Generating Linked-Data based Domain-Specific Sentiment Lexicons from Legacy Language and Semantic Resources

We present a methodology for legacy language resource adaptation that generates domain-specific sentiment lexicons organized around domain entities described with lexical information and sentiment words described in the context of these entities. We explain the steps of the methodology and we give a working example of our initial results. The resulting lexicons are modelled as Linked Data resources by use of established formats for Linguistic Linked Data (lemon, NIF) and for linked sentiment expressions (Marl), thereby contributing and linking to existing Language Resources in the Linguistic Linked Open Data cloud.

Event-based time series data preprocessing: application to traffic flow time series

Traffic flow time series data are usually high dimensional and very complex. Also they are sometimes imprecise and distorted due to data collection sensor malfunction. Additionally, events like congestion caused by traffic accidents add more uncertainty to real-time traffic conditions, making traffic flow forecasting a complicated task. This article presents a new data preprocessing method targeting multidimensional time series with a very high number of dimensions and shows its application to real traffic flow time series from the California Department of Transportation (PEMS web site). The proposed method consists of three main steps. First, based on a language for defining events in multidimensional time series, mTESL, we identify a number of types of events in time series that corresponding to either incorrect data or data with interference. Second, each event type is restored utilizing an original method that combines real observations, local forecasted values and historical data. Third, an exponential smoothing procedure is applied globally to eliminate noise interference and other random errors so as to provide good quality source data for future work.

Linked-data based domain-specific sentiment lexicons

In this paper we present a dataset componsed of domain-specific sentiment lexicons in six languages for two domains. We used existing collections of reviews from Trip Advisor, Amazon, the Stanford Network Analysis Project and the OpinRank Review Dataset. We use an RDF model based on the lemon and Marl formats to represent the lexicons. We describe the methodology that we applied to generate the domain-specific lexicons and we provide access information to our datasets.

Aeroelastic effects in a traffic sign panel induced by a passing vehicle

Here, a simple theoretical model of the vehicle induced flow and its effects on traffic sign panels is presented. The model is a continuation of a previous one by Sanz-Andrés and coworkers, now including the flexibility of the panel (and, therefore, the flow effects associated to the motion of the panel). Through the paper an aeroelastic one-degree-of-freedom model is developed and the flow effects are computed from unsteady potential theory. The influence of panel's mechanical properties (mass, damping ratio, and stiffness) in the motion induced forces are numerically analyzed.

Vehicle-induced loads on traffic sign panels

The determination of the loads on traffic sign panels in the current standards does not, in general, take into account the vehicle-induced loads, as explained by Quinn, Baker and Wright (QBW in what follows) (J. Wind Eng. Ind. Aerodyn. 89 (2001) 831). On the other hand, a report from Cali and Covert (CC) (J. Wind Eng. Ind. Aerodyn. 84 (2000) 87) indicates that in highway sign support structures, vehicle-induced loads have led to premature failures in some cases. The aim of this paper is to present a mathematical model for the vehicle-induced load on a flat sign panel, simple enough to give analytical results, but able to explain the main characteristics of the phenomenon. The results of the theoretical model help to explain the behaviour observed in the experiments performed in previous studies.

‘Data Augmentation’ per reti neurali profonde per la classificazione del segnale EEG

I recenti sviluppi nel campo dell’intelligenza artificiale hanno permesso una più adeguata classificazione del segnale EEG. Negli ultimi anni è stato dimostrato come sia possibile ottenere ottime performance di classificazione impiegando tecniche di Machine Learning (ML) e di Deep Learning (DL), facendo uso, per quest’ultime, di reti neurali convoluzionali (Convolutional Neural Networks, CNN). In particolare, il Deep Learning richiede molti dati di training mentre spesso i dataset per EEG sono limitati ed è difficile quindi raggiungere prestazioni elevate. I metodi di Data Augmentation possono alleviare questo problema. Partendo da dati reali, questa tecnica permette, la creazione di dati artificiali fondamentali per aumentare le dimensioni del dataset di partenza. L’applicazione più comune è quella di utilizzare i Data Augmentation per aumentare le dimensioni del training set, in modo da addestrare il modello/rete neurale su un numero di campioni più esteso, riducendo gli errori di classificazione. Partendo da questa idea, i Data Augmentation sono stati applicati in molteplici campi e in particolare per la classificazione del segnale EEG. In questo elaborato di tesi, inizialmente, vengono descritti metodi di Data Augmentation implementati nel corso degli anni, utilizzabili anche nell’ambito di applicazioni EEG. Successivamente, si presentano alcuni studi specifici che applicano metodi di Data Augmentation per migliorare le presentazioni di classificatori basati su EEG per l’identificazione dello stato sonno/veglia, per il riconoscimento delle emozioni, e per la classificazione di immaginazione motoria.

Real-Time Recognition System for Traffic Signs

The aim of this thesis project is to develop the Traffic Sign Recognition algorithm for real time. Inreal time environment, vehicles move at high speed on roads. For the vehicle intelligent system itbecomes essential to detect, process and recognize the traffic sign which is coming in front ofvehicle with high relative velocity, at the right time, so that the driver would be able to pro-actsimultaneously on instructions given in the Traffic Sign. The system assists drivers about trafficsigns they did not recognize before passing them. With the Traffic Sign Recognition system, thevehicle becomes aware of the traffic environment and reacts according to the situation.The objective of the project is to develop a system which can recognize the traffic signs in real time.The three target parameters are the system’s response time in real-time video streaming, the trafficsign recognition speed in still images and the recognition accuracy. The system consists of threeprocesses; the traffic sign detection, the traffic sign recognition and the traffic sign tracking. Thedetection process uses physical properties of traffic signs based on a priori knowledge to detect roadsigns. It generates the road sign image as the input to the recognition process. The recognitionprocess is implemented using the Pattern Matching algorithm. The system was first tested onstationary images where it showed on average 97% accuracy with the average processing time of0.15 seconds for traffic sign recognition. This procedure was then applied to the real time videostreaming. Finally the tracking of traffic signs was developed using Blob tracking which showed theaverage recognition accuracy to 95% in real time and improved the system’s average response timeto 0.04 seconds. This project has been implemented in C-language using the Open Computer VisionLibrary.

