Search

Various country-wide guidelines for speed limits setting make use of diverse parameters specifying properties of roads, their environment, as well as traffic intensity, statistical distribution of measured vehicle speeds and history of crash accidents. We argue, that although such extensive data are not present in public geospatial datasets, they can be inferred as latent features in machine learning models trained to predict speed limits. To verify this hypothesis we performed an experiment, in which we extracted tag-based and geometrical features from Open Street Map for Poland and applied various classification methods to predict class labels corresponding to speed limits. In spite of the fact that the datasets were imbalanced (with majority classes corresponding to default speed limits) we obtained F1 scores ranging from 0.72 to 0.91 for lower speed roads. The neural network classifier implemented with Tensorflow framework turned out to be the most efficient.

Authorship attribution aims at identifying the author of an unseen text document based on text samples originating from different authors. In this paper we focus on authorship attribution of Polish texts using stylometric features based on part of speech (POS) tags. Polish language is characterized by high inflection level and in consequence over 1000 POS tags can be distinguished. This allows building a sufficiently large feature space by extracting POS information from documents and performing their classification with use of machine learning methods. We report results of experiments conducted with Weka workbench using combinations of the following features: POS tags, an approximation of their bigrams and simple document statistics.

Authorship attribution aims at distinguishing texts written by different authors using text features representing their styles. In this paper we investigate stylometric features for the Polish language based on Part of Speech (POS) tagging (including POS bigrams) and function words. Due to high inflection level of Polish language the feature space tends to be very large. This in particular concerns POS n-grams. Focusing on POS bigrams, we propose their simplified representation allowing to keep the feature space compact. We report experiments, in which authorship attribution was conducted for varying in lengths documents, with use of classifiers from the Weka library. We evaluate classification results for combinations of the following features: POS tags, POS bigrams, function words and simple document statistics. Experiments indicate that the developed features provide good classification performance.

For various IT systems security is considered a key quality factor. In particular, it might be crucial for video surveillance systems, as their goal is to provide continuous protection of critical infrastructure and other facilities. Risk assessment is an important activity in security management; it aims at identifying assets, threats and vulnerabilities, analysis of implemented countermeasures and their effectiveness in mitigating risks. This paper discusses an application of a new risk assessment method, in which risk calculation is based on Fuzzy Cognitive Maps (FCMs) to a complex automated video surveillance system. FCMs are used to capture dependencies between assets and FCM based reasoning is applied to aggregate risks assigned to lower-level assets (e.g. cameras, hardware, software modules, communications, people) to such high level assets as services, maintained data and processes. Lessons learned indicate, that the proposed method is an efficient and low-cost approach, giving instantaneous feedback and enabling reasoning on effectiveness of security system.

For contemporary software systems, security is considered to be a key quality factor and the analysis of IT security risk becomes an indispensable stage during software deployment. However, performing risk assessment according to methodologies and standards issued for the public sector or large institutions can be too costly and time consuming. Current business practice tends to circumvent risk assessment by defining sets of standard safeguards and applying them to all developed systems. This leads to a substantial gap: threats are not re-evaluated for particular systems and the selection of security functions is not based on risk models. This paper discusses a new lightweight risk assessment method aimed at filling this gap. In this proposal, Fuzzy Cognitive Maps (FCMs) are used to capture dependencies between assets, and FCM-based reasoning is performed to calculate risks. An application of the method is studied using an example of an e-health system providing remote telemonitoring, data storage and teleconsultation services. Lessons learned indicate that the proposed method is an efficient and low-cost approach, giving instantaneous feedback and enabling reasoning on the effectiveness of the security system.

This paper presents a system for extraction of concepts from unstructured Polish texts. Here concepts are understood as n-grams, whose words satisfy specific grammatical constraints. Detection and transformation of concepts to their normalized form are performed with rules defined in a language, which combines elements of colored and fuzzy Petri nets. We apply a user friendly method for specification of samples of transformation patterns that are further compiled to rules. To improve accuracy and performance, we recently introduced rule management mechanisms, which are based on two relations between rules: partial refinement and covering. The implemented methods include filtering with metarules and removal of redundant rules (i.e. these covered by other rules). We report results of experiments, which aimed at extracting specific concepts (actions) using a ruleset refactored with the developed rule management techniques.

