Today, healthcare fraud poses a significant issue, encompassing everything from falsified billing claims and phantom services to the excessive prescription of opioid medications and medical identity theft. These deceptive activities cause substantial financial losses, erode patient trust, compromise healthcare quality, and threaten patient safety. In this paper, we introduce SSI-MedRx, a healthcare system based on blockchain technology and Self-Sovereign Identity (SSI). It is designed to ensure cross-border interoperability, preserve patient privacy, and prevent challenging healthcare frauds, including medical identity theft, phantom billing, kickbacks, and opioid overprescribing. By design, our system empowers patients by granting them complete control over their personal and health data. This shift toward patient-centric data management can potentially reduce the risk of data breaches, enhance care coordination, and improve overall healthcare outcomes.

Modeling count data using suitable statistical distributions has been instrumental for analyzing the patterns it conveys. However, failing to address critical aspects, like overdispersion, jeopardizes the effectiveness of such an analysis. In this paper, overdispersed count data is modeled using the Dirichlet Multinomial (DM) distribution by maximizing its likelihood using a fixed-point iteration algorithm. This is achieved by estimating the DM distribution parameters while comparing the recent Languasco-Migliardi (LM), and the Yu-Shaw (YS) procedures, which address the well-known computational difficulties of evaluating its log-likelihood. Experiments were conducted using multiple datasets from different domains spanning polls, images, and IoT network traffic. They all showed the superiority of the LM procedure as it succeeded at estimating the DM parameters at the designated level of accuracy in all experiments, while the YS procedure failed to produce sufficiently accurate results (or any results at all) in several experiments. Moreover, the LM procedure achieved a speedup that ranged from 2-fold to 20-fold over YS.

The advent of Industry 5.0 represents a paradigm shift towards a more human-centric approach in manufacturing, focusing on integrating human operators with advanced technological systems. Despite significant progress in predictive maintenance for machinery, there is a notable gap in predictive assessment technologies to safeguard human operators. This paper introduces a novel conceptual framework designed to fill this gap by leveraging predictive technologies and methodologies to proactively monitor human operators in Industry 5.0 paradigm settings. Our framework emphasizes the importance of human well-being and safety by integrating data collection, advanced analytics, and targeted intervention techniques. Through a literature review of related works and a detailed exposition of our framework, we highlight its potential to enhance operational efficiency, environmental sustainability, and, importantly, the overall welfare of the workforce. This research underlines the critical need for a balanced focus on both technological advancement and the well-being of human operators, proposing a preemptive approach that aligns with the pillars of Industry 5.0. We discuss the implications of our findings for future research, particularly the need for ethical data collection practices, real-time data processing techniques, and personalized interventions. The proposed framework categorizes conceptual approaches and introduces recent innovations in predictive assessment technologies, outlining the way for more sustainable, efficient, and human-centric industrial environments.

Summary Smartwatches are becoming increasingly ubiquitous as they offer new capabilities to develop sophisticated applications that make daily life easier and more convenient for consumers. The services provided include applications for mobile payment, ticketing, identification, access control, etc. While this makes modern smartwatches very powerful devices, it also makes them very attractive targets for attackers. Indeed, PINs and Pattern Lock have been widely used in smartwatches for user authentication. However, such authentication methods are not robust against various forms of cybersecurity attacks, such as side channel, phishing, smudge, shoulder surfing, and video‐recording attacks. Moreover, the recent adoption of hardware‐based solutions, like the Trusted Execution Environment (TEE), can mitigate only partially such problems. Thus, the user's security and privacy are at risk without a strong authentication scheme in place. In this work, we propose 2GesturePIN, a new authentication framework that allows users to authenticate securely to their smartwatches and related sensitive services through solely two gestures. 2GesturePIN leverages the rotating bezel or crown, which are the most intuitive ways to interact with a smartwatch, as a dedicated hardware. 2GesturePIN improves the resilience of the regular PIN authentication method against state‐of‐the‐art cybersecurity attacks while maintaining a high level of usability.

In the last months, the market for personal wearable devices has been booming significantly, and, in particular, smartwatches are starting to assume a fundamental role in the Bring Your Own Device (BYOD) arena as well as in the more general Internet of Things (IoT) ecosystem, by acting both as sensitive data sources and as user identity proxies. These new roles, complementing the more traditional personal assistance and telemetry/tracking ones, open new perspectives in their integration in complex IoT-based critical infrastructures such as e-payment, health care monitoring, and emergency systems, as well as in their usage as remote control facilities in smart services. Users can access their IoT devices at any time from any place through smartwatches. We argue that this new scenario calls for a strengthened and more resilient authentication of users on these devices, despite their limitations in terms of dimensions and hardware constraints that may considerably affect the usability of security mechanisms. In this article, we present an innovative authentication scheme targeted at smartwatches, namely CirclePIN, that provides both resilience to most common attacks and a high level of usability in tests with real users.

