Model Checking Tap Withdrawal in C. Elegans

We present what we believe to be the first formal verification of a biologically realistic (nonlinear ODE) model of a neural circuit in a multicellular organism: Tap Withdrawal (TW) in \emph{C. Elegans}, the common roundworm. TW is a reflexive behavior exhibited by \emph{C. Elegans} in response to vibrating the surface on which it is moving; the neural circuit underlying this response is the subject of this investigation. Specifically, we perform reachability analysis on the TW circuit model of Wicks et al. (1996), which enables us to estimate key circuit parameters. Underlying our approach is the use of Fan and Mitra's recently developed technique for automatically computing local discrepancy (convergence and divergence rates) of general nonlinear systems. We show that the results we obtain are in agreement with the experimental results of Wicks et al. (1995). As opposed to the fixed parameters found in most biological models, which can only produce the predominant behavior, our techniques characterize ranges of parameters that produce (and do not produce) all three observed behaviors: reversal of movement, acceleration, and lack of response

An Industry Driven Genre Classification Application using Natural Language Processing

With the advent of digitized music, many online streaming companies such as Spotify have capitalized on a listener’s need for a common stream platform. An essential component of such a platform is the recommender systems that suggest to the constituent user base, related tracks, albums and artists. In order to sustain such a recommender system, labeling data to indicate which genre it belongs to is essential. Most recent academic publications that deal with music genre classification focus on the use of deep neural networks developed and applied within the music genre classification domain. This thesis attempts to use some of the highly sophisticated techniques, such as Hierarchical Attention Networks that exist within the text classification domain in order to classify tracks of different genres. In order to do this, the music is first separated into different tracks (drums, vocals, bass and accompaniment) and converted into symbolic text data. Due to the sophistication of the distributed machine learning system (over five computers, each possessing a graphical processing units greater than a GTX 1070) present in this thesis, it is capable of classifying contemporary genres with an impressive peak accuracy of over 93%, when comparing the results with that of competing classifiers. It is also argued that through the use text classification, the ex- pert domain knowledge which musicians and people involved with musicological techniques, can be attracted to improving reccomender systems within the music information retrieval research domain

Examining the Effect of the Think-Aloud Instructional Strategy on ELL Student Performance in Middle School Mathematics Classrooms

This research study was based on Vygotsky\u27s learning theory, the Zone of Proximal Development (ZPD). The Think Aloud strategy provides an effective scaffolding technique that is advocated in Vygotsky\u27s conceptualization of ZPD. This study examined the effect of the think-aloud instructional strategy using academic language and problem-solving thought process on English Language Learners\u27 student performance with solving word problems when teachers implement the protocol in middle school mathematics classrooms. This empirical study utilized a quantitative single-case research design for data collection and data analysis. The data collection occurred during the concurrent learning model due to the COVID-19 pandemic. In the single-case research study, the data were analyzed using the multiple baseline design composed of a baseline without think-aloud and treatment with the think-aloud strategy. The multiple baselines revealed seven trends, including task performance, academic language usage, a proportional relationship between task performance and academic language usage, gender differences, speaking vs. writing, the complexity of the content, and learning model in the pandemic. The findings from data analysis of various statistical measures revealed that the think-aloud approach positively impacted ELLs\u27 problem-solving performance and academic language usage in multiple ways. The results were analyzed along with the study\u27s potential limitations to make recommendations for future research studies

Segmenting Images Using Hybridization of K-Means and Fuzzy C-Means Algorithms

Image segmentation is an essential technique of image processing for analyzing an image by partitioning it into non-overlapped regions each region referring to a set of pixels. Image segmentation approaches can be divided into four categories. They are thresholding, edge detection, region extraction and clustering. Clustering techniques can be used for partitioning datasets into groups according to the homogeneity of data points. The present research work proposes two algorithms involving hybridization of K-Means (KM) and Fuzzy C-Means (FCM) techniques as an attempt to achieve better clustering results. Along with the proposed hybrid algorithms, the present work also experiments with the standard K-Means and FCM algorithms. All the algorithms are experimented on four images. CPU Time, clustering fitness and sum of squared errors (SSE) are computed for measuring clustering performance of the algorithms. In all the experiments it is observed that the proposed hybrid algorithm KMandFCM is consistently producing better clustering results