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Marked mix-nets were introduced by Pereira and Rivest as a mechanism to allow very efficient mixing that ensures privacy but at the cost of not guaranteeing integrity. This is useful in a number of e-voting schemes such as STAR-Vote and Selene. However, the proposed marked mix-net construction comes with no proof of security and, as we show in this paper, does not provide privacy even in the presence of a single corrupt authority. Fortunately, the attack that we present is easy to prevent and we show several possible ways to address it. Finally while the original marked mix-net paper worked with ElGamal, we identify conditions that the adopted encryption scheme should satisfy in order to be appropriate for a marked mix-net. This opens the possibility of building marked mix-nets based on intractability assumptions which are believed to hold in the presence of a quantum computer.

Our life depends more and more on intelligent machines that help us carry out our work or have completely taken our place in tasks which used to be wholly human. This also applies to the field of care and assistance for the sick and fragile: indeed, carebots are increasingly present by the patient’s bed and can collaborate in care work. However carebots are not objects like others that we use in our everyday life: unlike the tools through which we do things, robots have (and will more and more have) some level of autonomy. We intend to consider the type of relationship it is right to build with these devices and ask whether intelligent robots deserve some moral and legal relevance. The robot may be seen as a slave or an entity with which we can form friendships or loving relationships: we will maintain that it is our responsibility to prepare for future scenarios in which increasingly intelligent, autonomous machines will be not mere tools, but significant life companions.

The ever-increasing number of published scientific articles has prompted the need for automated, data-driven approaches to summarizing the content of scientific articles. The Computational Linguistics Scientific Document Summarization Shared Task (CL-SciSumm 2019) has recently fostered the study and development of new text mining and machine learning solutions to the summarization problem customized to the academic domain. In CL-SciSumm, a Reference Paper (RP) is associated with a set of Citing Papers (CPs), all containing citations to the RP. In each CP, the text spans (i.e., citances) have been identified that pertain to a particular citation to the RP. The task of identifying the spans of text in the RP that most accurately reflect the citance is addressed using supervised approaches. This paper proposes a new, more effective solution to the CL-SciSumm discourse facet classification task, which entails identifying for each cited text span what facet of the paper it belongs to from a predefined set of facets. It proposes also to extend the set of traditional CL-SciSumm tasks with a new one, namely the discourse facet summarization task. The idea behind is to extract facet-specific descriptions of each RP consisting of a fixed-length collection of RP’s text spans. To tackle both the standard and the new tasks, we propose machine learning supported solutions based on the extraction of a selection of discriminating words, called pivot words. Predictive features based on pivot words are shown to be of great importance to rate the pertinence and relevance of a text span to a given facet. The newly proposed facet classification method performs significantly better than the best performing CL-SciSumm 2019 participant (i.e., the classification accuracy has increased by + 8%), whereas regression methods achieved promising results for the newly proposed summarization task.

[Context and motivation] Modern software engineering processes have shifted from traditional upfront requirements engineering (RE) to a more continuous way of conducting RE, particularly including data-driven approaches. [Question/problem] However, current research on data-driven RE focuses more on leveraging certain techniques such as natural language processing or machine learning than on making the concept fit for facilitating its use in the entire software development process. [Principal ideas/results] In this paper, we propose a research agenda composed of six distinct research directions. These include a data-driven RE infrastructure, embracing data heterogeneity, context-aware adaptation, data analysis and decision support, privacy and confidentiality, and finally process integration. Each of these directions addresses challenges that impede the broader use of data-driven RE. [Contribution] For researchers, our research agenda provides topics relevant to investigate. For practitioners, overcoming the underlying challenges with the help of the proposed research will allow to adopt a data-driven RE approach and facilitate its seamless integration into modern software engineering. For users, the proposed research will enable the transparency, control, and security needed to trust software systems and software providers. This work has been supported by: the Spanish project GENESIS (TIN2016-79269-R), the Christian Doppler Forschungsgesellschaft, the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development, and the Austrian Science Fund (FWF) under the grant numbers J3998-N31, P28519-N31, and P30525-N31.

Logical frameworks like LF allow to specify the syntax and (natural deduction) inference rules for syntax/proof-checking a wide variety of logical systems. A crucial feature that is missing for prototyping logics is a way to specify basic proof automation. We try to alleviate this problem by generating \(\lambda \)Prolog (ELPI) inference predicates from logic specifications and controlling them by logic-independent helper predicates that encapsulate the prover characteristics. We show the feasibility of the approach with three experiments: We directly automate ND calculi, we generate tableau theorem provers and model generators.

