Computer Science

Critical thinking skills needed in learning process to train students to solve problems. In addition, the 21st century need the individuals who have analytical and critical thinking. To prepare students to face the challenges of 21st century life, the critical thinking skills need to be developed. Empowering critical thinking skills can be done through the implementation of Remap STAD learning models. Remap STAD can enhance students' critical thinking skills because it was required students to read in advance, so the students will be trained to read. After reading activity students make a concept map from what they have been read. Furthermore, using Remap STAD learning models can enhance students' thinking skills beacuse students can share their idea in group work.

Hip-hop pedagogy is the use of hip-hop artifacts and culture as resources to a curriculum or instruction. Applying hip-hop pedagogy to academic interventions has been suggested to effectively increase the value and connection to course content as well as the ability to re-envision content into ones that reflect underrepresented students of color's lived experiences. As there is a gap in African American and non-White Latino American performance in STEM compared to their White and Asian American counterparts, and a high demand in prospective STEM occupations (namely computer and information technology), hip-hop pedagogy can be leveraged to improve STEM identity to successively improve STEM performance. This paper represents the first stage of a case study to analyze and validate hip-hop pedagogical tools for computing and STEM instruction and outreach. This paper describes a qualitative meta-synthesis of hip-hop pedagogy and associated STEM outreach programs. Findings on hip-hop pedagogy in computing and STEM outreach interventions are discussed to aid in supporting and creating future hip-hop pedagogical interventions.

Indoor localization has been an important area of research for many years. A more recent development in this space is towards device-free localization techniques. We present a thorough survey and results from our attempt to increase the accuracy and robustness relating to the device-free indoor-localization problem. Using WiFi network signals for such localization tasks, almost near-perfect accuracy could be reached. We build on top of two [1] [2] of the recent works that use WiFi Channel State Information (CSI), to test and experiment indoor localization accuracy at room level. Our work evaluates the achievable improvements by experimenting with classical machine learning and deep learning models. We also briefly discuss the instability of CSI data due to drift over time.

Choosing a model with too few parameters can involve making unrealistically simple assumptions and lead to high bias, poor prediction, and missed opportunities for insight. Such models are not flexible enough to describe the sample or the population well. A model with too many parameters can fit the observed data very well, but be too closely tailored to it. Such models may generalize poorly. Penalized-likelihood information criteria, such as Akaike's Information Criterion (AIC), the Bayesian Information Criterion (BIC), the Consistent AIC, and the Adjusted BIC, are widely used for model selection. However, different criteria sometimes support different models, leading to uncertainty about which criterion is the most trustworthy. In some simple cases the comparison of two models using information criteria can be viewed as equivalent to a likelihood ratio test, with the different models representing different alpha levels (i.e., different emphases on sensitivity or specificity; L...

We propose a copula-based joint modeling framework for mixed longitudinal responses. Our approach permits all model parameters to vary with time, and thus will enable researchers to reveal dynamic response-predictor relationships and response-response associations. We call the new class of models TIMECOP because we model dependence using a time-varying copula. We develop a one-step estimation procedure for the TIMECOP parameter vector, and also describe how to estimate standard errors. We investigate the finite sample performance of our procedure via three simulation studies, one of which shows that our procedure performs well under ignorable missingness. We also illustrate the applicability of our approach by analyzing binary and continuous responses from the Women's Interagency HIV Study and a smoking cessation program.

A tapered fork-shaped antenna having small ground for ultra-wide band (UWB) antenna is proposed in this paper. Finite element method has been successfully employed to simulate and optimize the feed line, ground, and tapered fork-shaped diameter to enhance the performance of the antenna in terms of bandwidth obviously for the ultra-wideband purposes. An acceptable impedance matching performance has been achieved, which is a band wider than the UWB band that is defined by the Federal Communications Commission (FCC). In this paper, the detailed design parameters including the key elements in bandwidth enhancement is presented. The results of the finite element simulations have been presented as well indicating the return loss and radiation pattern of the proposed antenna.

A novel method to develop an Ultra-Wide Band (UWB) antenna is proposed in which a defected ground structure technique is employed. The proposed design is excited via a microstrip feed line which is optimized for the best impedance matching performance. The slotted ground is to enhance the UWB performance. The design parameters of the antenna have been optimized using high frequency finite element-based simulation software for the best gain, bandwidth and efficiency performance. The final design successfully meets the requirements of the UWB and even exceeds them, having a bandwidth of 2.55 to 10.85 GHz. The design parameters and the simulation results are reported in this paper and include the radiation pattern at different frequencies within the operating band.

Information criteria have been popularly used in model selection and proved to possess nice theoretical properties. For classification, Claeskens et al. (2008) proposed support vector machine information criterion for feature selection and provided encouraging numerical evidence. Yet no theoretical justification was given there. This work aims to fill the gap and to provide some theoretical justifications for support vector machine information criterion in both fixed and diverging model spaces. We first derive a uniform convergence rate for the support vector machine solution and then show that a modification of the support vector machine information criterion achieves model selection consistency even when the number of features diverges at an exponential rate of the sample size. This consistency result can be further applied to selecting the optimal tuning parameter for various penalized support vector machine methods. Finite-sample performance of the proposed information criterion...