Spam campaign detection, analysis, and formalization

Les courriels Spams (courriels indésirables ou pourriels) imposent des coûts annuels extrêmement lourds en termes de temps, d’espace de stockage et d’argent aux utilisateurs privés et aux entreprises. Afin de lutter efficacement contre le problème des spams, il ne suffit pas d’arrêter les messages de spam qui sont livrés à la boîte de réception de l’utilisateur. Il est obligatoire, soit d’essayer de trouver et de persécuter les spammeurs qui, généralement, se cachent derrière des réseaux complexes de dispositifs infectés, ou d’analyser le comportement des spammeurs afin de trouver des stratégies de défense appropriées. Cependant, une telle tâche est difficile en raison des techniques de camouflage, ce qui nécessite une analyse manuelle des spams corrélés pour trouver les spammeurs. Pour faciliter une telle analyse, qui doit être effectuée sur de grandes quantités des courriels non classés, nous proposons une méthodologie de regroupement catégorique, nommé CCTree, permettant de diviser un grand volume de spams en des campagnes, et ce, en se basant sur leur similarité structurale. Nous montrons l’efficacité et l’efficience de notre algorithme de clustering proposé par plusieurs expériences. Ensuite, une approche d’auto-apprentissage est proposée pour étiqueter les campagnes de spam en se basant sur le but des spammeur, par exemple, phishing. Les campagnes de spam marquées sont utilisées afin de former un classificateur, qui peut être appliqué dans la classification des nouveaux courriels de spam. En outre, les campagnes marquées, avec un ensemble de quatre autres critères de classement, sont ordonnées selon les priorités des enquêteurs. Finalement, une structure basée sur le semiring est proposée pour la représentation abstraite de CCTree. Le schéma abstrait de CCTree, nommé CCTree terme, est appliqué pour formaliser la parallélisation du CCTree. Grâce à un certain nombre d’analyses mathématiques et de résultats expérimentaux, nous montrons l’efficience et l’efficacité du cadre proposé.

Profile detection through source code static analysis

The present article reflects the progress of an ongoing master’s dissertation on language engineering. The main goal of the work here described, is to infer a programmer’s profile through the analysis of his source code. After such analysis the programmer shall be placed on a scale that characterizes him on his language abilities. There are several potential applications for such profiling, namely, the evaluation of a programmer’s skills and proficiency on a given language or the continuous evaluation of a student’s progress on a programming course. Throughout the course of this project and as a proof of concept, a tool that allows the automatic profiling of a Java programmer is under development. This tool is also introduced in the paper and its preliminary outcomes are discussed.

Modelling human behaviour and event detection from videos

Automatic analysis of human behaviour in large collections of videos is gaining interest, even more so with the advent of file sharing sites such as YouTube. However, challenges still exist owing to several factors such as inter- and intra-class variations, cluttered backgrounds, occlusion, camera motion, scale, view and illumination changes. This research focuses on modelling human behaviour for action recognition in videos. The developed techniques are validated on large scale benchmark datasets and applied on real-world scenarios such as soccer videos. Three major contributions are made. The first contribution is in the area of proper choice of a feature representation for videos. This involved a study of state-of-the-art techniques for action recognition, feature extraction processing and dimensional reduction techniques so as to yield the best performance with optimal computational requirements. Secondly, temporal modelling of human behaviour is performed. This involved frequency analysis and temporal integration of local information in the video frames to yield a temporal feature vector. Current practices mostly average the frame information over an entire video and neglect the temporal order. Lastly, the proposed framework is applied and further adapted to real-world scenario such as soccer videos. A dataset consisting of video sequences depicting events of players falling is created from actual match data to this end and used to experimentally evaluate the proposed framework.

Call recognition and individual identification of fish vocalizations based on automatic speech recognition: An example with the Lusitanian toadfish

The study of acoustic communication in animals often requires not only the recognition of species specific acoustic signals but also the identification of individual subjects, all in a complex acoustic background. Moreover, when very long recordings are to be analyzed, automatic recognition and identification processes are invaluable tools to extract the relevant biological information. A pattern recognition methodology based on hidden Markov models is presented inspired by successful results obtained in the most widely known and complex acoustical communication signal: human speech. This methodology was applied here for the first time to the detection and recognition of fish acoustic signals, specifically in a stream of round-the-clock recordings of Lusitanian toadfish (Halobatrachus didactylus) in their natural estuarine habitat. The results show that this methodology is able not only to detect the mating sounds (boatwhistles) but also to identify individual male toadfish, reaching an identification rate of ca. 95%. Moreover this method also proved to be a powerful tool to assess signal durations in large data sets. However, the system failed in recognizing other sound types.

