839 research outputs found

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Finding an effective problem-solving heuristic instructional approach for circle geometry

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    Abstracts in English, Afrikaans and ZuluThis research study carried out an investigation into finding a contemporary problem- solving instructional approach that will be effective for teaching and learning of mathematics in South African schools, with specific focus on circle geometry. Prior to conducting this study, a retrospection was done into the mathematical practices implemented, in schools in South Africa, by researchers, educational practitioners and stakeholders such as Non-Governmental Organisations. The aforementioned unanimously identified the instructional approaches for teaching and learning of mathematics, particularly, the traditional teaching and learning approach, as problematic and counter-productive, and this might be contributing to poor learners’ performances. In a bid to replace the obsolete traditional approach, the researcher in this study recommended: “teaching thinking skills” and “teaching effective problem-solving instructional approaches” as more appropriate. With regards to teaching thinking skills, the infusion approach (teaching thinking skills, along with content instructions), was highlighted. For teaching effective problem-solving, Polya’s Problem-Solving Model, was investigated. To ensure an effective design and implementation of the proposed problem-solving instructional approach, the APOS theory (ACE teaching cycle) was adopted. Also, the teaching and learning of circle geometry was carried out in a collaborative classroom setting. This proposed instructional approach was tentatively, labelled as “IPAC mathematics problem-solving instructional model’’ or simply, the “IPAC model”. This was an acronym for the four elements of this new approach, namely - the infusion approach, Polya’s approach, and APOS theory in a collaborative learning classroom. Two groups of Grade 11 mathematics learners served as participants for this study: group 1 - 11A had 30 learners (the control group) and group 2- 11B had 32 learners (the experimental group). Data collected methods for this study were: observations of participants in their natural classroom settings, recorded videos, questionnaires, photograph of participants’ work (classwork/homework and standardized tests). This study followed a mixed-method research design, hence, both quantitative and qualitative data analyses procedures were implemented. The quantitative data was analysed by implementing inferential statistics and descriptive statistics, while the APOS theory analysis was used to analyse the qualitative facet of the collected data. During the APOS theory analysis, content analysis was done on participants’ written responses to each of the four standardized tests’ data. The content analysis was carried out on the written responses of participants, from both the control and the experimental groups. The research findings that emanated from this study were the following: that this new method of teaching and learning is valid, practical and effective; there was a statistically significant improvement in the test scores of participants who were taught by the new instructional approach; participants’ conceptual understanding, procedural fluency, strategic competence and mathematical reasoning skills were enhanced; participants’ problem-solving competence improved, during and after the intervention; the IPAC model guided the majority of the participants to operate at the object and schema levels in relation to the APOS theory mental conceptions. Lastly, the ACE teaching instructional approach significantly guided and enhanced participants’ cognitive engagement and development, which ultimately, optimized their problem-solving competence. Based on these research findings, the researcher recommended among others, that the new instructional approach - the IPAC model, should be implemented for teaching and learning of circle geometry in South African schools. The researcher also recommended that cultivation of thinking skills and implementation of effective problem-solving instructional approaches should be prioritized in mathematics classrooms in South Africa. The researcher established from this study