In the fight against illegal football streaming, Italy introduced Piracy Shield, a platform through which copyright holders can notify the national regulator (AGCOM), which in turn orders ISPs to block infringing resources -- such as IP addresses and Fully Qualified Domain Names (FQDNs) -- within 30 minutes. In this paper, we present the first investigation into the platform's real-world impact by reconstructing and analyzing its blocking activity. Our analysis shows that the platform causes significant collateral damage. Indiscriminate IP-level blocking has disrupted and continues to disrupt hundreds of legitimate, non-streaming websites. At the same time, the platform's effectiveness may have been undermined by streamers who evaded enforcement by migrating to new infrastructure and unfiltered IP address space. Based on these findings, we call on Italian authorities and policymakers to critically reconsider the platform's core blocking principles. The evidence suggests that its broad impact on legitimate services and the potential national security risks outweigh its intended benefits.

Wearable robots are designed to provide physical assistance and rehabilitation training. Light-weight designs are desirable for human usage and parallel robots are quite suitable due to low moving inertia. One of the challenges of using wearable parallel robots is to compute the end-effector position/orientation from joint angle measurements, as the forward kinematics problem is computationally difficult. It becomes even more challenging if the kinematic model changes from the nominal model due to deflection of the mechanical members, manufacturing tolerances, or misalignments of the joints between the machine and the user.Artificial neural networks have been used to provide solution of the forward kinematics problem for parallel robots. However, their efficacy has not yet been studied in wearable applications where the linkages of the robots are not fully rigid. In this paper, we conducted a case study to investigate the performance of using neural network models to compute forward kinematics of a wearable robot. Our results show that the performance of neural networks is superior to numerical approaches and produces reasonable solutions even at the boundary of the robot workspace.

Gait training is widely used to treat gait abnormalities. Traditional gait measurement systems are limited to instrumented laboratories. Even though gait measurements can be made in these settings, it is challenging to estimate gait parameters robustly in real-time for gait rehabilitation, especially when walking over-ground. In this paper, we present a novel approach to track the continuous gait cycle during overground walking outside the laboratory. In this approach, we instrument standard footwear with a sensorized insole and an inertial measurement unit. Artificial neural networks are used on the raw data obtained from the insoles and IMUs to compute the continuous percentage of the gait cycle for the entire walking session. We show in this paper that when tested with novel subjects, we can predict the gait cycle with a Root Mean Square Error (RMSE) of 7.2%. The onset of each cycle can be detected within an RMSE time of 41.5 ms with a 99% detection rate. The algorithm was tested with 18840 strides collected from 24 adults. In this paper, we tested a combination of fully-connected layers, an Encoder-Decoder using convolutional layers, and recurrent layers to identify an architecture that provided the best performance.

Various leg exoskeletons have been designed for gait rehabilitation. The transparency of these exoskeletons is crucial to their effectiveness in gait training. The weight and inertia of an exoskeleton are two important factors affecting its transparency. In this study, using a light-weight leg exoskeleton C-ALEX, we conducted a series of experiments to explore the effect of exoskeleton weight and inertia on the natural walking of twelve healthy subjects. They walked in C-ALEX under three levels of mass added to the leg: (i) no added mass, (ii) 1.8 kg, and (iii) 3.6 kg, and three different setups of C-ALEX: (i) freewalking without C-ALEX, (ii) with C-ALEX, and (iii) with C-ALEX compensating for the weight of the added mass. The result shows that increasing exoskeleton mass increases step length, decreases step height, and reduces maximum knee flexion. After weight compensation, the step height, and the maximum knee flexion partially restored, but the step length did not, implying that the inertia is responsible for the change in step length. The study demonstrates that compensating for weight alone cannot eliminate the changes due to exoskeleton mass. On the other hand, reducing the overall mass of the exoskeleton can better preserve the natural gait of the subjects.

Current geocasting algorithms for VANETs are being designed to enable either private or reliable communications, but not both. Existing algorithms preserve privacy by minimizing the information used for routing, and sacrifice message delivery success. On the other hand, reliable protocols often store node information that can be used to compromise a vehicle's privacy. We propose a secure, privacy-preserving geocasting protocol for VANETs that uses direction-based dissemination and ensures confidentiality. Privacy is achieved via unlinkable pseudonymous channels, and encryption and authentication with a public key technique. To reduce message duplication, we apply dynamic traffic restriction and probabilistic forwarding techniques, which depend on message rate and cumulative payload, as well as the value of the angle of spreading of the direction-based scheme. Our analysis shows that due to dynamic traffic restriction, node density does not meaningfully affect reliability, while the angle of spreading does have a significant influence.