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Russia’s Eurasian view of the world brings together anti-Western and state-centric elements. Placed at the center of its own geo-political sphere of influence and civilizational milieu, Russia’s worldview is self-contained and insular. What Russian policy slights is the global context in which its primacy over a heterogeneous Eurasia is embedded and which, when disregarded, can impose serious costs. This paper traces the broad contours of Russia’s geopolitical and civilizational Eurasianism, linking it to earlier scholarship on regions and civilization. We also explore selected aspects of Russia’s foreign security (Crimea and Ukraine) and economic (energy) policies as well as the constraints they encounter in an increasingly global world that envelops Russia and Eurasia in a larger context.

No Man’s Sky is an open world space procedural exploration game which allows players to traverse space in space ships, land on and explore planets. A group of archaeogamers (archaeologists interested in video games for varying reasons) decided to treat the game as an archaeological site, and within the No Man’s Sky Archaeological Survey explore, catalogue findings, and analyze objects and constructs within the game from an archaeological perspective. One of the aspects of this activity was to create a Code of Ethics – this paper describes the creation of the Code, the difficulties in implementation of the Code, and offers some recommendations to game developers who wish to encourage similar archaeological exploration within their own games.

When deploying machine learning models in real-world applications, an open-world learning framework is needed to deal with both normal in-distribution inputs and undesired out-of-distribution (OOD) inputs. Open-world learning frameworks include OOD detectors that aim to discard input examples which are not from the same distribution as the training data of machine learning classifiers. However, our understanding of current OOD detectors is limited to the setting of benign OOD data, and an open question is whether they are robust in the presence of adversaries. In this paper, we present the first analysis of the robustness of open-world learning frameworks in the presence of adversaries by introducing and designing oodAdvExamples. Our experimental results show that current OOD detectors can be easily evaded by slightly perturbing benign OOD inputs, revealing a severe limitation of current open-world learning frameworks. Furthermore, we find that oodAdvExamples also pose a strong threat to adversarial training based defense methods in spite of their effectiveness against in-distribution adversarial attacks. To counteract these threats and ensure the trustworthy detection of OOD inputs, we outline a preliminary design for a robust open-world machine learning framework.

[ES] El objetivo de este proyecto es la realización del diseño de un videojuego de aventura y exploración con puzles en un entorno 3D mundo abierto, primando la experiencia de vivir este entorno. En una época marcada por los avances tecnológicos donde los videojuegos supondrían un nuevo paso en la exploración de nuevos territorios, se ofrece al jugador un mundo virtual desconocido como alternativa a la sobreinformación entorno al planeta Tierra. Influenciado en títulos como Journey, el personaje principal se define como un solitario explorador contemplativo que interacciona amigablemente con la diversidad animal del mundo que recorre. Al protagonista, como en la saga de The Legend of Zelda o los clásicos de la animación de Disney, le acompaña un personaje no jugable que actúa a modo de voz de conciencia, reforzando estos valores. La producción artística incluirá el diseño de este mundo y sus escenarios, además de los personajes protagonistas, así como las diferentes habilidades del personaje principal, ligadas a una serie de props como las máscaras intercambiables que el usuario podrá equipar. También se desarrollará el diseño de diversas criaturas y el estudio de sus diferentes interacciones con el jugador. Las claves estéticas del trabajo parten de referentes del Art Nouveau como Alfons Mucha, el ilustrador Moebius o ilustradores contemporáneos como Matt Rhodes., [EN] The main purpose of this project is the production and design of an adventure, exploration and puzzles on a 3D open world environment video game, prevailing the experience of living this environment. At a time marked by the technological advances where video games would mean the next step in the exploration of unknown paths, it is offered to the player a new undiscovered virtual world like an alternative to the excess of information about planet Earth. Based on titles like Journey, the main character is defined like lonesome contemplative explorer which kindly interacts with the animal diversity of the world he¿s travelling. This leading figure like in the ¿The legend of Zelda¿ saga or the Disney¿s animation classics, is accompanied by a non-playable figure who acts like a conscious voice, beefing up these values. The artistic production will include the design of this world and its scenarios, in addition the leading characters, as well as the multiple skills of the main character, linked to some props like the exchanging masks that could be equipped by the player. It will be developed the design of some creatures as the studio of its multiple interactions with the gamester. The aesthetic working keys are based on some art leaders of the Art Nouveau like Alfons Mucha, the illustrator Moebius and some others contemporary illustrators like Matt Rhodes.

