Understanding the performance of interactive applications

Many if not most computer systems are used by human users. The performance of such interactive systems ultimately affects those users. Thus, when measuring, understanding, and improving system performance, it makes sense to consider the human user's perspective. Essentially, the performance of interactive applications is determined by the perceptible lag in handling user requests. So, when characterizing the runtime of an interactive application we need a new approach that focuses on the perceptible lags rather than on overall and general performance characteristics. Such a new characterization approach should enable a new way to profile and improve the performance of interactive applications. Imagine a way that would seek out these perceptible lags and then investigate the causes of these lags. Performance analysts could simply optimize responsible parts of the software, thus eliminating perceptible lag for interactive applications. Unfortunately, existing profiling approaches either incur significant overhead that makes them impractical for an interactive scenario, or they lack the ability to provide insight into the causes of long latencies. An effective approach for interactive applications has to fulfill several requirements such as an accurate view of the causes of performance problems and insignificant perturbation of the interactive application. We propose a new profiling approach that helps developers to understand and improve the perceptible performance of interactive applications and satisfies the above needs

Learning an Interactive Segmentation System

Many successful applications of computer vision to image or video manipulation are interactive by nature. However, parameters of such systems are often trained neglecting the user. Traditionally, interactive systems have been treated in the same manner as their fully automatic counterparts. Their performance is evaluated by computing the accuracy of their solutions under some fixed set of user interactions. This paper proposes a new evaluation and learning method which brings the user in the loop. It is based on the use of an active robot user - a simulated model of a human user. We show how this approach can be used to evaluate and learn parameters of state-of-the-art interactive segmentation systems. We also show how simulated user models can be integrated into the popular max-margin method for parameter learning and propose an algorithm to solve the resulting optimisation problem.Comment: 11 pages, 7 figures, 4 table

Сryptocurrency and Internet of Things: Problems of Implementation and Realization

IoT (Internet of Things) requires the implementation of digital encryption of information, transaction support and recording of all events for security. It can provide cryptocurrencies protocols with adding an additional possibility of payments. This opportunity is not so much demanded at the hardware level as in the software implementation. We have discovered that IoT devices are widely used for illegal purposes for trusts or network consolidated attacks, and virtually no legal and useful ways of using their hardware-distributed capabilities. Standardization and compatibility in IOT network should become the main tools for the possibility of introducing new solutions, testing their utility, performance and safety. The standardization of a new approach to interactive protocols in the IOT network and the Internet with a finance approach is now inevitable. We need new IEEE standards for cryptocurrencies and IoT functioning. They must include standards for protocol functioning, transaction validation and saving, privacy and security support. Cryptocurrencies and IoT interaction diagram were proposed. The IoT network devices technology will be in the future instance of the smart class of digital-physical systems, which also encompasses technologies such as smart homes, intelligent transportation systems, smart cities etc. The financial aspect for purchasing software, services, solutions and sales of the resulting benefits will complement this network with additional capabilities. The development of standards for the financial level of functioning is also necessary.IoT (Internet of Things) requires the implementation of digital encryption of information, transaction support and recording of all events for security. It can provide cryptocurrencies protocols with adding an additional possibility of payments. This opportunity is not so much demanded at the hardware level as in the software implementation. We have discovered that IoT devices are widely used for illegal purposes for trusts or network consolidated attacks, and virtually no legal and useful ways of using their hardware-distributed capabilities. Standardization and compatibility in IOT network should become the main tools for the possibility of introducing new solutions, testing their utility, performance and safety. The standardization of a new approach to interactive protocols in the IOT network and the Internet with a finance approach is now inevitable. We need new IEEE standards for cryptocurrencies and IoT functioning. They must include standards for protocol functioning, transaction validation and saving, privacy and security support. Cryptocurrencies and IoT interaction diagram were proposed. The IoT network devices technology will be in the future instance of the smart class of digital-physical systems, which also encompasses technologies such as smart homes, intelligent transportation systems, smart cities etc. The financial aspect for purchasing software, services, solutions and sales of the resulting benefits will complement this network with additional capabilities. The development of standards for the financial level of functioning is also necessary