This paper provides an overview of the main developments of the Tech United Eindhoven RoboCup @Home team. Tech United uses an advanced world modeling system called the Environment Descriptor. It allows straightforward implementation of localization, navigation, exploration, object detection & recognition, object manipulation and robot-robot cooperation skills based on the most recent state of the world. Other important features include object and people detection via deep learning methods, a GUI, speech recognition, natural language interpretation and a chat interface combined with a conversation engine. Recent developments that aided with obtaining the victory during RoboCup 2019 include pointing detection, usage of HSR’s display, a people detector and the addition of a custom keyboard in the chat interface.

Reproducibility of Science is considered as one of the main principles of the scientific method, and refers to the ability of an experiment to be accurately reproduced, by third person, in complex experiment every detail matters to ensure the correct reproducibility. In the context of the ICCS 2011, Elsevier organized the executable paper grand challenge a contest to improve the way scientific information is communicated and used. While during this contest the focus was on developing methods and technique to realize the idea of executable papers, in this paper we focus on the operational issues related to the creation a viable service with a predefined QoS.

Distributed Ledgers (DLs), also known as blockchains, provide decentralised, tamper-free registries of transactions among partners that distrust each other. For the scientific community, DLs have been proposed to decentralise and make more transparent each step of the scientific workflow. For the particular case of dissemination and peer-reviewing, DLs can provide the cornerstone to realise open decentralised publishing systems where social interactions between peers are tamper-free, enabling trustworthy computation of bibliometrics. In this paper, we propose the use of DL-backed smart contracts to track a subset of social interactions for scholarly publications in a decentralised and reliable way, yielding Smart Papers. We show how our Smart Papers approach complements current models for decentralised publishing, and analyse cost implications.

In digital libraries, much of the reading activity is still done on printed copies of documents. We show how digital pen and paper technologies can be used to support readers by automatically creating interactive paper versions of digital documents during the printing process that enable users to activate embedded hyperlinks to other documents and services from printed versions. The approach uses a special printer driver that allows information about hyperlinks to be extracted and stored at print time. Users can then activate hyperlinks in the printed document with a digital pen.

According to the data from the Scopus publication database, as analyzed in several recent studies, more than 70,000 papers have been published in the area of Software Engineering (SE) since late 1960’s. According to our recent work, 43% of those papers have received no citations at all. Since citations are the most commonly used metric for measuring research (academic) impact, these figures raise questions (doubts) about the (non-existing) impact of such a large set of papers. It is a reality that typical academic reward systems encourage researchers to publish more papers and do not place a major emphasis on research impact. To shed light on the issue of volume (quantity) versus citation-based impact of SE research papers, we conduct and report in this paper a quantitative bibliometrics assessment in four aspects: (1) quantity versus impact of different paper types (e.g., conference versus journal papers), (2) ratios of uncited (non-impactful) papers, (3) quantity versus impact of papers originating from different countries, and (4) quantity versus impact of papers by each of the top-10 authors (in terms of number of papers). To achieve the above objective, we conducted a quantitative exploratory bibliometrics assessment, comprised of four research questions, to assess quantity versus impact of SE papers with respect to the aspects discussed above. We extracted the data through a systematic, automated and repeatable process from the Scopus paper database, which we also used in two previous papers. Our results show that the distribution of SE publications has a major inequality in terms of impact overall, and also when categorized in terms of the above four aspects. The situation in the SE literature is similar to the other areas of science as studied by previous bibliometrics studies. Also, among our results is the fact that journal articles and conference papers have been cited 12.6 and 3.6 times on average, confirming the expectation that journal articles have more impact, in general, than conference papers. Also, papers originated from English-speaking countries have in general more visibility and impact (and consequently citations) when compared to papers originated from non-English-speaking countries. Our results have implications for improvement of academic reward systems, which nowadays mainly encourage researchers to publish more papers and usually neglect research impact. Also, our results can help researchers in non-English-speaking countries to consider improvements to increase their research impact of their upcoming papers., Vahid Garousi was partially supported by several internal grants provided by the Hacettepe University and the Scientific and Technological Research Council of Turkey (TUBITAK). Joao M. Fernandes was supported by FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope UID/CEC/00319/2013.

This book constitutes the refereed proceedings of the 5th Computer Games Workshop, CGW 2016, and the 5th Workshop on General Intelligence in Game-Playing Agents, GIGA 2016, held in conjunction with the 25th International Conference on Artificial Intelligence, IJCAI 2016, in New York, USA, in July 2016.The 12 revised full papers presented were carefully reviewed and selected from 25 submissions. The papers address all aspects of artificial intelligence and computer game playing. They discuss topics such as Monte-Carlo methods; heuristic search; board games; card games; video games; perfect and imperfect information games; puzzles and single player games; multi-player games; combinatorial game theory; applications; computational creativity; computational game theory; evaluation and analysis; game design; knowledge representation; machine learning; multi-agent systems; opponent modeling; planning.

