Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of Cultural Learning in Populations of Neural Networks

Population learning can be described as the iterative Darwinian process of fitness-based selection and genetic transfer of information leading to populations of higher fitness and is often simulated using genetic algorithms. Cultural learning describes the process of information transfer between individuals in a population through non-genetic means. Cultural learning has been simulated by combining genetic algorithms and neural networks using a teacher-pupil scenario where highly fit individuals are selected as teachers and instruct the next generation. By examining the innate fitness of a population (i.e., the fitness of the population measured before any cultural learning takes place), it is possible to examine the effects of cultural learning on the population's genetic makeup. Our model explores the effect of cultural learning on a population and employs three benchmark sequential decision tasks as the evolutionary task for the population: connect-four, tic-tac-toe, and blackjack. Experiments are conducted with populations employing population learning alone and populations combining population and cultural learning. The article presents results showing the gradual transfer of knowledge from genes to the cultural process, illustrated by the simultaneous decrease in the population's innate fitness and the increase of its acquired fitness measured after learning takes place.
Engineering Approach for an Adaptable Recommender System Using Web Mining

A current trend in e-commerce systems is to incorporate instruments for personalized product recommendation in order to obtain new customers and retain existing ones. In this paper, we integrate software engineering and web mining techniques in the development of a recommender web system able to predict the preferences of its users and adapt the user interface to their necessities. The personalization and adaptation tasks use a data mining predictive model induced by a decision tree algorithm. The system is also endowed with an internationalization automatic mechanism that facilitates the visualization of the user’s interface in different languages.
Neural Predictors of Giving in to Temptation in Daily Life

The ability to control desires, whether for food, sex, or drugs, enables people to function successfully within society. Yet, in tempting situations, strong impulses often result in self-control failure. Although many triggers of self-control failure have been identified, the question remains as to why some individuals are more likely than others to give in to temptation. In this study, we combined functional neuroimaging and experience sampling to determine if there are brain markers that predict whether people act on their food desires in daily life. We examined food-cue-related activity in the nucleus accumbens (NAcc), as well as activity associated with response inhibition in the inferior frontal gyrus (IFG). Greater NAcc activity was associated with greater likelihood of self-control failures, whereas IFG activity supported successful resistance to temptations. These findings demonstrate an important role for the neural mechanisms underlying desire and self-control in people’s real-world experiences of temptations.
Hidden Markov models applied to on-line handwritten isolated character recognition

Hidden Markov models are used to model the generation of handwritten, isolated characters. Models are trained on examples using the Baum-Welch optimization routine. Then, given the models for the alphabet, unknown characters can be classified using maximum-likelihood classification. Experiments have been conducted, and an average error rate of 6.9% was achieved over the alphabet consisting of the lowercase English alphabet.
Structured Prediction with Perceptron: Theory and Algorithms

Structured prediction problem is a special case of machine learning problem where both the inputs and outputs are structures such as sequences, trees, and graphs, rather than plain single labels or values. Many important natural language processing (NLP) problems are structured prediction problems, including Part-Of-Speech tagging, parsing, and machine translation. This survey investigates how perceptron, the simplest supervised machine learning algorithm, can be adapted to handle structured inputs and outputs. In particular, for the estimation problem that searches for the right output structure, introducing structures leads to combinatorial explosion and the inference becomes extremely time-consuming. Various dynamic programming techniques and approximations are developed to speed up the search. For the learning problem, especially supervised learning with perceptron, searching structures with approximations makes the standard perceptron learning algorithm unconvergable. To address this problem, new learning methods are invented for structured learning. In addition, complicated structured prediction problems usually involve unobserved structures as hidden variables. A latent variable perceptron model is also discussed to handle those problems.
La construction des reseaux d’entreprises, une contribution par les oppositions paradoxales : le cas d'un réseau d'entreprises horticoles de la région Angevine

Les reseaux d’entreprises sont des formes organisationnelles conduisant a un certain nombre d’avantages pour les entreprises qui en sont membres. Pour ces dernieres, cette forme organisationnelle presente l’interet de ne pas supprimer leur autonomie ni leur independance, tout en beneficiant des avantages lies aux rapprochements inter-organisationnels. De par cette particularite, les reseaux comprennent ainsi deux niveaux organisationnels distincts mais indissociables : le niveau organisationnel des entreprises membres et le niveau organisationnel du reseau. Avant de beneficier des avantages auxquels conduit l’organisation en reseau, il est necessaire qu’il soit construit par les organisations qui en sont a l’origine. Dans le temps, la construction du reseau conduit a un certain renforcement de son degre de neguentropie, se traduisant par une complexification sur le long-terme. Cette complexification s’opere tout en conservant l’autonomie et l’independance des organisations qui en sont membres. Peu de recherches se sont interessees a cette question de la construction des reseaux. Cette these propose d’y repondre un utilisant l’approche par les oppositions paradoxales (approche par les paradoxes et approche par les dialectiques), qui permet de prendre en consideration l’indissociabilite du double niveau organisationnel des reseaux. Par le biais d’une etude de cas appliquee a un reseau d’entreprises dans le secteur horticole de la region angevine, nous cherchons a identifier comment les objectifs de chacun des deux niveaux organisationnels contribuent a la construction du reseau en question et au renforcement de son degre de neguentropie.
Seven Pervasive Statistical Flaws in Cognitive Training Interventions

