pinecone/core-2020-05-10-deduplication huggingface dataset:
User evaluation of an interactive learning framework for single-arm and dual-arm robots

The final publication is available at link.springer.comSocial robots are expected to adapt to their users and, like their human counterparts, learn from the interaction. In our previous work, we proposed an interactive learning framework that enables a user to intervene and modify a segment of the robot arm trajectory. The framework uses gesture teleoperation and reinforcement learning to learn new motions. In the current work, we compared the user experience with the proposed framework implemented on the single-arm and dual-arm Barrett’s 7-DOF WAM robots equipped with a Microsoft Kinect camera for user tracking and gesture recognition. User performance and workload were measured in a series of trials with two groups of 6 participants using two robot settings in different order for counterbalancing. The experimental results showed that, for the same task, users required less time and produced shorter robot trajectories with the single-arm robot than with the dual-arm robot. The results also showed that the users who performed the task with the single-arm robot first experienced considerably less workload in performing the task with the dual-arm robot while achieving a higher task success rate in a shorter time.Peer ReviewedPostprint (author's final draft
Co-design and robots: A case study of a robot dog for aging people

© Springer International Publishing AG 2016.The day-to-day experiences of aging citizens differ significantly from young, technologically savvy engineers. Yet, well-meaning engineers continue to design technologies for aging citizens, informed by skewed stereotypes of aging without deep engagements from these users. This paper describes a co-design project based on the principles of Participatory Design that sought to provide aging people with the capacity to co-design technologies that suit their needs. The project combined the design intuitions of both participants and designers, on equal footing, to produce a companion robot in the form of a networked robotic dog. Besides evaluating a productive approach that empowers aging people in the process of co-designing and evaluating technologies for themselves, this paper presents a viable solution that is playful and meaningful to these elderly people; capable of enhancing their independence, social agency and well-being
A review, timeline, and categorization of learning design tools

Enabling teachers to define or portray efficient teaching ideas for\ud
sharing, reuse or adaptation has attracted the interest of Learning Design\ud
researchers and has led to the development of a variety of learning design tools.\ud
In this paper, we introduce a multi-dimensional framework for the analysis of\ud
learning design tools and use it to review twenty-nine tools currently available to\ud
researchers and practitioners. Lastly, we categorise these tools according to the\ud
main functionality that they offer
Parallelizing heuristics for generating synchronizing sequences

Synchronizing sequences are used in the context of finite state machine based testing in order to initialize an implementation to a particular state. The cubic complexity of even the fastest heuristic algorithms known in the literature to construct a synchronizing sequence can be a problem in practice. In order to scale the performance of synchronizing heuristics, some algorithmic improvements together with a parallel implementation of these heuristics are proposed in this paper. An experimental study is also presented which shows that the improved/parallel implementation can yield a considerable speedup over the sequential implementation
Construction of Capacities from Overlap Indexes

In many problems, it is crucial to find a relation between groups of data. Such relation can be expressed, for instance, in terms of an appropriate fuzzy measure or capacity([10, 21]) which reflects the way the different data are connected to each other [20]. In this chapter, taking into account this fact and following the developments in [8],we introduce a method to build capacities ([20, 21]) directly from the data (inputs) of a given problem. In order to do so, we make use of the notions of overlap function and overlap index ([5, 12, 13, 7, 4, 14, 16]) for constructing capacities which reflect how different data are related to each other.\ud
This paper is organized as follows: after providing some preliminaries, we analyse, in Section 3, some properties of overlap functions and indexes. In Sections 4 we discuss a method for constructing capacities from overlap functions and overlap indexes. Finally, we present some conclusions and references
Moving real-time linked data query evaluation to the client

Traditional RDF stream processing engines work completely server-side, which contributes to a high server cost. For allowing a large number of concurrent clients to do continuous querying, we extend the low-cost Triple Pattern Fragments (TPF) interface with support for timesensitive queries. In this poster, we give the overview of a client-side rdf stream processing engine on top of tpf. Our experiments show that our solution significantly lowers the server load while increasing the load on the clients. Preliminary results indicate that our solution moves the complexity of continuously evaluating real-time queries from the server to the client, which makes real-time querying much more scalable for a large amount of concurrent clients when compared to the alternatives
An ontology to semantically declare and describe functions

