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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.

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.

International audience; We propose a compositional specification theory for reason- ing about components that interact by synchronisation of input and out- put (I/O) actions, in which the specification of a component constrains the temporal ordering of interactions with the environment. Such a the- ory is motivated by the need to support composability of components, in addition to modelling environmental assumptions, and reasoning about run-time behaviour. Models can be specified operationally by means of I/O labelled transition systems augmented by an inconsistency predi- cate on states, or in a purely declarative manner by means of traces. We introduce a refinement preorder that supports safe-substitutivity of components. Our specification theory includes the operations of parallel composition for composing components at run-time, logical conjunction for independent development, and quotient for incremental development. We prove congruence properties of the operations and show correspon- dence between the operational and declarative frameworks.

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.

Deductive software verification aims at formally verifying that all possible behaviors of a given program satisfy formally defined, possibly complex properties, where the verification process is based on logical inference. We follow the trajectory of the field from its inception in the late 1960s via its current state to its promises for the future, from pen-and-paper proofs for programs written in small, idealized languages to highly automated proofs of complex library or system code written in mainstream languages. We take stock of the state-of-art and give a list of the most important challenges for the further development of the field of deductive software verification.

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.

Part 1: Long and Short Papers (Continued); International audience; In this paper we introduce TicQR – a photo-based checkbox-enabled interface which bridges the physical and digital document domains, allowing automatic download or processing of useful data from paper documents. There is a long demonstrated need for people to be able to connect between printed material and digital information and services. By using a combination of image recognition and QR codes we are able to detect user marks on paper documents via a single photograph taken with a standard smart phone. This information can then be used to access the equivalent digital content, save contacts or URLs, or even order goods directly from local retailers.

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.

While collaborative filtering and citation analysis have been well studied for research paper recommender systems, content-based approaches typically restrict themselves to straightforward application of the vector space model. However, various types of metadata containing potentially useful information are usually available as well. Our work explores several methods to exploit this information in combination with different similarity measures.

We present a demonstration system for incremental spoken human–machine dialogue for task-centric domains that includes a controller for verbal and nonverbal behavior for virtual agents. The dialogue management components can handle uncertainty in input and resolve it interactively with high responsivity, and state tracking is aware of momentary events such as interruptions by the user. Aside from adaptable dialogue strategies, such as for grounding, the system includes a multimodal floor management controller that attempts to limit the influence of idiosyncratic dialogue behavior on the part of our primary user groups – older adults and people with cognitive impairments – both of which have previously participated in pilot studies using the platform.

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.

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.

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.

This chapter studies and analyzes Memetic Differential Evolution (MDE) Frameworks for designing digital filters, which aim at detecting paper defects produced during an industrial process. MDE Frameworks employ the Differential Evolution (DE) as an evolutionary framework and a list of local searchers adaptively coordinated by a control scheme. Here, three different variants of MDE are taken into account and their features and performance are compared. The binomial explorative features of the DE framework in contraposition to the exploitative features of the local searcher are analyzed in detail in light of the stagnation prevention problem, typical for the DE. Much emphasis in this chapter is given to the various adaptation systems and to their applicability this image processing problem.

We demonstrate a collaborative knowledge management platform in which communities representing autonomously developed information systems build ontologies to achieve semantic interoperabil- ity between those systems. The tool is called GOSPL, which stands for Grounding Ontologies with Social Processes and natural Language, and supports the method bearing the same name. Ontologies in GOSPL are hybrid, meaning that concepts are both described informally in natural language and formally. Agreements on these two levels are made simulta- neously and the social interaction between and across communities drive the ontology evolution process

In this paper we analyze the use of different types of statistical tests for the correlation of anomaly detection alerts. We show that the Granger Causality Test, one of the few proposals that can be extended to the anomaly detection domain, strongly depends on good choices of a parameter which proves to be both sensitive and difficult to estimate. We propose a different approach based on a set of simpler statistical tests, and we prove that our criteria work well on a simplified correlation task, without requiring complex configuration parameters.

