Incremental Lifecycle Validation Of Knowledge-based Systems Through Commonkads

This dissertation introduces a novel validation method for knowledge-based systems (KBS). Validation is an essential phase in the development lifecycle of knowledge-based systems. Validation ensures that the system is valid, reliable and that it reflects the knowledge of the expert and meets the specifications. Although many validation methods have been introduced for knowledge-based systems, there is still a need for an incremental validation method based on a lifecycle model. Lifecycle models provide a general framework for the developer and a mapping technique from the system into the validation process. They support reusability, modularity and offer guidelines for knowledge engineers to achieve high quality systems. CommonKADS is a set of models that helps to represent and analyze knowledge-based systems. It offers a de facto standard for building knowledge-based systems. Additionally, CommonKADS is a knowledge representation-independent model. It has powerful models that can represent many domains. Defining an incremental validation method based on a conceptual lifecycle model (such as CommonKADS) has a number of advantages such as reducing time and effort, ease of implementation when having a template to follow, well-structured design, and better tracking of errors when they occur. Moreover, the validation method introduced in this dissertation is based on case testing and selecting an appropriate set of test cases to validate the system. The validation method defined makes use of results of prior test cases in an incremental validation procedure. This facilitates defining a minimal set of test cases that provides complete and effective system coverage. CommonKADS doesn’t define validation, verification or testing in any of its models. This research seeks to establish a direct relation between validation and lifecycle models, and introduces a validation method for KBS embedded into CommonKAD

The unspoken global race for artificial intelligence

Two men walk into a bar, the first one says: “robots will conquer our civilisation and make us their servants within ten years”, the second one responds: “No, the principle of artificial intelligence (AI) is a far-fetched goal that will never see light”. The bartender smiles, analyses their facial expressions, assigns a sentiment score to their sentences, evaluates their historical drinking trends, and decides to pour the first one a glass of gin and tonic, and the second one a glass of Scotch. Here is the spoiler: both men are lying; and the bartender is a robot. Not a funny joke, but a reality that is shadowing all conventional discussions about the future prospects of AI. In order to avoid such binary discussions about the goodness and possibilities of machine intelligence, and to eliminate the ‘hype’ surrounding the topic, this article aims to unveil the slowly cooking, quietly simmering, unspoken truths of the inevitable global arms race of AI

Why an open mind on open science could reshape human knowledge

In the year 1610, Galileo observed a ring-like shape around a distant planet (Saturn). After realising the significance of his discovery, Galileo wanted to record it to be able to claim it as his own contribution once it was announced. To do that, he wrote a letter to a colleague stating the following: "smaismrmilmepoetaleumibunenugttauiras"

Thoughts on the future of human knowledge and machine intelligence

Throughout history, nations and armies have brawled for knowledge. The burning of the Library of Alexandria, the destruction of Xianyang Palace’s archives, the secret investigations of the Dead Sea Scrolls, the destruction of the Mayan Codex, and many other examples illustrate the continuous human quest for owning knowledge or eliminating it from an enemy

Cybersecurity Law: Legal Jurisdiction and Authority

Cybersecurity threats affect all aspects of society; critical infrastructures (such as networks, corporate systems, water supply systems, and intelligent transportation systems) are especially prone to attacks and can have tangible negative consequences on society. However, these critical cyber systems are generally governed by multiple jurisdictions, for instance the Metro in the Washington, D.C. area is managed by the states of Virginia and Maryland, as well as the District of Columbia (DC) through Washington Metropolitan Area Transit Authority (WMATA). Additionally, the water treatment infrastructure managed by DC Water consists of waste water input from Fairfax and Arlington counties, and the district (i.e. DC). Additionally, cyber attacks usually launch from unknown sources, through unknown switches and servers, and end up at the destination without much knowledge on their source or path. Certain infrastructures are shared amongst multiple countries, another idiosyncrasy that exacerbates the issue of governance. This law paper however, is not concerned with the general governance of these infrastructures, rather with the ambiguity in the relevant laws or doctrines about which authority would prevail in the context of a cyber threat or a cyber-attack, with a focus on federal vs. state issues, international law involvement, federal preemption, technical aspects that could affect lawmaking, and conflicting responsibilities in cases of cyber crime. A legal analysis of previous cases is presented, as well as an extended discussion addressing different sides of the argument.Comment: This report is developed for partial fulfillment of the requirements for the degree of Juris Masters of Law at GMU's Antonin Scalia Law Schoo

