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A Study to Examine the Impact of the Paideia Seminar Reading Intervention Program at a School in Connecticut
No full textLow achievement in reading comprehension has been an issue that has plagued grade 8 students for many years. Empirical literature strongly suggested that implementing a dialogic discussion-based reading intervention, which involves students participating in small group discussions regarding a piece of text, could lead to deeper comprehension of text and thus higher reading achievement. Historically, reading comprehension interventions within the school that was used in this study, have not focused on implementing dialogic discussion strategies as part of a reading comprehension intervention. As a result, per iReady reading comprehension diagnostic data, the reading comprehension skills of grade 8 students at this school have remained chronically low for many years. The purpose of this study was to determine the impact of the Paideia Seminar reading intervention on the reading comprehension skills of grade 8 English Language Arts students. More precisely, through a mixed methods methodology and utilizing Rosenblatt’s Theory of Transactional Reading, this study determined the impact of the Paideia Seminar reading intervention on the reading comprehension skills of grade 8 English Language Arts students. Findings suggested that implementation of a student dialogue-based reading intervention could lead to an increase in reading comprehension skills as evidenced by the iReady reading comprehension reading diagnostic assessment. More explicitly, student participation in this Paideia Seminar reading intervention led to an improvement in reading comprehension skills. During this study, there were some limitations that did become evident. However, major results revealed that the incorporation of a student dialogue-based intervention within an English Language Arts curriculum can change how teachers teach reading
Proposed Framework to Improving Performance of Familial Classification in Android Malware
No full textBecause of the recent developments in hardware and software technologies for mobile phones, people depend on their smartphones more than ever before. Today, people conduct a variety of business, health, and financial transactions on their mobile devices. This trend has caused an influx of mobile applications that require users' sensitive information. As these applications increase so too have the number of malicious applications increased, which may compromise users' sensitive information. Between all smartphone, Android receives major attention from security practitioners and researchers due to the large number of malicious applications. For the past twelve years, Android malicious applications have been clustered into groups for better identification. Characterizing the malware families can improve the detection process and understand the malware patterns. However, in the research community, detecting new malware families is a challenge. In this research, a framework is proposed to improve the performance of familial classification in Android malware. The framework is named a Reverse Engineering Framework (RevEng). Within RevEng, applications' permissions were selected and then fed into machine learning algorithms. Through our research, we created a reduced set of permissions using Extremely Randomized Trees algorithm that achieved high accuracy and a shorter execution time. Furthermore, we conducted two approaches based on the extracted information. The first approach used a binary value representation of the permissions. The second approach used the features' importance. We represented each selected permission in latter approach by its weight value instead of its binary value in the former approach. We conducted a comparison between the results of our two approaches and other relevant works. Our approaches achieved better results in both accuracy and time performance with a reduced number of permissions
Digit Recognition Based on Specialization, Decomposition and Holistic Processing
No full textWith the introduction of the Convolutional Neural Network (CNN) and other classical algorithms, facial and object recognition have made significant progress. However, in a situation where there are few label examples or the environment is not ideal, such as lighting conditions, orientations, and so on, performance is disappointing. Various methods, such as data augmentation and image registration, have been used in an effort to improve accuracy; nonetheless, performance remains far from human efficiency. Advancement in cognitive science has provided us with valuable insight into how humans achieve high accuracy in identifying and discriminating between different faces and objects. These researches help us understand how the brain uses the features in the face to form a holistic representation and subsequently uses it to discriminate between faces. Our objective and contribution in this paper is to introduce a computational model that leverages these techniques, being used by our brain, to improve robustness and recognition accuracy. The hypothesis is that the biological model, our brain, achieves such high efficiency in face recognition because it is using a two-step process. We therefore postulate that, in the case of a handwritten digit, it will be easier for a learning model to learn invariant features and to generate a holistic representation than to perform classification. The model uses a variational autoencoder to generate holistic representation of handwritten digits and a Neural Network(NN) to classify them. The results obtained in this research show the effectiveness of decomposing the recognition tasks into two specialize sub-tasks, a generator, and a classifier.https://doi.org/10.3390/make203001
Autism Spectrum Disorders (ASD) and Learning Support Systems in Post-Secondary Education
