1,430 research outputs found

    On the Significance of Distance in Machine Learning

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    Avstandsbegrepet er grunnleggende i maskinlæring. Hvordan vi velger å måle avstand har betydning, men det er ofte utfordrende å finne et passende avstandsmål. Metrisk læring kan brukes til å lære funksjoner som implementerer avstand eller avstandslignende mål. Vanlige dyplæringsmodeller er sårbare for modifikasjoner av input som har til hensikt å lure modellen (adversarial examples, motstridende eksempler). Konstruksjon av modeller som er robuste mot denne typen angrep er av stor betydning for å kunne utnytte maskinlæringsmodeller i større skala, og et passende avstandsmål kan brukes til å studere slik motstandsdyktighet. Ofte eksisterer det hierarkiske relasjoner blant klasser, og disse relasjonene kan da representeres av den hierarkiske avstanden til klasser. I klassifiseringsproblemer som må ta i betraktning disse klasserelasjonene, kan hierarkiinformert klassifisering brukes. Jeg har utviklet en metode kalt /distance-ratio/-basert (DR) metrisk læring. I motsetning til den formuleringen som normalt anvendes har DR-formuleringen to gunstige egenskaper. For det første er det skala-invariant med hensyn til rommet det projiseres til. For det andre har optimale klassekonfidensverdier på klasserepresentantene. Dersom rommet for å konstruere modifikasjoner er tilstrekklig stort, vil man med standard adversarial accuracy (SAA, standard motstridende nøyaktighet) risikere at naturlige datapunkter blir betraktet som motstridende eksempler. Dette kan være en årsak til SAA ofte går på bekostning av nøyaktighet. For å løse dette problemet har jeg utviklet en ny definisjon på motstridende nøyaktighet kalt Voronoi-epsilon adversarial accuracy (VAA, Voronoi-epsilon motstridende nøyaktighet). VAA utvider studiet av lokal robusthet til global robusthet. Klassehierarkisk informasjon er ikke tilgjengelig for alle datasett. For å håndtere denne utfordringen har jeg undersøkt om klassifikasjonsbaserte metriske læringsmodeller kan brukes til å utlede klassehierarkiet. Videre har jeg undersøkt de mulige effektene av robusthet på feature space (egenskapsrom). Jeg fant da at avstandsstrukturen til et egenskapsrom trent for robusthet har større likhet med avstandsstrukturen i rådata enn et egenskapsrom trent uten robusthet.The notion of distance is fundamental in machine learning. The choice of distance matters, but it is often challenging to find an appropriate distance. Metric learning can be used for learning distance(-like) functions. Common deep learning models are vulnerable to the adversarial modification of inputs. Devising adversarially robust models is of immense importance for the wide deployment of machine learning models, and distance can be used for the study of adversarial robustness. Often, hierarchical relationships exist among classes, and these relationships can be represented by the hierarchical distance of classes. For classification problems that must take these class relationships into account, hierarchy-informed classification can be used. I propose distance-ratio-based (DR) formulation for metric learning. In contrast to the commonly used formulation, DR formulation has two favorable properties. First, it is invariant of the scale of an embedding. Secondly, it has optimal class confidence values on class representatives. For a large perturbation budget, standard adversarial accuracy (SAA) allows natural data points to be considered as adversarial examples. This could be a reason for the tradeoff between accuracy and SAA. To resolve the issue, I proposed a new definition of adversarial accuracy named Voronoi-epsilon adversarial accuracy (VAA). VAA extends the study of local robustness to global robustness. Class hierarchical information is not available for all datasets. To handle this challenge, I investigated whether classification-based metric learning models can be used to infer class hierarchy. Furthermore, I explored the possible effects of adversarial robustness on feature space. I found that the distance structure of robustly trained feature space resembles that of input space to a greater extent than does standard trained feature space.Doktorgradsavhandlin

    An Empirical Research Agenda for the Forensic Sciences

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    After the National Academy of Sciences issued a stunning report in 2009 on the unscientific state of many forensic science subfields, forensic science has undergone internal and external scrutiny that it had managed to avoid for decades. Although some reform efforts are underway, forensic science writ large has yet to embrace and settle upon an empirical research agenda that addresses knowledge gaps pertaining to the reliability of its methods. Our paper addresses this problem by proposing a preliminary set of fourteen empirical studies for the forensic sciences. Following a brief discussion of the courtroom treatment of forensic science evidence, we sketch a series of studies that should be conducted to increase understanding of what forensic examiners are doing, how accurately they are doing it, and how cognitive bias may affect the work product. We also propose several studies that examine how the specific questions examiners are asked might affect the validity and persuasiveness of examiners’ responses. We conclude by affirming the importance of developing a research culture within the forensic sciences that includes a commitment to conducting, participating in, and relying upon high quality empirical research

    Malware Triage Approach using a Task Memory based on Meta-Transfer Learning Framework