Applications built on top of the Semantic Web are emerging as a novel solution in different areas, such as decision making and route planning. However, to connect results of these solutions -i.e., the semantically annotated data - with real-world applications, this semantic data needs to be connected to actionable events. A lot of work has been done (both semantically as non-semantically) to describe and define Web services, but there is still a gap on a more abstract level, i.e., describing interfaces independent of the technology used. In this paper, we present a data model, specification, and ontology to semantically declare and describe functions independently of the used technology. This way, we can declare and use actionable events in semantic applications, without restricting ourselves to programming language-dependent implementations. The ontology allows for extensions, and is proposed as a possible solution for semantic applications in various domains
Graph-based editing of linked data mappings using the RMLEditor

Linked Data is in many cases generated from (semi-) structured data. This generation is supported by several tools, a number of which use a mapping language to facilitate the Linked Data generation. However, knowledge of this language and other used technologies is required to use the tools, limiting their adoption by non-Semantic Web experts. We demonstrate the RMLEditor: a graphical user interface that utilizes graphs to easily visualize the mappings that deliver the RDF representation of the original data. The required amount of knowledge of the underlying mapping language and the used technologies is kept to a minimum. The RMLEditor lowers the barriers to create Linked Data by aiming to also facilitate the editing of mappings by non-experts
Continuous client-side query evaluation over dynamic linked data

Existing solutions to query dynamic Linked Data sources extend the SPARQL language, and require continuous server processing for each query. Traditional SPARQL endpoints already accept highly expressive queries, so extending these endpoints for time-sensitive queries increases the server cost even further. To make continuous querying over dynamic Linked Data more affordable, we extend the low-cost Triple Pattern Fragments (TPF) interface with support for time-sensitive queries. In this paper, we introduce the TPF Query Streamer that allows clients to evaluate SPARQL queries with continuously updating results. Our experiments indicate that this extension significantly lowers the server complexity, at the expense of an increase in the execution time per query. We prove that by moving the complexity of continuously evaluating queries over dynamic Linked Data to the clients and thus increasing bandwidth usage, the cost at the server side is significantly reduced. Our results show that this solution makes real-time querying more scalable for a large amount of concurrent clients when compared to the alternatives
To explore or to exploit? Learning humans' behaviour to maximize interactions with them

Assume a robot operating in a public space (e.g., a library, a museum) and serving visitors as a companion, a guide or an information stand. To do  that, the robot has to interact with humans, which presumes that it actively searches for humans in order to interact with them. This paper addresses the problem how to plan robot's actions in order to maximize the number of such interactions in the case human behavior is not known in advance. We formulate this problem as the exploration/exploitation problem and design several strategies for the robot. The main contribution of the paper than lies in evaluation and comparison of the designed strategies on two datasets. The evaluation shows interesting properties of the strategies, which are discussed
Which are harder? Soft skills or hard skills?

This paper describes some technical and employability skills that are essential for our students to succeed in a career in software development. We conducted research aimed at understanding the students' problems when required to develop these skills. We explain our techniques for observing skills gaps. Knowledge about these gaps enables us to intervene and suggest remedial action. We discuss how we create opportunities for our students to enhance their skills, based on our experience and the findings of our research.http://www.springer.comseries/7899Computer Scienc
Towards a generic DSL for automated marking systems

The automated static and dynamic assessment of programs makes it practical to increase the learning opportunities of large student classes through the regular assessment of programming assignments. Automatic assessments are traditionally specified in tool-specific languages which are closely linked to the functionality and implementation of a particular tool. This paper considers existing specification languages for assessments and proposes a generic and extensible domain-specific language for the specification of programming assignment assessments.http://www.springer.comseries/7899Computer Scienc
The Kadison-Singer Property

This book gives a complete classification of all algebras with the Kadison-Singer property, when restricting to separable Hilbert spaces. The Kadison-Singer property deals with the following question: given a Hilbert space H and an abelian unital C*-subalgebra A of B(H), does every pure state on A extend uniquely to a pure state on B(H)? This question has deep connections to fundamental aspects of quantum physics, as is explained in the foreword by Klaas Landsman. The book starts with an accessible introduction to the concept of states and continues with a detailed proof of the classification of maximal Abelian von Neumann algebras, a very explicit construction of the Stone-Cech compactification and an account of the recent proof of the Kadison-Singer problem. At the end accessible appendices provide the necessary background material.