There is heightened concern over deliberately inaccurate news. Recently, so-called deepfake videos and images that are modified by or generated by artificial intelligence techniques have become more realistic and easier to create. These techniques could be used to create fake announcements from public figures or videos of events that did not happen, misleading mass audiences in dangerous ways. Although some recent research has examined accurate detection of deepfakes, those methodologies do not generalize well to real-world scenarios and are not available to the public in a usable form. In this project, we propose a system that will robustly and efficiently enable users to determine whether or not a video posted online is a deepfake. We approach the problem from the journalists' perspective and work towards developing a tool to fit seamlessly into their workflow. Results demonstrate accurate detection on both within and mismatched datasets.

In the age of pervasive computing and social networks, it has become commonplace to retrieve opinions about digital contents in games. In the case of multi-player, open world gaming, in fact even in “old-school” single players games, it is evident the need for adding new features in a game depending on users comments and needs. However this is a challenging task that usually requires considerable design and programming efforts, and more and more patches to games, with the inevitable consequence of loosing interest in the game by players over years. This is particularly a hard problem for all games that do not intend to be designed as interactive novels. Process Content Generation (PCG) of new contents could be a solution to this problem, but usually such techniques are used to design new maps or graphical contents. Here we propose a novel PCG technique able to introduce new contents in games by means of new story-lines and quests. We introduce new intelligent agents and events in the world: their attitudes and behaviors will promote new actions in the game, leading to the involvement of players in new gaming content. The whole methodology is driven by Social Media Analysis contents about the game, and by the use of formal planning techniques based on Multi-Agents models. © 2019 Elsevier Ltd

Covering vast geographical and cultural areas, the wingspan of Kenneth White’s poetic programme covers a cross-cultural, transdisciplinary field of study. The chapter examines White’s work, focusing on the interconnectedness and intertwining of the poetic subject with the natural world. It analyses White’s concepts of intellectual nomadism and the open world by means of tracing the main philosophical influences that inform his writing. It examines the poet’s explorations of landscape and mindscape, stemming from philosophical contemplation and spiritual realisation. The analysis attempts to follow the poet’s mapping of extensive territories of the globe in the context of the problem of proper dwelling, investigating the ways in which it mediates place in a phenomenological relationship. It explores the poetic subject’s self-identification with its surroundings, which stresses the interfolding of a mind with the natural world. Finally, the chapter also considers the syncretism of poetic forms employed by White, ranging from haiku to ‘diamond’ poems, to polyphonic itinerary poems.

At present, object recognition studies are mostly conducted in a closed lab setting with classes in test phase typically in training phase. However, real-world problem is far more challenging because: i) new classes unseen in the training phase can appear when predicting; ii) discriminative features need to evolve when new classes emerge in real time; and iii) instances in new classes may not follow the "independent and identically distributed" (iid) assumption. Most existing work only aims to detect the unknown classes and is incapable of continuing to learn newer classes. Although a few methods consider both detecting and including new classes, all are based on the predefined handcrafted features that cannot evolve and are out-of-date for characterizing emerging classes. Thus, to address the above challenges, we propose a novel generic end-to-end framework consisting of a dynamic cascade of classifiers that incrementally learn their dynamic and inherent features. The proposed method injects dynamic elements into the system by detecting instances from unknown classes, while at the same time incrementally updating the model to include the new classes. The resulting cascade tree grows by adding a new leaf node classifier once a new class is detected, and the discriminative features are updated via an end-to-end learning strategy. Experiments on two real-world datasets demonstrate that our proposed method outperforms existing state-of-the-art methods., This paper has been accepted by CIKM 2019

Behavioral profiling of players in digital games is a key challenge in game analytics, representing a particular challenge in Open-World Games. These games are characterized by large virtual worlds and few restrictions on player affordances. In these games, incorporating the spatial and temporal dimensions of player behavior is necessary when profiling behavior, as these dimensions are important to the playing experience. We present analyses that apply cluster analysis and the DEDICOM decompositional model to profile the behavior of more than 5,000 players of the major commercial title Just Cause 2 integrating both spatio-temporal trails and behavioral metrics. The application of DEDICOM to profile the spatio-temporal behavior of players is demonstrated for the purpose of analysing the entire play history of Just Cause 2 players, but also for the more detailed analysis of a single mission. This showcases the applicability of spatio-temporal profiling to condense player behavior across large sample sizes, across different scales of investigation. The method presented here provides a means to build profiles of player activity in game environments with high degrees of freedom across different scales of analysis - from a small segment to the entire game.

Crimes and vignettes are placed throughout the game city space using procedural systems to find appropriate locations. Editing of roads or buildings necessitates re-authoring the placement of dynamic encounters in that area of the environment. In many cases the dynamic encounters may have already undergone "final" manual adjustment to the space. To accommodate iterations on city layout, we add three main improvements to our procedural systems: preserve hand-authored work if it continues to meet specifications; place encounter components with higher fidelity; and provide the artists and designers guidance for crimes and vignettes needing more attention.

