Miracle-dz/UniFAR-FTUs huggingface dataset:
The Council of Europe’s citizenship conception in ‘Education for Democratic Citizenship’: a critical discourse analysis of two textbooks

ABSTRACT This paper presents a neocommunitarian conception of citizenship identified in two textbooks of the programme ‘Education for Democratic Citizenship’, organised by the Council of Europe. Critical discourse analysis is applied to the key themes of the textbooks T-Kit 7: Under construction: Citizenship Youth and Europe and Compass Manual enclosing citizenship and human rights discourses, respectively. An intra-disciplinary discussion follows drawing on critical political economy. The findings of the analysis are that the textbooks exhibit an abstract representation of social reality as well as moral relativism stemming from neoliberal communitarianism. The identification of the neocommunitarian conception of citizenship reveals the power of citizenship education today.
Misco: a MapReduce framework for mobile systems

The proliferation of increasingly powerful, ubiquitous mobile devices has created a new and powerful sensing and computational environment. Software development and application deployment in such distributed mobile settings is especially challenging due to issues of failures, concurrency, and lack of easy programming models. We present a framework which provides a powerful software abstraction that hides many of such complexities from the application developer. We design and implement a mobile MapReduce framework targeted at any device which supports Python and network connectivity. We have implemented our system on a testbed of Nokia N95 8GB smartphones and demonstrated the feasibility and performance of our approach.
Insertions/Deletions-Associated Nucleotide Polymorphism in Arabidopsis thaliana

Although high levels of within-species variation are commonly observed, a general mechanism for the origin of such variation is still lacking. Insertions and deletions (indels) are a widespread feature of genomes and we hypothesize that there might be an association between indels and patterns of nucleotide polymorphism. Here, we investigate flanking sequences around 18 indels (>100 bp) among a large number of accessions of the plant, Arabidopsis thaliana. We found two distinct haplotypes, i.e., a nucleotide dimorphism, present around each of these indels and dimorphic haplotypes always corresponded to the indel-present/-absent patterns. In addition, the peaks of nucleotide diversity between the two divergent alleles were closely associated with these indels. Thus, there exists a close association between indels and dimorphisms. Further analysis suggests that indel-associated substitutions could be an important component of genetic variation shaping nucleotide polymorphism in Arabidopsis. Finally, we suggest a mechanism by which indels might generate these highly divergent haplotypes. This study provides evidence that nucleotide dimorphisms, which are frequently regarded as evidence of frequency-dependent selection, could be explained simply by structural variation in the genome.
Entropy-Based Incremental Variational Bayes Learning of Gaussian Mixtures

Variational approaches to density estimation and pattern recognition using Gaussian mixture models can be used to learn the model and optimize its complexity simultaneously. In this brief, we develop an incremental entropy-based variational learning scheme that does not require any kind of initialization. The key element of the proposal is to exploit the incremental learning approach to perform model selection through efficient iteration over the variational Bayes optimization step in a way that the number of splits is minimized. The method starts with just one component and adds new components iteratively by splitting the worst fitted kernel in terms of evaluating its entropy. Our experimental results, on synthetic and real data sets show the effectiveness of the approach outperforming other state-of-the-art incremental component learners.
Fine-grained location-free planarization in wireless sensor networks

Extracting planar graph from network topologies is of great importance for efficient protocol design in wireless ad hoc and sensor networks. Previous techniques of planar topology extraction are often based on ideal assumptions, such as UDG communication model and accurate node location measurements. To make these protocols work effectively in practice, we need extract a planar topology in a location-free and distributed manner with small stretch factor. Current location-free methods cannot provide any guarantee on the stretch factor of the constructed planar topologies. In this work, we present a fine-grained and location-free network planarization method. Compared with existing location-free planarization approaches, our method can extract a high-quality planar graph, called TPS (Topological Planar Simplification), from the communication graph using local connectivity information. TPS is proved to be a planar graph and has a constant stretch factor for a large class of network instances. We evaluate our design through extensive simulations and compare with the state-of-the-art approaches. The simulation results show that our method produces planar graphs with a small constant stretch factor, often less than 1.5.
High-Dimensional Statistical and Data Mining Techniques

