Geo-Information Harvesting from Social Media Data

As unconventional sources of geo-information, massive imagery and text messages from open platforms and social media form a temporally quasi-seamless, spatially multi-perspective stream, but with unknown and diverse quality. Due to its complementarity to remote sensing data, geo-information from these sources offers promising perspectives, but harvesting is not trivial due to its data characteristics. In this article, we address key aspects in the field, including data availability, analysis-ready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data. We then showcase some exemplary geographic applications. In addition, we present the first extensive discussion of ethical considerations of social media data in the context of geo-information harvesting and geographic applications. With this effort, we wish to stimulate curiosity and lay the groundwork for researchers who intend to explore social media data for geo-applications. We encourage the community to join forces by sharing their code and data.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin

A Networked Dataflow Simulation Environment for Signal Processing and Data Mining Applications

In networked signal processing systems, dataflow graphs can be used to describe the processing on individual network nodes. However, to analyze the correctness and performance of these systems, designers must understand the interactions across these individual "node-level'' dataflow graphs --- as they communicate across the network --- in addition to the characteristics of the individual graphs. In this thesis, we present a novel simulation environment, called the NS-2 -- TDIF SIMulation environment (NT-SIM). NT-SIM provides integrated co-simulation of networked systems and combines the network analysis capabilities provided by the Network Simulator (ns) with the scheduling capabilities of a dataflow-based framework, thereby providing novel features for more comprehensive simulation of networked signal processing systems. Through a novel integration of advanced tools for network and dataflow graph simulation, our NT-SIM environment allows comprehensive simulation and analysis of networked systems. We present two case studies that concretely demonstrate the utility of NT-SIM in the contexts of a heterogeneous signal processing and data mining system design

Automatic Mobile Video Remixing and Collaborative Watching Systems

In the thesis, the implications of combining collaboration with automation for remix creation are analyzed. We first present a sensor-enhanced Automatic Video Remixing System (AVRS), which intelligently processes mobile videos in combination with mobile device sensor information. The sensor-enhanced AVRS system involves certain architectural choices, which meet the key system requirements (leverage user generated content, use sensor information, reduce end user burden), and user experience requirements. Architecture adaptations are required to improve certain key performance parameters. In addition, certain operating parameters need to be constrained, for real world deployment feasibility. Subsequently, sensor-less cloud based AVRS and low footprint sensorless AVRS approaches are presented. The three approaches exemplify the importance of operating parameter tradeoffs for system design. The approaches cover a wide spectrum, ranging from a multimodal multi-user client-server system (sensor-enhanced AVRS) to a mobile application which can automatically generate a multi-camera remix experience from a single video. Next, we present the findings from the four user studies involving 77 users related to automatic mobile video remixing. The goal was to validate selected system design goals, provide insights for additional features and identify the challenges and bottlenecks. Topics studied include the role of automation, the value of a video remix as an event memorabilia, the requirements for different types of events and the perceived user value from creating multi-camera remix from a single video. System design implications derived from the user studies are presented. Subsequently, sport summarization, which is a specific form of remix creation is analyzed. In particular, the role of content capture method is analyzed with two complementary approaches. The first approach performs saliency detection in casually captured mobile videos; in contrast, the second one creates multi-camera summaries from role based captured content. Furthermore, a method for interactive customization of summary is presented. Next, the discussion is extended to include the role of users’ situational context and the consumed content in facilitating collaborative watching experience. Mobile based collaborative watching architectures are described, which facilitate a common shared context between the participants. The concept of movable multimedia is introduced to highlight the multidevice environment of current day users. The thesis presents results which have been derived from end-to-end system prototypes tested in real world conditions and corroborated with extensive user impact evaluation

An integrated task manager for virtual command and control

The Task Manager is a desktop/tablet PC interface to the Battlespace research project that provides interactions and displays for supervisory control of unmanned aerial vehicles. Utilizing a north-up map display, the Task Manager provides a direct-manipulation interface to the units involved in an engagement. Used in two primary modes, the Task Manager can be used either in a planning/review mode that can be used to generate mission scenarios or a live-streaming mode that connects to a live Battlespace simulation via a network connection to edit and update path information on the fly. The goal of this research is to combine the precision of 2D mouse and pen-based interaction with the increased situational awareness provided by 3D battlefield visualizations like the Battlespace application. Combined use of these interfaces, either by a single operator or a small team of operators with task-specific roles, is proposed to produce a more favorable ratio of operators to units in field operations with superior decision-making capabilities due to the specific nature of the interfaces

