Programming Languages and Systems

This open access book constitutes the proceedings of the 29th European Symposium on Programming, ESOP 2020, which was planned to take place in Dublin, Ireland, in April 2020, as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The actual ETAPS 2020 meeting was postponed due to the Corona pandemic. The papers deal with fundamental issues in the specification, design, analysis, and implementation of programming languages and systems

Dependent Object Types

A scalable programming language is one in which the same concepts can describe small as well as large parts. Towards this goal, Scala unifies concepts from object and module systems. In particular, objects can contain type members, which can be selected as types, called path-dependent types. Focusing on path-dependent types, we develop a type-theoretic foundation for Scala: the calculus of Dependent Object Types (DOT). We derive DOT from System F, we add a lower bound to each type variable, in addition to its usual upper bound, (2) in System D, we turn each type variable into a regular term variable containing a type, (3) for a full subtyping lattice, we add intersection and union types, (4) for objects, we consolidate all values into records, (5) for objects that close over a self, we introduce a recursive type, binding a self term variable, (6) for recursive types, we first extend the theory in typing and then also in subtyping. Through this bottom-up exploration, we discover a sound, uniform yet powerful design for DOT. We devise strategies and techniques for proving soundness that scale through this iterative step-by-step process: (1) "pushback" of subtyping transitivity or subsumption, to concisely capture inversion of subtyping or typing, (2) distinction between concrete vs. abstract context variables, to resolve tension between preservation of types vs. preservation of type abstractions, (3) and, specifically for big-step semantics, a type that closes over an environment, to relate context-dependent types across closures. While ultimately, we have developed sound models of DOT in both big-step and small-step operational semantics, historically, the shift to big-step semantics has been helpful in focusing the requirements. In particular, by developing a novel big-step soundness proof for System F<:, calculi like System D<: emerge as straightforward generalizations, almost like removing artificial restrictions. Interesting in their own right, our type soundness techniques for definitional interpreters extend to mutable references without use of co-induction. The DOT calculus finally grounds languages like Scala in firm theory. The DOT calculus helps in finding bugs in Scala, and in understanding feature interaction better as well as requirements. The DOT calculus serves as a good basis for future work which studies extensions or encodings on top of the core, bridging the gap from DOT to Dotty / Scala