that the developed IPAC model will serve as an effective and a reliable pedagogical tool which can address some of the teaching and learning challenges teachers and learners encounter in mathematics classrooms.Hierdie navorsingstudie het 'n ondersoek gedoen na die vind van 'n kontemporĂȘre probleemoplossende onderrigbenadering wat effektief sal wees vir onderrig en leer van wiskunde in Suid-Afrikaanse skole, met spesifieke fokus op sirkelmeetkunde. Voor die uitvoering van hierdie studie is 'n terugblik gedoen na die wiskundige praktyke wat in skole in Suid-Afrika geĂŻmplementeer is deur navorsers, opvoedkundige praktisyns en belanghebbendes soos nie-regeringsorganisasies. Die instruksionele benaderings vir onderrig en leer van wiskunde, veral die tradisionele onderrig-en-leerbenadering, is eenparig geĂŻdentifiseer as problematies en teenproduktief, en dit kan dalk bydra tot swak leerders se prestasies. In 'n poging om die uitgediende tradisionele benadering te vervang, het die navorser in hierdie studie aanbeveel: "onderrig van denkvaardighede" en "onderrig van effektiewe probleemoplossende onderrigbenaderings" as meer gepas. Met betrekking tot die onderrig van denkvaardig hede, is die infusiebenadering (onderrig van denkvaardighede, tesame met inhoudsinstruksies), uitgelig. Vir die onderrig van effektiewe probleemoplossing is Polya se probleemoplossingsmodel ondersoek. Om 'n effektiewe ontwerp en implementering van die voorgestelde probleemoplossende onderrigbenadering te verseker, is die APOS-teorie (GOS-onderrigsiklus) aanvaar. Die onderrig en leer van sirkelmeetkunde is ook in 'n samewerkende klaskameropset uitgevoer. Hierdie voorgestelde onderrigbenadering is voorlopig, gemerk as "IPAC wiskunde probleemoplossing instruksionele model" of eenvoudig die "IPAC model". Dit was 'n akroniem vir die vier elemente van hierdie nuwe benadering, naamlik - die infusiebenadering, Polya se benadering en APOS-teorie in 'n samewerkende leerklaskamer. Twee groepe graad 11-wiskunde-leerders het as deelnemers vir hierdie studie gedien: groep 1 - 11A het 30 leerders (die kontrolegroep) en groep 2- 11B het 32 leerders (die eksperimentele groep). Data wat ingesamel is metodes vir hierdie studie was: waarnemings van deelnemers in hul natuurlike klaskamerinstellings, opgeneemde video's, vraelyste, foto van deelnemers se werk (klaswerk/huiswerk en gestandaardiseerde toetse). Hierdie studie het 'n gemengde-metode navorsingsontwerp gevolg, dus is beide kwantitatiewe en kwalitatiewe data-ontledingsprosedures geĂŻmplementeer. Die kwantitatiewe data is ontleed deur inferensiĂ«le statistiek en beskrywende statistiek te implementeer, terwyl die APOS teorie-analise gebruik is om te analiseer die kwalitatiewe faset van die versamelde data. Tydens die APOS-teorie-analise is inhoudsontleding gedoen op deelnemers se geskrewe antwoorde op elk van die vier gestandaardiseerde toetse se data. Die inhoudsanalise is uitgevoer op die geskrewe reaksie van deelnemers, van beide die kontrole- en die eksperimentele groepe. Die navorsingsbevindinge wat uit hierdie studie voortgespruit het, was die volgende: dat hierdie nuwe metode van onderrig en leer geldig, prakties en effektief is; daar was 'n statisties beduidende verbetering in die toetstellings van deelnemers wat deur die nuwe onderrigbenadering onderrig is; deelnemers se konseptuele begrip, prosedurele vlotheid, strategiese bevoegdheid en wiskundige redenasievaardighede is verbeter; deelnemers se probleemoplossingsbevoegdheid het verbeter, tydens en na die intervensie; die IPAC-model het die meerderheid van die deelnemers gelei om op die objek- en skemavlakke te werk in verhouding tot die APOS-teorie se verstandelike opvattings. Laastens het die GOS-onderrigbenadering die deelnemers se kognitiewe betrokkenheid en ontwikkeling aansienlik gelei en verbeter, wat uiteindelik hul probleemoplossingsbevoegdheid geoptimaliseer het. Op grond van hierdie navorsingsbevindinge het die navorser onder andere aanbeveel dat die nuwe onderrigbenadering - die IPAC-model, geĂŻmplementeer moet word vir onderrig en leer van sirkelmeetkunde in Suid-Afrikaanse skole. Die navorser het ook aanbeveel dat die kweek van denkvaardighede en implementering van effektiewe probleemoplossende onderrigbenaderings in wiskundeklaskamers in Suid-Afrika geprioritiseer moet word. Die navorser het uit hierdie studie vasgestel