Recent work has shown that flow estimation from a pair of images can be formulated as a supervised learning task to be resolved with convolutional neural networks (CNN). However, the basic straightforward CNN methods estimate optical flow with motion and occlusion boundary blur. To tackle this problem, we propose a tiny diagnostic dataset called FlowClevr to quickly evaluate various modules that can use to enhance standard CNN architectures. Based on the experiments of the FlowClevr dataset, we find that a deformable module can improve model prediction accuracy by around 30% to 100% in most tasks and more significantly reduce boundary blur. Based on these results, we are able to design modifications to various existing network architectures improving their performance. Compared with the original model, the model with the deformable module clearly reduces boundary blur and achieves a large improvement on the MPI sintel dataset, an omni-directional stereo (ODS) and a novel omni-directional optical flow dataset.

The Daily Show är ett halvtimmeslångt amerikanskt humorprogram som sänds på kabelkanalen Comedy Central. Programmet tar upp nyheter, främst om politik, på ett humoristiskt sätt med hjälp av klipp från etablerade nyhetskanaler. Vår undersökning syftar till att ta reda på hur The Daily Show förändrats efter valet 2008 då Barack Obama valdes till president. Vi har jämfört inslagen i 32 The Daily Show-avsnitt från hösten 2005 med lika många från samma period 2009. Extra fokus har lagts på hur programmet behandlar nyhetsmedier. Dessutom har vi undersökt programmets gästlistor från hela dessa år, samt tagit hjälp av en tidigare studie från 2007 för att se vilken sorts gäster som får framträda och om det skett någon förändring. Vår studie visar att The Daily Show gått mot att kritisera och rikta satir mot nyhetsmedier som CNN och Fox News i större utsträckning än tidigare. Särskilt Fox News har fått en mycket större andel av programmets uppmärksamhet. Färre underhållare finns med som gäster, till förmån för fler politiska kommentatorer, journalister och författare. Programmet fokuserar till största del på sakfrågor och inte enskilda personer.

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Magistro darbo objektas – „islamo grėsmės“ įvaizdis televizijoje. Darbo tikslas – išanalizuoti islamo grėsmės įvaizdžio formavimą televizijoje. Pagrindiniai darbo uždaviniai: Apibrėžti islamo sampratą ir jo „funkcijas“ islamiškose kultūrose, bei apžvelgti šiuolaikinį islamą; įvertinti, kaip yra kuriami įvaizdžiai televizijoje; išsiaiškinti ar yra ryšys tarp islamo ir terorizmo; bei išanalizuoti islamo, kaip grėsmės, įvaizdžio formavimą televizijoje. Išsamiau susipažinus su islamu ir musulmonų požiūriu į pasaulį bei trumpai apžvelgus terorizmo istoriją, Korano ir terorizmo sąryšį bei naujai atsiradusį terminą „islamo fobija“, o galiausiai atlikus detalią Amerikos kabelinio naujienų kanalo CNN reportažų analizę, prieita prie išvados, kad televizija ne tik kuria įvaizdžius, bet daro didelę įtaką žmonių supratimui apie vieną ar kitą objektą, šiuo atveju islamo, kaip grėsmės, įvaizdžio sukūrimą vakariečių ne musulmonų akyse. Po rugsėjo 11- osios musulmonai yra priversti aiškinti požiūrį į šią tragediją ir ar islamas yra šios tragedijos priežastis, islamo ir dvasinio džihado santykį. Daugelis islamo vertinimų, reakcijų į musulmonų doktriną ir musulmonų – krikščionių susidūrimų dažnai vis dar apibūdinami tos dienos įvykių šviesoje. Šiuo metu, praėjus šešiems metams po įvykusių teroristinių išpuolių Jungtinėse Amerikos Valstijose, už kurią atsakomybę prisiėmė musulmoniškoji teroristinė organizacija Al-Qaeda, musulmonams skiriamas išskirtinis dėmesys jų didžiajai religijai islamui... [toliau žr. visą tekstą] / Today exclusive attention focuses on Muslims, their religion Islam, exclusive and distinctive lifestyle and the ever growing threat – terrorism. Now is obviously, a time in which Islam to Muslims is not only religion, it is law and morality, culture and mind rule of life. this is Muslims political ideology. The power of religion has always been strong in the Muslim world, but in these last decades, Islam as political ideology has become very strong and in some places has become radical. Strong processes of globalization, Israel’s rise and western culture expansion within the Muslim world, has been resisted by its people, this resistance is still growing and growing. In this place Islam is already not only the religion, but political ideology, which exists to represent and to protect Muslims, as strong players in world religions and region maps. We can’t forget that every fifth person in the world is Muslim: in fifty seven countries they are the majority. The main purpose of the paper was to detect „islam threat“ in television (CNN television), so the main attention is to detect how ”Islam threat” images are made in television. To detect why Islam is represented as a threat, when Muslims say, that Islam is a religion of peace. To detect these dimensions (aspects) the main object of the paper is to measure “Islam threat” in television. Paper tasks are: to characterize Islam and its functions in Islamic cultures and to present the modern Islam; To rate and to understand how... [to full text]

