From Traditional to Modern : Domain Adaptation for Action Classification in Short Social Video Clips

Short internet video clips like vines present a significantly wild distribution compared to traditional video datasets. In this paper, we focus on the problem of unsupervised action classification in wild vines using traditional labeled datasets. To this end, we use a data augmentation based simple domain adaptation strategy. We utilise semantic word2vec space as a common subspace to embed video features from both, labeled source domain and unlablled target domain. Our method incrementally augments the labeled source with target samples and iteratively modifies the embedding function to bring the source and target distributions together. Additionally, we utilise a multi-modal representation that incorporates noisy semantic information available in form of hash-tags. We show the effectiveness of this simple adaptation technique on a test set of vines and achieve notable improvements in performance.Comment: 9 pages, GCPR, 201

Using nondeterministic learners to alert on coffee rust disease

Motivated by an agriculture case study, we discuss how to learn functions able to predict whether the value of a continuous target variable will be greater than a given threshold. In the application studied, the aim was to alert on high incidences of coffee rust, the main coffee crop disease in the world. The objective is to use chemical prevention of the disease only when necessary in order to obtain healthier quality products and reductions in costs and environmental impact. In this context, the costs of misclassifications are not symmetrical: false negative predictions may lead to the loss of coffee crops. The baseline approach for this problem is to learn a regressor from the variables that records the factors affecting the appearance and growth of the disease. However, the number of errors is too high to obtain a reliable alarm system. The approaches explored here try to learn hypotheses whose predictions are allowed to return intervals rather than single points. Thus,in addition to alarms and non-alarms, these predictors identify situations with uncertain classification, which we call warnings. We present 3 different implementations: one based on regression, and 2 more based on classifiers. These methods are compared using a framework where the costs of false negatives are higher than that of false positives, and both are higher than the cost of warning prediction

Neyman-Pearson classification algorithms and NP receiver operating characteristics

In many binary classification applications, such as disease diagnosis and spam detection, practitioners commonly face the need to limit type I error (that is, the conditional probability of misclassifying a class 0 observation as class 1) so that it remains below a desired threshold. To address this need, the Neyman-Pearson (NP) classification paradigm is a natural choice; it minimizes type II error (that is, the conditional probability of misclassifying a class 1 observation as class 0) while enforcing an upper bound, α, on the type I error. Despite its century-long history in hypothesis testing, the NP paradigm has not been well recognized and implemented in classification schemes. Common practices that directly limit the empirical type I error to no more than α do not satisfy the type I error control objective because the resulting classifiers are likely to have type I errors much larger than α, and the NP paradigm has not been properly implemented in practice. We develop the first umbrella algorithm that implements the NP paradigm for all scoring-type classification methods, such as logistic regression, support vector machines, and random forests. Powered by this algorithm, we propose a novel graphical tool for NP classification methods: NP receiver operating characteristic (NP-ROC) bands motivated by the popular ROC curves. NP-ROC bands will help choose α in a data-adaptive way and compare different NP classifiers. We demonstrate the use and properties of the NP umbrella algorithm and NP-ROC bands, available in the R package nproc, through simulation and real data studies

Anomaly detection in smart city wireless sensor networks

Aquesta tesi proposa una plataforma de detecció d’intrusions per a revelar atacs a les xarxes de sensors sense fils (WSN, per les sigles en anglès) de les ciutats intel·ligents (smart cities). La plataforma està dissenyada tenint en compte les necessitats dels administradors de la ciutat intel·ligent, els quals necessiten accés a una arquitectura centralitzada que pugui gestionar alarmes de seguretat en un sistema altament heterogeni i distribuït. En aquesta tesi s’identifiquen els diversos passos necessaris des de la recollida de dades fins a l’execució de les tècniques de detecció d’intrusions i s’avalua que el procés sigui escalable i capaç de gestionar dades típiques de ciutats intel·ligents. A més, es comparen diversos algorismes de detecció d’anomalies i s’observa que els mètodes de vectors de suport d’una mateixa classe (one-class support vector machines) resulten la tècnica multivariant més adequada per a descobrir atacs tenint en compte les necessitats d’aquest context. Finalment, es proposa un esquema per a ajudar els administradors a identificar els tipus d’atacs rebuts a partir de les alarmes disparades.Esta tesis propone una plataforma de detección de intrusiones para revelar ataques en las redes de sensores inalámbricas (WSN, por las siglas en inglés) de las ciudades inteligentes (smart cities). La plataforma está diseñada teniendo en cuenta la necesidad de los administradores de la ciudad inteligente, los cuales necesitan acceso a una arquitectura centralizada que pueda gestionar alarmas de seguridad en un sistema altamente heterogéneo y distribuido. En esta tesis se identifican los varios pasos necesarios desde la recolección de datos hasta la ejecución de las técnicas de detección de intrusiones y se evalúa que el proceso sea escalable y capaz de gestionar datos típicos de ciudades inteligentes. Además, se comparan varios algoritmos de detección de anomalías y se observa que las máquinas de vectores de soporte de una misma clase (one-class support vector machines) resultan la técnica multivariante más adecuada para descubrir ataques teniendo en cuenta las necesidades de este contexto. Finalmente, se propone un esquema para ayudar a los administradores a identificar los tipos de ataques recibidos a partir de las alarmas disparadas.This thesis proposes an intrusion detection platform which reveals attacks in smart city wireless sensor networks (WSN). The platform is designed taking into account the needs of smart city administrators, who need access to a centralized architecture that can manage security alarms in a highly heterogeneous and distributed system. In this thesis, we identify the various necessary steps from gathering WSN data to running the detection techniques and we evaluate whether the procedure is scalable and capable of handling typical smart city data. Moreover, we compare several anomaly detection algorithms and we observe that one-class support vector machines constitute the most suitable multivariate technique to reveal attacks, taking into account the requirements in this context. Finally, we propose a classification schema to assist administrators in identifying the types of attacks compromising their networks