This study explores the short-run spillover effects of popular research papers. We consider the publicity of ‘Male Organ and Economic Growth: Does Size Matter?’ as an exogenous shock to economics discussion paper demand, a natural experiment of a sort. In particular, we analyze how the very substantial visibility influenced the downloads of Helsinki Center of Economic Research discussion papers. Difference in differences and regression discontinuity analysis are conducted to elicit the spillover patterns. This study finds that the spillover effect to average economics paper demand is positive and statistically significant. It seems that hit papers increase the exposure of previously less downloaded papers. We find that part of the spillover effect could be attributable to Internet search engines’ influence on browsing behavior. Conforming to expected patterns, papers residing on the same web page as the hit paper evidence very significant increases in downloads which also supports the spillover thesis. JEL Classification A11, C21 MSC Classification 97K80 Electronic supplementary material The online version of this article (doi:10.1186/2193-1801-2-168) contains supplementary material, which is available to authorized users.

Paper documents are often described as ‘information rich’, in contrast with electronic documents. This ethnographic study examines Lotus NOTES in a sub-section of the Irish civil service, with particular reference to the concurrent use of electronic and paper documents. The sub-section examines disagreements with regard to claims by Irish citizens for particular government benefits. The study describes how meta-information contained in paper case files is perceived as necessary for the work of the organization, thus restricting the use of electronic case files in NOTES as a shared information system. However, this reliance on paper files derives not only from the information rich properties of paper documents, but also from the desire of some workers to protect their occupational status by defining, as necessary for their job, information which is only available in paper documents and which only they can interpret. This dependence on paper documents also reduces the amount of information that can be shared within the organization. This paper suggests that, only if the perceived threat posed by the information system were reduced in some way would user innovations in work practices and greater sharing of information within the organization become possible.

In this paper we present a survey of the work on type-based useless-code elimination for higher-order functional programs. After some historical remarks on the motivations and early approaches we give an informal but complete account of the techniques and results developed at the Computer Science Department of the University of Torino. In particular, we focus on the fact that, for each of the type-based techniques developed, there is an optimal program simplification.

This book constitutes the thoroughly refereed post-conference proceedings of the 10th International Conference on Adaptive Multimedia Retrieval, AMR 2012, held in Copenhagen, Denmark, in October 2012.The 17 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers cover topics of state of the art contributions, features and classification, location context, language and semantics, music retrieval, and adaption and HCI.

The number of references per paper, perhaps the best single index of a journal's scholarliness, has been studied in different disciplines and periods. In this paper we present a four decade study of eight engineering journals. A data set of over 70000 references was generated after automatic data gathering and manual inspection for errors. Results show a significant increase in the number of references per paper, the average rises from 8 in 1972 to 25 in 2013. This growth presents an acceleration around the year 2000, consistent with a much easier access to search engines and documents produced by the generalization of the Internet., 11 pages, 4 figures. Published online on August 31, 2013

International audience; This book constitutes the refereed proceedings of the Computer Games Workshop, CGW 2013, held in Beijing, China, in August 2013, in conjunction with the Twenty-third International Conference on Artificial Intelligence, IJCAI 2013. The 9 revised full papers presented were carefully reviewed and selected from 15 submissions. The papers cover a wide range of topics related to computer games. They discuss six games that are played by humans in practice: Chess, Domineering, Chinese Checkers, Go, Goofspiel, and Tzaar. Moreover, there are papers about the Sliding Tile Puzzle, an application, namely, Cooperative Path-Finding Problems, and on general game playing.

The present volume contains the proceedings of the seventh international workshop on Formal Aspects of Security and Trust (FAST 2010), held in Pisa, Italy, 16–17 September 2010, as part of the 8th IEEE International Conference on Software Engineering and Formal Methods (SEFM 2010). FAST aims to foster cooperation among researchers in the areas of security and trust. As computing and network infrastructures become increasingly pervasive, and as they carry increasing economic activity, society needs well-matched security and trust mechanisms. These interactions increasingly span several enterprises and involve loosely structured communities of individuals. Participants involved in these activities must control interactions with their partners based on trust policies and business logic. Trust-based decisions effectively determine the security goals for shared information and for access to sensitive or valuable resources. FAST sought original papers focusing on formal aspects of the following topics: security and trust policy models; security protocol design and analysis; formal models of trust and reputation; logics for security and trust; distributed trust management systems; trust-based reasoning; digital assets protection; data protection; privacy and id issues; information flow analysis; language-based security; security and trust aspects of ubiquitous computing; validation/analysis tools; web service security/trust/privacy; grid security; security risk assessment; and case studies. The proceedings of this, the seventh FAST workshop, contains a paper by Dusko Pavlovic based on his invited talk. It also comprises 14 revised papers selected out of 42 submissions. Each paper was reviewed by at least three members of the Program Committee, whom we wish to thank for their valuable efforts. We are also grateful to the organizers of SEFM 2010 for having accepted FAST 2010 as an affiliated event and for providing a perfect environment for running the workshop. Last but not least, many thanks to Andrei Voronkov, who allowed us to use the free conference software system EasyChair, which greatly simplified the work of the Program Committee.

