Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Verification through Model Checking of Medical Critical Intelligent Systems

Software systems based on Artificial Intelligence (AI) and Machine Learning (ML) are being widely adopted in various scenarios, from online shopping to medical applications. When developing these systems, one needs to take into account that they should be verifiable to make sure that they are in accordance with their requirements. In this work we propose a framework to perform online verification of ML models, through the use of model checking. In order to validate the proposal, we apply it to the medical domain to help qualify medical risk. The results reveal that we can efficiently use the framework to determine if a patient is close to the multidimensional decision boundary of a risk score model. This is particularly relevant since patients in these circumstances are the ones more likely to be misclassified. As such, our framework can be used to help medical teams make better informed decisions.
High accuracy of pooled DNA genotyping by 2b-RAD sequencing in the Pacific white shrimp, Litopenaeus vannamei

Using pooled DNA genotyping to estimate the proportional contributions from multiple families in a pooled sample is of particular interest for selective breeding in aquaculture. We compared different pooled libraries with separate 2b-RAD sequencing of Litopenaeus vannamei individuals to assess the effect of different population structures (different numbers of individuals and families) on pooled DNA sequencing, the accuracy of parent sequencing of the DNA pools and the effect of SNP numbers on pooled DNA sequencing. We demonstrated that small pooled DNA genotyping of up to 53 individuals by 2b-RAD sequencing could provide a highly accurate assessment of population allele frequencies. The accuracy increased as the number of individuals and families increased. The allele frequencies of the parents from each pool were highly correlated with those of the pools or the corresponding individuals in the pool. We chose 500–28,000 SNPs to test the effect of SNP number on the accuracy of pooled sequencing, and no linear relationship was found between them. When the SNP number was fixed, increasing the number of individuals in the mixed pool resulted in higher accuracy of each pooled genotyping. Our data confirmed that pooled DNA genotyping by 2b-RAD sequencing could achieve higher accuracy than that of individual-based genotyping. The results will provide important information for shrimp breeding programs.
Federated Learning for Energy-Efficient Task Computing in Wireless Networks

In this paper, the problem of minimizing energy consumption for task computation and transmission in a cellular network with mobile edge computing (MEC) capabilities is studied. In the considered network, each user needs to process a computational task at each time slot. A part of the task can be transmitted to a base station (BS) that can use its powerful computational ability to process the tasks offloaded from its users. Since the data size of each user’s computational task varies over time, the BSs must dynamically adjust the resource allocation scheme to meet the users’ needs. This problem is posed as an optimization problem whose goal is to minimize the energy consumption for task computing and transmission via adjusting user association scheme as well as their task and power allocation scheme. To solve this problem, a support vector machine (SVM)-based federated learning (FL) is proposed to determine the user association proactively. Given the user association, the BS can collect the information related to the computational tasks of its associated users using which, the transmit power and task allocation of each user will be optimized and the energy consumption of each user is also minimized. The proposed SVM-based FL method enables the BS and users to cooperatively build a global SVM model that can determine all users’ association without any transmission of users’ historical association and computational task offloading. Simulations using real data on city cellular traffic from the OMNILab at Shanghai Jiao Tong University show that the proposed algorithm can reduce the users’ energy consumption by up to 20.1% compared to the conventional centralized SVM method.
Lumbar lordosis angle value analysis and bone tissue density in the ls section in women after 50 years old.

OBJECTIVE Introduction: The lumbar lordosis angle measured by radiology under normal conditions is between 135-1400. Any deviation from the values is associated with a change in static-dynamic loads and should translate into a change in bone tissue within the lumbar-sacral border, which results from a change in the distribution of load forces acting on individual parts of the vertebral bodies. The aim: To determine the relationship between individual variants of lordosis (hypolordosis, hyperlordosis, norm) and the general density of the spongy part of individual vertebral bodies and in selected zones of the lumbosacral zone (L5 / S1). PATIENTS AND METHODS Material and methods: The study group was a population of women over 50 (n = 277), it was divided into three subgroups, i.e. women with: normal, shallow and deepened lordosis. In the sagittal view, the density of vertebral bodies from L1 to S1 was assessed. RESULTS Results: The difference in the overall density of individual stems between normal lordosis and extreme values was significant (p≥0.05) only for the L1, L2 and S1 vertebrae, whereas in the case of an excessive lordosis angle, the density of spongy bone tissue showed significant differences for all measurement points. CONCLUSION Conclusions: Deviations from the correct values of the lumbar lordosis angle result in a change in compressive and shear forces, which is reflected in the reconstruction of bonetissue. This can be a clue to differentiate the physiotherapy program of women reporting back pain - depending on the size of the lumbar lordosis angle.
Machine Learning-based Inter-Beam Inter-Cell Interference Mitigation in mmWave

