Anomaly Detection Using Predictive Convolutional Long Short-Term Memory Units

Automating the segmentation of anomalous activities within long video sequences is complicated by the ambiguity of how such events are defined. This thesis approaches the problem by learning generative models with which meaningful sequences can be identified in videos using limited supervision. We propose two types of end-to-end trainable Convolutional Long Short-Term Memory (Conv-LSTM) networks that are able to predict the subsequent video sequence from a given input. The first is an encoder decoder based model that learns spatio-temporal features from stacked non-overlapping image patches, and the second is an autoencoder based model that utilizes max-pooling layers to learn an abstraction of the entire image. The networks learn to model “normal” activities from usual events. Regularity scores are derived from the reconstruction errors of a set of predictions with abnormal video sequences yielding lower regularity scores, as they diverge further from the actual sequence with time. The models utilize a composite structure and examine the effects of “conditioning” to learn more meaningful representations. The best model is chosen based on the reconstruction and prediction accuracies. The Conv-LSTM models are evaluated both qualitatively and quantitatively, demonstrating competitive results on multiple anomaly detection datasets. Conv-LSTM units are shown to provide competitive results for modeling and predicting learned events when compared to state-to-the-art methods

Thesis Inquiry & Process: Something About Reality

This thesis documents the employment of a system of Process and Inquiry as it serves as a structural foundation for an investigation of the manner in which Reality is represented in visual portraiture. Through a vigorous exploration of the concept of Reality and its singular nature, it is hypothesized that intrinsically unique experiences could potentially be communicated through the perceptive abilities of the emotional quotient. The effects of media, timing, complexity, abstraction, and authenticity are examined for their effect on the apparent clarity of concepts transmitted in this manner. The inquiry ultimately manifests in the daily creation of self-portraiture, as well as a multimedia exhibition inspired by the theatrical arts, that speak to the communal understanding of the Human Experience

Effects of Urban Stormwater Runoff on Fathead Minnows: Mitigating Potential of Best Management Practices

Aquatic ecosystems located near urban landscapes are often contaminated by a complex mixture of contaminants of emerging concern (CECs). These landscapes are defined by an abundance of impervious surfaces that act as conduits during precipitation events moving contaminants into aquatic ecosystems. Prior research on the introduction of CECs into surface waters frequently focused on municipal wastewater treatment plants and agricultural runoff. This study investigates the effects of urban stormwater runoff on fathead minnows. In addition, I examined the mitigating potential of retention ponds and iron-enhanced sand filtration (IESF) as best management practices. I collected inflow and outflow water samples following precipitation events during snow melt, spring flush, and summer rains from seven stormwater ponds across the greater metropolitan area of St. Paul, MN, USA. CECs were commonly detected in stormwater runoff with greater concentrations in inflows when compared to pond outflows. In some instances, CEC concentrations rivaled those reported for treated wastewater effluent. Endpoints measured include survival, growth, foraging efficiency, and predator avoidance performance. Results indicated that seasonality had a significant effect on all biological outcomes (

Energy-efficient Transitional Near-* Computing

Studies have shown that communication networks, devices accessing the Internet, and data centers account for 4.6% of the worldwide electricity consumption. Although data centers, core network equipment, and mobile devices are getting more energy-efficient, the amount of data that is being processed, transferred, and stored is vastly increasing. Recent computer paradigms, such as fog and edge computing, try to improve this situation by processing data near the user, the network, the devices, and the data itself. In this thesis, these trends are summarized under the new term near-* or near-everything computing. Furthermore, a novel paradigm designed to increase the energy efficiency of near-* computing is proposed: transitional computing. It transfers multi-mechanism transitions, a recently developed paradigm for a highly adaptable future Internet, from the field of communication systems to computing systems. Moreover, three types of novel transitions are introduced to achieve gains in energy efficiency in near-* environments, spanning from private Infrastructure-as-a-Service (IaaS) clouds, Software-defined Wireless Networks (SDWNs) at the edge of the network, Disruption-Tolerant Information-Centric Networks (DTN-ICNs) involving mobile devices, sensors, edge devices as well as programmable components on a mobile System-on-a-Chip (SoC). Finally, the novel idea of transitional near-* computing for emergency response applications is presented to assist rescuers and affected persons during an emergency event or a disaster, although connections to cloud services and social networks might be disturbed by network outages, and network bandwidth and battery power of mobile devices might be limited

New techniques in television to provide research in three-dimensional real-time or near real-time imagery and reduced cost systems for teleconferencing and educational uses, part 1

The results are presented of a continuing research and development program the objective of which is to develop a reduced bandwidth television system and a technique for television transmission of holograms. The result of the former is a variable frame rate television system, the operation of which was demonstrated for both black-and-white and color signals. This system employs a novel combination of the inexpensive mass storage capacity of a magnetic disc with the reliability of a digital system for time expansion and compression. Also reported are the results of a theoretical analysis and preliminary feasibility experiment of an innovative system for television transmission of holograms using relatively conventional TV equipment along with a phase modulated reference wave for production of the original interference pattern

Studying users’ adaptation to Android's run-time fine-grained access control system

© 2018 Elsevier Ltd The advent of the sixth Android version brought a significant security and privacy advancement to its users. The platform's security model has changed dramatically, allowing users to grant or deny access to resources when requested by applications during run-time. This improvement changed the traditional coarse-grained permission system and it was anticipated for a long time by privacy-aware users. In this paper, we present a pilot study that aims to analyze how Android users adapted to the run-time permission model. We gathered anonymous data from 52 participants, who downloaded an application we developed and answered questions related to the run-time permission model. Their answers suggest that most of them positively accepted the new model. We also collected data that describe users’ permission settings for each installed application on their devices. Our analysis shows that individuals make consistent choices regarding the resources they allow to various applications to access. In addition, the results of this pilot study showcase that on a second data collection round (occurred one month after the first phase of our experiments), 50% of the respondents did not change a single permission on their devices and only 2.26% of installed applications (on average) presented altered permission settings

Multi-sensor human action recognition with particular application to tennis event-based indexing

The ability to automatically classify human actions and activities using vi- sual sensors or by analysing body worn sensor data has been an active re- search area for many years. Only recently with advancements in both fields and the ubiquitous nature of low cost sensors in our everyday lives has auto- matic human action recognition become a reality. While traditional sports coaching systems rely on manual indexing of events from a single modality, such as visual or inertial sensors, this thesis investigates the possibility of cap- turing and automatically indexing events from multimodal sensor streams. In this work, we detail a novel approach to infer human actions by fusing multimodal sensors to improve recognition accuracy. State of the art visual action recognition approaches are also investigated. Firstly we apply these action recognition detectors to basic human actions in a non-sporting con- text. We then perform action recognition to infer tennis events in a tennis court instrumented with cameras and inertial sensing infrastructure. The system proposed in this thesis can use either visual or inertial sensors to au- tomatically recognise the main tennis events during play. A complete event retrieval system is also presented to allow coaches to build advanced queries, which existing sports coaching solutions cannot facilitate, without an inordi- nate amount of manual indexing. The event retrieval interface is evaluated against a leading commercial sports coaching tool in terms of both usability and efficiency