Most of the recognition systems presume a controlled, well-defined research setting, where all possible classes that can appear during a test are known a priori. This environment is referred to as the ``closed-world'' model, while the ``open-world'' model implies that unknown classes can be incorporated into a recognition algorithm whilst being predicted. Therefore, recognition systems that operate in the real world have to deal with these unknown categories. Our objective was not only to detect data that originate from categories unseen during training, but to identify similarities between pieces of unknown data and then form new classes by automatically labeling them. Our Double Probability Model was extended by an image clustering algorithm, in which Kernel K-means was used. A new procedure, namely the Cluster Classification algorithm for the detection of unknowns and automated labeling, is proposed. These approaches facilitate the transition from open-set recognition to an open-world problem. The Fisher Vector (FV) was used for the mathematical representation of the images and then a Support Vector Machine introduced as a classifier. The measurement of similarity was based on the FV representations. Experiments were conducted on the Caltech101 and Caltech256 datasets of images and the Rand Index was evaluated over the unknown data. The results showed that our proposed Cluster Classification algorithm was able to yield almost the same Rand Index, even though the number of unknown categories increased.

In our increasingly connected and open World, randomness has become an endangered species. We may soon not have anything private, all out communication, interaction with others becomes publicly available. The only method to secure (temporarily) communication is mixing it with randomness – encoding it with random keys. But massive reuse of the same sources of randomness and rapid development of technology often reveals that used sources were not perfectly random. The Internet security is topdown, based on higher-level certificates, but we can never be quite certain with 'given from above' products in their quality – in order to beat each other producers are 'cutting corners' and even the high-level security certificates are available on Internet dark markets. This clearly shows in tremendous increase of all kind of security accidents, so there is an urgent need for new, independent sources of randomness. Mathematical treatment of randomness is based on infinite concepts, thus useless in practice with devices with finite memory (humans, computers, Internet Of Things). Here is introduced a definition for randomness based on devices with finite memory – k-randomness; it is shown, how this allows to create new randomness in computer games; numerous tests show, that this source is quite on par with established sources of randomness. Besides algorithmically-generated randomness is in computer games present also human-generated randomness-when competing players try to beat each other they invent new moves and tactics, i.e. introduce new randomness. This randomness appears in the sequence of players moves and when combined with the sequences of moves of other players can be used for generating secret keys for symmetric encryption in multi-player game communication system. The method does not use public-key step for creation of shared secret (the key), thus the encryption system does not need any upper-level security authorities. publishedVersion

A fundamental assumption in most contemporary person re-identification research, is that all query persons that need to be re-identified belong to a closed gallery of known persons, i.e., they have been observed and a representation of their appearance is available. For several real-world applications, this closed-world assumption does not hold, as image queries may contain people that the re-identification system has never observed before. In this work, we remove this constraining assumption. To do so, we introduce a novelty detection mechanism that decides whether a person in a query image exists in the gallery. The re-identification of persons existing in the gallery is easily achieved based on the persons representation employed by the novelty detection mechanism. The proposed method operates on a hybrid person descriptor that consists of both supervised (learnt) and unsupervised (hand-crafted) components. A series of experiments on public, state of the art datasets and in comparison with state of the art methods shows that the proposed approach is very accurate in identifying persons that have not been observed before and that this has a positive impact on re-identification accuracy.

Classic supervised learning makes the shut world suspicion that the classes found in testing probably showed up in preparing. Be that as it may, this supposition that is frequently disregarded in actual applications. For instance, in a web-based media website, new subjects rise continually furthermore, in internet business, new classes of items show up every day. A model that can't identify new/inconspicuous issues or items is hard to work well in such open conditions. An alluring model using in such situations must have the option to (1) reject models from inconspicuous classes (not showed up in preparing) and (2) gradually become familiar with the new/concealed types to extend the current model. This is called open-world learning (OWL). This paper proposes another OWL technique dependent on meta-learning. The essential oddity is that the model keeps up just a powerful arrangement of seen classes that permits new courses to be included or erased with no requirement for model re-preparing. Each class is spoken to by a little understanding of preparing models.

