Histogram of gradients of Time-Frequency Representations for Audio scene detection

This paper addresses the problem of audio scenes classification and contributes to the state of the art by proposing a novel feature. We build this feature by considering histogram of gradients (HOG) of time-frequency representation of an audio scene. Contrarily to classical audio features like MFCC, we make the hypothesis that histogram of gradients are able to encode some relevant informations in a time-frequency {representation:} namely, the local direction of variation (in time and frequency) of the signal spectral power. In addition, in order to gain more invariance and robustness, histogram of gradients are locally pooled. We have evaluated the relevance of {the novel feature} by comparing its performances with state-of-the-art competitors, on several datasets, including a novel one that we provide, as part of our contribution. This dataset, that we make publicly available, involves $19$ classes and contains about $900$ minutes of audio scene recording. We thus believe that it may be the next standard dataset for evaluating audio scene classification algorithms. Our comparison results clearly show that our HOG-based features outperform its competitor

Flexible RDF data extraction from Wiktionary - Leveraging the power of community build linguistic wikis

We present a declarative approach implemented in a comprehensive opensource framework (based on DBpedia) to extract lexical-semantic resources (an ontology about language use) from Wiktionary. The data currently includes language, part of speech, senses, definitions, synonyms, taxonomies (hyponyms, hyperonyms, synonyms, antonyms) and translations for each lexical word. Main focus is on flexibility to the loose schema and configurability towards differing language-editions ofWiktionary. This is achieved by a declarative mediator/wrapper approach. The goal is, to allow the addition of languages just by configuration without the need of programming, thus enabling the swift and resource-conserving adaptation of wrappers by domain experts. The extracted data is as fine granular as the source data in Wiktionary and additionally follows the lemon model. It enables use cases like disambiguation or machine translation. By offering a linked data service, we hope to extend DBpedia’s central role in the LOD infrastructure to the world of Open Linguistics.

Towards Commentary-Driven Soccer Player Analytics

Open information extraction (open IE) has been shown to be useful in a number of NLP Tasks, such as question answering, relation extraction, and information retrieval. Soccer is the most watched sport in the world. The dynamic nature of the game corresponds to the team strategy and individual contribution, which are the deciding factors for a team’s success. Generally, companies collect sports event data manually and very rarely they allow free-access to these data by third parties. However, a large amount of data is available freely on various social media platforms where different types of users discuss these very events. To rely on expert data, we are currently using the live-match commentary as our rich and unexplored data-source. Our aim out of this commentary analysis is to initially extract key events from each game and eventually key entities like players involved, player action and other player related attributes from these key events. We propose an end-to-end application to extract commentaries and extract player attributes from it. The study will primarily depend on an extensive crowd labelling of data involving precautionary periodical checks to prevent incorrectly tagged data. This research will contribute significantly towards analysis of commentary and acts as a cheap tool providing player performance analysis for smaller to intermediate budget soccer club

Development of sustainable tools (Database and software) for Marine Litter Data management

Entregable del proyecto CleanAtlanticEste entregable describe diversas herramientas de utilidad para la gestión de datos de basura marina

The Dictionary of Accessible Communication

Terminology on Accessible Communication has primarily evolved and been published within the borders of a given country with no or only little exchange across these borders. Since English can be regarded as the „lingua franca“ of science, this first German–English dictionary of Accessible Communication will help to promote international exchange and an international discourse on this topic by attempting to define concepts that go beyond the scope of a single-country centered approach. The terminological work for this dictionary is based on the German Handbook of Accessible Communication. Most of the handbook’s contents are language-independent and applicable to other recipient communities. On the basis of the German terminology, the English equivalents, definitions and explanations were researched. The dictionary contributes to the development of a standardised terminology across languages and cultures

