14 research outputs found

    Mars Image Content Classification: Three Years of NASA Deployment and Recent Advances

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    The NASA Planetary Data System hosts millions of images acquired from the planet Mars. To help users quickly find images of interest, we have developed and deployed content-based classification and search capabilities for Mars orbital and surface images. The deployed systems are publicly accessible using the PDS Image Atlas. We describe the process of training, evaluating, calibrating, and deploying updates to two CNN classifiers for images collected by Mars missions. We also report on three years of deployment including usage statistics, lessons learned, and plans for the future.Comment: Published at the Thirty-Third Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-21). IAAI Innovative Application Award. 10 pages, 11 figures, 6 table

    Integrating Machine Learning for Planetary Science: Perspectives for the Next Decade

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    Machine learning (ML) methods can expand our ability to construct, and draw insight from large datasets. Despite the increasing volume of planetary observations, our field has seen few applications of ML in comparison to other sciences. To support these methods, we propose ten recommendations for bolstering a data-rich future in planetary science.Comment: 10 pages (expanded citations compared to 8 page submitted version for decadal survey), 3 figures, white paper submitted to the Planetary Science and Astrobiology Decadal Survey 2023-203

    Anomaly-based insider threat detection with expert feedback and descriptions

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    Abstract. Insider threat is one of the most significant security risks for organizations, hence insider threat detection is an important task. Anomaly detection is a one approach to insider threat detection. Anomaly detection techniques can be categorized into three categories with respect to how much labelled data is needed: unsupervised, semi-supervised and supervised. Obtaining accurate labels of all kinds of incidents for supervised learning is often expensive and impractical. Unsupervised methods do not require labelled data, but they have a high false positive rate because they operate on the assumption that anomalies are rarer than nominals. This can be mitigated by introducing feedback, known as expert-feedback or active learning. This allows the analyst to label a subset of the data. Another problem is the fact that models often are not interpretable, thus it is unclear why the model decided that a data instance is an anomaly. This thesis presents a literature review of insider threat detection, unsupervised and semi-supervised anomaly detection. The performance of various unsupervised anomaly detectors are evaluated. Knowledge is introduced into the system by using state-of-the-art feedback technique for ensembles, known as active anomaly discovery, which is incorporated into the anomaly detector, known as isolation forest. Additionally, to improve interpretability techniques of creating rule-based descriptions for the isolation forest are evaluated. Experiments were performed on CMU-CERT dataset, which is the only publicly available insider threat dataset with logon, removable device and HTTP log data. Models use usage count and session-based features that are computed for users on every day. The results show that active anomaly discovery helps in ranking true positives higher on the list, lowering the amount of data analysts have to analyse. Results also show that both compact description and Bayesian rulesets have the potential to be used in generating decision-rules that aid in analysing incidents; however, these rules are not correct in every instance.Poikkeamapohjainen sisäpiiriuhkien havainta palautteen ja kuvauksien avulla. Tiivistelmä. Sisäpiirinuhat ovat yksi vakavimmista riskeistä organisaatioille. Tästä syystä sisäpiiriuhkien havaitseminen on tärkeää. Sisäpiiriuhkia voidaan havaita poikkeamien havaitsemismenetelmillä. Nämä menetelmät voidaan luokitella kolmeen oppimisluokkaan saatavilla olevan tietomäärän perusteella: ohjaamaton, puoli-ohjattu ja ohjattu. Täysin oikein merkatun tiedon saaminen ohjattua oppimista varten voi olla hyvin kallista ja epäkäytännöllistä. Ohjaamattomat oppimismenetelmät eivät vaadi merkattua tietoa, mutta väärien positiivisten osuus on suurempi, koska nämä menetelmät perustuvat oletukseen että poikkeamat ovat harvinaisempia kuin normaalit tapaukset. Väärien positiivisten osuutta voidaan pienentää ottamalla käyttöön palaute, jolloin analyytikko voi merkata osan datasta. Tässä opinnäytetyössä tutustutaan ensin sisäpiiriuhkien havaitsemiseen, mitä tutkimuksia on tehty ja ohjaamattomaan ja puoli-ohjattuun poikkeamien havaitsemiseen. Muutamien lupaavien ohjaamattomien poikkeamatunnistimien toimintakyky arvioidaan. Järjestelmään lisätään tietoisuutta havaitsemisongelmasta käyttämällä urauurtavaa active anomaly discovery -palautemetelmää, joka on tehty havaitsinjoukoille (engl. ensembles). Tätä arvioidaan Isolation Forest -havaitsimen kanssa. Lisäksi, jotta analytiikko pystyisi paremmin käsittelemään havainnot, tässä työssä myös arvioidaan sääntöpohjaisten kuvausten luontimenetelmä Isolation Forest -havaitsimelle. Kokeilut suoritettiin käyttäen julkista CMU-CERT:in aineistoa, joka on ainoa julkinen aineisto, missä on muun muuassa kirjautumis-, USB-laite- ja HTTP-tapahtumia. Mallit käyttävät käyttöluku- ja istuntopohjaisia piirteitä, jotka luodaan jokaista käyttäjää ja päivää kohti. Tuloksien perusteella Active Anomaly Discovery auttaa epäilyttävämpien tapahtumien sijoittamisessa listan kärkeen vähentäen tiedon määrä, jonka analyytikon tarvitsee tutkia. Kompaktikuvakset (engl. compact descriptions)- ja Bayesian sääntöjoukko -menetelmät pystyvät luomaan sääntöjä, jotka kuvaavat minkä takia tapahtuma on epäilyttävä, mutta nämä säännöt eivät aina ole oikein

