Development of iSpeak: A voice activated Relationship Management System

A constant source of frustration for subscribers of mobile telephony in Nigeria is the quality of customer care service. The ubiquitous IVR systems deployed by service providers often ends in long and winding texting of digits that terminate in calls to agents with poor CRM attitudes. Automation of most of the functions of the human agent goes a long way in mitigating this problem. This paper describes iSpeak – a system designed to reduce the human–to–human (H2H) interaction in the complaint-lodging and solution provision process to a minimal level where it is not possible to eradicate it totally by a replacement with human–to–system (H2S) interactivity. iSpeak has an inherent capacity for improving the efficiency and drastically cutting CRM cost of corporate organizations. This comes with the attendant advantage of improved business-customer relationship. Keywords – Automatic Speech Recognition, Customer Care Service, Speech-control, Customer Voice Model, Voice Print, Voice Recognition

UA66/8/3 Annual Report

Annual report created by and about WKU Geography & Geology

Orange Value Fund Research Reports and Valuations

Academic finance courses teach the underlying principles of portfolio management. Diversify your portfolio to eliminate idiosyncratic risk, match your portfolio beta to the risk appetite of your investors, invest in companies exhibiting earnings per share growth, and your fund will beat the market. More or less, this is the mindset of the herd, but following the herd never gets you ahead. Warren Buffet “attempts to be fearful when others are greedy and to be greedy only when others are fearful.” True investors ignore the movements in the market, and focus their energies on discovering unrecognized intrinsic value. As such, value investing hinges on the belief that markets are inefficient because inefficient markets result in short term prices unrepresentative of intrinsic value, thus presenting investment opportunities. I was introduced to the principles of value investing through my participation in the Orange Value Fund, a value investing hedge fund administered through the Whitman School of Management. Fernando Diz, an associate professor at the business school, is the managing director of the fund. The fund models its investment approach after Martin J. Whitman’s investment philosophy. The philosophy is simple: buy “safe” and “cheap” investments, but the practice is difficult. To be considered “safe,” a company must pass a rigorous examination of its industry, competition, top management, directors, and capital structure. To be considered “cheap,” the security must be trading at a significant discount to its estimated intrinsic value. My Capstone Project is a compilation of five research reports on the following companies: the Bristow Group, EnCana Corporation (now EnCana and Cenovus Energy), Ship Finance International Limited, Google, and GameStop Corporation

Deep Attentive Survival Analysis in Limit Order Books: Estimating Fill Probabilities with Convolutional-Transformers

One of the key decisions in execution strategies is the choice between a passive (liquidity providing) or an aggressive (liquidity taking) order to execute a trade in a limit order book (LOB). Essential to this choice is the fill probability of a passive limit order placed in the LOB. This paper proposes a deep learning method to estimate the filltimes of limit orders posted in different levels of the LOB. We develop a novel model for survival analysis that maps time-varying features of the LOB to the distribution of filltimes of limit orders. Our method is based on a convolutional-Transformer encoder and a monotonic neural network decoder. We use proper scoring rules to compare our method with other approaches in survival analysis, and perform an interpretability analysis to understand the informativeness of features used to compute fill probabilities. Our method significantly outperforms those typically used in survival analysis literature. Finally, we carry out a statistical analysis of the fill probability of orders placed in the order book (e.g., within the bid-ask spread) for assets with different queue dynamics and trading activity