Pandemics and big data tyrannies

Tyranny typically occurs when citizens share their data (willingly) to achieve safety but end up losing control of it, writes Feras A. Batarseh

ExClaim: Explainable Neural Claim Verification Using Rationalization

With the advent of deep learning, text generation language models have improved dramatically, with text at a similar level as human-written text. This can lead to rampant misinformation because content can now be created cheaply and distributed quickly. Automated claim verification methods exist to validate claims, but they lack foundational data and often use mainstream news as evidence sources that are strongly biased towards a specific agenda. Current claim verification methods use deep neural network models and complex algorithms for a high classification accuracy but it is at the expense of model explainability. The models are black-boxes and their decision-making process and the steps it took to arrive at a final prediction are obfuscated from the user. We introduce a novel claim verification approach, namely: ExClaim, that attempts to provide an explainable claim verification system with foundational evidence. Inspired by the legal system, ExClaim leverages rationalization to provide a verdict for the claim and justifies the verdict through a natural language explanation (rationale) to describe the model's decision-making process. ExClaim treats the verdict classification task as a question-answer problem and achieves a performance of 0.93 F1 score. It provides subtasks explanations to also justify the intermediate outcomes. Statistical and Explainable AI (XAI) evaluations are conducted to ensure valid and trustworthy outcomes. Ensuring claim verification systems are assured, rational, and explainable is an essential step toward improving Human-AI trust and the accessibility of black-box systems.Comment: Published at 2022 IEEE 29th ST

ACWA: An AI-driven Cyber-Physical Testbed for Intelligent Water Systems

This manuscript presents a novel state-of-the-art cyber-physical water testbed, namely: The AI and Cyber for Water and Agriculture testbed (ACWA). ACWA is motivated by the need to advance water supply management using AI and Cybersecurity experimentation. The main goal of ACWA is to address pressing challenges in the water and agricultural domains by utilising cutting-edge AI and data-driven technologies. These challenges include Cyberbiosecurity, resources management, access to water, sustainability, and data-driven decision-making, among others. To address such issues, ACWA consists of multiple topologies, sensors, computational nodes, pumps, tanks, smart water devices, as well as databases and AI models that control the system. Moreover, we present ACWA simulator, which is a software-based water digital twin. The simulator runs on fluid and constituent transport principles that produce theoretical time series of a water distribution system. This creates a good validation point for comparing the theoretical approach with real-life results via the physical ACWA testbed. ACWA data are available to AI and water domain researchers and are hosted in an online public repository. In this paper, the system is introduced in detail and compared with existing water testbeds; additionally, example use-cases are described along with novel outcomes such as datasets, software, and AI-related scenarios

Validation of knowledge-based systems through CommonKADS

This paper defines a method that can be used for validating knowledge-based systems (KBS) throughout their entire lifecycle. Method\u27s name is MAVERICK. It stands for Method for Automated Validation Embedded into the Reusable and Incremental CommonKADS. The lack of suitable, rigorous and general validation methods has become a serious obstacle to user acceptance of KBS for critical applications. In spite of recent significant advances in validation of KBS, it still remains an open problem. The ideas presented in this paper are based on the concept that validation should be performed in a structured and guided manner, integrated within a knowledge-based systems\u27 lifecycle development method. We define an incremental validation method for KBS based on extracting test cases from CommonKADS. Furthermore, we introduce our method for reducing the number of test cases and thus reducing validation\u27s effort and cost