    Visualizing Image Content to Explain Novel Image Discovery

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    The initial analysis of any large data set can be divided into two phases: (1) the identification of common trends or patterns and (2) the identification of anomalies or outliers that deviate from those trends. We focus on the goal of detecting observations with novel content, which can alert us to artifacts in the data set or, potentially, the discovery of previously unknown phenomena. To aid in interpreting and diagnosing the novel aspect of these selected observations, we recommend the use of novelty detection methods that generate explanations. In the context of large image data sets, these explanations should highlight what aspect of a given image is new (color, shape, texture, content) in a human-comprehensible form. We propose DEMUD-VIS, the first method for providing visual explanations of novel image content by employing a convolutional neural network (CNN) to extract image features, a method that uses reconstruction error to detect novel content, and an up-convolutional network to convert CNN feature representations back into image space. We demonstrate this approach on diverse images from ImageNet, freshwater streams, and the surface of Mars.Comment: Under Revie

    Computational creativity applications in engineering

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    We investigated and implemented two computational creativity applications in generating engineering processes and building material designs respectively. To synthesize engineering processes, we developed a full system that generates engaging Rube Goldberg Machine designs. We first describe the use of case-based reasoning (CBR) and an existing knowledge base to yield a combinatorial design space for experiments. We then apply automated planning techniques to generate experiment procedures. We further use functional modeling to represent the experiment devices and demonstrate how that representation enables the planner to generate a valid Rube Goldberg Machine. Finally, a semantic similarity metric is proposed to evaluate the quality of a generated chain of experiments. To discover concrete formulas as building materials with desired properties, we use a conditional variational autoencoder (CVAE), a type of semisupervised generative model. Our model is trained using open data from the UCI Machine Learning Repository joined with environmental impact data computed using a web-based tool. We demonstrate that the CVAE can design concrete formulas with lower emissions and natural resource usage while meeting design requirements. To ensure fair comparison between extant and generated formulas, we also train regression models to predict the environmental impacts and strength of discovered formulas. With these results, a construction engineer may create a formula that meets structural needs and best addresses local environmental concerns

    Systematic construction of anomaly detection benchmarks from real data

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    Research in anomaly detection suffers from a lack of realis-tic and publicly-available problem sets. This paper discusses what properties such problem sets should possess. It then introduces a methodology for transforming existing classi-fication data sets into ground-truthed benchmark data sets for anomaly detection. The methodology produces data sets that vary along three important dimensions: (a) point diffi-culty, (b) relative frequency of anomalies, and (c) clustered-ness. We apply our generated datasets to benchmark several popular anomaly detection algorithms under a range of dif-ferent conditions. 1
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