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关键词： Improving classroom teaching   Simulations   Pedagogical issues   Teaching/learning strategies  

摘要： Pedagogical design for science learning in classrooms often involves tension between the scientific community expectations and traditional curricular expectations. To address this issue, this study proposed a light-weight inquiry activity that can be pragmatically implemented in regular classrooms based on the minimalism principle and the teacherled collaboration principle. Data gathered from 49 middle school students indicated that after learning in the light-weight inquiry condition, students demonstrated significant enhancements in the target science knowledge. In particular, the students in the light-weight inquiry condition displayed significantly higher levels of enhancement in scientific literacy than those who learned in the traditional lecturing condition. Furthermore, the students perceived a higher level of deep motivation and strategy but a lower level of memorizing science facts and calculating and practicing when they learned in the light-weight inquiry condition. The proposed pedagogical design demonstrated a positive impact on the learning of science knowledge, scientific literacy, and conceptions of learning and approaches to learning science. However, an unexpected effect was also observed, showing that the light-weight inquiry activity might also trigger surface motivation reflecting students' fear of failure in tests and the orientation to meet external expectations. The implications of the educational practice are discussed, and directions for future studies are also addressed.

关键词： Computer science   Dynamic gesture recognition models   Machine learning   Arabic sign language   Classification  

摘要： Arabic Sign Language (ArSL) is similar to other sign languages in terms of the way it is gestured and interpreted and used as a medium of communication among the hearing-impaired and the communities in which they live in. Research investigating sensor utilization and natural user interfaces to facilitate ArSL recognition and interpretation, is lacking. Previous research has demonstrated that there is not a single classifier modeling approach that can be suitable for all hand gesture recognition tasks, therefore, this research investigated which combination of algorithms, set with different parameters used with a sensor device, produce higher ArSL recognition accuracy results in a gesture recognition system. This research proposed a dynamic prototype model (DPM) using Kinect as a sensor to recognize certain ArSL gestured dynamic words. The DPM used eleven predictive models of three algorithms (SVM, RF, KNN) based on different parameter settings. Research findings indicated that highest recognition accuracy rates for the dynamic words gestured were achieved by the SVM models, with linear kernel and cost parameter = 0.035.

关键词： Young adults   Minors   Capital punishment   Correctional treatment programs   Teenagers   Juvenile justice  

摘要： Roper v. Simmons and its progeny fundamentally altered juvenile justice jurisprudence. In the aftermath of these cases, scholars devoted research to the force behind the Supreme Court's requirement that children have "a meaningful opportunity for release," state judicial and legislative responses to the cases, and possible extensions of these cases to abolish certain juvenile sentencing and confinement practices. This Note takes a different tack. The Roper trilogy's central principle declares juveniles to be distinct from adults in terms of cognitive and socioemotional development. The trilogy then makes these differences relevant to criminal liability and punishment. But these differences come into greater relief for adolescents, particularly girls, who have suffered trauma in the form of sexual abuse. This Note thus examines the Roper trilogy's application to girls who have been sexually abused and subsequently swept into the criminal justice system, the so-called sexual-abuse-to-prison pipeline. In finding that these adolescents are meaningfully different from their peers in their socioemotional and cognitive development, not to mention different from adults, this Note argues that we should change juvenile justice law to conform to the Roper trilogy's "children are different" principle. These girls' unique circumstances require alterations to jury instructions and broader latitude for adolescent psychologists to contextualize their actions and behaviors. Moreover, to the extent that mens . rea analysis is intended to determine actual criminal culpability, that analysis should similarly evolve to account for these girls' histories of sexual abuse. But the analysis cannot stop here. Broader research is urgently needed to extend this analysis to children of all genders who have survived childhood sexual abuse. And adolescents who have suffered other types of trauma similarly deserve to have their actions understood in light of their experiences.

