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Modeling a Problem-Solving Approach Through Computational Thinking for Teaching Programming

IEEE TRANSACTIONS ON EDUCATION 2024年第2期67卷 282-291页

作者： Zebel-Al Tareq Yusof, Raja Jamilah Raja Univ Malaya Fac Comp Sci & Informat Technol Dept Software Engn Kuala Lumpur 50306 Malaysia

Contribution: A problem-solving approach (PSA) model derived from major computational thinking (CT) concepts. This model can be utilized to formulate solutions for different algorithmic problems and translate them into effective active learning methods. Background: Different teaching approaches for programming are widely available;however, being able to formulate an algorithmic solution computationally and then transform it into code is essential for students. Research Questions: What are the effective teaching approaches for fostering the development of problem-solving and programming skills? How do CT concepts contribute to the formulation of a PSA model for programming problems and its translation into an effective teaching method? How can an effective teaching method that utilizes the PSA model be identified and distinguished from other approaches? Methodology: A preliminary study pointed out the difficulties experienced when teaching programming, inspiring the formulation of a PSA model that used CT concepts. An experimental study on problem-based and game-based programming workshops that utilized the PSA model through sorting algorithms was performed on experimental groups consisting of 30 students each. A syntax-based programming workshop consisting of 30 students was used as the control group. All the participants were recruited through a pretest that incorporated basic programming questions. The participants had to answer a posttest after the workshop. Findings: The results showed that the participants exhibited no significant difference between the pretest and posttest for the syntax-based learning (SBL). However, there is a significant difference between the pretest and posttest of both the problem-based learning (PBL) and the game-based learning (GBL) workshops. There was no significant difference significant difference for the pretest scores of all three workshops. The analysis of the posttest further confirmed that the experimental groups (PBL and GBL)

关键词： Programming profession Education Problem-solving sorting Computational modeling Conferences Software algorithms Active learning approach model computational thinking (CT) computer science computing skills game-based learning (GBL) problem-based learning (PBL) problem-solving instructional methods sorting algorithm syntax-based learning (SBL)

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Quicksort asymptotics

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JOURNAL OF algorithmS 2002年第1期44卷 4-28页

作者： Fill, JA Janson, S Johns Hopkins Univ Dept Math Sci Baltimore MD 21218 USA Univ Uppsala Dept Math S-75105 Uppsala Sweden

The number of comparisons X, used by Quicksort to sort an array of it distinct numbers has mean mu(n) of order n log n and standard deviation of order it. Using different methods, Regnier and Rosler each showed that the normalized variate Y-n := (X-n - mu(n))/n converges in distribution, say to Y;the distribution of Y can be characterized as the unique fixed point with zero mean of a certain distributional trans formal ion. We provide the first rates of convergence for the distribution of Y-n to that of F, using various metrics. In particular, we establish the bound 2n(-1/2) in the d(2)-metric, and the rate O(n(epsilon-(1/2))) for Kolmogorov-Smirnov distance, for any positive (C) 2002 Elsevier Science (USA). All rights reserved.

关键词： Quicksort sorting algorithm asymptotics limit distribution rate of convergence Kolmogorov-Smirnov distance density moment generating function numerical analysis d(p)-metric coupling

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DEVELOPMENT OF A FIELD-PORTABLE IMAGING-SYSTEM FOR SCENE CLASSIFICATION USING MULTISPECTRAL DATA FUSION algorithmS

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IEEE AEROSPACE AND ELECTRONIC SYSTEMS MAGAZINE 1994年第9期9卷 13-19页

作者： PRESTON, E BERGMAN, T GORENFLO, R HERMANN, D KOPALA, E KUZMA, T LAZOFSON, L ORKIS, R Battelle Columbus OH USA Battelle Memorial Institute Columbus OH US Battelle Memorial Inst. Columbus OH USA

