Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Drone Detection and Acoustic Scene Classification with Deep Learning

Vemula, Hari Charan. M.S., Department of computer science and Engineering, Wright State University, 2018. Multiple Drone Detection and Acoustic Scene Classification with Deep Learning. Classification of environmental scenes and detection of events in one’s environment from audio signals enables one to create better-planning agents, intelligent navigation systems, pattern recognition systems, and audio surveillance systems. This thesis will explore the use of Convolutional Neural Networks(CNN’S) with spectrograms and raw audio waveforms as inputs to Deep Neural Networks with hand engineered features extracted from large-scale feature extraction schemes to identify the acoustic scenes and events. The first part focuses on building an audio pattern recognition system capable of detecting the if there are zero, one, or two DJI phantoms in the scene within the range of a stereo microphone. The ability to distinguish the presence multiple UAV’s could be used to augment information from other sensors less capable of making such determinations. The second part of the thesis focuses on building an acoustic scene detector to Task 1a in the DCASE2018 challenge(http://dcase.community/challenge2018/index). In both cases, this document will explain the pre-processing techniques, CNN and DNN architectures used, data augmentation methods including the use of Generative Adversarial Networks(GAN’s), and performance results compared to existing benchmarks when available. This thesis will conclude with a discussion of how one might expand the techniques in the construction of commercial off the shelf audio scene classifier for multiple UAV detections.
Chemoprevention of Oral Squamous Cell Carcinoma: Optimizing Efficacy with Personalized Local Drug Delivery Strategies

ii Dedication . vii Acknowledgements . viii Vita . ix List of Tables . xiv List of Figures . xv Chapter 1: Introduction to Oral Squamous Cell Carcinoma Chemoprevention . 1 1.1 Statement of the Problem . 1 1.2 Premalignant Lesions of OSCC – Managing Progression . 2 1.3 Therapeutic Targets for OSCC Chemoprevention . 4 1.3.1 Chemoprevention Targets within Keratinocyte Differentiation Pathways . 5 1.3.2 Adjunctive Chemopreventive Targets – Blocking Invasive Capacity and Angiogenesis . 7 1.4 Personalizing OSCC Chemoprevention . 10 1.5 References . 11 Chapter 2: Optimizing Therapeutic Efficacy of Chemopreventive Agents: A Criticial Review of Delivery Strategies in Oral Cancer . 14 2.1 Abstract . 14 2.2 Introduction . 15 2.3 Oral Cancer Chemoprevention Clinical Trials: Systemic Delivery . 18 2.4 Oral Cancer Chemoprevention Clinical Trials: Local Delivery . 25 2.5 Discussion . 36 xii 2.6 References . 38 Chapter 3: Nanoparticles for Local Drug Delivery to the Oral Mucosa: Proof of Principle Studies . 46 3.1 Abstract . 46 3.2 Introduction . 47 3.3 Materials and Methods . 50 3.4 Results . 59 3.5 Discussion . 69 3.6 References . 76 Chapter 4: Evaluation of a Mucoadhesive Fenretinide Patch for Local Intraoral Delivery: A Strategy to Re-introduce Fenretinide for Oral Cancer Chemoprevention . 82 4.1 Abstract . 82 4.2 Introduction . 83 4.3 Materials and Methods . 85 4.4 Results . 94 4.5 Discussion . 101 4.6 References . 108 Chapter 5: A Personalized Approach to the Management of Oral Squamous Cell Carcinoma . 111 5.1 Abstract . 111 5.2 Introduction . 112 5.3 Materials and Methods . 114 5.4 Results . 118 5.5 Discussion . 124 5.6 References . 126 Chapter 6: Executive Summary and Future Research Directions . 127 6.1 Nanoparticles for Local Drug Delivery to the Oral Mucosa: Proof of Principle Studies . 127 6.2 Evaluation of a Mucoadhesive Fenretinide Patch for Local Intraoral Delivery: A Strategy to Re-introduce Fenretinide for Oral Cancer Chemoprevention . 128 6.3 A Personalized Approach to the Management of Oral Squamous Cell Carcinoma . 129 6.4 Future Research Directions . 130 Bibliography . 132
Remote sensing and machine learning for tree detection and classification in forestry applications

