Bottom up approach to manage data privacy policy through the front end filter paradigm

An increasing number of business services for private companies and citizens are accomplished trough the web and mobile devices. Such a scenario is characterized by high dynamism and untrustworthiness, as a large number of applications exchange different kinds of data. This poses an urgent need for effective means in preserving data privacy. This paper proposes an approach, inspired to the front-end trust filter paradigm, to manage data privacy in a very flexible way. Preliminary experimentation suggests that the solution could be a promising path to follow for web-based transactions which will be very widespread in the next future

Visual Localization in the Presence of Appearance Changes Using the Partial Order Kernel

Visual localization across seasons and under varying weather and lighting conditions is a challenging task in robotics. In this paper, we present a new sequence-based approach to visual localization using the Partial Order Kernel (POKer), a convolution kernel for string comparison, that is able to handle appearance changes and is robust to speed variations. We use multiple sequence alignment to construct directed acyclic graph representations of the database image sequences, where sequences of images of the same place acquired at different times are represented as alternative paths in a graph. We then use the POKer to compute the pairwise similarities between these graphs and the query image sequences obtained in a subsequent traversal of the environment, and match the corresponding locations. We evaluated our approach on a dataset which features extreme appearance variations due to seasonal changes. The results demonstrate the effectiveness of our approach, where it achieves higher precision and recall than two state-of-the-art baseline method

Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge

Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95)

Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge

Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95)

Organic working fluid optimization for space power cycles

The merits of organic fluid space power cycles are surveyed and compared with those of alternate options. Selection of an optimum working fluid is rccognized as an important tool to improve system performance. The main characteristics of organic power cycles are shown to be predictable with a good level of accuracy through a generaI method, which requests the knowledge of a limited information about the fluid properties: speci/ic heat in the ideai gas state, a portion of the saturation curve, and the criticaI pararn- eters. On the ground of such a theory the adoption of fluids with a relatively complcx rnolecular structure and condensation at the lowest practically admissible reduccd temperature allow a better efficiency than achievable with the lise of toluene, which is taken as a reference fluido The influence of turbine cfficicncy actually achievable in real machines on cycle performance is then addrcsscd; performance diagrams of optimized turbines in the power range of interest for space cycles are calculated and presented. It is shown that only the combined optimization of thermal and fluid dynamic variables leads to the dcfinition of an optimum working Iluid and power cycle. A c1ass of fluids is exarnincd, that of the rnethyl-substituted benzenes, offering a wide variation of thcrrnnl prope ties. A thorough optimization that considers a wide range of power outputs, one- and two-stage turbines, saturated and superheated cycles is performed. For a power output of about 30 kW trimethylbenzene is found to offer the best overall efficiency, a moderate maxirnum pressure, rcasonable turbine dimensions, and rotating speed. A thermodynamic conver- sion efficiency in excess of 30 percent seems achievable at a rnaxirnurn tempera- ture of 360Q C for a condensation temperature of 60°C. Such energy perfor- mance suggests that ORC systems could represent a viable multifuel prime mover option also for terrestrial power generation. Thermal stability of the proposcd fluid is experimentall; investigated and found to be similar to that of tolucne, but its definite evaluation is shown to require further testing

Understanding perceived trust to reduce regret

Trust is fundamental for promoting the use of online services, such as e-commerce or e-health. Understanding how users perceive trust online is a precondition to create trustworthy marketplaces. In this article, we present a domain-independent general trust perception model that helps us to understand how users make online trust decisions and how we can help them in making the right decisions, which minimize future regret. We also present the results of a user study describing the weight that different factors in the model (e.g., security, look&feel, and privacy) have on perceived trust. The study identifies the existence of a positive correlation between the user's knowledge and the importance placed on factors such as security and privacy. This indicates that the impact factors as security and privacy have on perceived trust is higher in users with higher knowledge. Keywords: perceived trust; trust decision; regret; security; privac