This study explores the Australian and New Zealand media-government relationship during foreign instability and regional military intervention. It offers a critique of print media coverage and political communication during the 2002-2003 Solomon Islands crisis and the subsequent Regional Assistance Mission to the Solomon Islands. By reviewing the Indexing Hypothesis and CNN Effect, this thesis considers media and government data from the year preceding the intervention. By investigating the media-government relationship in the Pacific region, this study builds on the literature that has so far primarily focused on American and European led interventions. Previous research has illustrated the advantages and limitations to specific methodological practises. This study has drawn from the current literature to form a unique methodical approach. The methods to test the Australian and New Zealand media-government relationship include content analysis, and qualitative techniques for use in four complementary tests. Findings from this study indicate that while there is some degree of the media using the political elite as a cue for newsworthy issues, the media appear to often report independently from the political elite perspectives. The political elite set the range of debate, and while the media stay within this range, they appear to sensationalise certain aspects of the debate. Government also appear to benefit from this media behaviour as it uses the media to gauge responses during the policy formation process.

This thesis presents detailed observation of different Convolutional Neural Network (CNN) architecture which had assisted Computer Vision researchers to achieve state-of-the-art performance on classification, detection, segmentation and much more to name image analysis challenges. Due to the advent of deep learning, CNN had been used in almost all the computer vision applications and that is why there is utter need to understand the miniature details of these feature extractors and find out their pros and cons of each feature extractor meticulously. In order to perform our experimentation, we decided to explore an object detection task using a particular model architecture which maintains a sweet spot between computational cost and accuracy. The model architecture which we had used is LSTM-Decoder. The model had been experimented with different CNN feature extractor and found their pros and cons in variant scenarios. The results which we had obtained on different datasets elucidates that CNN plays a major role in obtaining higher accuracy and we had also achieved a comparable state-of-the-art accuracy on Pedestrian Detection Dataset. In extension to object detection, we also implemented two different model architectures which find shoulder keypoints. So, One of our idea can be explicated as follows: using the detected annotation from object detection, a small cropped image is generated which would be feed into a small cascade network which was trained for detection of shoulder keypoints. The second strategy is to use the same object detection model and fine tune their weights to predict shoulder keypoints. Currently, we had generated our results for shoulder keypoint detection. However, this idea could be extended to full-body pose Estimation by modifying the cascaded network for pose estimation purpose and this had become an important topic of discussion for the future work of this thesis.

Ingeniera Civil Eléctrica / El reconocimiento (o detección) de objetos es un área activa y en continua mejora de la visión computacional. Recientemente se han introducido distintas estrategias para mejorar el desempeño y disminuir los costos y el tiempo de detección. Entre estas, se encuentran la generación de Object Proposals (regiones en la imágen donde hay alta probabilidad de encontrar un objeto) para acelerar la etapa de localización, como respuesta al paradigma de ventana deslizante; el cada vez más popular uso de redes Deep Learning y, en particular, para la clasi cación y detección de imágenes, las redes convolucionales (CNN). Si bien existen diversos trabajos que utilizan ambas técnicas, todos ellos se centran en tener una buena performance en conocidas bases de datos y competencias en lugar de estudiar su comportamiento en problemas reales y el efecto que tiene la modi cación de arquitecturas de redes convencionales y la elección adecuada de un sistema de generación de proposals. En este trabajo de título, entonces, se tiene como objetivo principal el caracterizar métodos de generación de proposals para su uso en el reconocimiento de objetos con redes CNN, comparando el desempeño tanto de los proposals generados como del sistema completo en bases de datos fabricadas manualmente. Para estudiar el sistema completo, se comparan dos estructuras conocidas, llamadas R-CNN y Fast R-CNN, que utilizan de distintas formas ambas técnicas (generación de proposals y detección) y donde se considera en el estado del arte mejor Fast R-CNN. Se propone en este trabajo que esta hipótesis no es del todo cierta en el caso de que se trabaje con un número su cientemente bajo de proposals (donde las bases de datos acá construidas se enfocan en precisamente asegurar una cantidad baja de objetos de tamaños similares presentes en cada una: objetos sobre super cies y objetos de una sala de estar) y se acelere el proceso de clasi cación alterando el tamaño de entrada de la red convolucional utilizada. Se eligieron tres métodos de generación de Proposals de la literatura a partir de su desempe ño reportado, y fueron comparados en distintos escenarios sus tiempos de procesamiento, calidad de proposals generados (mediante análisis visual y numérico) en función del número generados de estos. El método llamado BING presenta una ventaja sustancial en términos del tiempo de procesamiento y tiene un desempeño competitivo medido con el recall (fracción de los objetos del ground truth correctamente detectados) para las aplicaciones escogidas. Para implementar R-CNN se entrenan dos redes del tipo SqueezeNet pero con entradas reducidas y seleccionando los 50 mejores proposals generados por BING se encuentra que para una red de entrada 64x64 se alcanza casi el mismo recall (~ 40%) que se obtiene con el Fast R-CNN original y con una mejor precisión, aunque es 5 veces más lento (0.75s versus 0.14s). El sistema R-CNN implementado en este trabajo, entonces, no sólo acelera entre 10 y 20 veces la etapa de generación de proposals en comparación a su implementación original, si no que el efecto de reducir la entrada de la red utilizada logra disminuir el tiempo de detección a uno que es sólo 5 veces más lento que Fast R-CNN cuando antes era hasta 100 veces más lento y con un desempeño equivalente.

