A novel Big Data analytics and intelligent technique to predict driver's intent

Modern age offers a great potential for automatically predicting the driver's intent through the increasing miniaturization of computing technologies, rapid advancements in communication technologies and continuous connectivity of heterogeneous smart objects. Inside the cabin and engine of modern cars, dedicated computer systems need to possess the ability to exploit the wealth of information generated by heterogeneous data sources with different contextual and conceptual representations. Processing and utilizing this diverse and voluminous data, involves many challenges concerning the design of the computational technique used to perform this task. In this paper, we investigate the various data sources available in the car and the surrounding environment, which can be utilized as inputs in order to predict driver's intent and behavior. As part of investigating these potential data sources, we conducted experiments on e-calendars for a large number of employees, and have reviewed a number of available geo referencing systems. Through the results of a statistical analysis and by computing location recognition accuracy results, we explored in detail the potential utilization of calendar location data to detect the driver's intentions. In order to exploit the numerous diverse data inputs available in modern vehicles, we investigate the suitability of different Computational Intelligence (CI) techniques, and propose a novel fuzzy computational modelling methodology. Finally, we outline the impact of applying advanced CI and Big Data analytics techniques in modern vehicles on the driver and society in general, and discuss ethical and legal issues arising from the deployment of intelligent self-learning cars

CLASSICAL LASSICAL AND BEHAVIOURAL FINANCE IN INVESTOR DECISION

Conceptual model of individual investor behavior presented in this paper aims to structure a part of the vast knowledge about investor behavior that is present in the finance field. The investment process could be seen as driven by dual mental processes (cognitive and affective) and the interplay between these systems contributes to bounded rational behavior manifested through various heuristics and biases. The investment decision is seen as a result of an interaction between the investor and the investment environmentinvestor behaviour; financial decisions making; cognitive modelling,;sentiments; market efficiency

차원 축소를 이용한 편향적 문맥에서의 단어 클러스터링

학위논문 (석사) -- 서울대학교 대학원 : 인문대학 언어학과, 2020. 8. 신효필.편향성(Bias)은 어떤 사물, 사람 혹은 그룹 등에서 한쪽에 불균형적으로 주어지는 가중치라고 정의할 수 있다. 최근에는 기계학습에서의 편향성 문제와, 자연언어처리에서 이러한 편향성을 완화하고자 하는 연구에 대한 관심이 늘고 있다. 본 연구의 목표는 언어에 존재하는 편향성을 확인하고 워드 임베딩에서 그 편향성이 어떻게 표현되고 있는지 살펴보는 것이다. 본 연구에서 사용하는 데이터는 Wikipedia Neutrality Corpus(WNC)이고 이에 대한 워드 임베딩으로는 Pryzant et al.(2019)의 편향성을 제거하는 모듈러 모델(modular model)을 이용하였다. 또한 K-means Clustering을 이용하여 편향성 정보를 포함한 v 벡터를 추가하기 전과 후의 워드 임베딩을 시각화하였고, 클러스터링(Clustering) 성능의 개선을 위해 주성분분석(Principal Component Analysis/PCA)을 사용하였다. 본 연구에서는 워드 임베딩에서 언어적 특징에 따라 클러스터링 되는 것과 같이 편향성을 갖는 단어들 역시 편향성의 유형(인식론적 편향성, 프레이밍에 따른 편향성, 인구학적 편향성 등)에 따라서 클러스터링 된다는 것을 확인할 수 있었다. 또한, 워드 임베딩이 모듈러 모델의 고유한 v 벡터와 결합할 경우 다양한 언어 정보를 포함하게 되므로, 이러한 연구는 편향성을 인식하고 제거하는 task뿐만 아니라 문맥(context) 정보를 이해하는 데에도 도움이 될 것이다.Bias can be defined as disproportionate weight in favor of or against one thing, person, or group compared with another. Recently, the issue of bias in machine learning and how to de-bias natural language processing has been a topic of increasing interest. This research examines bias in language, the effect of context on biased-judgements, and the clustering of biased- and neutral-judged words taken from biased contexts. The data for this study comes from the Wikipedia Neutrality Corpus (WNC) and its representation as word embeddings is from the bias neutralizing modular model by Pryzant et al. (2019). Visualization of the embeddings is done using K-means clustering to compare before and after the addition of the v vector, which holds bias information. Principal Component Analysis (PCA) is also used in an attempt to boost performance of clustering. This study finds that because the word embeddings cluster according linguistic features, the biased words also cluster according to bias type: epistemological bias, framing bias, and demographic bias. It also presents evidence that the word embeddings after being combined with the unique v vector from the modular model contain discrete linguistic information that helps not only in the task of detecting and neutralizing bias, but also recognizing context.1. Introduction 1 1.1. What is Bias? 1 1.2. De-biasing Techniques 7 1.3. Purpose and Significance of this Study 11 2. Background Information 13 2.1. Previous Research 13 2.2. Wikipedia Neutrality Corpus 18 2.3. Modular Model 20 2.4. Methodology 24 2.2.1. Clustering 25 2.2.2. Dimensionality Reduction Algorithm 28 3. Experiment 35 4. Results 43 4.1. Clustering of Entire Data Set 43 4.1.1. Most Frequently Biased-Judged Words 52 4.1.2. Cosine Similarity 58 4.2. Clustering of Small Random Sample 66 4.3. Significance of Results 69 5. Conclusion 71 References 73 Appendix 80 Abstract in Korean 83Maste

Domain-specific lexicon generation for emotion detection from text.

