On Some Test Statistics for Testing the Population Skewness and Kurtosis: An Empirical Study

The purpose of this thesis is to propose some test statistics for testing the skewness and kurtosis parameters of a distribution, not limited to a normal distribution. Since a theoretical comparison is not possible, a simulation study has been conducted to compare the performance of the test statistics. We have compared both parametric methods (classical method with normality assumption) and non-parametric methods (bootstrap in Bias Corrected Standard Method, Efron’s Percentile Method, Hall’s Percentile Method and Bias Corrected Percentile Method). Our simulation results for testing the skewness parameter indicate that the power of the tests differs significantly across sample sizes, the choice of alternative hypotheses and methods we chose. For testing the kurtosis parameter, the simulation results suggested that the classical method performs well when the data are from both normal and beta distributions and bootstrap methods are useful for uniform distribution especially when the sample size is large

On Some Statistics for Testing the Skewness in a Population: An Empirical Study

The purpose of this paper is to propose some test statistics for testing the skewness parameter of a distribution, not limited to a normal distribution. Since a theoretical comparison is not possible, a simulation study has been conducted to compare the performance of the test statistics. We have compared both parametric methods (classical method with normality assumption) and non-parametric methods (bootstrap in Bias Corrected Standard Method, Efron’s Percentile Method, Hall’s Percentile Method and Bias Corrected Percentile Method). Our simulation results indicate that the power of the tests differ significantly across sample sizes, the choice of alternative hypotheses and methods one choose. When the data are generated from a normal distribution, both classical method and Efron’s Percentile Method can attain a nominal size of 0.05, while other bootstrap methods cannot. However, for a skewed distribution, bootstrap methods show higher power with larger sample sizes whereas the classical method only performs well when the sample size is small

ImprovChat: An AI-enabled Dialogue Assistant Chatbot for English Language Learners (ELL)

This thesis asks, how can an AI-enabled Dialogue Assistant in the form of a chatbot suggest new and unexpected forms of response to English Language Learners? The thesis draws upon theories of improvisation and play, alongside computer science to facilitate text-based conversation in English for non-native speakers. ImprovChat is a digital solution for communication issues that I encounter every day. My experience with improvisational theatre also informs the ImprovChat project. For my thesis research-creation, I developed the web application by implementing API powered by machine learning to generate AI phrases. The phrase generation model is pre-trained using the Twitter feeds created by people who speak English as a first language. ImprovChat dialogue assistant will provide phrase suggestions; these could potentially be used in online chatting scenarios where the participants have two different native languages. It can also be used to inspire unexpected and playful forms of response

What is fair enough? Reconciling complementors’ needs for fairness management on digital platforms

Digital platforms (DPs) provide individuals with alternative opportunities for earning incomes, attracting skyrocketing individuals to work as DP complementors. This gives rise to the superior power of DP owners by which complementors can be treated unfairly while they are vulnerable to seeking redress as they are legally autonomous from the underlying DPs. Unfair DP treatment can threaten complementors’ survival and be detrimental to the DP’s long-term development. Yet, there is a lack of a holistic understanding of the fairness perceived by DP complementors and their fairness needs can be addressed by DP owners. To address these gaps, we conducted a case study of complementors on content platforms (i.e., content creators) by accounting for their perceptions of DP fairness. Our study contributes to the DP fairness literature by 1) generating a holistic understanding of the DP fairness perceived by complementors, and 2) developing fairness-addressing actions that can be adopted by DPs

An Experimental Study on Attribute Validity of Code Quality Evaluation Model

Regarding the practicality of the quality evaluation model, the lack of quantitative experimental evaluation affects the effective use of the quality model, and also a lack of effective guidance for choosing the model. Aiming at this problem, based on the sensitivity of the quality evaluation model to code defects, a machine learning-based quality evaluation attribute validity verification method is proposed. This method conducts comparative experiments by controlling variables. First, extract the basic metric elements; then, convert them into quality attributes of the software; finally, to verify the quality evaluation model and the effectiveness of medium quality attributes, this paper compares machine learning methods based on quality attributes with those based on text features, and conducts experimental evaluation in two data sets. The result shows that the effectiveness of quality attributes under control variables is better, and leads by 15% in AdaBoostClassifier; when the text feature extraction method is increased to 50 - 150 dimensions, the performance of the text feature in the four machine learning algorithms overtakes the quality attributes; but when the peak is reached, quality attributes are more stable. This also provides a direction for the optimization of the quality model and the use of quality assessment in different situations

LOWA: Localize Objects in the Wild with Attributes

We present LOWA, a novel method for localizing objects with attributes effectively in the wild. It aims to address the insufficiency of current open-vocabulary object detectors, which are limited by the lack of instance-level attribute classification and rare class names. To train LOWA, we propose a hybrid vision-language training strategy to learn object detection and recognition with class names as well as attribute information. With LOWA, users can not only detect objects with class names, but also able to localize objects by attributes. LOWA is built on top of a two-tower vision-language architecture and consists of a standard vision transformer as the image encoder and a similar transformer as the text encoder. To learn the alignment between visual and text inputs at the instance level, we train LOWA with three training steps: object-level training, attribute-aware learning, and free-text joint training of objects and attributes. This hybrid training strategy first ensures correct object detection, then incorporates instance-level attribute information, and finally balances the object class and attribute sensitivity. We evaluate our model performance of attribute classification and attribute localization on the Open-Vocabulary Attribute Detection (OVAD) benchmark and the Visual Attributes in the Wild (VAW) dataset, and experiments indicate strong zero-shot performance. Ablation studies additionally demonstrate the effectiveness of each training step of our approach