In this paper, we address inter-beam inter-cell interference mitigation in 5G networks that employ millimetre-wave (mmWave), beamforming and non-orthogonal multiple access (NOMA) techniques. Those techniques play a key role in improving network capacity and spectral efficiency by multiplexing users on both spatial and power domains. In addition, the coverage area of multiple beams from different cells can intersect, allowing more flexibility in user-cell association. However, the intersection of coverage areas also implies increased inter-beam inter-cell interference, i.e. interference among beams formed by nearby cells. Therefore, joint user-cell association and inter-beam power allocation stand as a promising solution to mitigate inter-beam, inter-cell interference. In this paper, we consider a 5G mmWave network and propose a reinforcement learning algorithm to perform joint user-cell association and inter-beam power allocation to maximize the sum rate of the network. The proposed algorithm is compared to a uniform power allocation that equally divides power among beams per cell. Simulation results present a performance enhancement of 13 – 30% in network’s sum-rate corresponding to the lowest and highest traffic loads, respectively.
LIV-LAM: LiDAR and Visual Localization and Mapping

This paper presents a framework for Simultaneous Localization and Mapping (SLAM) by combining a novel method for object discovery and localization from a monocular camera with depth information provided by Light Detection and Ranging (LiDAR). One major challenge in vision is discovering unknown objects without prior training/supervision, in the wild, and on-the-fly. In our framework, no training samples are available prior to the deployment. We develop an efficient proposal-matching method to discover object temporal saliency, and then finetune these frequently matched object proposals according to tracking information. Detected features of the objects are used as landmark features, and are merged with the LiDAR data in the proposed LIV-LAM (LiDAR and Visual Localization and Mapping). Compared to most visual SLAM or LiDAR-based SLAM, the novelty of this method is the computationally-efficient object detection and localization for feature set-and-match, in order to increase the accuracy of the generated map. The results show that the presented method is superior in both accuracy and efficiency of the maps generated by LiDAR.
Multi-Tier Location-Based Joint Channel Assignment and Power Control Architecture for Mobile Ad-Hoc Networks

Previously proposed Space Division Multiple Access (SDMA) schemes for Vehicular Ad-hoc Networks (VANETs) are based on having uniform cell (i.e., space division) size, shape, and transmission range parameters throughout the entire network geographic area; this is a problem because it creates a ‘one size fits all’ type of inefficiency, preventing the network from reacting to localized network density extremes in an optimal and targeted manner. We propose a novel SDMA-based scheme that enables nodes located in neighboring geographic areas with different node densities to use different cell size, shape and transmission range parameters that are optimal for the node density in each of the neighboring areas; this is accomplished by a multi-tier, joint channel assignment and power control architecture that enables neighboring cells to instantly adapt to local density variations by allowing the cells to dynamically merge (in sparse areas), or split (in dense areas), in a predictable manner, so that local message throughput and latency performance are kept within a desired range, bringing SDMA-based VANETs one step closer to a feasible deployment.
Dynamic Orchestration of Security Services at Fog Nodes for 5G IoT

Fog Computing is one of the edge computing paradigms that envisages being the proximate processing and storage infrastructure for a multitude of IoT appliances. With its dynamic deployability as a medium level cloud service, fog nodes are enabling heterogeneous service provisioning infrastructure that features scalability, interoperability, and adaptability. Out of the various 5G based services possible with the fog computing platforms, security services are imperative but minimally investigated direct live. Thus, in this research, we are focused on launching security services in a fog node with an architecture capable of provisioning on-demand service requests. As the fog nodes are constrained on resources, our intention is to integrate light-weight virtualization technology such as Docker for forming the service provisioning infrastructure. We managed to launch multiple security instances configured to be Intrusion Detection and Prevention Systems (IDPSs) on the fog infrastructure emulated via a Raspberry Pi-4 device. This environment was tested with multiple network flows to validate its feasibility. In our proposed architecture, orchestration strategies performed by the security orchestrator were stated as guidelines for achieving pragmatic, dynamic orchestration with fog in IoT deployments. The results of this research guarantee the possibility of developing an ambient security service model that facilitates IoT devices with enhanced security.
Optimization of Current Excitation for Identification of Battery Electrochemical Parameters based on Analytic Sensitivity Expression

