Miracle-dz/UniFAR-FTUs huggingface dataset:
An Object-Oriented Model Based on the Specialization of Real-Time UML/MARTE and Hybrid Automata to Realize Industrial Hybrid Dynamic Systems

In this paper, we present a hybrid control design, which is followed up by the Real-Time Unified Modeling Language (UML) and MARTE, in combination with hybrid automata to intensively deploy controllers of industrial Hybrid Dynamic Systems (HDS). The paper shows out step-by-step the specification of an industrial HDS modeled by the specialization of hybrid automata to capture the control requirements. The design artifacts of industrial HDS are then carried out by specializing the Real-Time UML/MARTE that allow finding out quickly the main control capsules, their ports and communication protocols, in order to precisely model and closely allocate control structures and dynamic behaviors for implementing controllers of systems. The important realization hypotheses of timing concurrency are next shown out that permit the designed capsules to perform the control evolutions for HDS applications. Finally, this proposed model is applied to develop a horizontal planar trajectory-tracking controller of a low-cost turtle-shaped autonomous underwater vehicle.
Early Detection of Diabetes Mellitus Using Differentially Private SGD in Federated Learning

Diabetes mellitus is a chronic disease that appears when the pancreas does not produce enough insulin or the body does not correctly use its insulin. If not adequately managed or diagnosed on time, this pathology can cause a lot of damage to the body organs, such as the heart, eyes, kidneys, and so on. Research carried out through machine learning has made it possible to have increasingly efficient and precise models for detecting and preventing type 2 diabetes. However, most of the models mentioned do not offer guarantees on the privacy of patient data used during the training process. In addition, these models are generally stored in a centralized repository, where the analysis is performed with full access to sensitive content, implying increased attack risks on confidentiality and privacy. This paper proposes a Differentially Private Stochastic Gradient Descent applied to the Federated Averaging (DPSGDFedAvg) model for diabetes prediction using the Pima Indian dataset. In first results, we obtained an accuracy between 60% and 70% with a raised level of privacy. We demonstrate in this work the feasibility and effectiveness of the DPSGDFedAvg model in offering a raised level of privacy and maintaining utility of the global FL model.
Multi-Agent Reinforcement Learning With Contribution-Based Assignment Online Routing In SDN

Emerging applications place critical QoS requirements on the Internet. Networks need to guarantee different quality of service (QoS) requirements for different data flows for various Internet services. Improvements in traffic classification techniques, software-defined networking (SDN), and programmable network devices make it possible to quickly identify user requirements and control the routing of fine-grained traffic. In this paper, we propose CBR, an online routing algorithm using multi-agent deep reinforcement learning. CBR uses GCN to extract topology features, designs different reward functions to learn appropriate routing policies for different types of traffic demands, and organizes agents to generate routes in a hop-by-hop approach. In addition, to address the challenge of not being able to distinguish whether the actions made by each agent are critical or not due to shared reward values, CBR designs a new baseline to indicate their contribution level. Finally, to ensure reliability and speed up training, we use pre-training to learn shortest path rules to obtain initial parameters to speed up training and introduce a routing alternative mechanism to provide security for online routing. We conducted Mininet-based experiments using Abilene and GEANT network topologies. The experimental results show that CBR is able to simultaneously meet the demands of different service types for their requested traffic while performing well in terms of reliability in the case of link failures.
A Proposal of an End-to-End DoA Estimation System Aided by Deep Learning

Direction of arrival (DoA) estimation is a technique used in, for instance, radar systems, source localization, and wireless channel estimation, and improving its accuracy is becoming more important as its applications expand. In previous works, we verified that deep neural networks (DNN) trained offline are a viable tool for achieving great on-grid DoA estimation performance, even compared to the traditional root multiple signal classification (root-MUSIC) algorithm. Moreover, we separately proposed the signal-to-noise ratio (SNR)-based DNN selection method in order to fully leverage the DNN learning potential at specific SNRs; and the superposition of two DNNs (staggered DNNs) in order to overcome the estimation failure caused by incident radio waves at the grid border. In this paper, we propose an end-to-end DoA estimation system, which consists of three modules: DoA, SNR and source number estimators. Here, we expand the performance of the DoA estimator module by presenting a new algorithm more efficient in detecting DoA from the DNN output, and by using the combination of the two previously mentioned methods: SNR-based DNN selection and staggered DNNs. Also, an SNR estimation scheme with better precision is presented.
Impact Analysis of Crypto Miner Malware Attacks Using Android Debug Bridge (ADB) Vulnerabilities via TCP/IP on Android-Based Raspberry Pi 4 IoT Device

