Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of digital storytelling in improving the third graders' writing skills

The aim of this action research was to investigate the effects of digital storytelling in improving the writing skills of third grade students enrolled in rural primary schools. The writing performances of the students were measured before and after the teaching procedures of digital storytelling. Then, the process of narrative writing with digital storytelling was profoundly and carefully explored through observation and field notes, interviews, audio and video records, student diaries and documents, and student products. The results indicated that digital storytelling enhanced students’ ideas, organization, word choice, sentence fluency, and conventions in terms of writing quality. Similarly, the digital storytelling improved story elements and word counts in stories. In terms of the quality of students’ digital stories, the results demonstrated a steady progress in the elements of digital stories, and the technology literacy and competency of students throughout the process. Besides, the digital storytelling modified the process of narrative writing, and emerged as a beneficial tool to overcome the digital divide by developing students’ new literacy perception, competency, and skills. The digital storytelling also created learning community by improving interactions among students in the classroom, and increased their motivation to write.
Saliency Detection with Multi-Scale Superpixels

We propose a salient object detection algorithm via multi-scale analysis on superpixels. First, multi-scale segmentations of an input image are computed and represented by superpixels. In contrast to prior work, we utilize various Gaussian smoothing parameters to generate coarse or fine results, thereby facilitating the analysis of salient regions. At each scale, three essential cues from local contrast, integrity and center bias are considered within the Bayesian framework. Next, we compute saliency maps by weighted summation and normalization. The final saliency map is optimized by a guided filter which further improves the detection results. Extensive experiments on two large benchmark datasets demonstrate the proposed algorithm performs favorably against state-of-the-art methods. The proposed method achieves the highest precision value of 97.39% when evaluated on one of the most popular datasets, the ASD dataset.
Effect of genre, speaker, and word class on the realization of given and new information

There is much evidence in the literature that speakers tend to deaccent discourse-given entities, while accenting new ones. However, speakers do not always follow this simple strategy and the causes for such variation are not yet well understood. In this paper, we describe several new forms of variability in the relationship between given/new information and accenting behavior, variation due to individual differences and to word class. We present results indicating that different speakers have different strategies for making new words prominent. We analyze two word-classes – nouns and verbs – in a corpus of spontaneous and read direction-giving monologues, and show that speakers use different combinations of pitch, intensity and inter-word pauses to distinguish between given and new information. Most interestingly, we find that in both genres all speakers tend to produce given verbs with higher intensity than new verbs. Index Terms: prosody, information status, given/new information, accenting.
Using the Sociocognitive-Transformative Approach in Writing Classrooms: Effects on L2 Learners’ Writing Performance

ABSTRACT The current study used a scale-based approach and complexity, accuracy, and fluency (CAF) analysis to comprehensively capture the effects of the sociocognitive-transformative approach on 2nd language (L2) learners’ writing performance. The study involved 66 preuniversity intermediate L2 students from 4 different English classes. I randomly assigned 2 sections to the control group (N = 31) and the other 2 sections to the treatment group (N = 35). Findings from this pretest–posttest nonequivalent control group design revealed that students who received the treatment significantly improved their writing performance and outperformed the students in the control group. In terms of CAF, findings showed that the treatment group improved their fluency and complexity but not their accuracy. The findings further revealed that the treatment group outperformed the control group in 2 of the 3 components of CAF. I discuss implications for L2 writing pedagogy and future studies.
Optimized Wavelet Packet decomposition based on Minimum Probability of Error Signal Representation

This work addresses the problem of optimal Wavelet packet (WP) filter bank decomposition based on the minimum probability of error signal representation (MPE-SR) principle. The problem is formulated as a complexity regularized optimization, where the tree-indexed structure of the WP family is explored to find conditions for reducing this problem to a type of minimum cost tree pruning, a method wellunderstood in regression and classification trees (CART) and tree-structured vector quantization (TSVQ). For estimating the conditional mutual information (CMI), the fidelity criterion adopted in the optimal tree pruning problem, a non-parametric approach based on product adaptive partitions is proposed extending the Darbellay-Vajda tree-structured data dependent algorithm. Finally, experimental evaluation within an automatic speech recognition task shows that MPE-SR solutions for the WP decomposition problem are consistent with well understood empirically determined speech features, and the derived feature representations yield competitive performances with respect to standard feature extraction techniques. Index Terms Minimum probability of error Signal Representation, Bayes decision approach, basis selection, treestructured bases and Wavelet packet (WP), complexity regularization, mutual information, minimum cost tree pruning, family pruning problem, data-dependent partition, nonparametric mutual information estimation. February 6, 2008 DRAFT SUBMITTED FOR REVIEW, IEEE TRANSACTIONS ON SIGNAL PROCESSING 1
Graduate Counseling Psychology Training in Sex and Sexuality: An Exploratory Analysis

