Miracle-dz/UniFAR-FTUs huggingface dataset:
Hip-Hop Citizens: Arts-Based, Culturally Sustaining Civic Engagement Pedagogy.

Amid concerns about the decreasing political engagement of young people, scholars and policy makers have begun discussing the “civic achievement gap,” disparities in civic capacity between low-income students and Students of Color and their White, wealthier counterparts. While this scholarship raises important issues, it often relies on a narrow view of civic engagement, downplaying alternative forms of civic activity and the variability of civic life across contexts. This can lead to deficit-based models of civic education that bypass the opportunity to tap into the many less-visible ways youth, particularly those from low-income Communities of Color, are already engaged in civic life. In this article, Paul J. Kuttner offers as an alternative approach, youth cultural organizing, which engages young people in catalyzing change in their communities through the arts and other forms of cultural expression, drawing on shared cultural resources. He presents a theoretical framework for this culturally sustainin.
Achievable Rates for  $K$-User Gaussian Interference Channels

The aim of this paper is to study the achievable rates for a K-user Gaussian interference channel (G-IFC) for any signal-to-noise ratio using a combination of lattice and algebraic codes. Lattice codes are first used to transform the G-IFC into a discrete input--output noiseless channel, and subsequently algebraic codes are developed to achieve good rates over this new alphabet. In this context, a quantity called efficiency is introduced which reflects the effectiveness of the algebraic coding strategy. This paper first addresses the problem of finding high-efficiency algebraic codes. A combination of these codes with Construction-A lattices is then used to achieve nontrivial rates for the original G-IFC.
Notice of RetractionRecometh: Using CBR and characteristic weights to recommend a software development methodology in software engineering

In the development of software or information systems exists some properties and characteristics that can be improved and consequently increase the performance of development in a project. In this paper is proposed an approach of a recommendation system that will allow choosing the most appropriate software development methodology for a custom project from a set of characteristics like cost, time, number of employers, etc.
A Note on the Fractional-Order Cellular Neural Networks

This paper deals on fractional-order cellular neural networks (CNNs). It is based on the well-known concept of the CNNs, where mathematical model of the CNN consists a fractional order derivative. Traditional first order cell is replaced by fractional order (non-integer) one. We present the method to derive such kind of the fractional order cell. An illustrative example has been used to demonstrate and investigate the existence of the fractional order CNN. We present the fractional three-cell CNN where the total order of system is less than three, namely 2.7, and the corresponding state space trajectories are shown as well.
Social support and coping strategies of parents raising a child with autism spectrum disorder

ABSTRACT The current study sought to explore the psychological and social needs of parents within the same family unit raising a child with autism spectrum disorder (ASD). Mothers (N = 10) and fathers (N = 9) of children with ASD attending an early intervention centre completed a semi-structured interview investigating their experiences of raising a child with ASD. Both genders reported adopting problem-focused coping strategies, engaging in ‘me time’ activities, and disengaging from a stressor in an effort to cope. More mothers reported engaging in emotion-focused strategies and accessing social support for emotional and practical support. The presence of traditional gender roles emerged as a potentially significant factor in understanding how mothers and fathers adopt different types of coping strategies.
A novel method to extract text from compound document images

To separate the text embedded in colored and/or complex backgrounds, a novel segmentation algorithm to separate the text from the image in a complicated document in which the text overlaps the background was presented, and this work could be seen as a new view to realize the multi-thresholding segmentation method. In the first step, the curve fitting using least square method was carried out to fit the image histogram; in the second step, the image was split into several layers including text layers and background layers. These layers were merged by some given rules to simplify the image processing period; at last, all the text layers were processed using different techniques to pick up the text document successfully. Experiments were carried out with large number of such images and it shows that the proposed method outperforms the common used segmentation methods and has preferable applicability.
WapMetrics: A tool for computing UML design metrics forWeb applications

Many companies are still asking how to assess and predict the maintenance cost of their software. Measures of software maintenance cost can be taken either late or early in the development process. Early measures of software maintenance cost are beneficial because they can help in allocating project resources efficiently, predicting the effort of maintenance tasks and controlling the maintenance process. This paper describes a tool for computing early metrics from UML class diagrams based on the Web Application Extension (WAE) for UML. A case study is used to show the usefulness and effectiveness of the tool.
Foreground-Background Segmentation of Video Sequences

Automatic segmentation of foreground from background in video sequences has attracted lots of attention in computer vision. Two different implementations have been completed during the summer. The first one is known as “background subtraction”. The foreground is segmented from the background by directly subtracting a mean background image B from each frame, and retaining those parts of the frame that differ the most from B.
Stability and Segregation in Group Formation