Packet analysis for network forensics: A comprehensive survey

Packet analysis is a primary traceback technique in network forensics, which, providing that the packet details captured are sufficiently detailed, can play back even the entire network traffic for a particular point in time. This can be used to find traces of nefarious online behavior, data breaches, unauthorized website access, malware infection, and intrusion attempts, and to reconstruct image files, documents, email attachments, etc. sent over the network. This paper is a comprehensive survey of the utilization of packet analysis, including deep packet inspection, in network forensics, and provides a review of AI-powered packet analysis methods with advanced network traffic classification and pattern identification capabilities. Considering that not all network information can be used in court, the types of digital evidence that might be admissible are detailed. The properties of both hardware appliances and packet analyzer software are reviewed from the perspective of their potential use in network forensics

Design of hardware architectures for HMM–based signal processing systems with applications to advanced human-machine interfaces

In questa tesi viene proposto un nuovo approccio per lo sviluppo di interfacce uomo–macchina. In particolare si tratta il caso di sistemi di pattern recognition che fanno uso di Hidden Markov Models per la classificazione. Il progetto di ricerca è partito dall’ideazione di nuove tecniche per la realizzazione di sistemi di riconoscimento vocale per parlato spontaneo. Gli HMM sono stati scelti come lo strumento algoritmico di base per la realizzazione del sistema. Dopo una fase di studio preliminare gli obiettivi sono stati estesi alla realizzazione di una architettura hardware in grado di fornire uno strumento riconfigurabile che possa essere utilizzato non solo per il riconoscimento vocale, ma in qualsiasi tipo di classificatore basato su HMM. Il lavoro si concentra quindi sullo sviluppo di architetture hardware dedicate, ma nuovi risultati sono stati ottenuti anche a livello di applicazione per quanto riguarda la classificazione di segnali elettroencefalografici attraverso gli HMM. Innanzitutto state sviluppata una architettura a livello di sistema applicabile a qualsiasi sistema di pattern recognition che faccia usi di HMM. L’architettura stata concepita in modo tale da essere utilizzabile come un sistema stand–alone. Definita l’architettura, un processore hardware per HMM, completamente riconfigurabile, stato decritto in linguaggio VHDL e simulato con successo. Un array parallelo di questi processori costituisce di fatto il nucleo di processamento dell’architettura sviluppata. Sulla base del progetto in VHDL, due piattaforme di prototipaggio rapido basate su FPGA sono state selezionate per dei test di implementazione. Diverse configurazioni costituite da array paralleli di processori HMM sono state implementate su FPGA. Le soluzioni che offrivano un miglior compromesso tra prestazioni e quantità di risorse hardware utilizzate sono state selezionate per ulteriori analisi. Un sistema software per il pattern recognition basato su HMM stato scelto come sistema di riferimento per verificare la corretta funzionalità delle architetture implementate. Diversi test sono stati progettati per validare che il funzionamento del sistema corrispondesse alle specifiche iniziali. Le versioni implementate del sistema sono state confrontate con il software di riferimento sulla base dei risultati forniti dai test. Dal confronto è stato possibile appurare che le architetture sviluppate hanno un comportamento corrispondente a quello richiesto. Infine le implementazioni dell’array parallelo di processori HMM `e sono state applicate a due applicazioni reali: un riconoscitore vocale, ed un classificatore per interfacce basate su segnali elettroencefalografici. In entrambi i casi l’architettura si è dimostrata in grado di gestire l’applicazione senza alcun problema. L’uso del processamento hardware per il riconoscimento vocale apre di fatto la strada a nuovi sviluppi nel campo grazie al notevole incremento di prestazioni ottenibili in termini di tempo di esecuzione. L’applicazione al processamento dell’EEG, invece, introduce di fatto un approccio completamente nuovo alla classificazione di questo tipo di segnali, e mostra come in futuro potrebbe essere possibile lo sviluppo di interfacce basate sulla classificazione dei segnali generati dal pensiero spontaneo. I possibili sviluppi del lavoro iniziato con questa tesi sono molteplici. Una direzione possibile è quella dell’implementazione completa dell’architettura proposta come un sistema stand–alone riconfigurabile per l’accelerazione di sistemi per pattern recognition di qualsiasi natura purchè basati su HMM. Le potenzialità di tale sistema renderebbero possibile la realizzazione di classificatiori in tempo reale con un alto grado di complessità, e quindi allo sviluppo di interfacce realmente multimodali, con una vasta gamma di applicazioni, dai sistemi di per lo spazio a quelli di supporto per persone disabili.In this thesis a new approach is described for the development of human–computer interfaces. In particular the case of pattern recognition systems based on Hidden Markov Models have been taken into account. The research started from he development of techniques for the realization of natural language speech recognition systems. The Hidden Markov Model (HMM) was chosen as the main algorithmic tool to be used to build the system. After the early work the goal was extended to the development of an hardware architecture that provided a reconfigurable tool to be used in any pattern recognition task, and not only in speech recognition. The whole work is thus focused on the development of dedicated hardware architectures, but also some new results have been obtained on the classification of electroencephalographic signals through the use of HMMs. Firstly a system–level architecture has been developed to be used in HMM based pattern recognition systems. The architecture has been conceived in order to be able to work as a stand–alone system. Then a VHDL description has been made of a flexible and completely reconfigurable hardware HMM processor and the design was successfully simulated. A parallel array of these processors is actually the core processing block of the developed architecture. Then two suitable FPGA based, fast prototyping platforms have been identified to be the targets for the implementation tests. Different configurations of parallel HMM processor arrays have been set up and mapped on the target FPGAs. Some solutions have been selected to be the best in terms of balance between performance and resources utilization. Furthermore a software HMM based pattern recognition system has been chosen to be the reference system for the functionality of the implemented subsystems. A set of tests have been developed with the aim to test the correct functionality of the hardware. The implemented system was compared to the reference system on the basis of the tests’ results, and it was found that the behavior was the one expected and the required functionality was correctly achieved. Finally the implementation of the parallel HMM array was tested through its application to two real–world applications: a speech recognition task and a brain–computer interface task. In both cases the architecture showed to be functionally suitable and powerful enough to handle the task without problems. The application of the hardware processing to speech recognition opens new perspectives in the design of this kind of systems because of the dramatic increment in performance. The application to brain–computer interface is really interesting because of a new approach in the classification of EEG that shows how could be possible a future development of interfaces based on the classification of spontaneous thought. The possible evolution directions of the work started with this thesis are many. Effort could be spent of the implementation of the developed architecture as a stand–alone reconfigurable system suitable for any kind of HMM–based pattern recognition task. The potential performance of such a system could open the way to extremely complex real–time pattern recognition systems, and thus to the realization of truly multimodal interfaces, with a variety of applications, from space to aid systems for the impaired