Resource-constrained systems are becoming more and more common as users migrate from PCs to mobile devices and as IoT systems enter the mainstream. At the same time, it is not acceptable to reduce the level of security hence it is necessary to accommodate the required security into the system-imposed resource constraints. This paper introduces BAdDroIds, a mobile application leveraging machine learning for detecting malware on resource constrained devices. BAdDroIds executes in background and transparently analyzes the applications as soon as they are installed, i.e., before infecting the device. BAdDroIds relies on static analysis techniques and features provided by the Android OS to build up sound and complete models of Android apps in terms of permissions and API invocations. It uses ad-hoc supervised classification techniques to allow resource-efficient malware detection. By exploiting the intrinsic nature of data, it has been possible to implement a state-of-the-art data-driven model which provides deep insights on the detection problem and can be efficiently executed on the device itself as it requires a very limited computational effort. Besides its limited resource footprint, BAdDroIds is extremely effective: An extensive experimental evaluation shows that it outperforms the currently available solutions in terms of accuracy, which is around 99 percent.

The market for invoice financing has been steadily growing in the last few years and has been the third financing market in size in 2016. Most solutions in this field are based on private platforms and even the new proposals based on blockchain are mostly adopting a private, permissioned blockchain. In this paper, we propose an idea based on a public blockchain that allows both fully open and group-restricted auctioning of invoices. Furthermore, our proposal introduces a reputation system that is based on the past behavior of entities, as it is photographed by the public blockchain, to allow insurance companies modulate the cost of the insurance contracts they offer. This combination guarantees the complete transparency and tamperproof-ness of a public blockchain, while it allows reducing insurance costs and fraud possibilities.

Smartwatches offer new capabilities to develop sophisticated applications that make daily life easier and more convenient for consumers and are becoming increasingly ubiquitous. The kind of services these devices are capable to provide include applications for mobile payment, ticketing, identification, access control, etc. While this makes modern smartwatches very powerful devices, it also makes them very attractive targets for attackers. PINs and Pattern Lock have been widely used in smartwatches for user authentication, however, those types of passwords are not robust against various forms of attacks, such as side channel, phishing, smudge, shoulder surfing, and videorecording attacks. In this work, we propose 2GesturePIN, a new authentication method that allows users to authenticate securely to their smartwatches and sensitive services through solely two gestures. It leverages the rotating bezel or the crown which are the most intuitive channels to interact with a smartwatch. 2GesturePIN enhances the resilience of the regular PIN to common attacks while maintaining a high level of usability.

In the today's mobile communications scenario, smartphones offer new capabilities to develop sophisticated applications that seem to make daily life easier and more convenient for users. Such applications, which may involve mobile ticketing, identification, access control operations, etc., are often accessible through social network aggregators, that assume a fundamental role in the federated identity management space. While this makes modern smartphones very powerful devices, it also makes them very attractive targets for spyware injection. This kind of malware is able to bypass classic authentication measures and steal user credentials even when a secure element is used, and can, therefore, perform unauthorized mobile access to social network services without the user's consent. Such an event allows stealing sensitive information or even a full identity theft. In this work, we address this issue by introducing BrightPass, a novel authentication mechanism based on screen brightness. BrightPass allows users to authenticate safely with a PIN-based confirmation in the presence of specific operations on sensitive data. We compare BrightPass with existing schemes, in order to show its usability and security within the social network arena. Furthermore, we empirically assess the security of BrightPass through experimentation. Our tests indicate that BrightPass protects the PIN code against automatic submissions carried out by malware while granting fast authentication phases and reduced error rates.

This paper investigates the feasibility of constructing power-consumption-based sensors for the identification of security threats (e.g. battery-drain attacks) on Android based mobile devices. In particular, this paper proposes a measurement methodology and high-level models for the energy consumpt ion of two very important hardware subsystems in a mobile device, namely the Wi-Fi and the CPU. The measuring methodology and the high-level models are then compared to others described in the literature and validated through actual experiments. Finally, the proposed methodology is tested with an energy oriented variant of the ping-flood attack performed while a legitimate application is running. Experimental results show that the measurement methodology is sound, precise and reliable in detecting the onset of an attack.

One of the fundamental security elements in cellular networks is the authentication procedure performed by means of the Subscriber Identity Module that is required to grant access to network services and hence protect the network from unauthorized usage. Nonetheless, in this work we present a new kind of denial of service attack based on properly crafted SIM-less devices that, without any kind of authentication and by exploiting some specific features and performance bottlenecks of the UMTS network attachment process, are potentially capable of introducing significant service degradation up to disrupting large sections of the cellular network coverage. The knowledge of this attack can be exploited by several applications both in security and in network equipment manufacturing sectors.

