Robust network monitoring in the presence of non-cooperative traffic queries

We present the design of a predictive load shedding scheme for a network monitoring platform that supports multiple and competing traffic queries. The proposed scheme can anticipate overload situations and minimize their impact on the accuracy of the traffic queries. The main novelty of our approach is that it considers queries as black boxes, with arbitrary (and highly variable) input traffic and processing cost. Our system only requires a high-level specification of the accuracy requirements of each query to guide the load shedding procedure and assures a fair allocation of computing resources to queries in a non-cooperative environment. We present an implementation of our load shedding scheme in an existing network monitoring system and evaluate it with a diverse set of traffic queries. Our results show that, with the load shedding mechanism in place, the monitoring system can preserve the accuracy of the queries within predefined error bounds even during extreme overload conditions.     

Self-adaptive utility-based web session management

In the Internet, where millions of users are a click away from your site, being able to dynamically classify the workload in real time, and predict its short term behavior, is crucial for proper self-management and business efficiency. As workloads vary significantly according to current time of day, season, promotions and linking, it becomes impractical for some ecommerce sites to keep over-dimensioned infrastructures to accommodate the whole load. When server resources are exceeded, session-based admission control systems allow maintaining a high throughput in terms of properly finished sessions and QoS for a limited number of sessions; however, by denying access to excess users, the website looses potential customers.In the present study we describe the architecture of AUGURES, a system that learns to predict Web user’s intentions for visiting the site as well its resource usage. Predictions are made from information known at the time of their first request and later from navigational clicks. For this purpose we use machine learning techniques and Markov-chain models. The system uses these predictions to automatically shape QoS for the most profitable sessions, predict short-term resource needs, and dynamically provision servers according to the expected revenue and the cost to serve it. We test the AUGURES prototype on access logs from a high-traffic, online travel agency, obtaining promising results.     

Observer-invariant histopathology using genetics-based machine learning

Prostate cancer accounts for one-third of noncutaneous cancers diagnosed in US men and is a leading cause of cancer-related death. Advances in Fourier transform infrared spectroscopic imaging now provide very large data sets describing both the structural and local chemical properties of cells within prostate tissue. Uniting spectroscopic imaging data and computer-aided diagnoses (CADx), our long term goal is to provide a new approach to pathology by automating the recognition of cancer in complex tissue. The first step toward the creation of such CADx tools requires mechanisms for automatically learning to classify tissue types—a key step on the diagnosis process. Here we demonstrate that genetics-based machine learning (GBML) can be used to approach such a problem. However, to efficiently analyze this problem there is a need to develop efficient and scalable GBML implementations that are able to process very large data sets. In this paper, we propose and validate an efficient GBML technique——based on an incremental genetics-based rule learner. exploits massive parallelisms via the message passing interface (MPI) and efficient rule-matching using hardware-implemented operations. Results demonstrate that is capable of performing prostate tissue classification efficiently, making a compelling case for using GBML implementations as efficient and powerful tools for biomedical image processing.     

Multimodal focus attention and stress detection and feedback in an augmented driver simulator

This paper presents a driver simulator, which takes into account the information about the user’s state of mind (level of attention, fatigue state, stress state). The user’s state of mind analysis is based on video data and biological signals. Facial movements such as eyes blinking, yawning, head rotations, etc., are detected on video data: they are used in order to evaluate the fatigue and the attention level of the driver. The user’s electrocardiogram and galvanic skin response are recorded and analyzed in order to evaluate the stress level of the driver. A driver simulator software is modified so that the system is able to appropriately react to these critical situations of fatigue and stress: some audio and visual messages are sent to the driver, wheel vibrations are generated and the driver is supposed to react to the alert messages. A multi-threaded system is proposed to support multi-messages sent by the different modalities. Strategies for data fusion and fission are also provided. Some of these components are integrated within the first prototype of OpenInterface: the multimodal similar platform.     

Formal Specification and Quantitative Analysis of a Constellation of Navigation Satellites

Navigation satellites are a core component of navigation satellite‐based systems such as Global Positioning System, Global Navigation Satellite System and Galileo, which provide location and timing information for a variety of uses. Such satellites are designed for operating on orbit to perform tasks and have lifetimes of 10 years or more. Reliability, availability and maintainability analysis of systems has been indispensable in the design phase of satellites in order to achieve minimum failures or to increase mean time between failures and thus to plan maintenance strategies, optimise reliability and maximise availability. In this paper, we present formal models of both a single satellite and a navigation satellite constellation and logical specification of their reliability, availability and maintainability properties, respectively. The probabilistic model checker PRISM has been used to perform automated analysis of these quantitative properties. Copyright © 2014 John Wiley & Sons, Ltd.     

Hybridized electronic states between CdSe nanoparticles and conjugated organic ligands: A theoretical and ultrafast photo-excited carrier dynamics study

Formation of densely packed thin films of semiconductor nanocrystals is advantageous for the exploitation of their unique optoelectronic properties for real-world applications. Here we investigate the fundamental role of the structure of the bridging ligand on the optoelectronic properties of the resulting hybrid film. In particular, we considered hybrid films formed using the same CdSe nanocrystals and two organic ligands that have the same bidentate dithiocarbamate binding moiety, but differ in their bridging structures, one bridged by ethylene, the other by phenylene that exhibits conjugation. Based on the results of photo-excited carrier dynamics experiments combined with theoretical calculations on the electronic states of bridged CdSe layers, we show that only the phenylene-based ligand presents a strong hybridization of the molecular HOMO state with CdSe layers, that is a marker of formation of an effective bridge. We argue that this hybridization spread favors the hopping of photo-excited carriers between nanocrystals, which may explain the reported larger photo-currents in phenylene-based hybrid films than those observed in ethylene-based ones.

Open image in new window

     

Complexes With Biologically Active Ligands. Part 6 Ni(II) Coordination Compounds of Hydrazine and Heterocyclic Sulfonamides as Inhibitors of the Zinc Enzyme Carbonic Anhydrase

Ternary Ni(II) complexes of hydrazine and eight heterocyclic sulfonamides possessing carbonic anhydrase (CA) inhibitory properties, were prepared and characterized by elemental analysis, spectroscopic, magnetic, thermogravimetric, and conductimetric measurements. The complexes behave as strong inhibitors for two isozymes (I and II) of carbonic anhydrase.