This paper presents a model of group formation based on the assumption that individuals prefer to associate with people similar to them. It is shown that, in general, if the number of groups that can be formed is bounded, then a stable partition of the society into groups may not exist. A partition is defined as stable if none of the individuals would prefer be in a different group than the one he is in. However, if individuals’ characteristics are one-dimensional, then a stable partition always exists. We give sufficient conditions for stable partitions to be segregating (in the sense that, for example, low-characteristic individuals are in one group and high-characteristic ones are in another) and Pareto efficient. In addition, we propose a dynamic model of individual myopic behavior describing the evolution of group formation to an eventual stable, segregating, and Pareto efficient partition.
Everybody producer -A tracking system of interesting person using SOS and the cellular phone-

In this paper, we propose a video streaming system which can freely select the view angel of a streamed video. If the streamed video was existed a frame-out, a user cannot select freely. Then, SOS (stereo omni-directional system) which does not a frame-out exist is used. Moreover, the TV phone which can send and receive the video movie is used for real time, the video movie acquired by SOS is sent from a server to a user, and the image acquired from the camera of a cellular phone is sent from a user to a server. A user can change the view angle by detected the optical flow of the image which acquired by moved cellular phone intuitively.
Identifying auxiliary web images using combination of analyses

As the Web gains more popularity, Web sites become richer in media. Besides text, another most common form of media is image. A Web page can utilize images in various ways such as to illustrate stories, to summarize data and to decorate the page. This leads to a large amount of images embedded in Web pages. However, not all Web images are informative, i.e., engaged with the page for the purpose of delivering useful information. The uninformative or auxiliary images are, for example, logos and banner advertisements. The benefit of classifying Web images as ``informative" or "auxiliary" is the efficient use of available resources. The auxiliary images are insignificant and can be ignored in many tasks including search engine's indexing, for the sake of conciseness of search results, and Web page printing, to reduce ink usage. This paper proposes a solution for the HP Multimedia Grand Challenge to identify informative multimedia contents in Web pages. Our approach is based on a supervised machine learning model trained from a set of 32 features gathered from content analysis of images, Web page layout, and domain name. We adopt the Support Vector Machines (SVM) algorithm to train the classifier. The model is optimized by a grid search technique to select the appropriate set of kernel parameters. The evaluation results based on the 10-fold cross-validation yielded the classification accuracy of 94.08%. The classification results are used to annotate each image accordingly, as in the prototype implementtaion, each image is highlighted with different border color.
THE AUTOMATION AND EVALUATION OF NESTED CLADE PHYLOGEOGRAPHIC ANALYSIS

Abstract Nested clade phylogeographic analysis (NCPA) is a popular method for reconstructing the demographic history of spatially distributed populations from genetic data. Although some parts of the analysis are automated, there is no unique and widely followed algorithm for doing this in its entirety, beginning with the data, and ending with the inferences drawn from the data. This article describes a method that automates NCPA, thereby providing a framework for replicating analyses in an objective way. To do so, a number of decisions need to be made so that the automated implementation is representative of previous analyses. We review how the NCPA procedure has evolved since its inception and conclude that there is scope for some variability in the manual application of NCPA. We apply the automated software to three published datasets previously analyzed manually and replicate many details of the manual analyses, suggesting that the current algorithm is representative of how a typical user will perform NCPA. We simulate a large number of replicate datasets for geographically distributed, but entirely random-mating, populations. These are then analyzed using the automated NCPA algorithm. Results indicate that NCPA tends to give a high frequency of false positives. In our simulations we observe that 14% of the clades give a conclusive inference that a demographic event has occurred, and that 75% of the datasets have at least one clade that gives such an inference. This is mainly due to the generation of multiple statistics per clade, of which only one is required to be significant to apply the inference key. We survey the inferences that have been made in recent publications and show that the most commonly inferred processes (restricted gene flow with isolation by distance and contiguous range expansion) are those that are commonly inferred in our simulations. However, published datasets typically yield a richer set of inferences with NCPA than obtained in our random-mating simulations, and further testing of NCPA with models of structured populations is necessary to examine its accuracy.
Associations between Diurnal 24-Hour Rhythm in Ambulatory Heart Rate Variability and the Timing and Amount of Meals during the Day Shift in Rotating Shift Workers

