Graphical User Interface Layout Language Using Combinators

While Java is a popular general-purpose programming language, there are certain areas where the syntax of Java is lacking for the task at hand. One of them is in the area of layout handling, i.e., the task of placing controls in a Graphical User Interface (GUI) with regard to their relative position and size. This is because the syntax of Java is targeted towards imperative programming, where code is written in the form of a list of instructions. A list of instructions does not adequately mirror the hierarchical structure of a layout. To overcome that weakness, this thesis describes and evaluates a new domainspecific programming language designed specifically for layout handling, named Swing GUI Layout Language (SGLL). One of the primary features of SGLL is the use of combinators, a concept used in functional languages. We propose that combinators are a more intuitive concept compared to the approach taken by Java, which involves adding controls to a layout m anager. Furthermore, we suggest that e li mination of clutter and better s upport for the abstractions in layout handling can provide an increase in programmer productivity and understandability of the source code. In this thesis, we focus on the GridLayout manager class, since it is rather easy to understand and provides a good starting point. To validate our approach, we evaluated Java and SGLL in both productivity and understandability. We found out that SGLL does provide a significant improvement in productivity and understandability for the task of layout handling

SEQ2SEQ++: a multitasking-based seq2seq model to generate meaningful and relevant answers

Question-answering chatbots have tremendous potential to complement humans in various fields. They are implemented using either rule-based or machine learning-based systems. Unlike the former, machine learning-based chatbots are more scalable. Sequence-to-sequence (Seq2Seq) learning is one of the most popular approaches in machine learning-based chatbots and has shown remarkable progress since its introduction in 2014. However, chatbots based on Seq2Seq learning show a weakness in that it tends to generate answers that can be generic and inconsistent with the questions, thereby becoming meaningless and, therefore, may lower the chatbot adoption rate. This weakness can be attributed to three issues: question encoder overfit, answer generation overfit, and language model influence. Several recent methods utilize multitask learning (MTL) to address this weakness. However, the existing MTL models show very little improvement over single-task learning, wherein they still generate generic and inconsistent answers. This paper presents a novel approach to MTL for the Seq2Seq learning model called SEQ2SEQ++, which comprises a multifunctional encoder, an answer decoder, an answer encoder, and a ternary classifier. Additionally, SEQ2SEQ++ utilizes a dynamic tasks loss weight mechanism for MTL loss calculation and a novel attention mechanism called the comprehensive attention mechanism. Experiments on NarrativeQA and SQuAD datasets were conducted to gauge the performance of the proposed model in comparison with two recently proposed models. The experimental results show that SEQ2SEQ++ yields noteworthy improvements over the two models on bilingual evaluation understudy, word error rate, and Distinct-2 metrics

Silhouette-based multi-view human action recognition in video

In this paper, a human action recognition method is presented where pose features are represented using contour points of the human silhouette, and actions are learned by using sequences of multi-view contour points. The differences and divergences among actors performing the same action are handled by considering variations in shape and speed. Experimental results on the IXMAS dataset show promising success rates, exceeding that of existing multi-view human action recognition state-of-the-art techniques

Temporal trends analysis for dengue outbreak and network threats severity prediction accuracy improvement

Time series analysis is one of the major techniques in capturing trends and pattern of the occurrence for future forecasting. Existing but scarce work have developed temporal-based techniques which target to either predict movement (increase or decrease) or quantify the possibility of the predicted event to happen. Man of these techniques focus on the values of the time series attribute but there is no available work on dengue or intrusion logs that focus on temporal trend analysis based on temporal relations mining. In this work the proposed technique utilize the temporal trends analysis of the observational attributes towards the pattern of the target’s attribute values. In this work, we propose a new temporal trends analysis approach that uses temporal relation mining in forecasting dengue outbreak and cyber intrusion.We leverage the temporal abstractions and temporal logic to define patterns with the goal to optimize prediction accuracy. From the experiment conducted, the results showed that the proposed approach has better prediction as compared to the baseline

A comparative study of evolving fuzzy grammar and machine learning techniques for text categorization

Several methods have been studied in text categorization and mostly are inspired by the statistical distribution features in the texts, such as the implementation of Machine Learning (ML) methods. However, there is no work available that investigates the performance of ML-based methods against the text expression-based method, especially for incident and medical case categorization. Meanwhile, these two domains are becoming ever more popular, due to a growing interest of automation in security intelligence and health services. This paper presents a text expression-based method called Evolving Fuzzy Grammar (EFG) and evaluates its performance against the conventional ML methods of Naïve Bayes, support vector machine, k-nearest neighbour, adaptive booting, and decision tree. The incident dataset used is a real dataset that was taken from the World Incidents Tracking System, while Image CLEF 2009 was used as the source for radiology case reports. The results suggested variations of strength and weakness of each method in both categorization tasks, where a standard evaluation technique (i.e., recall, precision, and F-measure) was used. In both domains, the SMO and IBk methods were the best, while AdaBoost was the worst. It was also observed that the medical dataset was easier to categorize than the incident. Although EFG was ranked second lowest, it obtained the highest precision score in the bombing categorization, the highest score in armed attack recall, and was averagely ranked in the top three for the medical case categorization. It was also noted that the text expression-based method used in EFG was the most verbose and expressive, when compared to the ML methods. This indicates that EFG is a viable method in text categorization and may serve as an alternative approach to such a task