Battelle scientists have assembled a reconfigurable multispectral imaging and classification system which can be taken into the field to support automated real-time target/background discrimination. The system may be used for a variety of applications including environmental remote sensing, industrial inspection and medical imaging. This paper discusses hard tactical target and runway detection applications performed with the multispectral system. The Battelle-developed system consists of a passive, multispectral imaging electro-optical (EO) sensor suite and a real-time digital data collection and data fusion image processor. The EO sensor suite, able to collect imagery in 12 distinct wavebands from the ultraviolet (UV) through the long wave infrared (LWIR), consists of five charge-coupled device (CCD) cameras and two thermal IR imagers integrated on a common portable platform. The data collection and processing system consists of video switchers, recorders and a real-time sensor fusion/classification hardware system which combines any three input wavebands to perform real-time data fusion by applying ''look-up tables,'' derived from tailored neural network algorithms, to classify the imaged scene pixel by pixel. The result is then visualized in a video format on a full color, 9-inch, active matrix Liquid Crystal Display (LCD). A variety of classification algorithms including artificial neural networks and data clustering techniques were successfully optimized to perform pixel-level classification of imagery in complex scenes comprised of tactical targets, buildings, roads, aircraft runways, and vegetaton. algorithms implemented included unsupervised maximum likelihood, Linde Buzo Gray, and ''fuzzy'' clustering algorithms along with Multilayer Perceptron and Learning Vector Quantization (LVQ) neural networks. Supervised clustering of the data was also evaluated. To assess classification robustness, algorithms were tested on imagery recorded over broad periods of tim

关键词： CCD image sensors aircraft instrumentation data acquisition feedforward neural nets image processing equipment image recognition infrared detectors infrared imaging learning (artificial intelligence) liquid crystal displays maximum likelihood estimation INFRARED DETECTORS sorting algorithm Infrared imaging data integration Data fusion algorithms image processing equipment LIQUID CRYSTAL DISPLAY maximum likelihood estimation CCD image sensors feedforward neural nets Image recognition

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Adaptive Order-of-Addition Experiments via the Quick-Sort algorithm

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TECHNOMETRICS 2023年第3期65卷 396-405页

作者： Lin, Dennis K. J. Chen, Jianbin Purdue Univ Dept Stat W Lafayette IN USA Beijing Inst Technol Sch Math & Stat Beijing 100081 Peoples R China

The order-of-addition (OofA) experiment has received a great deal of attention in the recent literature. The primary goal of the OofA experiment is to identify the optimal order in a sequence of m components. All the existing methods are model-dependent and are limited to small number of components. The appropriateness of the resulting optimal order heavily depends on (a) the correctness of the underlying assumed model, and (b) the goodness of model fitting. Moreover, these methods are not applicable to deal with large m (e.g., m >= 7 ). With this in mind, this article proposes an efficient adaptive methodology, building upon the quick-sort algorithm, to explore the optimal order without any model specification. Compared to the existing work, the run sizes of the proposed method needed to achieve the optimal order are much smaller. Theoretical supports are given to illustrate the effectiveness of the proposed method. The proposed method is able to obtain the optimal order for large m (e.g., m >= 20 ). Numerical experiments are used to demonstrate the effectiveness of the proposed method.

关键词： Design of experiment Optimal order Sequential design sorting algorithm

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The use of multi-temporal Landsat Normalized Difference Vegetation Index (NDVI) data for mapping fuel models in Yosemite National Park, USA

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2003年第8期24卷 1639-1651页

作者： Van Wagtendonk, JW Root, RR US Geol Survey Western Ecol Res Ctr Yosemite Field Stn El Portal CA 95318 USA US Geol Survey Rocky Mt Mapping Ctr Denver Fed Ctr Denver CO 80225 USA

