An Information-Theoretic Framework for Consistency Maintenance in Distributed Interactive Applications

Distributed Interactive Applications (DIAs) enable geographically dispersed users to interact with each other in a virtual environment. A key factor to the success of a DIA is the maintenance of a consistent view of the shared virtual world for all the participants. However, maintaining consistent states in DIAs is difficult under real networks. State changes communicated by messages over such networks suffer latency leading to inconsistency across the application. Predictive Contract Mechanisms (PCMs) combat this problem through reducing the number of messages transmitted in return for perceptually tolerable inconsistency. This thesis examines the operation of PCMs using concepts and methods derived from information theory. This information theory perspective results in a novel information model of PCMs that quantifies and analyzes the efficiency of such methods in communicating the reduced state information, and a new adaptive multiple-model-based framework for improving consistency in DIAs. The first part of this thesis introduces information measurements of user behavior in DIAs and formalizes the information model for PCM operation. In presenting the information model, the statistical dependence in the entity state, which makes using extrapolation models to predict future user behavior possible, is evaluated. The efficiency of a PCM to exploit such predictability to reduce the amount of network resources required to maintain consistency is also investigated. It is demonstrated that from the information theory perspective, PCMs can be interpreted as a form of information reduction and compression. The second part of this thesis proposes an Information-Based Dynamic Extrapolation Model for dynamically selecting between extrapolation algorithms based on information evaluation and inferred network conditions. This model adapts PCM configurations to both user behavior and network conditions, and makes the most information-efficient use of the available network resources. In doing so, it improves PCM performance and consistency in DIAs

Exponentially Modified Peak Functions in Biomedical Sciences and Related Disciplines

In many cases relevant to biomedicine, a variable time, which features a certain distribution, is required for objects of interest to pass from an initial to an intermediate state, out of which they exit at random to a final state. In such cases, the distribution of variable times between exiting the initial and entering the final state must conform to the convolution of the first distribution and a negative exponential distribution. A common example is the exponentially modified Gaussian (EMG), which is widely used in chromatography for peak analysis and is long known as ex-Gaussian in psychophysiology, where it is applied to times from stimulus to response. In molecular and cell biology, EMG, compared with commonly used simple distributions, such as lognormal, gamma, and Wald, provides better fits to the variabilities of times between consecutive cell divisions and transcriptional bursts and has more straightforwardly interpreted parameters. However, since the range of definition of the Gaussian component of EMG is unlimited, data approximation with EMG may extend to the negative domain. This extension may seem negligible when the coefficient of variance of the Gaussian component is small but becomes considerable when the coefficient increases. Therefore, although in many cases an EMG may be an acceptable approximation of data, an exponentially modified nonnegative peak function, such as gamma-distribution, can make more sense in physical terms. In the present short review, EMG and exponentially modified gamma-distribution (EMGD) are discussed with regard to their applicability to data on cell cycle, gene expression, physiological responses to stimuli, and other cases, some of which may be interpreted as decision-making. In practical fitting terms, EMG and EMGD are equivalent in outperforming other functions; however, when the coefficient of variance of the Gaussian component of EMG is greater than ca. 0.4, EMGD is preferable

Algorithms for the Analysis of Spatio-Temporal Data from Team Sports

Modern object tracking systems are able to simultaneously record trajectories—sequences of time-stamped location points—for large numbers of objects with high frequency and accuracy. The availability of trajectory datasets has resulted in a consequent demand for algorithms and tools to extract information from these data. In this thesis, we present several contributions intended to do this, and in particular, to extract information from trajectories tracking football (soccer) players during matches. Football player trajectories have particular properties that both facilitate and present challenges for the algorithmic approaches to information extraction. The key property that we look to exploit is that the movement of the players reveals information about their objectives through cooperative and adversarial coordinated behaviour, and this, in turn, reveals the tactics and strategies employed to achieve the objectives. While the approaches presented here naturally deal with the application-specific properties of football player trajectories, they also apply to other domains where objects are tracked, for example behavioural ecology, traffic and urban planning

History of Computer Art

A large text presents the history of Computer Art. The history of the artistic uses of computers and computing processes is reconstructed from its beginnings in the fifties to its present state. It points out hypertextual, modular and generative modes to use computing processes in Computer Art and features examples of early developments in media like cybernetic sculptures, video tools, computer graphics and animation (including music videos and demos), video and computer games, pervasive games, reactive installations, virtual reality, evolutionary art and net art. The functions of relevant art works are explained more detailed than is usual in such histories. From October 2011 to December 2012 the chapters have been published successively in German (The English translation started in August 2013 and was completed in June 2014)