A fundamental assumption in most contemporary person re-identification research, is that all query persons that need to be re-identified belong to a closed gallery of known persons, i.e., they have been observed and a representation of their appearance is available. For several real-world applications, this closed-world assumption does not hold, as image queries may contain people that the re-identification system has never observed before. In this work, we remove this constraining assumption. To do so, we introduce a novelty detection mechanism that decides whether a person in a query image exists in the gallery. The re-identification of persons existing in the gallery is easily achieved based on the persons representation employed by the novelty detection mechanism. The proposed method operates on a hybrid person descriptor that consists of both supervised (learnt) and unsupervised (hand-crafted) components. A series of experiments on public, state of the art datasets and in comparison with state of the art methods shows that the proposed approach is very accurate in identifying persons that have not been observed before and that this has a positive impact on re-identification accuracy.

Next generation machine learning requires stepping away from classical batch learning towards interactive and adaptive learning. This is essential to cope with demanding machine learning applications we have already today. Our workshop at ECCV 2018 in Munich therefore served as a discussion forum for experts in this field and in the following we give a brief overview. Please note that this discussion paper has not been not peer-reviewed and only contains the subjective summary of the workshop organizers.

Intelligent Interactive Systems work well in well defined contexts. Therefore, current research focuses on developing specialised systems for specific tasks. However, real life situations violate these restrictions. Even with a specialised task focus Intelligent Interactive Systems still need to be able to react in a meaningful way in real world situations. In this paper, we propose a set of context factors to determine context and a coarse dependency structure for predicting behaviour. We argue that with such a limited set of context factors it will be possible to model context-specific behaviours for different contexts. Also, it can serve as a basis to determine the complexity of a situation.

Virtually every connection to an Internet service is preceded by a DNS lookup which is performed without any traffic-level protection, thus enabling manipulation, redirection, surveillance, and censorship. To address these issues, large organizations such as Google and Cloudflare are deploying recently standardized protocols that encrypt DNS traffic between end users and recursive resolvers such as DNS-over-TLS (DoT) and DNS-over-HTTPS (DoH). In this paper, we examine whether encrypting DNS traffic can protect users from traffic analysis-based monitoring and censoring. We propose a novel feature set to perform the attacks, as those used to attack HTTPS or Tor traffic are not suitable for DNS' characteristics. We show that traffic analysis enables the identification of domains with high accuracy in closed and open world settings, using 124 times less data than attacks on HTTPS flows. We find that factors such as location, resolver, platform, or client do mitigate the attacks performance but they are far from completely stopping them. Our results indicate that DNS-based censorship is still possible on encrypted DNS traffic. In fact, we demonstrate that the standardized padding schemes are not effective. Yet, Tor -- which does not effectively mitigate traffic analysis attacks on web traffic -- is a good defense against DoH traffic analysis.

In the study of literature, different authors adhere to diverse criteria and therefore have produced special concepts that in many elements may even coincide, however in particular elements may differ from one author to another. One of the most renowned authors who has studied literature and literary criticism and has created theoretical concepts for these studies is Northrop Frye, a contemporary scholar whose views on literature and literary criticism have been expressed in a very broad and theoretically supported way, particularly in his work "Anatomy of Criticism", an important work in theoretical studies related to the arts and literature as well as on literary criticism of art in general. The work opens with a “controversial introduction”, to continue then with the main part - Four Essays (First, Second, Third, Fourth Essay), in which the following are respectively added: historical criticism; ethical critique; arch-criticism and rhetorical criticism, to conclude with an Endeavour to conclude and some other notes that help make it easier to be scrutinised. "On the one hand, it seems to us that the world is a "sealed" book that lets itself to be read just once, because if there is a law that governs the gravity of the planet, this law is either right or wrong. Compared to that, the universality of a book seems to us to be an open world (Umberto Eco, For Literature, Tirana, 2007).

Commonsense knowledge relations are crucial for advanced NLU tasks. We examine the learnability of such relations as represented in CONCEPTNET, taking into account their specific properties, which can make relation classification difficult: a given concept pair can be linked by multiple relation types, and relations can have multi-word arguments of diverse semantic types. We explore a neural open world multi-label classification approach that focuses on the evaluation of classification accuracy for individual relations. Based on an in-depth study of the specific properties of the CONCEPTNET resource, we investigate the impact of different relation representations and model variations. Our analysis reveals that the complexity of argument types and relation ambiguity are the most important challenges to address. We design a customized evaluation method to address the incompleteness of the resource that can be expanded in future work., Comment: RELATIONS - Workshop on meaning relations between phrases and sentences (co-located with IWCS). May 2019, Gothenburg, Sweden