The objective of this study was to test the applicability of using Normalized Difference Vegetation Index (NDVI) values derived from a temporal sequence of six Landsat Thematic Mapper (TM) scenes to map fuel models for Yosemite National Park, USA. An unsupervised classification algorithm was used to define 30 unique spectral-temporal classes of NDVI values. A combination of graphical, statistical and visual techniques was used to characterize the 30 classes and identify those that responded similarly and could be combined into fuel models. The final classification of fuel models included six different types: short annual and perennial grasses, tall perennial grasses, medium brush and evergreen hardwoods, short-needled conifers with no heavy fuels, long-needled conifers and deciduous hardwoods, and short-needled conifers with a component of heavy fuels. The NDVI, when analysed over a season of phenologically distinct periods along with ancillary data, can elicit information necessary to distinguish fuel model types. Fuels information derived from remote sensors has proven to be useful for initial classification of fuels and has been applied to fire management situations on the ground.

关键词： EVERGREEN Yosemite National Park heavy oil Fuel models Conifers Remote sensing equipment ancillary data normalized difference vegetation index sorting algorithm

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A method for classification accuracy evaluation for a precipitation-oriented classifier

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2005年第21期26卷 4785-4800页

作者： Anthis, AI Cracknell, AP Reg Meteorol Ctr Hellen Tact Air Force Hellen Natl Meteorol Serv Larisa Greece Univ Dundee Div Elect Engn & Phys Dundee Scotland

A method of classification accuracy evaluation for a cloud and precipitation classifier applied to geostationary meteorological satellite data is presented. The method has been developed to evaluate the accuracy of a rather precise classification algorithm. The algorithm produces nine classes, four of which involve precipitation. The classes are: (1) clear or insignificant cloud, (2) low thin cloud with no rain, (3) low or middle thin cloud with no rain, (4) low or middle thick cloud with no rain, (5) middle or high cloud with no rain, (6) middle or high cloud with the possibility of rain, (7) middle or high cloud with light-moderate precipitation, (8) middle-high cloud with moderate-heavy precipitation, (9) heavy thunderstorm. The evaluation classifier has been tested for its accuracy (ground truth) using comparison between actual meteorological weather reports and classification results derived from the algorithm applied. For the estimation of classification accuracy, the omission/commission method is applied between the observed and the classification-produced values. The classifier used has proved to be very reliable for classifying major cloud types and precipitation, tested during the synoptic situation of depression systems approaching the south Balkan Peninsula from the west. In that synoptic situation, different intensities of rainfall as well as heavy thunderstorm were present, and the results are very satisfactory. The method can be used to evaluate classification results produced by algorithms applied to meteorological satellite data, classifying precipitation areas as well as the heaviness of precipitation.

关键词： Classifiers Cloud Types algorithms mean point rain Satellite Imagery sorting algorithm

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Video Behavior Analysis Using Topic Models and Rough Sets

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IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE 2013年第1期8卷 56-67页

作者： Zhao, Liang Shang, Lin Gao, Yang Yang, Yubin Jia, Xiuyi Nanjing Univ Nanjing Jiangsu Peoples R China Nanjing Univ Sci & Technol Nanjing Jiangsu Peoples R China

Video behavior analysis is both meaningful and challenging in computer vision. As the monitoring equipment become readily cheaper and available, massive video data have been generated. As abnormal behaviors are rare, manual surveillance may drastically increase the chances of false positives or false negatives. It is hence useful to achieve automatic surveillance. Abnormal behaviors are usually complex and short. It is however difficult to completely list the abnormal behaviors. We can view abnormalities as outliers. Sometimes abnormalities co-occur with normalities, which cannot be distinguished in some feature space.

关键词： Analytical models Classification algorithms Computational modeling Computer vision Data models Feature extraction Rough sets Rough sets Feature extraction ANALYSIS MODELS monitoring equipment data models Eigenvector behavioral research abnormal behavior computer vision sorting algorithm Computational modeling

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Brownian Motus and Clustered Binary Insertion Sort methods: An efficient progress over traditional methods