Machine learning has been very actively used in analysis of remote sensing data and for a wide range of applications such as agriculture, land and ice surface change detection, etc. In this paper, we explore use of machine learning methods for forest inventory, since it has great impact on economic and ecological sustainability. Our approach is based on UAV data from hyperspectral and LiDAR sensors. A particular case described in the present paper showcases how aerial survey data achieves high classification accuracy of forest tree species. Moreover, applicability of different data types such as LiDAR and hyperspectral (NIR and VIS) for the task at hand is investigated. In the paper we present the solution in its full scope, starting from remote and in-situ data acquisition and pre-processing, to automatic tree species classification and recognition using machine learning algorithms, as well, as further analysis of the results. In the initial stage, during an expedition to the northern region of Arkhangelsk (Russia), airborne data (LiDAR and hyperspectral) and manually recording of trees locations and species were collected. Afterwards, an object-based classification has been performed by detecting individual tree crowns beforehand with the help of LiDAR data. Various hyperspectral features have been calculated for use in classification algorithms complemented by on-ground spectroscopic benchmark data. In this paper, we prove applicability of the proposed method and workflow in real life application. Results validation was done using data from the observation parcels, where trees were manually labeled. The next step is design of a system with finely tuned filters, which will make possible robust species classification at a cost much lower than hyperspectral imaging.
Comparative Review of Cross-Domain Generative Adversarial Networks

This paper provides the comparative analysis between two recent image-to-image translation models that based on Generative Adversarial Networks. The first one is UNIT which consists of coupled GANs and variational autoencoders (VAEs) with shared-latent space, and the second one is Star-GAN which contains a single GAN model. Given training data from two different domains from dataset CelebA, these two models learn translation task in two directions. The term domain denotes as a set of images sharing the same attribute value. So, the attributes that are prepared: eye glasses, blond hair, beard, smiling and age. Five UNIT models are trained separately, while only one Star-GAN model is trained. For evaluation, we conduct some experiments and provide a quantitative comparison using direct metric GAM (Generative Adversarial Metric) to quantify the ability of generalization and the ability of generating photorealistic photos. The experimental results show the superiority of cross-model UNIT over multi-model StarGAN on generating age and eye glasses attributes, and the equivalent performance to synthesize other attributes.
The Effects of Green Vegetable Juice (Angelica Keiskei) Supplementation on Plasma Lipids and Antioxidant Status in Smokers

It has been suggested that green juice supplementation may have some health promoting benefits. We evaluated the effects of green juice (Angelica keiskei) consumption on parameters of lipid profiles and plasma antioxidant status in healthy male smokers. Fifty-four smokers were supplemented with 300 ml of green juice for 6 weeks while maintaining their normal diet. Blood samples were collected on week 0 and week 6 in order to evaluate plasma lipid profiles (total cholesterol, triglyceride, LDL-cholesterol, HDL-cholesterol) , plasma antioxidant vitamin levels (ascorbic acid, -tocopherol, -tocopherol, -carotene, -carotene, cryptoxanthin and lycopene) , the degree of LDL oxidation and GOT, GPT levels for liver function. Plasma ascorbic acid level remained at the same level. However, -tocopherol and -tocopherol normalized by total cholesterol (p -carotene (p
Impact of Education Based on Health Belief Model in Isfahanese Housewives in Preventing H. pylori infection

Background & Objective: The educational programs and informing people related to the prevention of diseases is neces-sary. Due to the prevalence of Helicobacter pylori )H. Pylori( infection and its association with gastric cancer, this study was carried out to determine the effect of educational interference based on Health Belief Model on health behaviors related to H. Pylori prevention among house wives. Materials & Methods: In this semi-experimental study, 84 housewives of almost similar demographic characteristics were randomly categorized into two groups )case and control(. Before starting any educational program, the questionnaires were completed by these two groups. The case group received the considered interference )4 educational sessions(. The partici-pants in both groups completed the questionnaires 2 months after the intervention. Data was analyzed using SPSS18 by independent-t, paired T test and Mann Whitney statistical tests. Results: After the intervention of educational programs a significant difference (P<0.001) between the two groups with regards to attitude, knowledge, distances health belief model and practice was shown. Conclusion: Education based on health belief model, increase the knowledge and improve the attitude and practice of housewives about Health Behaviors Prevention of Helicobacter infection.
GCC-PHAT Cross-Correlation Audio Features for Simultaneous Sound Event Localization and Detection (SELD) on Multiple Rooms

In this work, we show a simultaneous sound event localization and detection (SELD) system, with enhanced acoustic features, in which we propose using the well-known Generalized Cross Correlation (GCC) PATH algorithm, to augment the magnitude and phase regular Fourier spectra features at each frame. GCC-PHAT has already been used for some time to calculate the Time Difference of Arrival (TDoA) in simultaneous audio signals, in moderately reverberant environments, using classic signal processing techniques, and can assist audio source localization in current deep learning machines. The neural net architecture we used is a Convolutional Recurrent Neural Network (CRNN), and is tested using the sound database prepared for the Task 3 of the 2019 DCASE Challenge. In the challenge results, our proposed system was able to achieve 20.8 of direction of arrival error, 85.6% frame recall, 86.5% F-score and 0.22 error rate detection in evaluation samples.
Semantically Aligned Sentence-Level Embeddings for Agent Autonomy and Natural Language Understanding