No full textAutism Spectrum Disorder (ASD) is a condition affecting numerous individuals and families. As the improvement of ASD analysis continues and diagnostic coding is updated, opportunities for an earlier detection can be made. This early detection has resulted in more cases being identified, but has also led to more services and a better understanding of the health science of the condition. Although ASD consideration can relate to a broad range of conditions, the diagnostic deficits put in place by the American Psychiatric Association are the criteria used for this review. With better support systems in place and inclusive learning being a common theme in elementary and high schools, adult ASD students are moving into post-secondary education at an increased rate. Colleges and universities are well-versed in disability services, however, ASD students require a specialized kind of support as many of these students display a variety of deficits related not only to the classroom, but the college environment as a whole. In this review, five methods of post-secondary support are reviewed; mentoring, transition programs, assistive technology, coaching, and universal design. These support methods are then compared with the diagnostic coding for ASD to provide recommendations of best practices. Although each method has shown positive feedback from ASD students, none fully meet all of the diagnostic criteria and it is likely that the best option of support would be a combination of methods
The Nature of the Association Between Food Allergy and Anxiety in Children and Their Parents
Get PDFFood allergy is an adverse reaction to the ingestion of certain food items such as nuts, eggs, and cow’s milk. It affects all age groups but is particularly worrisome in children who may have severe or even fatal reactions. The incidence of food allergies is estimated to be approximately 4-6% among children up to 18 years of age in the US. The swiftness of onset of food allergies, the fact that they may be triggered by the ingestion of commonly available food substances, and the difficulty of diagnosis and selection of preventive medication often causes high levels of stress and anxiety among children and their parents or caregivers. Parents are often faced with challenges related to the food that is accessible to their children as well as with the quality of medical attention that is readily available if an allergic reaction does occur. A systematic review was performed to investigate the relative lack of information that children suffering from food allergies and their parents encounter, allowing for a broad set of recommendations to dispel some doubts, lower anxiety levels, and improve quality of life for the entire family unit. Online databases were searched to create a list of articles which investigated issues related to quality of life and association between food allergy and anxiety among children and their parents. One of the effective strategies for coping with food challenges was found to be the oral food challenge, in which patients are requested to intentionally ingest suspected allergens. During oral food challenges, patients are continuously clinically monitored for an onset of reactions, which is the protocol required to establish a definitive diagnosis of food allergy. It was also found that food allergy impacts the quality of life of caregivers, although pediatric allergist specialty care may lower the degree of this impairment to an extent through correct diagnosis and appropriate counseling
Sequential Evolutionary Operations of Trigonometric Simplex Designs for High-Dimensional Unconstrained Optimization Applications
No full textThis dissertation proposes a novel mathematical model for the Amoeba or the Nelder-Mead simplex optimization (NM) algorithm. The proposed Hassan NM (HNM) algorithm allows components of the reflected vertex to adapt to different operations, by breaking down the complex structure of the simplex into multiple triangular simplexes that work sequentially to optimize the individual components of mathematical functions. When the next formed simplex is characterized by different operations, it gives the simplex similar reflections to that of the NM algorithm, but with rotation through an angle determined by the collection of nonisometric features. As a consequence, the generating sequence of triangular simplexes is guaranteed that not only they have different shapes, but also they have different directions, to search the complex landscape of mathematical problems and to perform better performance than the traditional hyperplanes simplex. To test reliability, efficiency, and robustness, the proposed algorithm is examined on three areas of large-scale optimization categories: systems of nonlinear equations, nonlinear least squares, and unconstrained minimization. The experimental results confirmed that the new algorithm delivered better performance than the traditional NM algorithm, represented by a famous Matlab function, known as fminsearch. In addition, the new trigonometric simplex design provides a platform for further development of reliable and robust sparse autoencoder software (SAE) for intrusion detection system (IDS) applications. The proposed error function for the SAE is designed to make a trade-off between the latent state representation for more mature features and network regularization by applying the sparsity constraint in the output layer of the proposed SAE network. In addition, the hyperparameters of the SAE are tuned based on the HNM algorithm and were proved to give a better capability of extracting features in comparison with the existing developed algorithms. In fact, the proposed SAE can be used for not only network intrusion detection systems, but also other applications pertaining to deep learning, feature extraction, and pattern analysis. Results from experimental tests showed that the different layers of the enhanced SAE could efficiently adapt to various levels of learning hierarchy. Finally, additional tests demonstrated that the proposed IDS architecture could provide a more compact and effective immunity system for different types of network attacks with a significant detection accuracy of 99.63% and an F-measure of 0.996, on average, when penalizing sparsity constraint directly on the synaptic weights within the network
Fasting and Prayer: Can It Help in the Resolution of Modern Diseases of Culture?