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.

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.

Stated preference elicitation techniques, such as discrete choice experiments and best-worst scaling, are now widely used in health research to explore the public’s choices and preferences. In this paper, we propose an alternative stated preference elicitation technique, which we refer to as ‘trio-wise’. We explain this new technique, its relative advantages, modeling framework, and how it compares to the best-worst scaling method. To better illustrate the differences and similarities, we utilize best-worst scaling Case2, where individuals make best and worst (most and least) choices for the attribute levels that describe a single profile. We demonstrate this new preference elicitation technique using an empirical case study that explores preferences among the general public for ways to involve them in decisions concerning the health care system. Our findings show that the best-worst scaling and trio-wise preference elicitation techniques both retrieve similar preferences. However, the capability of our trio-wise method to provide additional information on the strength of rank preferences and its ability to accommodate indifferent preferences lead us to prefer it over the standard best-worst scaling technique.

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.

Enabling intuitive interaction in system design remains an art more than a science. This difficulty is exacerbated when the diversity of device and end user group is considered. In this paper, it is argued that conventional interaction modalities are unsuitable in many circumstances and that alternative modalities need be considered. Specifically the case of implicit interaction is considered, and the paper discusses how its use may lead to more satisfactory experiences. Specifically, harnessing implicit interaction in conjunction with the traditional explicit interaction modality, can enable a more intuitive and natural interactive experience. However, the exercise of capturing and interpreting implicit interaction is problematic and is one that lends itself to the adoption of AI techniques. In this position paper, the potential of lightweight intelligent agents is proposed as a model for harmonising the explicit and implicit components of an arbitrary interaction. Science Foundation Ireland 12M embargo until January 2012 - AV 10/05/2011

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.

The design of easy-to-use mobile systems for collecting and handling emergency medical care data in the field can significantly improve the effectiveness of rescue operations. In particular, this paper focuses on the design and evaluation of a mobile application that replaces ambulance run paper sheets. First, we discuss the limitations of traditional ambulance run paper sheets. Then, we present the PDA-based system we have developed. Finally, we discuss in detail the usability study we have carried out with first responders.

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.

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.

[EN]The game of Diplomacy has been used as a test case for complex automated negotiations for a long time, but to date very few successful negotiation algorithms have been implemented for this game. We have therefore decided to include a Diplomacy tournament within the annual Automated Negotiating Agents Competition (ANAC). In this paper we present the setup and the results of the ANAC 2017 Diplomacy Competition and the ANAC 2018 Diplomacy Challenge. We observe that none of the negotiation algorithms submitted to these two editions have been able to significantly improve the performance over a non-negotiating baseline agent. We analyze these algorithms and discuss why it is so hard to write successful negotiation algorithms for Diplomacy. Finally, we provide experimental evidence that, despite these results, coalition formation and coordination do form essential elements of the game.

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.

The possibility of clustering objects represented by structured data with possibly non-trivial geometry certainly is an interesting task in pattern recognition. Moreover, in the Big Data era, the possibility of clustering huge amount of (structured) data challenges computer science and pattern recognition researchers alike. The aim of this paper is to bridge the gap on large-scale structured data clustering. Specifically, following a previous work, in this paper a parallel and distributed k-medoids clustering implementation is proposed and tested on real-world biological structured data, namely pathway maps (graphs) and primary structure of proteins (sequences). Furthermore, two methods for medoids’ evaluation are proposed and compared in terms of scalability, based on exact and approximate procedures, respectively. Computational results show that the proposed implementation is flexible with respect to the dissimilarity measure and the input space adopted, with satisfactory results in terms of scalability.