Minecraft er et apen verden-spill hvor spilleren har stor frihet til selv a velge sine aktiviteter ogbestemme spillets mal. De kreative mulighetene i spillet har gjort at laerere over hele verden har tattdet i bruk i undervisning. En skoleversjon av spillet har ogsa blitt utviklet, der laereren har mulighettil a planlegge, styre og overvake elevaktiviteten. Det overordnede malet med denne studien er aforsta bedre hvordan spillerens frihet endres nar spillet gar fra a vaere et mal i seg selv til a bli etverktoy for laering. I denne artikkelen ser jeg pa fem ulike spillelementer som bare finnes i skoleversjonenav Minecraft, og som er laget for a hjelpe laereren til a kontrollere og styre elevaktivitet ispillet. Med utgangspunkt i Foucaults begrep maktens mikrofysikk, analyserer jeg hvordan de femspillelementene er foreslatt brukt i en hjelpetekst fra utvikleren og hvordan de faktisk blir brukt itre undervisningsopplegg. Endelig forsoker jeg, a beskrive hvilke subjektsposisjoner som spillet ogundervisningsoppleggene tilbyr laerere som bruker spillet i klasserommet. English abstract Minecraft is an open world game where the player has a great deal of freedom to choose theirown activities and determine the goals of the game. The creative possibilities of the game havemade teachers all over the world use it in their classrooms. A school edition of the game has alsobeen lauched where the teacher has the opportunity to plan, manage and monitor student activity.The overall goal of this study is to understand better how the player’s freedom changes when thegame goes from being as a means on its own to becoming a learning tool. In this article, I lookat five different game features that are unique for the educational version of Minecraft. These aredesigned to help the teacher surveil and control student activity in the game. Based on Foucault’sconcept of the microphysics of power, I investigate how the five game elements are described in amanual and how they are actually used in three lesson plans. Finally, I try to describe which subjectpositions the game and the lesson plans offer teachers who use the game in classrooms.

Die virtuelle Realität ist ein fantastischer Ort, frei von Einschränkungen und vielen Möglichkeiten. Für Ingenieure ist dies der perfekte Ort, um Wissenschaft und Technik zu erleben, es fehlt jedoch die Infrastruktur, um die virtuelle Realität zugänglich zu machen, insbesondere für technische Anwendungen. Diese Arbeit bescheibt die Entstehung einer Softwareumgebung, die eine einfachere Entwicklung von Virtual-Reality-Anwendungen und deren Implementierung in immersiven Hardware-Setups ermöglicht. Virtual Engineering, die Verwendung virtueller Umgebungen für Design-Reviews während des Produktentwicklungsprozesses, wird insbesondere von kleinen und mittleren Unternehmen nur äußerst selten eingesetzt. Die Hauptgründe sind nicht mehr die hohen Kosten für professionelle Virtual-Reality-Hardware, sondern das Fehlen automatisierter Virtualisierungsabläufe und die hohen Wartungs- und Softwareentwicklungskosten. Ein wichtiger Aspekt bei der Automatisierung von Virtualisierung ist die Integration von Intelligenz in künstlichen Umgebungen. Ontologien sind die Grundlage des menschlichen Verstehens und der Intelligenz. Die Kategorisierung unseres Universums in Begriffe, Eigenschaften und Regeln ist ein grundlegender Schritt von Prozessen wie Beobachtung, Lernen oder Wissen. Diese Arbeit zielt darauf ab, einen Schritt zu einem breiteren Einsatz von Virtual-Reality-Anwendungen in allen Bereichen der Wissenschaft und Technik zu entwickeln. Der Ansatz ist der Aufbau eines Virtual-Reality-Authoring-Tools, eines Softwarepakets zur Vereinfachung der Erstellung von virtuellen Welten und der Implementierung dieser Welten in fortschrittlichen immersiven Hardware-Umgebungen wie verteilten Visualisierungssystemen. Ein weiteres Ziel dieser Arbeit ist es, das intuitive Authoring von semantischen Elementen in virtuellen Welten zu ermöglichen. Dies sollte die Erstellung von virtuellen Inhalten und die Interaktionsmöglichkeiten revolutionieren. Intelligente immersive Umgebungen sind der Schlüssel, um das Lernen und Trainieren in virtuellen Welten zu fördern, Prozesse zu planen und zu überwachen oder den Weg für völlig neue Interaktionsparadigmen zu ebnen.

Increasing amounts of available data have led to a heightened need for representing large-scale probabilistic knowledge bases. One approach is to use a probabilistic database, a model with strong assumptions that allow for efficiently answering many interesting queries. Recent work on open-world probabilistic databases strengthens the semantics of these probabilistic databases by discarding the assumption that any information not present in the data must be false. While intuitive, these semantics are not sufficiently precise to give reasonable answers to queries. We propose overcoming these issues by using constraints to restrict this open world. We provide an algorithm for one class of queries, and establish a basic hardness result for another. Finally, we propose an efficient and tight approximation for a large class of queries.