INTRODUCTION Because of the rapid growth of applications in computational biology, computer and information sciences has resulted in large volumes of high dimensional data (HDD) becoming available. In a DNA microarray dataset, there exist hundreds or thousands of dimensions, each corresponding to the gene expression of biological samples. In textual data, there exists high number of dimensions where each dimension corresponds to a word. In customer purchase behavior data, there also exists a high number of dimensions, corresponding to merchandises. Similar situation is present in market basket data where dimensions are the products of a transaction Analyzing these datasets is more difficult than other datasets because of the dimension problem and because traditional statistical techniques are no longer applicable. This is because, most of the traditional approaches were based on the assumption that the observation number should be more than the variable number (n>p). Recent technologies provide an opportunity for researchers to measure lots of variables however their highcost limits the number of observations. The number of variables exceeds the number of observations (n<p) and the classical statistical techniques fail. Thus, more adapted techniques are proposed to analyze these types of data (Donoho, 2000; Verleysen, 2003). In this chapter, we will give an overview of these adapted approaches and a brief review of the most popular statistical and data mining techniques in the analysis of HDD.
Reconsidering the “Conventional Wisdom” of the Relationship between False Alarms and Risk Beliefs

The purpose of this research was to examine how false alarm experience of weather-related emergencies affects the relationship between perceived danger and desire for warnings. Participants reported how dangerous they perceived each event to be, whether they believed warnings should be distributed, whether they had experienced the event, and whether they had experienced a false alarm of the event. Participants indicated strong agreement for the desire for weather warnings. Although the literature implies that people who have experienced many false alarms may perceive that danger as less severe in the future due to the “crywolf effect,” our data suggest that people have a heightened risk perception and desire for warnings of those events. Because memories inform mental representations that guide decision-making in uncertain situations, it is imperative that future research continue to examine what people have stored in memory of false alarm experiences to settle debates in the contentious literature of false alarms and risk perception.
Preliminary Validation of a High Docosahexaenoic Acid (DHA) and -Linolenic Acid (ALA) Dietary Oil Blend: Tissue Fatty Acid Composition and Liver Proteome Response in Atlantic Salmon (Salmo salar) Smolts

Marine oils are important to human nutrition as the major source of docosahexaenoic acid (DHA), a key omega-3 long-chain (≥C20) polyunsaturated fatty acid (n-3 LC-PUFA) that is low or lacking in terrestrial plant or animal oils. The inclusion of fish oil as main source of n-3 LC-PUFA in aquafeeds is mostly limited by the increasing price and decreasing availability. Fish oil replacement with cheaper terrestrial plant and animal oils has considerably reduced the content of n-3 LC-PUFA in flesh of farmed Atlantic salmon. Novel DHA-enriched oils with high alpha-linolenic acid (ALA) content will be available from transgenic oilseeds plants in the near future as an alternative for dietary fish oil replacement in aquafeeds. As a preliminary validation, we formulated an oil blend (TOFX) with high DHA and ALA content using tuna oil (TO) high in DHA and the flaxseed oil (FX) high in ALA, and assessed its ability to achieve fish oil-like n-3 LC-PUFA tissue composition in Atlantic salmon smolts. We applied proteomics as an exploratory approach to understand the effects of nutritional changes on the fish liver. Comparisons were made between fish fed a fish oil-based diet (FO) and a commercial-like oil blend diet (fish oil + poultry oil, FOPO) over 89 days. Growth and feed efficiency ratio were lower on the TOFX diet. Fish muscle concentration of n-3 LC-PUFA was significantly higher for TOFX than for FOPO fish, but not higher than for FO fish, while retention efficiency of n-3 LC-PUFA was promoted by TOFX relative to FO. Proteomics analysis revealed an oxidative stress response indicative of the main adaptive physiological mechanism in TOFX fish. While specific dietary fatty acid concentrations and balances and antioxidant supplementation may need further attention, the use of an oil with a high content of DHA and ALA can enhance tissue deposition of n-3 LC-PUFA in relation to a commercially used oil blend.
Design of a one million neuron single FPGA neuromorphic system for real-time multimodal scene analysis

In this paper, we present an architecture and corresponding analysis for large-scale neuromorphic systems using a digital approach where neurons are abstracted as arithmetic logic units and communication processors. After presenting the architecture, we establish a few basic architectural principles, particularly the scaling of the system to large arrays. We demonstrate the reality of a single chip million neuron system built from these principles in commercial off the shelf FPGA.
Empirical evidence for AMBER alert as crime control theater: a comparison of student and community samples

