Parental evaluation of a telemonitoring service for children with Type 1 Diabetes

Introduction In the past years, we developed a telemonitoring service for young patients affected by Type 1 Diabetes. That service provides data to the clinical staff and offers an important tool to the parents, that are able to oversee in real time their children. The aim of this work was to analyze the parents' perceived usefulness of the service. Methods The service was tested by the parents of 31 children enrolled in a seven-day clinical trial during a summer camp. To study the parents' perception we proposed and analyzed two questionnaires. A baseline questionnaire focused on the daily management and implications of their children's diabetes, while a post-study one measured the perceived benefits of telemonitoring. Questionnaires also included free text comment spaces. Results Analysis of the baseline questionnaires underlined the parents' suffering and fatigue: 51% of total responses showed a negative tendency and the mean value of the perceived quality of life was 64.13 in a 0-100 scale. In the post-study questionnaires about half of the parents believed in a possible improvement adopting telemonitoring. Moreover, the foreseen improvement in quality of life was significant, increasing from 64.13 to 78.39 ( p-value\u2009=\u20090.0001). The analysis of free text comments highlighted an improvement in mood, and parents' commitment was also proved by their willingness to pay for the service (median\u2009=\u2009200\u2009euro/year). Discussion A high number of parents appreciated the telemonitoring service and were confident that it could improve communication with physicians as well as the family's own peace of mind

Sensor networks and personal health data management: software engineering challenges

The advances of 5G, sensors, and information technologies enabled proliferation of smart pervasive sensor networks. 5G mobile networks provide low-power, high-availability, high density, and high-throughput data capturing by sensor networks and continuous streaming of multiple measured variables. Rapid progress in sensors that can measure vital signs, advances in the management of medical knowledge, and improvement of algorithms for decision support, are fueling a technological disruption to health monitoring. The increase in size and complexity of wireless sensor networks and expansion into multiple areas of health monitoring creates challenges for system design and software engineering practices. In this paper, we highlight some of the key software engineering and data-processing issues, along with addressing emerging ethical issues of data management. The challenges associated with ensuring high dependability of sensor network systems can be addressed by metamorphic testing. The proposed conceptual solution combines data streaming, filtering, cross-calibration, use of medical knowledge for system operation and data interpretation, and IoT-based calibration using certified linked diagnostic devices. Integration of blockchain technologies and artificial intelligence offers a solution to the increasing needs for higher accuracy of measurements of vital signs, high-quality decision-making, and dependability, including key medical and ethical requirements of safety and security of the data

How do interval scales help us with better understanding IR evaluation measures?

Evaluation measures are the basis for quantifying the performance of IR systems and the way in which their values can be processed to perform statistical analyses depends on the scales on which these measures are defined. For example, mean and variance should be computed only when relying on interval scales. In our previous work we defined a theory of IR evaluation measures, based on the representational theory of measurement, which allowed us to determine whether and when IR measures are interval scales. We found that common set-based retrieval measures\u2014namely precision, recall, and F-measure\u2014always are interval scales in the case of binary relevance while this does not happen in the multi-graded relevance case. In the case of rank-based retrieval measures\u2014namely AP, gRBP, DCG, and ERR\u2014only gRBP is an interval scale when we choose a specific value of the parameter p and define a specific total order among systems while all the other IR measures are not interval scales. In this work, we build on our previous findings and we carry out an extensive evaluation, based on standard TREC collections, to study how our theoretical findings impact on the experimental ones. In particular, we conduct a correlation analysis to study the relationship among the above-mentioned state-of-the-art evaluation measures and their scales. We study how the scales of evaluation measures impact on non parametric and parametric statistical tests for multiple comparisons of IR system performance. Finally, we analyse how incomplete information and pool downsampling affect different scales and evaluation measures

ICN PATTA: ICN privacy attack through traffic analysis

PATTA is the first privacy attack based on network traffic analysis in Information-Centric Networking. PATTA aims to automatically identify the category of requested content by sniffing the communication towards the first hop router. PATTA applies text processing and machine learning techniques to content names in content-oriented architectures. We evaluate PATTA in a simulated network, achieving an accuracy in determining a real-time content category equal to 96%

Do not let Next-Intent Vulnerability be your next nightmare: type system-based approach to detect it in Android apps

Android is currently the most widespread operating system (OS) worldwide, but also the most prone to attacks. Despite the challenges faced by Industry and Academia to improve the Android OS security, it still has several vulnerabilities. Among those, the severity of the Next-Intent Vulnerability (NIV) can be immediately grasped. Android apps are made of components, which by default are private and cannot be targeted by other apps on the same phone. However, NIV allows any app to access the private components of a different app, eventually generating a crash or stealing sensitive data. NIV occurs when there is a chain of calls among different components based on the Intent messaging model and there is no control over the reliability of the first component triggering the call. NIV was first detected in 2013, but it is still an open issue. In this paper, we present Next-Intent Vulnerability Detector (NI VD), a novel approach to detect NIV in Android apps by relying on type systems. NI VD applies the inference rules of its type system to the app execution paths containing a sequence of calls to three NIV-related Android APIs. Compared to the state-of-the-art, NI VD is faster and more efficient, without losing precision in detecting NIV. Finally, through NI VD Google Photos was found to be vulnerable, and we disclosed the finding on the Google official bug report website (issue number 124342801)

Spatial bloom filter in named data networking: A memory efficient solution

Among the possible future Internet architectures, Information Centric Networking (ICN) is the most promising one and researchers working on the Named Data Networking (NDN) project are putting efforts towards its deployment in a real scenario. To properly handle content names, the different components of an NDN network need efficient and scalable data structures. In this paper, we propose a new data structure to support the NDN forwarding procedure by replacing the current Forwarding Information Base (FIB): the Spatial Bloom Filter (SBF), a probabilistic data structure that guarantees fast lookup and efficient memory consumption. Through a set of simulations run to compare the performance of FIB and SBF, we found that the latter uses less than 5 KB of data to handle 106 queried interests, with a (negligible) probability 10-4 of false positive events. Conversely, the FIB requires up to 2.5 GB of data in disadvantageous cases, e.g. when interests are composed of a considerable number of components

Repack Me If You Can: An Anti-Repackaging Solution Based on Android Virtualization

A growing trend in repackaging attacks exploits the Android virtualization technique, in which malicious code can run together with the victim app in a virtual container. In such a scenario, the attacker can directly build a malicious container capable of hosting the victim app instead of tampering with it, thus neglecting any anti-repackaging protection developed so far. Also, existing anti-virtualization techniques are ineffective since the malicious container can intercept - and tamper with - such controls at runtime. So far, only two solutions have been specifically designed to address virtualization-based repackaging attacks. However, their effectiveness is limited since they both rely on static taint analysis, thus not being able to evaluate code dynamically loaded at runtime. To mitigate such a problem, in this paper we introduce MARVEL, the first methodology that allows preventing both traditional and virtualization-based repackaging attacks. MARVEL strongly relies on the virtualization technique to build a secure virtual environment where protected apps can run and be checked at runtime. To assess the viability and reliability of MARVEL, we implemented it in a tool, i.e., MARVELoid, that we tested by protecting 4000 apps with 24 different configurations of the protection parameters (i.e., 96k protection combinations). MARVELoid was able to inject the protection into 97.3% of the cases, with a processing time of 98 seconds per app on average. Moreover, we evaluated the runtime overhead on 45 apps, showing that the introduced protection has a negligible impact in terms of average CPU (<5%) and memory overhead (<0.5%)