The quality of data plays an important role in determining the accuracy of battery parameter identification/estimation, and hence data optimization for estimation has received increasing attention lately. The main idea is to design input excitation that is most sensitive to the target parameter(s) under estimation. However, most existing studies are hampered by the complexity of sensitivity computation/optimization, and need to impose heuristic patterns on the input to facilitate the optimization. Consequently, they are not capable of finding the ultimate optimal input pattern, and explaining the underlying mechanism. This is especially the case for the parameters of the first-principle electrochemical battery model. This paper aims at performing direct optimization of input excitation with no imposed pattern to find the ultimate optimal profile for estimating battery electrochemical parameters. The practice is enabled by the analytic expressions of sensitivity derived in our previous work. Based on the optimization results, we will explore the features/patterns of optimal profiles for different parameters by correlating to the analytic sensitivity expressions.
A 120-GHz Wireless Link Using 3D-Printed Lens With Flexible Dielectric Fiber Feed and 28-nm CMOS Transceiver

This letter presents the design of a 120-GHz wireless link. The link incorporates a 28-nm CMOS ON–OFF keying transceiver with on-chip antennas to launch the mm-wave signal into a flexible dielectric fiber. The flexible fiber is connected to a 3D-printed high-gain lens proposed to overcome the high free-space path loss to achieve a 15-dBm EIRP. The receiver consists of a low-noise amplifier with 24.9 dB of voltage gain and covers 42-GHz BW−3dB. An efficient self-mixing envelope detector demodulates the amplified signal back to baseband. Using the proposed flexible fiber and wireless topology, a data rate of 11 Gb/s is demonstrated and distance of 1 m is reached with a power consumption <80 mW.
Positive Unknown Inputs Filters Design for Positive Linear Systems

This paper concerns the design of new positive filters for positive linear systems. In fact, we propose a new positive full order filter for positive linear systems subject to unknown inputs and bounded disturbances. The designed filter is always nonnegative at any time and converges asymptotically to the real state vector. This paper is among first attempts to design positive unknown input filters (PUIF) for positive linear systems. The proposed approach is based on the unbiasedness of the estimation error and by imposing the positivity of the design parameters; then a new method to avoid the derivative of the disturbance vector in the filtering error dynamics is proposed. Based on the extended strictly positive real (ESPR) design, this problem is solved by applying the Linear Matrix Inequalities (LMI)-based ESPR Lemma. Note that all structural constraints on filter matrices are addressed in terms of LMI formulation. An algorithm that summarizes the different steps of the proposed positive filter design is given. A numerical example is finally given to illustrate the effectiveness of the proposed method.
Optimal Energy and Thermal Management of Hybrid Battery Packs Using Convex Optimization

The concept of Hybrid Energy Storage Systems (HESS) has recently re-emerged in the context of electrification of medium- to heavy-duty freight vehicles. While HESS offer the opportunity to capitalize on the advantages of high energy density and high power density Lithium ion batteries to reduce the overall pack weight and improve durability, they introduce new complexities in the pack energy and thermal management strategies.This paper presents a method for the optimization of the control policy and thermal management strategy for HESS for an extended range hybrid electric vehicle based upon convex optimization. Starting from a zero-order equivalent circuit model of the battery pack and the formulation of an energy-optimal control problem, the equations are convexified to optimize the instantaneous power split over a prescribed duty cycle, and the method is benchmarked against dynamic programming (DP). To manage the pack temperature, a radiator model is convexified and incorporated into the overall optimization framework.
Design and Nonlinear Control of a Haptic Glove for Virtual Palpation

This paper presents the design and kinematic analysis of a Haptic Glove for medical elastographic imaging virtual palpation. Of the 12 degrees of freedom present in the index finger, middle finger, and thumb of the hand, the design constrains 5 and controls 6 with pneumatic air cylinder actuators, allowing uncontrolled, but measured motion in the remaining degree of freedom. Nearly linear bijective transfer functions between the actuator positions and joint angles are found in closed form for all 6 actuated joints. A nonlinear force and stiffness controller based on backstepping-sliding mode algorithm was designed and implemented.
Learning to Learn Sequential Network Attacks Using Hidden Markov Models

The global surge of cyber-attacks in the form of sequential network attacks has propelled the need for robust intrusion detection and prediction systems. Such attacks are difficult to reveal using current intrusion detection systems, since each individual attack phase may appear benign when examined outside of its context. In addition, there are challenges in building supervised learning models for such attacks, since there are limited labelled datasets available. Hence, there is a need for updating already built models to specific operational environments and for addressing the concept drift. A hidden Markov model (HMM) is a popular framework for sequential modelling, however, in addition to the above challenges, the model parameters are difficult to optimise. This paper proposes a transfer learning (TL) approach that exploits already learned knowledge, gained from a labelled source dataset, and adapts it on a different, unlabelled target dataset. The datasets may be from a different but related domain. Five unsupervised HMM techniques are developed utilising a TL approach and evaluated against conventional machine learning approaches. Baum-Welch (BW), Viterbi training, gradient descent, differential evolution (DE) and simulated annealing, are deployed for the detection of attack stages in the network traffic, as well as, forecasting both the next most probable attack stage and its method of manifestation. Specifically, for the prediction of the three next most likely states and observations, TL with DE achieved a maximum accuracy improvement of 48.3%, and 27.4%, respectively. Finally, the actual detection prediction for the three next most probable states and methods of manifestation reaches 78.9% and 96.3% using TL with BW and DE, respectively.
Accuracy vs. Cost Trade-off for Machine Learning Based QoE Estimation in 5G Networks