FemtoDet: An Object Detection Baseline for Energy Versus Performance Tradeoffs

Efficient detectors for edge devices are often optimized for parameters or speed count metrics, which remain in weak correlation with the energy of detectors. However, some vision applications of convolutional neural networks, such as always-on surveillance cameras, are critical for energy constraints. This paper aims to serve as a baseline by designing detectors to reach tradeoffs between energy and performance from two perspectives: 1) We extensively analyze various CNNs to identify low-energy architectures, including selecting activation functions, convolutions operators, and feature fusion structures on necks. These underappreciated details in past work seriously affect the energy consumption of detectors; 2) To break through the dilemmatic energy-performance problem, we propose a balanced detector driven by energy using discovered low-energy components named \textit{FemtoDet}. In addition to the novel construction, we improve FemtoDet by considering convolutions and training strategy optimizations. Specifically, we develop a new instance boundary enhancement (IBE) module for convolution optimization to overcome the contradiction between the limited capacity of CNNs and detection tasks in diverse spatial representations, and propose a recursive warm-restart (RecWR) for optimizing training strategy to escape the sub-optimization of light-weight detectors by considering the data shift produced in popular augmentations. As a result, FemtoDet with only 68.77k parameters achieves a competitive score of 46.3 AP50 on PASCAL VOC and 1.11 W $\&$ 64.47 FPS on Qualcomm Snapdragon 865 CPU platforms. Extensive experiments on COCO and TJU-DHD datasets indicate that the proposed method achieves competitive results in diverse scenes.Comment: ICCV 202

Application of 25 MHz B-Scan Ultrasonography to Determine the Integrity of the Posterior Capsule in Posterior Polar Cataract

Purpose. To report the application of 25 MHz B-scan ultrasonography (MHzB) to determine the integrity of the posterior capsule (PC) in posterior polar cataract (PPC). Methods. Patients with whom PPC was clinically diagnosed using slit lamp microscopy who underwent 25 MHzB before phacoemulsification were retrospectively reviewed. The status of the PC was determined by 25 MHzB before phacoemulsification and confirmed during cataract surgery. Results. In total, 21 eyes in 14 clinically diagnosed PPC patients were enrolled in this study. Out of 25 MHzB images, 19 PCs were found to be intact, while 2 showed dehiscence before cataract surgery. During phacoemulsification, 17 PCs were observed to be intact, while 4 PCs showed posterior capsule rupture (PCR). These 4 PCR cases included the above 2 eyes, in which preexisting dehiscence was detected by 25 MHzB. The other 2 PCR cases showed high reflectivity between high echoes in posterior opacities and the PC, indicating synechia between the PPC and PC. Conclusion. This is the first report to show that 25 MHzB can be used to clearly visualize the status of the PC in PPC. These results, in turn, could be used to select the appropriate treatment and to thereby avoid further complications during PPC surgery

Geometrical and electrical modulation on the transport property of silicene constrictions

We study the electrical modulation of the transport properties of silicene constrictions with different geometrical structures by adopting the tight-binding model and non-equilibrium Green's function method. The band structure and transmission properties are discussed under the influence of the external electric field and potential energy. Especially, we investigate the effects of the position and width of the central scattering region on the conductance with increasing of Fermi energy. We find that the conductance significantly depends on the position and the width. Interestingly, the symmetrical structure of the central region can induce a resonance effect and significantly enlarge the system's conductance. Obviously, we obtain an effective method to adjust the transport property of the silicene heterojunctions. Correspondingly, we propose a novel two-channel structure with an excellent performance on the conductance compared to the one-channel structure with the same total width.Comment: 7 pages, 8 figure

Perspectives on Privacy in the Post-Roe Era: A Mixed-Methods of Machine Learning and Qualitative Analyses of Tweets

Abortion is a controversial topic that has long been debated in the US. With the recent Supreme Court decision to overturn Roe v. Wade, access to safe and legal reproductive care is once again in the national spotlight. A key issue central to this debate is patient privacy, as in the post-HITECH Act era it has become easier for medical records to be electronically accessed and shared. This study analyzed a large Twitter dataset from May to December 2022 to examine the public's reactions to Roe v. Wade's overruling and its implications for privacy. Using a mixed-methods approach consisting of computational and qualitative content analysis, we found a wide range of concerns voiced from the confidentiality of patient-physician information exchange to medical records being shared without patient consent. These findings may inform policy making and healthcare industry practices concerning medical privacy related to reproductive rights and women's health.Comment: Paper accepted for the proceedings of the 2023 American Medical Informatics Association Annual Symposium (AMIA