Refining the imprecise meaning of non-determinism in the Web by strategic games

Nowadays interactions with the World Wide Web are ubiquitous. Users interact through a number of steps consisting of site calls and handling results that can be automatized as orchestrations. Orchestration results have an inherent degree of uncertainty due to incomplete Web knowledge and orchestration semantics are characterized in terms of imprecise probabilistic choices. We consider two aspects in this imprecise semantic characterization. First, when local knowledge (even imprecise) of some part of the Web increases, this knowledge goes smoothly through the whole orchestration. We deal formally with this aspect introducing orchestration refinements. Second, we analyze refinement under uncertainty in the case of parallel composition. Uncertain knowledge is modeled by an uncertainty profile. Such profiles allow us to look at the uncertainty through a zero-sum game, called angel/daemon-game. We propose to use the structure of the Nash equilibria to refine uncertainty. In this case the information improves not through cooperation but through the angel and daemon competition.Peer ReviewedPostprint (author's final draft

User-oriented recommender systems in retail

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

User-oriented recommender systems in retail

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

Machine Learning Based Detection and Evasion Techniques for Advanced Web Bots.

Web bots are programs that can be used to browse the web and perform different types of automated actions, both benign and malicious. Such web bots vary in sophistication based on their purpose, ranging from simple automated scripts to advanced web bots that have a browser fingerprint and exhibit a humanlike behaviour. Advanced web bots are especially appealing to malicious web bot creators, due to their browserlike fingerprint and humanlike behaviour which reduce their detectability. Several effective behaviour-based web bot detection techniques have been pro- posed in literature. However, the performance of these detection techniques when target- ing malicious web bots that try to evade detection has not been examined in depth. Such evasive web bot behaviour is achieved by different techniques, including simple heuris- tics and statistical distributions, or more advanced machine learning based techniques. Motivated by the above, in this thesis we research novel web bot detection techniques and how effective these are against evasive web bots that try to evade detection using, among others, recent advances in machine learning. To this end, we initially evaluate state-of-the-art web bot detection techniques against web bots of different sophistication levels and show that, while the existing approaches achieve very high performance in general, such approaches are not very effective when faced with only advanced web bots that try to remain undetected. Thus, we propose a novel web bot detection framework that can be used to detect effectively bots of varying levels of sophistication, including advanced web bots. This framework comprises and combines two detection modules: (i) a detection module that extracts several features from web logs and uses them as input to several well-known machine learning algo- rithms, and (ii) a detection module that uses mouse trajectories as input to Convolutional Neural Networks (CNNs). Moreover, we examine the case where advanced web bots utilise themselves the re- cent advances in machine learning to evade detection. Specifically, we propose two novel evasive advanced web bot types: (i) the web bots that use Reinforcement Learning (RL) to update their browsing behaviour based on whether they have been detected or not, and (ii) the web bots that have in their possession several data from human behaviours and use them as input to Generative Adversarial Networks (GANs) to generate images of humanlike mouse trajectories. We show that both approaches increase the evasiveness of the web bots by reducing the performance of the detection framework utilised in each case. We conclude that malicious web bots can exhibit high sophistication levels and com- bine different techniques that increase their evasiveness. Even though web bot detection frameworks can combine different methods to effectively detect such bots, web bots can update their behaviours using, among other, recent advances in machine learning to in- crease their evasiveness. Thus, the detection techniques should be continuously updated to keep up with new techniques introduced by malicious web bots to evade detection

Konzeption und Entwicklung eines Robot Cognition Processors für adaptive Demontageanwendungen