Crypto-jacking attacks on IoT devices increase by 22% in 2021 compared to those in 2020. Crypto-jacking attacks using crypto miner malware were sent to the IoT devices via Android Debug Bridge (ADB) port, where the IoT devices such as smart TVs, phones, IP cameras and others running the Android operating system that ADB port was open. This study was made to find out how the attack workflow occurs and the possible impact on the device if it is exposed to the attack. The research was conducted by simulating Crypto-jacking Malware crypto-miner attacks using vulnerabilities in ADB via TPC/IP and then analyzing the impact of malware on devices using the hybrid analysis method. It was revealed that the malware used a lot of computing resources as seen from the CPU Clock Speed increasing up to eight times compared to when the malware was not running. The CPU Utilization Presentation also saw an increase when the malware was running, and the CPU computing resource usage increased up to 100%. In addition, the device temperature also experienced a significant trend of increasing, up to more than 24°C when the malware started running. Meanwhile, the memory usage did increase but not significantly and did not have much effect on the device.
Look, Tell and Match: Refining Video-Text Retrieval with Semantic Information

Video-text retrieval aims to search videos from the database by a natural sentence. However, the core problem of this challenging task is the modality gap between video and text data. As videos are made of abundant visual patterns and texts depict abstract and general concepts. When considering videotext matching, the extracted features from videos and texts remain an asymmetric scale information problem. In this paper, a novel Semantic Enhanced Cross-Modal Retrieval (SECMR) method is proposed to align videos and texts at a symmetric scale. Specifically, we utilize a generative model to predict semantic information from given videos and refine the visual feature with enhanced semantic information. Then we utilize a contrastive learning method to align refined videos and texts in a common space. Extensive experiments demonstrate that our proposed SECMR outperforms the-state-of-the-art counterparts by 1.3%, 3.6%, 4.1% in the metric R@1, R@5, R@10, respectively. Our codes and trained models are released at: https://anonymous.4open. science/r/SECMR-2104.
Vehicular Cloud Computing for Population Evacuation Optimization

This study focuses on online evacuation management. We design a framework to solve dynamic population evacuation (DPE) problems by creating an initial evacuation plan and employing inter-vehicle communication under a cloud computing architecture. For creating an initial plan, we formulate and solve dynamically (i) the shelter allocation problem (SAP) to choose a shelter node representing safety, and (ii) dynamic traffic assignment (DTA) to choose the best path leading towards the chosen destinations. The DPE process then begins with the planning phase to design a plan for the evacuation operation. Afterward, a vehicular ad hoc network (VANET) is deployed according to vehicular cloud computing (VCC) to make it possible for evacuees to share information and have online guidance from the server. We use vehicular communication to revise vehicle decisions initially taken by the plan, considering the dynamic progression of the catastrophe risk and congestion levels. To approve our new framework, we apply it to a real test case of Luxembourg city. We measure the clearance time and the mean evacuation time of the proposed solution and compare them with the results of other planning methods in the literature. Results show that online solving of DPE can minimize mean evacuation time and network clearance time by more than 10% compared to other models with only planning optimization. In addition, we analyze the performance of the proposed framework by changing the penetration rate of connected vehicles, and we observe that the increase in penetration rate leads to a decrease in network clearance time.
Electronic correlation effects in a model of endohedral Mg (Mg@C60)

We have studied theoretically the atomic structure and ground-state photoionization cross-section in a model of endohedral Mg (Mg@C60) with particular emphasis on doubly excited bound and autoionizing (AI) states. We modelled Mg@C60 as a system of two active electrons outside a frozen, doubly charged ionic core, confined to a spherically symmetric potential well of finite depth. We employed a suitable B-spline basis set within a CI approach to describe the two-electron states of the confined system. In addition, we have calculated the ground-state photoionization cross-section in a limited energy range above the first ionization threshold. We found that doubly excited perturbers appear in each regular singly excited Rydberg series of bound states. They are composed of two-electron configurations built with those one-electron orbitals mostly affected by the confining potential. Moreover, the low-lying part of the AI spectrum for each symmetry analysed is perturbed by states novel to the confined system, whose configuration expansion is again dominated by the one-electron orbitals mostly affected by the confining potential and whose AI widths vary over a wide range of values. Finally, the ground-state photoionization cross-section in a limited energy range (a few eV) above the first ionization threshold is structured by the AI resonances in a way markedly different from the corresponding Mg atom cross-section.
3D Deformable Protein Shapes Classification based on Triangles-Stars and Composite Deep Neural Networks