It has not hitherto been clarified whether there is an association between dietary behavior and circadian variation in autonomic nervous system activity among shift workers. This study examines diurnal 24-h rhythm in heart rate variability (HRV) and dietary behavior among rotating shift workers, while taking into account the sleep-wake cycle and physical activity. The subjects were 11 female and 2 male nurses or caregivers working in a rotating 2-shift system at a health care facility. All the subjects were asked to undergo 24-h electrocardiograph and step count recordings, and to record the time of each meal and the amounts of each food and beverage consumed. Coarse graining spectral analysis was used for approximately 10-min segments of HRV to derive the total power (TOT: >0.04 Hz) of the periodic components and the integrated power of periodic components in the low-frequency (LF: 0.04–0.15 Hz) and high-frequency (HF: >0.15 Hz) ranges. Then the ratio of HF power to TOT (HF nu) and the ratio of LF power to HF power (LF/HF) were calculated to assess cardiac vagal tone and cardiac sympathovagal balance, respectively. Single cosinor analysis was used to obtain 24-h period variations in both variables of HRV. Acrophases of HF nu and LF/HF expressed in time since awakening were significantly (p<0.05) delayed for subjects having breakfast at a later time after awakening. Multivariable regression analysis indicated that the timing of breakfast, the ratio of energy intake at dinner to total energy intake, and total energy intake were correlated to the acrophases of HF nu and/or LF/HF. These results suggest that the phase angle between circadian variation in cardiac autonomic nervous system activity and the sleep-wake cycle may be associated with dietary behavior in shift workers.
Comparing Reuse Mechanisms for Model Transformation Languages: Design for an Empirical Study

Reuse mechanisms for model transformation languages help avoid duplication, thereby increasing maintainability and enabling a more systematic overall development process. As the introduction of such reuse mechanisms to model transformation languages is still in its preliminary stages, however, language designers are currently faced with the challenge of choosing from amongst diverse proposed approaches. Although there are a few surveys comparing syntactic and semantic differences, there is still a need for empirical studies on the actual effectiveness of different reuse mechanisms for transformation developers. In this paper, therefore, we present a study design for a controlled experiment to investigate the benefits and drawbacks of two specific reuse mechanisms for model transformation languages: rule refinement and variability-based rules. Both mechanisms are tailored to graph-based model transformation languages, yet represent two contrasting reuse paradigms: modularizing rules by composing them from smaller, shared fragments, versus maintaining a single, integrated representation via variability annotations. We propose to compare these two approaches using comprehension and bug-finding tasks to investigate understandability, and bug-fixing and modification tasks to study changeability.
Design and Evaluation of MagnetViz&#x2014;A Graph Visualization Tool

MagnetViz was designed for the interactive manipulation of force-directed graph layouts, allowing the user to obtain visualizations based on the graph topology and/or the attributes of its nodes and edges. The user can introduce virtual magnets anywhere in the graph and these can be set to attract nodes and edges that fulfill user-defined criteria. When a magnet is placed, the force-directed nature of the layout forces it to reorganize itself in order to reflect the changes in the balance of forces, consequently changing the visualization into one that is more semantically relevant to the user. This paper describes MagnetViz's concepts, illustrating them with examples and a case study based on a usage scenario. We also describe how the MagnetViz has evolved since its original version and present the evaluation of its latest version. This evaluation consists of two user studies aiming at assessing generated layout quality and how well the concepts can be apprehended and employed, and a task taxonomy assessment focusing on establishing which graph visualization tasks the technique is able to handle.
Providing a supportive alternative education environment for at-risk students

ABSTRACT Many factors cause student disengagement from school that subsequently result in high dropout rates. Alternative education (AE) programs provide a different pathway for at-risk youths who do not meet the goals, standards, and requirements of traditional educational settings. However, educational agencies have vastly different interpretations regarding AE program components and operations. The absence of a consistent national protocol as to what components or criteria define an effective AE program causes confusion among practitioners and stakeholders. In the study being reported, a research-based multidimensional framework was established in the hope of building an operational AE program model.
Ribozymes Targeted to Stearoyl–ACP Δ9 Desaturase mRNA Produce Heritable Increases of Stearic Acid in Transgenic Maize Leaves

Ribozymes are RNAs that can be designed to catalyze the specific cleavage or ligation of target RNAs. We have explored the possibility of using ribozymes in maize to downregulate the expression of the stearoyl–acyl carrier protein (Δ9) desaturase gene. Based on site accessibility and catalytic activity, several ribozyme constructs were designed and transformed into regenerable maize lines. One of these constructs, a multimer hammerhead ribozyme linked to a selectable marker gene, was shown to increase leaf stearate in two of 13 maize lines. There were concomitant decreases in Δ9 desaturase mRNA and protein. The plants with the altered stearate phenotype were shown to express ribozyme RNA. The ribozyme-mediated trait was heritable, as evidenced by stearate increases in the leaves of the R1 plants derived from a high-stearate line. The increase in stearate correlated with the presence of the ribozyme gene. A catalytically inactive version of this ribozyme did not produce any significant effect in transgenic maize. This is evidence that ribozymes can be used to modulate the expression of endogenous genes in maize.
Towards Statistical Planning for Marketing Strategies