In this manuscript, we explore the need for inclusion of sex-positivity training in counseling psychology doctoral program curricula. We surveyed 25 counseling psychology doctoral program administrators throughout the United States and asked about the inclusion of human sexuality coursework and training opportunities as well as sex positivity within their curricula. We report our findings, noting the number of programs that include human sexuality coursework, incorporate sex positivity, discuss sex positivity, and discuss sex positivity from a multicultural lens. The implications of these findings for research, practice, training, and advocacy within counseling psychology are discussed.
Accelerated contrast‐enhanced whole‐heart coronary MRI using low‐dimensional‐structure self‐learning and thresholding

We sought to evaluate the efficacy of prospective random undersampling and low‐dimensional‐structure self‐learning and thresholding reconstruction for highly accelerated contrast‐enhanced whole‐heart coronary MRI. A prospective random undersampling scheme was implemented using phase ordering to minimize artifacts due to gradient switching and was compared to a randomly undersampled acquisition with no profile ordering. This profile‐ordering technique was then used to acquire contrast‐enhanced whole‐heart coronary MRI in 10 healthy subjects with 4‐fold acceleration. Reconstructed images and the acquired zero‐filled images were compared for depicted vessel length, vessel sharpness, and subjective image quality on a scale of 1 (poor) to 4 (excellent). In a pilot study, contrast‐enhanced whole‐heart coronary MRI was also acquired in four patients with suspected coronary artery disease with 3‐fold acceleration. The undersampled images were reconstructed using low‐dimensional‐structure self‐learning and thresholding, which showed significant improvement over the zero‐filled images in both objective and subjective measures, with an overall score of 3.6 ± 0.5. Reconstructed images in patients were all diagnostic. Low‐dimensional‐structure self‐learning and thresholding reconstruction allows contrast‐enhanced whole‐heart coronary MRI with acceleration as high as 4‐fold using clinically available five‐channel phased‐array coil. Magn Reson Med, 2012. © 2012 Wiley Periodicals, Inc.
An Architecture for Flexible Problem Solving

The literature on problem solving in both humans and machines has revealed a diverse set of strategies that operate in different manners. In this paper, we review this great variety of techniques and propose a five-stage framework for problem solving that accounts for this variation in terms of differences in strategic knowledge used at each stage. We describe the framework and its implementation in some detail, including its encoding of problems and their solutions, its representation of domain-level and strategy-level knowledge, and its overall operation. We present evidence of the framework’s generality and its ability to support many distinct problem-solving strategies, including one that is novel and interesting. We also report experiments that show the framework’s potential for empirical comparisons of different techniques. We conclude by reviewing other work on flexible approaches to problem solving and considering some directions for future research.
Is Gifted Education a Bright Idea ? Assessing the Impact of Gifted and Talented Programs on Students

We identify the impact of gifted and talented (GT) services on students by exploiting a discontinuity in eligibility requirements and find no impact on achievement for marginal students despite substantially higher peer quality and advanced course-taking. We then examine the impact of attending a GT magnet on higher achieving students through randomized lotteries and find that, despite exposure to higher quality teachers and peers, only science achievement improves compared to neighborhood GT programs. We find that the relative ranking of students change, as do their grades, indicating that either invidious comparison peer effects or teaching targeting may be important. 1 Department of Economics, 204 McElhinney Hall, Houston, TX 77204-5019. Correspondence should be made to Scott Imberman at simberman@uh.edu or Steven Craig at scraig@uh.edu. ©2012 by Sa Bui, Steven Craig and Scott Imberman. All rights reserved. We would like to thank Aimee Chin, Dennis Epple, Jason Imbrogno, Chinhui Juhn, Brian Kovak, Jacob Vigdor and seminar participants at APPAM, AEFP, NBER Economics of Education Meeting, SOLE, Carnegie Mellon University, Georgetown University, University of Houston, University of Maryland, and the employees of an anonymous school district for all of their guidance and assistance in this project. Financial support from the University of Houston Small Grants program is gratefully appreciated. All errors remain our own.
CASA: Contention-Aware Scratchpad Memory Allocation for Online Hybrid On-Chip Memory Management