This elementary account of the Kadison-Singer conjecture is very well-suited for graduate students interested in operator algebras and states, researchers who are non-specialists of the field, and/or interested in fundamental quantum physics.nrpages: x+140 pp.status: publishe
Future demand response services for blocks of buildings

Research surrounding demand response (DR) is beginning to consider how blocks of buildings can operate collectively within energy networks. DR at the level of a block of buildings involves near real-time optimisation of energy demand, storage and supply (including self-production) using intelligent energy management systems with the objective of reducing the difference between peak-power demand and minimum night-time demand, thus reducing costs and greenhouse gas emissions. To enable this it will be necessary to integrate and augment the telemetry and control technologies embedded in current building management systems and identify potential revenue sources:  both of which vary according to local and national contexts. This paper discusses how DR in blocks of buildings might be achieved. The ideas proposed are based on a current EU funded collaborative research project called “Demand Response in Blocks of Buildings” (DR-BOB), and are envisaged to act as a starting-point for future research and innovation
Digital strategies to a local cultural tourism development: Project e-Carnide

Digital humanities and smart economy strategies are being seen as an important link between tourism and cultural heritage, as they may contribute to differentiate the audiences and to provide different approaches. Carnide is a peripheral neighbourhood of Lisbon with an elderly population, visible traces of rurality, and strong cultural and religious traditions. The academic project e-Carnide concerns its tangible and intangible cultural heritage and the data dissemination through a website and a mobile app, with textual and visual information. The project aims to analyse the impact of technological solutions on cultural tourism development in a sub-region, involving interdisciplinary research in heritage, history of art, ethnography, design communication and software engineering and the collaboration between the university and local residents in a dynamic and innovative way. Framed by a theoretical approach about the role of smart economy for the cultural tourism development in peripheral areas, this paper focuses on a case study, dealing with documents, interviews and observations, in order to understand how the e-Carnide project evolves. The study comprises an analysis about the strengths, weaknesses, opportunities and threats (SWOT analysis) of the project in view to realize its social and cultural implications and to appreciate how it can be applied in other similar and enlarged projects. Results of the research indicates that the new technological strategies can promote the involvement of the population in the knowledge of its own heritage as a factor of cultural and creative tourism development centred on an authentic and immersive experience of the places
Towards the formal verification of data-intensive applications through metric temporal logic

We present an approach for the automated formal verification of distributed systems based on the Storm technology. The approach is based on a formal model of the behavior of Storm topologies given in terms of the CLTLoc metric temporal logic extended with counters. We present a tool-supported mechanism to automatically generate formal models from high-level description of Storm topologies. The Zot formal verification tool is then used to check whether some desired properties hold for the modeled system or not. The analyzed properties concern the growth of the queues of the nodes of the Storm topology. Some experiments performed on example topologies show how the timing features of the modeled system influence the behavior of the queues of the nodes
An Event-B Development Process for the Distributed BIP Framework

We present a refinement-based methodology to design correct by construction distributed systems specified as Event-B models. Starting from an Event-B machine, the studied process proposes successive steps in order to split and schedule the computation of complex events and then to map them on subcomponents. The specification of these steps is done through two domain specific languages. From these specifications, two refinements are generated. Eventually, a distributed code architecture is also generated. The correctness of the process relies on the correctness of the refinements and the translation. We target the distributed BIP framework
An SMT-Based Approach to the Formal Analysis of MARTE/CCSL

International audienceMARTE (abbreviated for Modeling and Analysis of Real-Time and Embedded systems) is a UML profile which provides a generalmodeling framework to design and analyze real-time embedded systems. CCSL (abbreviated for Clock Constraint Specification Language) is aformal language companion to MARTE, used to specify the constraints between the occurrences of events in real-time embedded systems. Many approaches have been proposed to the formal analysis of CCSL such as simulation and model checking. We propose in this paper an SMT-based approach to the formal analysis of CCSL. It is well-known that the SMT-based approach can effectively overcome the state-explosion problem for model checking, and can also be used for theorem proving. The latter feature allows us to prove the invalidity of CCSL constraints, which most of the existing approaches lack. We implement the proposed approach in a prototype tool clyzer on top of K framework and use Z3 as theunderlying SMT solver
Relevancer: Finding and labeling relevant  information in tweet collections

Contains fulltext :
                  161497.pdf (publisher's version ) (Open Access)We introduce a tool that supports knowledge workers who want to gain insights from a tweet collection, but due to time constraints cannot go over all tweets. Our system first pre-processes, de-duplicates, and clusters the tweets. The detected clusters are presented to the expert as so-called information threads. Subsequently, based on the information thread labels provided by the expert, a classifier is trained that can be used to classify additional tweets. As a case study, the tool is evaluated on a tweet collection based on the key terms ‘genocide’ and ‘Rohingya’. The average precision and recall of the classifier on six classes is 0.83 and 0.82 respectively. At this level of performance, experts can use the tool to manage tweet collections efficiently without missing much information.8th International Conference on Social Informatics (SocInfo 2016), 15 november 201
Predicting online extremism, content adopters, and interaction
  reciprocity