dat die ontwikkelde IPAC-model sal dien as 'n effektiewe en betroubare pedagogiese hulpmiddel wat sommige van die onderrig- en leeruitdagings wat onderwysers en leerders in wiskundeklaskamers ondervind, kan aanspreek.Lolu cwaningo luqukethe uphenyo mayelana nokuthola ikhambi elingaxazulula ekutholeni indlela eqondile engaletha imiphumela ewusizo ekufundiseni nasekufundeni kwezibalo ezikoleni zaseMzansi Africa, ezophinde ibhekane ngqo ne circle Geometry. Ngaphambi kokuba kuqale lolu cwaningo, kube nolunye ucwaningo olunzulu olwenziwe ngezinye izindlela esezivele zikhona mayelana nezibalo, ezikoleni zaseMzansi Africa, lwenziwa ngabacwaningi, izifundiswa ezingo ncweti Kanye nezinhlangano ezizimele. Inhlangano ebizwa nge okushiwo ngenhla luhlonze indlela eqondile yokufundisa nokufunda izibalo, ikakhulukazi, indlela ejwayelekile yokwenza, njengezindlela eziyinkinga nezingahambisani, futhi lokhu ngungaba yimbangela ekungenzini kahle kwabafundi. Emkhankasweni wokushintsha lolu hlelo oludala lokwenza olungasasizi, uMhlaziyi kulolu cwaningo uncome ukuthi: “ikhono elufundisa ukuzicabangela” Kanye “nekhono lokufundisa elisebenzayo ukuzixazululela izinkinga” njengendlela okuyiyo efanele. Mayelana nekhono elifundisa ukuzicabangela, indlela eyiqophelo (ikhono elifundisa ukuzicabangela, elihambisana nemigomo equkethwe), luthintiwe. Mayelana nohlelo oluwusizo ekuxazululeni izinkinga, uhlelo luka Polya lokuxazulula izinkinga luphenyiwe. Ukuqinisekisa ukuthi uhlelo olusebenzayo futhi oluzosentsenziswa ekuphakamiseni indlela eqondile enemigomo ekuxazululeni izinkinga yokwenza, i APOS theory (ACE teaching cycle) iyona ekhethiwe. Okunye, uhlelo lokufundisa nokufunda i circle geometry lukhishiwe endleleni ehlanganisayo yokuhlala egunjini lokufunda. Okwamanje Lolu hlelo oluphakanyisiwe lokufundisa, lubekwe njenge “IPAC indlela yezibalo eqondile yokuxazulula izinkinga enemigomo” . Lokhu kuyigama elifinqiwe elakhiwe izinhlamvu ezine kule ndlela entsha ebizwa nge infusion approach, Polya’s approach, Kanye ne APOS theory egunjini lokufunda elihlanganisile. Amaqembu amabili ebanga le shuminanye labafundi bezibalo basentshenzisiwe ukubamba iqhaza kulolu cwaningo: iqembu lokuqala ibanga 11A ebelinabafundi abangu 30 (iqembu labaqondisi) bese iqembu lesibili ibanga 11B ebelinabafundi abangu 32 (iqembu elenzayo). Ucwaningo oluqoqiwe lwalendlela lube kanje: imibono yalaba ebekade bebambe iqhaza egunjini lokufunda obuhleliwe, baqophe amavidiyo, babhala imibuzo, bathatha izithombe zalaba ekade bembambe iqhaza lwalomsebenzi wokubamba iqhaza. (imisebenzi yasegunjini lokufunda/imisebenzi yasekhaya Kanye nokwenza uvivinyo). Lolu phenyo lulandele uhlelo oluxubile okuwuhlelo lokuphenya, yingakho zombili lezi zinhlelo zokuqukethwe nokuseZingeni zokuqoqa uphenyo olwenziwe zisentshenzisiwe. Uhlelo lokuqukethwe lemininingwane lusentshenzisiwe ukuhlaziya ngokusebenzisa uhlelo lokuqoqa okutholakele Kanye nohlelo lokwenza okutholakele, futhi kube kwenziwa ne APOS theory analysis ukuhlaziya okusezingeni eliphezulu zigxenye zonke lwemininingwane eqoqiwe. Ngesikhathi se APOS theory analysis, ukuhlaziywa kokuqukethwe okwenziwe ababambe iqhaza babhale okwenzekile ngesikhathi benza lezi zivivinyo ezine ezibekiwe. Uhlelo lokuhlaziya okuqukethwe lwenziwe labhalwa yilaba kade bebambe iqhaza, kuwo womabili amaqembu , elokuqondisa nelokwenza. Uphenyo olutholakele kulolu hlelo lunje: lolu hlelo lokufundisa nokufunda luyasebenza, luyenzeka, futhi lunomehluko: ngokwezibalo kube nomehluko omkhulu oncono ezibalweni zalabo ekade bebambe iqhaza besebenzisa indlela entsha yemigomo: bonke ekade bebambe iqhaza bathole ithuba lokuthi kuthuthuke amakhono abo ekwazini ukuqonda ukuzicabangela, ekwazini ukwenza izinto ezinomehluko eyinqubomgomo, ukumelana nezindlela eziningi eziphumelelisayo Kanye nekhono lokuqonda izibalo; ikhono lalabo ekade bebambe iqhaza ekuxazululeni izinkinga ngokusezingeni lithuthukile, ngesikhathi nangemuva kokwenza ucwaningo; I IPAC model ukwenzisa abaningi balaba ekade bebambe iqhaza kalula umsebenzi ngokuhlukana kwamazinga kusentsenziswa i APOS theory. Ekugcineni, indlela yokwenza ebizwa nge ACE teaching ikwazile okwenzisa kahle ngokusezingeni eliphezulu futhi yakhuphula labo ebekade bebambe iqhaza yaphinde yabathuthukisa, lokhu okwenze bakwazi ukuba sezingeni lokuphumelela ukuxazulula izinkinga. Ngenxa yalokhu okutholakale kucwaningo, umcwaningi