9,096 research outputs found
vONTSS: vMF based semi-supervised neural topic modeling with optimal transport
Recently, Neural Topic Models (NTM), inspired by variational autoencoders,
have attracted a lot of research interest; however, these methods have limited
applications in the real world due to the challenge of incorporating human
knowledge. This work presents a semi-supervised neural topic modeling method,
vONTSS, which uses von Mises-Fisher (vMF) based variational autoencoders and
optimal transport. When a few keywords per topic are provided, vONTSS in the
semi-supervised setting generates potential topics and optimizes topic-keyword
quality and topic classification. Experiments show that vONTSS outperforms
existing semi-supervised topic modeling methods in classification accuracy and
diversity. vONTSS also supports unsupervised topic modeling. Quantitative and
qualitative experiments show that vONTSS in the unsupervised setting
outperforms recent NTMs on multiple aspects: vONTSS discovers highly clustered
and coherent topics on benchmark datasets. It is also much faster than the
state-of-the-art weakly supervised text classification method while achieving
similar classification performance. We further prove the equivalence of optimal
transport loss and cross-entropy loss at the global minimum.Comment: 24 pages, 12 figures, ACL findings 202
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The impact of employees' working relations in creating and retaining trust: the case of the Bahrain Olympic Committee
Introduction: This thesis investigates the impact of employees’ working relations in creating, maintaining and retaining trust in the Bahrain Olympic Committee (BOC).
Aim: The main aim of this thesis is to determine how the three groups of Organisational Trust variables, namely Social System Elements (SSE), Factors of Trustworthiness (FoT) and Third-Party Gossip (TPG), affect employees’ Organisational Trust (OTR) in the BOC and promote Organisational Citizenship Behaviour (OCB). To answer this main aim, a conceptual framework was created that focused on exploring the following research aims: (1) the interrelationship between SSE and FoT, (2) the effect of SSE on OTR, (3) the impact of TPG on OTR and (4) the effect of OTR on overall OCB.
Methodology: The study uses a mixed-method case study research style that included in-depth semi-structured interviews with 17 managers, an online questionnaire survey with 320 employees of the BOC and an analysis of the BOC’s Annual Reports from 2015 to 2018.
Results: The qualitative and quantitative findings indicate, firstly, that there is a significant interrelationship between SSE and FoT, establishing that SSE’s perception of organisational justice (OJ), including that FoTs benevolence and integrity as the most important factors in yielding employees’ trust in the BOC. Secondly, it has been established that SSEs have significant direct and indirect effects on OTR. Thirdly, negative and positive TPG concurrently occurred in the BOC and the prevalence of negative TPG poses more impact on OTR. Finally, this study’s findings demonstrated OTR’s effect in generating OCB, including that Civic Virtue was rated as the most preferred of the five OCB themes; this indicates the managers’ and the employees’ strong emotional attachment and support of the activities taking place at the BOC.
Contributions: Overall, this thesis substantially contributes to OTR literature, particularly in the context of the Middle East. It also proposes several insightful recommendations for future research and practical implications for practitioners in the field of Organisational Trust
Bayesian Forecasting in Economics and Finance: A Modern Review
The Bayesian statistical paradigm provides a principled and coherent approach
to probabilistic forecasting. Uncertainty about all unknowns that characterize
any forecasting problem -- model, parameters, latent states -- is able to be
quantified explicitly, and factored into the forecast distribution via the
process of integration or averaging. Allied with the elegance of the method,
Bayesian forecasting is now underpinned by the burgeoning field of Bayesian
computation, which enables Bayesian forecasts to be produced for virtually any
problem, no matter how large, or complex. The current state of play in Bayesian
forecasting in economics and finance is the subject of this review. The aim is
to provide the reader with an overview of modern approaches to the field, set
in some historical context; and with sufficient computational detail given to
assist the reader with implementation.Comment: The paper is now published online at:
https://doi.org/10.1016/j.ijforecast.2023.05.00
Multilevel latent class analysis with covariates: Analysis of cross-national citizenship norms with a two-stage approach
This paper focuses on the substantive application of multilevel LCA to the
evolution of citizenship norms in a diverse array of democratic countries. To
do so, we present a two-stage approach to fit multilevel latent class models:
in the first stage (measurement model construction), unconditional class
enumeration is done separately on both low and high level latent variables,
estimating only a part of the model at a time -- hence keeping the remaining
part fixed -- and then updating the full measurement model; in the second stage
(structural model construction), individual and/or group covariates are
included in the model. By separating the two parts -- first stage and second
stage of model building -- the measurement model is stabilized and is allowed
to be determined only by it's indicators. Moreover, this two-step approach
makes the inclusion/exclusion of a covariate a relatively simple task to
handle. Our proposal amends common practice in applied social science research,
where simple (low-level) LCA is done to obtain a classification of low-level
unit, and this is then related to (low- and high-level) covariates simply
including group fixed effects. Our analysis identifies latent classes that
score either consistently high or consistently low on all measured items, along
with two theoretically important classes that place distinctive emphasis on
items related to engaged citizenship, and duty-based norms
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Rigorous Experimentation For Reinforcement Learning
Scientific fields make advancements by leveraging the knowledge created by others to push the boundary of understanding. The primary tool in many fields for generating knowledge is empirical experimentation. Although common, generating accurate knowledge from empirical experiments is often challenging due to inherent randomness in execution and confounding variables that can obscure the correct interpretation of the results. As such, researchers must hold themselves and others to a high degree of rigor when designing experiments. Unfortunately, most reinforcement learning (RL) experiments lack this rigor, making the knowledge generated from experiments dubious. This dissertation proposes methods to address central issues in RL experimentation.
