Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science

(abridged for arXiv) With the first direct detection of gravitational waves, the Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) has initiated a new field of astronomy by providing an alternate means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO's first observing run.Comment: 27 pages, 8 figures, 1 tabl

What’s Happening Around the World? A Survey and Framework on Event Detection Techniques on Twitter

© 2019, Springer Nature B.V. In the last few years, Twitter has become a popular platform for sharing opinions, experiences, news, and views in real-time. Twitter presents an interesting opportunity for detecting events happening around the world. The content (tweets) published on Twitter are short and pose diverse challenges for detecting and interpreting event-related information. This article provides insights into ongoing research and helps in understanding recent research trends and techniques used for event detection using Twitter data. We classify techniques and methodologies according to event types, orientation of content, event detection tasks, their evaluation, and common practices. We highlight the limitations of existing techniques and accordingly propose solutions to address the shortcomings. We propose a framework called EDoT based on the research trends, common practices, and techniques used for detecting events on Twitter. EDoT can serve as a guideline for developing event detection methods, especially for researchers who are new in this area. We also describe and compare data collection techniques, the effectiveness and shortcomings of various Twitter and non-Twitter-based features, and discuss various evaluation measures and benchmarking methodologies. Finally, we discuss the trends, limitations, and future directions for detecting events on Twitter

Mining, Modeling, and Analyzing Real-Time Social Trails

Real-time social systems are the fastest growing phenomena on the web, enabling millions of users to generate, share, and consume content on a massive scale. These systems are manifestations of a larger trend toward the global sharing of the real-time interests, affiliations, and activities of everyday users and demand new computational approaches for monitoring, analyzing, and distilling information from the prospective web of real-time content. In this dissertation research, we focus on the real-time social trails that reflect the digital footprints of crowds of real-time web users in response to real-world events or online phenomena. These digital footprints correspond to the artifacts strewn across the real-time web like posting of messages to Twitter or Facebook; the creation, sharing, and viewing of videos on websites like YouTube; and so on. While access to social trails could benefit many domains there is a significant research gap toward discovering, modeling, and leveraging these social trails. Hence, this dissertation research makes three contributions: • The first contribution of this dissertation research is a suite of efficient techniques for discovering non-trivial social trails from large-scale real-time social systems. We first develop a communication-based method using temporal graphs for discovering social trails on a stream of conversations from social messaging systems like instant messages, emails, Twitter directed or @ messages, SMS, etc. and then develop a content-based method using locality sensitive hashing for discovering content based social trails on a stream of text messages like Tweet stream, stream of Facebook messages, YouTube comments, etc. • The second contribution of this dissertation research is a framework for modeling and predicting the spatio-temporal dynamics of social trails. In particular, we develop a probabilistic model that synthesizes two conflicting hypotheses about the nature of online information spread: (i) the spatial influence model, which asserts that social trails propagates to locations that are close by; and (ii) the community affinity influence model, which asserts that social trail prop- agates between locations that are culturally connected, even if they are distant. • The third contribution of this dissertation research is a set of methods for social trail analytics and leveraging social trails for prognostic applications like real-time content recommendation, personalized advertising, and so on. We first analyze geo-spatial social trails of hashtags from Twitter, investigate their spatio-temporal dynamics and then use this analysis to develop a framework for recommending hashtags. Finally, we address the challenge of classifying social trails efficiently on real-time social systems