We present a machine learning framework that leverages a mixture of metadata,
network, and temporal features to detect extremist users, and predict content
adopters and interaction reciprocity in social media. We exploit a unique
dataset containing millions of tweets generated by more than 25 thousand users
who have been manually identified, reported, and suspended by Twitter due to
their involvement with extremist campaigns. We also leverage millions of tweets
generated by a random sample of 25 thousand regular users who were exposed to,
or consumed, extremist content. We carry out three forecasting tasks, (i) to
detect extremist users, (ii) to estimate whether regular users will adopt
extremist content, and finally (iii) to predict whether users will reciprocate
contacts initiated by extremists. All forecasting tasks are set up in two
scenarios: a post hoc (time independent) prediction task on aggregated data,
and a simulated real-time prediction task. The performance of our framework is
extremely promising, yielding in the different forecasting scenarios up to 93%
AUC for extremist user detection, up to 80% AUC for content adoption
prediction, and finally up to 72% AUC for interaction reciprocity forecasting.
We conclude by providing a thorough feature analysis that helps determine which
are the emerging signals that provide predictive power in different scenarios.Comment: 9 pages, 3 figures, 8 table
Determining the veracity of rumours on Twitter

While social networks can provide an ideal platform for up-to-date information from individuals across the world, it has also proved to be a place where rumours fester and accidental or deliberate misinformation often emerges. In this article, we aim to support the task of making sense from social media data, and specifically, seek to build an autonomous message-classifier that filters relevant and trustworthy information from Twitter. For our work, we collected about 100 million public tweets, including users' past tweets, from which we identified 72 rumours (41 true, 31 false). We considered over 80 trustworthiness measures including the authors' profile and past behaviour, the social network connections (graphs), and the content of tweets themselves. We ran modern machine-learning classifiers over those measures to produce trustworthiness scores at various time windows from the outbreak of the rumour. Such time-windows were key as they allowed useful insight into the progression of the rumours. From our findings, we identified that our model was significantly more accurate than similar studies in the literature. We also identified critical attributes of the data that give rise to the trustworthiness scores assigned. Finally we developed a software demonstration that provides a visual user interface to allow the user to examine the analysis
Determining the Veracity of Rumours on Twitter

While social networks can provide an ideal platform for up-to-date
information from individuals across the world, it has also proved to be a place
where rumours fester and accidental or deliberate misinformation often emerges.
In this article, we aim to support the task of making sense from social media
data, and specifically, seek to build an autonomous message-classifier that
filters relevant and trustworthy information from Twitter. For our work, we
collected about 100 million public tweets, including users' past tweets, from
which we identified 72 rumours (41 true, 31 false). We considered over 80
trustworthiness measures including the authors' profile and past behaviour, the
social network connections (graphs), and the content of tweets themselves. We
ran modern machine-learning classifiers over those measures to produce
trustworthiness scores at various time windows from the outbreak of the rumour.
Such time-windows were key as they allowed useful insight into the progression
of the rumours. From our findings, we identified that our model was
significantly more accurate than similar studies in the literature. We also
identified critical attributes of the data that give rise to the
trustworthiness scores assigned. Finally we developed a software demonstration
that provides a visual user interface to allow the user to examine the
analysis.Comment: 21 pages, 6 figures, 2 table
Social Politics: Agenda Setting and Political Communication on Social
  Media

Social media play an increasingly important role in political communication.
Various studies investigated how individuals adopt social media for political
discussion, to share their views about politics and policy, or to mobilize and
protest against social issues. Yet, little attention has been devoted to the
main actors of political discussions: the politicians. In this paper, we
explore the topics of discussion of U.S. President Obama and the 50 U.S. State
Governors using Twitter data and agenda-setting theory as a tool to describe
the patterns of daily political discussion, uncovering the main topics of
attention and interest of these actors. We examine over one hundred thousand
tweets produced by these politicians and identify seven macro-topics of
conversation, finding that Twitter represents a particularly appealing vehicle
of conversation for American opposition politicians. We highlight the main
motifs of political conversation of the two parties, discovering that
Republican and Democrat Governors are more or less similarly active on Twitter
but exhibit different styles of communication. Finally, by reconstructing the
networks of occurrences of Governors' hashtags and keywords related to
political issues, we observe that Republicans form a tight core, with a
stronger shared agenda on many issues of discussion