A computational academic integrity framework

L'abast creixent i la naturalesa canviant dels programes acadèmics constitueixen un repte per a la integritat dels protocols tradicionals de proves i exàmens. L'objectiu d¿aquesta tesi és introduir una alternativa als enfocaments tradicionals d'integritat acadèmica, per a cobrir la bretxa del buit de l'anonimat i donar la possibilitat als instructors i administradors acadèmics de fer servir nous mitjans que permetin mantenir la integritat acadèmica i promoguin la responsabilitat, accessibilitat i eficiència, a més de preservar la privadesa i minimitzin la interrupció en el procés d'aprenentatge. Aquest treball té com a objectiu començar un canvi de paradigma en les pràctiques d'integritat acadèmica. La recerca en l'àrea de la identitat de l'estudiant i la garantia de l'autoria són importants perquè la concessió de crèdits d'estudi a entitats no verificades és perjudicial per a la credibilitat institucional i la seguretat pública. Aquesta tesi es basa en la noció que la identitat de l'alumne es compon de dues capes diferents, física i de comportament, en les quals tant els criteris d'identitat com els d'autoria han de ser confirmats per a mantenir un nivell raonable d'integritat acadèmica. Per a això, aquesta tesi s'organitza en tres seccions, cadascuna de les quals aborda el problema des d'una de les perspectives següents: (a) teòrica, (b) empírica i (c) pragmàtica.El creciente alcance y la naturaleza cambiante de los programas académicos constituyen un reto para la integridad de los protocolos tradicionales de pruebas y exámenes. El objetivo de esta tesis es introducir una alternativa a los enfoques tradicionales de integridad académica, para cubrir la brecha del vacío anonimato y dar la posibilidad a los instructores y administradores académicos de usar nuevos medios que permitan mantener la integridad académica y promuevan la responsabilidad, accesibilidad y eficiencia, además de preservar la privacidad y minimizar la interrupción en el proceso de aprendizaje. Este trabajo tiene como objetivo iniciar un cambio de paradigma en las prácticas de integridad académica. La investigación en el área de la identidad del estudiante y la garantía de la autoría son importantes porque la concesión de créditos de estudio a entidades no verificadas es perjudicial para la credibilidad institucional y la seguridad pública. Esta tesis se basa en la noción de que la identidad del alumno se compone de dos capas distintas, física y de comportamiento, en las que tanto los criterios de identidad como los de autoría deben ser confirmados para mantener un nivel razonable de integridad académica. Para ello, esta tesis se organiza en tres secciones, cada una de las cuales aborda el problema desde una de las siguientes perspectivas: (a) teórica, (b) empírica y (c) pragmática.The growing scope and changing nature of academic programmes provide a challenge to the integrity of traditional testing and examination protocols. The aim of this thesis is to introduce an alternative to the traditional approaches to academic integrity, bridging the anonymity gap and empowering instructors and academic administrators with new ways of maintaining academic integrity that preserve privacy, minimize disruption to the learning process, and promote accountability, accessibility and efficiency. This work aims to initiate a paradigm shift in academic integrity practices. Research in the area of learner identity and authorship assurance is important because the award of course credits to unverified entities is detrimental to institutional credibility and public safety. This thesis builds upon the notion of learner identity consisting of two distinct layers (a physical layer and a behavioural layer), where the criteria of identity and authorship must both be confirmed to maintain a reasonable level of academic integrity. To pursue this goal in organized fashion, this thesis has the following three sections: (a) theoretical, (b) empirical, and (c) pragmatic