Semantically Aligned Sentence-Level Embeddings for Agent Autonomy and Natural Language Understanding Nancy Ellen Fulda Department of Computer Science, BYU Doctor of Philosophy Many applications of neural linguistic models rely on their use as pre-trained features for downstream tasks such as dialog modeling, machine translation, and question answering. This work presents an alternate paradigm: Rather than treating linguistic embeddings as input features, we treat them as common-sense knowledge repositories that can be queried using simple mathematical operations within the embedding space, without the need for additional training. Because current state-of-the-art embedding models were not optimized for this purpose, this work presents a novel embedding model designed and trained specifically for the purpose of “reasoning in the linguistic domain”. Our model jointly represents single words, multi-word phrases, and complex sentences in a unified embedding space. To facilitate common-sense reasoning beyond straightforward semantic associations, the embeddings produced by our model exhibit carefully curated properties including analogical coherence and polarity displacement. In other words, rather than training the model on a smorgaspord of tasks and hoping that the resulting embeddings will serve our purposes, we have instead crafted training tasks and placed constraints on the system that are explicitly designed to induce the properties we seek. The resulting embeddings perform competitively on the SemEval 2013 benchmark and outperform state-ofthe-art models on two key semantic discernment tasks introduced in Chapter 8. The ultimate goal of this research is to empower agents to reason about low-level behaviors in order to fulfill abstract natural language instructions in an autonomous fashion. An agent equipped with an embedding space of su cient caliber could potentially reason about new situations based on their similarity to past experience, facilitating knowledge transfer and one-shot learning. As our embedding model continues to improve, we hope to see these and other abilities become a reality.
Multimodal Biometric Recognition under Unconstrained Settings

In recent years, it has been recognized that the path forward, regarding biometrics, concerns the development of systems that work in an accurate and robust way, independent of the conditions under which input data is acquired. With growing difficulties in performing biometric recognition under these new challenging scenarios, the use of multiple biometric trait representations, is also gaining growing attention by the research community. By combining multiple sources of biometric information, increased performance and robustness is achieved, when compared with most traditional systems based on a single representation. In the present thesis, the idea of multimodal biometric recognition performed on data acquired under less controlled scenarios is explored. We start this analysis with a series of unimodal approaches based on face and periocular data. Both modalities are assessed for less than ideal acquisition scenarios, characterized by variable illumination and occlusion conditions, with the establishment of a recognition pipeline based on the universal background model (UBM) approach, with proven performance in the field of speaker verification. Furthermore, regarding face recognition, we propose a cognitively motivated hierarchical framework, based on the global-to-local flow of object recognition by the human brain. On the other hand, for periocular data, we explore usability in mobile devices, making use of transfer learning principles to achieve inter-operable biometric recognition across multiple devices. Face biometrics is further explored, from a multispectral point-of-view, using the novel Intel RealSense family of sensors to perform multimodal recognition in a series of alternative face image representations such as RGB, IR and depth. With the work presented throughout this thesis, a series of state-of-the-art results were achieved, in a wide array of application scenarios, with a considerable margin for progression still left by the constant evolution of both technology, research trends within the field of biometrics and its applicability in society nowadays and in the near future.
Detector of power quality disturbance signal with random phase offset

The wavelet- or wavelet packet-based power quality disturbance (PQD) signal detection is highly sensitive to random phase offset owing to their shift-variant characteristics, which has not yet been well discussed in the literature. In this letter, we newly define the wavelet packet transform modulus (WPTM), introduce the two WPTM metrics (MV and RMS) with an optimum factor k modifying conventionally defined universal threshold, and present a WPTM-based PQD detector using those metrics that is robust against severe channel conditions with random phase offset. Simulation results verify that the presented method greatly reduces false edge detection rate (FER) (< 10 %), while maintaining a high detection rate (DR), when compared to existing wavelet- or wavelet packet-based methods.
Skeletal muscle adaptations in cachectic, tumor-bearing rats

