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关键词： interactivity   life stories   poetic gameplay   interactive feedback loop   agency  

摘要： A life story's ability to evoke the emotion experienced by a protagonist is crucial to its success. Authors of interactive life stories sometimes strategically alter the interactive feedback loop to help convey this subjective experience. Using Mitchell's conception of defamiliarizing poetic gameplay, this study identifies poetic gameplay devices, which creatively alter the feedback loop for emotional narrative impact. The article suggests extending the term "poetic gameplay" beyond interactive devices whose primary goal is critical appreciation of aesthetic form, to techniques directed at deepening a player's narrative involvement, via alterations to interactivity designed to evoke emotions that mirror a protagonist's experience. Close readings of 19 interactive life stories identified 13 devices, which fall into two categories: alterations to manipulation rules (involving local agency) and alterations to goal rules (involving higher level agency). The findings reveal some of the expressive possibilities of interactivity in digital narrative.

关键词： Throughput   Predictive models   Bandwidth   Data centers   Computational modeling   Convergence   Computer science   Data center networks   throughput prediction   network utility maximization  

摘要： As modern network applications (e.g., large data analytics) become more distributed and can conduct application-layer traffic adaptation, they demand better network visibility to better orchestrate their data flows. As a result, the ability to predict the available bandwidth for a set of flows has become a fundamental requirement of today's networking systems. While there are previous studies addressing the case of non-reactive flows, the prediction for reactive flows, e.g., flows managed by TCP congestion control algorithms, still remains an open problem. In this paper, we take the first step to solving this problem in a data center network. To address both theoretical and practical challenges, we introduce a novel learning-based prediction system based on the NUM model, with two key techniques named fast factor learning (FFL) and efficient flow sampling. We adopt novel techniques to overcome practical concerns such as scalability, convergence and unknown system parameters. A system, Prophet, is proposed leveraging the emerging technologies of Software Defined Networking (SDN) to realize the model. Evaluations demonstrate that our solution achieves significant accuracy in a wide range of settings.

摘要： The author argues that U.S. states should expand access to computer science (CS) education in order to increase participation in CS, principally by addressing equity in professional development, education policy, and practical application of policy. The article mentions the Expanding Computing Education Pathways (ECEP) Alliance, social inequity, and expertise in CS education. The article presents examples of expansion in states such as Rhode Island and Nevada.

关键词： Black Press   African American Press   Hollywood   NAACP   Black caricatures   Walter White  

摘要： In the early decades of the twentieth century, Hollywood created its depictions of African Americans. Many of the images, which first appeared in Hollywood films and then on television, were derived from "Dixie," a term used to reference the antebellum American South, during a time when African Americans were enslaved. This article examines the account, given by the African American Press, of the ongoing dispute over black imagery between Hollywood and the NAACP. The heightened voice of the African American Press ultimately helped to push for the infusing of black presence in popular culture with the goal of depicting the possibilities of an integrated American society. The NAACP and the African American Press emerged as the leading voices in challenging Hollywood's black caricature culture, after recognizing that harmful black representation was injurious to the burgeoning civil rights movement of the mid-twentieth century.

关键词： Prediction algorithms   Heterogeneous networks   Diseases   Prediction methods   Biology   Computer science   Feature extraction   Matrix completion   miRNA target predict-ion   recommendation algorithm  

摘要： microRNAs (miRNAs) are small non-coding RNAs which modulate the stability of gene targets and their rates of translation into proteins at transcriptional level and post-transcriptional level. miRNA dysfunctions can lead to human diseases because of dysregulation of their targets. Correct miRNA target prediction will lead to better understanding of the mechanisms of human diseases and provide hints on curing them. In recent years, computational miRNA target prediction methods have been proposed according to the interaction rules between miRNAs and targets. However, these methods suffer from high false positive rates due to the complicated relationship between miRNAs and their targets. The rapidly growing number of experimentally validated miRNA targets enables predicting miRNA targets with high precision via accurate data analysis. Taking advantage of these known miRNA targets, a novel recommendation system model (miRTMC) for miRNA target prediction is established using a new matrix completion algorithm. In miRTMC, a heterogeneous network is constructed by integrating the miRNA similarity network, the gene similarity network, and the miRNA-gene interaction network. Our assumption is that the latent factors determining whether a gene is the target of miRNA or not are highly correlated, i.e., the adjacency matrix of the heterogeneous network is low-rank, which is then completed by using a nuclear norm regularized linear least squares model under non-negative constraints. Alternating direction method of multipliers (ADMM) is adopted to numerically solve the matrix completion problem. Our results show that miRTMC outperforms the competing methods in terms of various evaluation metrics. Our software package is available at https://***/hjiangcsu/miRTMC.

