ON META-NETWORKS, DEEP LEARNING, TIME AND JIHADISM

Il terrorismo di stampo jihadista rappresenta una minaccia per la società e una sfida per gli scienziati interessati a comprenderne la complessità. Questa complessità richiede costantemente nuovi sviluppi in termini di ricerca sul terrorismo. Migliorare la conoscenza empirica rispetto a tale fenomeno può potenzialmente contribuire a sviluppare applicazioni concrete e, in ultima istanza, a prevenire danni all’uomo. In considerazione di tali aspetti, questa tesi presenta un nuovo quadro metodologico che integra scienza delle reti, modelli stocastici e apprendimento profondo per far luce sul terrorismo jihadista sia a livello esplicativo che predittivo. In particolare, questo lavoro compara e analizza le organizzazioni jihadiste più attive a livello mondiale (ovvero lo Stato Islamico, i Talebani, Al Qaeda, Boko Haram e Al Shabaab) per studiarne i pattern comportamentali e predirne le future azioni. Attraverso un impianto teorico che si poggia sulla concentrazione spaziale del crimine e sulle prospettive strategiche del comportamento terroristico, questa tesi persegue tre obiettivi collegati utilizzando altrettante tecniche ibride. In primo luogo, verrà esplorata la complessità operativa delle organizzazioni jihadiste attraverso l’analisi di matrici stocastiche di transizione e verrà presentato un nuovo coefficiente, denominato “Normalized Transition Similarity”, che misura la somiglianza fra paia di gruppi in termini di dinamiche operative. In secondo luogo, i processi stocastici di Hawkes aiuteranno a testare la presenza di meccanismi di dipendenza temporale all’interno delle più comuni sotto-sequenze strategiche di ciascun gruppo. Infine, il framework integrerà la meta-reti complesse e l’apprendimento profondo per classificare e prevedere i target a maggiore rischio di essere colpiti dalle organizzazioni jihadiste durante i loro futuri attacchi. Per quanto riguarda i risultati, le matrici stocastiche di transizione mostrano che i gruppi terroristici possiedono un ricco e complesso repertorio di combinazioni in termini di armi e obiettivi. Inoltre, i processi di Hawkes indicano la presenza di diffusa self-excitability nelle sequenze di eventi. Infine, i modelli predittivi che sfruttano la flessibilità delle serie temporali derivanti da grafi dinamici e le reti neurali Long Short-Term Memory forniscono risultati promettenti rispetto ai target più a rischio. Nel complesso, questo lavoro ambisce a dimostrare come connessioni astratte e nascoste fra eventi possano essere fondamentali nel rivelare le meccaniche del comportamento jihadista e come processi memory-like (ovvero molteplici comportamenti ricorrenti, interconnessi e non randomici) possano risultare estremamente utili nel comprendere le modalità attraverso cui tali organizzazioni operano.Jihadist terrorism represents a global threat for societies and a challenge for scientists interested in understanding its complexity. This complexity continuously calls for developments in terrorism research. Enhancing the empirical knowledge on the phenomenon can potentially contribute to developing concrete real-world applications and, ultimately, to the prevention of societal damages. In light of these aspects, this work presents a novel methodological framework that integrates network science, mathematical modeling, and deep learning to shed light on jihadism, both at the explanatory and predictive levels. Specifically, this dissertation will compare and analyze the world's most active jihadist terrorist organizations (i.e. The Islamic State, the Taliban, Al Qaeda, Boko Haram, and Al Shabaab) to investigate their behavioral patterns and forecast their future actions. Building upon a theoretical framework that relies on the spatial concentration of terrorist violence and the strategic perspective of terrorist behavior, this dissertation will pursue three linked tasks, employing as many hybrid techniques. Firstly, explore the operational complexity of jihadist organizations using stochastic transition matrices and present Normalized Transition Similarity, a novel coefficient of pairwise similarity in terms of strategic behavior. Secondly, investigate the presence of time-dependent dynamics in attack sequences using Hawkes point processes. Thirdly, integrate complex meta-networks and deep learning to rank and forecast most probable future targets attacked by the jihadist groups. Concerning the results, stochastic transition matrices show that terrorist groups possess a complex repertoire of combinations in the use of weapons and targets. Furthermore, Hawkes models indicate the diffused presence of self-excitability in attack sequences. Finally, forecasting models that exploit the flexibility of graph-derived time series and Long Short-Term Memory networks provide promising results in terms of correct predictions of most likely terrorist targets. Overall, this research seeks to reveal how hidden abstract connections between events can be exploited to unveil jihadist mechanics and how memory-like processes (i.e. multiple non-random parallel and interconnected recurrent behaviors) might illuminate the way in which these groups act