Multimodal Video Analysis and Modeling

From recalling long forgotten experiences based on a familiar scent or on a piece of music, to lip reading aided conversation in noisy environments or travel sickness caused by mismatch of the signals from vision and the vestibular system, the human perception manifests countless examples of subtle and effortless joint adoption of the multiple senses provided to us by evolution. Emulating such multisensory (or multimodal, i.e., comprising multiple types of input modes or modalities) processing computationally offers tools for more effective, efficient, or robust accomplishment of many multimedia tasks using evidence from the multiple input modalities. Information from the modalities can also be analyzed for patterns and connections across them, opening up interesting applications not feasible with a single modality, such as prediction of some aspects of one modality based on another. In this dissertation, multimodal analysis techniques are applied to selected video tasks with accompanying modalities. More specifically, all the tasks involve some type of analysis of videos recorded by non-professional videographers using mobile devices.Fusion of information from multiple modalities is applied to recording environment classification from video and audio as well as to sport type classification from a set of multi-device videos, corresponding audio, and recording device motion sensor data. The environment classification combines support vector machine (SVM) classifiers trained on various global visual low-level features with audio event histogram based environment classification using k nearest neighbors (k-NN). Rule-based fusion schemes with genetic algorithm (GA)-optimized modality weights are compared to training a SVM classifier to perform the multimodal fusion. A comprehensive selection of fusion strategies is compared for the task of classifying the sport type of a set of recordings from a common event. These include fusion prior to, simultaneously with, and after classification; various approaches for using modality quality estimates; and fusing soft confidence scores as well as crisp single-class predictions. Additionally, different strategies are examined for aggregating the decisions of single videos to a collective prediction from the set of videos recorded concurrently with multiple devices. In both tasks multimodal analysis shows clear advantage over separate classification of the modalities.Another part of the work investigates cross-modal pattern analysis and audio-based video editing. This study examines the feasibility of automatically timing shot cuts of multi-camera concert recordings according to music-related cutting patterns learnt from professional concert videos. Cut timing is a crucial part of automated creation of multicamera mashups, where shots from multiple recording devices from a common event are alternated with the aim at mimicing a professionally produced video. In the framework, separate statistical models are formed for typical patterns of beat-quantized cuts in short segments, differences in beats between consecutive cuts, and relative deviation of cuts from exact beat times. Based on music meter and audio change point analysis of a new recording, the models can be used for synthesizing cut times. In a user study the proposed framework clearly outperforms a baseline automatic method with comparably advanced audio analysis and wins 48.2 % of comparisons against hand-edited videos

A Validation Assessment of THUNDER 6.5\u27s Intelligence, Surveillance, and Reconnaissance Module

A validation assessment of THUNDER 6.5\u27s Intelligence, Surveillance, and Reconnaissance (ISR) module is accomplished using formulational and experimental validation techniques. A comparison of ISR purposes and processes according to military doctrine is made with the purposes and processes of ISR implemented within THUNDER 6.5. This comparison provides an overview of the process, an understanding of the level of aggregation within THUNDER, insight into possible problem areas in THUNDER, and a basis for improving THUNDER ISR processes. Sensitivity analysis of the ISR parameters as they relate to the Quality, Quantity, and Timeliness of ISR is also presented to provide insight into the responsiveness of THUNDER to changes in ISR capability for selected battle outcomes. Linear Regression and a Face-Centered Central Composite Design were used to generate a response surface. Ninety-percent confidence intervals were used to determine differences in mean response among the full factorial design points

Multi-modal video analysis for early fire detection

In dit proefschrift worden verschillende aspecten van een intelligent videogebaseerd branddetectiesysteem onderzocht. In een eerste luik ligt de nadruk op de multimodale verwerking van visuele, infrarood en time-of-flight videobeelden, die de louter visuele detectie verbetert. Om de verwerkingskost zo minimaal mogelijk te houden, met het oog op real-time detectie, is er voor elk van het type sensoren een set ’low-cost’ brandkarakteristieken geselecteerd die vuur en vlammen uniek beschrijven. Door het samenvoegen van de verschillende typen informatie kunnen het aantal gemiste detecties en valse alarmen worden gereduceerd, wat resulteert in een significante verbetering van videogebaseerde branddetectie. Om de multimodale detectieresultaten te kunnen combineren, dienen de multimodale beelden wel geregistreerd (~gealigneerd) te zijn. Het tweede luik van dit proefschrift focust zich hoofdzakelijk op dit samenvoegen van multimodale data en behandelt een nieuwe silhouet gebaseerde registratiemethode. In het derde en tevens laatste luik van dit proefschrift worden methodes voorgesteld om videogebaseerde brandanalyse, en in een latere fase ook brandmodellering, uit te voeren. Elk van de voorgestelde technieken voor multimodale detectie en multi-view lokalisatie zijn uitvoerig getest in de praktijk. Zo werden onder andere succesvolle testen uitgevoerd voor de vroegtijdige detectie van wagenbranden in ondergrondse parkeergarages

Holistic Network Defense: Fusing Host and Network Features for Attack Classification

This work presents a hybrid network-host monitoring strategy, which fuses data from both the network and the host to recognize malware infections. This work focuses on three categories: Normal, Scanning, and Infected. The network-host sensor fusion is accomplished by extracting 248 features from network traffic using the Fullstats Network Feature generator and from the host using text mining, looking at the frequency of the 500 most common strings and analyzing them as word vectors. Improvements to detection performance are made by synergistically fusing network features obtained from IP packet flows and host features, obtained from text mining port, processor, logon information among others. In addition, the work compares three different machine learning algorithms and updates the script required to obtain network features. Hybrid method results outperformed host only classification by 31.7% and network only classification by 25%. The new approach also reduces the number of alerts while remaining accurate compared with the commercial IDS SNORT. These results make it such that even the most typical users could understand alert classification messages