A collection of coauthored papers is the new norm for doctoral dissertations in the natural and biomedical sciences, yet there is no consensus on how to partition authorship credit between PhD candidates and their coauthors. Guidelines for PhD programs vary but tend to specify only a suggested range for the number of papers to be submitted for evaluation, sometimes supplemented with a requirement for the PhD candidate to be the principal author on the majority of submitted papers. Here I use harmonic counting to quantify the actual amount of authorship credit attributable to individual PhD graduates from two Scandinavian universities in 2008. Harmonic counting corrects for the inherent inflationary and equalizing biases of routine counting methods, thereby allowing the bibliometrically identifiable amount of authorship credit in approved dissertations to be analyzed with unprecedented accuracy. Unbiased partitioning of authorship credit between graduates and their coauthors provides a post hoc bibliometric measure of current PhD requirements, and sets a de facto baseline for the requisite scientific productivity of these contemporary PhD’s at a median value of approximately 1.6 undivided papers per dissertation. Comparison with previous census data suggests that the baseline has shifted over the past two decades as a result of a decrease in the number of submitted papers per candidate and an increase in the number of coauthors per paper. A simple solution to this shifting baseline syndrome would be to benchmark the amount of unbiased authorship credit deemed necessary for successful completion of a specific PhD program, and then monitor for departures from this level over time. Harmonic partitioning of authorship credit also facilitates cross-disciplinary and inter-institutional analysis of the scientific output from different PhD programs. Juxtaposing bibliometric benchmarks with current baselines may thus assist the development of harmonized guidelines and transparent transnational quality assurance procedures for doctoral programs by providing a robust and meaningful standard for further exploration of the causes of intra- and inter-institutional variation in the amount of unbiased authorship credit per dissertation. Paid Open Access

Organizational best practices are unstructured, emergent processes that freely coordinate actors engaged in reaching organizations' goals. In recent years we are witnessing the wide adoption of social software (blogs, microblogs, wiki, forums, shared calendars, etc.) as primary technological tools to support organizational best practices, fostering their creation, evolution and sharing, allowing their continuous refinement and alignment with the organization's mission and evolving know-how. While organizational best practices and social software tools are good candidates to support specific processes within the organization (and among organizations) they also present several issues, when compared to classic BPM tools - those based on structured coordination and well-defined process models: since they have no explicit representation it is hard to analyze them (by analytic techniques or by simulation), to monitor their evolution and to support their execution; moreover it is hard to extract explicit knowledge from them. In this paper we present a set of tools that complement social software in creating a real coordination platform, mitigating some of the aforementioned issues.

Information technology has great potential for transforming the health care system, improving quality of care. With the increasing expansion of health information systems, the Electronic Health Record (EHR) has become one of the finest sources for clinical information aggregators in the context of digital health. The EHR is a core part of a hospital information system, as well as a service on duty of the patient to improve the treatment of patients. It can be considered as a longitudinal electronic record of patient heath information, for example vital signs, medical history or laboratory data, generated by one or more encounters in any care delivery setting. As the EHR offers many potential opportunities for healthcare systems, it is important to take steps to improve the system. With this in mind, a study of the features present on a Portuguese EHR was made. The basis of this study was an adoption model that evaluates the EHR system accordingly to its current features. After this study, the EHR will be ranked into one of the existing eight stages.

Security and trust policy models.- security protocol design and analysis.- formal models of trust and reputation.- logics for security and trust.- distributed trust management systems.- trust-based reasoning.- digital assets protection.- data protection.- privacy and ID issues.- information flow analysis.- language-based security.- security and trust aspects of ubiquitous computing.- validation/analysis tools.- web service security/trust/privacy.- grid security.- security risk assessment.- case studies.

Ongoing efforts among cities to reinvigorate streets have encouraged innovations in using smart data to understand pedestrian activities. Empowered by advanced algorithms and computation power, data from smartphone applications, GPS devices, video cameras, and other forms of sensors can help better understand and promote street life and pedestrian activities. Through adopting a pedestrian-oriented and place-based approach, this paper reviews the major environmental components, pedestrian behavior, and sources of smart data in advancing this field of computational urban science. Responding to the identified research gap, a case study that hybridizes different smart data to understand pedestrian jaywalking as a reflection of urban spaces that need further improvement is presented. Finally, some major research challenges and directions are also highlighted.