Discovering Regularity in Point Clouds of Urban Scenes

Despite the apparent chaos of the urban environment, cities are actually replete with regularity. From the grid of streets laid out over the earth, to the lattice of windows thrown up into the sky, periodic regularity abounds in the urban scene. Just as salient, though less uniform, are the self-similar branching patterns of trees and vegetation that line streets and fill parks. We propose novel methods for discovering these regularities in 3D range scans acquired by a time-of-flight laser sensor. The applications of this regularity information are broad, and we present two original algorithms. The first exploits the efficiency of the Fourier transform for the real-time detection of periodicity in building facades. Periodic regularity is discovered online by doing a plane sweep across the scene and analyzing the frequency space of each column in the sweep. The simplicity and online nature of this algorithm allow it to be embedded in scanner hardware, making periodicity detection a built-in feature of future 3D cameras. We demonstrate the usefulness of periodicity in view registration, compression, segmentation, and facade reconstruction. The second algorithm leverages the hierarchical decomposition and locality in space of the wavelet transform to find stochastic parameters for procedural models that succinctly describe vegetation. These procedural models facilitate the generation of virtual worlds for architecture, gaming, and augmented reality. The self-similarity of vegetation can be inferred using multi-resolution analysis to discover the underlying branching patterns. We present a unified framework of these tools, enabling the modeling, transmission, and compression of high-resolution, accurate, and immersive 3D images

Computational Analysis of Greek folk music of the Aegean islands

Αν και έχουν αναπτυχθεί νεότερα και πιο ανεπτυγμένα μοντέλα υπολογιστικής μουσικής ανάλυσης με στόχο την αύξηση διαθέσιμης πληροφορίας στον κλάδο της μουσικολογίας, υπάρχει πολύ λίγη έρευνα στην υπολογιστική ανάλυση δημοτικής μουσικής γενικότερα και ελληνικής δημοτικής μουσικής ειδικότερα. Στόχος της παρούσας εργασίας είναι η διερεύνηση ποικίλων τύπων μουσικών χαρακτηριστικών και προτύπων στη δημοτική μουσική των νησιών του Αιγαίου και η παροχή χρήσιμης πληροφορίας σχετικά με τη δομή και το περιεχόμενο του εν λόγω είδους. Επιπρόσθετα, με στόχο τη σύγκριση μουσικών αποσπασμάτων χορών Συρτού και Μπάλου, αλλά και γεωγραφικών περιοχών από τις οποίες προέρχονται, 73 αποσπάσματα συγκεντρώθηκαν συνολικά σε μια βάση δεδομένων και αναλύθηκαν. Η εξαγωγή χαρακτηριστικών και η ανάλυση προτύπων ανέδειξαν μελωδικές και ρυθμικές διαφορές τόσο ανάμεσα στα δύο είδη χορών όσο και στις διάφορες νησιωτικές περιοχές, ενώ υπήρξαν επίσης ποικίλες ομοιότητες σε όλο το σύνολο των δεδομένων.While newer, advanced computational music analysis models have been developed with the intentions of increasing available information in this field, very little research exists on the computational analysis of folk music in general and Greek folk music in specific. The aim of this study was to examine various types of musical features and patterns in the folk music of the Aegean islands and provide useful information about the structure and the content of this music style. In addition, to compare the tunes of Syrtos and Mpalos dances, but also the various island regions from which they originate, a total of 73 tunes were included in the constructed dataset and the analyses. Feature extraction and pattern analysis revealed that there are indeed melodic and temporal differences both between the two dance types and between the island regions, while there were also various important similarities throughout the whole dataset

