An agent-driven semantical identifier using radial basis neural networks and reinforcement learning

Due to the huge availability of documents in digital form, and the deception possibility raise bound to the essence of digital documents and the way they are spread, the authorship attribution problem has constantly increased its relevance. Nowadays, authorship attribution,for both information retrieval and analysis, has gained great importance in the context of security, trust and copyright preservation. This work proposes an innovative multi-agent driven machine learning technique that has been developed for authorship attribution. By means of a preprocessing for word-grouping and time-period related analysis of the common lexicon, we determine a bias reference level for the recurrence frequency of the words within analysed texts, and then train a Radial Basis Neural Networks (RBPNN)-based classifier to identify the correct author. The main advantage of the proposed approach lies in the generality of the semantic analysis, which can be applied to different contexts and lexical domains, without requiring any modification. Moreover, the proposed system is able to incorporate an external input, meant to tune the classifier, and then self-adjust by means of continuous learning reinforcement.Comment: Published on: Proceedings of the XV Workshop "Dagli Oggetti agli Agenti" (WOA 2014), Catania, Italy, Sepember. 25-26, 201

Using Modularity Metrics to assist Move Method Refactoring of Large System

For large software systems, refactoring activities can be a challenging task, since for keeping component complexity under control the overall architecture as well as many details of each component have to be considered. Product metrics are therefore often used to quantify several parameters related to the modularity of a software system. This paper devises an approach for automatically suggesting refactoring opportunities on large software systems. We show that by assessing metrics for all components, move methods refactoring an be suggested in such a way to improve modularity of several components at once, without hindering any other. However, computing metrics for large software systems, comprising thousands of classes or more, can be a time consuming task when performed on a single CPU. For this, we propose a solution that computes metrics by resorting to GPU, hence greatly shortening computation time. Thanks to our approach precise knowledge on several properties of the system can be continuously gathered while the system evolves, hence assisting developers to quickly assess several solutions for reducing modularity issues

Improving files availability for BitTorrent using a diffusion model

The BitTorrent mechanism effectively spreads file fragments by copying the rarest fragments first. We propose to apply a mathematical model for the diffusion of fragments on a P2P in order to take into account both the effects of peer distances and the changing availability of peers while time goes on. Moreover, we manage to provide a forecast on the availability of a torrent thanks to a neural network that models the behaviour of peers on the P2P system. The combination of the mathematical model and the neural network provides a solution for choosing file fragments that need to be copied first, in order to ensure their continuous availability, counteracting possible disconnections by some peers

Specification and verification issues in a process language

PhD ThesisWhile specification formalisms for reactive concurrent systems are now reasonably well-understood theoretically, they have not yet entered common, widespread design practice. This motivates the attempt made in this work to enhance the applicability of an important and popular formal framework: the CSP language, endowed with a failure-based denotational semantics and a logic for describing failures of processes. The identification of behaviour with a set of failures is supported by a convincing intuitive reason: processes with different failures can be distinguished by easily realizable experiments. But, most importantly, many interesting systems can be described and studied in terms of their failures. The main technique employed for this purpose is a logic in which process expressions are required to satisfy an assertion with each failure of the behaviour they describe. The theory of complete partial orders, with its elegant treatment of recursion and fixpoint-based verification, can be applied to this framework. However, in spite of the advantages illustrated, the practical applicability of standard failure semantics is impaired by two weaknesses. The first is its inability to describe many important systems, constructed by connecting modules that can exchange values of an infinite set across ports invisible to the environment. This must often be assumed for design and verification purposes (e.g. for the many protocols relying upon sequence numbers to cope with out-of-sequence received messages). Such a deficiency is due to the definition of the hiding operator in standard failure semantics. This thesis puts forward a solution based on an interesting technical result about infinite sets of sequences. Another difficulty with standard failure semantics is its treatment of divergence, the phenomenon in which some components of a system interact by performing an infinite, uninterrupted sequence of externally invisible actions. Within failure semantics, divergence cannot be abstracted from on the basis of the implicit fairness assumption that, if there is a choice leading out of divergence, it will eventually be made. This 'fair abstraction' is essential for the verification of many important systems, including communication protocols. The solution proposed in this thesis is an extended failure semantics which records refused traces, rather than just actions. Not only is this approach compatible with fair abstraction, but it also permits, like ordinary failure semantics, verification in a compositional calculus with fixpoint induction. Rather interestingly, these results can be obtained outside traditional fixpoint theory, which cannot be applied in this case. The theory developed is based on the novel notion of 'trace-based' process functions. These can be shown to possess a particular fixpoint that, unlike the least fixpoint of traditional treatments, is compatible with fair abstraction. Moreover, they form a large class, sufficient to give a compositional denotational semantics to a useful eSP-like process language. Finally, a logic is proposed in which the properties of a process' extended failures can be expressed and analyzed; the methods developed are applied to the verification of two example communication protocols: a toy one and a large case study inspired by a real transport protocol

