The usage of signatures for authentication is widely accepted, and remains one of the most familiar biometric in our society. Efforts to digitalise and automate the verification of these signatures are hot topics in the field of Machine Learning, and a plethora of different tools and methods have been developed and adapted for this purpose. The intention of this report is to study the authentication of handwritten signatures on digital whiteboards, and how to most effectively set up a dual verification system based on Hidden Markov models (HMMs) and global aggregate features such as average speed. The aim is to gauge which features are Suitable for determining that a signature is in fact genuine Suitable for rejecting forgeries Unsuitable for gauging the authenticity of a signature all together In addition, we take a look at the configuration of the HMMs themselves, in order to find good configurations for The number of components used in the model What type of covariance to use The best threshold to draw the line between a genuine signature and a forgery For the research, we collected a total of 200 signatures and 400 forgeries, gathered from 10 different people on digital whiteboards. We concluded that the best configurations of our HMMs had 11 components, used a full covariance model, and observed about five features, where pressure, angle and speed were the most important. Among the global features, we discarded 11 out of 35 due to either strong correlation with other features, or contained too little discriminatory information. The strongest global features were the ones pertaining to speed, acceleration, direction, and curvature. Using the combined verification we obtained an EER of 7 %, which is in the typical range of contemporary studies. We conclude that the best way to combine global feature verification with local HMM verification is to perform both separately, and only accept signatures that are admissible by both, with a tolerance level for the global and local verifications of 1.2 and 2.5 standard deviations, respectively. / Användandet av signaturer för autentisering är allmänt accepterat, och är fortfarande den mest använda biometriken i vårt samhälle. Arbetet med att digitalisera och automatisera verifieringen av dessa signaturer är ett populärt ämne inom maskininlärning, och en uppsjö av olika verktyg och metoder har utvecklats och anpassats för detta ändamål. Avsikten med denna studie är att bestämma hur man mest framgångsrikt kan inrätta ett verifikationssystem för handskrivna signatures på digitala whiteboards baserat på dolda Markovmodeller (HMMs) och globalt aggregerade attribut. Syftet är att bedöma vilka features som är Lämpliga för att bestämma huruvida en signatur är äkta Lämpliga för att avvisa förfalskningar Olämpliga för att mäta äktheten hos en signatur över huvud taget Utöver detta studerar vi HMM-konfigurationen själv, i syfte att hitta bra konfigurationer för Antalet komponenter som används i modellen Vilken typ av kovarians som ger bäst resultat Det bästa tröskelvärdet vid vilken att dra gränsen för huruvida en signatur är äkta eller förfalskad För forskningen samlade vi totalt in 200 signaturer och 400 förfalskningar från 10 olika personer med hjälp av digitala whiteboards. Vi drog slutsatsen att de bästa konfigurationerna hade 11 komponenter, använde komplett kovarians, och använde cirka fem features, där tryck, vinkel och hastighet var det viktigaste. Bland våra globala features kastade vi 11 av 35 på grund av att de antingen korrelerade för starkt med andra features, eller på grund av att de innehöll för lite information för att utröna huruvida en signatur var äkta eller ej. Våra bästa globala features var de som hänförde sig till hastighet, acceleration, riktning och krökning. Genom att använda den kombinerade verifieraren fick vi en EER på 7 %, vilket är i linje med liknande studier. Vi drog även slutsatsen att det bästa sättet att kombinera global verifiering med lokal HMM-verifiering är att utföra dem separat och endast acceptera signaturer som godkänns av bägge två. Den bästa toleransnivån för den globala och lokala verifieraren var 1,2 och 2,5 standardavvikelser, respektive.

yes / This paper presents a methodology for fault detection, fault prediction and fault isolation based on the integration of hidden Markov modelling (HMM) and Bayesian networks (BN). This addresses the nonlinear and non-Gaussian data characteristics to support fault detection and prediction, within an explainable hybrid framework that captures causality in the complex engineered system. The proposed methodology is based on the analysis of the pattern of similarity in the log-likelihood (LL) sequences against the training data for the mixture of Gaussians HMM (MoG-HMM). The BN model identifies the root cause of detected/predicted faults, using the information propagated from the HMM model as empirical evidence. The feasibility and effectiveness of the presented approach are discussed in conjunction with the application to a real-world case study of an automotive exhaust gas Aftertreatment system. The paper details the implementation of the methodology to this case study, with data available from real-world usage of the system. The results show that the proposed methodology identifies the fault faster and attributes the fault to the correct root cause. While the proposed methodology is illustrated with an automotive case study, its applicability is much wider to the fault detection and prediction problem of any similar complex engineered system.