Automatic speech recognition for European Portuguese

Dissertação de mestrado em Informatics EngineeringThe process of Automatic Speech Recognition (ASR) opens doors to a vast amount of possible improvements in customer experience. The use of this type of technology has increased significantly in recent years, this change being the result of the recent evolution in ASR systems. The opportunities to use ASR are vast, covering several areas, such as medical, industrial, business, among others. We must emphasize the use of these voice recognition systems in telecommunications companies, namely, in the automation of consumer assistance operators, allowing the service to be routed to specialized operators automatically through the detection of matters to be dealt with through recognition of the spoken utterances. In recent years, we have seen big technological breakthrough in ASR, achieving unprecedented accuracy results that are comparable to humans. We are also seeing a move from what is known as the Traditional approach of ASR systems, based on Hidden Markov Models (HMM), to the newer End-to-End ASR systems that obtain benefits from the use of deep neural networks (DNNs), large amounts of data and process parallelization. The literature review showed us that the focus of this previous work was almost exclusively for the English and Chinese languages, with little effort being made in the development of other languages, as it is the case with Portuguese. In the research carried out, we did not find a model for the European Portuguese (EP) dialect that is freely available for general use. Focused on this problem, this work describes the development of a End-to-End ASR system for EP. To achieve this goal, a set of procedures was followed that allowed us to present the concepts, characteristics and all the steps inherent to the construction of these types of systems. Furthermore, since the transcribed speech needed to accomplish our goal is very limited for EP, we also describe the process of collecting and formatting data from a variety of different sources, most of them freely available to the public. To further try and improve our results, a variety of different data augmentation techniques were implemented and tested. The obtained models are based on a PyTorch implementation of the Deep Speech 2 model. Our best model achieved an Word Error Rate (WER) of 40.5%, in our main test corpus, achieving slightly better results to those obtained by commercial systems on the same data. Around 150 hours of transcribed EP was collected, so that it can be used to train other ASR systems or models in different areas of investigation. We gathered a series of interesting results on the use of different batch size values as well as the improvements provided by the use of a large variety of data augmentation techniques. Nevertheless, the ASR theme is vast and there is still a variety of different methods and interesting concepts that we could research in order to seek an improvement of the achieved results.O processo de Reconhecimento Automático de Fala (ASR) abre portas para uma grande quantidade de melhorias possíveis na experiência do cliente. A utilização deste tipo de tecnologia tem aumentado significativamente nos últimos anos, sendo esta alteração o resultado da evolução recente dos sistemas ASR. As oportunidades de utilização do ASR são vastas, abrangendo diversas áreas, como médica, industrial, empresarial, entre outras. É de realçar que a utilização destes sistemas de reconhecimento de voz nas empresas de telecomunicações, nomeadamente, na automatização dos operadores de atendimento ao consumidor, permite o encaminhamento automático do serviço para operadores especializados através da detecção de assuntos a tratar através do reconhecimento de voz. Nos últimos anos, vimos um grande avanço tecnológico em ASR, alcançando resultados de precisão sem precedentes que são comparáveis aos atingidos por humanos. Por outro lado, vemos também uma mudança do que é conhecido como a abordagem tradicional, baseados em modelos de Markov ocultos (HMM), para sistemas mais recentes ponta-a-ponta que reúnem benefícios do uso de redes neurais profundas, em grandes quantidades de dados e da paralelização de processos. A revisão da literatura efetuada mostra que o foco do trabalho anterior foi quase que exclusivamente para as línguas inglesa e chinesa, com pouco esforço no desenvolvimento de outras línguas, como é o caso do português. Na pesquisa realizada, não encontramos um modelo para o dialeto português europeu (PE) que se encontre disponível gratuitamente para uso geral. Focado neste problema, este trabalho descreve o desenvolvimento de um sistema de ASR ponta-a-ponta para o PE. Para atingir este objetivo, foi seguido um conjunto de procedimentos que nos permitiram apresentar os conceitos, características e todas as etapas inerentes à construção destes tipos de sistemas. Além disso, como a fala transcrita necessária para cumprir o nosso objetivo é muito limitada para PE, também descrevemos o processo de coleta e formatação desses dados em uma variedade de fontes diferentes, a maioria delas disponíveis gratuitamente ao público. Para tentar melhorar os nossos resultados, uma variedade de diferentes técnicas de aumento de dados foram implementadas e testadas. Os modelos obtidos são baseados numa implementação PyTorch do modelo Deep Speech 2. O nosso melhor modelo obteve uma taxa de erro de palavras (WER) de 40,5% no nosso corpus de teste principal, obtendo resultados ligeiramente melhores do que aqueles obtidos por sistemas comerciais sobre os mesmos dados. Foram coletadas cerca de 150 horas de PE transcritas, que podem ser utilizadas para treinar outros sistemas ou modelos de ASR em diferentes áreas de investigação. Reunimos uma série de resultados interessantes sobre o uso de diferentes valores de batch size, bem como as melhorias fornecidas pelo uso de uma grande variedade de técnicas de data augmentation. O tema ASR é vasto e ainda existe uma grande variedade de métodos diferentes e conceitos interessantes que podemos investigar para melhorar os resultados alcançados