Processes in organisations such as hospitals, may deviate from intended standard processes, due to unforeseeable events and the complexity of the organisation. For hospitals, the knowledge of actual patient streams for patient populations (e.g., severe or non-severe cases) is important for quality control and improvement. Process discovery from event data in electronic health records can shed light on the patient flows, but their comparison for different populations is cumbersome and time-consuming. In this paper, we present an approach for the automatic comparison of process models extracted from events in electronic health records. Concretely, we propose to compare processes for different patient populations by cross-log conformance checking, and standard graph similarity measures obtained from the directed graph underlying the process model. Results from a case study on breast cancer care show that average fitness and precision of cross-log conformance checks provide good indications of process similarity and therefore can guide the direction of further investigation for process improvement.

In the field of eXplainable AI (XAI), robust “blackbox” algorithms such as Convolutional Neural Networks (CNNs) are known for making high prediction performance. However, the ability to explain and interpret these algorithms still require innovation in the understanding of influential and, more importantly, explainable features that directly or indirectly impact the performance of predictivity. A number of methods existing in literature focus on visualization techniques but the concepts of explainability and interpretability still require rigorous definition. In view of the above needs, this paper proposes an interaction-based methodology–Influence score (I-score)—to screen out the noisy and non-informative variables in the images hence it nourishes an environment with explainable and interpretable features that are directly associated to feature predictivity. The selected features with high I-score values can be considered as a group of variables with interactive effect, hence the proposed name interaction-based methodology. We apply the proposed method on a real world application in Pneumonia Chest X-ray Image data set and produced state-of-the-art results. We demonstrate how to apply the proposed approach for more general big data problems by improving the explainability and interpretability without sacrificing the prediction performance. The contribution of this paper opens a novel angle that moves the community closer to the future pipelines of XAI problems. In investigation of Pneumonia Chest X-ray Image data, the proposed method achieves 99.7% Area-Under-Curve (AUC) using less than 20,000 parameters while its peers such as VGG16 and its upgraded versions require at least millions of parameters to achieve on-par performance. Using I-score selected explainable features allows reduction of over 98% of parameters while delivering same or even better prediction results.

Process model matching refers to the creation of correspondences between activities of process models. Applications of process model matching are manifold, reaching from model validation over harmonization of process variants to effective management of process model collections. Recently, this demand led to the development of different techniques for process model matching. Yet, these techniques are heuristics and, thus, their results are inherently uncertain and need to be evaluated on a common basis. Currently, however, the BPM community lacks established data sets and frameworks for evaluation. The Process Model Matching Contest 2013 aimed at addressing the need for effective evaluation by defining process model matching problems over published data sets. This paper summarizes the setup and the results of the contest. Besides a description of the contest matching problems, the paper comprises short descriptions of all matching techniques that have been submitted for participation. In addition, we present and discuss the evaluation results and outline directions for future work in this field of research

Toponym extraction and disambiguation have received much attention in recent years. Typical fields addressing these topics are information retrieval, natural language processing, and semantic web. This paper addresses two problems with toponym extraction and disambiguation. First, almost no existing works examine the extraction and disambiguation interdependency. Second, existing disambiguation techniques mostly take as input extracted named entities without considering the uncertainty and imperfection of the extraction process. In this paper we aim to investigate both avenues and to show that explicit handling of the uncertainty of annotation has much potential for making both extraction and disambiguation more robust. We conducted experiments with a set of holiday home descriptions with the aim to extract and disambiguate toponyms. We show that the extraction confidence probabilities are useful in enhancing the effectiveness of disambiguation. Reciprocally, retraining the extraction models with information automatically derived from the disambiguation results, improves the extraction models. This mutual reinforcement is shown to even have an effect after several automatic iterations.keywordstoponyms extractiontoponym disambiguationuncertain annotations.

In systems where process executions are not strictly enforced by a predefined process model, obtaining reliable performance information is not trivial. In this paper, we analyzed an event log of a real-life process, taken from a Dutch financial institute, using process mining techniques. In particular, we exploited the alignment technique [2] to gain insights into the control flow and performance of the process execution. We showed that alignments between event logs and discovered process models from process discovery algorithms reveal insights into frequently occurring deviations and how such insights can be exploited to repair the original process models to better reflect reality. Furthermore, we showed that the alignments can be further exploited to obtain performance information. All analysis in this paper is performed using plug-ins within the open-source process mining toolkit ProM.