The prospect of enhancing cognition is undoubtedly among the most exciting research questions currently bridging psychology, neuroscience, and evidence-based medicine. Yet, convincing claims in this line of work stem from designs that are prone to several shortcomings, thus threatening the credibility of training-induced cognitive enhancement. Here, we present seven pervasive statistical flaws in intervention designs: (i) lack of power; (ii) sampling error; (iii) continuous variable splits; (iv) erroneous interpretations of correlated gain scores; (v) single transfer assessments; (vi) multiple comparisons; and (vii) publication bias. Each flaw is illustrated with a Monte Carlo simulation to present its underlying mechanisms, gauge its magnitude, and discuss potential remedies. Although not restricted to training studies, these flaws are typically exacerbated in such designs, due to ubiquitous practices in data collection or data analysis. The article reviews these practices, so as to avoid common pitfalls when designing or analyzing an intervention. More generally, it is also intended as a reference for anyone interested in evaluating claims of cognitive enhancement.
The impact of foreign direct investment on developing economies and the environment

All of us that are concerned about the environment should ask if the increase in capital mobility associated with the world-wide process of liberalization, deregulation and privatization known as the Neo-liberal global regime has contributed to the problems of higher emissions, ozone layer destruction, and pollution of water sources, as well as to create false economic bubbles that lead to increase consumption in these regions and force the poor to destroy the environment in order to survive and cope with the roles their society demands. Neo-liberal practices such as those enforced in developing countries like Colombia, seeking to attract foreign investment to push their economies tend to generate a false aggregated demand growth, that in most cases is not sustainable in the long term, and thus high global unemployment, unleash destructive competitive processes, and weaken government's ability to regulate business in the citizens best interests. The forces of global Neo-liberalism are now so powerful that it has become difficult if not impossible for countries like Colombia to maintain non-Neo-liberal economic structures, in which countries are forced to deregulate FDI policies and receive inflows of capital no matter the terms and the objectives as long as it helps to maintain consumption levels.
Learning Agents for Storage Devices Management in the Smart Grid

A notable feature in the smart grid is the widespread usage of energy storage devices. How to manage those storage devices is a key problem for the smart grid. In this paper, a novel adaptive agent learning ZIPEM algorithm is presented for management of the storage devices. A system with such algorithm allows multi-agent learning that leads to optimal energy storage strategy. The experimental results show that load factor during peak time reduced significantly, and it is possible to save up to 6 percent per consumer on electricity expenses with a storage device of 2 kWh. Moreover, emissions of carbon-dioxide from power generation processes can decrease by 6.3 percent.
Scents and scents-ability: pollution disrupts chemical social recognition and shoaling in fish

Chemical cues are of enormous importance in mediating the behaviour of animals, enabling them to navigate throughout their habitats, to detect the presence of predators or prey and for social recognition—identifying and discriminating between conspecifics. In many species of freshwater fish, social recognition is known to be based primarily on chemical cues. Such recognition mechanisms are vulnerable to disruption by the presence of anthropogenic contaminants in the aquatic environment. Here we show that acute exposure to low, environmentally relevant dosages of the ubiquitous contaminant, 4-nonylphenol, can seriously affect social recognition and ultimately social organization in fishes. A 1 hour 0.5 μg l−1 dose was sufficient to alter the response of members of a shoaling fish species (juvenile banded killifish, Fundulus diaphanus) to conspecific chemical cues. Dosages of 1–2 μg l−1 caused killifish to orient away from dosed conspecifics, in both a flow channel and an arena. Given the overall importance of shoaling as an adaptive strategy against predators and for locating food, it is likely that its disruption by anthropogenic contaminants would have serious implications for fishes' fitness.
A novel hybrid approach for human silhouette segmentation

In this work we propose a novel algorithm for human silhouette segmentation, which combines characteristics from a number of well established and state of the art algorithms, such as the Gaussian mixture models, the Self Organizing Maps and the Illumination Sensitive method. The proposed algorithm is evaluated against user-defined ground truth segmentation for two different types of indoor video sequences, one of which was obtained by a hemispheric camera. The behavior of the algorithm with respect to its controlling parameters is investigated and its computational burden is studied.
Image enhancement based on lifting wavelet transform