Image Captioning (IC) aims to generate a coherent and comprehensive textual description that summarizes the complex content of an image. It is a combination of computer vision and natural language processing techniques to encode the visual features of an image and translate them into a sentence. In the context of remote sensing (RS) analysis, IC has been emerging as a new research area of high interest since it not only recognizes the objects within an image but also describes their attributes and relationships. In this thesis, we propose several IC methods for RS image analysis. We focus on the design of different approaches that take into consideration the peculiarity of RS images (e.g. spectral, temporal and spatial properties) and study the benefits of IC in challenging RS applications. In particular, we focus our attention on developing a new decoder which is based on support vector machines. Compared to the traditional decoders that are based on deep learning, the proposed decoder is particularly interesting for those situations in which only a few training samples are available to alleviate the problem of overfitting. The peculiarity of the proposed decoder is its simplicity and efficiency. It is composed of only one hyperparameter, does not require expensive power units and is very fast in terms of training and testing time making it suitable for real life applications. Despite the efforts made in developing reliable and accurate IC systems, the task is far for being solved. The generated descriptions are affected by several errors related to the attributes and the objects present in an RS scene. Once an error occurs, it is propagated through the recurrent layers of the decoders leading to inaccurate descriptions. To cope with this issue, we propose two post-processing techniques with the aim of improving the generated sentences by detecting and correcting the potential errors. They are based on Hidden Markov Model and Viterbi algorithm. The former aims to generate a set of possible states while the latter aims at finding the optimal sequence of states. The proposed post-processing techniques can be injected to any IC system at test time to improve the quality of the generated sentences. While all the captioning systems developed in the RS community are devoted to single and RGB images, we propose two captioning systems that can be applied to multitemporal and multispectral RS images. The proposed captioning systems are able at describing the changes occurred in a given geographical through time. We refer to this new paradigm of analysing multitemporal and multispectral images as change captioning (CC). To test the proposed CC systems, we construct two novel datasets composed of bitemporal RS images. The first one is composed of very high-resolution RGB images while the second one of medium resolution multispectral satellite images. To advance the task of CC, the constructed datasets are publically available in the following link: https://disi.unitn.it/~melgani/datasets.html. Finally, we analyse the potential of IC for content based image retrieval (CBIR) and show its applicability and advantages compared to the traditional techniques. Specifically, we focus our attention on developing a CBIR systems that represents an image with generated descriptions and uses sentence similarity to search and retrieve relevant RS images. Compare to traditional CBIR systems, the proposed system is able to search and retrieve images using either an image or a sentence as a query making it more comfortable for the end-users. The achieved results show the promising potentialities of our proposed methods compared to the baselines and state-of-the art methods.

The hidden Markov model (HMM) is one of the workhorse tools in, for example, statistical signal processing and machine learning. It has found applications in a vast number of fields, ranging all the way from bioscience to speech recognition to modeling of user interactions in social networks. In an HMM, a latent state transitions according to Markovian dynamics. The state is only observed indirectly via a noisy sensor – that is, it is hidden. This type of model is at the center of this thesis, which in turn touches upon three main themes. Firstly, we consider how the parameters of an HMM can be estimated from data. In particular, we explore how recently proposed methods of moments can be combined with more standard maximum likelihood (ML) estimation procedures. The motivation for this is that, albeit the ML estimate possesses many attractive statistical properties, many ML schemes have to rely on local-search procedures in practice, which are only guaranteed to converge to local stationary points in the likelihood surface – potentially inhibiting them from reaching the ML estimate. By combining the two types of algorithms, the goal is to obtain the benefits of both approaches: the consistency and low computational complexity of the former, and the high statistical efficiency of the latter. The filtering problem – estimating the hidden state of the system from observations – is of fundamental importance in many applications. As a second theme, we consider inverse filtering problems for HMMs. In these problems, the setup is reversed; what information about an HMM-filtering system is exposed by its state estimates? We show that it is possible to reconstruct the specifications of the sensor, as well as the observations that were made, from the filtering system’s posterior distributions of the latent state. This can be seen as a way of reverse engineering such a system, or as using an alternative data source to build a model. Thirdly, we consider Markov decision processes (MDPs) – systems with Markovian dynamics where the parameters can be influenced by the choice of a control input. In particular, we show how it is possible to incorporate prior information regarding monotonic structure of the optimal decision policy so as to accelerate its computation. Subsequently, we consider a real-world application by investigating how these models can be used to model the treatment of abdominal aortic aneurysms (AAAs). Our findings are that the structural properties of the optimal treatment policy are different than those used in clinical practice – in particular, that younger patients could benefit from earlier surgery. This indicates an opportunity for improved care of patients with AAAs. / <p>QC 20180301</p>