Deep learning models for the automatic analysis of volleyball plays

Collecting and analysing data is an important element in any field of human activity and research. Even in sports, collecting and analyzing statistical data is attracting a growing interest. Some exemplar use cases are: improvement of technical/tactical aspects for team coaches, definition of game strategies based on the opposite team play or evaluation of the performance of players. Other advantages are related to taking more precise and impartial judgment in referee decisions: a wrong decision can change the outcomes of important matches. Finally, it can be useful to provide better representations and graphic effects that make the game more engaging for the audience during the match. Nowadays it is possible to delegate this type of task to automatic software systems that can use cameras or even hardware sensors to collect images or data and process them. One of the most efficient methods to collect data is to process the video images of the sporting event through mixed techniques concerning machine learning applied to computer vision. As in other domains in which computer vision can be applied, the main tasks in sports are related to object detection, player tracking, and to the pose estimation of athletes. The goal of the present thesis is to apply different models of CNNs to analyze volleyball matches. Starting from video frames of a volleyball match, we reproduce a bird's eye view of the playing court where all the players are projected, reporting also for each player the type of action she/he is performing.

Data sensitivity detection in chat interactions for privacy protection

In recent years, there has been exponential growth in using virtual spaces, including dialogue systems, that handle personal information. The concept of personal privacy in the literature is discussed and controversial, whereas, in the technological field, it directly influences the degree of reliability perceived in the information system (privacy ‘as trust’). This work aims to protect the right to privacy on personal data (GDPR, 2018) and avoid the loss of sensitive content by exploring sensitive information detection (SID) task. It is grounded on the following research questions: (RQ1) What does sensitive data mean? How to define a personal sensitive information domain? (RQ2) How to create a state-of-the-art model for SID?(RQ3) How to evaluate the model? RQ1 theoretically investigates the concepts of privacy and the ontological state-of-the-art representation of personal information. The Data Privacy Vocabulary (DPV) is the taxonomic resource taken as an authoritative reference for the definition of the knowledge domain. Concerning RQ2, we investigate two approaches to classify sensitive data: the first - bottom-up - explores automatic learning methods based on transformer networks, the second - top-down - proposes logical-symbolic methods with the construction of privaframe, a knowledge graph of compositional frames representing personal data categories. Both approaches are tested. For the evaluation - RQ3 – we create SPeDaC, a sentence-level labeled resource. This can be used as a benchmark or training in the SID task, filling the gap of a shared resource in this field. If the approach based on artificial neural networks confirms the validity of the direction adopted in the most recent studies on SID, the logical-symbolic approach emerges as the preferred way for the classification of fine-grained personal data categories, thanks to the semantic-grounded tailor modeling it allows. At the same time, the results highlight the strong potential of hybrid architectures in solving automatic tasks.

The Lea Grating Test In Assessing Detection Grating Acuity In Normal Infants Less Than 4 Months Of Age.

To assess binocular detection grating acuity using the LEA GRATINGS test to establish age-related norms in healthy infants during their first 3 months of life. In this prospective, longitudinal study of healthy infants with clear red reflex at birth, responses to gratings were measured at 1, 2, and 3 months of age using LEA gratings at a distance of 28 cm. The results were recorded as detection grating acuity values, which were arranged in frequency tables and converted to a one-octave scale for statistical analysis. For the repeated measurements, analysis of variance (ANOVA) was used to compare the detection grating acuity results between ages. A total of 133 infants were included. The binocular responses to gratings showed development toward higher mean values and spatial frequencies, ranging from 0.55 ± 0.70 cycles per degree (cpd), or 1.74 ± 0.21 logMAR, in month 1 to 3.11 ± 0.54 cpd, or 0.98 ± 0.16 logMAR, in month 3. Repeated ANOVA indicated differences among grating acuity values in the three age groups. The LEA GRATINGS test allowed assessment of detection grating acuity and its development in a cohort of healthy infants during their first 3 months of life.

Determination Of Tetrakis(hydroxymethyl)phosphonium Sulfate In Commercial Formulations And Cooling Water By Capillary Electrophoresis With Contactless Conductivity Detection.

A novel capillary electrophoresis method using capacitively coupled contactless conductivity detection is proposed for the determination of the biocide tetrakis(hydroxymethyl)phosphonium sulfate. The feasibility of the electrophoretic separation of this biocide was attributed to the formation of an anionic complex between the biocide and borate ions in the background electrolyte. Evidence of this complex formation was provided by (11) B NMR spectroscopy. A linear relationship (R(2) = 0.9990) between the peak area of the complex and the biocide concentration (50-900 μmol/L) was found. The limit of detection and limit of quantification were 15.0 and 50.1 μmol/L, respectively. The proposed method was applied to the determination of tetrakis(hydroxymethyl)phosphonium sulfate in commercial formulations, and the results were in good agreement with those obtained by the standard iodometric titration method. The method was also evaluated for the analysis of tap water and cooling water samples treated with the biocide. The results of the recovery tests at three concentration levels (300, 400, and 600 μmol/L) varied from 75 to 99%, with a relative standard deviation no higher than 9%.

Human Herpesvirus Infections Of The Central Nervous System: Laboratory Diagnosis Based On Dna Detection By Nested Pcr In Plasma And Cerebrospinal Fluid Samples.