The traditional image enhancement algorithms enhance noise signal in image while they enhance image, which leads to the de-scent of information entropy. Designing image enhancement systems with good nature is a goal which all researchers pursue. Method of lifting can best deal with MRA Multi-resolution Analysis method which is time and memory consuming which impedes its real-time application. The process of lifting method was introduced. Method of image enhancement based on lifting wavelet was introduced. The algorithm and traditional wavelet image enhancement algorithms are compared. Result of the experiment indicate, the quality of image have been improved. It is better than traditional wavelet image enhancement algorithms.
Lysis of ras oncogene-transformed cells by specific cytotoxic T lymphocytes elicited by primary in vitro immunization with mutated ras peptide

Ras protooncogenes are activated by characteristic point mutations in a wide variety of malignancies. The expressed p21ras proteins are oncogenic by virtue of single substituted amino acids, usually at position 12 or 61 of the 189-residue p21ras protein. In the current study, the ability of class I major histocompatibility complex (MHC)- restricted T cells to recognize the altered segment of a transforming p21ras protein and to lyse cells transformed by the corresponding ras oncogene was examined. Synthetic ras peptides encompassing the common activating substitution of leucine for glutamine at position 61 were constructed with an amino acid motif appropriate for binding to the H- 2Kb murine class I MHC molecule. Cytotoxic T lymphocytes (CTL) specific for bound ras leucine 61 peptide were elicited by in vitro immunization of normal lymphocytes with synthetic peptides. The ras peptide-induced CTL specifically lysed syngeneic fibroblasts transformed by an activated ras gene encoding oncogenic p21ras protein containing the same single amino acid substitution. Thus, in some circumstances, mutated p21ras protein can serve as a tumor-specific antigen.
A fast algorithm for moving objects detection based on model switching

A new method is proposed to improve background modeling speed. First, the pixels in current frame are classified into two classes according to average background to reduce the computing load. Second, different models for instance kernel or GMM based algorithm are used necessarily to deal with 'dead lock' of scene. Third, a kernel density estimation based on neighbor correlation is used to decrease the false positives'. Last, the two algorithm detection results are fused to detect moving object by the label of pixel. In this paper, a novel description of correlation about the pixel with its around pixels and a strategy of background modeling are proposed. Experimental results of outdoor complex scene demonstrate that the new algorithm is robustness to noise and good for real-time moving object detection.
The logical meaning of Expansion

The Expansion property considered by researchers in Social Choice is shown to correspond to a logical property of nonmonotonic consequence relations that is the pure, i.e., not involving connectives, version of a previously known weak rationality condition. The assumption that the union of two definable sets of models is definable is needed for the soundness part of the result.
Emotional contagion and group polarization: experimental evidence on facebook

Information, rumors, debates shape and reinforce the perception of reality and heavily impact public opinion. Indeed, the way in which individuals influence each other is one of the foundational challenges in several disciplines such as sociology, social psychology, and economics. One of the most fascinating and powerful mechanisms of social contagion is that of group polarization. The phenomenon manifests when like-minded people discuss and reinforce their shared views thus ending up in a more extreme position. The core of the research work presented in this thesis explores the phenomenon of group polarization on online social media. We focus on the Italian and US pages providing scientific and conspiracy information and we analyze a) users’ emotional dynamics and b) their response to dissenting information. We offer tight quantitative evidence about the existence of echo chambers on online social media. Users tend to promote their beliefs and to form highly polarized groups. Furthermore, dealing with untrusted opponents in online discussion results for users in a major commitment with respect to their own echo chamber.
Fast and high quality overlap repair for patch-based texture synthesis

Patch-based texture synthesis has proven to produce high quality textures faster than pixel-based approaches. Previous algorithms differ in how the regions of overlap between neighboring patches are treated. We present an approach that produces higher quality overlap regions than simple blending of patches or computing good boundaries, however, that is faster than resynthesizing invalid pixels using a classical per-pixel synthesis algorithm: we use a k-nearest neighbor (knn) data structure, obtained from the input texture in a precomputation step. Results from our implementation show that the algorithm produces high-quality textures, where the time complexity of the synthesis stage is linear in the number of resynthesized pixels and, therefore, scales well with the size of the input texture
Classifier fusion for robust ICAO compliant face analysis