Sequence to sequence model performance for education chatbot

Chatbot for education has great potential to complement human educators and education administrators. For example, it can be around the clock tutor to answer and clarify any questions from students who may have missed class. A chatbot can be implemented either by ruled based or artificial intelligence based. However, unlike the ruled-based chatbots, artificial intelligence based chatbots can learn and become smarter overtime and is more scalable and has become the popular choice for chatbot researchers recently. Recurrent Neural Network based Sequence-to-sequence (Seq2Seq) model is one of the most commonly researched model to implement artificial intelligence chatbot and has shown great progress since its introduction in 2014. However, it is still in infancy and has not been applied widely in educational chatbot development. Introduced originally for neural machine translation, the Seq2Seq model has been adapted for conversation modelling including question-answering chatbots. However, in-depth research and analysis of optimal settings of the various components of Seq2Seq model for natural answer generation problem is very limited. Additionally, there has been no experiments and analysis conducted to understand how Seq2Seq model handles variations is questions posed to it to generate correct answers. Our experiments add to the empirical evaluations on Seq2Seq literature and provides insights to these questions. Additionally, we provide insights on how a curated dataset can be developed and questions designed to train and test the performance of a Seq2Seq based question-answer model

Enhanced normalization approach addressing stop-word complexity in compound-word schema labels

An extensive review of the existing schema matching approaches discovered an area of improvement in the field of semantic schema matching. Normalization and lexical annotation methods using WordNet have been somewhat successful in general cases. However, in the presence of stop-words these approaches result in poor accuracy. Stop-words have previously been ignored in most studies resulting in false negative conclusions. This paper proposes NORMSTOP (NORMalizer of schemata having STOP-words) as an improved schema normalization approach that addresses the complexity of stop-words (e.g. ‘by’, ‘at’, ‘and,’ or’) in Compound Word (CW) schema labels. Using a combined set of WordNet features, NORMSTOP isolates these labels during the preprocessing stage and resets the base-form to a relevant WordNet term, or an annotable compound noun. When tested on the same real dataset used in the earlier approach - (NORMS or NORMalizer of Schemata), NORMSTOP shows up to 13% improvement in annotation recall measurement. This level of improvement takes the overall schema matching process another step closer to perfect accuracy; while its absence exposes a gap in expectation, especially in today’s databases, where stop-words are in abundance

Examining text categorization methods for incidents analysis

Text mining saves the necessity to sift through vast amount of documents manually to find relevant information. This paper focuses on text categorization, one of the tasks under text mining. This paper introduces fuzzy grammar as a technique for building text classifier and investigates the performance of fuzzy grammar against other machine learning methods such as decision table, support vector machine, statistic, nearest neighbor and boosting. Incidents data set was used where the focus was given on classifying the incidents events. Results have shown that fuzzy grammar has gotten promising results among the other benchmark machine learning methods

Credit card default prediction using machine learning techniques

Credit risk plays a major role in the banking industry business. Banks' main activities involve granting loan, credit card, investment, mortgage, and others. Credit card has been one of the most booming financial services by banks over the past years. However, with the growing number of credit card users, banks have been facing an escalating credit card default rate. As such data analytics can provide solutions to tackle the current phenomenon and management credit risks. This paper provides a performance evaluation of credit card default prediction. Thus, logistic regression, rpart decision tree, and random forest are used to test the variable in predicting credit default and random forest proved to have the higher accuracy and area under the curve. This result shows that random forest best describe which factors should be considered with an accuracy of 82 % and an Area under Curve of 77 % when assessing the credit risk of credit card customers

Enhancements to the sequence-to-sequence-based natural answer generation models

There is a great interest shown by academic researchers to continuously improve the sequence-to-sequence (Seq2Seq) model for natural answer generation (NAG) in chatbots. The Seq2Seq model shows a weakness whereby the model tends to generate answers that are generic, meaningless and inconsistent with the questions. However, a comprehensive literature review on the factors contributing to the weakness and potential solutions are still missing. Therefore, this review article fills the gap by reviewing Seq2Seq based natural answer generation-based literature to identify those factors and proposed methods to address the weakness. This literature review identified several factors such as input question is not sufficient to determine a meaningful output, usage of cross-entropy function as the loss function during training, infrequent words in training data, language model influence which generates answers not relevant to the question, utilization of teacher forcing method during training which results in exposure bias, long sentences and inability to consider dialogue history as the factors. Additionally, this literature review also identified and reviewed the methods proposed to address the weakness such as utilizing additional embedding and encoders, using different loss functions and training approaches, as well as utilizing other mechanisms like copying source word(s) and paying attention to a certain portion of the input. For discussion, these methods are categorized into four broad categories which are Structural Modifications, Augmented Learning, Beam Search and Complementary Mechanisms. Additionally, the paper highlights unexplored areas in Seq2Seq modeling and proposes potential future works for natural answer generation