AI planning has traditionally dealt with developing sophisticated plans for achieving well-defined goals in welldefined domains. In this paper we consider the problem of building marketing plans for massive customers to achieve a company’s financial goal in a business-planning domain. Corporations and institutions are often interested in strategic planning for marketing strategies to target their customers and outperform their competitors. For example, a stockbroker company may draft a marketing plan for retaining valuable customers or for switching a potential customer to a true customer. Planning in these applications consists of market segmentation, marketing-action selection and validation. For such problems, the traditional planning frameworks no longer apply. Instead, planning is done based on statistical reasoning of previous cases and patterns. In this paper, we present a novel framework that incorporates data mining, case based reasoning and planning to support marketing-strategy planning. In our approach, we discover case bases by data mining on the customer database and formulate plans based on the mined cases or “ role-models” . These plans are not guaranteed to work for each individual; however, based on previous experience, they have a high probability of succeeding. We explore the tradeoff among time, space and quality of computation in this framework. We demonstrate the effectiveness of the methods through empirical results.
Visual Maritime Attention Using Multiple Low-Level Features and Naïve Bayes Classification

This paper presents a framework for Visual Attention Detection in maritime scenes. The focus is to provide an early processing stage for high resolution images captured by maritime surveillance platforms. The framework groups multiple low-level features that are designed specifically for maritime scenarios with different distance measurements. Integrated in the framework is a detector for sea and sky that aids in background segmentation. A Naive Bayes Classifier is used to produce Attention Maps of the input images. Experiments using ground truthed images show the technique is effective on a large dataset of maritime images and outperforms state of the art generic saliency detectors.
UCS Protein Rng3p Is Essential for Myosin-II Motor Activity during Cytokinesis in Fission Yeast

UCS proteins have been proposed to operate as co-chaperones that work with Hsp90 in the de novo folding of myosin motors. The fission yeast UCS protein Rng3p is essential for actomyosin ring assembly and cytokinesis. Here we investigated the role of Rng3p in fission yeast myosin-II (Myo2p) motor activity. Myo2p isolated from an arrested rng3-65 mutant was capable of binding actin, yet lacked stability and activity based on its expression levels and inactivity in ATPase and actin filament gliding assays. Myo2p isolated from a myo2-E1 mutant (a mutant hyper-sensitive to perturbation of Rng3p function) showed similar behavior in the same assays and exhibited an altered motor conformation based on limited proteolysis experiments. We propose that Rng3p is not required for the folding of motors per se, but instead works to ensure the activity of intrinsically unstable myosin-II motors. Rng3p is specific to conventional myosin-II and the actomyosin ring, and is not required for unconventional myosin motor function at other actin structures. However, artificial destabilization of myosin-I motors at endocytic actin patches (using a myo1-E1 mutant) led to recruitment of Rng3p to patches. Thus, while Rng3p is specific to myosin-II, UCS proteins are adaptable and can respond to changes in the stability of other myosin motors.
On viewpoint complexity of 3 D scenes

In this paper we try to give a comprehensive definition of the notion of viewpoint complexity for 3D scenes. We show that this notion is very useful in various areas of computer graphics and that its accurate measurement permits important improvements in these areas. Methods of more or less accurate computation of viewpoint complexity for a scene are given.
Artificial neural network-based error compensation procedure for low-cost encoders

An artificial neural network-based error compensation method is proposed for improving the accuracy of resolver-based 16-bit encoders by compensating for their respective systematic error profiles. The error compensation procedure, for a particular encoder, involves obtaining its error profile by calibrating it on a precision rotary table, training the neural network by using a part of these data and then determining the corrected encoder angle by subtracting the ANN-predicted error from the measured value of the encoder angle. Since it is not guaranteed that all the resolvers will have exactly similar error profiles because of the inherent differences in their construction on a micro scale, the ANN has been trained on one error profile at a time and the corresponding weight file is then used only for compensating the systematic error of this particular encoder. The systematic nature of the error profile for each of the encoders has also been validated by repeated calibration of the encoders over a period of time and it was found that the error profiles of a particular encoder recorded at different epochs show near reproducible behaviour. The ANN-based error compensation procedure has been implemented for four encoders by training the ANN with their respective error profiles and the results indicate that the accuracy of encoders can be improved by nearly an order of magnitude from quoted values of ∼ ±6 arc-min to ∼ ±0.65 arc-min when their corresponding ANN-generated weight files are used for determining the corrected encoder angle.