A Computational Academic Integrity Framework

L'abast creixent i la naturalesa canviant dels programes acadèmics constitueixen un repte per a la integritat dels protocols tradicionals de proves i exàmens. L'objectiu d'aquesta tesi és introduir una alternativa als enfocaments tradicionals d'integritat acadèmica, per a cobrir la bretxa del buit de l'anonimat i donar la possibilitat als instructors i administradors acadèmics de fer servir nous mitjans que permetin mantenir la integritat acadèmica i promoguin la responsabilitat, accessibilitat i eficiència, a més de preservar la privadesa i minimitzin la interrupció en el procés d'aprenentatge. Aquest treball té com a objectiu començar un canvi de paradigma en les pràctiques d'integritat acadèmica. La recerca en l'àrea de la identitat de l'estudiant i la garantia de l'autoria són importants perquè la concessió de crèdits d'estudi a entitats no verificades és perjudicial per a la credibilitat institucional i la seguretat pública. Aquesta tesi es basa en la noció que la identitat de l'alumne es compon de dues capes diferents, física i de comportament, en les quals tant els criteris d'identitat com els d'autoria han de ser confirmats per a mantenir un nivell raonable d'integritat acadèmica. Per a això, aquesta tesi s'organitza en tres seccions, cadascuna de les quals aborda el problema des d'una de les perspectives següents: (a) teòrica, (b) empírica i (c) pragmàtica.El creciente alcance y la naturaleza cambiante de los programas académicos constituyen un reto para la integridad de los protocolos tradicionales de pruebas y exámenes. El objetivo de esta tesis es introducir una alternativa a los enfoques tradicionales de integridad académica, para cubrir la brecha del vacío anonimato y dar la posibilidad a los instructores y administradores académicos de usar nuevos medios que permitan mantener la integridad académica y promuevan la responsabilidad, accesibilidad y eficiencia, además de preservar la privacidad y minimizar la interrupción en el proceso de aprendizaje. Este trabajo tiene como objetivo iniciar un cambio de paradigma en las prácticas de integridad académica. La investigación en el área de la identidad del estudiante y la garantía de la autoría son importantes porque la concesión de créditos de estudio a entidades no verificadas es perjudicial para la credibilidad institucional y la seguridad pública. Esta tesis se basa en la noción de que la identidad del alumno se compone de dos capas distintas, física y de comportamiento, en las que tanto los criterios de identidad como los de autoría deben ser confirmados para mantener un nivel razonable de integridad académica. Para ello, esta tesis se organiza en tres secciones, cada una de las cuales aborda el problema desde una de las siguientes perspectivas: (a) teórica, (b) empírica y (c) pragmática.The growing scope and changing nature of academic programmes provide a challenge to the integrity of traditional testing and examination protocols. The aim of this thesis is to introduce an alternative to the traditional approaches to academic integrity, bridging the anonymity gap and empowering instructors and academic administrators with new ways of maintaining academic integrity that preserve privacy, minimize disruption to the learning process, and promote accountability, accessibility and efficiency. This work aims to initiate a paradigm shift in academic integrity practices. Research in the area of learner identity and authorship assurance is important because the award of course credits to unverified entities is detrimental to institutional credibility and public safety. This thesis builds upon the notion of learner identity consisting of two distinct layers (a physical layer and a behavioural layer), where the criteria of identity and authorship must both be confirmed to maintain a reasonable level of academic integrity. To pursue this goal in organized fashion, this thesis has the following three sections: (a) theoretical, (b) empirical, and (c) pragmatic

Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications

The last decade has seen a revolution in the theory and application of machine learning and pattern recognition. Through these advancements, variable ranking has emerged as an active and growing research area and it is now beginning to be applied to many new problems. The rationale behind this fact is that many pattern recognition problems are by nature ranking problems. The main objective of a ranking algorithm is to sort objects according to some criteria, so that, the most relevant items will appear early in the produced result list. Ranking methods can be analyzed from two different methodological perspectives: ranking to learn and learning to rank. The former aims at studying methods and techniques to sort objects for improving the accuracy of a machine learning model. Enhancing a model performance can be challenging at times. For example, in pattern classification tasks, different data representations can complicate and hide the different explanatory factors of variation behind the data. In particular, hand-crafted features contain many cues that are either redundant or irrelevant, which turn out to reduce the overall accuracy of the classifier. In such a case feature selection is used, that, by producing ranked lists of features, helps to filter out the unwanted information. Moreover, in real-time systems (e.g., visual trackers) ranking approaches are used as optimization procedures which improve the robustness of the system that deals with the high variability of the image streams that change over time. The other way around, learning to rank is necessary in the construction of ranking models for information retrieval, biometric authentication, re-identification, and recommender systems. In this context, the ranking model's purpose is to sort objects according to their degrees of relevance, importance, or preference as defined in the specific application.Comment: European PhD Thesis. arXiv admin note: text overlap with arXiv:1601.06615, arXiv:1505.06821, arXiv:1704.02665 by other author

Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications

The last decade has seen a revolution in the theory and application of machine learning and pattern recognition. Through these advancements, variable ranking has emerged as an active and growing research area and it is now beginning to be applied to many new problems. The rationale behind this fact is that many pattern recognition problems are by nature ranking problems. The main objective of a ranking algorithm is to sort objects according to some criteria, so that, the most relevant items will appear early in the produced result list. Ranking methods can be analyzed from two different methodological perspectives: ranking to learn and learning to rank. The former aims at studying methods and techniques to sort objects for improving the accuracy of a machine learning model. Enhancing a model performance can be challenging at times. For example, in pattern classification tasks, different data representations can complicate and hide the different explanatory factors of variation behind the data. In particular, hand-crafted features contain many cues that are either redundant or irrelevant, which turn out to reduce the overall accuracy of the classifier. In such a case feature selection is used, that, by producing ranked lists of features, helps to filter out the unwanted information. Moreover, in real-time systems (e.g., visual trackers) ranking approaches are used as optimization procedures which improve the robustness of the system that deals with the high variability of the image streams that change over time. The other way around, learning to rank is necessary in the construction of ranking models for information retrieval, biometric authentication, re-identification, and recommender systems. In this context, the ranking model's purpose is to sort objects according to their degrees of relevance, importance, or preference as defined in the specific application.Comment: European PhD Thesis. arXiv admin note: text overlap with arXiv:1601.06615, arXiv:1505.06821, arXiv:1704.02665 by other author

Machine learning-based Naive Bayes approach for divulgence of Spam Comment in Youtube station

In the 21st Century, web-based media assumes an indispensable part in the interaction and communication of civilization. As an illustration of web-based media viz. YouTube, Facebook, Twitter, etc., can increase the social regard of a person just as a gathering. Yet, every innovation has its pros as well as cons. In some YouTube channels, a machine-made spam remark is produced on that recordings, moreover, a few phony clients additionally remark a spam comment which creates an adverse effect on that YouTube channel.  The spam remarks can be distinguished by using AI (artificial intelligence) which is based on different Algorithms namely Naive Bayes, SVM, Random Forest, ANN, etc. The present investigation is focussed on a machine learning-based Naive Bayes classifier ordered methodology for the identification of spam remarks on YouTub

Machine learning-based Naive Bayes approach for divulgence of Spam Comment in Youtube station

In the 21st Century, web-based media assumes an indispensable part in the interaction and communication of civilization. As an illustration of web-based media viz. YouTube, Facebook, Twitter, etc., can increase the social regard of a person just as a gathering. Yet, every innovation has its pros as well as cons. In some YouTube channels, a machine-made spam remark is produced on that recordings, moreover, a few phony clients additionally remark a spam comment which creates an adverse effect on that YouTube channel.  The spam remarks can be distinguished by using AI (artificial intelligence) which is based on different Algorithms namely Naive Bayes, SVM, Random Forest, ANN, etc. The present investigation is focussed on a machine learning-based Naive Bayes classifier ordered methodology for the identification of spam remarks on YouTub

The impact of collarette region-based convolutional neural network for iris recognition

Iris recognition is a biometric technique that reliably and quickly recognizes a person by their iris based on unique biological characteristics. Iris has an exceptional structure and it provides very rich feature spaces as freckles, stripes, coronas, zigzag collarette area, etc. It has many features where its growing interest in biometric recognition lies. This paper proposes an improved iris recognition method for person identification based on Convolutional Neural Networks (CNN) with an improved recognition rate based on a contribution on zigzag collarette area - the area surrounding the pupil - recognition. Our work is in the field of biometrics especially iris recognition; the iris recognition rate using the full circle of the zigzag collarette was compared with the detection rate using the lower semicircle of the zigzag collarette. The classification of the collarette is based on the Alex-Net model to learn this feature, the use of the couple (collarette/CNN) allows for noiseless and more targeted characterization and also an automatic extraction of the lower semicircle of the collarette region, finally, the SVM training model is used for classification using grayscale eye image data taken from (CASIA-iris-V4) database. The experimental results show that our contribution proves to be the best accurate, because the CNN can effectively extract the image features with higher classification accuracy and because our new method, which uses the lower semicircle of the collarette region, achieved the highest recognition accuracy compared with the old methods that use the full circle of collarette region