Detecting a malicious hacker intruding on a network system can be difficult. This challenge is made even more complex by the network activity generated by normal users and by the fact that it is impossible to know the hacker’s exact actions. Instead, the defender of the network system has to infer the hacker’s actions by statistics collected by the intrusion detection system, IDS. This thesis investigates the performance of hidden Markov models, HMM, to detect an intrusion automatically under different background activities generated by normal users. Furthermore, background subtraction techniques with inspiration from computer vision are investigated to see if normal users’ activity can be filtered out to improve the performance of the HMMs.The results suggest that the performance of HMMs are not sensitive to the type of background activity but rather to the number of normal users present. Furthermore, background subtraction enhances the performance of HMMs slightly. However, further investigations into how background subtraction performs when there are many normal users must be done before any definitive conclusions. / Det kan vara svårt att upptäcka en hackare som gör intrång i ett nätverkssystem. Utmaningen blir ännu större genom nätverksaktiviteten som genereras av vanliga användare och av det faktum att det är omöjligt att veta hackarens exakta handlingar. Istället måste nätverkssystemets försvarare använda insamlad data från intrångsdetekteringssystemet, IDS, för att estimera hackarens handlingar. Detta arbete undersöker förmågan hos dolda Markovmodeller, HMM, att automatiskt upptäcka dataintrång under olika typer av bakgrundsaktiviteter som genereras av normala användare. Dessutom undersöks bakgrundssubtraktionstekniker med inspiration från datorseende för att se om normala användares aktivitet kan filtreras bort för att förbättra prestanda hos HMM. Resultaten tyder på att prestandan för HMM inte är känsliga för typen av bakgrundsaktivitet utan snarare för antalet närvarande normala användare. Dessutom förbättrar bakgrundssubtraktion prestandan hos HMM. Det krävs dock mer forskning för att dra definitiva slutsatser kring vilken effekt bakgrundssubstitution har när antalet normala användare är stort.

Objective: Children Born of War (CBOW) are an international and timeless phenomenon that exists in every country involved in war or armed conflict. Nevertheless, little is known on a systematic level about those children, who are typically fathered by a foreign or enemy soldier and born to a local mother. In particular, the identity issues that CBOW often report have remained largely uninvestigated. In the current qualitative study we began filling this gap in the scientific literature by asking how CBOW construct their identity in self-descriptions. Method: We utilized thematic content analysis of N = 122 German CBOWs' answers to an open-ended questionnaire item asking how they see themselves and their identity in the context of being a CBOW. Results: We identified five key themes in CBOW' identity accounts. Loneliness and lack of belonging appeared as a paramount aspect of their self-descriptions next to narratives about belonging and positive relationship. On a less interpersonal basis, we found fighting and surviving and searching for truth and completion overarching aspects of their identities. There were also few accounts growing up unaffected by the fact of being born a CBOW. Although all themes portray different perspectives, they all (but the last one) clearly indicate the impeded circumstances under which CBOW had to grow up. Conclusions: Integrating our findings with existing interdisciplinary literature regarding identity, we discuss implications for future research and clinical and political practice.

The purpose of the study was to examine the impact of internalized stigma of mental illness on the career development of college students with hidden disabilities. The availability of research investigating career variables within this population is limited and is primarily focused within the vocational rehabilitation arena. Therefore, one of the goals of the current study was to link separate bodies of literature on college students with disabilities, career development, and internalized stigma of mental illness. The second goal was to examine the interaction of internalized stigma of mental illness between career decision self-efficacy and career exploration on the perceived likelihood of achieving hoped for occupational possible selves (OPS). The study included college students with hidden disabilities and investigated variables related to mental illness and career. Participants were administered a background information questionnaire, the Career Decision Self-Efficacy scale (CDSE-SF), selected subscales of the Career Exploration Survey (CES), and the Internalized Stigma of Mental Illness scale (ISMI). Contrary to hypotheses, career decision making self-efficacy, career self-exploration, and internalized stigma of mental illness did not have a direct effect on the perceived likelihood of achieving hoped for OPS. However, career environment exploration did have a direct and positive association with perceived likelihood of achieving hoped for OPS. Results further indicated internalized stigma of mental illness did not moderate the effect of career decision self-efficacy and career exploration on the perceived likelihood of achieving one's hoped for occupational self. Study implications, limitations, and future directions are discussed.