uncome ukuthi kokunye, indlela entsha yokwenza ngemigomo – i-IPAC, kumele isentshenziswe ekufundiseni nasekufundeni i circle geometry ezikoleni zaseMzansi Africa. Umcwaningi uphinde waphakamisa ukuthi ukuthuthukisa ikhono lokuzicabangela nokwenziwa kwezindlela ezisebenzayo zokuxazulula izinkinga kumele zibekwe phambili emagunjini okufunda izibalo eMzansi Africa. Umcwaningi ubeke indlela eseqophelweni eliphezulu eyisisekelo kusukela kwisifundo esenziwe yokuthi i IPAC model iyona esebenza njenge ndlela eyithuluzi elibonakalayo futhi elinemiphumela emihle ethembekile, engakwazi ukubhekana nezinkinga futhi ixazulule izinqinamba zokufundisa nokufunda ezikoleni, lezi othisha nabafundi ababhekana nazo egunjini lokufundela izibaloEducational StudiesD. Phil. (Education

    A Closer Look into Recent Video-based Learning Research: A Comprehensive Review of Video Characteristics, Tools, Technologies, and Learning Effectiveness

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    People increasingly use videos on the Web as a source for learning. To support this way of learning, researchers and developers are continuously developing tools, proposing guidelines, analyzing data, and conducting experiments. However, it is still not clear what characteristics a video should have to be an effective learning medium. In this paper, we present a comprehensive review of 257 articles on video-based learning for the period from 2016 to 2021. One of the aims of the review is to identify the video characteristics that have been explored by previous work. Based on our analysis, we suggest a taxonomy which organizes the video characteristics and contextual aspects into eight categories: (1) audio features, (2) visual features, (3) textual features, (4) instructor behavior, (5) learners activities, (6) interactive features (quizzes, etc.), (7) production style, and (8) instructional design. Also, we identify four representative research directions: (1) proposals of tools to support video-based learning, (2) studies with controlled experiments, (3) data analysis studies, and (4) proposals of design guidelines for learning videos. We find that the most explored characteristics are textual features followed by visual features, learner activities, and interactive features. Text of transcripts, video frames, and images (figures and illustrations) are most frequently used by tools that support learning through videos. The learner activity is heavily explored through log files in data analysis studies, and interactive features have been frequently scrutinized in controlled experiments. We complement our review by contrasting research findings that investigate the impact of video characteristics on the learning effectiveness, report on tasks and technologies used to develop tools that support learning, and summarize trends of design guidelines to produce learning video

    User-oriented recommender systems in retail

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    User satisfaction is considered a key objective for all service provider platforms, regardless of the nature of the service, encompassing domains such as media, entertainment, retail, and information. While the goal of satisfying users is the same across different domains and services, considering domain-specific characteristics is of paramount importance to ensure users have a positive experience with a given system. User interaction data with a system is one of the main sources of data that facilitates achieving this goal. In this thesis, we investigate how to learn from domain-specific user interactions. We focus on recommendation as our main task, and retail as our main domain. We further explore the finance domain and the demand forecasting task as additional directions to understand whether our methodology and findings generalize to other tasks and domains. The research in this thesis is organized around the following dimensions: 1) Characteristics of multi-channel retail: we consider a retail setting where interaction data comes from both digital (i.e., online) and in-store (i.e., offline) shopping; 2) From user behavior to recommendation: we conduct extensive descriptive studies on user interaction log datasets that inform the design of recommender systems in two domains, retail and finance. Our key contributions in characterizing multi-channel retail are