Since their first release, 5G systems have been enhanced with Network Data Analytics Functionalities (NWDAF) as well as with the ability to interact with 3rd parties’ Application Functions (AFs). Such capabilities enable a variety of potentials, unimaginable for earlier generation networks, notable examples being 5G built-in Machine Learning (ML) mechanisms for QoE estimation, subject of this paper. In this work, an ML-based mechanism for video streaming QoE estimation in 5G networks is presented and evaluated. The mechanism relies on an ML algorithm embedded in NWDAF, the collection of 5G network KPIs, and the collection of QoE information from video streaming service provider, i.e., the 3rd party AF. The mechanism has been evaluated in terms of QoE estimation accuracy against the cost in terms of required input sources and data for the estimation, and its performance has been compared to alternative methodologies not making use of ML. The evaluation, via simulation activity, clearly highlights the benefits of the proposed mechanism. Based on the derived results, the required input sources are ranked with respect to their importance.
Portfolio Theory Application for 5G Heterogeneous Cloud-RAN Infrastructure

Mobile telecommunication infrastructure has been considered as the deployment of predefined network components at the end-user access. The advent of novel 5G technologies allows the re-conception of the traditional model through which network services are offered to network User Equipment (UE) devices, through the introduction of new network architectures. Cloud-based Radio Access Networks (Cloud-RANs) add to the network flexibility, allowing on-demand instantiation of new base stations. The introduction of heterogeneous technologies in the cells, and the fact that mobile devices are equipped with diverse wireless interfaces create fertile ground for the operators to select the technologies through which each UE in a cell is served, towards maximizing the perceived QoE. In this direction, we apply the Portfolio Theory, stemming from the finance field, to reveal that the appropriate combinations of networking technologies and traffic allocation can achieve the demanded throughput. In this work we provide a detailed description of this architecture and apply the Portfolio Theory for selecting the appropriate combinations of radio access technologies, towards achieving minimum volatility in throughput performance of a heterogeneous Cloud-RAN. We investigate our approach with proof-of-concept testbed experiments that showcase the applicability and performance of Portfolio Theory in real wireless environment conditions.
Optimal Power Allocation for Cooperative DS-CDMA systems

In this paper, we derive the optimal power to be allocated to source and relays in cooperative DS-CDMA systems. The study is valid for any channel. The analysis is carried for dual-hop and multihop systems in the presence of an arbitrary number of DF relays. A significant performance enhancement (2 dB) is observed when the proposed power allocation strategy is performed with respect to uniform power allocation. Our results are new since they take into account multipath propagation between different nodes whereas previous studies are valid for a single path channel.
Experimenting Long Range Wide Area Network in an e-Health Environment: Discussion and Future Directions

Wearable devices/sensors and wireless networking play a pivotal role in enabling e-Health environments demanding biometry monitoring outside hospital facilities. In this regard, the Long-Range Wide-Area Network (LoRaWAN) technology is considered the most adopted wide area network since it promises ubiquitous connectivity in outdoor e-Health applications while keeping network structures and simple management, recently gained interest from the research and industrial community. However, the coexistence of high-dense wireless sensors brings several issues to LoRaWAN, such as high interference and channel congestion. In this paper, we introduce assessments on the LoRaWAN in an e-Health scenario by modeling wireless sensor traffic and implementing it on a network simulator. Simulation results suggest that while LoRaWAN can be extremely useful, by providing communication to thousands of simultaneous users, but its MAC layer structure and design limits significantly. We also introduce future research directions driven by the simulation results and LoRaWAN characteristics, for the goal to improve LoRaWAN performance in an e-Health scenario.
Protective role of vitamin C against lindane toxicity on the histo-architecture of the epididymis and vas deferens of male mice ( Mus musculus )

We studied the protective effects of vitamin C against the toxic effects of lindane on the epididymis and vas deferens of male mice. There were four treatments: controls (untreated), lindane (20 mg/kg), lindane plus vitamin C (10 mg/kg) and vitamin C only. Lindane induced histopathological alterations in the epididymis and vas deferens characterized by a reduced epithelium with degenerative changes in the muscular layers and connective tissues, and a lack of spermatozoa in the lumen of both epididymis and vas deferens. These changes were restored towards normalcy by vitamin C. These results suggest that vitamin C has some protective role against lindane toxicity in epididymis and vas deferens of male mice.