关键词： Imaging   Dynamic range   Image reconstruction   Computer science   Streaming media   Lighting   Optimization   High dynamic range imaging   deep learning   sparse stacked autoencoders   sparse representations  

摘要： Bracketed High Dynamic Range (HDR) imaging architectures acquire a sequence of Low Dynamic Range (LDR) images in order to either produce a HDR image or an "optimally" exposed LDR image, achieving impressive results under static camera and scene conditions. However, in real world conditions, ghost-like artifacts and noise effects limit the quality of HDR reconstruction. We address these limitations by introducing a post-acquisition snapshot HDR enhancement scheme that generates a bracketed sequence from a small set of LDR images, and in the extreme case, directly from a single exposure. We achieve this goal via a sparse-based approach where transformations between differently exposed images are encoded through a dictionary learning process, while we learn appropriate features by employing a stacked sparse autoencoder (SSAE) based framework. Via experiments with real images, we demonstrate the improved performance of our method over the state-of-the-art, while our single-shot based HDR formulation provides a novel paradigm for the enhancement of LDR imaging and video sequences.

关键词： Food science   Food technology   Computer science   Adaptive neuro fuzzy inference system (ANFIS)   Drying   Moisture ratio   Effective diffusivity   Activation energy   Rehydration ratio  

摘要： This study applied Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the moisture ratio (MR) during the drying process of yam slices (Dioscorea rotundata) in a hot air convective dryer. Also the effective diffusivity, activation energy, and rehydration ratio were calculated. The experiments were carried out at three (3) drying air temperatures (50, 60, and 70 degrees C), air velocities (0.5, 1, and 1.5 m/s), and slice thickness (3, 6, and 9 mm), and the obtained experimental data were used to check the usefulness of ANFIS in the yam drying process. The result showed efficient applicability of ANFIS in predicting the MR at any time of the drying process with a correlation value (R-2) of 0.98226 and root mean square error value (RMSE) of 0.01702 for the testing stage. The effective diffusivity increased with an increase in air velocity, air temperature, and thickness and the values (6.382E -09 to 1.641E -07 m(2)/s). The activation energy increased with an increase in air velocity, but fluctuate within the air temperatures and thickness used (10.59-54.93 KJ/mol). Rehydration ratio was highest at air velocityxair temperaturexthickness (1.5 m/s x 70 degrees C x 3 mm), and lowest at air velocity x air temperature x thickness (0.5 m/s x 70 degrees C x 3 mm). The result showed that the drying kinetics of Dioscorea rotundata existed in the falling rate period. The drying time decreased with increased temperature, air velocity, and decreased slice thickness. These established results are applicable in process and equipment design, analysis and prediction of hot air convective drying of yam (Dioscorea rotundata) slices.

关键词： Support vector machines   Software   Prediction algorithms   Predictive models   Testing   Software algorithms   Computer science   Cost-sensitive learning   data imbalance   ranking-oriented defect prediction (RODP)  

摘要： Context: Ranking-oriented defect prediction (RODP) ranks software modules to allocate limited testing resources to each module according to the predicted number of defects. Most RODP methods overlook that ranking a module with more defects incorrectly makes it difficult to successfully find all of the defects in the module due to fewer testing resources being allocated to the module, which results in much higher costs than incorrectly ranking the modules with fewer defects, and the numbers of defects in software modules are highly imbalanced in defective software datasets. Cost-sensitive learning is an effective technique in handling the cost issue and data imbalance problem for software defect prediction. However, the effectiveness of cost-sensitive learning has not been investigated in RODP models. Aims: In this article, we propose a cost-sensitive ranking support vector machine (SVM) (CSRankSVM) algorithm to improve the performance of RODP models. Method: CSRankSVM modifies the loss function of the ranking SVM algorithm by adding two penalty parameters to address both the cost issue and the data imbalance problem. Additionally, the loss function of the CSRankSVM is optimized using a genetic algorithm. Results: The experimental results for 11 project datasets with 41 releases show that CSRankSVM achieves 1.12%-15.68% higher average fault percentile average (FPA) values than the five existing RODP methods (i.e., decision tree regression, linear regression, Bayesian ridge regression, ranking SVM, and learning-to-rank (LTR)) and 1.08%-15.74% higher average FPA values than the four data imbalance learning methods (i.e., random undersampling and a synthetic minority oversampling technique;two data resampling methods;RankBoost, an ensemble learning method;IRSVM, a CSRankSVM method for information retrieval). Conclusion: CSRankSVM is capable of handling the cost issue and data imbalance problem in RODP methods and achieves better performance. Therefore, CSRankSVM is reco