We present a novel extension to embedding-based knowledge graph completion models which enables them to perform open-world link prediction, i.e. to predict facts for entities unseen in training based on their textual description. Our model combines a regular link prediction model learned from a knowledge graph with word embeddings learned from a textual corpus. After training both independently, we learn a transformation to map the embeddings of an entity's name and description to the graph-based embedding space. In experiments on several datasets including FB20k, DBPedia50k and our new dataset FB15k-237-OWE, we demonstrate competitive results. Particularly, our approach exploits the full knowledge graph structure even when textual descriptions are scarce, does not require a joint training on graph and text, and can be applied to any embedding-based link prediction model, such as TransE, ComplEx and DistMult., Comment: 8 pages, accepted to AAAI-2019

Knowledge Graph Completion (KGC) aims at complementing missing relationships between entities in a Knowledge Graph (KG). While closed-world KGC approaches utilizing the knowledge within KG could only complement very limited number of missing relations, more and more approaches tend to get knowledge from open-world resources such as online encyclopedias and newswire corpus. For instance, a recent proposed open-world KGC model called ConMask learns embeddings of the entity’s name and parts of its text-description to connect unseen entities to the KG. However, this model does not make full use of the rich feature information in the text descriptions, besides, the proposed relationship-dependent content masking method may easily miss to find the target-words. In this paper, we propose to use a Multiple Interaction Attention (MIA) mechanism to model the interactions between the head entity description, head entity name, the relationship name, and the candidate tail entity descriptions, to form the enriched representations. Our empirical study conducted on two real-world data collections shows that our approach achieves significant improvements comparing to state-of-the-art KGC methods.

This paper presents a planning system based on semantic reasoning for a general-purpose service robot, which is aimed at behaving more intelligently in domains that contain incomplete information, under-specified goals, and dynamic changes. First, Two kinds of data are generated by Natural Language Processing module from the speech: (i) action frames and their relationships, (ii) the modifier used to indicate some property or characteristic of a variable in the action frame. Next, the task’s goals are generated from these action frames and modifiers. These goals are represented as AI symbols, combining world state and domain knowledge, which are used to generate plans by an Answer Set Programming solver. Finally, the plan’s actions are executed one by one, and continuous sensing grounds useful information, which makes the robot use contingent knowledge to adapt to dynamic changes and faults. For each action in the plan, the planner gets its preconditions and effects from domain knowledge, so during the execution of the task, the environmental changes, especially those conflict with the actions, not only the action being performed but also the subsequent actions, can be detected and handled as early as possible. A series of case studies are used to evaluate the system and verify its ability to acquire knowledge through dialogue with users, solve problems with the acquired causal knowledge, and plan for complex tasks autonomously in the open world.

Due to the incomplete knowledge, how to handle the uncertain risk factors in failure mode and effects analysis (FMEA) is still an open issue. This paper proposes a new generalized evidential FMEA (GEFMEA) model to handle the uncertain risk factor, which may not be included in the conventional FMEA model. In GEFMEA, not only the conventional risk factors, the occurrence, severity, and detectability of the failure mode, but also the other incomplete risk factors are taken into consideration. In addition, the relative importance among all these risk factors is well addressed in the proposed method. GEFMEA is based on the generalized evidence theory, which is efficient in handling incomplete information in the open world. The efficiency and some merit of the proposed method are verified by the numerical example and a real case study on aircraft turbine rotor blades.

Agriculture has an important role in providing food for the community. The Increasing of open world trade, especially in food products, causes prices of domestic food products to be affected by the situation and conditions of international food prices. To protect farmers, the Law Number 19 of 2013 concerning Protection and Empowerment of Farmers has been enacted. In the implementation of the law, especially Article 30 Paragraph (1), received many complaints from Indonesian trading partners, such as the United States and New Zealand who stated that these rules were inconsistent with the GATT provisions. The research method in writing this journal is a normative juridical approach method. In summary, the conclusions from the results of the first discussion, the protection of farmers in addressing world trade is regulated in Article 30 Paragraph (1) of the article aimed at protecting farmers as food producers from loss of price risk due to uncontrolled import of agricultural commodities. Second, Responding to the DS 477-478 WTO ruling it is necessary to revise Article 30 Paragraph (1) because it is contrary to the principle of quantitative restrictive prohibition. Changes to the article should still be used as an instrument to protect farmers as food producers by harmonizing the provisions contained in the WTO