Evaluation of Red Colour Segmentation Algorithms in Traffic Signs Detection

Colour segmentation is the most commonly used method in road signs detection. Road sign contains several basic colours such as red, yellow, blue and white which depends on countries.The objective of this thesis is to do an evaluation of the four colour segmentation algorithms. Dynamic Threshold Algorithm, A Modification of de la Escalera’s Algorithm, the Fuzzy Colour Segmentation Algorithm and Shadow and Highlight Invariant Algorithm. The processing time and segmentation success rate as criteria are used to compare the performance of the four algorithms. And red colour is selected as the target colour to complete the comparison. All the testing images are selected from the Traffic Signs Database of Dalarna University [1] randomly according to the category. These road sign images are taken from a digital camera mounted in a moving car in Sweden.Experiments show that the Fuzzy Colour Segmentation Algorithm and Shadow and Highlight Invariant Algorithm are more accurate and stable to detect red colour of road signs. And the method could also be used in other colours analysis research. The yellow colour which is chosen to evaluate the performance of the four algorithms can reference Master Thesis of Yumei Liu.

Evaluation of a method for fusing LIDAR data and multispectral images for building detection

In this paper, we describe the evaluation of a method for building detection by the Dempster-Shafer fusion of LIDAR data and multispectral images. For that purpose, ground truth was digitised for two test sites with quite different characteristics. Using these data sets, the heuristic model for the probability mass assignments of the method is validated, and rules for the tuning of the parameters of this model are discussed. Further we evaluate the contributions of the individual cues used in the classification process to the quality of the classification results. Our results show the degree to which the overall correctness of the results can be improved by fusing LIDAR data with multispectral images.

Data mining and visualization for anomaly detection and diagnosis in civil structures

Peer reviewed

Data-driven Signal Processing per Jamming Detection in Sistemi Satellitari

In questo elaborato vengono analizzate differenti tecniche per la detection di jammer attivi e costanti in una comunicazione satellitare in uplink. Osservando un numero limitato di campioni ricevuti si vuole identificare la presenza di un jammer. A tal fine sono stati implementati i seguenti classificatori binari: support vector machine (SVM), multilayer perceptron (MLP), spectrum guarding e autoencoder. Questi algoritmi di apprendimento automatico dipendono dalle features che ricevono in ingresso, per questo motivo è stata posta particolare attenzione alla loro scelta. A tal fine, sono state confrontate le accuratezze ottenute dai detector addestrati utilizzando differenti tipologie di informazione come: i segnali grezzi nel tempo, le statistical features, le trasformate wavelet e lo spettro ciclico. I pattern prodotti dall’estrazione di queste features dai segnali satellitari possono avere dimensioni elevate, quindi, prima della detection, vengono utilizzati i seguenti algoritmi per la riduzione della dimensionalità: principal component analysis (PCA) e linear discriminant analysis (LDA). Lo scopo di tale processo non è quello di eliminare le features meno rilevanti, ma combinarle in modo da preservare al massimo l’informazione, evitando problemi di overfitting e underfitting. Le simulazioni numeriche effettuate hanno evidenziato come lo spettro ciclico sia in grado di fornire le features migliori per la detection producendo però pattern di dimensioni elevate, per questo motivo è stato necessario l’utilizzo di algoritmi di riduzione della dimensionalità. In particolare, l'algoritmo PCA è stato in grado di estrarre delle informazioni migliori rispetto a LDA, le cui accuratezze risentivano troppo del tipo di jammer utilizzato nella fase di addestramento. Infine, l’algoritmo che ha fornito le prestazioni migliori è stato il Multilayer Perceptron che ha richiesto tempi di addestramento contenuti e dei valori di accuratezza elevati.

National Traffic Data Acquisition Conference (NATDAC '94). Proceedings, volume I. Final report.

Federal Highway Administration, Washington, D.C.

Implementing Green IT approach for transferring Big Data over Parallel Data Link

The whole research of the current Master Thesis project is related to Big Data transfer over Parallel Data Link and my main objective is to assist the Saint-Petersburg National Research University ITMO research team to accomplish this project and apply Green IT methods for the data transfer system. The goal of the team is to transfer Big Data by using parallel data links with SDN Openflow approach. My task as a team member was to compare existing data transfer applications in case to verify which results the highest data transfer speed in which occasions and explain the reasons. In the context of this thesis work a comparison between 5 different utilities was done, which including Fast Data Transfer (FDT), BBCP, BBFTP, GridFTP, and FTS3. A number of scripts where developed which consist of creating random binary data to be incompressible to have fair comparison between utilities, execute the Utilities with specified parameters, create log files, results, system parameters, and plot graphs to compare the results. Transferring such an enormous variety of data can take a long time, and hence, the necessity appears to reduce the energy consumption to make them greener. In the context of Green IT approach, our team used Cloud Computing infrastructure called OpenStack. It’s more efficient to allocated specific amount of hardware resources to test different scenarios rather than using the whole resources from our testbed. Testing our implementation with OpenStack infrastructure results that the virtual channel does not consist of any traffic and we can achieve the highest possible throughput. After receiving the final results we are in place to identify which utilities produce faster data transfer in different scenarios with specific TCP parameters and we can use them in real network data links.