Miracle-dz/UniFAR-FTUs huggingface dataset:
Grammar model-based program evolution

In evolutionary computation, genetic operators, such as mutation and crossover, are employed to perturb individuals to generate the next population. However these fixed, problem independent genetic operators may destroy the sub-solution, usually called building blocks, instead of discovering and preserving them. One way to overcome this problem is to build a model based on the good individuals, and sample this model to obtain the next population. There is a wide range of such work in genetic algorithms; but because of the complexity of the genetic programming (GP) tree representation, little work of this kind has been done in GP. In this paper, we propose a new method, grammar model-based program evolution (GMPE) to evolved GP program. We replace common GP genetic operators with a probabilistic context-free grammar (SCFG). In each generation, an SCFG is learnt, and a new population is generated by sampling this SCFG model. On two benchmark problems we have studied, GMPE significantly outperforms conventional GP, learning faster and more reliably.
Satisfiability based test generation for stuck-at fault coverage in RTL circuits using VHDL

Due to the increasing complexity of modern circuit design, verification has become the major bottleneck of the entire design process. Most efforts are to verify the correctness of the initial Register-Transfer Level (RTL) descriptions written in Hardware Description Language (HDL). A digital system, in general, consists of two main parts: a data path and controller. A validation test set is used to verify controller behaviour and, the controller behaviour exercised by test sequences in a validation test set are reused for detecting faults in the data path. Such controller behaviours are said to be compatible with the corresponding pre-computed test vectors/responses, resulting in the detection of a majority of stuck-at faults in the data path RTL modules. Also, since test generation is performed at the RTL and the controller behaviour is predetermined, test generation time is reduced. The Justification algorithm is used to quickly identify whether the controller behaviour is compatible with pre-computed test pattern and it is a basic factor in affecting the efficiency of algorithms for test generation. Adding Design For Testability (DFT) elements is equivalent to modifying these clauses, such that all the unsatisfiable segment becomes satisfiable. A greedy algorithm is used to select circuit variables for DFT and minimizes the number of DFT elements added to a circuit.
A New Architecture for Underwater Acoustic Sensor Networks

Underwater acoustic sensor networks (UASNs), have ability to monitor and predict underwater environment and gather scientific data. Essentially UASNs consist of underwater sensor nodes, underwater automatic vehicles (AUVs) and stations that are located on surface of water, which is used as gateways to prepare radio link communication with offshore stations. Quality of service in these networks is limited with some parameters such as, bandwidth of acoustic channel, propagation delay of sound and high-level ambient noise in the water. In this paper, at first, several aspects of underwater acoustic communications are investigated, after that; different architectures of UASN are discussed. In addition, we propose a new multipath scheme for UASN, which can improve end-to-end packet error rate while achieving a good balance between the overall energy efficiency and the end-to-end packet delay. Simulation results show that our method is highly energy-efficient with low endto-end packet delays.
Estimating the amenibility of new domains for deception detection

In this paper we present an initial experiment in the estimation of the amenability of new domains to true/false classification. We choose four domains, two of which have been classified for deception, and use the out-ofrank distance measure on n-grams to aid in deciding whether the third and fourth domains are amenable to T/F classification. We then use a classifier covered in the literature to train on the verified domains and test on the new domains to determine whether the relative distance measure can be a predictor of classification accuracy.
Challenges in Bioinformatics for Statistical Data Miners

Starting with possible definitions of statistical data mining and bioinformatics, this article will give a general side-by-side view of both fields, emphasising on the statistical data mining part and its techniques, illustrate possible synergies and discuss how statistical data miners may collaborate in bioinformatics’ challenges in order to unlock the secrets of the cell. What is statistics and why is it needed? Statistics is the science of “learning from data”. It includes everything from planning for the collection of data and subsequent data management to end-of-the-line activities, such as drawing inferences from numerical facts called data and presentation of results. Statistics is concerned with one of the most basic of human needs: the need to find out more about the world and how it operates in face of variation and uncertainty. Because of the increasing use of statistics, it has become very important to understand and practise statistical thinking. Or, in the words of H. G. Wells: “Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write”. But, why is statistics needed? Knowledge is what we know. Information is the communication of knowledge. Data are known to be crude information and not knowledge by itself. The way leading from data to knowledge is as follows: from data to information (data become information when they become relevant to the decision problem); from information to facts (information becomes facts when the data can support it); and finally, from facts to knowledge (facts become knowledge when they are used in the successful completion of the decision process). That is why we need statistics and statistical thinking. Statistics arose from the need to place knowledge on a systematic evidence base. What is data mining? Data mining has been defined in almost as many ways as there are authors who have written about it. Because it sits at the interface between statistics, computer science, artificial intelligence, machine learning, database management and data visualisation (to name some of the fields), the definition changes with the perspective of the user. Here is a not so random sample of a few:
Three contaminated sites in southern Italy. The Neonatal Environment and Health Outcomes cohort: protocol for a longitudinal birth cohort study