Data-driven methods for simulation and forecasting of financial time series

This thesis develops data-driven methods for the simulation and forecasting of financial time series. The contributions are structured into four main components. In the first part, we propose Tail-GAN, a novel nonparametric approach that combines a Generative Adversarial Network (GAN) with the joint elicitability property of Value-at-Risk (VaR) and Expected Shortfall (ES) for learning to simulate price scenarios that preserve tail risk features for a set of benchmark trading strategies. In the second part, we investigate the impact of order flow imbalance (OFI) on price movements in equity markets in a multi-asset setting. Our results show that, once the information from multiple levels is integrated into the OFI, multi-asset models with cross-impact do not provide additional explanatory power for contemporaneous impact compared to a sparse model without the cross-impact terms. We show however that cross-asset OFIs do improve the forecasting of future returns. In the third part, we apply machine learning models to forecast intraday realized volatility (RV), by exploiting commonality in intraday volatility by pooling stocks together, and by incorporating a proxy for market volatility. Neural networks dominate linear regression and tree-based models in terms of performance, and remain robust and competitive on unseen stocks not included in the training set, thus providing new empirical evidence for a universal volatility mechanism among stocks. We also propose a new approach to forecasting one-day-ahead RVs using past intraday RVs as predictors, and expose interesting time-of-day effects that aid the forecasting mechanism. In the last part, we develop a method for forecasting the realized covariance matrix of asset returns in the U.S. equity market by exploiting the predictive information of graphs in volatility and correlation. Specifically, we augment the Heterogeneous Autoregressive (HAR) model via neighborhood aggregation on these graphs. The results generally suggest that the augmented model incorporating graph information yields both statistically and economically significant improvements for out-of-sample performance over the traditional models

Recent Advances in Stock Market Prediction Using Text Mining: A Survey

Market prediction offers great profit avenues and is a fundamental stimulus for most researchers in this area. To predict the market, most researchers use either technical or fundamental analysis. Technical analysis focuses on analyzing the direction of prices to predict future prices, while fundamental analysis depends on analyzing unstructured textual information like financial news and earning reports. More and more valuable market information has now become publicly available online. This draws a picture of the significance of text mining strategies to extract significant information to analyze market behavior. While many papers reviewed the prediction techniques based on technical analysis methods, the papers that concentrate on the use of text mining methods were scarce. In contrast to the other current review articles that concentrate on discussing many methods used for forecasting the stock market, this study aims to compare many machine learning (ML) and deep learning (DL) methods used for sentiment analysis to find which method could be more effective in prediction and for which types and amount of data. The study also clarifies the recent research findings and its potential future directions by giving a detailed analysis of the textual data processing and future research opportunity for each reviewed study

Applications of Deep Neural Networks

Deep learning is a group of exciting new technologies for neural networks. Through a combination of advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output. Deep learning allows a neural network to learn hierarchies of information in a way that is like the function of the human brain. This course will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN), and reinforcement learning. Application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation will be covered. High-Performance Computing (HPC) aspects will demonstrate how deep learning can be leveraged both on graphical processing units (GPUs), as well as grids. Focus is primarily upon the application of deep learning to problems, with some introduction to mathematical foundations. Readers will use the Python programming language to implement deep learning using Google TensorFlow and Keras. It is not necessary to know Python prior to this book; however, familiarity with at least one programming language is assumed