On Singles, Couples and Extended Families. Measuring Overlapping between Latin Vallex and Latin WordNet

Different lexical resources may pursue different views on lexical meaning. However, all of them deal with lexical items as common basic components, which are described according to criteria that may vary from one resource to another. In this paper, we present a method for measuring the degree of similarity between a valency-based lexical resource and a WordNet. This is motivated by both theoretical and practical reasons. As for the former, we wonder if there are lexical classes that "impose" themselves regardless of the fact that they are explicitly recorded as such in source lexical resources. As for the latter, our work wants to contribute to the research task dealing with merging lexical resources. In order to apply and evaluate our method, we propose a normalized coefficient of overlapping that measures the overlapping rate between a valency lexicon and a WordNet. In particular, in the context of the exploitation of the linguistic resources for ancient languages built over the last decade, we compute and evaluate the overlapping between a selection of homogeneous lexical subsets extracted from two lexical resources for Latin.
Expression and Physiological Relevance of Agrobacterium tumefaciens Phosphatidylcholine Biosynthesis Genes

ABSTRACT Phosphatidylcholine (PC), or lecithin, is the major phospholipid in eukaryotic membranes, whereas only 10% of all bacteria are predicted to synthesize PC. In Rhizobiaceae, including the phytopathogenic bacterium Agrobacterium tumefaciens, PC is essential for the establishment of a successful host-microbe interaction. A. tumefaciens produces PC via two alternative pathways, the methylation pathway and the Pcs pathway. The responsible genes, pmtA (coding for a phospholipid N-methyltransferase) and pcs (coding for a PC synthase), are located on the circular chromosome of A. tumefaciens C58. Recombinant expression of pmtA and pcs in Escherichia coli revealed that the individual proteins carry out the annotated enzyme functions. Both genes and a putative ABC transporter operon downstream of PC are constitutively expressed in A. tumefaciens. The amount of PC in A. tumefaciens membranes reaches around 23% of total membrane lipids. We show that PC is distributed in both the inner and outer membranes. Loss of PC results in reduced motility and increased biofilm formation, two processes known to be involved in virulence. Our work documents the critical importance of membrane lipid homeostasis for diverse cellular processes in A. tumefaciens.
Behavior Selection Based Navigation and Obstacle Avoidance Approach Using Visual and Ultrasonic Sensory Information for Quadruped Robots

A robot functioning in an environment may exhibit various forms of behavior emerge from the interaction with its environment through sense, control and plan activities. Hence, this paper introduces a behaviour selection based navigation and obstacle avoidance algorithm with effective method for adapting robotic behavior according to the environment conditions and the navigated terrain. The developed algorithm enable the robot to select the suitable behavior in real-time to avoid obstacles based on sensory information through visual and ultrasonic sensors utilizing the robot's ability to step over obstacles, and move between surfaces of different heights. In addition, it allows the robot to react in appropriate manner to the changing conditions either by fine-tuning of behaviors or by selecting different set of behaviors to increase the efficiency of the robot over time. The presented approach has been demonstrated on quadruped robot in several different experimental environments and the paper provides an analysis of its performance.
A Subspace Clustering Extension for the KNIME Data Mining Framework

Analyzing databases with many attributes per object is a recent challenge. For these high dimensional data it is known that traditional clustering algorithms fail to detect meaningful patterns. As a solution subspace clustering techniques were introduced. They analyze arbitrary subspace projections of the data to detect clustering structures. In this demonstration, we introduce the first subspace clustering extension for the well-established KNIME data mining framework. While KNIME offers a variety of data mining functionalities, subspace clustering is missing so far. Our novel extension provides a multitude of algorithms, data generators, evaluation measures, and visualization techniques specifically designed for subspace clustering. It deeply integrates into the KNIME framework allowing a flexible combination of the existing KNIME features with the novel subspace components. The extension is available on our website.
Investigation of the Hammerstein hypothesis in the modeling of electrically stimulated muscle

To restore functional use of paralyzed muscles by automatically controlled stimulation, an accurate quantitative model of the stimulated muscles is desirable. The most commonly used model for isometric muscle has had a Hammerstein structure, in which a linear dynamic block is preceded by a static nonlinear function. To investigate the accuracy of the Hammerstein model, the responses to a pseudo-random binary sequence (PRES) excitation of normal human plantarflexors, stimulated with surface electrodes, were used to identify a Hammerstein model but also four local models which describe the responses to small signals at different mean levels of activation. Comparison of the local models with the linearized Hammerstein model showed that the Hammerstein model concealed a fivefold variation in the speed of response. Also, the small-signal gain of the Hammerstein model was in error by factors up to three. We conclude that, despite the past widespread use of the Hammerstein model, it is not an accurate representation of isometric muscle. On the other hand, local models, which are more accurate predictors, can be identified from the responses to short PRES sequences. The utility of local models for controller design is discussed.
A Novel Local Classification Method using Growing Neural Gas and Proximal Support Vector Machines