Introduction Exposure to environmental contaminants during pregnancy is one of the determinants of child’s future health outcomes. The effect of environmental pollution on pregnant women living in heavily polluted areas is of special interest and, in this context, the Neonatal Environment and Health Outcomes (NEHO) cohort will focus on the investigation of (1) toxicants transferred from the environment to the mother and from the mother to the developing fetus and (2) the influence of toxicants on pregnancy outcomes, fetal development and health status during infancy. Because the human placenta is positioned at the interface between the maternal/external environment and the embryo, it can be considered a highly informative matrix regarding many key pregnancy events that can shape infant’s future health. Methods and analysis The NEHO cohort will enrol an estimated total of 800 pregnant women in three selected National Priority Contaminated Sites in southern Italy. Epidemiological data, concerning maternal health status, lifestyle and pregnancy, are obtained through questionnaires provided to the mother starting from the last 2 months of pregnancy. At delivery, maternal blood, umbilical cord blood and placenta tissue are collected to assess contaminant levels and to clarify how toxicants interact with the placental domain. Furthermore, placental transcriptome is studied in order to explore the interferences of toxicants on the role of the placenta in maternal/fetal interplay. Regular follow-up is planned at 6, 12 and 24 months. Ethics and dissemination The study has been approved by all the Ethics Committees of the three National Priority Contaminated Sites involved: the Ethics Committee of the University Hospitals of Messina (18 September 2017, n. 9/2017); the Ethics Committee ‘Catania 2’ (11 July 2017, n. 38/2017/CECT2); the Ethics Committee of the Region of Calabria (20 July 2017, n. 173). Findings will be disseminated in the scientific community and on a regional basis for appropriate policy actions.
Altered temporal patterns of anxiety in aged and amyloid precursor protein (APP) transgenic mice

Both normal aging and dementia are associated with dysregulation of the biological clock, which contributes to disrupted circadian organization of physiology and behavior. Diminished circadian organization in conjunction with the loss of cholinergic input to the cortex likely contributes to impaired cognition and behavior. One especially notable and relatively common circadian disturbance among the aged is “sundowning syndrome,” which is characterized by exacerbated anxiety, agitation, locomotor activity, and delirium during the hours before bedtime. Sundowning has been reported in both dementia patients and cognitively intact elderly individuals living in institutions; however, little is known about temporal patterns in anxiety and agitation, and the neurobiological basis of these rhythms remains unspecified. In the present study, we explored the diurnal pattern of anxiety-like behavior in aged and amyloid precursor protein (APP) transgenic mice. We then attempted to treat the observed behavioral disturbances in the aged mice using chronic nightly melatonin treatment. Finally, we tested the hypothesis that time-of-day differences in acetylcholinesterase and choline acetyltransferase expression and general neuronal activation (i.e., c-Fos expression) coincide with the behavioral symptoms. Our results show a temporal pattern of anxiety-like behavior that emerges in elderly mice. This behavioral pattern coincides with elevated locomotor activity relative to adult mice near the end of the dark phase, and with time-dependent changes in basal forebrain acetylcholinesterase expression. Transgenic APP mice show a similar behavioral phenomenon that is not observed among age-matched wild-type mice. These results may have useful applications to the study and treatment of age- and dementia-related circadian behavioral disturbances, namely, sundowning syndrome.
A Composite DiscretdContinuous Control of Robot Manipulators

In this report, a composite control scheme for the control of robot manipulators is proposed. Due to the modeling error or environmental uncertainties, robot motion may present a significant positioning error by using a conventional Computer-Torque Method. To improve tracking capability of robot manipulators, sliding mode control and nonlinear control algorithms have been introduced, but computation is costly, and thus a fast motion execution using simple computer sources is impossible. To solve this problem, we present a composite control algorithm to control robot motion combining a discrete feedforward component and a continuous feedback component. The discrete feedforward component provides a nominal torque computed using the robot dynamics and compensates for dynamic coupling between the links. This part can be updated in a luge sampling time, and can be computed off-line generally, thus real time computation is decreased. The continuous feedback control component uses a structure of Variable Structure System and provides a robust control to disturbances during the sliding mode. This part can be digitally implemented using a short sampling time, and thus a fast motion of a multi-degree freedom robot manipulator can be executed by using a simple computer, or even a single board computer with an %bit CPU. The stability of the proposed multiple-rate control scheme is proven in the paper and efficiency of the control scheme has been demonstrated by simulations of a three-link robot subject to parameter and payload uncertainties. ~~~. ~. -.
Automatic liver tissue segmentation in microscopic images using fusion color space and multiscale morphological reconstruction