No full textDiseases of modern civilization represent the single largest driver of morbidity and mortality in the Western world. The predominant medical paradigm examines the health of the body nearly exclusively in terms of physiological and mental health, with a particular focus on physical and biochemical associations. This potential bias minimizes the role of the spiritual dimension of health. While there has been movement towards the investigation of Far Eastern practices, the extant literature from a religious perspective is wanting, despite 2000 years of theory and praxis. A literature review was performed examining the Orthodox Christian ascetic practices of fasting and hesychastic prayer, respecting the teleological and empirical evidence for their potential roles in benefiting human health. Additionally, a comparative analysis was conducted to provide further clinical validation from ostensibly similar practices of other traditions, e.g., Islamic fasting, secular intermittent fasting, yoga, meditation, and mindfulness-based stress reduction therapy. This evaluation demonstrated that a sufficient hypothetical framework exists for further exploration of these practices. Consequentially, a case series protocol was proposed for future investigation into the potential of Orthodox Christian asceticism in the resolution of the diseases of modern civilization
The Treatment of Binge-Eating Disorder
No full textBinge Eating Disorder (BED) is a serious health issue causing psychological and emotional distress, binge eating episodes, depression, body image issues, weight loss or weight gain, gastrointestinal issues, and social and relationship disturbances. BED is treatable using the three cornerstone methods that must be implemented long-term to recover. The three cornerstone treatments are psychotherapy, pharmaceutical therapy, and nutrition meal plans. There are other natural agents and therapies that can be used together with the three cornerstone treatments which are supplements, natural agents, meditation, acupuncture, and physical activity. The purpose of this dissertation was to determine which psychotherapies and pharmaceutical drugs work best in the recovery of BED. Psychotherapies were compared in this dissertation and it was found that Cognitive Behavioral Therapy (CBT) was shown to be the most effective in the recovery of BED. CBT was compared to Interpersonal Therapy (IPT) and Behavioral Weight Loss Therapy (BWL) {P<.05; odds ratios: BWL vs. CBT, 2.3; BWL vs. IPT, 2.6; and CBT vs. IPT, 1.2}. The medications presented in this dissertation showed mixed results in the recovery of BED. Nutrition meal plans are recommended to all BED patients, however, there is no consensus on an optimal specific dietary plan for BED. The most effective nutrition plans appear to be foods that do not trigger cravings or spike blood sugar levels. The trial outcomes were methodically segmented targeting high-risk populations that meet the criteria for BED. The most used outcome or assessment methods in the researched trials for psychotherapies were the Eating Disorders Examination Score and questionnaires. The most used outcome or assessment methods in the researched trials for medications were Clinical Global Impressions and the Hamilton Rating Scale for Depression
Can Private Equity Investments Succeed Without Innovation? Strategic Decision Guidelines To Enhance Business Acquisition Performance
No full textThere is sufficient evidence that many private equity (PE) leveraged buyouts (LBOs) fail financially and during this process, destroy significant value for their investors, employees, partners, suppliers, customers, and the economy in the communities in which these businesses operate. PE firms acquire companies and manage them for a short time-period, typically under 7 years, for the purpose of selling them at a higher price and with a high return on investment (ROI). The capital structure of such an acquisition is shaped as an LBO via an investment mix of 20-30 percent in equity and 70-80 percent in debt, which forces the LBO to assume a large debt. During the PE-LBO ownership period, PE partners focus on improving the LBO performance and on increasing its valuation with the objective of exiting the investment with large ROIs for their investors. To facilitate their objectives, the PE firm aligns the LBO’s leadership with its goals through changes in the leadership team and creative incentive plans. This research study investigates the influence of PE on innovation in PE LBOs, exploring factors that impact innovation and linking them to reasons for acquisition failures. Primarily, factors studied herein explore hypotheses about the effects of short-term ownership, management restructuring, management incentive plans, and debt size on new product development and innovation. Through the implementation of case study research utilizing surveys of LBO executives, this study uncovers challenges and opportunities that impact PE LBO acquisitions and gleans insight into potential mechanisms for successful financial outcomes. As a result, this study details a rigorous, strategic, and systematic platform that highlights three PE engagement phases with an LBO company. These phases include the acquisition phase, the planning phase, and the execution phase. This platform facilitates decision making and provides guidelines and recommendations to help increase leadership focus on innovation and enhance the success rate of related investments and the future success of LBOs. Notably, these guidelines are also applicable to the broader merger and acquisition (M&A) market
A Program Evaluation of an English Learner Program in a Southwestern Connecticut Urban School District
No full textThe need for academic supports for English Learners enrolled in public schools has increased significantly in the last decade. Accountability for success in school expands yearly and can impact crucial components such as staffing and funding. The purpose of this study was to evaluate the implementation of an English Learner Program designed to support students in one school district. This study utilized a mixed method program evaluation design to determine the impact of an English Learner Program on the teaching and learning taking place in a southwestern Connecticut urban school district.
The purpose of this research was to examine the impact of the English Learner Program after all the components were initially implemented. The components, based on the Second Language Acquisition theory, included professional development for all staff in the district, intervention for English Learners based on language acquisition, and progress monitoring of student performance. Data collected detailed the impact that the English Learner Program had on the progression of instruction for English Learners and their academic achievement. Both qualitative and quantitative data was collected and analyzed from numerous sources within the district including teachers and students. The findings concluded that the simultaneous implementation of the theory driven components of the program impacted teachers to deliver effective instruction for English Learners, and caused the growth in Language Assessment Scale scores for elementary English Learner students between 2018 and 2019. The results provided the district with specific information about the current impact and future implementation of the English Learner Program