Error management in ATLAS TDAQ : an intelligent systems approach

This thesis is concerned with the use of intelligent system techniques (IST) within a large distributed software system, specifically the ATLAS TDAQ system which has been developed and is currently in use at the European Laboratory for Particle Physics(CERN). The overall aim is to investigate and evaluate a range of ITS techniques in order to improve the error management system (EMS) currently used within the TDAQ system via error detection and classification. The thesis work will provide a reference for future research and development of such methods in the TDAQ system. The thesis begins by describing the TDAQ system and the existing EMS, with a focus on the underlying expert system approach, in order to identify areas where improvements can be made using IST techniques. It then discusses measures of evaluating error detection and classification techniques and the factors specific to the TDAQ system. Error conditions are then simulated in a controlled manner using an experimental setup and datasets were gathered from two different sources. Analysis and processing of the datasets using statistical and ITS techniques shows that clusters exists in the data corresponding to the different simulated errors. Different ITS techniques are applied to the gathered datasets in order to realise an error detection model. These techniques include Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Cartesian Genetic Programming (CGP) and a comparison of the respective advantages and disadvantages is made. The principle conclusions from this work are that IST can be successfully used to detect errors in the ATLAS TDAQ system and thus can provide a tool to improve the overall error management system. It is of particular importance that the IST can be used without having a detailed knowledge of the system, as the ATLAS TDAQ is too complex for a single person to have complete understanding of. The results of this research will benefit researchers developing and evaluating IST techniques in similar large scale distributed systems

Error management in ATLAS TDAQ : an intelligent systems approach

This thesis is concerned with the use of intelligent system techniques (IST) within a large distributed software system, specifically the ATLAS TDAQ system which has been developed and is currently in use at the European Laboratory for Particle Physics(CERN). The overall aim is to investigate and evaluate a range of ITS techniques in order to improve the error management system (EMS) currently used within the TDAQ system via error detection and classification. The thesis work will provide a reference for future research and development of such methods in the TDAQ system. The thesis begins by describing the TDAQ system and the existing EMS, with a focus on the underlying expert system approach, in order to identify areas where improvements can be made using IST techniques. It then discusses measures of evaluating error detection and classification techniques and the factors specific to the TDAQ system. Error conditions are then simulated in a controlled manner using an experimental setup and datasets were gathered from two different sources. Analysis and processing of the datasets using statistical and ITS techniques shows that clusters exists in the data corresponding to the different simulated errors. Different ITS techniques are applied to the gathered datasets in order to realise an error detection model. These techniques include Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Cartesian Genetic Programming (CGP) and a comparison of the respective advantages and disadvantages is made. The principle conclusions from this work are that IST can be successfully used to detect errors in the ATLAS TDAQ system and thus can provide a tool to improve the overall error management system. It is of particular importance that the IST can be used without having a detailed knowledge of the system, as the ATLAS TDAQ is too complex for a single person to have complete understanding of. The results of this research will benefit researchers developing and evaluating IST techniques in similar large scale distributed systems.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Deficient data classification with fuzzy learning

This thesis first proposes a novel algorithm for handling both missing values and imbalanced data classification problems. Then, algorithms for addressing the class imbalance problem in Twitter spam detection (Network Security Problem) have been proposed. Finally, the security profile of SVM against deliberate attacks has been simulated and analysed.<br /

Insider Threat Detection on the Windows Operating System using Virtual Machine Introspection

Existing insider threat defensive technologies focus on monitoring network traffic or events generated by activities on a user\u27s workstation. This research develops a methodology for signaling potentially malicious insider behavior using virtual machine introspection (VMI). VMI provides a novel means to detect potential malicious insiders because the introspection tools remain transparent and inaccessible to the guest and are extremely difficult to subvert. This research develops a four step methodology for development and validation of malicious insider threat alerting using VMI. Six core use cases are developed along with eighteen supporting scenarios. A malicious attacker taxonomy is used to decompose each scenario to aid identification of observables for monitoring for potentially malicious actions. The effectiveness of the identified observables is validated through the use of two data sets, one containing simulated normal and malicious insider user behavior and the second from a computer network operations exercise. Compiled Memory Analysis Tool - Virtual (CMAT-V) and Xen hypervisor capabilities are leveraged to perform VMI and insider threat detection. Results of the research show the developed methodology is effective in detecting all defined malicious insider scenarios used in this research on Windows guests