Learning in Dynamic Data-Streams with a Scarcity of Labels

Analysing data in real-time is a natural and necessary progression from traditional data mining. However, real-time analysis presents additional challenges to batch-analysis; along with strict time and memory constraints, change is a major consideration. In a dynamic stream there is an assumption that the underlying process generating the stream is non-stationary and that concepts within the stream will drift and change over time. Adopting a false assumption that a stream is stationary will result in non-adaptive models degrading and eventually becoming obsolete. The challenge of recognising and reacting to change in a stream is compounded by the scarcity of labels problem. This refers to the very realistic situation in which the true class label of an incoming point is not immediately available (or will never be available) or in situations where manually labelling incoming points is prohibitively expensive. The goal of this thesis is to evaluate unsupervised learning as the basis for online classification in dynamic data-streams with a scarcity of labels. To realise this goal, a novel stream clustering algorithm based on the collective behaviour of ants (Ant Colony Stream Clustering (ACSC)) is proposed. This algorithm is shown to be faster and more accurate than comparative, peer stream-clustering algorithms while requiring fewer sensitive parameters. The principles of ACSC are extended in a second stream-clustering algorithm named Multi-Density Stream Clustering (MDSC). This algorithm has adaptive parameters and crucially, can track clusters and monitor their dynamic behaviour over time. A novel technique called a Dynamic Feature Mask (DFM) is proposed to ``sit on top’’ of these stream-clustering algorithms and can be used to observe and track change at the feature level in a data stream. This Feature Mask acts as an unsupervised feature selection method allowing high-dimensional streams to be clustered. Finally, data-stream clustering is evaluated as an approach to one-class classification and a novel framework (named COCEL: Clustering and One class Classification Ensemble Learning) for classification in dynamic streams with a scarcity of labels is described. The proposed framework can identify and react to change in a stream and hugely reduces the number of required labels (typically less than 0.05% of the entire stream)

A Comprehensive Classification of Business Activities in the Market of Intellectual Property Rights-related Services

Technology and intellectual property markets have witnessed great developments in the last few decades. Due to intellectual property rights gaining more importance and technology companies opening up their innovation processes, a wide range of intellectual property rights related services have emerged in the last two decades. The goal of this research is to develop a comprehensive classification system of intellectual property rights related services (IPSC). The classification is created by applying an ontology engineering process. The IPSC consists of 72 various IPR services divided into six main categories (100 Legal Service; 200 IP Consulting; 300 Matchmaking and Trading; 400 IP Portfolio Processing; 500 IPR-related Financial Service; 600 IPR-related Communication Service). The implications of the thesis are directed to policy makers, technology transfer managers, C-level executives and innovation researchers. The IPSC enables practitioners and researchers to organize industry data that can be thereafter analyzed for better strategy and policy making. In addition, this contributes towards organizing a more transparent and single intellectual property market.:Acknowledgements I Abstract II Contents IV List of Figures VI List of Tables VII 1. Introduction 1 1.1. Introduction to Technology Markets 1 1.2. Explanation of Key Concepts 5 1.3. Research Questions and Goals 9 1.4. Readers Guide 13 2. Literature Review 15 2.1. Intellectual Property Markets State of the Art Review 15 2.2. Ontology Engineering State of the Art Review 22 3. Methodology 26 3.1. Methontology 26 3.2. Planning the IPSC 29 3.3. Specification 30 3.4. Conceptualization 31 3.5. Formalization 32 3.6. Integration 32 3.7. Evaluation 33 3.8. Documentation 33 3.9. Realization and Maintenance 33 4. Data description and collection framework 34 5. Applying Methontology 46 5.1. Knowledge Acquisition and Planning the IPSC 46 5.2. Specification 46 5.3. Conceptualization 47 5.4. Formalization 54 100 Legal Service 56 200 IP Consulting 60 300 Matchmaking and Trading 65 400 IP Portfolio Processing 72 500 IPR-related Financial Service 76 600 IPR-related Communication Service 81 5.5. Integration 86 5.6. Evaluation 95 5.7. Documentation 104 5.8. Realization and Maintenance of the IPSC 106 6. Interview Results and Further Discussions 108 6.1. Implications for Industry 108 6.2. Contributions of the IPSC 110 6.3. Limitations of the IPSC and Future Work 112 7. Conclusions 116 References 120 List of experts interviewed and the date of interview 129 Appendices 13