This article focuses on building a prototyping for immersive captioning following a user-centric approach. This methodology is characterised by following a bottom-up approach, where usability and user needs are at the heart of the development. Recent research on user requirements for captioning in immersive environments has shown that there is both a need for improvement and a wealth of research opportunities. The final aim is to identify how to display captions for an optimal viewing experience. This work began four years ago with some partial findings. We build from the lessons learnt, focussing on the user-centric design requirements cornerstone: prototyping. Our prototype framework integrates methods used in existing solutions aiming at instant contrast-and-compare functionalities. The first part of the article presents the state of the art for user requirements identifying the reasons behind the development of the prototyping framework. The second part of the article describes the two-stage framework development. The initial framework concept answered to the challenges resulting from the previous research. As soon as the first framework was developed, it became obvious that a second improved solution was required, almost as a showcase on how ideas can quickly be implemented for user testing, and for users to elicit requirements and creative solutions. The article finishes with a list of functionalities, resulting in new caption modes, and the opportunity of becoming a comprehensive immersive captions testbed, where tools such as eye-tracking, or physiological testing devices could be testing captions across any device with a web browser.

Structured reporting contributes to the completeness of radiology reports and improves quality. Both the content and the structure are essential for successful implementation of structured reporting. Contextual structured reporting is tailored to a specific scenario and can contain information retrieved from the context. Critical findings detected by imaging need urgent communication to the referring physician. According to guidelines, the occurrence of this communication should be documented in the radiology reports and should contain when, to whom and how was communicated. In free-text reporting, one or more of these required items might be omitted. We developed a contextual structured reporting template to ensure complete documentation of the communication of critical findings. The WHEN and HOW items were included automatically, and the insertion of the WHO-item was facilitated by the template. A pre- and post-implementation study demonstrated a substantial improvement in guideline adherence. The template usage improved in the long-term post-implementation study compared with the short-term results. The two most often occurring categories of critical findings are “infection / inflammation” and “oncology”, corresponding to the a large part of urgency level 2 (to be reported within 6 h) and level 3 (to be reported within 6 days), respectively. We conclude that contextual structured reporting is feasible for required elements in radiology reporting and for automated insertion of context-dependent data. Contextual structured reporting improves guideline adherence for communication of critical findings.

Telemedicine and eHealth refer to the use of information and communication technology (ICT) embedded in software programs with highspeed telecommunications systems for delivery, management, and monitoring of healthcare services. Application of telemedicine have become timely while providing great potentials to protect both medical practitioners and patients, as well as limit social mobility of patients contributing to reduce the spread of the virus. This study employs data from the existing literature to describe the application of telemedicine and eHealth as a proactive measure to improve clinical care. Findings from this study present the significance of telemedicine and current applications adopted during the pandemic. More importantly, the findings present practical application of telemedicine and eHealth for clinical services. Also, polices initiated across the world to promote management of COVID-19 are discussed. Respectively, this study suggests that telemedicine and eHealth can be adopted in times of health emergency, as a convenient, safe, scalable, effective, and green method of providing clinical care. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Modal transition systems (MTSs) and featured transition systems (FTSs) are widely recognised as fundamental behavioural models for software product lines. This short paper summarises the contributions published in [3]: MTSs with variability constraints (MTS\(\upsilon \)s) are equally expressive as FTSs. This is proved by giving sound and complete transformations of the latter into the former, and of the former into the latter. The benefits of this result are twofold. First, it contributes to the expressiveness hierarchy of such basic models studied in the literature. Second, it provides an automatic algorithm from FTSs to MTS\(\upsilon \)s that preserves the original (compact) branching structure, thus paving the way for model checking FTSs with the variability model checker VMC.

The Internet of Things (IoT) is part of a new paradigm where it is possible to integrate every sensor to the Internet, allowing it to be even more immersive and pervasive. Mastering these technologies, such as pervasive and smart places, is a challenge, especially when the goal is to achieve a closer interaction between citizens and applications. The rapid development and exciting innovation create an opportunity to stimulate a variety of new tools for tourists. Taking into account the versatility of IoT sensors for different applications, in this position paper we outline our perspective of ambient intelligence in smart tourism. info:eu-repo/semantics/acceptedVersion

GDPLL(k) grammars have been introduced as a tool for the construction of syntactic pattern recognition-based systems. The grammars have been successfully used in several different applications. The practical experience with the implementation of a syntactic pattern recognition system based on GDPLL(k) grammars has served to define methodological guidelines for constructing such systems. In the paper key methodological issues are presented.