Evaluating the performance of an RL algorithm is the most common type of experiment in RL literature. Most performance evaluations are often incapable of answering a specific research question and produce misleading results. Thus, the first issue we address is how to create a performance evaluation procedure that holds up to scientific standards.
Despite the prevalence of performance evaluation, these types of experiments produce limited knowledge, e.g., they can only show how well an algorithm worked and not why, and they require significant amounts of time and computational resources. As an alternative, this dissertation proposes that scientific testing, the process of conducting carefully controlled experiments designed to further the knowledge and understanding of how an algorithm works, should be the primary form of experimentation.
Lastly, this dissertation provides a case study using policy gradient methods, showing how scientific testing can replace performance evaluation as the primary form of experimentation. As a result, this dissertation can motivate others in the field to adopt more rigorous experimental practices
Measurement of the Environmental Impact of Materials
Throughout their life cycles—from production, usage, through to disposal—materials and products interact with the environment (water, soil, and air). At the same time, they are exposed to environmental influences and, through their emissions, have an impact on the environment, people, and health. Accelerated experimental testing processes can be used to predict the long-term environmental consequences of innovative products before these actually enter the environment. We are living in a material world. Building materials, geosynthetics, wooden toys, soil, nanomaterials, composites, wastes and more are research subjects examined by the authors of this book. The interactions of materials with the environment are manifold. Therefore, it is important to assess the environmental impact of these interactions. Some answers to how this task can be achieved are given in this Special Issue
Likelihood Asymptotics in Nonregular Settings: A Review with Emphasis on the Likelihood Ratio
This paper reviews the most common situations where one or more regularity
conditions which underlie classical likelihood-based parametric inference fail.
We identify three main classes of problems: boundary problems, indeterminate
parameter problems -- which include non-identifiable parameters and singular
information matrices -- and change-point problems. The review focuses on the
large-sample properties of the likelihood ratio statistic. We emphasize
analytical solutions and acknowledge software implementations where available.
We furthermore give summary insight about the possible tools to derivate the
key results. Other approaches to hypothesis testing and connections to
estimation are listed in the annotated bibliography of the Supplementary
Material
Examples of works to practice staccato technique in clarinet instrument
Klarnetin staccato tekniğini güçlendirme aşamaları eser çalışmalarıyla uygulanmıştır. Staccato
geçişlerini hızlandıracak ritim ve nüans çalışmalarına yer verilmiştir. Çalışmanın en önemli amacı
sadece staccato çalışması değil parmak-dilin eş zamanlı uyumunun hassasiyeti üzerinde de
durulmasıdır. Staccato çalışmalarını daha verimli hale getirmek için eser çalışmasının içinde etüt
çalışmasına da yer verilmiştir. Çalışmaların üzerinde titizlikle durulması staccato çalışmasının ilham
verici etkisi ile müzikal kimliğe yeni bir boyut kazandırmıştır. Sekiz özgün eser çalışmasının her
aşaması anlatılmıştır. Her aşamanın bir sonraki performans ve tekniği güçlendirmesi esas alınmıştır.
Bu çalışmada staccato tekniğinin hangi alanlarda kullanıldığı, nasıl sonuçlar elde edildiği bilgisine
yer verilmiştir. Notaların parmak ve dil uyumu ile nasıl şekilleneceği ve nasıl bir çalışma disiplini
içinde gerçekleşeceği planlanmıştır. Kamış-nota-diyafram-parmak-dil-nüans ve disiplin
kavramlarının staccato tekniğinde ayrılmaz bir bütün olduğu saptanmıştır. Araştırmada literatür
taraması yapılarak staccato ile ilgili çalışmalar taranmıştır. Tarama sonucunda klarnet tekniğin de
kullanılan staccato eser çalışmasının az olduğu tespit edilmiştir. Metot taramasında da etüt
çalışmasının daha çok olduğu saptanmıştır. Böylelikle klarnetin staccato tekniğini hızlandırma ve
güçlendirme çalışmaları sunulmuştur. Staccato etüt çalışmaları yapılırken, araya eser çalışmasının
girmesi beyni rahatlattığı ve istekliliği daha arttırdığı gözlemlenmiştir. Staccato çalışmasını yaparken
doğru bir kamış seçimi üzerinde de durulmuştur. Staccato tekniğini doğru çalışmak için doğru bir
kamışın dil hızını arttırdığı saptanmıştır. Doğru bir kamış seçimi kamıştan rahat ses çıkmasına
bağlıdır. Kamış, dil atma gücünü vermiyorsa daha doğru bir kamış seçiminin yapılması gerekliliği
vurgulanmıştır. Staccato çalışmalarında baştan sona bir eseri yorumlamak zor olabilir. Bu açıdan
çalışma, verilen müzikal nüanslara uymanın, dil atış performansını rahatlattığını ortaya koymuştur.
