Artificial Neural Network Context-Aware Reasoning Engine Model for Predicting User-Ranked Activities

The choice of users’ activity in a context-aware environment depends on users’ preferences and background. Users tend to rank concurrent activities and select their preferred activity. Researchers and developers of context-aware applications have sought various mechanisms to implement context reasoning engines. Recent implementations use Artificial Neural Networks (ANN) and other machine learning techniques to develop a context-aware reasoning engine to predict users’ activities. However, the complexities of these mechanisms overwhelm the processing capabilities and storage capacity of mobile devices. The study models a context-aware reasoning engine using a multi-layered perceptron with a gradient descent back-propagation algorithm to predict activity from user-ranked activities using a stochastic learning mode with a constant learning rate. The work deduced that working with specific rules in training a neural network is not always applicable. Training a network without approximation of neuron’s output to the nearest whole number increases the accuracy level of the network at the end of the training.

Combining Logic and Natural Language Processing to Support Investment Management

This paper presents an application that we developed to assist users with the creation of an investment profile for the selection of financial assets. It consists of a natural language interface, an automatic translation to a declarative FO(.) knowledge base, and the IDP reasoning engine with multiple forms of logical inference. The application speeds up the investment profile creation process, and reduces the considerable inherent operational risk linked to the creation of investment profiles

NACRE - A Nogood And Clause Reasoning Engine

NACRE, for Nogood And Clause Reasoning Engine, is a constraint solver written in C++. It is based on a modular architecture designed to work with generic constraints while implementing several state-of-the-art search methods and heuristics. Interestingly, its data structures have been carefully designed to play around nogoods and clauses, making it suit- able for implementing learning strategies. NACRE was submitted to the CSP MiniTrack of the 2018 and 2019 XCSP3 [8] competitions where it took the first place. This paper gives a general description of NACRE as a framework. We present its kernel, the available search algorithms, and the default settings (notably, used for XCSP3 competitions), which makes NACRE efficient in practice when used as a black-box solver.

CBR-Based Decision Support Methodology for Cybercrime Investigation: Focused on the Data-Driven Website Defacement Analysis

Criminal profiling is a useful technique to identify the most plausible suspects based on the evidence discovered at the crime scene. Similar to offline criminal profiling, in-depth profiling for cybercrime investigation is useful in analysing cyberattacks and for speculating on the identities of the criminals. Every cybercrime committed by the same hacker or hacking group has unique traits such as attack purpose, attack methods, and target. These unique traits are revealed in the evidence of cybercrime; in some cases, these unique traits are well hidden in the evidence such that it cannot be easily perceived. Therefore, a complete analysis of several factors concerning cybercrime can provide an investigator with concrete evidence to attribute the attacks and narrow down the scope of the criminal data and grasp the criminals in the end. We herein propose a decision support methodology based on the case-based reasoning (CBR) for cybercrime investigation. This study focuses on the massive data-driven analysis of website defacement. Our primary aim in this study is to demonstrate the practicality of the proposed methodology as a proof of concept. The assessment of website defacement was performed through the similarity measure and the clustering processing in the reasoning engine based on the CBR. Our results show that the proposed methodology that focuses on the investigation enables a better understanding and interpretation of website defacement and assists in inferring the hacker’s behavioural traits from the available evidence concerning website defacement. The results of the case studies demonstrate that our proposed methodology is beneficial for understanding the behaviour and motivation of the hacker and that our proposed data-driven analytic methodology can be utilized as a decision support system for cybercrime investigation.

Performance Evaluation of the Semantic Web Reasoners

The major benefit of working on Ontology Web Language (OWL) is its ability to define semantics such that the information becomes more valuable. To realize the full power of semantics, it is essential to integrate a reasoning engine to it. The software codes that perform inferences are often referred to as reasoning engines or reasoners. The reasoners can be classified into categories: tableau based and rule based reasoners. The rule based reasoners combines the assertions with a set of logical rules to infer new knowledge chunks. The Jena framework offers several ways to integrate rule based reasoners programmatically. The operation is similar to creating a more advanced model from a simpler one. The objective of this paper is to list and classify the reasoners according to OWL 2 profiles thereafter the focus of this study is to develop a model which evaluate the performance of Semantic Web Reasoner based on few parameters.