The purpose of this paper is to analyze the state of the art in the field of memory support systems. In particular, this analysis is focalized on systems supporting prospective memory, which is the form of memory that reminds an individual to perform a planned action or intention that, for some reason, can't be performed in the moment in which is formulated, but must be postponed to a further moment.

Green Security is a new research field defining and investigating security solutions under an energy-aware perspective. Green Security aims at: (1) evaluating the actual security mechanisms in order to assess their energy consumption; (2) building new security mechanisms by considering energy costs from the design phase. In this paper, we first provide a definition of Green Security and formalism to model it, then we provide a use case showing how it is possible to model the energy consumption of two Intrusion Detection System (IDS) strategies, finally we leverage this model to assess the energy leakage due to the late discovery of bad packets.

Several studies both in the field of psychology and in the field of physiology relate stress to the difficulty of transferring information from short term memory to medium term memory. This difficulty causes a reduction in personal efficiency and generates heavy frustration as things to do tend to come to mind with no relation to the user current environment, thus further increasing the stress level of the subjects. We claim that the provision to subjects of timely information about the activities that could be efficiently performed in their current environment could reduce this phenomenon and thus increase personal efficiency and remove the related sense of frustration. In this paper we describe the prototype of a system capable of gathering user needs from verbal commands, translate those needs into queries to a GIS, build a map of locations of interest for the user expressed needs and provide timely prompts as soon as the user current environment guarantees the possibility to efficiently perform an action that satisfies one of the identified needs.

Summary form only given. Two of the the two main threads in distributed computing that recently show noticeable progress are grid computing and the development of Web-based services. It is our opinion that the adoption of the standards fostered by the Web-service community could improve the level of usability and interoperability of grid systems and lead to the development of metacomputing systems based on reusable, interoperable components. Up to now the adoption of Web services in grid projects has never penetrated the core computational model. This is mainly due to the fact that Web services use by default the SOAP protocol which is extremely ill suited to computational science applications. We show how we leveraged the Web service invocation framework (WSIF) to develop a new high performance binding based on XDR coding and how we improved the general WSIF performance by developing an extension based on a fully static service invocation.

This paper describes the Harness-PVM emulation module that makes it possible to run legacy PVM applications within the Harness metacomputing system environment. We show benchmark results suggesting that component- and Java-based frameworks may provide substantial benefits without significantly compromising performance. Augmenting or implementing grids using such component based systems can thus be advantageous in a number of situations.

Software infrastructures that support metacomputing are evolving from traditional monolithic, platform-specific systems to component and service-based frameworks. We demonstrate that contrary to popular belief, such modular software systems are capable of delivering good to excellent performance, support legacy as well as new application programming paradigms, and deliver enhanced functionality. The Harness system, a software backplane enabling reconfigurable distributed concurrent computing is used to emulate the PVM programming environment. Numerical kernel benchmarks show that application performance results using the emulator and native versions are within a few percent of each other. Coupled with the ability to leverage pre-existing and specialized modules, our experiences suggest that service-oriented computational grids may be constructed rapidly and effectively via such component-based architectural frameworks that deliver full functionality, without compromising efficiency.

The recent progress of RISC technology has led to the feeling that a significant percentage of image processing applications, which in the past required the use of special purpose computer architectures or of ad hoc hardware, can now be implemented in software on low cost general purpose platforms. We decided to undertake the study described in this paper to understand the extent to which this feeling corresponds to reality. We selected a set of reference RISC-based systems to represent RISC technology, and identified a set of basic image processing tasks to represent the image processing domain. We measured the performance and studied the behavior of the reference systems in the execution of the basic image processing tasks by running a number of experiments based on different program organizations. The results of these experiments are summarized in a table, which can be used by image processing application designers to evaluate whether RISC-based platforms are able to deliver the computing power required for a specific application.

Motion estimation is a key technique in most algorithms for video compression and particularly in the MPEG and H.261 standards. The most frequently used technique is based on a Full Search Block Matching Algorithm which is highly computing intensive and requires the use of special purpose architectures to obtain real-time performance. We propose an approach to the parallel implementation of the Full Search Block Matching Algorithm which is suitable for implementation on massively parallel architectures ranging from large scale SIMD computers to dedicated processor arrays realized in ASICs. While the first alternative can be used for the implementation of high performance coders the second alternative is particularly attractive for low cost video compression devices. This paper describes the approach proposed for the parallel implementation of the Full Search Block Matching Algorithm and the implementation of such an approach in an ASIC.