This paper addresses the problem of modeling and automated recognition of video events. We propose to use Linear Temporal Logic as a language for events specification and Fuzzy Semantic Petri Nets (FSPN) as a tool for their recognition. FSPN are Petri nets coupled with an underlying fuzzy ontology. The ontology stores assertions (facts) concerning classification of objects and detected relations. Fuzzy predicates querying the ontology content are used as guards of transitions in FSPN. Tokens carry information on objects participating in a scenario and are equipped with weights indicating likelihood of their assignment to places. In turn, the places correspond to scenario steps. We describe a prototype detection system consisting of an FSPN interpreter, the fuzzy ontology, and a set of predicate evaluators. Initial tests yielding promising results are reported.

The motivation for our work was an idea of integrating formal verification with business processes modeling. In the presented approach ArchiMate was selected as a language used for definition of processes. We describe a procedure, which extracts behavioral elements from ArchiMate specification and transforms them into a corresponding representation used by NuSMV model checker. Then, we focus on time efficiency of the verification task. We give results of tests performed on a set of artificial process specifications, as well as on a complex business process, whose development was supported by the implemented solution. We compare three semantics of ArchiMate process definitions and discuss their influence on model complexity and verification time.

We present recently developed solution allowing extraction of concepts from unstructured Polish texts with special focus on correct morphological forms of obtained concept names. As Polish is a highly inflected language, detected names need to be transformed following Polish grammar rules. We propose a user-friendly method for specification of transformation patterns, which is based on a simple annotations language. Annotations prepared by a user are compiled into transformation rules. During the concept extraction process the input document is split into sentences and the rules are applied to sequences of words comprised in sentences. Recognized strings forming concept names are aggregated at various levels and assigned with scores. We report also results of initial experiments performed on a medical text.

We investigate an application of model checking techniques to automated verification of business processes expressed in ArchiMate language. As a verification tool the state of the art symbolic model checker NuSMV is used. The proposed approach consists in fully automated translation of behavioral elements embedded in ArchiMate models into a corresponding representation in NuSMV language and then verifying its properties specified in CTL. Since our goal is to build an interactive verification tool, we focus on time efficiency of the verification process. We report results of tests performed on artificial process models of various complexity, as well as on a real business process example. The results show, that the described approach can be applied successfully, however, verification of complex business process specifications may face the problem of state space explosion. In such a case, to make the verification feasible, various reductions and simplifications can be applied.

Part 2: Algorithms; International audience; In this paper we investigate the problem of verification of business processes specified with ArchiMate language. The proposed solution employs model checking techniques. As a verification platform the state of the art symbolic model checker NuSMV is used.We describe a method of fully automated translation of behavioral elements embedded in ArchiMate models into a representation in NuSMV language, which is then submitted to verification with respect to requirements expressed in CTL. The requirements specification can be entered by user, but we also propose to derive some of them automatically, based on analysis of control flows within business processes. The solution was implemented as a plugin to Archi, a popular ArchiMate modeling tool. Application of the method is presented on an example of a small business process.

The motivation for the presented work was the need for solution allowing efficient estimation of traffic parameters in based on sparse measurements covering a small fraction road segments within large urban road network. The solution was intended to be deployed within the Dynamic Map, an important component of the INSIGMA system used for route planning and traffic control. We have chosen to base the estimation model on Fuzzy Cognitive Maps (FCM) formalism and apply an Evolutionary Algorithm to learn the model parameters. The main argument in favor of FCM is their simplicity and the speed of calculations that can be required for real-time updates of traffic parameters. This paper discusses a novel evolutionary algorithm for FCM learning and demonstrates feasibility and efficiency of such approach by giving results of tests performed on road networks of various size.

A common taxonomy of data warehouse architectures comprises five basic approaches: Centralized, Independent Data Mart, Federated, Hub-and-Spoke and Data Mart Bus. However, for many real world cases, an applied data warehouse architecture can be their combination. In this paper we propose a Data Warehouse Architecture Reference Model (DWARM), which unifies known architectural styles and provides options for adaptation to fit particular purposes of a developed data warehouse system. The model comprises 11 layers grouping containers (data stores, sources and consumers), as well as processes, covering typical functional groups: ETL, data storage, data integration and delivery. Actual data warehouse architecture can be obtained by tailoring (removing unnecessary components) and instantiating (creating required layers and components of a given type).

We examine properties of a peer to peer network comprising several agents that store various types of local data and exchange them through established communication channels. We propose a communication model applicable to a developed platform for data integration between various security agencies and we focus on analysis of consequences of established channels, e.g. an unintended information leakage or a presence of data silos that can be an impediment for cooperation. To detect such situations efficiently, we do not concentrate on exchanged data itself, but on a belief related to known classes of data. In the analyses we use a model, in which communications and belief states are expressed as matrix operations of linear algebra. We show that applying this model we can efficiently reason about the data that can potentially be exchanged between agents not linked directly and about the ranges, which can be reached by the data during communication flows.