An accurate local thermal sensation model is indispensable for the effective development of personalized conditioning systems in office environments. The output of such a model relies on the accurate prediction of local skin temperatures, which in turn depend on reliable input data of the local clothing thermal resistance and clothing area factor. However, for typical office clothing ensembles, only few local datasets are available in the literature. In this study, the dry thermal resistance was measured for 23 typical office clothing ensembles according to EN-ISO 15831 on a sweating agile manikin. For 6 ensembles, the effects of different air speeds and body movement were also included. Local clothing area factors were estimated based on 3D scans. Local differences can be found between the measured local insulation values and local area factors of this study and the data of other studies. These differences are likely due to the garment fit on the manikin and reveal the necessity of reporting clothing fit parameters (e.g., ease allowance) in the publications. The increased air speed and added body movement mostly decreased the local clothing insulation. However, the reduction is different for all body parts, and therefore cannot be generalized. This study also provides a correlation between the local clothing insulation and the ease allowance for body parts covered with a single layer of clothing. In conclusion, the need for well-documented measurements is emphasized to get reproducible results and to choose accurate clothing parameters for thermo-physiological and thermal sensation modeling. Electronic supplementary material The online version of this article (10.1007/s00484-018-1625-0) contains supplementary material, which is available to authorized users.

The past few years have seen a tremendous increase in often irregularly structured data that can be represented most naturally and efficiently in the form of graphs. Making sense of incessantly growing graphs is not only a key requirement in applications like social media analysis or fraud detection but also a necessity in many traditional enterprise scenarios. Thus, a flexible approach for multidimensional analysis of graph data is needed. Whereas many existing technologies require up-front modelling of analytical scenarios and are difficult to adapt to changes, our approach allows for ad-hoc analytical queries of graph data. Extending our previous work on graph summarization, in this position paper we lay the foundation for large graph analytics to enable business intelligence on graph-structured data.

This position paper describes an approach to predict the performances of a Web application already in the early stages of application development. It leverages the wealth of information of MDWE solutions to automatically obtain accurate representations of the running application in terms of layered queue networks (LQNs), i.e., analytical models simulating the behavior of the system and computing the performances mathematically. In particular, the paper discusses how a MDWE methodology can be exploited to generate such performance models and presents a proof of concept example.

In this short paper we compare the communication requirements for three Smart Grid scenarios with the availability of broadband and mobile communication networks in Europe. We show that only in the most demanding case - where data is collected and transmitted every second - a standard GSM/GPRS connection is not enough. Whereas in the less demanding scenarios it is almost all of the European households that can be covered by a standard broadband technology for use with Smart Grid.

Medical workstations have developed into the super-assistants of radiologists. The overwhelming production of images, hardware that rapidly became cheaper and powerful 3D visualization and quantitative analysis software have all pushed the developments from simple PACS viewing into a really versatile viewing environment. This chapter gives an overview of these developments, aimed at radiologists’ readership. Many references and internet Open image in new window are given which discuss the topics in more depth than is possible in this short paper. This paper is necessarily incomplete.

A confusingly wide variety of temporally asymmetric learning rules exists related to reinforcement learning and/or to spike-timing dependent plasticity, many of which look exceedingly similar, while displaying strongly different behavior. These rules often find their use in control tasks, for example in robotics and for this rigorous convergence and numerical stability is required. The goal of this article is to review these rules and compare them to provide a better overview over their different properties. Two main classes will be discussed: temporal difference (TD) rules and correlation based (differential hebbian) rules and some transition cases. In general we will focus on neuronal implementations with changeable synaptic weights and a time-continuous representation of activity. In a machine learning (non-neuronal) context, for TD-learning a solid mathematical theory has existed since several years. This can partly be transferred to a neuronal framework, too. On the other hand, only now a more complete theory has also emerged for differential Hebb rules. In general rules differ by their convergence conditions and their numerical stability, which can lead to very undesirable behavior, when wanting to apply them. For TD, convergence can be enforced with a certain output condition assuring that the delta-error drops on average to zero (output control). Correlation based rules, on the other hand, converge when one input drops to zero (input control). Temporally asymmetric learning rules treat situations where incoming stimuli follow each other in time. Thus, it is necessary to remember the first stimulus to be able to relate it to the later occurring second one. To this end different types of so-called eligibility traces are being used by these two different types of rules. This aspect leads again to different properties of TD and differential Hebbian learning as discussed here. Thus, this paper, while also presenting several novel mathematical results, is mainly meant to provide a road map through the different neuronally emulated temporal asymmetrical learning rules and their behavior to provide some guidance for possible applications.

The European Physical Journal Special Topics, 195 (1), ISSN:1951-6355, ISSN:1951-6401

Driven by a large number of potential applications in areas like bioinformatics, information retrieval and social network analysis, the problem setting of inferring relations between pairs of data objects has recently been investigated quite intensively in the machine learning community. To this end, current approaches typically consider datasets containing crisp relations, so that standard classification methods can be adopted. However, relations between objects like similarities and preferences are in many real-world applications often expressed in a graded manner. A general kernel-based framework for learning relations from data is introduced here. It extends existing approaches because both crisp and valued relations are considered, and it unifies existing approaches because different types of valued relations can be modeled, including symmetric and reciprocal relations. This framework establishes in this way important links between recent developments in fuzzy set theory and machine learning. Its usefulness is demonstrated on a case study in document retrieval.

