Constraint specification by example in a meta-CASE tool

Meta-CASE tools offer the ability to specialise and customise diagram-based software modelling editors. Constraints play a major role in these specialisation and customisation tasks. However, constraint definition is complicated. This thesis addresses the problem of constraint specification complexity in meta-CASE tools. Constraint Specification by Example (CSBE), a novel variant of Programming by Example, is proposed as a technique that can simplify and facilitate constraint specification in meta-CASE tools. CSBE involves a user presenting visual examples of diagrams to the tool which engages in a synergistic interaction with the user, based on system inference and additional user input, to arrive at the user’s intended constraint. A prototype meta-CASE tool has been developed that incorporates CSBE. This prototype was used to perform several empirical studies to investigate the feasibility and potential advantages of CSBE. An empirical study was conducted to evaluate the performance in terms of effectiveness, efficiency and user satisfaction of CSBE compared to a typical form-filling technique. Results showed that users using CSBE correctly specified significantly more constraints and required less time to accomplish the task. Users reported higher satisfaction when using CSBE. A second empirical online study has been conducted with the aim of discovering the preference of participants for positive or negative natural language polarity when expressing constraints. Results showed that subjects preferred positive constraint expression over negative expression. A third empirical study aimed to discover the effect of example polarity (negative vs. positive) on the performance of CSBE. A multi-polarity tool offering both positive and negative examples scored significantly higher correctness in a significantly shorter time to accomplish the task with a significantly higher user satisfaction compared to a tool offering only one example polarity. A fourth empirical study examined user-based addition of new example types and inference rules into the CSBE technique. Results demonstrated that users are able to add example types and that performance is improved when they do so. Overall, CSBE has been shown to be feasible and to offer potential advantages compared to other commonly-used constraint specification techniques

An Interactive Automation for Human Biliary Tree Diagnosis Using Computer Vision

The biliary tree is a network of tubes that connects the liver to the gallbladder, an organ right beneath it. The bile duct is the major tube in the biliary tree. The dilatation of a bile duct is a key indicator for more major problems in the human body, such as stones and tumors, which are frequently caused by the pancreas or the papilla of vater. The detection of bile duct dilatation can be challenging for beginner or untrained medical personnel in many circumstances. Even professionals are unable to detect bile duct dilatation with the naked eye. This research presents a unique vision-based model for biliary tree initial diagnosis. To segment the biliary tree from the Magnetic Resonance Image, the framework used different image processing approaches (MRI). After the image’s region of interest was segmented, numerous calculations were performed on it to extract 10 features, including major and minor axes, bile duct area, biliary tree area, compactness, and some textural features (contrast, mean, variance and correlation). This study used a database of images from King Hussein Medical Center in Amman, Jordan, which included 200 MRI images, 100 normal cases, and 100 patients with dilated bile ducts. After the characteristics are extracted, various classifiers are used to determine the patients’ condition in terms of their health (normal or dilated). The findings demonstrate that the extracted features perform well with all classifiers in terms of accuracy and area under the curve. This study is unique in that it uses an automated approach to segment the biliary tree from MRI images, as well as scientifically correlating retrieved features with biliary tree status that has never been done before in the literature

PDF Malware Detection Based on Optimizable Decision Trees

Portable document format (PDF) files are one of the most universally used file types. This has incentivized hackers to develop methods to use these normally innocent PDF files to create security threats via infection vector PDF files. This is usually realized by hiding embedded malicious code in the victims’ PDF documents to infect their machines. This, of course, results in PDF malware and requires techniques to identify benign files from malicious files. Research studies indicated that machine learning methods provide efficient detection techniques against such malware. In this paper, we present a new detection system that can analyze PDF documents in order to identify benign PDF files from malware PDF files. The proposed system makes use of the AdaBoost decision tree with optimal hyperparameters, which is trained and evaluated on a modern inclusive dataset, viz. Evasive-PDFMal2022. The investigational assessment demonstrates a lightweight and accurate PDF detection system, achieving a 98.84% prediction accuracy with a short prediction interval of 2.174 μSec. To this end, the proposed model outperforms other state-of-the-art models in the same study area. Hence, the proposed system can be effectively utilized to uncover PDF malware at a high detection performance and low detection overhead