Gelecek nesillere edinilen bilgi ve birikimlerin aktarılması ve geliştirici olması teşvik edilmiştir.
Çıkacak eserlerin nasıl çözüleceği, staccato tekniğinin nasıl üstesinden gelinebileceği anlatılmıştır.
Staccato tekniğinin daha kısa sürede çözüme kavuşturulması amaç edinilmiştir. Parmakların
yerlerini öğrettiğimiz kadar belleğimize de çalışmaların kaydedilmesi önemlidir. Gösterilen azmin ve
sabrın sonucu olarak ortaya çıkan yapıt başarıyı daha da yukarı seviyelere çıkaracaktır
Model Diagnostics meets Forecast Evaluation: Goodness-of-Fit, Calibration, and Related Topics
Principled forecast evaluation and model diagnostics are vital in fitting probabilistic models and forecasting outcomes of interest. A common principle is that fitted or predicted distributions ought to be calibrated, ideally in the sense that the outcome is indistinguishable from a random draw from the posited distribution. Much of this thesis is centered on calibration properties of various types of forecasts.
In the first part of the thesis, a simple algorithm for exact multinomial goodness-of-fit tests is proposed. The algorithm computes exact -values based on various test statistics, such as the log-likelihood ratio and Pearson\u27s chi-square. A thorough analysis shows improvement on extant methods. However, the runtime of the algorithm grows exponentially in the number of categories and hence its use is limited.
In the second part, a framework rooted in probability theory is developed, which gives rise to hierarchies of calibration, and applies to both predictive distributions and stand-alone point forecasts. Based on a general notion of conditional T-calibration, the thesis introduces population versions of T-reliability diagrams and revisits a score decomposition into measures of miscalibration, discrimination, and uncertainty. Stable and efficient estimators of T-reliability diagrams and score components arise via nonparametric isotonic regression and the pool-adjacent-violators algorithm. For in-sample model diagnostics, a universal coefficient of determination is introduced that nests and reinterprets the classical in least squares regression.
In the third part, probabilistic top lists are proposed as a novel type of prediction in classification, which bridges the gap between single-class predictions and predictive distributions. The probabilistic top list functional is elicited by strictly consistent evaluation metrics, based on symmetric proper scoring rules, which admit comparison of various types of predictions
On the Principles of Evaluation for Natural Language Generation
Natural language processing is concerned with the ability of computers to understand natural language texts, which is, arguably, one of the major bottlenecks in the course of chasing the holy grail of general Artificial Intelligence. Given the unprecedented success of deep learning technology, the natural language processing community has been almost entirely in favor of practical applications with state-of-the-art systems emerging and competing for human-parity performance at an ever-increasing pace. For that reason, fair and adequate evaluation and comparison, responsible for ensuring trustworthy, reproducible and unbiased results, have fascinated the scientific community for long, not only in natural language but also in other fields. A popular example is the ISO-9126 evaluation standard for software products, which outlines a wide range of evaluation concerns, such as cost, reliability, scalability, security, and so forth. The European project EAGLES-1996, being the acclaimed extension to ISO-9126, depicted the fundamental principles specifically for evaluating natural language technologies, which underpins succeeding methodologies in the evaluation of natural language.
Natural language processing encompasses an enormous range of applications, each with its own evaluation concerns, criteria and measures. This thesis cannot hope to be comprehensive but particularly addresses the evaluation in natural language generation (NLG), which touches on, arguably, one of the most human-like natural language applications. In this context, research on quantifying day-to-day progress with evaluation metrics lays the foundation of the fast-growing NLG community. However, previous works have failed to address high-quality metrics in multiple scenarios such as evaluating long texts and when human references are not available, and, more prominently, these studies are limited in scope, given the lack of a holistic view sketched for principled NLG evaluation.
In this thesis, we aim for a holistic view of NLG evaluation from three complementary perspectives, driven by the evaluation principles in EAGLES-1996: (i) high-quality evaluation metrics, (ii) rigorous comparison of NLG systems for properly tracking the progress, and (iii) understanding evaluation metrics. To this end, we identify the current state of challenges derived from the inherent characteristics of these perspectives, and then present novel metrics, rigorous comparison approaches, and explainability techniques for metrics to address the identified issues.
We hope that our work on evaluation metrics, system comparison and explainability for metrics inspires more research towards principled NLG evaluation, and contributes to the fair and adequate evaluation and comparison in natural language processing
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