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    To enhance the efficiency of incident response triage operations, it is not cost-effective to defend all systems equally in a complex cyber environment. Instead, prioritizing the defense of critical functionality and the most vulnerable systems is desirable. Threat intelligence is crucial for guiding Security Operations Center (SOC) analysts' focus toward specific system activity and provides the primary contextual foundation for interpreting security alerts. This paper explores novel approaches for improving incident response triage operations, including dealing with attacks and zero-day malware. This solution for rapid prioritization of different malware have been raised to formulate fast response plans to minimize socioeconomic damage from the massive growth of malware attacks in recent years, it can also be extended to other incident response. We propose a malware triage approach that can rapidly classify and prioritize different malware classes to address this concern. We utilize a pre-trained ResNet18 network based on Siamese Neural Network (SNN) to reduce the biases in weights and parameters. Furthermore, our approach incorporates external task memory to retain the task information of previously encountered examples. This helps to transfer experience to new samples and reduces computational costs, without requiring backpropagation on external memory. Evaluation results indicate that the classification aspect of our proposed method surpasses other similar classification techniques in terms of performance. This new triage strategy based on task memory with meta-learning evaluates the level of similarity matching across malware classes to identify any risky and unknown malware (e.g., zero-day attacks) so that a defense of those that support critical functionality can be conducted

    Proceedings of the First Workshop on Computing News Storylines (CNewsStory 2015)

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    This volume contains the proceedings of the 1st Workshop on Computing News Storylines (CNewsStory 2015) held in conjunction with the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2015) at the China National Convention Center in Beijing, on July 31st 2015. Narratives are at the heart of information sharing. Ever since people began to share their experiences, they have connected them to form narratives. The study od storytelling and the field of literary theory called narratology have developed complex frameworks and models related to various aspects of narrative such as plots structures, narrative embeddings, characters’ perspectives, reader response, point of view, narrative voice, narrative goals, and many others. These notions from narratology have been applied mainly in Artificial Intelligence and to model formal semantic approaches to narratives (e.g. Plot Units developed by Lehnert (1981)). In recent years, computational narratology has qualified as an autonomous field of study and research. Narrative has been the focus of a number of workshops and conferences (AAAI Symposia, Interactive Storytelling Conference (ICIDS), Computational Models of Narrative). Furthermore, reference annotation schemes for narratives have been proposed (NarrativeML by Mani (2013)). The workshop aimed at bringing together researchers from different communities working on representing and extracting narrative structures in news, a text genre which is highly used in NLP but which has received little attention with respect to narrative structure, representation and analysis. Currently, advances in NLP technology have made it feasible to look beyond scenario-driven, atomic extraction of events from single documents and work towards extracting story structures from multiple documents, while these documents are published over time as news streams. Policy makers, NGOs, information specialists (such as journalists and librarians) and others are increasingly in need of tools that support them in finding salient stories in large amounts of information to more effectively implement policies, monitor actions of “big players” in the society and check facts. Their tasks often revolve around reconstructing cases either with respect to specific entities (e.g. person or organizations) or events (e.g. hurricane Katrina). Storylines represent explanatory schemas that enable us to make better selections of relevant information but also projections to the future. They form a valuable potential for exploiting news data in an innovative way.JRC.G.2-Global security and crisis managemen

    University Rankings: Evidence and a Conceptual Framework

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    University ranking has high public visibility, the ranking business has flourished, and institutions of higher education have not been able to ignore it. This study of university ranking presents general considerations of ranking and institutional responses to it, particularly considering reactions to ranking, ranking as a self-fulfilling prophecy, and ranking as a means of transforming qualities into quantities. The authors present a conceptual framework of university ranking based on three propositions and carry out a descriptive statistical analysis of U.S. and international ranking data to evaluate those propositions. The first proposition of university ranking is that ranking systems are demarcated by a high degree of stability, equilibrium, and path dependence. The second proposition links ranking to institutional identity. The third proposition posits that rankings function as a catalyst for institutional isomorphism. The conclusion reviews some important new developments in university ranking

    Audiovisual processing for sports-video summarisation technology

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    In this thesis a novel audiovisual feature-based scheme is proposed for the automatic summarization of sports-video content The scope of operability of the scheme is designed to encompass the wide variety o f sports genres that come under the description ‘field-sports’. Given the assumption that, in terms of conveying the narrative of a field-sports-video, score-update events constitute the most significant moments, it is proposed that their detection should thus yield a favourable summarisation solution. To this end, a generic methodology is proposed for the automatic identification of score-update events in field-sports-video content. The scheme is based on the development of robust extractors for a set of critical features, which are shown to reliably indicate their locations. The evidence gathered by the feature extractors is combined and analysed using a Support Vector Machine (SVM), which performs the event detection process. An SVM is chosen on the basis that its underlying technology represents an implementation of the latest generation of machine learning algorithms, based on the recent advances in statistical learning. Effectively, an SVM offers a solution to optimising the classification performance of a decision hypothesis, inferred from a given set of training data. Via a learning phase that utilizes a 90-hour field-sports-video trainmg-corpus, the SVM infers a score-update event model by observing patterns in the extracted feature evidence. Using a similar but distinct 90-hour evaluation corpus, the effectiveness of this model is then tested genencally across multiple genres of fieldsports- video including soccer, rugby, field hockey, hurling, and Gaelic football. The results suggest that in terms o f the summarization task, both high event retrieval and content rejection statistics are achievable