Im Rahmen der perspektivischen Einführung einer Kreislaufwirtschaft sind ökonomische und ökologische Aspekte entscheidend für die Attraktivität der Umsetzung in beteiligten Wirtschaftsunternehmen. Die Demontage stellt innerhalb von Verwertungsprozessen in diesen Konzepten einen wichtigen Schritt dar, der aufgrund von hoher Varianten- und Zustandsvielfalt überwiegend manuell ausgeführt wird. Diese Forschungsarbeit untersucht die Möglichkeiten der nachhaltigen Verbesserung des Demontageprozesses durch selektive (Teil-)Automatisierung mit Hilfe eines Konzeptes aus dem Bereich der kognitiven Robotik. Es wird dabei auf Grundlage der Anforderungen aus realen Demontageprozessen ein System entwickelt, das in einer agentenbasierten Modulstruktur die Funktionsumfänge bietet, die für eine autonome, flexible Demontageplanung unter Berücksichtigung von Produkt- und Lebenszyklusdaten erforderlich sind und die effiziente Ausführung der Demontageoperationen im Rahmen einer Mensch-Roboter-Kollaboration erlauben. Grundlage für die entwickelten Module stellt ein standardisiertes Informationsmanagement-Konzept dar, welches die Anlagenebene der Demontage technisch mit allen beteiligten Stakeholdern der zirkulären Wertschöpfungskette verknüpft. Mit Hilfe von Industrie 4.0 Technologien, wie beispielsweise dem Einsatz von KI-unterstützten Entscheidungssystemen oder einer intelligenten Bilderkennungseinheit können so produktindividuelle Verwertungsszenarien innerhalb der Kreislaufwirtschaft bestimmt werden, welche die Schlüsselposition der Demontage am Beginn der zirkulären Wertschöpfungskette bestmöglich nutzen. Die Untersuchungen des Systemkonzeptes am Beispiel der Moduldemontage von Elektrofahrzeug-Batterien zeigen, dass mit dem entwickelten Konzept eine Verbesserung gegenüber manueller Demontageoperationen erzielt werden kann. Die Verknüpfung der Systemeinheiten lässt sich durch die verwendeten Interoperabilitätsstandards skalieren und erlaubt so auch den industriellen Einsatz. Durch bidirektionale Kommunikationsstrukturen wird gezeigt, dass es möglich ist validierte Prozessinformationen aus einer Demontageeinheit an anderen Stellen zu nutzen. Dies reduziert den effektiven Aufwand im Umgang mit einer hohen Variantenvielfalt. Die Verwendung der entwickelten Modulkonzepte ist grundsätzlich auch in angrenzenden Feldern möglich, erfordert jedoch weitere Entwicklungs- und Abstimmungsarbeit. Aus den Ergebnissen dieser Konzeptentwicklung folgen zahlreiche Weiterentwicklungs- und Anwendungspotenziale für Robotiksysteme im Bereich der kreislaufwirtschaftlichen Verwertungsprozesse. Vor dem Hintergrund der Notwendigkeit der Rückgewinnung kritischer Elemente und einer effizienten Ressourcennutzung durch höherwertige (Teil-)Nutzungs- und Verwertungsoptionen, ist der Einsatz hierauf aufbauender Konzepte eine lohnenswerte Zukunftsperspektive.In the pursuance of a Circular Economy, both economic and ecological aspects are crucial for the implementation in private companies. The disassembly process itself is a very important step in end-oflife utilization and because of the high variance of products and their conditions it is mainly carried out manually. This work investigates the possibilities of a sustainable improvement of such processes by selective automation with cognitive robotics. Based on requirements of real disassembly cases, a robot system is conceptualized and developed which is able to facilitate an autonomous, flexible disassembly planning while taking both product and lifecycle data into account. Furthermore, the execution of the disassembly process in the concept is carried out as a human-machine-collaboration. The overall foundation of the system is an information management concept which connects shopfloor level disassembly with all stakeholders within the circular value chain. Using Industry 4.0 technologies, for instance AI decision systems or an intelligent image recognition, part-individual utilization scenarios can be defined this way. The investigation of the system concept on the case study of module disassembly of electric vehicle batteries shows that automation is both more effective and efficient in comparison to manual operations. Interfaces are highly scalable because of the interoperability standards used, preparing the concept to be implemented in industry. Moreover, bidirectional communication pipelines enable the exchange of valid process knowledge between several stakeholders, reducing the effort of dealing with a high variance of products and conditions. Transfer of the concept to other fields of industry or recycling operations is possible but requires further development for the actual use case. Conclusively, the concept developed opens up a manifold of different application scenarios for cognitive robotics in the Circular Economy domain. Keeping the necessity of recovering critical elements and the reuse of valuable components in mind, an implementation of future concepts based on this approach is a perspective worthwhile