ABSTRACT While the AMBER Alert system is intended to facilitate the rescue of abducted children, it has practical and psychological limitations. Participants indicated their emotions and perceptions about Alerts before and after reading a vignette that manipulated details about a child abduction. Results indicate that the Alert system fits some criteria of Crime Control Theater (CCT). CCT polices are emotion-based legal actions that appear to address crime but fail to do so and have unintended consequences. Participants experienced panic about child abduction and believed the system is an effective tool which should be used no matter the unintended consequences. Emotions and panic positively related to perceptions of the system. Still, perceptions were not particularly positive, indicating that some participants recognize the system's limitations. Female and community participants generally had more positive perceptions than males and students, especially when experiencing high emotions or panic. Reading about an abduction reduced emotions overall and led to more positive perceptions (but only in the ‘AMBER Alert success’ condition). Reading about an ‘Alert failure’ did not affect perceptions. Results highlight the role of emotion in shaping perceptions of the system. As with other CCT policies, lawmakers should rely less on community sentiment and more on science when adopting legislation.
Improving robot vision models for object detection through interaction

We propose a method for learning specific object representations that can be applied (and reused) in visual detection and identification tasks. A machine learning technique called Cartesian Genetic Programming (CGP) is used to create these models based on a series of images. Our research investigates how manipulation actions might allow for the development of better visual models and therefore better robot vision. This paper describes how visual object representations can be learned and improved by performing object manipulation actions, such as, poke, push and pick-up with a humanoid robot. The improvement can be measured and allows for the robot to select and perform the `right' action, i.e. the action with the best possible improvement of the detector.
GreenScilab-Crop, an open source software for plant simulation and parameter estimation

GreenLab is a mathematical model simulating the dynamics of plant organogenesis, biomass production and allocation, and plant three-dimensional structure. Being a functional-structural model, its software implementation and test are costly. On the other hand, software is a necessary tool to understand, analyze and apply the model. With increasing scientific publications on GreenLab, need for a freely-accessible software increased in the community of plant modeling. Implementation of GreenLab has been done in Scilab, named GreenScilab, which simulates generic plant structures from trees to crops. In this paper, a new version of GreenScilab dedicated to herbaceous plants, called GreenScilab-Crop, is presented. It allows flexible control on plants, especially the position-dependent organ growth delay. A case study is made on tomato plant for simulation and parameter identification.
Accurate and Computationally Efficient Tensor-Based Subspace Approach for Multidimensional Harmonic Retrieval

In this paper, parameter estimation for R-dimensional (R -D) sinusoids with R >; 2 in additive white Gaussian noise is addressed. With the use of tensor algebra and principal-singular-vector utilization for modal analysis, the sinusoidal parameters at one dimension are first accurately estimated according to an iterative procedure which utilizes the linear prediction property and weighted least squares. The damping factors and frequencies in the remaining dimensions are then solved such that pairing of the R-D parameters is automatically achieved. Algorithm modification for a single R -D tone is made and it is proved that the frequency estimates are asymptotically unbiased while their variances approach Cramér-Rao lower bound at sufficiently high signal-to-noise ratio conditions. Computer simulations are also included to compare the proposed approach with conventional R -D harmonic retrieval schemes in terms of mean square error performance and computational complexity.
VSUM: summarizing from videos

Summarization on produced type of video data (like news or movies) is to find important segments that contain rich information. Users could obtain the important messages by reading summaries rather than full documents. The research in this area could be divided into two parts: (I) image processing (IP) perspective, and (2) NLP (nature language processing) perspective. The former put emphasis on the detection of key frames, while the later focused on the extraction of important concepts. This paper proposes a video summarization system, VSUM. VSUM first identifies all caption words, and then adopts a technique to find the important segments. An external thesaurus is also used in VSUM to enhance the summary extraction process. The experimental results show that VSUM could perform well even if the accuracy of OCR (optical character recognition) is not sophisticating.
Intertwiners and \ade Lattice Models

Intertwiners between \ade lattice models are presented and the general theory developed. The intertwiners are discussed at three levels: at the level of the adjacency matrices, at the level of the cell calculus intertwining the face algebras and at the level of the row transfer matrices. A convenient graphical representation of the intertwining cells is introduced. The utility of the intertwining relations in studying the spectra of the \ade models is emphasized. In particular, it is shown that the existence of an intertwiner implies that many eigenvalues of the \ade row transfer matrices are exactly in common for a finite system and, consequently, that the corresponding central charges and scaling dimensions can be identified.
Liver Patient Classification Using Intelligent Techniques

