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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.

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.

With the proliferation of graphics processing units (GPU) supporting general-purpose computing (GPGPU), many computationally demanding applications are being redesigned to exploit the capabilities offered by massively parallel computing platforms. This paper presents a Bees Algorithm (BA) for the Quadratic Assignment Problem (QAP) implemented on the CUDA platform. The motivations for our work were twofold: firstly, we wanted to develop a dedicated algorithm to solve the QAP showing both time and optimization performance, secondly, we planned to check if the capabilities offered by popular GPUs can be exploited to accelerate hard optimization tasks requiring high computational power. The paper describes both sequential and parallel algorithm implementations, as well as reports results of tests.

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.

This paper presents a Particle Swarm Optimization (PSO) algorithm for the Quadratic Assignment Problem (QAP) implemented on OpenCL platform. Motivations to our work were twofold: firstly we wanted to develop a dedicated algorithm to solve the QAP showing both time and optimization performance, secondly we planned to check, if the capabilities offered by popular GPUs can be exploited to accelerate hard optimization tasks requiring high computational power. We were specifically targeting low-cost popular devices, with limited capabilities. The paper discusses the algorithm and its parallel implementation, as well as reports results of tests.

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).

This paper describes DWARM, an ontology formalizing a new data warehouse architecture reference model intended do capture common five architectural approaches, as well as to provide means for describing complex hybrid architectures that emerge due to observed evolution of business and technology. The ontology defines concepts, e.g. layers, processes, containers and property classes, as well as relations that can be used to construct precise architectural models. Formalization of architecture description as an ontology gives an opportunity to perform automatic or semiautomatic validation and assessment.

We describe a prototype implementation of a real time traffic monitoring service that uses GPS positioning information received from moving vehicles to calculate average speed and travel time and assign them to road segments. The primary factor for reliability of determined parameters is the correct calculation of a vehicle location on a road segment, which is realized by a map-matching algorithm. We present an a new incremental map-matching algorithm based on Hidden Markov Model (HMM). A HMM state corresponds to a road segment and a sensor reading to an observation. The HMM model is updated on arrival of new GPS data by alternating operations: expansion and contraction. In the later step a part of determined trajectory is output. We present also results of conducted experiments.

Map-matching algorithms aim at establishing a vehicle location on a road segment based on positioning data from a variety of sensors: GPS receivers, WiFi or cellular radios. They are integral part of various Intelligent Transportation Systems (ITS) including fleet management, vehicle tracking, navigation services, traffic monitoring and congestion detection. Our work was motivated by an idea of developing an algorithm that can be both utilized for tracking individual vehicles and for monitoring traffic in real-time. We propose a new incremental map-matching algorithm that constructs of a sequence of Hidden-Markov Models (HMMs). Starting from an initial HMM, the next models are developed by alternating operations: expansion and contraction. In the later, the map-matched trace is output. We discuss results of initial experiments conducted for 20 GPS traces, which to test algorithm robustness, were modified by introduction of noise and/or downsampled.

With the growing number of vehicles traveling on public roads, traffic congestion has become a serious problem, resulting in more unpredictable travel times, increased fuel consumption and pollution. Intelligent Transportation Systems (ITS), already being developed by several countries, aim to improve safety, mobility and environmental performance. The goal of the INSIGMA project is to develop a system providing functionality of a typical ITS: real-time traffic monitoring, route planning and traffic control. In this paper we discuss the concepts and solutions developed within the project: dynamic map, sensors – videodetector and GPS tracker – as well as advanced route planning and traffic control algorithms.

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.