Database applications are a very pervasive tool that enable businesses to make the most out of the data they collect and generate. Furthermore, they can also be used to provide services on top of such data that can access, process, modify and explore it. It was argued in the work this paper extends that when client applications that access a database directly run on public or semipublic locations that are not highly secured (such as a reception desk), the database credentials used could be stolen by a malicious user. To prevent such an occurrence, solutions such as virtual private networks (VPNs) can be used to secure access to the database. However, VPNs can be bypassed by accessing the database from within the business network in an internal attack, among other problems. A methodology called Secure Proxied Database Connectivity (SPDC) is presented which aims to push the database credentials out of the client applications and divides the information required to access them between a proxy and an authentication server, while supporting existing tools and protocols that provide access to databases, such as JDBC. This approach will be shown and further detailed in this paper in terms of attack scenarios, implementation and discussion. published

Data extraction and preparation are the most time-consuming phases of any process mining project. Due to the variability on the sources of event data, it remains a highly manual process in most of the cases. Moreover, it is very difficult to obtain reliable event data in enterprise systems that are not process-aware. Some techniques, like redo log process mining, try to solve these issues by automating the process as much as possible, and enabling event extraction in systems that are not process aware. This paper presents the challenges faced by redo log, and traditional process mining, comparing both approaches at theoretical and practical levels. Finally, we demonstrate that the data obtained with redo log process mining in a real-life environment is, at least, as valid as the one extracted by the traditional approach.

Process discovery, one of the key challenges in process mining, aims at discovering process models from process execution data stored in event logs. Most discovery algorithms assume that all data in an event log conform to correct execution of the process, and hence, incorporate all behaviour in their resulting process model. However, in real event logs, noise and irrelevant infrequent behaviour are often present. Incorporating such behaviour results in complex, incomprehensible process models concealing the correct and/or relevant behaviour of the underlying process. In this paper, we propose a novel general purpose filtering method that exploits observed conditional probabilities between sequences of activities. The method has been implemented in both the ProM toolkit and the RapidProM framework. We evaluate our approach using real and synthetic event data. The results show that the proposed method accurately removes irrelevant behaviour and, indeed, improves process discovery results.

This paper presents some intermediate results of the EU-IST project WASP that aims to develop an integrated model for implementing applications using wireless sensor networks. In this paper we present our approach to programming sensor networks. The main contribution concerns the separation of three abstraction levels leaving more room for standardization than with current practices. In addition, we propose to program the network from an overall perspective rather than programming individual nodes. For doing this we present two programming models that complement one another. The proposed programming model is event-based, corresponding closely to the nature of wireless sensors. The paper shows our approaches by giving several examples and ends with a description of wireless sensor networks related services and gives an outlook on future work.

One of the aims of process mining is to retrieve a process model from a given event log. However, current techniques have problems when mining processes that contain non-trivial constructs and/or when dealing with the presence of noise in the logs. To overcome these problems, we try to use genetic algorithms to mine process models. The non-trivial constructs are tackled by choosing an internal representation that supports them. The noise problem is naturally tackled by the genetic algorithm because, per definition, these algorithms are robust to noise. The definition of a good fitness measure is the most critical challenge in a genetic approach. This paper presents the current status of our research and the pros and cons of the fitness measure that we used so far. Experiments show that the fitness measure leads to the mining of process models that can reproduce all the behavior in the log, but these mined models may also allow for extra behavior. In short, the current version of the genetic algorithm can already be used to mine process models, but future research is necessary to always ensure that the mined models do not allow for extra behavior. Thus, this paper also discusses some ideas for future research that could ensure that the mined models will always only reflect the behavior in the log.