two-fold. First, we propose a neural model that makes use of sales in multiple shopping channels in order to improve the performance of demand forecasting in a target channel. Second, we provide the first study of user behavior in a multi-channel retail setting, which results in insights about the channel-specific properties of user behavior, and their effects on the performance of recommender systems. We make three main contributions in designing user-oriented recommender systems. First, we provide a large-scale user behavior study in the finance domain, targeted at understanding financial information seeking behavior in user interactions with company filings. We then propose domain-specific user-oriented filing recommender systems that are informed by the findings of the user behavior analysis. Second, we analyze repurchasing behavior in retail, specifically in the grocery shopping domain. We then propose a repeat consumption-aware neural recommender for this domain. Third, we focus on scalable recommendation in retail and propose an efficient recommender system that explicitly models users' personal preferences that are reflected in their purchasing history

    Artificial Collective Intelligence Engineering: a Survey of Concepts and Perspectives

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    Collectiveness is an important property of many systems--both natural and artificial. By exploiting a large number of individuals, it is often possible to produce effects that go far beyond the capabilities of the smartest individuals, or even to produce intelligent collective behaviour out of not-so-intelligent individuals. Indeed, collective intelligence, namely the capability of a group to act collectively in a seemingly intelligent way, is increasingly often a design goal of engineered computational systems--motivated by recent techno-scientific trends like the Internet of Things, swarm robotics, and crowd computing, just to name a few. For several years, the collective intelligence observed in natural and artificial systems has served as a source of inspiration for engineering ideas, models, and mechanisms. Today, artificial and computational collective intelligence are recognised research topics, spanning various techniques, kinds of target systems, and application domains. However, there is still a lot of fragmentation in the research panorama of the topic within computer science, and the verticality of most communities and contributions makes it difficult to extract the core underlying ideas and frames of reference. The challenge is to identify, place in a common structure, and ultimately connect the different areas and methods addressing intelligent collectives. To address this gap, this paper considers a set of broad scoping questions providing a map of collective intelligence research, mostly by the point of view of computer scientists and engineers. Accordingly, it covers preliminary notions, fundamental concepts, and the main research perspectives, identifying opportunities and challenges for researchers on artificial and computational collective intelligence engineering.Comment: This is the author's final version of the article, accepted for publication in the Artificial Life journal. Data: 34 pages, 2 figure

    Guiding Quality Assurance Through Context Aware Learning

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    Software Testing is a quality control activity that, in addition to finding flaws or bugs, provides confidence in the software’s correctness. The quality of the developed software depends on the strength of its test suite. Mutation Testing has shown that it effectively guides in improving the test suite’s strength. Mutation is a test adequacy criterion in which test requirements are represented by mutants. Mutants are slight syntactic modifications of the original program that aim to introduce semantic deviations (from the original program) necessitating the testers to design tests to kill these mutants, i.e., to distinguish the observable behavior between a mutant and the original program. This process of designing tests to kill a mutant is iteratively performed for the entire mutant set, which results in augmenting the test suite, hence improving its strength. Although mutation testing is empirically validated, a key issue is that its application is expensive