Scratchpad memory (SPM) has been increasingly used in embedded systems due to its higher efficiency in terms of energy and area compared to that of ordinary cache. A hybrid on-chip memory architecture that combines SPM with a mini-cache has been proposed. One key issue for hybrid on-chip memory architectures is to reduce the number of off-chip memory accesses and energy consumption. Existing methods achieve this by moving the most frequently accessed data into SPM. However, these methods may be ineffective because the main source of off-chip memory accesses may not be the most frequently accessed data. Instead, most off-chip memory accesses are caused by cache misses, so reducing the latter will reduce the former. Cache misses are mainly caused by data contending for cache lines. Therefore, this paper proposes a contention-aware SPM allocation method for hybrid on-chip management. The number of cache misses for a page is used as a metric to determine whether a page should be moved to SPM. When the number of misses for a page exceeds a threshold, the page is moved to SPM, reducing cache contention. Experimental results show that the proposed method can reduce the energy delay product by 35% to 53% compared to a cache-only on-chip memory architecture and 19% to 31% compared to an existing hybrid on-chip memory architecture.
Matters of Care: Speculative Ethics in More than Human Worlds

Miriam Ticktin is an associate professor and chair of the Anthropology Department at the New School for Social Research. She is the author of Casualties of Care: Immigration and the Politics of Humanitarianism in France (University of California Press, 2011), and co-editor of In the Name of Humanity: The Government of Threat and Care (Duke University Press, 2010). Her work explores the category of humanity and the politics of who and what it includes and excludes.
Right Anterior Prefrontal Cortex Activation during Semantic Monitoring and Working Memory

Areas of the adult human brain used for semantic monitoring were identified using positron emission tomography. For a series of tasks, subjects viewed a list of familiar English nouns and monitored the words for names of dangerous animals. The monitoring task used here also contained an instruction to keep track of the number or percentage of targets for report after the scan. Surface characteristics of the tasks such as stimulus rate, number of targets, and whether subjects were asked to count or estimate the number of targets were varied across multiple conditions within and between subjects. A passive word viewing condition was used as the control in all subjects. Reliable activations were identified in anterior and dorsal right prefrontal cortex [Brodmann areas (BA) 9 and 10] and left extrastriate cortex. The right prefrontal cortical locations are similar to areas that have been activated during many episodic memory tasks. This surprising finding led to a thorough review of the literature for examples of other activations within 16-mm vector distance of this right prefrontal area. Activations in the vicinity of right BA10 due to episodic memory retrieval, to various forms of working memory, and to miscellaneous tasks were found. The right prefrontal activations in the current experiment and the additional working memory and miscellaneous tasks demonstrate that, although right BA10 is routinely activated by episodic retrieval tasks, it is not uniquely activated by episodic retrieval tasks.
Posting selfies and body image in young adult women: The selfie paradox.

This exploratory study was designed to investigate how young women make sense of their decision to post selfies, and perceived links between selfie posting and body image. Eighteen 19-22 year old British women were interviewed about their experiences of taking and posting selfies, and interviews were analysed using inductive thematic analysis. Women linked selfie posting to the “ideal” body, identity management, and body exposure; objectifying their own and others’ selfies, and trying to portray an image that was as close to “ideal” as possible. Women differentiated between their “unreal,” digitally manipulated online selfie identity and their “real” identity outside of Facebook and Instagram . Bodies were expected to be covered, and sexualised selfies were to be avoided. Results challenge conceptualisations of women as empowered and self-determined selfie posters; although women sought to control their image online, posting was constrained by postfeminist notions of what was considered socially appropriate to post.
Psychometric evaluation of Utrecht Work Engagement Scale among academic staff in universities of Pakistan

The purpose of present study was to evaluate the psychometric properties of Utrecht Work Engagement Scale (UWES) among academic staff in the universities of Pakistan. The UWES was administered to a sample of 400 academic staff working in twelve universities of Pakistan. The reliability was determined by calculating Cronbach Alpha Coefficients and Inter-Items statistics, whereas validity analyses were done by running Exploratory and Confirmatory Factor Analyses. The reliability analyses revealed that three sub scales of UWES possessed sufficiently high internal consistencies ( = 0.87 & r=0.65). On the other side, the Exploratory Factor Analysis revealed that all items had mean factor loadings up to 0.80, which determined the construct validity of scale. Moreover the results of Confirmatory Factor Analysis showed that three-factor model demonstrated better fit as compared to one or two factors model. Thus the overall results showed that UWES possesses good psychometric properties and it can be considered as reliable and valid tool of assessing Working Engagement among academic staff in Pakistan.
International Menu Costs and Price Dynamics