In this paper, a new pattern recognition method is presented. It is based on the combination of two techniques. The first is a modified version of growing neural gas, and the second is a set of proximal support vector machines. The aim of the former is to obtain a topology in the network that defines different local regions into the input space; and the goal of the latter is to fit a set of local classifiers for each one of the regions. The efficiency of the algorithm is validated on two data sets and is compared to another standard algorithm. The results obtained by the method presented exhibit a good performance in all cases.
Estimation of complex valued permeability of cable armour steel

This paper presents a model based technique to estimate the complex valued permeability of cable armour steel. An efficient analytical model is derived for the linearized mutual impedance of a transformer coil built on a core of magnetic armour steel. A numerical residue calculation is used to solve the related inverse problem based on impedance data. The analytical model is validated using commercial finite element (FEM) software to establish that edge effects can be neglected. The numerical residue calculation is investigated by studying its convergence based on a simple rectangular quadrature rule in comparison to the composite Simpson's rule. When there are no measurement errors, both methods converge with an unexpected high order (superconvergence). However, in practice the estimation performance will be governed by measurement and model errors. Assuming that there are Gaussian measurement errors, the present performance of the estimation technique is quantified and investigated by means of the Cramér-Rao lower bound. In future, the proposed method will be useful as an input to general calculations of power losses in three-phase power cables.
Sensitivity analysis of mathematical models of signalling pathways

Modeling the dynamic behavior of signal transduction pathways is an important topic in systems biology. Mathematical models complement experimental technologies used to identify the molecular components and interactions in a system of interest. In this paper, we illustrate different types of mathematical approaches that are used to model signaling network behavior. Here, we review the basic methods of sensitivity analysis and apply them to the model of the system of membrane receptors. Four such receptors are considered: growth factor epidermal, low density lipoprotein, transferrin and vitellogenin receptor. We argue that application of sensitivity analysis methods provides an insight into how a signaling system controls the cell behavior.
Model checking of Control Interpreted Petri Nets

The paper presents an original approach to model checking of Control Interpreted Petri Nets. Petri Nets are currently used in the industry, but they are mostly verified only for structural properties. However, behavior properties are also of great importance. They can be verified using model checking technique. Model checking of specification allows to early detect subtle errors resulting from incorrect specification interpretation. Model description derived from Petri net is presented at RTL-level in such a way that it is easy to synthesize as reconfigurable logic controller or PLC as well as to formally verify for behavioral properties.
A New MCA-Based Divisive Hierarchical Algorithm for Clustering Categorical Data

Clustering categorical data faces two challenges, one is lacking of inherent similarity measure, and the other is that the clusters are prone to being embedded in different subspace. In this paper, we propose the first divisive hierarchical clustering algorithm for categorical data. The algorithm, which is based on Multiple Correspondence Analysis (MCA), is systematic, efficient and effective. In our algorithm, MCA plays an important role in analyzing the data globally. The proposed algorithm has five merits. First, our algorithm yields a dendrogram representing nested groupings of patterns and similarity levels at different granularities. Second, it is parameter-free, fully automatic and, most importantly, requires no assumption regarding the number of clusters. Third, it is independent of the order in which the data are processed. Forth, it is scalable to large data sets; and finally, using the novel data representation and Chi-square distance measures makes our algorithm capable of seamlessly discovering the clusters embedded in the subspaces. Experiments on both synthetic and real data demonstrate the superior performance of our algorithm.
ASR for Romanian Language

In this paper we will present progresses made in Automatic Speech Recognition (ASR) for Romanian language. The recognition system is based on statistical modelling with hidden Markov models (HMMs). The progresses concern enhancement of modelling by taking into account the context in form of triphones, improvement of speaker independence by applying a gender specific training and enlargement of the feature categories used to describe speech sequences derived not only from perceptual cepstral analysis but also from perceptual linear prediction.
On the Matching Error of the Iterative Closest Contour Point Algorithm for Underwater Navigation

The terrain-aided navigation (TAN) technique is well suited as a totally autonomous and long-duration workable navigation approach for underwater vehicles. This paper focuses on the accuracy of the iterative closest contour point (ICCP) algorithm, particularly on the aspects which influence the matching error of ICCP. The first aspect studied is the quantitative relationship of the matching errors with the terrain feature. The 6 (among a total of 17) terrain factors most influencing the accuracy of the ICCP algorithm are identified using cluster and correlation analysis. Subsequently, three statistical methods, including multiple regression, logistics analysis, and discriminant analysis, are applied to mathematically derive the three different types of relationship between the terrain factors and the matching errors. Each formula uses no more than 3 terrain factors to fit the matching errors. The other issue studied in this paper is the effect of factors such as initial inertial navigation system (INS) errors, the INS accuracy, the map resolution, the vehicle speed, the matching path length, and the sonar accuracy on the matching errors. Some conclusions are then drawn which are useful for the application and implementation of the ICCP algorithm.