Pattern matching is a pervasive and useful feature in functional programming. There have been many attempts to bring similar notions to Object-Oriented Programming (OOP) in the past. However, a key challenge in OOP is how pattern matching can coexist with the open nature of OOP data structures, while at the same time guaranteeing other desirable properties for pattern matching. This paper discusses several desirable properties for pattern matching in an OOP context and shows how existing approaches are lacking some of these properties. We argue that the traditional semantics of pattern matching, which is based on the order of patterns and adopted by many approaches, is in conflict with the openness of data structures. Therefore we suggest that a more restricted, top-level pattern matching model, where the order of patterns is irrelevant, is worthwhile considering in an OOP context. To compensate for the absence of ordered patterns we propose a complementary mechanism for case analysis with defaults, which can be used when nested and/or multiple case analysis is needed. To illustrate our points we develop Castor: a meta-programming library inScala that adopts both ideas. Castor generates code that uses type-safe extensible visitors, and largely removes boilerplate code typically associated with visitors. We illustrate the applicability of our approach with a case study modularizing the interpreters in the famous book ”Types and Programming Languages”.

"Open World Games" have the potential for countless hours of exploration and fun through their huge game worlds. Procedural generation of game content by computer software is therefore becoming more and more important as a design technique. Using such techniques can cause problems in the coherence of the worlds that adversely affect the game experience. This work deals with the development and design of an open world game with a high degree of design coherence.

There is an increasing level of interest in self-driving cars. In fact, it is predicted that fully autonomous cars will roam the streets by 2020. For an autonomous car to drive by itself, it needs to learn. A safe and economic way to teach a self-driving car to drive by itself is through simulation. However, current car simulators are based on closed world assumptions, where all possible events are already known as design time. Nevertheless, during the training of a self-driving car, it is impossible to account for all the possible events in the open world, where several unknown events may arise (i.e., events that were not considered at design time). Instead of carrying out particular adaptations for known context events in the closed world, the system architecture should evolve to safely reach a new state in the open world. In this research work, our contribution is to extend a car simulator trained by means of machine learning to evolve at runtime with tactics when the simulation faces unknown context events.

This chapter presents some of the tools that are used in open-world games at Ubisoft. It provides a high-level survey of the sorts of tools for ambiences in open-world games that developers customize their own purposes. A set of simple tools are used instead of very complex system for everything. Hence, the memory and CPU cost is minimized. The most fundamental concept for ambiences is to split the game into sound regions. The regions represent those areas which are expected to hear different ambience sounds. One of the main problems with randomized effects is to keep enough aural variation so that players do not notice repetition, while keeping the memory footprint as low as possible. While there are many approaches to implement this transition, one good option is to use a "leaving distance" for each room in the game. The leaving distance is used as an automatic fade-out distance when the player is leaving a room for an outdoor ambience.

There is an ever-increasing complexity of the systems we engineer in modern society, which includes facing the convergence of the embedded world and the open world. This complexity creates increasing difficulty with providing assurance for factors including safety, security and performance. In such a context, the AQUAS project investigates the challenges arising from the inter-dependence of safety, security and performance of systems and aims at efficient solutions for the entire product life-cycle. The project builds on knowledge of partners gained in current or former EU projects and will demonstrate the newly developed methods and techniques for co-engineering across use cases spanning Space, Medicine, Transport and Industrial Control.

Dempster-Shafer evidence theory (DST) is widely used in intelligent information processing, especially for information fusion. Recently, measuring the information volume in the framework of DST draws a lot of attention. Many theories and tools have been proposed to model the uncertain degree in DST, including Deng entropy. However, Deng entropy and the other uncertainty measures in DST pay no attention to the uncertainty in the frame of discernment (FOD) in the open world, which is the reason of this paper. To address this issue, Deng entropy is extended to the open world in DST framework. With the extended Deng entropy (EDE) in the open world, the uncertain information represented by FOD and the mass function of the empty set now can be properly modelled while measuring the uncertain degree in DST. EDE can be regarded as a generalization of Deng entropy in the open world and it can be degenerated to Deng entropy in the closed world if the mass value of the empty set is zero. A few numerical examples are presented to verify the applicable and useful of the new measure.

Adaptive Virtual Textures (AVT) are an improvement upon Procedural Virtual Textures. This technique can be applied to a large open world and can achieve a much higher texture resolution when needed. With AVT, the artist can place thousands of projected decals with high-resolution textures on the terrain surface These decals will be baked together with terrain materials into virtual textures at runtime. Once baked, the rendering engine will directly display the virtual textures instead of rendering terrain materials and decals. Thus, the render performance is greatly improved.

