Efficient Data Driven Multi Source Fusion

Data/information fusion is an integral component of many existing and emerging applications; e.g., remote sensing, smart cars, Internet of Things (IoT), and Big Data, to name a few. While fusion aims to achieve better results than what any one individual input can provide, often the challenge is to determine the underlying mathematics for aggregation suitable for an application. In this dissertation, I focus on the following three aspects of aggregation: (i) efficient data-driven learning and optimization, (ii) extensions and new aggregation methods, and (iii) feature and decision level fusion for machine learning with applications to signal and image processing. The Choquet integral (ChI), a powerful nonlinear aggregation operator, is a parametric way (with respect to the fuzzy measure (FM)) to generate a wealth of aggregation operators. The FM has 2N variables and N(2N − 1) constraints for N inputs. As a result, learning the ChI parameters from data quickly becomes impractical for most applications. Herein, I propose a scalable learning procedure (which is linear with respect to training sample size) for the ChI that identifies and optimizes only data-supported variables. As such, the computational complexity of the learning algorithm is proportional to the complexity of the solver used. This method also includes an imputation framework to obtain scalar values for data-unsupported (aka missing) variables and a compression algorithm (lossy or losselss) of the learned variables. I also propose a genetic algorithm (GA) to optimize the ChI for non-convex, multi-modal, and/or analytical objective functions. This algorithm introduces two operators that automatically preserve the constraints; therefore there is no need to explicitly enforce the constraints as is required by traditional GA algorithms. In addition, this algorithm provides an efficient representation of the search space with the minimal set of vertices. Furthermore, I study different strategies for extending the fuzzy integral for missing data and I propose a GOAL programming framework to aggregate inputs from heterogeneous sources for the ChI learning. Last, my work in remote sensing involves visual clustering based band group selection and Lp-norm multiple kernel learning based feature level fusion in hyperspectral image processing to enhance pixel level classification

Popularity Characterization and Modelling for User-generated Videos

User-generated content systems such as YouTube have become highly popular. It is difficult to under- stand and predict content popularity in such systems. Characterizing and modelling content popularity can provide deeper insights into system design trade-offs and enable prediction of system behaviour in advance. Borghol et al. collected two datasets of YouTube video weekly view counts over eight months in 2008/09, namely a “recently-uploaded” dataset and a “keyword-search” dataset, and analyzed the popular- ity characteristics of the videos in the recently-uploaded dataset including the video popularity evolution over time. Based on the observed characteristics, they developed a model that can generate synthetic video weekly view counts whose characteristics with respect to video popularity evolution match those observed in the recently-uploaded dataset. For this thesis, new weekly view count data was collected over two months in 2011 for the videos in the recently-uploaded and keyword-search datasets of Borghol et al. This data was used to evaluate the accuracy of the Borghol et al. model when used to generate synthetic view counts for a much longer time period than the eight month period previously considered. Although the model yielded distributions of total (lifetime) video view counts that match the empirical distributions, significant differences between the model and em- pirical data were observed. These differences appear to arise because of particular popularity characteristics that change over time rather than being week-invariant as assumed in the model. This thesis also characterizes how video popularity evolves beyond the eight month period considered by Borghol et al., and studies the characteristics of the keyword-search dataset with respect to content popu- larity, popularity evolution, and sampling biases. Finally, the thesis studies the popularity characteristics of the videos in the recently-uploaded and keyword-search datasets for which additional view count data could not be collected, owing to the removal of these videos from YouTube

Book Success Prediction with Pretrained Sentence Embeddings and Readability Scores

Predicting the potential success of a book in advance is vital in many applications. This could help both publishers and readers in their decision-making process whether or not a book is worth publishing and reading, respectively. In this paper, we propose a model that leverages pretrained sentence embeddings along with various readability scores for book success prediction. Unlike previous methods, the proposed method requires no count-based, lexical, or syntactic features. Instead, we use a convolutional neural network over pretrained sentence embeddings and leverage different readability scores through a simple concatenation operation. Our proposed model outperforms strong baselines for this task by as large as 6.4\% F1-score points. Moreover, our experiments show that according to our model, only the first 1K sentences are good enough to predict the potential success of books

A comparative study of the outcome of displaced fractures neck of femur treated with unipolar prosthesis and fenestrated bipolar prosthesis in active elderly patients