The prediction of fatigue using speech as a biosignal

Automatic systems for estimating operator fatigue have application in safety-critical environments. We develop and evaluate a system to detect fatigue from speech recordings collected from speakers kept awake over a 60-hour period. A binary classification system (fatigued/not-fatigued) based on time spent awake showed good discrimination, with 80 % unweighted accuracy using raw features, and 90 % with speaker-normalized features. We describe the data collection, feature analysis, machine learning and cross-validation used in the study. Results are promising for real-world applications in domains such as aerospace, transportation and mining where operators are in regular verbal communication as part of their normal working activities

Aprendizaje evolutivo supervisado: Uso de histograma de gradiente y algoritmo de enjambre de partículas para detección y seguimiento de peatones en secuencia de imágenes infrarrojas

Recently, tracking and pedestrian detection from various images have become one of the major issues in the field of image processing and statistical identification.  In this regard, using evolutionary learning-based approaches to improve performance in different contexts can greatly influence the appropriate response.  There are problems with pedestrian tracking/identification, such as low accuracy for detection, high processing time, and uncertainty in response to answers.  Researchers are looking for new processing models that can accurately monitor one's position on the move.  In this study, a hybrid algorithm for the automatic detection of pedestrian position is presented.  It is worth noting that this method, contrary to the analysis of visible images, examines pedestrians' thermal and infrared components while walking and combines a neural network with maximum learning capability, wavelet kernel (Wavelet transform), and particle swarm optimization (PSO) to find parameters of learner model. Gradient histograms have a high effect on extracting features in infrared images.  As well, the neural network algorithm can achieve its goal (pedestrian detection and tracking) by maximizing learning.  The proposed method, despite the possibility of maximum learning, has a high speed in education, and results of various data sets in this field have been analyzed. The result indicates a negligible error in observing the infrared sequence of pedestrian movements, and it is suggested to use neural networks because of their precision and trying to boost the selection of their hyperparameters based on evolutionary algorithms

Predicting Ad Liking and Purchase Intent: Large-Scale Analysis of Facial Responses to Ads

Billions of online video ads are viewed every month. We present a large-scale analysis of facial responses to video content measured over the Internet and their relationship to marketing effectiveness. We collected over 12,000 facial responses from 1,223 people to 170 ads from a range of markets and product categories. The facial responses were automatically coded frame-by-frame. Collection and coding of these 3.7 million frames would not have been feasible with traditional research methods. We show that detected expressions are sparse but that aggregate responses reveal rich emotion trajectories. By modeling the relationship between the facial responses and ad effectiveness, we show that ad liking can be predicted accurately (ROC AUC = 0.85) from webcam facial responses. Furthermore, the prediction of a change in purchase intent is possible (ROC AUC = 0.78). Ad liking is shown by eliciting expressions, particularly positive expressions. Driving purchase intent is more complex than just making viewers smile: peak positive responses that are immediately preceded by a brand appearance are more likely to be effective. The results presented here demonstrate a reliable and generalizable system for predicting ad effectiveness automatically from facial responses without a need to elicit self-report responses from the viewers. In addition we can gain insight into the structure of effective ads.MIT Media Lab ConsortiumNEC CorporationMAR