The dynamic I–V curve method was recently introduced for the efficient experimental generation of reduced neuron models. The method extracts the response properties of a neuron while it is subject to a naturalistic stimulus that mimics in vivo-like fluctuating synaptic drive. The resulting history-dependent, transmembrane current is then projected onto a one-dimensional current–voltage relation that provides the basis for a tractable non-linear integrate-and-fire model. An attractive feature of the method is that it can be used in spike-triggered mode to quantify the distinct patterns of post-spike refractoriness seen in different classes of cortical neuron. The method is first illustrated using a conductance-based model and is then applied experimentally to generate reduced models of cortical layer-5 pyramidal cells and interneurons, in injected-current and injected- conductance protocols. The resulting low-dimensional neuron models—of the refractory exponential integrate-and-fire type—provide highly accurate predictions for spike-times. The method therefore provides a useful tool for the construction of tractable models and rapid experimental classification of cortical neurons.

Collaborative activities, in which students actively interact with each other, have proved to provide significant learning benefits. In Computer-Supported Collaborative Learning (CSCL), these collaborative activities are assisted by technologies. However, the use of computers does not guarantee collaboration, as free collaboration does not necessary lead to fruitful learning. Therefore, practitioners need to design CSCL scripts that structure the collaborative settings so that they promote learning. However, not all teachers have the technical and pedagogical background needed to design such scripts. With the aim of assisting teachers in designing effective CSCL scripts, we propose a model to support the selection of reusable good practices (formulated as patterns) so that they can be used as a starting point for their own designs. This model is based on a pattern ontology that computationally represents the knowledge captured on a pattern language for the design of CSCL scripts. A preliminary evaluation of the proposed approach is provided with two examples based on a set of meaningful interrelated patters computationally represented with the pattern ontology, and a paper prototyping experience carried out with two teaches. The results offer interesting insights towards the implementation of the pattern ontology in software tools.

Due to the growth of data and wide spread usage of Machine Learning (ML) by non-experts, automation and scalability are becoming key issues for ML. This paper presents an automated and scalable framework for ML that requires minimum human input. We designed the framework for the domain of telecommunications risk management. This domain often requires non-ML-experts to continuously update supervised learning models that are trained on huge amounts of data. Thus, the framework uses Automated Machine Learning (AutoML), to select and tune the ML models, and distributed ML, to deal with Big Data. The modules included in the framework are task detection (to detect classification or regression), data preprocessing, feature selection, model training, and deployment. In this paper, we focus the experiments on the model training module. We first analyze the capabilities of eight AutoML tools: Auto-Gluon, Auto-Keras, Auto-Sklearn, Auto-Weka, H2O AutoML, Rminer, TPOT, and TransmogrifAI. Then, to select the tool for model training, we performed a benchmark with the only two tools that address a distributed ML (H2O AutoML and TransmogrifAI). The experiments used three real-world datasets from the telecommunications domain (churn, event forecasting, and fraud detection), as provided by an analytics company. The experiments allowed us to measure the computational effort and predictive capability of the AutoML tools. Both tools obtained high- quality results and did not present substantial predictive differences. Nevertheless, H2O AutoML was selected by the analytics company for the model training module, since it was considered a more mature technology that presented a more interesting set of features (e.g., integration with more platforms). After choosing H2O AutoML for the ML training, we selected the technologies for the remaining components of the architecture (e.g., data preprocessing and web interface)., This work was executed under the project IRMDA - Intelligent Risk Management for the Digital Age, Individual Project, NUP: POCI-01-0247-FEDER-038526, co- funded by the Incentive System for Research and Technological Development, from the Thematic Operational Program Competitiveness of the national framework program - Portugal2020.

Nowadays, with the advancement of smart technologies, the Internet of Things (IoT) emerged as a booming technology that can provide better quality and facilities for the residents of smart cities. Smart cities can offer several services and have several applications in healthcare, transportation, education etc. Despite such a potential vision, the privacy of users on these IoT devices is a major concern. Most authentication schemes do not provide privacy and anonymity to legitimate users. to tackle this problem, we propose an efficient Zero Knowledge-based authentication scheme in the paper that authenticates devices on the network without knowing the information about user identity or revealing any other data entered by users. To explain our system framework at the micro-level, we apply our privacy-preserving scheme to IoT based healthcare applications, but it can easily be extended to the more general use cases where privacy-preserving authentication is required. This paper’s second major contribution is designing the data encryption algorithm ZKNimble that is mainly suitable for lightweight devices. Once the user is authenticated using Zero Knowledge Proof, the ZKNimble cipher can be used for legitimate users’ encryption and decryption processes.