Background: Fractures of the neck or femur are among the most serious surgical problems affecting older groups. These fractures constitute a large burden on families and society due to the inability to stand and walk. Objectives were to compare the functional outcomes of unipolar (Austin-Moore) versus uncemented bipolar hemiarthroplasty in displaced fractures neck femur among these active elderly patients.  Methods: This prospective interventional study was carried out at NITOR, Dhaka, Bangladesh in total 60 patients. Among them, 30 patients were treated with unipolar and 30 patients were treated with fenestrated bipolar prosthesis through a lateral approach. Results: There was no significant age difference between the two groups. Regarding the functional outcome, 6 (20%) patients in the unipolar group and 11 (36.67%) patients in the bipolar group had excellent outcomes. About the same number of patients had good outcomes 36.67% in the unipolar and 43.33% in the bipolar group; the fair and poor outcome was more in the unipolar group than the bipolar group. So, the final outcome after 6 months of operation, 57.57% of patients had satisfactory results in the unipolar group whereas 80% of patients had satisfactory results in the bipolar group. The average Harris hip score was 77.14±14.58 in the unipolar group and 84.63±10.15 in the bipolar group and the p=0.01 which is below 0.05. So, the result is statistically significant.  Conclusions: Uncemented bipolar hemiarthroplasty with a fenestrated stem can give better functional outcomes for displaced intracapsular femoral neck fractures in active elderly patients compared to Austin-Moore prostheses

Medication wastage and its impact on environment: evidence from Malaysia

The purpose of this study is to investigate the critical factors that have impact on environment causes of unused medication. The current study is a descriptive cross-sectional audit involving with patients based on a structured questionnaire format with answer sets. The data is analyzed using partial least square method. The results revealed that excess supplied, expired medicine, changed treatment and side effects have a significant impact on unused medication. In addition, overall unused medication has a significant relationship with environmental effect. In contrast, although excess supplied and side effects have not significant impact on environmental effect, but expired medicine and changed treatment have a significant impact on environmental effect. This survey results suggested; there are few factors which increased the volume of leftover medicine and it has led to an enhanced international awareness of the potential detrimental effects on the environment. More exertion is necessary to raise awareness of people in general as an initial step in promoting behavioral change in connection to medication wastage

Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks

Information fusion is an essential part of numerous engineering systems and biological functions, e.g., human cognition. Fusion occurs at many levels, ranging from the low-level combination of signals to the high-level aggregation of heterogeneous decision-making processes. While the last decade has witnessed an explosion of research in deep learning, fusion in neural networks has not observed the same revolution. Specifically, most neural fusion approaches are ad hoc, are not understood, are distributed versus localized, and/or explainability is low (if present at all). Herein, we prove that the fuzzy Choquet integral (ChI), a powerful nonlinear aggregation function, can be represented as a multi-layer network, referred to hereafter as ChIMP. We also put forth an improved ChIMP (iChIMP) that leads to a stochastic gradient descent-based optimization in light of the exponential number of ChI inequality constraints. An additional benefit of ChIMP/iChIMP is that it enables eXplainable AI (XAI). Synthetic validation experiments are provided and iChIMP is applied to the fusion of a set of heterogeneous architecture deep models in remote sensing. We show an improvement in model accuracy and our previously established XAI indices shed light on the quality of our data, model, and its decisions.Comment: IEEE Transactions on Fuzzy System

Financial Development and Foreign Direct Investment-The Moderating Role of Quality Institutions

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Internationalization of SMEs: A Developing Country Perspective

Internationalization has become increasingly important to the competitiveness of firms of all sizes, including small and medium-sized enterprises (SMEs). SMEs play a crucial role in the development of lower-income countries. In Bangladesh, SMEs account for between 80 and 85 percent of industrial employment and 23 percent of total employment and are critical to economic growth. Though the literature on firm internationalization is well established, the internationalization process of SMEs from developing countries, such as Bangladesh, remains relatively under-explored. The main aim of this study is to explore factors that hinder the internationalization of SMEs in a developing country, with Bangladesh serving as the context of the investigation. Qualitative research methods were adopted, comprising semi-structured interviews with leaders of 16 SMEs in Bangladesh. Six major themes were identified as hindrances to the firms’ internationalization: (1) lack of market knowledge, (2) lack of family support, (3) the proliferation of ‘scammer buyers’, (4) the (negative) involvement of third parties, (5) mismanagement of domestic ports, and (6) unregulated local market. Regarding positive factors, only one theme emerged from the data, the strong support from the local government, which provides considerable backing for local SMEs with international ambitions. This study’s primary contribution and originality lie in the context of the investigation, with Bangladesh primarily overlooked in the international business literature. Therefore, the study presents several novel insights into the internationalization process of SMEs