] Title: Believable Decision Making in Large Scale Open World Games for Ambient Characters Author: Tomáš Plch Department / Institute: Department of Software and Computer Science Education Supervisor of the doctoral thesis: Mgr. Cyril Brom, Ph. D., Department of Software and Computer Science Education Abstract: Large scale open worlds for computer games are inhabited by populations of Non- Player Characters (NPC). Believability of these NPCs is key in presenting immersive gameplay to the player. Managing complexity of NPC behaviors is a fundamental game development problem. This thesis is focused on increasing believability of NPCs' behaviors by providing an enhanced language for specifying action selection for these characters. The language is based on the Behavior Tree paradigm combined with object-oriented programming. We introduce our language's mechanisms that enable a developer to create complex, yet maintainable behaviors for individual NPCs. Second, we introduce our mechanism called Intelligent Environment aimed at maintaining a believable game environment able to adapt to player's actions and NPC's behaviors. Thirdly, we present our Smart construct concept which provides NPCs with context relevant behaviors from dedicated behavior containers to employ them when present at locations, using objects,...

In a typical real-world application of re-id, a watch-list (gallery set) of a handful of target people (e.g. suspects) to track around a large volume of non-target people are demanded across camera views, and this is called the open-world person re-id. Different from conventional (closed-world) person re-id, a large portion of probe samples are not from target people in the open-world setting. And, it always happens that a non-target person would look similar to a target one and therefore would seriously challenge a re-id system. In this work, we introduce a deep open-world group-based person re-id model based on adversarial learning to alleviate the attack problem caused by similar non-target people. The main idea is learning to attack feature extractor on the target people by using GAN to generate very target-like images (imposters), and in the meantime the model will make the feature extractor learn to tolerate the attack by discriminative learning so as to realize group-based verification. The framework we proposed is called the adversarial open-world person re-identification, and this is realized by our Adversarial PersonNet (APN) that jointly learns a generator, a person discriminator, a target discriminator and a feature extractor, where the feature extractor and target discriminator share the same weights so as to makes the feature extractor learn to tolerate the attack by imposters for better group-based verification. While open-world person re-id is challenging, we show for the first time that the adversarial-based approach helps stabilize person re-id system under imposter attack more effectively.

In this thesis we tackle the open world person re-identification task in which the people we want to re-identify (probe) might not appear in the database of known identities (gallery). For a given probe person, the goal is to find out whether he is present in the gallery or not and if so, who he is. Our first contribution is based on a verification formulation of the problem. A linear transformation of the features is learnt so that the distance between features of the same person are below a threshold and that of distinct people are above that same threshold so that it is easy to determine whether two sets of images represent the same person or not. Our other contributions are based on collaborative sparse representations. A usual way to use collaborative sparse representation for re-identification is to approximate the feature of a probe image by a sparse linear combination of gallery elements, where all the known identities collaborate but only the most similar elements are selected. Gallery identities are then ranked according to how much they contributed to the approximation. We propose to enhance the collaborative aspect so that collaborative sparse representations can be used not only as a ranking tool but also as a detection tool which rejects wrong matches. A bidirectional variant gives even more robust results by taking into account the fact that a good match is a match where there is a reciprocal relation in which both the probe and the gallery identities consider the other one as a good match. COPReV shows average performances but bidirectional collaboration enhanced sparse representation method outperforms state-of-the-art methods for open world scenarios., Dans cette thèse, nous nous sommes intéressés au problème de la ré-identification de personnes dans le cadre de bases d'identités ouvertes. Ré-identifier une personne suppose qu'elle a déjà été identifiée auparavant. La galerie fait référence aux identités connues. Dans le cas de bases d'identités ouvertes, la galerie ne contient pas toutes les identités possibles. Ainsi une personne requête peut être une des personnes de la galerie, mais peut aussi ne pas être présente dans la galerie. Ré-identifier en base ouverte consiste donc non seulement à ranger par ordre de similarité les identités galeries les plus semblables à la personne requête mais également à rejeter les personnes requêtes si elles ne correspondent à aucune personne de la galerie. Une de nos contributions, COPReV, s'appuie exclusivement sur des contraintes de vérification afin d'apprendre une projection des descripteurs telle que la distance entre les descripteurs d'une même personne soit inférieure à un seuil et que la distance entre les descripteurs de deux personnes distinctes soit supérieure au même seuil. Nos autres contributions se basent sur des méthodes parcimonieuses collaboratives qui sont performantes pour résoudre des tâches de classement. Nous proposons d'améliorer ces méthodes en introduisant un aspect vérification grâce à une collaboration élargie. De plus, une variante bidirectionnelle de cette approche la rend encore plus robuste et donne des résultats meilleurs que les autres approches actuelles de l'état de l'art dans le cadre de la ré-identification de personne en base d'identités ouverte.