In this paper, the problem of 3D protein deformable shape classification is addressed. Proteins are macromolecules with deformable shapes, their classification based only on their molecular surfaces is challenging problem. In addition, the protein shapes are related to their functions which makes their classification an important task. Triangular meshes (graphs) my be considered to represent the protein molecular surfaces. In this paper, we propose a new deep learning-based approach for 3D protein deformable shape classification. We propose a 3D deformable shape descriptor and a composite deep neural network. The shape descriptor is based on a set of global and local features resulting from the decomposition of protein 3D shapes into triangles-stars, following the different neighborhood order considered. The classification is performed by a composite deep neural network, each branch corresponding to a neighborhood order. The proposed approach is evaluated against 3D protein benchmark repositories and state-of-the-art methods. Our experimental results show and demonstrate the high performances and the effectiveness of our approach.
Best-Fit: Best Fit Employee Recommendation

In this fast-growing world, there is huge competition in the market for employees. It becomes a tough task from an HR perspective to keep the most talented resources in the company to benefit the productivity of the company because every employee is a valuable asset. Employees tend to shift their current jobs numerous times due to various reasons and therefore employee turnover becomes a serious issue in this challenging world. This paper focuses on resolving the problem of employee attrition using classification algorithms like random forest, logistic regression and SVM on the IBM attrition dataset. If a valuable employee leaves an organization or gets promoted it becomes a difficult and tedious task to replace the employee. Architecture has been proposed in this paper which uses Random forest, SVM, Decision tree classifiers and similarity techniques to find the closest employees suitable for the vacancy. This includes finding similarities in the skills, qualifications and experience. Pre-trained word vectors are used to generate GloVe embeddings for finding document similarity. A personality match between two employees is calculated by taking a big five personality test, followed by clustering and finding the euclidean distance between two answer vectors in the same cluster. Best-Fit will finally recommend best-fit employees on the basis of resume match, personality match and retention probability.
Interaction of a beam of fast electrons with solids

The interaction of a high current relativistic electron beam with solid targets is analyzed. The conditions of the beam separation from the target are presented and the energy losses due to collective effects are evaluated for the cases of metals and dielectrics. The energy losses in dielectrics are higher because of the ionization of atoms in the beam self-consistent electric field. The problem of ion acceleration at the solid-vacuum interface is also considered.
Long-term stability of membrane transducers for proximity sensing

We report on the long-term stability of micromachined silicon membrane resonators for ultrasound based object detection and proximity sensing. The proximity sensor system measures distances up to 10 cm using either a phase or an acoustic Fabry-Perot measurement method. In these applications the transducer elements are continuously excited at their fundamental resonance using the thermomechanical excitation principle. The success of the sensor system depends on the reliability and stability of the micromachined devices, which are fabricated using an industrial pressure sensor process.
Simulation of Nonlinear Waves in a Magnetic Flux Tube near the Quiet Solar Photospheric Network

Recent high-resolution observations from photospheric magnetograms made with the SOHO/Michelson Doppler Imager instrument and Swedish Vacuum Solar Telescope on La Palma showed that magnetic flux tubes in the quiet photospheric network of the solar photosphere are highly dynamic objects with small-scale substructures. We investigate nonlinear waves propagating along a magnetic flux tube in weakly ionized plasmas with high plasma beta ( β ≃ 1) by using three-dimensional neutral-MHD equations. We investigate the wave propagation along a magnetic flux tube with weak current for the two cases of uniform density along the flux tube and density inhomogeneity due to solar gravity. It is shown that shear Alfvén waves are excited due to localized predominantly rotational perturbations, which might be induced in the quiet photospheric network boundaries. Excited waves with strong upflow of wave energy can propagate only upward along the flux tube when the density inhomogeneity due to the gravity is taken into account. We apply the simulation results to the problem of coronal heating from the quiet photospheric network of the solar photosphere.
Two-photon polymerization of cylinder microstructures by femtosecond Bessel beams

In this work, we present an approach to modulate femtosecond laser beams into Bessel beams with a spatial light modulator (SLM) for two-photon polymerization applications. Bessel beams with different parameters are generated and annular optical fields are produced at the focal plane of the objective. Uniform cylinder microstructures are fabricated by a single illumination during a few seconds without stage translation. By modulating the holograms encoded on the SLM, the diameters of the fabricated annular structures can be flexibly controlled in a wide range with no need of changing the optical elements and realignment of the optical path.
Effect of Ultrasonic Vibration on Coefficient of Friction during Strip Drawing

Effect of ultrasonic vibration on coefficient of friction between a rigid steel die and plastically deformed metal has been studied. The coefficient of friction was measured during a strip‐drawing operation with and without application of ultrasonic vibration. The application of ultrasonic vibration during the drawing process substantially reduces the coefficient of friction and die stresses.
Photonic crystals for fluid sensing in the subterahertz range

The authors present experimental results on photonic crystals in the subterahertz range for fluid-sensing applications. Using a standard machining process, a two-dimensional photonic crystal consisting of a triangular array of air holes in the dielectric medium was produced. The insertion of fluids with different refractive indices into the active region affects the transmission profile in the stop band. The experiment agrees well with theoretical calculations.
Numerical Simulation of Scavenging of Small Particles by Charged Droplets