In this paper, I analyze how the pricing behavior of firms systematically differs across domes- tic and export markets in terms of frequency, timing and size of price changes. First, I contrast domestic and export pricing decisions for the same products showing that (i) domestic producer prices change approximately twice as often as export producer prices, (ii) the probability of syn- chronized price adjustment across markets is 21% for upwards adjustments and 14% for downwards adjustments, (iii) the size of export price changes is substantially larger than the size of domes- tic price changes and (iv) there are strong seasonality effects in the data including a year-end synchronization effect. Second, I show that economic fundamentals such as inflation, productiv- ity, demand, exchange rates and market structure can only partially explain adjustment decisions and cross-market synchronization. Third, I present a dynamic menu cost model of price-setting, and attribute the remaining unexplained part in adjustment decisions to di erences in menu costs across countries. I calculate the implied export and domestic market menu costs from the data and estimate that export menu costs are 1.5% of period steady state revenues and three times as large as domestic market menu costs.
DO WAXY STOMATAL PLUGS IMPACT LEAF GAS EXCHANGE IN A RAIN FOREST GYMNOSPERM AGATHIS ROBUSTA

Summary. Functions suggested for stomatal plugs (occlusion of the stomatal pore by wax or cutin) include: reduction of water loss, protection against insects and fungi, preventing entry of water into stomatal pores and preventing the formation of a water film on leaves. By removing plugs experimentally, we investigated their impact on gas exchange, cuticular water loss and water film formation in the rain forest tree, Agathis robusta. The results showed that under saturating PFD, waxy plugs significantly decreased stomatal conductance, transpiration and photosynthesis in A. robusta in contrast to the results of Feild et al. (1998) for D. winteri. Although most species in dry environments lack stomatal plugs, in alpine regions where conifers commonly grow, seasonal soil-water deficit occurs when soil is frozen, and the leaves can still be exposed to dry air and high sunlight (Roberts, 2000). This might explain the presence of stomatal plugs in some of conifer species. The present results also showed that stomatal plugs did not affect the formation of a water film on leaf surfaces of A. robusta. This result was again in contrast
What Affects Authors’ and Editors’ Use of Reporting Guidelines? Findings from an Online Survey and Qualitative Interviews

OBJECTIVES To identify and understand, through data from multiple sources, some of the factors that affect authors' and editors' decisions to use reporting guidelines in the publication of health research. DESIGN Mixed methods study comprising an online survey and semi-structured interviews with a sample of authors (online survey: n = 56; response rate = 32%; semi-structured interviews: n = 5) and journal editors (online survey: n = 43; response rate = 27%; semi-structured interviews: n = 6) involved in publishing health and medical research. Participants were recruited from an earlier study examining the effectiveness of the TREND reporting guideline. RESULTS Four types of factors interacted to affect authors' and editors' likelihood of reporting guideline use; individual (e.g., having multiple reasons for use of reporting guidelines); the professional culture in which people work; environmental (e.g., policies of journals); and, practical (e.g., having time to use reporting guidelines). Having multiple reasons for using reporting guidelines was a particularly salient factor in facilitating reporting guidelines use for both groups of participants. CONCLUSIONS Improving the completeness and consistency of reporting of research studies is critical to the integrity and synthesis of health research. The use of reporting guidelines offers one potentially efficient and effective means for achieving this, but decisions to use (or not use) reporting guidelines take many factors into account. These findings could be used to inform future studies that might, for example, test the factors that we have identified within a wider theoretical framework for understanding changes in professional practices. The use of reporting guidelines by senior professionals appears to shape the expectations of what constitutes best practice and can be assimilated into the culture of a field or discipline. Without evidence of effectiveness of reporting guidelines, and sustained, multifaceted efforts to improve reporting, little progress seems likely to be made.
Detecting Extreme Rank Anomalous Collections

Anomaly or outlier detection has a wide range of applications, including fraud and spam detection. Most existing studies focus on detecting point anomalies, i.e., individual, isolated entities. However, there is an increasing number of applications in which anomalies do not occur individually, but in small collections. Unlike the majority, entities in an anomalous collection tend to share certain extreme behavioral traits. The knowledge essential in understanding why and how the set of entities becomes outliers would only be revealed by examining at the collection level. A good example is web spammers adopting common spamming techniques. To discover this kind of anomalous collections, we introduce a novel definition of anomaly, called Extreme Rank Anomalous Collection. We propose a statistical model to quantify the anomalousness of such a collection, and present an exact as well as a heuristic algorithms for finding top-K extreme rank anomalous collections. We apply the algorithms on real Web spam data to detect spamming sites, and on IMDB data to detect unusual actor groups. Our algorithms achieve higher precisions compared to existing spam and anomaly detection methods. More importantly, our approach succeeds in finding meaningful anomalous collections in both datasets.