In this paper we propose a new method for the segmentation of digital two-dimensional color liver tissue images acquired by an optical microscope from histological segments of the liver of hamster. The sections are acquired by cutting real livers of amoebic liver abscess. This work is part of a medical research project on studying the process of amoebiasis, which harms the human liver, being an important and dangerous disease. The new method is based on a fusion of various results of application of color histogram and multiscale morphological filter, which uses size and color characteristics. As a result, the images are segmented in four classes: the liver cell nuclei, the cytoplasm, stained cells, and the background. For the evaluation and for testing the reliability of the proposed segmentation algorithm, we use a set of real 2D color images of a hamster's liver provided through routine experimentation by the Experimental Pathology Unit at the National Autonomous University of Mexico.
Transfer rule and bilingual dictionary automatic induction in the ReTraTos project

In this paper we present the ReTraTos methodology to automatically induce bilingual resources —transfer rules and bilingual dictionaries— from parallel corpora. These resources are very useful in Machine Translation (MT) and other bilingual Natural Language Processing (NLP) applications. As a result, several automatic approaches have been proposed to avoid the extensive hard work employed to manually build these resources. The automatic approaches described in this paper aim at building bilingual dictionaries and shallow-transfer rules by extracting knowledge from word-aligned and part-of-speech tagged parallel corpora. Experiments carried out with Brazilian Portuguese–Spanish and Brazilian Portuguese–English parallel texts show that the proposed methodologies can speed the development of these valuable computational resources and, thus, help the development of MT systems for new pairs of languages. Furthermore, the rule induction methodology is innovative in the way rules are identified and filtered. http://www.dc. ufscar.br/ ̃helenacaseli/pdf/2007/TeseDoutorado.pdf
The Price Impacts of Linking the EU ETS to the CDM

Many countries have begun to look increasingly towards the Clean Development Mechanism (CDM) as one of the key tools to increase the cost-effectiveness of fulfilling their compliance requirements with the Kyoto Protocol. It is believed, from a theoretical point of view, linking emissions trading schemes to the Kyoto's flexible mechanisms can lead to a decrease in carbon prices and so a reduction of the overall compliance costs. This paper provides an analysis of the relationship between the European Union Emissions Trading Scheme (EU ETS) and the CDM. I take into consideration dynamic interactions between European Allowance (EUA) prices and Certified Emission Reduction (CER) prices. I use time-series econometric techniques to test for the existence of long-run links and causal relations between the prices. Further, the generalised impulse response analysis has been chosen in order to investigate temporal interactions among the variables. The results show that CER prices do not have a statistically significant effect on EUA prices; rather, it is the EUA prices which have driven CER prices during the period investigated. The policy constraints on the availability of CERs emanating from supplementarity and additionality criteria, stipulated by the Kyoto, is one of the major factors that can be hypothesised to account for this finding.
The difference between polytechnic students’ learning styles and their higher order thinking skills level

In order to help students learn thinking skills more effectively and improve their academic performance, learning styles preferred by students must be identified. The research purpose of this work was to analyse the difference between polytechnic students’ learning styles and the level of their higher order thinking skills (HOTS). A descriptive quantitative methodology study was conducted among 368 diploma students studying in three polytechnics in Malaysia. The students’ learning styles were identified through the Kolb Learning Styles Inventory while their HOTS level was identified through a set of questionnaires adapted from the Marzano Rubrics for Specific Tasks or Situations. The results indicated that ‘Doer’ is the most dominant learning style among polytechnic students. The results also showed that polytechnic students perceived their HOTS level to be moderate. The Cramer V analysis showed that there was no relationship between students’ learning styles and eight Marzano HOTS levels. There was also no significant difference between the Kolb Learning Styles and the Marzano HOTS levels. This indicated that regardless of the learning style possessed by the technical students i.e. Doer, Watcher, Thinker or Feeler, the level of HOTS of all the students was the same. This suggests that each student has a different learning style but that all of them possess equal opportunity and capability to learn and master HOTS.
Multichannel compressed sensing via source separation for hyperspectral images