Les protéines PentatricoPeptide-Repeats (PPR) représentent la plus grande famille de protéines de liaison à l’ARN connue. Elles sont caractérisées par la présence de motifs répétés en tandem d’environ 35 résidus ayant une structure hélice-tour-hélice. Depuis les premières études sur l’organisme modèle Arabidopsis thaliana, les protéines PPR ont aussi été découvertes chez d’autres espèces non-plantes, incluant les levures et l’humain. Cependant, la détection des protéines PPR en dehors des plantes est compliquée par le fait que les outils de recherche sont tous conçus pour les protéines de plantes. Récemment, une étude réalisée chez les levures a rapporté une méthode itérative semi-automatisée d’identification de PPR utilisant des profils Hidden Markov Models (HMM). Inspirés par cette approche, nous visons ici à développer une méthode complètement automatisée plus généralisable et sensible qui ne dépend pas du protéome de départ. Comme preuve de concept, nous avons choisi une espèce non reliée aux plantes possédant le plus grand nombre de protéines PPR en-dehors des plantes – le protiste marin unicellulaire Diplonema papillatum. Il s’agit d’un modèle émergent ayant reçu beaucoup d’intérêt pour l’excentricité de l’expression de son génome mitochondrial, pour lequel il a été suggéré que les protéines PPR jouent un rôle clé. Nous avons ici développé une approche itérative pour identifier et cataloguer les protéines PPR chez D. papillatum. Les fonctionnalités particulières de notre algorithme incluent l’inspection des intervalles de 30 à 40 résidus entre les motifs classiques déjà identifiés et l’utilisation des structures secondaires caractéristiques des motifs PPR pour valider les motifs candidats nouvellement identifiés. Au final, nous avons identifié près de 800 motifs PPR chez D.papillatum, dont plusieurs motifs « déviants » identifiés dans les espaces entre les motifs. La validation expérimentale des motifs candidats les plus prometteurs est en attente. / PentatricoPeptide-Repeat (PPR) proteins represent the largest family of RNA-binding proteins known. They are defined by containing tandemly arranged, ~35-residue long motifs assuming a helix-turn-helix structure, which are referred to as PPR motifs. Since the seminal studies undertaken in the model organism Arabidopsis, a few PPR proteins have been also discovered outside plants, including yeast and human. However, the detection of PPR proteins in non-plant eukaryotes is complicated by the fact that current search tools are tailored toward plants. Recently, a semi-automated method has been reported for identifying PPR motifs in yeast using iterative searches with profile Hidden Markov models (HMMs). Inspired by this work, we aimed to develop a fully automated, sensitive approach that can be used for detecting PPR proteins in any species, when using the corresponding proteome as input. For a proof of concept, we used a species that contains the largest number of PPR genes outside the plant kingdom –the unicellular protist Diplonema papillatum. This emerging model system has garnered much interest for the eccentricities of its mitochondrial gene expression, in which PPR proteins are posited to play a key role. Here, we have developed an iterative HMM-search method that comprehensively catalogues and classifies PPR motifs in D. papillatum. Particular features of our algorithm are that it inspects closely 30 to 40 residue-long intervals between readily identified (classical) motifs, makes use of the characteristic secondary structure of PPR motifs to validate newly detected candidate motifs. In total, we have identified around 800 PPR motifs in D. papillatum. Including several deviant candidates detected in ”gaps”. High ranking representatives of both classical and deviant motifs await experimental validation.

Modeling complex cognitive phenomena is a challenging task, especially when it is required to account for the functioning of a cognitive system interacting with an uncertain and changing environment. Psychometrics offers a heterogeneous corpus of computational tools to infer latent cognitive constructs from the observation of behavioral outcomes. However, there is not an explicit consensus regarding the optimal way to properly take into account the intrinsic dynamic properties of the environment, as well as the dynamic nature of cognitive states. In the present dissertation, we explore the potentials of relying on discrete state dynamic models to formally account for the unfolding of cognitive sub-processes in changing task environments. In particular, we propose Probabilistic Graphical Models (PGMs) as an ideal and unifying mathematical language to represent cognitive dynamics as structured graphs codifying (causal) relationships between cognitive sub-components which unfolds in discrete time. We propose several works demonstrating the advantage and the representational power of such a modeling framework, by providing dynamic models of cognition specified according to different levels of abstraction.

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