In this paper we describe a service that provides peripheral feedback on participation level of the audience in lectures and seminars to presenters. The peripheral display makes the lecturer aware of the attention level as well as the interest level of their students. We hypothesise that providing this kind of feedback can help lecturers or presenters to adjust their behaviour to the cognitive demands of the audience. In this paper we report on the results obtained from a focus group and two surveys that were carried out. Following that we describe the development of peripheral displays focusing on the design considerations and process of the teacher support service. We describe the service by addressing its technological components and visualisations. Finally we briefly discuss the issues to be considered for the evaluation of such an unobtrusive service.

Current Digital Rights Management (DRM) systems lack a functionality that allows a user to have more control over his content licenses to preserve his privacy or otherwise protect his interests. This paper presents an approach to increasing user's control of domain-based protected content without sacrificing content owner's control. The proposed method is based on a specific form of a delegation license and an activation mechanism. The paper also discusses a practical realization of the proposed concept as well as its cryptographic enforcement, architectural aspects and system complexity. The effectiveness and simplicity of the approach are illustrated by a practical realization of several motivating examples.

This paper studies ways to backdoor lattice-based systems following Young and Yung’s work on backdooring RSA and discrete-log based systems. For the NTRU encryption scheme we show how to build a backdoor and to change the system so that each ciphertext leaks information about the plaintext to the owner of the backdoor. For signature schemes the backdoor leaks information about the signing key to the backdoor owner. As in Young and Yung’s work the backdoor uses the freedom that random selections offer in the protocol to hide a secret message encrypted to the backdoor owner. The most interesting and very different part though is how to hide and retrieve the hidden messages.

3D scanning and printing has the potential to revolutionise the world. It offers a bridge between the virtual environment and the tangible world. The use of 3D scanners to capture and recreate defining objects is known as 3D virtualisation. It involves capturing a real-life scene using laser technology and representing its geometry using 3D modelling software or 3D printers. Despite being a relatively young technology, 3D printing has now become accessible and a part of modern industry. The printing of a 3D generated model can change the way in which an individual understands a concept, environment or communicates an idea. This has multiple benefits, for education, skills development, training and within the construction industry. However, using this technology relies on the operator having the skills and training required to generate accurate 3D models, and account for errors in the mesh after scanning. As such, this paper details the development into an automated 3D scanning system, and a cloud-based printing platform, where models are intelligently printed by multiple devices. Its development allows the readiness of 3D printing capabilities to unskilled users, who have no education or training in 3D model construction. Objects can be instantly manipulated and transferred into free-to-use open source graphic software. The access to detailed 3D model construction has never been so accessible to the untrained.

Process discovery algorithms typically aim at discovering process models from event logs. Most algorithms achieve this by solely using an event log, without allowing the domain expert to influence the discovery in any way. However, the user may have certain domain expertise which should be exploited to create better process models. In this paper, we address this issue of incorporating domain knowledge to improve the discovered process model. First, we present a verification algorithm to verify the presence of certain constraints in a process model. Then, we present three modification algorithms to modify the process model. The outcome of our approach is a Pareto front of process models based on the constraints specified by the domain expert and common quality dimensions of process mining.

Programming artificial intelligence (AI) to make fairness assessments of texts through top-down rules, bottom-up training, or hybrid approaches, has presented the challenge of defining cross-cultural fairness. In this paper a simple method is presented which uses vectors to discover if a verb is unfair (e.g., slur, insult) or fair (e.g., thank, appreciate). It uses already existing relational social ontologies inherent in Word Embeddings and thus requires no training. The plausibility of the approach rests on two premises. That individuals consider fair acts those that they would be willing to accept if done to themselves. Secondly, that such a construal is ontologically reflected in Word Embeddings, by virtue of their ability to reflect the dimensions of such a perception. These dimensions being: responsibility vs. irresponsibility, gain vs. loss, reward vs. sanction, joy vs. pain, all as a single vector (FairVec). The paper finds it possible to quantify and qualify a verb as fair or unfair by calculating the cosine similarity of the said verb���s embedding vector against FairVec - which represents the above dimensions. We apply this to Glove and Word2Vec embeddings. Testing on a list of verbs produces an F1 score of 95.7, which is improved to 97.0. Lastly, a demonstration of the method���s applicability to sentence measurement is carried out., This research was funded by the European Union���s Horizon 2020 research and innovation programme under the Next Generation Internet TRUST grant agreement no. 825618.