Performance Evaluation of Network Anomaly Detection Systems

Nowadays, there is a huge and growing concern about security in information and communication technology (ICT) among the scientific community because any attack or anomaly in the network can greatly affect many domains such as national security, private data storage, social welfare, economic issues, and so on. Therefore, the anomaly detection domain is a broad research area, and many different techniques and approaches for this purpose have emerged through the years. Attacks, problems, and internal failures when not detected early may badly harm an entire Network system. Thus, this thesis presents an autonomous profile-based anomaly detection system based on the statistical method Principal Component Analysis (PCADS-AD). This approach creates a network profile called Digital Signature of Network Segment using Flow Analysis (DSNSF) that denotes the predicted normal behavior of a network traffic activity through historical data analysis. That digital signature is used as a threshold for volume anomaly detection to detect disparities in the normal traffic trend. The proposed system uses seven traffic flow attributes: Bits, Packets and Number of Flows to detect problems, and Source and Destination IP addresses and Ports, to provides the network administrator necessary information to solve them. Via evaluation techniques, addition of a different anomaly detection approach, and comparisons to other methods performed in this thesis using real network traffic data, results showed good traffic prediction by the DSNSF and encouraging false alarm generation and detection accuracy on the detection schema. The observed results seek to contribute to the advance of the state of the art in methods and strategies for anomaly detection that aim to surpass some challenges that emerge from the constant growth in complexity, speed and size of today’s large scale networks, also providing high-value results for a better detection in real time.Atualmente, existe uma enorme e crescente preocupação com segurança em tecnologia da informação e comunicação (TIC) entre a comunidade científica. Isto porque qualquer ataque ou anomalia na rede pode afetar a qualidade, interoperabilidade, disponibilidade, e integridade em muitos domínios, como segurança nacional, armazenamento de dados privados, bem-estar social, questões econômicas, e assim por diante. Portanto, a deteção de anomalias é uma ampla área de pesquisa, e muitas técnicas e abordagens diferentes para esse propósito surgiram ao longo dos anos. Ataques, problemas e falhas internas quando não detetados precocemente podem prejudicar gravemente todo um sistema de rede. Assim, esta Tese apresenta um sistema autônomo de deteção de anomalias baseado em perfil utilizando o método estatístico Análise de Componentes Principais (PCADS-AD). Essa abordagem cria um perfil de rede chamado Assinatura Digital do Segmento de Rede usando Análise de Fluxos (DSNSF) que denota o comportamento normal previsto de uma atividade de tráfego de rede por meio da análise de dados históricos. Essa assinatura digital é utilizada como um limiar para deteção de anomalia de volume e identificar disparidades na tendência de tráfego normal. O sistema proposto utiliza sete atributos de fluxo de tráfego: bits, pacotes e número de fluxos para detetar problemas, além de endereços IP e portas de origem e destino para fornecer ao administrador de rede as informações necessárias para resolvê-los. Por meio da utilização de métricas de avaliação, do acrescimento de uma abordagem de deteção distinta da proposta principal e comparações com outros métodos realizados nesta tese usando dados reais de tráfego de rede, os resultados mostraram boas previsões de tráfego pelo DSNSF e resultados encorajadores quanto a geração de alarmes falsos e precisão de deteção. Com os resultados observados nesta tese, este trabalho de doutoramento busca contribuir para o avanço do estado da arte em métodos e estratégias de deteção de anomalias, visando superar alguns desafios que emergem do constante crescimento em complexidade, velocidade e tamanho das redes de grande porte da atualidade, proporcionando também alta performance. Ainda, a baixa complexidade e agilidade do sistema proposto contribuem para que possa ser aplicado a deteção em tempo real

Open dialogues for business model innovation

This thesis was previously held under moratorium from until 20th June 2018 until 30th July 2021.A growing body of research is highlighting how open innovative business models support the growth and economic success of new ideas and technologies. In this Ph.D., building on an action research study in SMEs, I develop the Open Business Model Innovation Framework that accounts for the interactions between value creation and active participation in the development of unmet needs to new business formations. I begin to unpack the process of open business model innovation development supporting the ability of SMEs to build and re-build their businesses.A growing body of research is highlighting how open innovative business models support the growth and economic success of new ideas and technologies. In this Ph.D., building on an action research study in SMEs, I develop the Open Business Model Innovation Framework that accounts for the interactions between value creation and active participation in the development of unmet needs to new business formations. I begin to unpack the process of open business model innovation development supporting the ability of SMEs to build and re-build their businesses