Challenges and perspectives of hate speech research

This book is the result of a conference that could not take place. It is a collection of 26 texts that address and discuss the latest developments in international hate speech research from a wide range of disciplinary perspectives. This includes case studies from Brazil, Lebanon, Poland, Nigeria, and India, theoretical introductions to the concepts of hate speech, dangerous speech, incivility, toxicity, extreme speech, and dark participation, as well as reflections on methodological challenges such as scraping, annotation, datafication, implicity, explainability, and machine learning. As such, it provides a much-needed forum for cross-national and cross-disciplinary conversations in what is currently a very vibrant field of research

Abyssal Ideology and the Amerindians of Guyana: An Eco-Crimes Analysis of Power, Discourse and Cognitive Injustice

Cognitive injustice- that is, the failure to recognize the plurality of epistemologies and the manner in which people across the globe provide meaning to their existence- should be the subject of critical criminological inquiry because it is directly linked to both environmental and social injustice. This dissertation presents a comparative and critical analysis of the discourses surrounding the indigenous peoples of Guyana, the Amerindians. Massaging the parameters of green criminology and the eco-crimes framework, I synthesize Norman Faircloughs methodology known as critical discourse analysis (CDA) and Boaventura de Sousa Santos theoretical framework of Abyssal Thinking in order to capture the Amerindian experience from the dawn of colonialism to recent conservation efforts, such as the countrys very first community-owned conservation area (C.O.C.A.). In my attempt to unmask cognitive injustice via discourse, I also demonstrate how the experiences and speech of the Amerindians can challenge this injustice by exercising what Santos refers to as Post-Abyssal Thinking

Computational Theory of Mind for Human-Agent Coordination

In everyday life, people often depend on their theory of mind, i.e., their ability to reason about unobservable mental content of others to understand, explain, and predict their behaviour. Many agent-based models have been designed to develop computational theory of mind and analyze its effectiveness in various tasks and settings. However, most existing models are not generic (e.g., only applied in a given setting), not feasible (e.g., require too much information to be processed), or not human-inspired (e.g., do not capture the behavioral heuristics of humans). This hinders their applicability in many settings. Accordingly, we propose a new computational theory of mind, which captures the human decision heuristics of reasoning by abstracting individual beliefs about others. We specifically study computational affinity and show how it can be used in tandem with theory of mind reasoning when designing agent models for human-agent negotiation. We perform two-agent simulations to analyze the role of affinity in getting to agreements when there is a bound on the time to be spent for negotiating. Our results suggest that modeling affinity can ease the negotiation process by decreasing the number of rounds needed for an agreement as well as yield a higher benefit for agents with theory of mind reasoning.</p

Gender-Sensitive and Gender-Effective Strategies in Preventing and Countering Violent Extremism

For over two decades, the global community has endeavored to prevent and combat violent extremism. In that time, states and civil society partners have increasingly recognized women’s role in this effort as critical to its success. The effective engagement of women, representing half of the world’s population, is now understood as necessary to a “localized, credible, inclusive, and resonant strateg[y] to build resilience to extremism.” However, gender-sensitive and gender-effective approaches to preventing and countering violent extremism (PCVE) have remained elusive. Women’s involvement in PCVE strategies continue to be largely marginal, often reinforcing harmful stereotypes, and sometimes resulting in negative consequences for gender equality. This is particularly true for women in minority ethnic and religious communities. The failure to effectively address the discriminatory impact of PCVE strategies on women not only hampers the efficacy of PCVE efforts but also impedes state party implementation of the Convention on the Elimination of All Forms of Discrimination against Women (CEDAW), 2 which requires elimination of discriminatory laws, policies and programming. The failure to maximally engage women in PCVE efforts as leaders, designers, targets and partners also represents a lost opportunity to implement CEDAW Article 4 measures to accelerate women’s equality. The International [Global] Human Rights Clinic at the University of Chicago Law School conducted this study with support from United Nations Office of the High Commissioner for Human Rights. The goal of the study is to contribute to a better understanding of how PCVE strategies and programming can effectively protect and promote women’s human rights and principles of gender equality. The study involved in-country engagements with stakeholders in Tunisia, Kenya and the United States. Through these interviews and an extensive literature review, the report assesses how such initiatives have engaged and impacted women and offers recommendations for improving strategies and interventions going forward. ISBN: 978-1-7334730-5-