This paper aims to analyze the green efficiency performance of the logistics industry in China’s 30 provinces from 2008 to 2017. We first evaluate the green efficiency of the logistics industry through the non-directional distance function (NDDF) method. Then, we use the functional clustering method funHDDC, which is one of the popular machine learning methods, to divide 30 provinces into 4 clusters and analyze the similarities and differences in green efficiency performance patterns among different groups. Further, we explore the driving factors of dynamic changes in green efficiency through the decomposition method. The main conclusions of this paper are as follows: (1) In general, the level of green efficiency is closely related to the geographical location. From the clustering results, we can find that most of the eastern regions belong to the cluster with higher green efficiency, while most of the western regions belong to the cluster with lower green efficiency. However, the green efficiency performance in several regions with high economic levels, such as Beijing and Shanghai, is not satisfactory. (2) Based on the analysis of decomposition results, the innovation effect of China’s logistics industry is the most obvious, but the efficiency change still needs to be improved, and technical leadership should be strengthened. Based on these conclusions, we further propose some policy recommendations for the green development of the logistics industry in China.

Blockchain has made an impact on today's technology by revolutionizing the financial industry in its utilization on cryptocurrency and the features it provided on decentralization. With the current trend of pursuing the decentralized Internet, many methods have been proposed to achieve decentralization considering different aspects of the current Internet model ranging from infrastructure and protocols to services and applications. This paper focuses on using Blockchain to provide a robust and secure decentralized computing system. The paper conducts a literature review on Blockchain-based methods capable for the decentralization of the future Internet. To achieve that decentralization, two research aspects of Blockchain have been investigated that are highly relevant in realizing the decentralized Internet. The first aspect is the consensus algorithms, which are vital components for decentralization of Blockchain. We have identified three consensus algorithms being PoP, Paxos, and PoAH to be more adequate for reaching consensus in Blockchain-enabled Internet architecture. The second aspect that we investigated is the impact of future Internet technologies on Blockchain, where their combinations with Blockchain would help to make it overcome its established flaws and be more optimized and applicable for Internet decentralization., Under Review in Cluster Computing, Springer

In March 2000 the European Participative Simulation environment for Integral Manufacturing enterprise renewal (PSIM) project was started. A continuous changing environment in Europe imposes new requirements for assembly management and the role of human factors. This project aims at enabling the ongoing and integral improvement of assembly processes using advanced simulation software and up-to-date knowledge of sociotechnical systems design and ergonomics. The focus of this paper is on the development of the Ergonomic/Sociotechnical (E/S) software tool. Attention is paid to procedural aspects, the test of the first paper and pencil version and the development of the software prototype.

Goal of the present paper is providing support to operations planning and management in complex scenarios. The authors are mainly focused on South Asia region, which is subject of experimental analysis by running an Intelligent Agents—driven HLA Federation. Simulation of investments and operations over an asymmetric mission environment with several parties, insurgents, terrorists and dynamic social framework is the aim. The scenario has various degrees of freedom and M&S enables evaluation of human behavior evolution and socio-psychological aspects. The presented models include Computer Generated Forces (CGF) driven by Intelligent Agents (IAs) that represents not only units on the battlefield, but also people and interest groups (i.e. Middle Class, Nomads, Clans). The study is focused on Civil Military Co-operations (CIMIC) and Psychological Operations (PSYOPs). The simulation is based on specific architecture that involves various federates playing different roles. Verification, Validation and Accreditation (VV&A) has been applied along the whole life cycle of the research, in order to determine the correctness and effectiveness of the results. The paper proposes experimental results obtained during the dynamic test of the federations.