A Distributed Ledger based infrastructure for Intelligent Transportation Systems

Intelligent Transportation Systems (ITS) are proposed as an efficient way to improve performances in transportation systems applying information, communication, and sensor technologies to vehicles and transportation infrastructures. The great amount of vehicles produced data, indeed, can potentially lead to a revolution in ITS development, making them more powerful multifunctional systems. To this purpose, the use of Vehicular Ad-hoc Networks (VANETs) can provide comfort and security to drivers through reliable communications. Meanwhile, distributed ledgers have emerged in recent years radically evolving the way that we used to consider finance, trust in communication and even renewing the concept of data sharing and allowing to establish autonomous, secured, trusted and decentralized systems. In this work an ITS infrastructure based on the combination of different emerging Distributed Ledger Technologies (DLTs) and VANETs is proposed, resulting in a transparent, self-managed and self-regulated system, that is not fully managed by a central authority. The intended design is focused on the user ability to use any type of DLT-based application and to transact using Smart Contracts, but also on the access control and verification over user’s vehicle produced data. Users "smart" transactions are achieved thanks to the Ethereum blockchain, widely used for distributed trusted computation, whilst data sharing and data access is possible thanks to the use of IOTA, a DLT fully designed to operate in the Internet of Things landscape, and IPFS, a protocol and a network that allows to work in a distributed file system. The aim of this thesis is to create a ready-to-work infrastructure based on the hypothesis that every user in the ITS must be able to participate. To evaluate the proposal, an infrastructure implementation is used in different real world use cases, common in Smart Cities and related to the ITS, and performance measurements are carried out for DLTs used