— Classification techniques have been widely used in the medical field for accurate classification than an individual classifier. This paper presents computational intelligence techniques for Liver Patient Classification. This paper evaluates the selected classification algorithms (J-48, Multi Layer Perceptron, Support Vector Machine, Random Forest and Bayesian Network) for the classification of liver patient datasets. This paper implements hybrid model construction and comparative analysis for improving prediction accuracy of liver patients in three phases. In first phase, classification algorithms are applied on the original liver patient datasets collected from UCI repository. In second phase, by the use of feature selection, a subset (data) of liver patient from whole liver patient datasets is obtained which comprises only significant attributes and then applying selected classification algorithms on obtained, significant subset of attributes. SVM algorithm is considered as the better performance algorithm, because it gives higher accuracy in respective to other classification algorithms before applying feature selection. But, Random Forest algorithm is considered as the better performance algorithm after applying feature selection. In third phase, the results of classification algorithms with and without feature selection are compared with each other. The results obtained from our experiments indicate that Random Forest algorithm outperformed all other techniques with the help of feature selection with an accuracy of 71.8696%.
Formalization of binary symmetric erasure channel based on infotheo

In this paper, we formalize the channel capacity of the binary symmetric erasure channel (BSEC) by using a proof-assistant system called Coq/SSReflect. This study is for the formalization of the fact that the binary symmetric channel (BSC) and binary erasure channel (BEC), which were previously formalized, are specializations of the BSEC.
Evaluation of lateral crural steal in nasal tip surgery.

IMPORTANCE Many techniques to increase nasal tip projection and rotation have been described. Some patients complain about aesthetic loss during the postoperative period. The lateral crural steal technique has been used to improve nasal tip rotation, definition, and projection. OBJECTIVE To evaluate the lateral crural steal technique comparing immediate and late postoperative results. DESIGN, SETTING, AND PARTICIPANTS Twenty consecutive patients in an academic tertiary care hospital underwent this rhinoplasty technique to improve nasal tip projection and rotation. INTERVENTION The patients' noses were measured in preoperative and immediate and late postoperative periods using a digital pachymeter. MAIN OUTCOME AND MEASURE Quantitative analysis of nasal length and nasal tip rotation and projection. RESULTS The average lateral crural steal was 4.2 mm. There was an increase in projection and rotation in all patients when comparing preoperative and immediate postoperative measurements. The differences in preoperative and immediate postoperative measurements in nasal length (46.5 mm to 40.5 mm) and nasolabial angle (89.5° to 109.2°) were statistically significant (P = .005 and P <.001, respectively). There was no statistical difference when comparing immediate and late postoperative periods. CONCLUSIONS AND RELEVANCE The technique described in this study was effective, with a high rate of patient satisfaction, and achieved a significant increase in the rotation of the nasal tip and a slight, but not statistically significant, increase in the projection of the nasal tip. LEVEL OF EVIDENCE 3.
Notice of RetractionThree Sub-swarms Exchange Particle Swarm Optimization Based on Different Evolvement Model

Particle swarm optimization and its modifications appear premature convergence for complex optimization problem, because particles' performance becomes same in seeking later period. In this paper, a new model is proposed to avoiding particles' performance same and possessing strong exploration capacity. Considering exploration and exploitation capacity diverse in different stage, the particle swarm is divided into three identical sub-swarms, with the first adopting the standard PSO model, the second adopting the cognition-only model, and the third adopting the proposed model. When the three sub-swarms evolve steady states independent, we exchange some particles between the three different sub-swarms, which can increase the information exchange between the sub-swarms, improve the population diversity and reduce the possibility of getting local extreme value. Four complex testing functions' results indicate that the proposed algorithm has greater globally optimal solution, better optimal efficiency and better performance than PSO and TS-PSO in many aspects.
Vehicle detection and speed estimation using cascade classifier and sub-pixel stereo matching

A new vision-based approach to accomplish automatic detection and speed measuring of vehicles is proposed in this paper. In the proposed method, a cascade classifier, based on Haar features, is trained on frontal view of vehicles and deployed for vehicle detection. A fast and accurate foreground segmentation algorithm is proposed to distinguish moving vehicles from the background and prune detection results. The refined detection result in each individual frame is aggregated with tracking results based on a combination of Kalman filter and Munkres assignment algorithm. To accomplish an accurate and real-time speed measurement, an efficient sub-pixel matching technique in conjunction with stereo-vision framework is applied to calculate per-frame displacements of vehicles. Experimental results show that the proposed method leads to more reliable detections and more accurate speed estimations as compared to competing algorithms.