A Credit-based Home Access Point (CHAP) to Improve Application Quality on IEEE 802.11 Networks

Increasing availability of high-speed Internet and wireless access points has allowed home users to connect not only their computers but various other devices to the Internet. Every device running different applications requires unique Quality of Service (QoS). It has been shown that delay- sensitive applications, such as VoIP, remote login and online game sessions, suffer increased latency in the presence of throughput-sensitive applications such as FTP and P2P. Currently, there is no mechanism at the wireless AP to mitigate these effects except explicitly classifying the traffic based on port numbers or host IP addresses. We propose CHAP, a credit-based queue management technique, to eliminate the explicit configuration process and dynamically adjust the priority of all the flows from different devices to match their QoS requirements and wireless conditions to improve application quality in home networks. An analytical model is used to analyze the interaction between flows and credits and resulting queueing delays for packets. CHAP is evaluated using Network Simulator (NS2) under a wide range of conditions against First-In-First- Out (FIFO) and Strict Priority Queue (SPQ) scheduling algorithms. CHAP improves the quality of an online game, a VoIP session, a video streaming session, and a Web browsing activity by 20%, 3%, 93%, and 51%, respectively, compared to FIFO in the presence of an FTP download. CHAP provides these improvements similar to SPQ without an explicit classification of flows and a pre- configured scheduling policy. A Linux implementation of CHAP is used to evaluate its performance in a real residential network against FIFO. CHAP reduces the web response time by up to 85% compared to FIFO in the presence of a bulk file download. Our contributions include an analytic model for the credit-based queue management, simulation, and implementation of CHAP, which provides QoS with minimal configuration at the AP

Robust and Efficient Activity Recognition from Videos

With technological advancement in embedded system design, powerful cameras have been embedded within smart phones, and wireless cameras can be easily deployed at street corners, traffic lights, big stadiums, train stations, etc. Besides, the growth of online media, surveillance, and mobile cameras have resulted in an explosion of videos being uploaded to social media sites such as Facebook and YouTube. The availability of such a vast volume of videos has attracted the computer vision community to conduct much research on human activity recognition since people are arguably the most interesting subjects of such videos. Automatic human activity recognition allows engineers and computer scientists to design smarter surveillance systems, semantically aware video indexes and also more natural human-computer interfaces. Despite the explosion of video data, the ability to automatically recognize and understand human activities is still rather limited. This is primarily due to multiple challenges inherent to the recognition task, namely large variability in human execution styles, the complexity of the visual stimuli in terms of camera motion, background clutter, viewpoint changes, etc., and the number of activities that can be recognized. In addition, the ability to predict future actions of objects based on past observed video frames is very useful. Therefore, in this thesis, we explore four designs to solve the problems we discussed earlier, namely (1) A semantics-based deep learning model, namely SBGAR, is proposed to do group activity recognition. This model achieves higher accuracy and efficiency than existing group activity recognition methods. (2) Despite its high accuracy, SBGAR has some limitations, namely (i) it requires a large dataset with caption information, (ii) activity recognition model is independent of the caption generation model and hence SBGAR may not perform well in some cases. To remove such limitations, we design ReHAR, a robust and efficient human activity recognition scheme. ReHAR can be used to recognize both single-person activities and group activities. (3) In many application scenarios, merely knowing what the moving agents are doing is not sufficient. It also requires predictions of future trajectories of moving agents. Thus, we propose GRIP, a graph-based interaction-aware motion intent prediction scheme. The scheme uses a graph to represent the relationships between two objects, e.g., human joints or traffic agents, and predict the motion intents of all observed objects simultaneously. (4) Action recognition and trajectory prediction schemes are typically deployed in resource-constrained devices. Thus, any technique that can accelerate the computation speed of our schemes is important. Hence, we propose a novel deep learning model decomposition method called DAC that is capable of factorizing an ordinary convolutional layer into two layers with much fewer parameters. DAC computes the corresponding weights for the newly generated layers directly from the weights of the original convolutional layer. Thus, no training (or fine-tuning) or any data is needed

Quality of service routing for real-time traffic

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Digital anthropology

The textbook supplements the lecture material with topical issues of the philosophy of neural technologies. The material belongs to the section "Philosophy of natural science and technology" of the lecture course on the philosophy and methodology of science. The natural-science aspects of human conscious-ness and technological trends in the evolution of convergent structures of digital ecosystems are described. The evolution of system computer engineering is analyzed