关键词： Analytical models   Heuristic algorithms   Computer science   Search problems   Computational complexity   Maintenance engineering   Games   2-D binary individual   constrained resources   heuristic   overlapping coalition formation (OCF)   subadditive tasks  

摘要： Overlapping coalition formation (OCF), which provides a natural framework for modeling scenarios where each agent can join and allocate their resources to several completely different coalitions at the same time, has become a very active topic in multiagent systems. For OCF in resource-constrained and subadditive task oriented domains, an agent may not possess sufficient resources to meet the needs of multiple coalitions simultaneously. As a result, there may exist many potential resource conflicts among the rival overlapping coalitions. To tackle such situations, we first present a natural variation of the traditional OCF model and analyze the size of the solution space and the computational complexity of the overlapping coalition structure generation (OCSG) problem. Next, we develop a generic task-oriented heuristic (TOH) for individual repairs that can be used in binary meta-heuristic algorithms to generate overlapping coalitions in a parallel manner. Moreover, we show how the proposed TOH repairs a 2-D individual to resolve resource conflicts and discuss several basic properties. Finally, to evaluate the effectiveness of TOH, we compare it with the existing agent-oriented heuristic for the OCSG problem. The empirical results demonstrate that TOH is of high efficiency and effectiveness in harsh environments with fierce competition over scarce resources.

关键词： Computer science   Serious game   Engagement player   Immersive   Soil tillage   Moldboard plow  

摘要： In most cases, problems that increase player involvement in immersive serious games do so by combining fun elements with a specific purpose. Previous studies have produced models of soil porosity and plow force that use the speed of plowing, the angle of the plow's eye, and the depth of the plow as the basis for a design strategy in immersion serious games. However, these studies have not been able to show the optimal strategy of engagement of the player in the game. In the domain of serious game concept learning, strategies can be formed based on real conditions or data from experimental results. In a serious game, the aim is to increase the player's knowledge so that the player gains knowledge by coming up with strategies to play the game. This research aims to increase the engagement of players by means of multi-objective optimization based on Pareto optima, with the objectivity of soil porosity and plow force that is affected by the speed of plowing, the angle of the plow's eye, and the depth of the plow. The results of this optimization are used as a basis for the design of strategies in a serious game in the form of Hierarchy Finite State Machine (HFSM). From the results of the study, it was found that there is an optimal area for the game strategy that is also an indicator of how to successfully process the soil tillage using a moldboard plow.

关键词： Computer science   Computer Vision   Image processing   Deep learning   Pedestrian attribute recognition  

摘要： Video surveillance applications have made great strides in making the world a safer place. Extracting visual attributes from a scene, such as the type of shoes, the type of clothing, carrying any object or not, or wearing any accessory etc., is a challenging problem and an efficient solution holds the key to a great number of applications. In this paper, we present a multi-branch convolutional neural network that uses depthwise separable convolution (DSC) layers to solve the pedestrian attribute recognition problem. Researchers have proposed various solutions over the years making use of convolutional neural networks (CNN), however, we introduce DSC layers to the CNN for the problem of pedestrian attribute recognition. In addition, we make a novel use of the different color spaces and create a 3-branch CNN, denoted as 3bCNN, that is efficient, especially with smaller datasets. We experiment on two benchmark datasets and show results with improvement over the state of the art.

关键词： Computer science   Data embedding   Data protection   Data hiding   Information security   Multimedia security  

摘要： In this digital era, transmitting data through a computer network has become common. Moreover, some applications have also been developed to do it. Nevertheless, users may not be aware of the security aspect of this data transmission, which can lead to disclosing this private message. In a case when a sensitive message is the object to transmit, a security mechanism should be applied. Data hiding is one of the methods introduced to work for this issue. In this algorithm, the message is embedded into the cover, such as an audio file, before being transmitted;on the other side, the recipient extracts it. However, the size of the message and the quality of the resulted stego data are still challenging. In this paper, we focus on these two problems by considering some factors: embedding space, embedding process, reducing, and smoothing steps. Firstly, the audio signal is discretized to obtain audio samples. Next, these samples are interpolated to provide spaces for hiding the secret. Considering that the quality of the generated stego audio is likely to drop, reducing and smoothing steps are designed. The experimental results show that this approach can improve the quality of the stego based on the specified payload capacity. That is, there is an increase in PSNR value of at least 10dB, depending on the payload size and the methods.