What can you verify and enforce at runtime?

The underlying property, its definition, and representation play a major role when monitoring a system. Having a suitable and convenient framework to express properties is thus a concern for runtime analysis. It is desirable to delineate in this framework the sets of properties for which runtime analysis approaches can be applied to. This paper presents a unified view of runtime verification and enforcement of properties in the Safety-Progress classification. First, we extend the Safety-Progress classification of properties in a runtime context. Second, we characterize the set of properties which can be verified (monitorable properties) and enforced (enforceable properties) at runtime. We propose in particular an alternative definition of ??property monitoring?? to the one classically used in this context. Finally, for the delineated sets of properties, we define specialized verification and enforcement monitors.     

Improving the performance of distributed virtual environment systems

The last years have witnessed a dramatic growth in the number as well as in the variety of distributed virtual environment systems. These systems allow multiple users, working on different client computers that are interconnected through different networks, to interact in a shared virtual world. One of the key issues in the design of scalable and cost-effective DVE systems is the partitioning problem. This problem consists of efficiently assigning the existing clients to the servers in the system and some techniques have been already proposed for solving it. This paper experimentally analyzes the correlation of the quality function proposed in the literature for solving the partitioning problem with the performance of DVE systems. Since the results show an absence of correlation, we also propose the experimental characterization of DVE systems. The results show that the reason for that absence of correlation is the nonlinear behavior of DVE systems with regard to the number of clients in the system. DVE systems reach saturation when any of the servers reaches 100 percent of CPU utilization. The system performance greatly decreases if this limit is exceeded in any server. Also, as a direct application of these results, we present a partitioning method that is targeted to keep all the servers in the system below a certain threshold value of CPU utilization, regardless of the amount of network traffic. Evaluation results show that the proposed partitioning method can improve DVE system performance, regardless of both the movement pattern of clients and the initial distribution of clients in the virtual world.     

The development and application of psychophysical methods in upper-extremity work tasks and task elements

This paper examines an approach to modeling the relationship between perceived acceptable work exposures and physical stressors in upper-extremity tasks using psychophysical methods. Several years of laboratory-based studies and results from a variety of simulated work tasks and task elements are summarized. The original impetus of these studies was founded in the pioneering work and successful application of psychophysical methods applied to manual materials handling tasks (e.g., lifting) generally beginning in the 1960s and 1970s. This approach provided unique and feasible solutions to work design problems involving exposure to the hazard of cumulative trauma. Presently, these methods were adapted to studying common upper-extremity tasks and task elements. Results provide conclusive evidence of the impact of required posture, force, gender and other variables on acceptable task frequency. These results and the psychophysical method in general, may be particularly helpful in establishing realistic and reasonable work design guidelines when workers are exposed to multiple, simultaneous hazards such as force, frequency, with deviated posture, etc, and in the absence of well-defined biomechanical or physiological-based models. Finally, a review of psychophysical theory and methods which can be applied to a wide range of occupational activities is provided.     

A multi-sequences MRI deep framework study applied to glioma classfication

Multimedia Tools and Applications - Glioma is one of the most important central nervous system tumors, ranked 15th in the most common cancer for men and women. Magnetic Resonance Imaging (MRI)...     

SLA-aware operational efficiency in AI-enabled service chains: challenges ahead

Information Systems and e-Business Management - Service providers compose services in service chains that require deep integration of core operational information systems across organizations....     

A model for evaluation and administration of security inobject-oriented databases

The integration of object-oriented programming concepts with databases is one of the most significant advances in the evolution of database systems. Many aspects of such a combination have been studied, but there are few models to provide security for this richly structured information. We develop an authorization model for object-oriented databases. This model consists of a set of policies, a structure for authorization rules, and algorithms to evaluate access requests against the authorization rules. User access policies are based on the concept of inherited authorization applied along the class structure hierarchy. We propose also a set of administrative policies that allow the control of user access and its decentralization. Finally, we study the effect of class structuring changes on authorization     

Robust control with uncertainty estimation for feedback linearizable systems: application to control of distillation columns

The goal of this paper is to describe a linearizing feedback adaptive control structure which leads to a high quality regulation of the output error in the presence of uncertainties and external disturbances. The controller consists of three elements: a nominal input–output linearizing compensator, a state observer and an uncertainty estimator, which provides the adaptive part of the control structure. In this way, the feedback controller, based on the disturbance observer, compensates for external disturbances and plant uncertainties. The effectiveness of the controller is demonstrated on a distillation column via numerical simulations. ©     

Assessing and discovering parallelism in C$$$$ code for heterogeneous platforms

Massively parallel architectures are mainly based on a parallel heterogeneous setup. They are composed by different computing devices that speed up specific code regions, named kernels. These kernels are usually executed offline in the corresponding devices. Porting applications to a specific heterogeneous platform is a costly task in terms of time and human resources. The key points in the porting process are the manual analysis of the source code and kernel detection. Each device of these heterogeneous platforms has their own restrictions, such as the memory allocation support. Kernels must be mapped with suitable computing devices. We introduced AKI as an automatic kernel identification and annotation tool that aims to identify potential kernels on C\(++\) sequential applications. AKI identifies those kernels that can be offlined on heterogeneous computing devices. To annotate these kernels, REPARA C++ attributes have been defined. This annotation mechanism can aid future automatic source-to-source transformation tools to facilitate the work for parallel heterogeneous platforms. AKI has been evaluated over all benchmarks included in the NAS suite. The benchmark suite incorporates a big set of realistic high performance applications. The evaluation results demonstrate that AKI is a competitive solution for identifying and annotating parallel code fragments (aka kernels).     

Integration of beamforming and uncertainty-of-observation techniques for robust ASR in multi-source environments

This paper presents a new approach for increasing the robustness of multi-channel automatic speech recognition in noisy and reverberant multi-source environments. The proposed method uses uncertainty propagation techniques to dynamically compensate the speech features and the acoustic models for the observation uncertainty determined at the beamforming stage. We present and analyze two methods that allow integrating classical multi-channel signal processing approaches like delay and sum beamformers or Zelinski-type Wiener filters, with uncertainty-of-observation techniques like uncertainty decoding or modified imputation. An analysis of the results on the PASCAL-CHiME task shows that this approach consistently outperforms conventional beamformers with a minimal increase in computational complexity. The use of dynamic compensation based on observation uncertainty also outperforms conventional static adaptation with no need of adaptation data.