Automated recognition of complex video events poses challenges related to: selection of formalisms for efficient event modeling and analysis, mapping semantic high-level concepts used in specifications on information extracted from video sequences, as well as managing uncertainty associated with this information. We propose Fuzzy Semantic Petri Nets (FSPN) as a tool aimed at solving the mentioned problems. FSPN are Petri nets coupled with an underlying fuzzy ontology. The ontology stores assertions (facts) concerning object classification and detected relations. Fuzzy predicates querying the ontology are used as transition guards. Places in FSPN represent scenario steps. Tokens carry information on objects participating in a scenario and have weights expressing likelihood of a step occurrence. FSPN enable detection of events occurring concurrently, analysis of various combinations of objects and reasoning about alternatives.

For various IT systems security is considered to be a key quality factor. In particular, for health care systems security is of uttermost importance, as it is related to patients’ health and safety. Risk assessment is an important activity in security management; it aims at identifying assets, threats and vulnerabilities, analysis of implemented countermeasures and their effectiveness in mitigating risks. This paper discusses a new risk assessment method, in which risk calculation is based on Fuzzy Cognitive Maps (FCMs) approach. FCMs are used to capture dependencies between assets and FCM based reasoning is applied to aggregate risks assigned to lower-level assets (e.g. hardware, software modules, communications, people) to such high level assets as services, maintained data and processes. An application of the method is studied on an example of e-health system providing remote telemonitoring, data storage and teleconsultation services. Lessons learned indicate, that the proposed method is an efficient and low-cost approach, giving instantaneous feedback and enabling reasoning on effectiveness of security system.

The paper reports an application of Architecture-based Tradeoff Analysis Method (ATAM) for early evaluation of the Dynamic Map architecture. The Dynamic Map is a complex information system, composed of spatial databases, storing static and dynamic data relevant for urban traffic, as well as a set of software modules responsible for data collection, interpretation and provision. Due to the complexity of the system, its size and key importance of its services to other subsystems, we decided to perform architecture evaluation using the ATAM method. To facilitate the task new tools supporting ATAM based assessment are proposed: Scenario Influence Matrix and Architectural Decision Matrix. Taking as example an excerpt from the system architecture, we present how they were used during the architecture evaluation. The gathered experience confirm usefulness of the tools, enabling ATAM to help detecting real flaws in a design and identify potential risks.

This paper presents the Dynamic Map system, one of the key products of the INSIGMA Project. The main focus is on the map and dynamic data storage subsystem, which utilizes a spatial database and is based on ontologies. First, the data models used are described, including the OpenStreetMap-based structure for the static map and the ontology-driven structure for dynamic parameters and events. The approach to generation of database structures from OWL is described in detail, followed by descriptions of the OSM import process, the GPS tracker module, the sensor state analyzer and the event interpreter. Finally, the planned future enhancements are outlined and discussed.

The paper presents IOEM, a methodology for ontology development elaborated for the INSIGMA project. Although prepared for a particular use, the methodology is quite general and can be used in a large variety of IT projects requiring ontology components. It is particularly suitable for large and geographically distributed software projects. The methodology is oriented towards applications of ontologies in various phases of a software lifecycle: development and run-time.

For many classes of problems it is difficult to make decisions, assessments or develop plans based on precise quantitative models. Fuzzy Cognitive Maps (FCM) are a well-known tool for qualitative analysis of systems that uses a simple representation of knowledge in the form of a graph of concepts linked by causal relationships. Advantages of FCMs are the ease of gathering and representing knowledge and the simplicity of reasoning techniques, very close to neural networks. The paper gives results of experiments aiming at application of FCMs to analysis of development scenarios for research and teaching units. The analyzes were conducted for four representative classes of academic units: strong, medium, weak with a development potential and weak; their results are discussed and several observations related to reasoning with FCMs are made.

In this paper we describe SOAROAD (SOA Related Ontology of Architectural Decisions), which was developed to support eva- luation of architectures of information systems using SOA techno- logies. The main goal of the ontology is to provide constructs for documenting service-oriented architectures, however, it is designed to support future reasoning. Building the ontology we focused on the requirements of Architecture Tradeoff Analysis Method (ATAM) which was chosen as reference methodology for architecture eva- luation.