This paper describes a novel framework for compressive sampling of multichannel signals that are highly correlated across the channels. In this work, we assume few number of independent sources are generating the multichannel observations based on a linear mixture model. Moreover, sources are assumed to have sparse/compressible representations in some orthonormal basis. The main contribution of this paper lies in rephrasing the compressed sampling of multichannel data as the compressive source separation problem by knowing the mixture parameters. A number of simulations measure the performance of our recovery algorithm. Comparing to the classical CS scheme-which recovers data of all channels separately- ours indicates a significant reduction in both, the number of measurements to be sent and the complexity of the decoding algorithm (i.e., scaling by the number of sources rather than the number of channels). We demonstrate an application of our scheme in acquisition of the Hyperspectral images. Our algorithm proposes an accurate, low cost and fast recovery from small number of the transmitted measurements that are taken by a low resolution camera.
Smaller splenium in children with nonverbal learning disability compared to controls, high-functioning autism and ADHD

The current study investigated morphological differences in the corpus callosum in children ages 8 to 18 years old with nonverbal learning disability (NLD; n = 19), high-functioning autism (HFA; n = 23), predominantly inattentive ADHD (ADHD:PI; n = 23), and combined type ADHD (ADHD:C; n = 25), as well as those demonstrating typical development (n = 57). Midsagittal area of the corpus callosum and five midsagittal anterior-to-posterior corpus callosum segments were examined using magnetic resonance imaging. Controlling for midsagittal brain area and age, no group differences were found for total corpus callosum area. This finding indicates that higher functioning children on the autistic spectrum do not have smaller corpus callosi as has been found in previous research with heterogeneous samples. Following segmentation of the corpus callosum, the NLD group was observed to have significantly smaller splenia compared to all other groups. Smaller splenia in the NLD group was associated with lower WASI PIQ scores but not WASI VIQ scores. Children with HFA were observed to have larger midbody areas than children with NLD and neurotypically developing children. Children with HFA and NLD demonstrated behavioral symptoms of inattention and hyperactivity similar to the ADHD groups indicating that corpus callosum differences seen in the NLD and HFA groups are not related to these behaviors.
Fast evaluation of sequence pair in block placement by longest common subsequence computation

Murata et al. (1996) introduced an elegant representation of block placement called sequence pair. All block placement algorithms which are based on sequence pairs use simulated annealing where the generation and evaluation of a large number of sequence pairs is required. Therefore, a fast algorithm is needed to evaluate each generated sequence pair, i.e. to translate the sequence pair to its corresponding block placement. This paper presents a new approach to evaluate a sequence pair based on comparing longest common subsequence in a pair of weighted sequences. We present a very simple and efficient O(n/sup 2/) algorithm to solve the sequence pair evaluation problem. We also show that using a more sophisticated data structure, the algorithm can be implemented to run in O(n log n) time. Both implementations of our algorithm are significantly faster than the previous O(n/sup 2/) graph-based algorithm. For example, we achieve 60/spl times/ speedup over the previous algorithm when input size n=128.
Adaptation of Engagement Questionnaire to Turkish for Science Classes: Validity and Reliability Study

The aim of this research was to adapt the Engagement Questionnaire in four dimensions (i.e. behavioral, emotional, cognitive, and agentic engagement) to Turkish to be used in science classes. For this purpose, firstly a pilot study was conducted with 153 middle school students, then the main study was conducted with 744 middle school students. Both studies supported four-factor structure of Turkish version of the EQ. In addition, reliabilities seemed to be sufficient. The results concerning the measurement invariance across gender revealed the data from both genders lead to equally valid conclusions regarding their engagement levels. Consequently, the findings of this study indicated that the Turkish version of EQ appears to be a valid and reliable instrument to measure and understand the middle school students’ engagement level in science classes.
A Quadtree Driven Image Fusion Quality Assessment

In this paper a new method to compute saliency of source images is presented. This work is an extension to universal quality index founded by Wang and Bovik and improved by Piella. It defines the saliency according to the change of topology of quadratic tree decomposition between source images and the fused image. The saliency function provides higher weight for the tree nodes that differs more in the fused image in terms topology. Quadratic tree decomposition provides an easy and systematic way to add a saliency factor based on the segmented regions in the images.
Joint resource allocation method in heterogeneous wireless networks based on genetic algorithm

The heterogeneous wireless networks integrate various types of radio access networks (RANs) as far as possible to provide multiple services on the common network platform. Joint resource allocation in these networks plays an important role in improving resource utilization. In this paper, we study the problem of maximizing system capacity for parallel multi-radio access scheme where multi-mode mobile terminals (MMTs) can transmit data simultaneously over multiple RANs. A bandwidth and power joint allocation method named MDGS was proposed, which based on the genetic algorithm (GA). Numerical results validate the effectiveness of the proposed approach.