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关键词： Computer science education   Culturally responsive computing   Rural education  

摘要： Students from rural places participate in computer science less than their urban and suburban peers (Google & Gallup, 2016). While current research into this phenomenon is in its infancy, research of other underrepresented populations has indicated utilizing culturally responsive computing may increase participation. In rural Nebraska and in much of the Midwest, agriculture is a very important cultural pillar. Accordingly, this thesis looks for potential overlap between computer science education and agriculture education. Eleven teachers were interviewed and ninety teachers were surveyed about their experiences with computer science applications in agriculture. Ultimately, the support for increased CS applications in agriculture classrooms is nearly universal, but teachers do point out some possible obstacles in implementing cross-curricular experiences. There is optimism that giving students hands-on experiences with modern computer-based agriculture technology may be a culturally responsive way to generate rural student interest in CS.

关键词： Computer engineering   Public health   Computer science   Bioinformatics  

摘要： Trauma is the leading cause of mortality in children and young adults. The initial resuscitation of injured patients is critical for identifying and managing life-threatening injuries. Despite the use of a standardized protocol, errors remain frequent during this initial evaluation. Computerized decision support has been proposed as a method for reducing errors in this setting. Medical activities are considered as key components of the clinical workflows. Automatic activity recognition during trauma resuscitation is necessary to be studied to generate the computerized decision for the next step and analyze the errors after the resuscitation. Video understanding developed rapidly these years due to the success of using deep learning methods in computer vision tasks. Our work, video-based activity recognition during clinical, is important because videos contain rich texture features for recognizing activities during clinical. We first present medical phase recognition during trauma resuscitation. Each Medical phase can be considered as a sequence of activities, which represents the progress of current resuscitation. Based on the protocal of the Advanced Trauma Life Support (ATLS), each trauma resuscitation case can be divided into five phases in sequential. The pre-arrival phase is focused on preparation for the patient, the primary survey for identifying and managing life-threatening injuries, the secondary survey phase for identifying additional injuries that need management, the post-secondary phases for initiating additional injury management, and the patient-departure phase for identifying the patient leaves the room. Identification of phases aids in the determination of errors in the type and order of activities. Decision support in this domain should reflect the priorities of each phase. Knowledge of the current phases aids in the prioritization of required activities based on the underlying goals in each. We used depth videos recorded using a Kinect-v2 as inpu

关键词： Optimization   Prediction algorithms   Mobile robots   Computer science   Tracking   Cameras   Object detection   Autonomous mobile robots   fractional order difference   template tracking   prediction of moving object position  

摘要： Optimization techniques are nowadays one of the most important branches of computer science due to the limitations of the computing power availability especially in cases of mobile platforms. Algorithms which can be used on such units have to be optimized for memory and computing power. In the paper we focused on optimization techniques for image analysis algorithms by limiting the number of necessary operations. The optimized algorithm has been designed to detect and follow a specified marker, known a priori, and is based on the correlation coefficient match between the acquired template and the current image. The acquired template is taken from the previous processed frame. The match operation is based on Pearson's correlation coefficient, so the whole mechanism is therefore highly demanding in terms of computing power. Optimization is performed mainly using the Region of Interest (ROI) to exclude irrelevant parts of the image. The algorithm is optimized using Grunvald - Letnikov fractional - order backward difference to estimate the position of the marker in a sequence of images. This limits the number of operations required to maintain the precision of the algorithm. Based on the position of the object in previous frames, a fractional order mathematical tool is able to estimate at relatively low cost and with high accuracy the probable position of the object in the incoming image. Here, we explain the workflow of the template detection and following algorithm, as well as the mathematical basis of the fractional order derivative optimization tool. The processor load connected to the optimized algorithm was reduced by over 35%.

摘要： Background of global forum. To explore the nature of computer science and promote the development of computer science discipline, Tsinghua University organized the first global forum during the 60th anniversary of Computer Science in Tsinghua. The forum aimed to bring to- gether the heads/deans of the top computer science departments/schools in the world to discuss the fundamentals of computer science, improve the quality of talent cultivation,

关键词： Energy   Computer science   Materials science   Mathematics   Mechanics   Physics   Thermodynamics  

摘要： The dissertation introduces the R2 Fill method, which is an algorithm for filling a region with particles isotropically. The method’s isotropy is tested geometrically and then mechanically with the Discrete Element Method (DEM). The DEM formulation used in this paper provides a way to model solid materials and eliminate residual stress created in particle packing algorithms. The R2 Fill method was tested over ranges of particle size distributions. At large size ranges the method generated a higher coordinate number and denser particle packing, with 79.718% fill at the maximum tested size range. The geometric isotropy was improved at higher size ranges yielding less than a 1% isotropic error. Lastly, we tested mechanical isotropy by using our DEM formulation to measure the stress-strain response under a tensile load at different orientations. The R2 Fill method produced mechanical isotropy within 2% error for the elastic moduli, shear moduli, and Poisson ratio. The second aim of the dissertation is to explore viscous damping and Dissipative Particle Dynamics’s (DPD) weighted damping effects on terminal velocity. We setup our simulation to match an experiment from Kelessidis [48] of a glass bead being dropped in water. A modified DPD formulation is outlined with parameter constants from Groot RD [51] that are a function of sigma. We found the solid-fluid viscoelastic damping had almost no effect on the terminal velocity. The model produced wake formation and pressure changes around the glass bead. The terminal velocity of the three sized glass beads tested decreased with increasing sigma & damper values non-linearly. The terminal velocities were independent of the viscous damping constant when  ō > 7 and had an asymptote at ō = 10. Lastly the simulation had an error of 1.88% compared to experimental data at ō = 8.5 and 2.48% average error from 8.5 <  < 9.5. The third aim was motivated by the low performance of contact-based heat conduction methods in low density par

关键词： Computer science   Neurosciences   Medical imaging  

关键词： Codes   Libraries   Recommender systems   Filtering   Measurement   Computer science   Labeling   Classification algorithms   Hybrid recommender systems   multi-label classification   digital libraries   universal decimal classification  

摘要： In this article we present a hybrid approach to recommending the Universal Decimal Classification (UDC) codes for unclassified documents. By recommending UDC codes to librarians, we can provide the decision support as part of a semi-automatic cataloguing process. As current approaches to recommending UDC codes are scarce and limited to certain fields of expertise, our motivation was to create a hybrid approach which covers all fields of expertise within the UDC hierarchy. The cascade hybrid approach combines the BM25 ranking function with a multi-label classifier based on a BERT (Bidirectional Encoder Representations from Transformers) deep neural network architecture. Additionally, lists of the most commonly used UDC codes within a document's origin organization are used as a final content-based filtering method. The BM25 ranking function is used to create an initial list of recommendations. The first cascade step re-ranks the initial list of recommendations using the list of recommendations produced by the multi-label BERT-based classifier. The second cascade step re-ranks the resulting recommendation list from the first cascade step using a list of most commonly used UDC codes within the document's organization. Finally, post-processing steps are applied to obtain the final list of recommended UDC codes. We present in detail the UDC structure, the used text corpus of documents and the functioning of our hybrid recommendation approach. We perform the analysis of the generated recommendation lists for the corpus from the Slovenian Open-Access Infrastructure using the metrics hit-ratio, normalized discounted cumulative gain, mean reciprocal rank and mean average precision. Our hybrid recommendation approach improves reference scores of individual methods for these metrics.

ISBN: (纸本)9781450395687

关键词： Computer science   Neuroscience   SARS-CoV-2   Seismology  

摘要： TheNature Podcastteam select some of their favourite stories from the past 12 months.