Optimization of thermal systems with sensitive optics, electronics, and structures

A strategy was investigated by which thermal designers for spacecraft could devise an optimal thermal control system to maintain the required temperatures, temperature differences, changes in temperature, and changes in temperature differences for specified equipment and elements of the spacecraft's structure. Thermal control is to be maintained by the coating pattern chosen for the external surfaces and heaters chosen to supplement the coatings. The approach is to minimize the thermal control power, thereby minimizing the weight of the thermal control system. Because there are so many complex computations involved in determining the optimal coating design a computerized approach was contemplated. An optimization strategy including all the elements considered by the thermal designer for use in the early stages of design, where impact on the mission is greatest, and a plan for implementing the strategy were successfully developed. How the optimization process may be used to optimize the design of the Space Telescope as a test case is demonstrated

A Flexible and Modular Framework for Implementing Infrastructures for Global Computing

We present a Java software framework for building infrastructures to support the development of applications for systems where mobility and network awareness are key issues. The framework is particularly useful to develop run-time support for languages oriented towards global computing. It enables platform designers to customize communication protocols and network architectures and guarantees transparency of name management and code mobility in distributed environments. The key features are illustrated by means of a couple of simple case studies

CAVIAR: Context-driven Active and Incremental Activity Recognition

Activity recognition on mobile device sensor data has been an active research area in mobile and pervasive computing for several years. While the majority of the proposed techniques are based on supervised learning, semi-supervised approaches are being considered to reduce the size of the training set required to initialize the model. These approaches usually apply self-training or active learning to incrementally refine the model, but their effectiveness seems to be limited to a restricted set of physical activities. We claim that the context which surrounds the user (e.g., time, location, proximity to transportation routes) combined with common knowledge about the relationship between context and human activities could be effective in significantly increasing the set of recognized activities including those that are difficult to discriminate only considering inertial sensors, and the highly context-dependent ones. In this paper, we propose CAVIAR, a novel hybrid semi-supervised and knowledge-based system for real-time activity recognition. Our method applies semantic reasoning on context-data to refine the predictions of an incremental classifier. The recognition model is continuously updated using active learning. Results on a real dataset obtained from 26 subjects show the effectiveness of our approach in increasing the recognition rate, extending the number of recognizable activities and, most importantly, reducing the number of queries triggered by active learning. In order to evaluate the impact of context reasoning, we also compare CAVIAR with a purely statistical version, considering features computed on context-data as part of the machine learning process

Orchestrating Tuple-based Languages

The World Wide Web can be thought of as a global computing architecture supporting the deployment of distributed networked applications. Currently, such applications can be programmed by resorting mainly to two distinct paradigms: one devised for orchestrating distributed services, and the other designed for coordinating distributed (possibly mobile) agents. In this paper, the issue of designing a pro- gramming language aiming at reconciling orchestration and coordination is investigated. Taking as starting point the orchestration calculus Orc and the tuple-based coordination language Klaim, a new formalism is introduced combining concepts and primitives of the original calculi. To demonstrate feasibility and effectiveness of the proposed approach, a prototype implementation of the new formalism is described and it is then used to tackle a case study dealing with a simplified but realistic electronic marketplace, where a number of on-line stores allow client applications to access information about their goods and to place orders

Network-aware Evaluation Environment for Reputation Systems

Parties of reputation systems rate each other and use ratings to compute reputation scores that drive their interactions. When deciding which reputation model to deploy in a network environment, it is important to find the most suitable model and to determine its right initial configuration. This calls for an engineering approach for describing, implementing and evaluating reputation systems while taking into account specific aspects of both the reputation systems and the networked environment where they will run. We present a software tool (NEVER) for network-aware evaluation of reputation systems and their rapid prototyping through experiments performed according to user-specified parameters. To demonstrate effectiveness of NEVER, we analyse reputation models based on the beta distribution and the maximum likelihood estimation

Context-Aware Data Association for Multi-Inhabitant Sensor-Based Activity Recognition