due to the large number of low-utility mutants that it introduces. Some mutants cannot be even killed as they are functionally equivalent to the original program. To reduce the application cost, it is imperative to limit the number of mutants to those that are actually useful. Since it requires manual analysis and test executions to identify such mutants, there is a lack of an effective solution to the problem. Hence, it remains unclear how to mutate and test a code efficiently. On the other hand, with the advancement in deep learning, several works in the literature recently focused on using it on source code to automate many nontrivial tasks including bug fixing, producing code comments, code completion, and program repair. The increasing utilization of deep learning is due to a combination of factors. The first is the vast availability of data to learn from, specifically source code in open-source repositories. The second is the availability of inexpensive hardware able to efficiently run deep learning infrastructures. The third and the most compelling is its ability to automatically learn the categorization of data by learning the code context through its hidden layer architecture, making it especially proficient in identifying features. Thus, we explore the possibility of employing deep learning to identify only useful mutants, in order to achieve a good trade-off between the invested effort and test effectiveness. Hence, as our first contribution, this dissertation proposes Cerebro, a deep learning approach to statically select subsuming mutants based on the mutants’ surrounding code context. As subsuming mutants reside at the top of the subsumption hierarchy, test cases designed to only kill this minimal subset of mutants kill all the remaining mutants. Our evaluation of Cerebro demonstrates that it preserves the mutation testing benefits while limiting the application cost, i.e., reducing all cost factors such as equivalent mutants, mutant executions, and the mutants requiring analysis. Apart from improving test suite strength, mutation testing has been proven useful in inferring software specifications. Software specifications aim at describing the software’s intended behavior and can be used to distinguish correct from incorrect software behaviors. Specification inference techniques aim at inferring assertions by generating and filtering candidate assertions through dynamic test executions and mutation testing. Due to the introduction of a large number of mutants during mutation testing such techniques are also computationally expensive, hence establishing a need for the selection of mutants that fit best for assertion inference. We refer to such mutants as Assertion Inferring Mutants. In our analysis, we find that the assertion inferring mutants are significantly different from the subsuming mutants. Thus, we explored the employability of deep learning to identify Assertion Inferring Mutants. Hence, as our second contribution, this dissertation proposes Seeker, a deep learning approach to statically select Assertion Inferring Mutants. Our evaluation demonstrates that Seeker enables an assertion inference capability comparable to the full mutation analysis while significantly limiting the execution cost. In addition to testing software in general, a few works in the literature attempt to employ mutation testing to tackle security-related issues, due to the fault-based nature of the technique. These works propose mutation operators to convert non-vulnerable code to vulnerable by mimicking common security bugs. However, these pattern-based approaches have two major limitations. Firstly, the design of security-specific mutation operators is not trivial. It requires manual analysis and comprehension of the vulnerability classes. Secondly, these mutation operators can alter the program semantics in a manner that is not convincing for developers and is perceived as unrealistic, thereby hindering the usability of the method. On the other hand, with the release of powerful language models trained on large code corpus, e.g. CodeBERT, a new family of mutation testing tools has arisen with the promise to generate natural mutants. We study the extent to which the mutants produced by language models can semantically mimic the behavior of vulnerabilities aka Vulnerability-mimicking Mutants. Designed test