The trajectories of fine aerosol particles in the vicinity of a free falling collector droplet and their deposition on it were investigated numerically by solving the equations of motion of the particle and the droplet in quiescent air. The droplet was assumed to be charged to one half of the Rayleigh limit. The Coulomb, image, Stokes, inertial, and gravitational forces acting upon the particle near the droplet were taken into consideration in the equations of motion. The equations of the droplet motion were also incorporated into the set of equations including the Coulomb and image forces on the droplet due to the particle charge. The flow field in the vicinity of the droplet was determined by numerical solution of the Navier-Stokes equations. The equations of particle motion were solved in threedimensional (3-D) space by the Runge-Kutta method of the fourth order. The collection efficiency of the particles on the droplet was determined by searching the limiting trajectory within the entire space. The results for particles charged to 10 elementary charges of the same and opposite polarity as the droplet, as well as the electrically neutral ones, were compared. The assumption on the charge of the particle was rather arbitrary. It was assumed that particles are not intentionally charged but only possess a charge generated by tribocharging due to random contacts and were independent of the particle size. Charging the collector causes the Coulomb forces between these 2 species to improve particle deposition on the droplet and in this way the aerosol is removed from the gas. For the aerosol particles charged to the same polarity as the collector, the collection efficiency is still higher than for uncharged particles due to the action of the image forces. In this case, the collection efficiency increases for smaller droplets and for particles with increasing diameter.
Analysing longitudinal count data with overdispersion

In many biomedical studies, longitudinal count data comprise repeated responses and a set of multidimensional covariates for a large number of individuals. When the response variable in such models is subject to overdispersion, the overdispersion parameter influences the marginal variance. In such cases, the overdispersion parameter plays a significant role in efficient estimation of the regression parameters. This raises the need for joint estimation of the regression parameters and the overdispersion parameter, the longitudinal correlations being nuisance parameters. In this paper, we develop a generalised estimating equations approach based on a general autocorrelation structure for the repeated overdispersed data. The asymptotic properties of the estimators of the main parameters are discussed, and the estimation methodology is illustrated by analysing data on epileptic seizure counts. Copyright Biometrika Trust 2002, Oxford University Press.
Interpolation-based super-resolution land cover mapping

Super-resolution mapping (SRM) is a technique to produce a land cover map with finer spatial resolution by using fractional proportion images as input. A two-step SRM approach has been widely used. First, a fine-resolution indicator map is estimated for each class from the coarse-resolution fractional image. All indicator maps are then combined to create the final fine-resolution land cover map. In this letter, three popular interpolation methods, Inverse Distance Weighted (IDW), Spline and Kriging, as well as four indicator map combination strategies, including the maximal value strategy and the sequential assignment strategy with and without normalization, were assessed. Based on the application to two simulated images, the performance of all SRM algorithms was assessed. The results show that the two-step SRM approach can obtain smoother land cover maps than hard classification. An increase in zoom factor results in the appearance of numerous small patches and linear artefacts in the SRM results. The accuracies of Spline and Kriging are similar and are both higher than that of IDW. The maximal value strategy can generate a smoother land cover map than the sequential assignment strategy in most cases, and a normalizing indicator value has a mixed effect on the result.
Accretion disk dynamics, photoionized plasmas, and stellar opacities

We present a brief review on the atomic kinetics, modeling and interpretation of astrophysical observations, and laboratory astrophysics experiments. The emphasis is on benchmarking of opacity calculations relevant for solar structure models, photoionized plasmas research, the magnetohydrodynamic numerical simulation of accretion disk dynamics, and a connection between radiation transport effects and plasma source geometry details. Specific cases of application are discussed with relevance to recent and proposed laboratory astrophysics experiments as well as Chandra and X-ray Multi-Mirror Mission Newton observations.
Adaptive discrete-continuous modeling of evolving discontinuities

Purpose – The purpose of this paper is to develop a method to model entire structures on a large scale, at the same time taking into account localized non-linear phenomena of the discrete microstructure of cohesive-frictional materials. Design/methodology/approach – Finite element (FEM) based continuum methods are generally considered appropriate as long as solutions are smooth. However, when discontinuities like cracks and fragmentation appear and evolve, application of models that take into account (evolving) microstructures may be advantageous. One popular model to simulate behavior of cohesive-frictional materials is the discrete element method (DEM). However, even if the microscale is close to the macroscale, DEMs are computationally expensive and can only be applied to relatively small specimen sizes and time intervals. Hence, a method is desirable that combines efficiency of FEM with accuracy of DEM by adaptively switching from the continuous to the discrete model where necessary. Findings – An exi.