Blockchain Inefficiency in the Bitcoin Peers Network

We investigate Bitcoin network monitoring the dynamics of blocks and transactions. We unveil that 43\% of the transactions are still not included in the Blockchain after 1h from the first time they were seen in the network and 20\% of the transactions are still not included in the Blockchain after 30 days, revealing therefore great inefficiency in the Bitcoin system. However, we observe that most of these `forgotten' transactions have low values and in terms of transferred value the system is less inefficient with 93\% of the transactions value being included into the Blockchain within 3h. The fact that a sizeable fraction of transactions is not processed timely casts serious doubts on the usability of the Bitcoin Blockchain for reliable time-stamping purposes and calls for a debate about the right systems of incentives which a peer-to-peer unintermediated system should introduce to promote efficient transaction recording.Comment: 15 pages, 8 figures, 3 table

The Accounting Network: how financial institutions react to systemic crisis

The role of Network Theory in the study of the financial crisis has been widely spotted in the latest years. It has been shown how the network topology and the dynamics running on top of it can trigger the outbreak of large systemic crisis. Following this methodological perspective we introduce here the Accounting Network, i.e. the network we can extract through vector similarities techniques from companies' financial statements. We build the Accounting Network on a large database of worldwide banks in the period 2001-2013, covering the onset of the global financial crisis of mid-2007. After a careful data cleaning, we apply a quality check in the construction of the network, introducing a parameter (the Quality Ratio) capable of trading off the size of the sample (coverage) and the representativeness of the financial statements (accuracy). We compute several basic network statistics and check, with the Louvain community detection algorithm, for emerging communities of banks. Remarkably enough sensible regional aggregations show up with the Japanese and the US clusters dominating the community structure, although the presence of a geographically mixed community points to a gradual convergence of banks into similar supranational practices. Finally, a Principal Component Analysis procedure reveals the main economic components that influence communities' heterogeneity. Even using the most basic vector similarity hypotheses on the composition of the financial statements, the signature of the financial crisis clearly arises across the years around 2008. We finally discuss how the Accounting Networks can be improved to reflect the best practices in the financial statement analysis

European Approaches to Japanese Language and Linguistics

In this volume European specialists of Japanese language present new and original research into Japanese over a wide spectrum of topics which include descriptive, sociolinguistic, pragmatic and didactic accounts. The articles share a focus on contemporary issues and adopt new approaches to the study of Japanese that often are specific to European traditions of language study. The articles address an audience that includes both Japanese Studies and Linguistics. They are representative of the wide range of topics that are currently studied in European universities, and they address scholars and students alike

PETAL: a python tool for deep analysis of biological pathways.

Abstract Summary Although several bioinformatics tools have been developed to examine signaling pathways, little attention has been given to ever long-distance crosstalk mechanisms. Here, we developed PETAL, a Python tool that automatically explores and detects the most relevant nodes within a KEGG pathway, scanning and performing an in-depth search. PETAL can contribute to discovering novel therapeutic targets or biomarkers that are potentially hidden and not considered in the network under study. Availabilityand implementation PETAL is a freely available open-source software. It runs on all platforms that support Python3. The user manual and source code are accessible from https://github.com/Pex2892/PETAL

Conservative and innovative Features in the Phonology of Hateruma Dialect

The Hateruma dialect belongs to Yaeyama Ryukyuan and is spoken on the Hateruma island as well as in the Shiraho village of Ishigaki island. The aim of this paper is to describe and analyse the phonetic and phonological characteristics of the Hateruma dialect, in order to point out all the uncommon features, most of which are unique within both Ryukyuan and mainland varieties, and their implication in the reconstruction of proto-Japanese. Beside several innovative forms, like the pharyngealized vowel, sonorant devoicing and noun-final consonant epenthesis, the Hateruma dialect is conservative in preserving certain distinctions which reflect better than any other Ryukyuan variety the distinction of kō/otsu series of Old Japanese and the corresponding reconstructed forms of proto-Japanese. A comparative analysis between cognates of the Hateruma dialect and Old Japanese shows how innovative sound changes may have contributed to preserve the distinction between syllables which have merged elsewhere