Skeletal muscle adaptations in cachectic, tumor-bearing rats Jeffrey S. Otis, Ph.D. Cancer cachexia is a debilitating, paraneoplastic syndrome commonly associated with late stage malignancy. It is estimated that ~25% of cancer-related deaths are due directly to complications arising from cachexia (Barton, 2001). Cachexia manifests as severe body wasting, primarily due to the loss of skeletal muscle mass. This study tested the hypothesis that muscle atrophy associated with cancer cachexia could be attenuated by using a unilateral, functional overload (FO) model applied concurrently with tumor development. To accomplish this, Morris hepatoma MH-7777 cells were implanted in adult female, Buffalo rats (n = 12) and allowed to incubate for 6 weeks. FO surgeries (n = 12) were performed five days prior to MH-7777 cell implantation. Over the course of six weeks, healthy, age, sex and strain-matched, vehicleinjected rats (n = 12) gained ~5% of body weight compared to tumor-bearing rats that lost ~6% of body weight when adjusted for tumor mass. Tumor-bearing animals experienced significant atrophy to gastrocnemius, tibialis anterior, extensor digitorum longus, plantaris and diaphragm muscles. FO successfully reversed plantaris muscle atrophy in cachectic, tumor-bearing rats (n=5). FO plantaris masses were ~24% larger than contralateral controls. However, this hypertrophic response was not as great as FO plantaris muscles from healthy, shamoperated controls (~44% larger than contralateral controls, n=5). FO plantaris muscles from tumor-bearing rats had ~1.5 fold increase in myonuclei/fiber ratios compared those of sham-operated, tumor-bearing controls (n = 6). Therefore, cancer cachexia did not prevent myonuclear accretion necessary for skeletal muscle hypertrophy.
Conditional Replicated Softmax for Topic Modelling with Metadata

CONDITIONAL REPLICATED SOFTMAX FOR TOPIC MODELLING WITH METADATA Charles Austria Advisors: University of Guelph, 2019 Dr. A. Ali Dr. T. Desmond Topic models are popular tools that model documents with the goal of extracting semantic topics from text. Documents often come with metadata such as authors, dates, or publication venues; however, current, state-of-the-art topic models do not incorporate metadata. This thesis introduces the conditional replicated softmax model, which is an undirected graphical model that models document word counts and document specific metadata using restricted Boltzmann machines. An additional input layer that is associated with the metadata is added to the replicated softmax model, thereby making the states of the hidden units conditional upon the metadata. This thesis compares the conditional replicated softmax model to other state-of-the-art topic models on the NIPS conference proceedings from 1987 to 1999. The learned topics appear richer and more interpretable relative to Dirichlet multinomial regression, but comparable to replicated softmax. Regardless, the added complexity of the new model was associated with higher test perplexity, which scores their ability to predict unseen documents from a test set, and higher penalized perplexity which penalizes perplexity for model complexity.
Creating a Virtual Reality Application with Game Elements for Teaching Geometry

This thesis has explored the potential benefits a Virtual Reality(VR) application with game elements might have for first-year high-school students learning geometry. First-year High-school students score an average of 2.4 on the exam for mathematics in 2017, and 3.0 for second-year students. This shows great potential for improvement. VR is effective for spatial visualization and interaction, which makes it a great tool for teaching geometry. The "design and creation" research method has been used for this project, which resulted in a VR learning application for geometry. The application is based on constructivism and pedagogical principles for learning geometry. Researchers do generally agree that constructivism is best suited for learning and teaching in VR. The application was evaluated in two phases. This was done by testing the application with high-school students, math teachers, an expert in didactics of mathematics and an expert in serious games. The findings suggest that a VR-application with game elements has the potential to help some students further their understanding of geometry through interactive figures and exploration. The results also indicate that VR can help increase the motivation and focus of students and that the use of game elements, such as environment, story, and puzzles, can further increase motivation. This study does also suggests important requirements to follow when creating a VR-application as an aid in teaching geometry. Further work should look more thoroughly into the learning outcome of the use of VR for learning geometry.
A Discrete Choice Experiment Application to School Choice

The objective of this paper is to analyse which school characteristics parents prefer in the Basque Country when choosing a primary school for their children, by carrying out a Discrete Choice Experiment applied to parental school choice. After conducting the experiment and gathering the answers, responses have been analysed using a Random Parameter Logit. Results indicate that, in stark contrast to other countries and regions, the most relevant school characteristics are the main language of instruction and religious orientation of the school. The main contribution of this work is the quantification, in monetary units, of the parental preferences regarding school characteristics in the Basque Country; as well as the first application of this methodology to School Choice in Spain.
An Evidence-Based Approach: The Emerging Role of Renal Protection in Hypertension; A Focus on Clinical and Economic Factors That Impact Formulary Decision Making

OBJECTIVE: To describe the new paradigm of evidence-based medicine (EBM) and the benefits of using EBM in making treatment decisions for individual patients. SUMMARY: Applying the knowledge gained from large clinical trials to patient care promotes consistency of treatment and optimal outcomes, helps establish national standards of patient care, and sets criteria to measure and reward performance-based medical practice. Implementing the principles of EBM, which rely on the rules of evidence and research, requires a commitment from medical schools, local health and medical licensing departments, physicians, pharmacists, professional associations, and managed care organizations. A review of results from landmark trials in hypertension, diabetic nephropathy, and end-stage renal disease describes the research for evidence-based therapies. A review of studies in the pharmacist’s expanding role in implementing evidence-based medicine shows the benefits of collaborative medical practices. CONCLUSION: Implementation of EBM in the managed care setting provides standards that have the potential to provide the best medical care at the lowest cost.