Emotions play a key role in effective and successful human communication. Text is popularly used on the internet and social media websites to express and share emotions, feelings and sentiments. However useful applications and services built to understand emotions from text are limited in effectiveness due to reliance on general purpose emotion lexicons that have static vocabulary and sentiment lexicons that can only interpret emotions coarsely. Thus emotion detection from text calls for methods and knowledge resources that can deal with challenges such as dynamic and informal vocabulary, domain-level variations in emotional expressions and other linguistic nuances. In this thesis we demonstrate how labelled (e.g. blogs, news headlines) and weakly-labelled (e.g. tweets) emotional documents can be harnessed to learn word-emotion lexicons that can account for dynamic and domain-specific emotional vocabulary. We model the characteristics of realworld emotional documents to propose a generative mixture model, which iteratively estimates the language models that best describe the emotional documents using expectation maximization (EM). The proposed mixture model has the ability to model both emotionally charged words and emotion-neutral words. We then generate a word-emotion lexicon using the mixture model to quantify word-emotion associations in the form of a probability vectors. Secondly we introduce novel feature extraction methods to utilize the emotion rich knowledge being captured by our word-emotion lexicon. The extracted features are used to classify text into emotion classes using machine learning. Further we also propose hybrid text representations for emotion classification that use the knowledge of lexicon based features in conjunction with other representations such as n-grams, part-of-speech and sentiment information. Thirdly we propose two different methods which jointly use an emotion-labelled corpus of tweets and emotion-sentiment mapping proposed in psychology to learn word-level numerical quantification of sentiment strengths over a positive to negative spectrum. Finally we evaluate all the proposed methods in this thesis through a variety of emotion detection and sentiment analysis tasks on benchmark data sets covering domains from blogs to news articles to tweets and incident reports

Role of sentiment classification in sentiment analysis: a survey

Through a survey of literature, the role of sentiment classification in sentiment analysis has been reviewed. The review identifies the research challenges involved in tackling sentiment classification. A total of 68 articles during 2015 – 2017 have been reviewed on six dimensions viz., sentiment classification, feature extraction, cross-lingual sentiment classification, cross-domain sentiment classification, lexica and corpora creation and multi-label sentiment classification. This study discusses the prominence and effects of sentiment classification in sentiment evaluation and a lot of further research needs to be done for productive results

Targeted aspect-based emotion analysis to detect opportunities and precaution in financial Twitter messages

Microblogging platforms, of which Twitter is a representative example, are valuable information sources for market screening and financial models. In them, users voluntarily provide relevant information, including educated knowledge on investments, reacting to the state of the stock markets in real-time and, often, influencing this state. We are interested in the user forecasts in financial, social media messages expressing opportunities and precautions about assets. We propose a novel Targeted Aspect-Based Emotion Analysis (tabea) system that can individually discern the financial emotions (positive and negative forecasts) on the different stock market assets in the same tweet (instead of making an overall guess about that whole tweet). It is based on Natural Language Processing (nlp) techniques and Machine Learning streaming algorithms. The system comprises a constituency parsing module for parsing the tweets and splitting them into simpler declarative clauses; an offline data processing module to engineer textual, numerical and categorical features and analyse and select them based on their relevance; and a stream classification module to continuously process tweets on-the-fly. Experimental results on a labelled data set endorse our solution. It achieves over 90% precision for the target emotions, financial opportunity, and precaution on Twitter. To the best of our knowledge, no prior work in the literature has addressed this problem despite its practical interest in decision-making, and we are not aware of any previous nlp nor online Machine Learning approaches to tabea.Xunta de Galicia | Ref. ED481B-2021-118Xunta de Galicia | Ref. ED481B-2022-093Financiado para publicación en acceso aberto: Universidade de Vigo/CISU

Earned Income Tax Credit: Path Dependence and the Blessing of Undertheorization

Some commentators have lamented that the Earned Income Tax Credit (EITC) is undertheorized—that its purpose is unclear—and that its design is therefore suboptimal. This Note explores the credit’s path-dependent past, which has resulted in a present-day EITC that manifests a diverse, uncoordinated assortment of policy purposes. Although the EITC’s ambiguity of purpose may yield policy inefficiencies, this Note argues that it also produces significant political benefits that would-be reformers who value the EITC’s many societal benefits should take into account before they attempt to enact any major overhaul

Language (Technology) is Power: A Critical Survey of "Bias" in NLP

We survey 146 papers analyzing "bias" in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing "bias" is an inherently normative process. We further find that these papers' proposed quantitative techniques for measuring or mitigating "bias" are poorly matched to their motivations and do not engage with the relevant literature outside of NLP. Based on these findings, we describe the beginnings of a path forward by proposing three recommendations that should guide work analyzing "bias" in NLP systems. These recommendations rest on a greater recognition of the relationships between language and social hierarchies, encouraging researchers and practitioners to articulate their conceptualizations of "bias"---i.e., what kinds of system behaviors are harmful, in what ways, to whom, and why, as well as the normative reasoning underlying these statements---and to center work around the lived experiences of members of communities affected by NLP systems, while interrogating and reimagining the power relations between technologists and such communities