Táto práca skúma vplyv globálnych masmédií na tvorbu medzinárodnej politiky počas vybraných konfliktov na Blízkom východe. Zameriava sa konkrétne na vojnu v Perzskom zálive a vojnu v Iraku. Jej hlavným cieľom je analýza, do akej miery môžu masmédiá ovplyvňovať proces tvorby medzinárodnej politiky resp. byť v nej využívané ako nástroj jej hlavných aktérov. Zhodnocuje, že masmédiá hrajú v medzinárodnej politike významnú úlohu ako nástroj propagandy. V budúcnosti sa stanú masmédiá kľúčovým faktorom pri zaisťovaní informačnej dominancie v nových konfliktoch. Masmédiá na Blízkom východe majú, ale zároveň potenciál stať sa nástrojom budovania demokracie.

Táto práca skúma vplyv globálnych masmédií na tvorbu medzinárodnej politiky počas vybraných konfliktov na Blízkom východe. Zameriava sa konkrétne na vojnu v Perzskom zálive a vojnu v Iraku. Jej hlavným cieľom je analýza, do akej miery môžu masmédiá ovplyvňovať proces tvorby medzinárodnej politiky resp. byť v nej využívané ako nástroj jej hlavných aktérov. Zhodnocuje, že masmédiá hrajú v medzinárodnej politike významnú úlohu ako nástroj propagandy. V budúcnosti sa stanú masmédiá kľúčovým faktorom pri zaisťovaní informačnej dominancie v nových konfliktoch. Masmédiá na Blízkom východe majú, ale zároveň potenciál stať sa nástrojom budovania demokracie.

Biometrics is the study of physical or behavioral traits that establishes the identity of a person. Forensics, physical security and cyber security are some of the main fields that use biometrics. Unlike traditional authentication systems—such as password based—biometrics cannot be lost, forgotten or shared. This is possible because biometrics establishes the identity of a person based on a physiological/behavioural characteristic rather than what the person possess or remembers. Biometrics has two modes of operation: identification and authentication. Identification finds the identity of a person among a group of persons. Authentication determines if the claimed identity of a person is truthful. Biometric person authentication is an alternative to passwords or graphical patterns. It prevents shoulder surfing attacks, i.e., people watching from a short distance. Nevertheless, biometric traits of conventional authentication techniques like fingerprints, face—and to some extend iris—are easy to capture and duplicate. This denotes a security risk for modern and future applications such as digital twins, where an attacker can copy and duplicate a biometric trait in order to spoof a biometric system. Researchers have proposed ECG as biometric authentication to solve this problem. ECG authentication conceals the biometric traits and reduces the risk of an attack by duplication of the biometric trait. However, current ECG authentication solutions require 10 or more seconds of an ECG signal in order to have accurate results. The accuracy is directly proportional to the ECG signal time-length for authentication. This is inconvenient to implement ECG authentication in an end-user product because a user cannot wait 10 or more seconds to gain access in a secure manner to their device. This thesis addresses the problem of spoofing by proposing an accurate and secure ECG biometric authentication system with relatively short ECG signal length for authentication. The system consists of an ECG acquisition from lead I (two electrodes), signal processing approaches for filtration and R-peak detection, a feature extractor and an authentication process. To evaluate this system, we developed a method to calculate the Equal Error Rate—EER—with non-normal distributed data. In the authentication process, we propose an approach based on Support Vector Machine—SVM—and achieve 4.5% EER with 4 seconds of ECG signal length for authentication. This approach opens the door for a deeper understanding of the signal and hence we enhanced it by applying a hybrid approach of Convolutional Neural Networks—CNN—combined with SVM. The purpose of this hybrid approach is to improve accuracy by automatically detect and extract features with Deep Learning—in this case CNN—and then take the output into a one-class SVM classifier—Authentication; which proved to outperform accuracy for one-class ECG classification. This hybrid approach reduces the EER to 2.84% with 4 seconds of ECG signal length for authentication. Furthermore, we investigated the combination of two different biometrics techniques and we improved the accuracy to 0.46% EER, while maintaining a short ECG signal length for authentication of 4 seconds. We fuse Fingerprint with ECG at the decision level. Decision level fusion requires information that is available from any biometric technique. Fusion at different levels—such as feature level fusion—requires information about features that are incompatible or hidden. Fingerprint minutiae are composed of information that differs from ECG peaks and valleys. Therefore fusion at the feature level is not possible unless the fusion algorithm provides a compatible conversion scheme. Proprietary biometric hardware does not provide information about the features or the algorithms; therefore, features are hidden and not accessible for feature level fusion; however, the result is always available for a decision level fusion.