Operations research proceedings 2019 : selected papers of the Annual International Conference of the German Operations Research Society (GOR), Dresden, Germany, September 4-6, 2019 / Janis S. Neufeld, Udo Buscher, Rainer Lasch, Dominik Möst, Jörn Schönberger, editors Annual International Conference of the German Operations Research Society, GOR2019, Dresden, Germany, 4 Sep 2019 - 6 Sep 2019; Cham : Springer, Operations Research Proceedings 725-731 (2020). doi:10.1007/978-3-030-48439-2_88, Published by Springer, Cham

In recent year the TPC Technology Conference on Performance Evaluation and Benchmarking (TPCTC) series have had significant influence in defining industry standards. The 11th TPC Technology Conference on Performance Evaluation and Benchmarking (TPCTC 2019) organized an industry panel on the “Role of the TPC in the Cloud Age”. This paper summaries the panel discussions.

This paper takes a software architect’s perspective to DevOps/CD and attempts to provide a consolidated view on the architecture styles for which empirical publications indicate to be suitable in the context of DevOps and CD. Following techniques from the evidence-based software engineering paradigm, we set out to answer a number of research questions pertaining to (1) the architecture characteristics important in DevOps/CD projects according to published literature, (2) the architectural styles found to work well in this context, (3) the application domains in which architecture characteristics and styles were evaluated, and (4) the empirical method being used by researchers on this topic. We applied a research protocol grounded on well-established systematic literature review guidelines, and evaluated sources published between 2009 and 2019. Our results indicate that (a) 17 software architecture characteristics are beneficial for CD and DevOps adoption, (b) micro-services are a dominant architectural style in this context, and (c) large-scale organizational contexts are the most studied, and (d) qualitative approaches (case study based) are the most applied research method.

Kernel PCA is a powerful feature extractor which recently has seen a reformulation in the context of Restricted Kernel Machines (RKMs). These RKMs allow for a representation of kernel PCA in terms of hidden and visible units similar to Restricted Boltzmann Machines. This connection has led to insights on how to use kernel PCA in a generative procedure, called generative kernel PCA. In this paper, the use of generative kernel PCA for exploring latent spaces of datasets is investigated. New points can be generated by gradually moving in the latent space, which allows for an interpretation of the components. Firstly, examples of this feature space exploration on three datasets are shown with one of them leading to an interpretable representation of ECG signals. Afterwards, the use of the tool in combination with novelty detection is shown, where the latent space around novel patterns in the data is explored. This helps in the interpretation of why certain points are considered as novel.

International audience; Complex system developments are more and more collaborative. Collaboration strategies largely depend on the development context at modelling, instantiation or enactment time. To put collaboration in action, we propose collaboration process patterns to define, reuse and enact collaborative software development processes. In this paper we describe the definition and application of collaboration patterns. Our patterns, inspired from workflow patterns of Van der Aalst, are described in CMSPEM, a Process Modelling Language developed in our team in 2014. In this paper, we briefly describe the CMSPEM metamodel and focus our presentation on two collaboration patterns: Duplicate in Sequence with Multiple Actors, Duplicate in Parallel with Multiple Actors and Merge. The approach is illustrated by a case study concerning the collaborative and representative process “Review a deliverable”.

Deep learning (DL) has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks. Especially in the area of computer vision and speech processing, DL has recently demonstrated better performance and generalisation properties, compared to classical machine learning approaches, which are based on the extraction of hand-crafted model-based features followed by classification. Hand gestures and speech constitute two of the most important modalities in human-to-human communication and man-machine interaction. In biomedical engineering, a lot of new work is directed towards electromyography-based gesture recognition. In this paper, we present a brief overview of DL methods for electromyography-based hand gesture recognition and then we select from literature a simple model based on Convolutional Neural Networks that we consider as the baseline model. The proposed modifications to the baseline model yield a 3% classification improvement. In the current paper, we concentrate on the explanatory analysis of this performance improvement. An ablation study identifies which modifications are the most important ones, and label smoothing is investigated to verify if the results can be improved by reducing a priori bias. The analysis helps in understanding the limitations of the model and exploring new ways to improve the performance.

In smart buildings, physical components (e.g., controllers, sensors, and actuators) are interconnected and communicate with each other using network protocols such as BACnet. Many smart building networks are now connected to the Internet, enabling attackers to exploit vulnerabilities in critical buildings. Network monitoring is crucial to detect such attacks and allow building operators to react accordingly. In this paper, we propose an intrusion detection system for building automation networks that detects known and unknown attacks, as well as anomalous behavior. It does so by leveraging protocol knowledge and specific BACnet semantics: by using this information, the alerts raised by our system are meaningful and actionable. To validate our approach, we use a real-world dataset coming from the building network of a Dutch university, as well as a simulated dataset generated in our lab facilities.