Network Capacity Bound for Personalized PageRank in Multimodal Networks

In a former paper the concept of Bipartite PageRank was introduced and a theorem on the limit of authority flowing between nodes for personalized PageRank has been generalized. In this paper we want to extend those results to multimodal networks. In particular we introduce a hypergraph type that may be used for describing multimodal network where a hyperlink connects nodes from each of the modalities. We introduce a generalisation of PageRank for such graphs and define the respective random walk model that can be used for computations. we finally state and prove theorems on the limit of outflow of authority for cases where individual modalities have identical and distinct damping factors.Comment: 28 pages. arXiv admin note: text overlap with arXiv:1702.0373

Adaptive Document Maps

Abstract. As document map creation algorithms like WebSOM are computationally expensive, and hardly reconstructible even from the same set of documents, new methodology is urgently needed to allow to construct document maps to handle streams of new documents entering document collection. This challenge is dealt with within this paper. In a multi-stage process, incrementality of a document map is warranted. 1 . The architecture of the experimental system allows for comparative evaluation of different constituent technologies for various stages of the process. The quality of the map generation process has been investigated based on a number of clustering and classification measures. Some conclusions concerning the impact of incremental, topic-sensitive approach on map quality are presented

The Confidence Database

Understanding how people rate their confidence is critical for the characterization of a wide range of perceptual, memory, motor and cognitive processes. To enable the continued exploration of these processes, we created a large database of confidence studies spanning a broad set of paradigms, participant populations and fields of study. The data from each study are structured in a common, easy-to-use format that can be easily imported and analysed using multiple software packages. Each dataset is accompanied by an explanation regarding the nature of the collected data. At the time of publication, the Confidence Database (which is available at https://osf.io/s46pr/) contained 145 datasets with data from more than 8,700 participants and almost 4 million trials. The database will remain open for new submissions indefinitely and is expected to continue to grow. Here we show the usefulness of this large collection of datasets in four different analyses that provide precise estimations of several foundational confidence-related effects

Function optimization by the immune metaphor

The main goal of the immune system is to protect an organism against pathogens. To be able to recognize unknown (i.e. never seen) pathogens, the immune system applies a number of methods allowing to maintain sufficient diversity of its receptors. The most important methods are clonal selection and suppression of ineffective receptors. In effect the immune system admits maturation affinity property: during its functioning it continuously improves its ability to recognize new types of pathogens. This idea had found many interesting computer-oriented applications. In this paper a simple and easy to implement algorithm for multi-modal as well as non-stationary functions optimization is proposed. It is based on clonal selection and cells suppression mechanisms. Empirical results confirming its usability for uni-, multi-modal and non-stationary functions optimization are presented, and a review of other immunity-based approaches is given

Evolutionary algorithm for learning Bayesian structures from data

In this paper we report an evolutionary approach to learning Bayesian networks from data. We explain reasons, which advocate such a non-deterministic approach. We analyze weaknesses of previous works and come to conclusion that we should operate in the search space native for the problem i.e. in the space of directed acyclic graphs instead of standard space of binary strings. This requires adaptation of evolutionary methodology into very specific needs. We propose quite new data representation and implementation of generalized genetic operators and then we present an efficient algorithm capable of learning complex networks without additional assumptions. We discuss results obtained with this algorithm. The approach presented in this paper can be extended with the possibility to absorb some suggestions from experts or obtained by means of data preprocessing

O metrykach odległości dla wielowymiarowych zbiorów danych wykorzystywanych w algorytmie selekcji negatywnej o wartościach rzeczywistych

One of the key parameters of algorithms for anomaly detection is the metric (norm) applied to calculate the distance between every two samples which reflect its proximity. It is especially important when we operate on real-valued high dimensional datasets, i.e. when we deal with the problem of intruders detection in computer networks. As observed, the most popular Euclidean norm becomes meaningless in higher than 15-dimensional space. This means that other norms should be investigated to improve the effectiveness of real-valued negative selection algorithms. In this paper we present results for the following norms: Minkowski, fractional distance and cosine.Jednym z kluczowych parametrów algorytmów wykrywania anomalii jest metryka (norma) służąca do obliczania odległości pomiędzy dwiema próbkami, która odzwierciedla ich podobieństwo. Jest ona szczególnie istotna w przypadkach operowania na zbiorach o wielu wymiarach takich, z jakimi mamy do czynienia w przypadku wykrywania intruzów w sieciach komputerowych. Zaobserwowano, że najczęściej stosowana norma euklidesowa staje się bezużyteczna w przestrzeniach o wymiarach większych niż 15. Oznacza to konieczność stosowania innych norm, które pozwoliłyby na zwiększenie skuteczności algorytmu selekcji negatywnej o wartościach rzeczywistych. W artykule prezentujemy wyniki uzyskane dla normy Minkowskiego, Lm, przy zmianach parametru m w zakresie (0, 2] oraz dla odległości kosinusowej

Rozproszony protokół enumeracji dla systemów z wartościowaniami

The paper presents a new algorithm for the problem of an enumeration protocol for nodes in a network. The new algorithm, contrary to previous ones, is local both in information access (neighbourhood only) and information stored (proportional to the number of neighbours). This property is achieved at the expense of the type of connectivity the network is assumed to exhibit.W pracy przedstawiono nowy algorytm enumeracji węzłów sieci. W odróżnieniu od dotychczasowych algorytmów jest on lokalny zarówno w sensie dostępu do informacji (uwzględnia się wyłącznie informacje pochodzące od sąsiadów aktualnie przetwarzanego węzła) jak i przechowywania informacji (ilość informacji jest proporcjonalna do liczby sąsiadów danego węzła). Cechę lokalności uzyskano zawężając rozważania do rodziny grafów triangulowanych, które odgrywają podstawową rolę w teorii sieci bayesowskich. Uogólnieniem tych ostatnich są systemy z wartościowaniami, nazywane też grafowymi systemami ekspertowymi, czyli struktury grafowe służące do reprezentacji niedeterministycznych zależności między zmiennymi (odpowiadają im węzły grafu)