Towards MARTE++ : an enhanced UML-based language to Model and Analyse Real-Time and Embedded Systems for the IoT age

This paper presents requirements for an enhanced version of the UML Profile for MARTE, the current standard of the OMG for the modelling and analysis of real-time embedded systems. Since its adoption by the OMG in 2009 and after the various additions along recent years, MARTE has been essayed in a number of application domains and validation approaches. This paper makes a review of these various efforts describing extensions, additional functionality, and modeling needs that may serve as inputs for the preparation of a formal request for proposals (RFP) at the OMG. Aspects that have been found useful to have in it include modern platforms like Multi-core, Many-core and GPUs, networking for broader domains like the Internet of Things, federation of all modelling artifacts involved in the development process, including tracing mechanisms embedded in the language to link design and run-time artifacts, and more elaborated kinds of quantitative analyses and extra functional properties, like energy and memory consumption, heat dissipation, and temperature distribution. Also methodological aspects like its specification as a profile and/or as a meta-model will need to be discussed. Finally, the standard needs to be reviewed against the new executable UML related specifications; particularly to be in alignment with those semantics of state machines and composite structures.This work receives funding from the Spanish Government under grant number TIN2014-56158-C4-2-P (M2C2), and from the Electronic Component Systems for European Leadership Joint Undertaking under grant agreement No 737494 (MegaM@RT2). This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation programme and Sweden, France, Spain, Italy, Finland, Czech Republic. We thank the anonymous reviewers for their insights and proposals of improvement

Guidance in the human–machine analytics process

In this paper, we list the goals for and the pros and cons of guidance, and we discuss the role that it can play not only in key low-level visualization tasks but also the more sophisticated model-generation tasks of visual analytics. Recent advances in artificial intelligence, particularly in machine learning, have led to high hopes regarding the possibilities of using automatic techniques to perform some of the tasks that are currently done manually using visualization by data analysts. However, visual analytics remains a complex activity, combining many different subtasks. Some of these tasks are relatively low-level, and it is clear how automation could play a role—for example, classification and clustering of data. Other tasks are much more abstract and require significant human creativity, for example, linking insights gleaned from a variety of disparate and heterogeneous data artifacts to build support for decision making. In this paper, we outline the potential applications of guidance, as well as the inputs to guidance. We discuss challenges in implementing guidance, including the inputs to guidance systems and how to provide guidance to users. We propose potential methods for evaluating the quality of guidance at different phases in the analytic process and introduce the potential negative effects of guidance as a source of bias in analytic decision making

On the Minimum Error Correction Problem for Haplotype Assembly in Diploid and Polyploid Genomes

International audienceFinding the global minimum energy conformation (GMEC) of a huge combinatorial search space is the key challenge in computational protein design (CPD) problems. Traditional algorithms lack a scalable and efficient distributed design scheme, preventing researchers from taking full advantage of current cloud infrastructures. We design cloud OSPREY (cOSPREY), an extension to a widely used protein design software OSPREY, to allow the original design framework to scale to the commercial cloud infrastructures. We propose several novel designs to integrate both algorithm and system optimizations, such as GMEC-specific pruning, state search partitioning, asynchronous algorithm state sharing, and fault tolerance. We evaluate cOSPREY on three different cloud platforms using different technologies and show that it can solve a number of large-scale protein design problems that have not been possible with previous approaches

Engineering Adaptive Model-Driven User Interfaces for Enterprise Applications

Enterprise applications such as enterprise resource planning systems have numerous complex user interfaces (UIs). Usability problems plague these UIs because they are offered as a generic off-the-shelf solution to end-users with diverse needs in terms of their required features and layout preferences. Adaptive UIs can help in improving usability by tailoring the features and layout based on the context-of-use. The model-driven UI development approach offers the possibility of applying different types of adaptations on the various UI levels of abstraction. This approach forms the basis for many works researching the development of adaptive UIs. Yet, several gaps were identified in the state-of-the-art adaptive model-driven UI development systems. To fill these gaps, this thesis presents an approach that offers the following novel contributions: - The Cedar Architecture serves as a reference for developing adaptive model-driven enterprise application user interfaces. - Role-Based User Interface Simplification (RBUIS) is a mechanism for improving usability through adaptive behavior, by providing end-users with a minimal feature-set and an optimal layout based on the context-of-use. - Cedar Studio is an integrated development environment, which provides tool support for building adaptive model-driven enterprise application UIs using RBUIS based on the Cedar Architecture. The contributions were evaluated from the technical and human perspectives. Several metrics were established and applied to measure the technical characteristics of the proposed approach after integrating it into an open-source enterprise application. Additional insights about the approach were obtained through the opinions of industry experts and data from real-life projects. Usability studies showed the approach’s ability to significantly improve usability in terms of end-user efficiency, effectiveness and satisfaction

多様なポストゲノムデータのためのアラインメントフリーなアルゴリズムの構造

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 今井 浩, 東京大学教授 小林 直樹, 東京大学教授 五十嵐 健夫, 東京大学教授 杉山 将, 東京大学講師 笠原 雅弘University of Tokyo(東京大学

A Dependently Typed Language with Nontermination

We propose a full-spectrum dependently typed programming language, Zombie, which supports general recursion natively. The Zombie implementation is an elaborating typechecker. We prove type saftey for a large subset of the Zombie core language, including features such as computational irrelevance, CBV-reduction, and propositional equality with a heterogeneous, completely erased elimination form. Zombie does not automatically beta-reduce expressions, but instead uses congruence closure for proof and type inference. We give a specification of a subset of the surface language via a bidirectional type system, which works up-to-congruence, and an algorithm for elaborating expressions in this language to an explicitly typed core language. We prove that our elaboration algorithm is complete with respect to the source type system. Zombie also features an optional termination-checker, allowing nonterminating programs returning proofs as well as external proofs about programs

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

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