Can Artificial Noise Boost Further the Secrecy of Dual-hop RIS-aided Networks?

In this paper, we quantify the physical layer security of a dual-hop regenerative relaying-based wireless communication system assisted by reconfigurable intelligent surfaces (RISs). In particular, the setup consists of a source node communicating with a destination node via a regenerative relay. In this setup, a RIS is installed in each hop to increase the source-relay and relay-destination communications reliability, where the RISs' phase shifts are subject to quantization errors. The legitimate transmission is performed under the presence of a malicious eavesdropper attempting to compromise the legitimate transmissions by overhearing the broadcasted signal from the relay. To overcome this problem, we incorporate a jammer to increase the system's secrecy by disrupting the eavesdropper through a broadcasted jamming signal. Leveraging the well-adopted Gamma and Exponential distributions approximations, the system's secrecy level is quantified by deriving approximate and asymptotic expressions of the secrecy intercept probability (IP) metric in terms of the main network parameters. The results show that the secrecy is enhanced significantly by increasing the jamming power and/or the number of reflective elements (REs). In particular, an IP of approximately $10^{-4}$ can be reached with $40$ REs and $10$ dB of jamming power-to-noise ratio even when the legitimate links' average signal-to-noise ratios are $10$-dB less than the eavesdropper's one. We show that cooperative jamming is very helpful in strong eavesdropping scenarios with a fixed number of REs, and the number of quantization bits does not influence the secrecy when exceeding $3$ bits. All the analytical results are endorsed by Monte Carlo simulations

Predicting long-term type 2 diabetes with support vector machine using oral glucose tolerance test

Diabetes is a large healthcare burden worldwide. There is substantial evidence that lifestyle modifications and drug intervention can prevent diabetes, therefore, an early identification of high risk individuals is important to design targeted prevention strategies. In this paper, we present an automatic tool that uses machine learning techniques to predict the development of type 2 diabetes mellitus (T2DM). Data generated from an oral glucose tolerance test (OGTT) was used to develop a predictive model based on the support vector machine (SVM). We trained and validated the models using the OGTT and demographic data of 1,492 healthy individuals collected during the San Antonio Heart Study. This study collected plasma glucose and insulin concentrations before glucose intake and at three time-points thereafter (30, 60 and 120 min). Furthermore, personal information such as age, ethnicity and body-mass index was also a part of the data-set. Using 11 OGTT measurements, we have deduced 61 features, which are then assigned a rank and the top ten features are shortlisted using minimum redundancy maximum relevance feature selection algorithm. All possible combinations of the 10 best ranked features were used to generate SVM based prediction models. This research shows that an individual’s plasma glucose levels, and the information derived therefrom have the strongest predictive performance for the future development of T2DM. Significantly, insulin and demographic features do not provide additional performance improvement for diabetes prediction. The results of this work identify the parsimonious clinical data needed to be collected for an efficient prediction of T2DM. Our approach shows an average accuracy of 96.80% and a sensitivity of 80.09% obtained on a holdout set

Data confidentiality for IoT networks: cryptographic gaps and physical-layer opportunities

The conventional solution for providing data confidentiality is by means of encryption (a branch of cryptography). However, encryption schemes are generally designed to provide a certain level of security without necessarily taking resource consumption into account. This poses an issue for Internet of Things (IoT) devices which are limited in terms of storage capacity and computational capabilities. In this paper, we discuss the capabilities of cryptographic solutions for providing data confidentiality and we evaluate whether these solutions are appropriate for IoT networks in terms of resource consumption. Based on the identified drawbacks of cryptographic solutions, we discuss opportunities within the area of physical-layer security (PLS). Finally, we provide an overview of PLS schemes which aim to enhance data confidentiality in IoT networks

Parents of Children with Cleft Lip Exhibit Heightened Visual Attention to the Perioral Area

Background:. Following high-quality surgical repair, children born with a cleft lip anomaly may still display lasting visual differences. We exposed control adults and parents of affected children to images of children with cleft deformity and compared their visual tracking patterns. The protocol investigated whether parental exposure to secondary cleft deformity heightens or diminishes visual attraction to this type of structural facial variation. Method:. Twenty participants (10 control adults, 10 parents of affected children) assessed 40 colored images of children's faces while their eye movements were tracked. Twenty-four control images and 16 repaired cleft lip images were displayed to observers. Nine bilateral facial aesthetic zones were considered as regions of interest. Percentage of time visually fixating within each region, and statistical differences in fixation duration percentage between the two participant groups and across the bilateral regions of interest were analyzed. Results:. While both groups of observers directed more visual attention to the nasal and oral regions of the cleft images than control images, parents of children with cleft lip spent significantly more time fixating on these areas (25% and 24% of the time, respectively) than did unaffected adults (14.6% and 19.3%; P < 0.001). Conclusions:. These results demonstrate that parents of cleft lip children exhibit heightened attention to this type of facial difference relative to the naive observer. These findings highlight that observer profile can meaningfully influence the perception of a facial deformity. Awareness of this information may enhance communication between surgeon and parents of an affected child by providing added insight into parental perspective

Fine-Tuned RNN-Based Detector for Electricity Theft Attacks in Smart Grid Generation Domain

In this article, we investigate the problem of electricity theft attacks on smart meters when malicious customers (i.e., adversaries) claim injecting more generated energy into the grid to get more profits from utility companies. These attacks can be applied by accessing the smart meters monitoring renewable-based distributed generation (DG), and manipulating the reading. In this article, we propose approaches that rely on data sources with only a single generator (i.e., solar only) and multifuel type; and address the crucial effects of slight perturbations that the attacker can add, which can deceive the detector. In particular, this article introduces an efficient multitask deep-learning-based detector that offers a higher detection rate, copes with different fuel types, and uses only single data sources. The proposed detector incorporates months and days as two additional features to boost the performance and properly guide the model to successful detection. The proposed method is then extended to consider small perturbations that attackers may use to launch successful attacks. We conduct extensive simulations for two different detectors, one for solar DG and the other for multiple fuel types (i.e., solar and wind). Using a realistic dataset, the results reveal that the proposed recurrent neural network-based detectors identify adversaries at a higher rate than the existing solutions, even with minimal perturbations and different fuel types