Real-time generation and adaptation of social companion robot behaviors

Social robots will be part of our future homes. They will assist us in everyday tasks, entertain us, and provide helpful advice. However, the technology still faces challenges that must be overcome to equip the machine with social competencies and make it a socially intelligent and accepted housemate. An essential skill of every social robot is verbal and non-verbal communication. In contrast to voice assistants, smartphones, and smart home technology, which are already part of many people's lives today, social robots have an embodiment that raises expectations towards the machine. Their anthropomorphic or zoomorphic appearance suggests they can communicate naturally with speech, gestures, or facial expressions and understand corresponding human behaviors. In addition, robots also need to consider individual users' preferences: everybody is shaped by their culture, social norms, and life experiences, resulting in different expectations towards communication with a robot. However, robots do not have human intuition - they must be equipped with the corresponding algorithmic solutions to these problems. This thesis investigates the use of reinforcement learning to adapt the robot's verbal and non-verbal communication to the user's needs and preferences. Such non-functional adaptation of the robot's behaviors primarily aims to improve the user experience and the robot's perceived social intelligence. The literature has not yet provided a holistic view of the overall challenge: real-time adaptation requires control over the robot's multimodal behavior generation, an understanding of human feedback, and an algorithmic basis for machine learning. Thus, this thesis develops a conceptual framework for designing real-time non-functional social robot behavior adaptation with reinforcement learning. It provides a higher-level view from the system designer's perspective and guidance from the start to the end. It illustrates the process of modeling, simulating, and evaluating such adaptation processes. Specifically, it guides the integration of human feedback and social signals to equip the machine with social awareness. The conceptual framework is put into practice for several use cases, resulting in technical proofs of concept and research prototypes. They are evaluated in the lab and in in-situ studies. These approaches address typical activities in domestic environments, focussing on the robot's expression of personality, persona, politeness, and humor. Within this scope, the robot adapts its spoken utterances, prosody, and animations based on human explicit or implicit feedback.Soziale Roboter werden Teil unseres zukünftigen Zuhauses sein. Sie werden uns bei alltäglichen Aufgaben unterstützen, uns unterhalten und uns mit hilfreichen Ratschlägen versorgen. Noch gibt es allerdings technische Herausforderungen, die zunächst überwunden werden müssen, um die Maschine mit sozialen Kompetenzen auszustatten und zu einem sozial intelligenten und akzeptierten Mitbewohner zu machen. Eine wesentliche Fähigkeit eines jeden sozialen Roboters ist die verbale und nonverbale Kommunikation. Im Gegensatz zu Sprachassistenten, Smartphones und Smart-Home-Technologien, die bereits heute Teil des Lebens vieler Menschen sind, haben soziale Roboter eine Verkörperung, die Erwartungen an die Maschine weckt. Ihr anthropomorphes oder zoomorphes Aussehen legt nahe, dass sie in der Lage sind, auf natürliche Weise mit Sprache, Gestik oder Mimik zu kommunizieren, aber auch entsprechende menschliche Kommunikation zu verstehen. Darüber hinaus müssen Roboter auch die individuellen Vorlieben der Benutzer berücksichtigen. So ist jeder Mensch von seiner Kultur, sozialen Normen und eigenen Lebenserfahrungen geprägt, was zu unterschiedlichen Erwartungen an die Kommunikation mit einem Roboter führt. Roboter haben jedoch keine menschliche Intuition - sie müssen mit entsprechenden Algorithmen für diese Probleme ausgestattet werden. In dieser Arbeit wird der Einsatz von bestärkendem Lernen untersucht, um die verbale und nonverbale Kommunikation des Roboters an die Bedürfnisse und Vorlieben des Benutzers anzupassen. Eine solche nicht-funktionale Anpassung des Roboterverhaltens zielt in erster Linie darauf ab, das Benutzererlebnis und die wahrgenommene soziale Intelligenz des Roboters zu verbessern. Die Literatur bietet bisher keine ganzheitliche Sicht auf diese Herausforderung: Echtzeitanpassung erfordert die Kontrolle über die multimodale Verhaltenserzeugung des Roboters, ein Verständnis des menschlichen Feedbacks und eine algorithmische Basis für maschinelles Lernen. Daher wird in dieser Arbeit ein konzeptioneller Rahmen für die Gestaltung von nicht-funktionaler Anpassung der Kommunikation sozialer Roboter mit bestärkendem Lernen entwickelt. Er bietet eine übergeordnete Sichtweise aus der Perspektive des Systemdesigners und eine Anleitung vom Anfang bis zum Ende. Er veranschaulicht den Prozess der Modellierung, Simulation und Evaluierung solcher Anpassungsprozesse. Insbesondere wird auf die Integration von menschlichem Feedback und sozialen Signalen eingegangen, um die Maschine mit sozialem Bewusstsein auszustatten. Der konzeptionelle Rahmen wird für mehrere Anwendungsfälle in die Praxis umgesetzt, was zu technischen Konzeptnachweisen und Forschungsprototypen führt, die in Labor- und In-situ-Studien evaluiert werden. Diese Ansätze befassen sich mit typischen Aktivitäten in häuslichen Umgebungen, wobei der Schwerpunkt auf dem Ausdruck der Persönlichkeit, dem Persona, der Höflichkeit und dem Humor des Roboters liegt. In diesem Rahmen passt der Roboter seine Sprache, Prosodie, und Animationen auf Basis expliziten oder impliziten menschlichen Feedbacks an

Models and Algorithms for Understanding and Supporting Learning Goals in Information Retrieval

While search technology is widely used for learning-oriented information needs, the results provided by popular services such as Web search engines are optimized primarily for generic relevance, not effective learning outcomes. As a result, the typical information trail that a user must follow while searching to achieve a learning goal may be an inefficient one, possibly involving unnecessarily difficult content, or material that is irrelevant to actual learning progress relative to a user's existing knowledge. My work addresses these problems through multiple studies where various models and frameworks are developed and tested to support particular dimensions of search as learning. Empirical analysis of these studies through user studies demonstrate promising results and provide a solid foundation for further work. The earliest work we focused on centered on developing a framework and algorithms to support vocabulary learning objectives in a Web document context. The proposed framework incorporates user information, topic information and effort constraints to provide a desirable combination of personalized and efficient (by word length) learning experience. Our user studies demonstrate the effectiveness of our framework against a strong commercial baseline's (Google search) results in both short- and long-term assessment. While topic-specific content features (such as frequency of subtopic occurrences) naturally play a role in influencing learning outcomes, stylistic and structural features of the documents themselves may also play a role. Using such features we construct robust regression models that show strong predictive strength for multiple measures of learning outcomes. We also show early evidence that regression models trained on one dataset of search as learning can show strong test-set predictions on an independent dataset of search as learning, suggesting a certain degree of generalizability of stylistic content features. The models developed in my work are designed to be as generalizable, scalable and efficient as possible to make it easier for practitioners in the field to improve how people use search engines for learning. Finally, we investigate how gaze-tracking and automatic question generation could be used to scale a form of active learning to arbitrary text material. Our results show promising potential for incorporating interactive learning experiences in arbitrary text documents on the Web. A major theme in these studies centers on understanding and improving how people learn when using Web search engines. We also put specific emphasis on long-term learning outcomes and demonstrate that our models and frameworks actually yield sustainable knowledge gains, both for passive and interactive learning. Taken together, these research studies provide a solid foundation for multiple promising directions in exploring search as learning.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155065/1/rmsyed_1.pd