cases failed by these mutants will also tackle mimicked vulnerabilities. In our analysis, we found that a very small subset of mutants is vulnerability-mimicking. Though, this set mimics more than half of the vulnerabilities in our dataset. Due to the absence of any defined features to identify vulnerability-mimicking mutants, as our third contribution, this dissertation introduces Mystique, a deep learning approach that automatically extracts features to identify vulnerability-mimicking mutants. Despite the scarcity, Mystique predicts vulnerability-mimicking mutants with a high prediction performance, demonstrating that their features can be automatically learned by deep learning models to statically predict these without the need of investing any effort in defining features. Since our vulnerability-mimicking mutants cannot mimic all the vulnerabilities, we perceive that these mutants are not a complete representation of all the vulnerabilities and there exists a need for actual vulnerability prediction approaches. Although there exist many such approaches in the literature, their performance is limited due to a few factors. Firstly, vulnerabilities are fewer in comparison to software bugs, limiting the information one can learn from, which affects the prediction performance. Secondly, the existing approaches learn on both, vulnerable, and supposedly non-vulnerable components. This introduces an unavoidable noise in training data, i.e., components with no reported vulnerability are considered non-vulnerable during training, and hence, results in existing approaches performing poorly. We employed deep learning to automatically capture features related to vulnerabilities and explored if we can avoid learning on supposedly non-vulnerable components. Hence, as our final contribution, this dissertation proposes TROVON, a deep learning approach that learns only on components known to be vulnerable, thereby making no assumptions and bypassing the key problem faced by previous techniques. Our comparison of TROVON with existing techniques on security-critical open-source systems with historical vulnerabilities reported in the National Vulnerability Database (NVD) demonstrates that its prediction capability significantly outperforms the existing techniques

    Le goût d'Orval: constructing the taste of Orval beer through narratives

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    This study explores the construction of taste through narratives, using Orval beer as a case study. Often found on lists of the best or most unique beers in the world, Orval is a bottle conditioned, dry-hopped strong Belgian ale with Brettanomyces yeast, creating an orange-hue beer topped with a large volume of white foam. It is both easy to drink and complex in flavour. Made in southeastern Belgium within the walls of a Trappist Abbey, Orval is closely associated with the country of Belgium, a pilgrimage site for beer lovers because of its unique and diverse beer culture. In 2016 “Beer Culture in Belgium” was inscribed on UNESCO’s Representative List of Intangible Cultural Heritage of Humanity. Orval beer also carries the Authentic Trappist Product label, ensuring that this product is brewed under the supervision of Trappist monks or nuns, within the Abbey walls, and is non-profit. Additionally, the beer has a unique, distinctive taste. This dissertation explores narratives that tell of all these aspects. The first section, Narrating Belgium, examines how social and economic histories build Belgium as a beer nation, and how conversion narratives of Belgian beer enthusiasts support this theory. The Narrating Trappist section examines how the Legend of Orval and the history of Orval Abbey create a sense of place for Orval beer and how the Authentic Trappist Product label helps construct its terroir. The last section, Narrating Taste, focuses on narratives of taste as shared in online reviews of Orval beer. I first conduct lexical and network analysis of reviews on Untappd, RateBeer, and BeerAdvocate before focusing specifically on themes found in BeerAdvocate reviews. Through ethnographic and textual research, this dissertation introduces a folkloristic approach to taste and argues that both contextual and sensory elements are essential in building taste through narratives
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