Recognizing the activities of daily living (ADLs) in multi-inhabitant settings is a challenging task. One of the major challenges is the so-called data association problem: how to assign to each user the environmental sensor events that he/she actually triggered? In this paper, we tackle this problem with a contextaware approach. Each user in the home wears a smartwatch, which is used to gather several high-level context information, like the location in the home (thanks to a micro-localization infrastructure) and the posture (e.g., sitting or standing). Context data is used to associate sensor events to the users which more likely triggered them. We show the impact of context reasoning in our framework on a dataset where up to 4 subjects perform ADLs at the same time (collaboratively or individually). We also report our experience and the lessons learned in deploying a running prototype of our method

ProCAVIAR: Hybrid Data-Driven and Probabilistic Knowledge-Based Activity Recognition

The recognition of physical activities using sensors on mobile devices has been mainly addressed with supervised and semi-supervised learning. The state-of-the-art methods are mainly based on the analysis of the user\u2019s movement patterns that emerge from inertial sensors data. While the literature on this topic is quite mature, existing approaches are still not adequate to discriminate activities characterized by similar physical movements. The context that surrounds the user (e.g., semantic location) could be used as additional information to significantly extend the set of recognizable activities. Since collecting a comprehensive training set with activities performed in every possible context condition is too costly, if possible at all, existing works proposed knowledge-based reasoning over ontological representation of context data to refine the predictions obtained from machine learning. A problem with this approach is the rigidity of the underlying logic formalism that cannot capture the intrinsic uncertainty of the relationships between activities and context. In this work, we propose a novel activity recognition method that combines semisupervised learning and probabilistic ontological reasoning. We model the relationships between activities and context as a combination of soft and hard ontological axioms. For each activity, we use a probabilistic ontology to compute its compatibility with the current context conditions. The output of probabilistic semantic reasoning is combined with the output of a machine learning classifier based on inertial sensor data to obtain the most likely activity performed by the user. The evaluation of our system on a dataset with 13 types of activities performed by 26 subjects shows that our probabilistic framework outperforms both a pure machine learning approach and previous hybrid approaches based on classic ontological reasoning

SmartFABER: Recognizing fine-grained abnormal behaviors for early detection of mild cognitive impairment

Objective: In an ageing world population more citizens are at risk of cognitive impairment, with negative consequences on their ability of independent living, quality of life and sustainability of healthcare systems. Cognitive neuroscience researchers have identified behavioral anomalies that are significant indicators of cognitive decline. A general goal is the design of innovative methods and tools for continuously monitoring the functional abilities of the seniors at risk and reporting the behavioral anomalies to the clinicians. SmartFABER is a pervasive system targeting this objective. Methods: A non-intrusive sensor network continuously acquires data about the interaction of the senior with the home environment during daily activities. A novel hybrid statistical and knowledge-based technique is used to analyses this data and detect the behavioral anomalies, whose history is presented through a dashboard to the clinicians. Differently from related works, SmartFABER can detect abnormal behaviors at a fine-grained level. Results: We have fully implemented the system and evaluated it using real datasets, partly generated by performing activities in a smart home laboratory, and partly acquired during several months of monitoring of the instrumented home of a senior diagnosed with MCI. Experimental results, including comparisons with other activity recognition techniques, show the effectiveness of SmartFABER in terms of recognition rates

Echinococcus granulosus "sensu stricto" in a captive ring-tailed lemur (Lemur catta) in Northern Italy

Cystic echinococcosis (CE) by Echinococcosis granulosus (Eg) infection was seen in a 13 years old male lemur, found dead in a zoo in Northern Italy. Necropsy revealed several transparent cysts in the lungs and in the abdominal cavity. Freefloating cysts of varying sizes were found in the peritoneal cavity, and no protoscolex was seen microscopically. Histologically, a multifocal severe parasitic granulomatous pneumonia was observed. Confirmation of E. granulosus "sensu stricto" was reached by PCR and sequencing. In view of the absence of definitive host in the zoo, located in non-endemic region for CE, it is speculated that infection introduced through translocation of lemur from endemic region (Southern Italy zoo)

Demo: Hybrid data-driven and context-aware activity recognition with mobile devices

We have designed and implemented a real-time hybrid activity recognition system which combines supervised learning on inertial sensor data from mobile devices and context-aware reasoning. We demonstrate how the context surrounding the user, combined with common knowledge about the relationship between this context and human activities, can significantly increase the ability to discriminate among activities when machine learning over inertial sensors has clear difficulties