Towards an interactive environment for the performance of Dubstep music

This Masters by Research project explores the integration of different concepts relating to the presence of the human body in Dubstep music performance. Three intended performance systems propose that the body is the logical site for the interactive control of live Dubstep music. The physicality and gestures of instrumentalists, choreographed dancers, and audience members will be examined in order to develop new and exciting ways to perform this genre in a live setting. The systems take on a three-tiered hierarchical approach on two levels in regards to the extraction of gestural information from human body movements, as well as in regards to the importance – and length – of musical phenomena and parameters that are under control. The characteristics of Dubstep music are defined and maintained within each interactive music system. A model for this each proposed system will be examined, including discussion of the technology and methodology employed in order to apply the two hierarchies and create the interactive environment

Effective Evaluation using Logged Bandit Feedback from Multiple Loggers

Accurately evaluating new policies (e.g. ad-placement models, ranking functions, recommendation functions) is one of the key prerequisites for improving interactive systems. While the conventional approach to evaluation relies on online A/B tests, recent work has shown that counterfactual estimators can provide an inexpensive and fast alternative, since they can be applied offline using log data that was collected from a different policy fielded in the past. In this paper, we address the question of how to estimate the performance of a new target policy when we have log data from multiple historic policies. This question is of great relevance in practice, since policies get updated frequently in most online systems. We show that naively combining data from multiple logging policies can be highly suboptimal. In particular, we find that the standard Inverse Propensity Score (IPS) estimator suffers especially when logging and target policies diverge -- to a point where throwing away data improves the variance of the estimator. We therefore propose two alternative estimators which we characterize theoretically and compare experimentally. We find that the new estimators can provide substantially improved estimation accuracy.Comment: KDD 201

Value Driven Representation for Human-in-the-Loop Reinforcement Learning

Interactive adaptive systems powered by Reinforcement Learning (RL) have many potential applications, such as intelligent tutoring systems. In such systems there is typically an external human system designer that is creating, monitoring and modifying the interactive adaptive system, trying to improve its performance on the target outcomes. In this paper we focus on algorithmic foundation of how to help the system designer choose the set of sensors or features to define the observation space used by reinforcement learning agent. We present an algorithm, value driven representation (VDR), that can iteratively and adaptively augment the observation space of a reinforcement learning agent so that is sufficient to capture a (near) optimal policy. To do so we introduce a new method to optimistically estimate the value of a policy using offline simulated Monte Carlo rollouts. We evaluate the performance of our approach on standard RL benchmarks with simulated humans and demonstrate significant improvement over prior baselines

Applications of agent architectures to decision support in distributed simulation and training systems

This work develops the approach and presents the results of a new model for applying intelligent agents to complex distributed interactive simulation for command and control. In the framework of tactical command, control communications, computers and intelligence (C4I), software agents provide a novel approach for efficient decision support and distributed interactive mission training. An agent-based architecture for decision support is designed, implemented and is applied in a distributed interactive simulation to significantly enhance the command and control training during simulated exercises. The architecture is based on monitoring, evaluation, and advice agents, which cooperate to provide alternatives to the dec ision-maker in a time and resource constrained environment. The architecture is implemented and tested within the context of an AWACS Weapons Director trainer tool. The foundation of the work required a wide range of preliminary research topics to be covered, including real-time systems, resource allocation, agent-based computing, decision support systems, and distributed interactive simulations. The major contribution of our work is the construction of a multi-agent architecture and its application to an operational decision support system for command and control interactive simulation. The architectural design for the multi-agent system was drafted in the first stage of the work. In the next stage rules of engagement, objective and cost functions were determined in the AWACS (Airforce command and control) decision support domain. Finally, the multi-agent architecture was implemented and evaluated inside a distributed interactive simulation test-bed for AWACS Vv\u27Ds. The evaluation process combined individual and team use of the decision support system to improve the performance results of WD trainees. The decision support system is designed and implemented a distributed architecture for performance-oriented management of software agents. The approach provides new agent interaction protocols and utilizes agent performance monitoring and remote synchronization mechanisms. This multi-agent architecture enables direct and indirect agent communication as well as dynamic hierarchical agent coordination. Inter-agent communications use predefined interfaces, protocols, and open channels with specified ontology and semantics. Services can be requested and responses with results received over such communication modes. Both traditional (functional) parameters and nonfunctional (e.g. QoS, deadline, etc.) requirements and captured in service requests