关键词： Computer science   Modified active contour energy function   Adaptive region-based segmentation   Adaptive threshold-based edge detection binary fusion segmentation   Fuzzy c-means clustering area classification-based morphological processing   Saturation component-based thresholding   Wavelet multiscale local-global enhancement  

摘要： Automatic detection of complex cracks on rough concrete surfaces via image processing is a challenging task. The most current effective methods involve deep learning schemes. These are usually computationally and structurally complex. Recently, relatively simplified algorithms were developed for effective segmentation of crack features. However, these approaches still could not consistently and accurately extract such features from extremely noisy images of rough concrete surfaces with complex crack patterns. This study describes crack feature segmentation algorithms based on wavelet coefficient adjustment, nonlinear filter pre-processing, saturation channel extraction, adaptive threshold-based edge detection and fuzzy clustering-based area classification. Additional modifications include a new energy function for active contour segmentation algorithm. Adaptive localized mask generation is also proposed for automatic region-based segmentation. Furthermore, a binary fusion stage is incorporated for improved edge feature extraction. The quantitative and visual evaluation of the proposed schemes show improvement in results compared to several recent state-of-the-art algorithms.

关键词： Computers  

关键词： Computer science   Housing classification   Urban areas   Developing countries   Convolutional neural networks   Satellite imagery  

摘要： The use of Deep Neural Networks for remote sensing scene image analysis is growing fast. Despite this, data sets on developing countries are conspicuously absent in the public domain for benchmarking machine learning algorithms, rendering existing data sets unrepresentative. Secondly, current literature uses low-level semantic scene image class definitions, which may not have many relevant applications in certain domains. To examine these problems, we applied Convolutional Neural Networks (CNN) to high-level scene image classification for identifying patterns in urban housing density in a developing country setting. An end-to-end model training workflow is proposed for this purpose. A method for quantifying spatial extent of urban housing classes which gives insight into settlement patterns is also proposed. The method consists of computing the ratio between area covered by a given housing class and total area occupied by all classes. In the current work this method is implemented based on grid count, whereby the number of predicted grids for one housing class is divided by the total grid count for all classes. Results from the proposed method were validated against building density data computed on Open-StreetMap data. Our results for scene image classification are comparable to current state-of-the-art, despite focusing only on most difficult classes in those works. We also contribute a new satellite scene image data set that captures some general characteristics of urban housing in developing countries. The data set has similar but also some distinct attributes to existing data sets.

关键词： Computer science   Machine learning   Energy efficiency   Approximate computing  

摘要： Smart sensors present in ubiquitous Internet of Things (IoT) devices often obtain high energy efficiency by carefully tuning how the sensing, the analog to digital (A/D) conversion and the digital serial transmission are implemented. Such tuning involves approximations, i.e. alterations of the sensed signals that can positively affect energy consumption in various ways. However, for many IoT applications, approximations may have an impact on the quality of the produced output, for example on the classification accuracy of a Machine Learning (ML) model. While the impact of approximations on ML algorithms is widely studied, previous works have focused mostly on processingapproximations. In this work, in contrast, we analyze how the signal alterations imposed by smart sensors impact the accuracy of ML classifiers. We focus in particular on data alterations introduced in the serial transmission from a smart sensor to a processor, although our considerations can also be extended to other sources of approximation, such as A/D conversion. Results on several types of models and on two different datasets show that ML algorithms are quite resilient to the alterations produced by smart sensors, and that the serial transmission energy can be reduced by up to 70% without a significant impact on classification accuracy. Moreover, we also show that, contrarily to expectations, the two generic approximation families identified in our work yield similar accuracy losses.

关键词： Task analysis   Processor scheduling   IEEE transactions   Data transfer   Schedules   Correlation   Computer science   Datacenter networks   network scheduling   task placement   cloud computing  

摘要： To improve the performance of data-intensive applications, existing datacenter schedulers optimize either the placement of tasks or the scheduling of network flows. The task scheduler strives to place tasks close to their input data (i.e., maximize data locality) to minimize network traffic, while assuming fair sharing of the network. The network scheduler strives to finish flows as quickly as possible based on their sources and destinations determined by the task scheduler, while the scheduling is based on flow properties (e.g., size, deadline, and correlation) and not bound to fair sharing. Inconsistent assumptions of the two schedulers can compromise the overall application performance. In this paper, we propose NEAT+, a task scheduling framework that leverages information from the underlying network scheduler and available compute resources to make task placement decisions. The core of NEAT+ is a task completion time predictor that estimates the completion time of a task under given network condition and a given network scheduling policy. NEAT+ leverages the predicted task completion times to minimize the average completion time of active tasks. Evaluation using ns2 simulations and real-testbed shows that NEAT+ improves application performance by up to 3.7x for the suboptimal network scheduling policies and up to 33% for the optimal network scheduling policy.