Biometrics is a huge and very fast growing domain of methods for uniquely recognizing humans based on one or more intrinsic physical or behavioral traits with applications in many different areas, e.g., surveillance, person verification and identification. The International Civil Aviation Organization (ICAO) provides a number of specifications to prepare automated recognition from travel document photos. The goal of these specifications is to increase security in civil aviation on the basis of standardized biometric data. Due to this international standard, there is a high demand for automatically checking face images to assist civil service employees in decision-making. In this work, we present a face normalization and analysis system implementing several parts of the ICAO specification. Our key contribution of this analysis is the fusion of different established classifiers to boost performance of the overall system. Our results show the superior checking quality on facial images due to utilizing classifier fusion compared to a single classifier decision.
Research on the Vulnerability Predicting Analysis Model for Binary Executables

In research for detecting vulnerability in binary executables, Fuzzing technology is commonly adopted and is effective for detecting vulnerabilities in binary executable. Because the test cases generation for Fuzzing technology is random, the efficiency of detecting vulnerability is very low. In this paper, we propose a predict analysis model for binary program vulnerability analysis, which is based upon the vulnerable factors that describe the complexity of the test binary executable. In order to improve the efficiency of vulnerability analysis, firstly, we predict the possible vulnerable positions in the binary executable, then we generate test cases to cover these possible vulnerable codes. The vulnerable factors defined in our predicting analysis model are the function Cyclomatic complexity, the function nest level, the loop number and the loop-write-memory instruction in the function. We extract the vulnerable factors of five large real-world binary programs, evaluate our vulnerability predicting analysis model on those five large real-world applications. The experimental results show that our predicting analysis can analyze the possible vulnerable factors which can cause the binary executable to occur exceptions and analyze the relations between the vulnerability of the binary program and the vulnerable factors. With the help of our predicting analysis model for binary program vulnerability analysis, we can generate targeted test cases to cover these possible vulnerabilities in binary executable.
mRNA Expression and DNA Methylation Analysis of Serotonin Receptor 2A (HTR2A) in the Human Schizophrenic Brain

Serotonin receptor 2A (HTR2A) is an important signalling factor implicated in cognitive functions and known to be associated with schizophrenia. The biological significance of HTR2A in schizophrenia remains unclear as molecular analyses including genetic association, mRNA expression and methylation studies have reported inconsistent results. In this study, we examine HTR2A expression and methylation and the interaction with HTR2A polymorphisms to identify their biological significance in schizophrenia. Subjects included 25 schizophrenia and 25 control post-mortem brain samples. Genotype and mRNA data was generated by transcriptome sequencing. DNA methylation profiles were generated for CpG sites within promoter-exon I region. Expression, genotype and methylation data were examined for association with schizophrenia. HTR2A mRNA levels were reduced by 14% (p = 0.006) in schizophrenia compared to controls. Three CpG sites were hypermethylated in schizophrenia (cg5 p = 0.028, cg7 p = 0.021, cg10 p = 0.017) and HTR2A polymorphisms rs6314 (p = 0.008) and rs6313 (p = 0.026) showed genetic association with schizophrenia. Differential DNA methylation was associated with rs6314 and rs6313. There was a strong correlation between HTR2A DNA methylation and mRNA expression. The results were nominally significant but did not survive the rigorous Benjamini-Hochberg correction for multiple testing. Differential HTR2A expression in schizophrenia in our study may be the result of the combined effect of multiple differentially methylated CpG sites. Epigenetic HTR2A regulation may alter brain function, which contributes to the development of schizophrenia.
Web navigation analysis and simulation using Ant Colony Optimization

This paper utilizes the Ant Colony Optimization algorithm to explore an unknown web site, mapping its structure and navigation routing so that accessibility and performance information can be attained. The investigation will also unveil changing structure of the web site adaptively such as new links, removed links, and unreachable links. As a consequence, coverage of all reachable nodes within the designated web site can be obtained, along with essential performance statistics to reflect near optimal accessible paths to any given node in the web site. By virtue of the simplicity of the Ant Colony Optimization algorithm, some straightforward mapping techniques were employed to entail opportunistic commercialization of the proposed algorithm.
Effects of cooperative learning on second graders’ learning from text

Two studies were conducted to investigate the effects of cooperative learning on second‐graders’ motivation and learning from text. In Study 1, students (n = 160) in cooperative learning groups were compared with their counterparts (n = 107) in traditional instruction groups. The results revealed a statistically significant difference between the two groups, with more favourable perceptions of teachers’ instructional practices and better reading comprehension in the instructional intervention groups than in the traditional instruction groups. In Study 2, 51 second‐graders participated in the instructional intervention programme. The results showed that students’ positive cooperative behaviour and attitudes were related to their motivation and reading comprehension. When students perceived that their peers were willing to help each other and were committed to the group, they tended to be more motivated and performed better in reading comprehension.