    Enhancing deep transfer learning for image classification

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    Though deep learning models require a large amount of labelled training data for yielding high performance, they are applied to accomplish many computer vision tasks such as image classification. Current models also do not perform well across different domain settings such as illumination, camera angle and real-to-synthetic. Thus the models are more likely to misclassify unknown classes as known classes. These issues challenge the supervised learning paradigm of the models and encourage the study of transfer learning approaches. Transfer learning allows us to utilise the knowledge acquired from related domains to improve performance on a target domain. Existing transfer learning approaches lack proper high-level source domain feature analyses and are prone to negative transfers for not exploring proper discriminative information across domains. Current approaches also lack at discovering necessary visual-semantic linkage and has a bias towards the source domain. In this thesis, to address these issues and improve image classification performance, we make several contributions to three different deep transfer learning scenarios, i.e., the target domain has i) labelled data; no labelled data; and no visual data. Firstly, for improving inductive transfer learning for the first scenario, we analyse the importance of high-level deep features and propose utilising them in sequential transfer learning approaches and investigating the suitable conditions for optimal performance. Secondly, to improve image classification across different domains in an open set setting by reducing negative transfers (second scenario), we propose two novel architectures. The first model has an adaptive weighting module based on underlying domain distinctive information, and the second model has an information-theoretic weighting module to reduce negative transfers. Thirdly, to learn visual classifiers when no visual data is available (third scenario) and reduce source domain bias, we propose two novel models. One model has a new two-step dense attention mechanism to discover semantic attribute-guided local visual features and mutual learning loss. The other model utilises bidirectional mapping and adversarial supervision to learn the joint distribution of source-target domains simultaneously. We propose a new pointwise mutual information dependant loss in the first model and a distance-based loss in the second one for handling source domain bias. We perform extensive evaluations on benchmark datasets and demonstrate the proposed models outperform contemporary works.Doctor of Philosoph

    Domain Adaptation and Privileged Information for Visual Recognition

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    The automatic identification of entities like objects, people or their actions in visual data, such as images or video, has significantly improved, and is now being deployed in access control, social media, online retail, autonomous vehicles, and several other applications. This visual recognition capability leverages supervised learning techniques, which require large amounts of labeled training data from the target distribution representative of the particular task at hand. However, collecting such training data might be expensive, require too much time, or even be impossible. In this work, we introduce several novel approaches aiming at compensating for the lack of target training data. Rather than leveraging prior knowledge for building task-specific models, typically easier to train, we focus on developing general visual recognition techniques, where the notion of prior knowledge is better identified by additional information, available during training. Depending on the nature of such information, the learning problem may turn into domain adaptation (DA), domain generalization (DG), leaning using privileged information (LUPI), or domain adaptation with privileged information (DAPI).;When some target data samples are available and additional information in the form of labeled data from a different source is also available, the learning problem becomes domain adaptation. Unlike previous DA work, we introduce two novel approaches for the few-shot learning scenario, which require only very few labeled target samples, and even one can be very effective. The first method exploits a Siamese deep neural network architecture for learning an embedding where visual categories from the source and target distributions are semantically aligned and yet maximally separated. The second approach instead, extends adversarial learning to simultaneously maximize the confusion between source and target domains while achieving semantic alignment.;In complete absence of target data, several cheaply available source datasets related to the target distribution can be leveraged as additional information for learning a task. This is the domain generalization setting. We introduce the first deep learning approach to address the DG problem, by extending a Siamese network architecture for learning a representation of visual categories that is invariant with respect to the sources, while imposing semantic alignment and class separation to maximize generalization performance on unseen target domains.;There are situations in which target data for training might come equipped with additional information that can be modeled as an auxiliary view of the data, and that unfortunately is not available during testing. This is the LUPI scenario. We introduce a novel framework based on the information bottleneck that leverages the auxiliary view to improve the performance of visual classifiers. We do so by introducing a formulation that is general, in the sense that can be used with any visual classifier.;Finally, when the available target data is unlabeled, and there is closely related labeled source data, which is also equipped with an auxiliary view as additional information, we pose the question of how to leverage the source data views to train visual classifiers for unseen target data. This is the DAPI scenario. We extend the LUPI framework based on the information bottleneck to learn visual classifiers in DAPI settings and show that privileged information can be leveraged to improve the learning on new domains. Also, the novel DAPI framework is general and can be used with any visual classifier.;Every use of auxiliary information has been validated extensively using publicly available benchmark datasets, and several new state-of-the-art accuracy performance values have been set. Examples of application domains include visual object recognition from RGB images and from depth data, handwritten digit recognition, and gesture recognition from video
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