Optimised meta-clustering approach for clustering Time Series Matrices

The prognostics (health state) of multiple components represented as time series data stored in vectors and matrices were processed and clustered more effectively and efficiently using the newly devised ‘Meta-Clustering’ approach. These time series data gathered from large applications and systems in diverse fields such as communication, medicine, data mining, audio, visual applications, and sensors. The reason time series data was used as the domain of this research is that meaningful information could be extracted regarding the characteristics of systems and components found in large applications. Also when it came to clustering, only time series data would allow us to group these data according to their life cycle, i.e. from the time which they were healthy until the time which they start to develop faults and ultimately fail. Therefore by proposing a technique that can better process extracted time series data would significantly cut down on space and time consumption which are both crucial factors in data mining. This approach will, as a result, improve the current state of the art pattern recognition algorithms such as K-NM as the clusters will be identified faster while consuming less space. The project also has application implications in the sense that by calculating the distance between the similar components faster while also consuming less space means that the prognostics of multiple components clustered can be realised and understood more efficiently. This was achieved by using the Meta-Clustering approach to process and cluster the time series data by first extracting and storing the time series data as a two-dimensional matrix. Then implementing an enhance K-NM clustering algorithm based on the notion of Meta-Clustering and using the Euclidean distance tool to measure the similarity between the different set of failure patterns in space. This approach would initially classify and organise each component within its own refined individual cluster. This would provide the most relevant set of failure patterns that show the highest level of similarity and would also get rid of any unnecessary data that adds no value towards better understating the failure/health state of the component. Then during the second stage, once these clusters were effectively obtained, the following inner clusters initially formed are thereby grouped into one general cluster that now represents the prognostics of all the processed components. The approach was tested on multivariate time series data extracted from IGBT components within Matlab and the results achieved from this experiment showed that the optimised Meta-Clustering approach proposed does indeed consume less time and space to cluster the prognostics of IGBT components as compared to existing data mining techniques

Behaviour Profiling using Wearable Sensors for Pervasive Healthcare

In recent years, sensor technology has advanced in terms of hardware sophistication and miniaturisation. This has led to the incorporation of unobtrusive, low-power sensors into networks centred on human participants, called Body Sensor Networks. Amongst the most important applications of these networks is their use in healthcare and healthy living. The technology has the possibility of decreasing burden on the healthcare systems by providing care at home, enabling early detection of symptoms, monitoring recovery remotely, and avoiding serious chronic illnesses by promoting healthy living through objective feedback. In this thesis, machine learning and data mining techniques are developed to estimate medically relevant parameters from a participant‘s activity and behaviour parameters, derived from simple, body-worn sensors. The first abstraction from raw sensor data is the recognition and analysis of activity. Machine learning analysis is applied to a study of activity profiling to detect impaired limb and torso mobility. One of the advances in this thesis to activity recognition research is in the application of machine learning to the analysis of 'transitional activities': transient activity that occurs as people change their activity. A framework is proposed for the detection and analysis of transitional activities. To demonstrate the utility of transition analysis, we apply the algorithms to a study of participants undergoing and recovering from surgery. We demonstrate that it is possible to see meaningful changes in the transitional activity as the participants recover. Assuming long-term monitoring, we expect a large historical database of activity to quickly accumulate. We develop algorithms to mine temporal associations to activity patterns. This gives an outline of the user‘s routine. Methods for visual and quantitative analysis of routine using this summary data structure are proposed and validated. The activity and routine mining methodologies developed for specialised sensors are adapted to a smartphone application, enabling large-scale use. Validation of the algorithms is performed using datasets collected in laboratory settings, and free living scenarios. Finally, future research directions and potential improvements to the techniques developed in this thesis are outlined

Machine Learning Guided Discovery and Design for Inertial Confinement Fusion

Inertial confinement fusion (ICF) experiments at the National Ignition Facility (NIF) and their corresponding computer simulations produce an immense amount of rich data. However, quantitatively interpreting that data remains a grand challenge. Design spaces are vast, data volumes are large, and the relationship between models and experiments may be uncertain. We propose using machine learning to aid in the design and understanding of ICF implosions by integrating simulation and experimental data into a common frame-work. We begin by illustrating an early success of this data-driven design approach which resulted in the discovery of a new class of high performing ovoid-shaped implosion simulations. The ovoids achieve robust performance from the generation of zonal flows within the hotspot, revealing physics that had not previously been observed in ICF capsules. The ovoid discovery also revealed deficiencies in common machine learning algorithms for modeling ICF data. To overcome these inadequacies, we developed a novel algorithm, deep jointly-informed neural networks (DJINN), which enables non-data scientists to quickly train neural networks on their own datasets. DJINN is routinely used for modeling data ICF data and for a variety of other applications (uncertainty quantification; climate, nuclear, and atomic physics data). We demonstrate how DJINN is used to perform parameter inference tasks for NIF data, and how transfer learning with DJINN enables us to create predictive models of direct drive experiments at the Omega laser facility. Much of this work focuses on scalar or modest-size vector data, however many ICF diagnostics produce a variety of images, spectra, and sequential data. We end with a brief exploration of sequence-to-sequence models for emulating time-dependent multiphysics systems of varying complexity. This is a first step toward incorporating multimodal time-dependent data into our analyses to better constrain our predictive models