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2018年 86卷 266-280页

作者： Goel, Shubham Kumar, Ravinder Thapar Inst Engn & Technol Comp Sci & Engn Dept Patiala 147004 Punjab India

sorting is the basic operation in every application of computer science. The paper proposes two novel sorting algorithms based on the concept of traditional Insertion Sort (IS). Firstly, Brownian Motus Insertion Sort (BMIS) based on IS is proposed. It is followed by Clustered Binary Insertion Sort (CBIS) based on the principles of Binary Insertion Sort (BIS). BIS is a binary search enhancement of IS which is a quite famous variant of it. Average case time complexity of BMIS is 0((0.54)root n);and that of CBIS is 0(n log n). The scenario which results into the worst case of IS is with the complexity of 0(n(2));and BIS with 0(n log n) is the best case scenario for BMIS and CBIS with complexity of 0(n). The probability of getting a worst case scenario for BMIS and CBIS is approximately zero. Comparison of proposed algorithms with IS and BIS has been performed at 25%, 50%, 75% and 100% level of randomness in the initial dataset. These results lead to prove our claim of devising efficient enhancements of IS. The results further reveal that performance of BMIS and CBIS will increase with a decrease in randomness level of the dataset in comparison to its counterparts. The number of comparisons required by BMIS and CBIS will approach to 0(n) with randomness level approach to zero. So, for nearly sorted datasets, our proposed BMIS and CBIS are the best choice. Both BMIS and CBIS are in-place, stable and online sorting algorithm. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Brownian Motus insertion sort Clustered Binary Insertion Sort Insertion sort Binary insertion sort sorting algorithm Online algorithm

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A comparison of classification algorithms for the identification of smoke plumes from satellite images

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STATISTICAL METHODS IN MEDICAL RESEARCH 2011年第2期20卷 131-156页

作者： Wan, V. Braun, W. J. Dean, C. B. Henderson, S. Simon Fraser Univ Dept Stat & Actuarial Sci Burnaby BC V5A 1S6 Canada Univ Western Ontario Dept Stat & Actuarial Sci London ON N6A 5B7 Canada Univ British Columbia Sch Environm Hlth Vancouver BC V5Z 1M9 Canada

Obtaining accurate measures of exposure to forest fire smoke is important for the assessment of health risk. Estimating exposure from air quality monitors is challenging because of the sparseness of the monitoring networks in remote areas. However, satellite imagery offers a novel and data-rich tool to provide visual information on smoke plumes. We will discuss statistical techniques for obtaining estimates of forest fire smoke plumes using classification algorithms on data from satellite imagery in order to develop automated processes for identifying exposure. The aim is to identify whether such methods may offer a high-resolution approach that provides a reliable estimate of smoke and a more thorough caption of the spatial distribution of smoke from fires than is currently available.

关键词： Health risk assessment Smoke visual information statistical techniques sorting algorithm

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Single-Molecule Study of Ribosome Hierarchic Dynamics at the Peptidyl Transferase Center

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BIOPHYSICAL JOURNAL 2010年第9期99卷 3002-3009页

作者： Altuntop, Mediha Esra Ly, Cindy Tu Wang, Yuhong Univ Houston Dept Biol & Biochem Houston TX 77004 USA

During protein biosynthesis the ribosome moves along mRNA in steps of precisely three nucleotides. The mechanism for this ribosome motion remains elusive. Using a classification algorithm to sort single-molecule fluorescence resonance energy transfer data into subpopulations, we found that the ribosome dynamics detected at the peptidyl transferase center are highly inhomogeneous. The pretranslocation complex has at least four subpopulations that sample two hybrid states, whereas the posttranslocation complex is mainly static. We observed transitions among the ribosome subpopulations under various conditions, including 1), in the presence of EF-G;2), spontaneously;3), in different buffers, and 4), bound to antibiotics. Therefore, these subpopulations represent biologically active ribosomes. One key observation indicates that the Hy2 hybrid state only exists in a fluctuating ribosome subpopulation, which prompts us to propose that ribosome dynamics are hierarchically arranged. This proposal may have important implications for the regulation of cellular translation rates.

关键词： Peptidyl Transferases Subpopulations Ribosomes sorting algorithm

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