The Stylometric Processing of Sensory Open Source Data

This research project’s end goal is on the Lone Wolf Terrorist. The project uses an exploratory approach to the self-radicalisation problem by creating a stylistic fingerprint of a person's personality, or self, from subtle characteristics hidden in a person's writing style. It separates the identity of one person from another based on their writing style. It also separates the writings of suicide attackers from ‘normal' bloggers by critical slowing down; a dynamical property used to develop early warning signs of tipping points. It identifies changes in a person's moods, or shifts from one state to another, that might indicate a tipping point for self-radicalisation. Research into authorship identity using personality is a relatively new area in the field of neurolinguistics. There are very few methods that model how an individual's cognitive functions present themselves in writing. Here, we develop a novel algorithm, RPAS, which draws on cognitive functions such as aging, sensory processing, abstract or concrete thinking through referential activity emotional experiences, and a person's internal gender for identity. We use well-known techniques such as Principal Component Analysis, Linear Discriminant Analysis, and the Vector Space Method to cluster multiple anonymous-authored works. Here we use a new approach, using seriation with noise to separate subtle features in individuals. We conduct time series analysis using modified variants of 1-lag autocorrelation and the coefficient of skewness, two statistical metrics that change near a tipping point, to track serious life events in an individual through cognitive linguistic markers. In our journey of discovery, we uncover secrets about the Elizabethan playwrights hidden for over 400 years. We uncover markers for depression and anxiety in modern-day writers and identify linguistic cues for Alzheimer's disease much earlier than other studies using sensory processing. In using these techniques on the Lone Wolf, we can separate their writing style used before their attacks that differs from other writing

Continuous User Authentication Using Multi-Modal Biometrics

It is commonly acknowledged that mobile devices now form an integral part of an individual’s everyday life. The modern mobile handheld devices are capable to provide a wide range of services and applications over multiple networks. With the increasing capability and accessibility, they introduce additional demands in term of security. This thesis explores the need for authentication on mobile devices and proposes a novel mechanism to improve the current techniques. The research begins with an intensive review of mobile technologies and the current security challenges that mobile devices experience to illustrate the imperative of authentication on mobile devices. The research then highlights the existing authentication mechanism and a wide range of weakness. To this end, biometric approaches are identified as an appropriate solution an opportunity for security to be maintained beyond point-of-entry. Indeed, by utilising behaviour biometric techniques, the authentication mechanism can be performed in a continuous and transparent fashion. This research investigated three behavioural biometric techniques based on SMS texting activities and messages, looking to apply these techniques as a multi-modal biometric authentication method for mobile devices. The results showed that linguistic profiling; keystroke dynamics and behaviour profiling can be used to discriminate users with overall Equal Error Rates (EER) 12.8%, 20.8% and 9.2% respectively. By using a combination of biometrics, the results showed clearly that the classification performance is better than using single biometric technique achieving EER 3.3%. Based on these findings, a novel architecture of multi-modal biometric authentication on mobile devices is proposed. The framework is able to provide a robust, continuous and transparent authentication in standalone and server-client modes regardless of mobile hardware configuration. The framework is able to continuously maintain the security status of the devices. With a high level of security status, users are permitted to access sensitive services and data. On the other hand, with the low level of security, users are required to re-authenticate before accessing sensitive service or data

Stylistic atructures: a computational approach to text classification

The problem of authorship attribution has received attention both in the academic world (e.g. did Shakespeare or Marlowe write Edward III?) and outside (e.g. is this confession really the words of the accused or was it made up by someone else?). Previous studies by statisticians and literary scholars have sought "verbal habits" that characterize particular authors consistently. By and large, this has meant looking for distinctive rates of usage of specific marker words -- as in the classic study by Mosteller and Wallace of the Federalist Papers. The present study is based on the premiss that authorship attribution is just one type of text classification and that advances in this area can be made by applying and adapting techniques from the field of machine learning. Five different trainable text-classification systems are described, which differ from current stylometric practice in a number of ways, in particular by using a wider variety of marker patterns than customary and by seeking such markers automatically, without being told what to look for. A comparison of the strengths and weaknesses of these systems, when tested on a representative range of text-classification problems, confirms the importance of paying more attention than usual to alternative methods of representing distinctive differences between types of text. The thesis concludes with suggestions on how to make further progress towards the goal of a fully automatic, trainable text-classification system