Decision-Theoretic Meta-reasoning in Partially Observable and Decentralized Settings

This thesis examines decentralized meta-reasoning. For a single agent or multiple agents, it may not be enough for agents to compute correct decisions if they do not do so in a timely or resource efficient fashion. The utility of agent decisions typically increases with decision quality, but decreases with computation time. The reasoning about one\u27s computation process is referred to as meta-reasoning. Aspects of meta-reasoning considered in this thesis include the reasoning about how to allocate computational resources, including when to stop one type of computation and begin another, and when to stop all computation and report an answer. Given a computational model, this translates into computing how to schedule the basic computations that solve a problem. This thesis constructs meta-reasoning strategies for the purposes of monitoring and control in multi-agent settings, specifically settings that can be modeled by the Decentralized Partially Observable Markov Decision Process (Dec-POMDP). It uses decision theory to optimize computation for efficiency in time and space in communicative and non-communicative decentralized settings. Whereas base-level reasoning describes the optimization of actual agent behaviors, the meta-reasoning strategies produced by this thesis dynamically optimize the computational resources which lead to the selection of base-level behaviors

Computational Approaches to Drug Profiling and Drug-Protein Interactions

Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a long period of stagnation in drug approvals. Due to the extreme costs associated with introducing a drug to the market, locating and understanding the reasons for clinical failure is key to future productivity. As part of this PhD, three main contributions were made in this respect. First, the web platform, LigNFam enables users to interactively explore similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly, two deep-learning-based binding site comparison tools were developed, competing with the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold relationships and has already been used in multiple projects, including integration into a virtual screening pipeline to increase the tractability of ultra-large screening experiments. Together, and with existing tools, the contributions made will aid in the understanding of drug-protein relationships, particularly in the fields of off-target prediction and drug repurposing, helping to design better drugs faster

Deep learning for real-time traffic signal control on urban networks

Real-time traffic signal controls are frequently challenged by (1) uncertain knowledge about the traffic states; (2) need for efficient computation to allow timely decisions; (3) multiple objectives such as traffic delays and vehicle emissions that are difficult to optimize; and (4) idealized assumptions about data completeness and quality that are often made in developing many theoretical signal control models. This thesis addresses these challenges by proposing two real-time signal control frameworks based on deep learning techniques, followed by extensive simulation tests that verifies their effectiveness in view of the aforementioned challenges. The first method, called the Nonlinear Decision Rule (NDR), defines a nonlinear mapping between network states and signal control parameters to network performances based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of processing traffic information in the near past. The NDR is implemented and tested within microscopic traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization procedure aiming to reduce delay, CO2 and black carbon emissions. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in optimizing different traffic and environmental objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The second method, called the Advanced Reinforcement Learning (ARL), employs the potential-based reward shaping function using Q-learning and 3rd party advisor to enhance its performance over conventional reinforcement learning. The potential-based reward shaping in this thesis obtains an opinion from the 3rd party advisor when calculating reward. This technique can resolve the problem of sparse reward and slow learning speed. The ARL is tested with a range of existing reinforcement learning methods. The results clearly show that ARL outperforms the other models in almost all the scenarios. Lastly, this thesis evaluates the impact of information availability and quality on different real-time signal control methods, including the two proposed ones. This is driven by the observation that most responsive signal control models in the literature tend to make idealized assumptions on the quality and availability of data. This research shows the varying levels of performance deterioration of different signal controllers in the presence of missing data, data noise, and different data types. Such knowledge and insights are crucial for real-world implementation of these signal control methods.Open Acces