Infections of the central nervous systems (CNS) present a diagnostic problem for which an accurate laboratory diagnosis is essential. Invasive practices, such as cerebral biopsy, have been replaced by obtaining a polymerase chain reaction (PCR) diagnosis using cerebral spinal fluid (CSF) as a reference method. Tests on DNA extracted from plasma are noninvasive, thus avoiding all of the collateral effects and patient risks associated with CSF collection. This study aimed to determine whether plasma can replace CSF in nested PCR analysis for the detection of CNS human herpesvirus (HHV) diseases by analysing the proportion of patients whose CSF nested PCR results were positive for CNS HHV who also had the same organism identified by plasma nested PCR. In this study, CSF DNA was used as the gold standard, and nested PCR was performed on both types of samples. Fifty-two patients with symptoms of nervous system infection were submitted to CSF and blood collection. For the eight HHV, one positive DNA result-in plasma and/or CSF nested PCR-was considered an active HHV infection, whereas the occurrence of two or more HHVs in the same sample was considered a coinfection. HHV infections were positively detected in 27/52 (51.9%) of the CSF and in 32/52 (61.5%) of the plasma, difference not significant, thus nested PCR can be performed on plasma instead of CSF. In conclusion, this findings suggest that plasma as a useful material for the diagnosis of cases where there is any difficulty to perform a CSF puncture.

Detection Of Explosives On The Surface Of Banknotes By Raman Hyperspectral Imaging And Independent Component Analysis.

The aim of this study was to develop a methodology using Raman hyperspectral imaging and chemometric methods for identification of pre- and post-blast explosive residues on banknote surfaces. The explosives studied were of military, commercial and propellant uses. After the acquisition of the hyperspectral imaging, independent component analysis (ICA) was applied to extract the pure spectra and the distribution of the corresponding image constituents. The performance of the methodology was evaluated by the explained variance and the lack of fit of the models, by comparing the ICA recovered spectra with the reference spectra using correlation coefficients and by the presence of rotational ambiguity in the ICA solutions. The methodology was applied to forensic samples to solve an automated teller machine explosion case. Independent component analysis proved to be a suitable method of resolving curves, achieving equivalent performance with the multivariate curve resolution with alternating least squares (MCR-ALS) method. At low concentrations, MCR-ALS presents some limitations, as it did not provide the correct solution. The detection limit of the methodology presented in this study was 50μgcm(-2).

A screening method for detection of hexavalent chromium levels in soils

A rapid and low cost method to determine Cr(VI) in soils based upon alkaline metal extraction at room temperature is proposed as a semi-quantitative procedure to be performed in the field. A color comparison with standards with contents of Cr(VI) in the range of 10 to 150 mg kg-1 was used throughout. For the different types of soils studied, more than 75% of the fortified soluble Cr(VI) were recovered for all levels of spike tested for both the proposed and standard methods. Recoveries of 83 and 99% were obtained for the proposed and the standard methods, respectively, taking into account the analysis of a heavily contaminated soil sample.

Histopathological events and detection of Metarhizium anisopliae using specific primers in infected immature stages of the fruit fly Anastrepha fraterculus (Wiedemann, 1830) (Diptera: Tephritidae)

The fungus Metarhizium anisopliae is used on a large scale in Brazil as a microbial control agent against the sugar cane spittlebugs, Mahanarva posticata and M. fimbriolata (Hemiptera., Cercopidae). We applied strain E9 of M. anisopliae in a bioassay on soil, with field doses of conidia to determine if it can cause infection, disease and mortality in immature stages of Anastrepha fraterculus, the South American fruit fly. All the events were studied histologically and at the molecular level during the disease cycle, using a novel histological technique, light green staining, associated with light microscopy, and by PCR, using a specific DNA primer developed for M. anisopliae capable to identify Brazilian strains like E9. The entire infection cycle, which starts by conidial adhesion to the cuticle of the host, followed by germination with or without the formation of an appressorium, penetration through the cuticle and colonisation, with development of a dimorphic phase, hyphal bodies in the hemocoel, and death of the host, lasted 96 hours under the bioassay conditions, similar to what occurs under field conditions. During the disease cycle, the propagules of the entomopathogenic fungus were detected by identifying DNA with the specific primer ITSMet: 5' TCTGAATTTTTTATAAGTAT 3' with ITS4 (5' TCCTCCGCTTATTGATATGC 3') as a reverse primer. This simple methodology permits in situ studies of the infective process, contributing to our understanding of the host-pathogen relationship and allowing monitoring of the efficacy and survival of this entomopathogenic fungus in large-scale applications in the field. It also facilitates monitoring the environmental impact of M. anisopliae on non-target insects.

Rastreamento automatico de trajetorias de jogadores de futebol por videogrametria : validação do metodo e analise dos resultados

Universidade Estadual de Campinas . Faculdade de Educação Física

Detection of human cytomegalovirus and Epstein-Barr virus in coronary atherosclerotic tissue

Previous studies indicated that patients with atherosclerosis are predominantly infected by human cytomegalovirus (HCMV), but rarely infected by type 1 Epstein-Barr virus (EBV-1). In this study, atheromas of 30 patients who underwent aortocoronary bypass surgery with coronary endartherectomy were tested for the presence of these two viruses. HCMV occurred in 93.3% of the samples and EBV-1 was present in 50% of them. Concurrent presence of both pathogens was detected in 43.3% of the samples.