Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep CNN-based Relay Selection in EH Full-Duplex IoT Networks with Short-Packet Communications

In this paper, we propose an efficient deep convolutional neural network-based relay selection (CNS) scheme to evaluate and improve the end-to-end throughput in energy harvesting full-duplex Internet-of-Things (IoT) networks. In this system, multiple full-duplex relays harvest energy from a power beacon to assist data transmission from a source node to multiple users under short packet communications. We propose a best relay best user (bR-bU) selection scheme to improve the diversity packet transmission. We then develop a deep convolutional neural network framework for relay selection and throughput prediction with high accuracy and low execution time. Simulation results show that the proposed CNS scheme achieves almost exactly the throughput of bR-bU one, while it considerably reduces computational complexity, suggesting a real-time configuration for IoT systems under complex scenarios. Moreover, the designed CNN model achieves the root-mean-square-error (RMSE) of 8.4 × 10−3 on the considered dataset, which exhibits the lowest RMSE as compared to the deep neural network and state-of-the-art machine learning approaches.
Beamforming Design and BBU Computation Resource Allocation for Power Minimization in Green C-RAN

This article focuses on the joint optimization of beamforming and baseband unit (BBU) computing resource allocation, with the goal of minimizing the network power consumption for the downlink cloud radio access network (C-RAN). To reduce the computational complexity, we split the joint optimization problem into two subproblems for transmission and computation respectively. The subproblem of minimizing power consumption for transmission is a network-wide beamforming design problem. By using positive semi-definite relaxation (SDR) technology, we transform it into a convex positive semi-definite programming (SDP) problem, which can be solved with effect. For the second subproblem, which intends to minimize the power consumption for computation, a computing resource allocation scheme based on the Simulated Annealing algorithm is proposed, which minimizes the active servers in the BBU pool to save power while meeting each user’s computing resource requirement. The simulation results demonstrate that the algorithm proposed in this paper has a better performance compared with the existing algorithms.
SINR-OP Based Robust AN-Aided Beamforming for Correlated MISO Eavesdropping Channels

Correlation between the main and eavesdropping channels damages secrecy, the achievable secrecy rate under high correlation is small not to satisfy some high-rate applications. This paper optimizes an artificial noise (AN)-aided beamformer by employing a novel quality-of-service criterion, namely signal-to-interference-plus-noise-ratio outage-probability (SINR-OP), and seeks to maximize the target outage SINR under transmit power and SINR-OP constraints. Under Gaussian channel state information uncertainties, a robust ellipsoid-bounding-based approach is employed to approximate the probabilistic constraints; then, a suboptimal solution is obtained. The simulation results verify the proposed AN-aided beamforming achieves the high target outage SINR at the destination.
An Efficient Constrained mm-Wave Hybrid Massive MIMO Beamforming for JSDM based NOMA

Massive MIMO and non-orthogonal multiple access (NOMA) are key technologies for next generation wireless systems due to their distinct advantages. We previously proposed a general framework on unification of code-domain NOMA and joint spatial division and multiplexing (JSDM) which is a spatial user-grouping based hybrid beamforming method for massive MIMO. In this paper, we propose a constrained analog beam-former design for JSDM based systems. Moreover, we propose joint group processing for the digital beamformer in order to reduce the interference introduced by constrained beamformers. We provide a comprehensive performance evaluation for different operational modes of JSDM with code-domain NOMA when constrained analog beamformers are utilized.
A Dual Waveform Differential Chirp Spread Spectrum Transceiver for LEO Satellite Communications

In recent years, chirp spread spectrum (CSS) has been used in the Long Range (LoRa) technology, which has been widely deployed for terrestrial low power and long-range wireless Internet of Things (IoT) communications. In order to ensure the connection of any object whatever its position on earth, constellations of satellites in low Earth orbit (LEO) can be used. However, the sensitivity of the receivers to establish earth-space CSS communications requires modifications. In this work, we propose a modification of the LoRa physical layer that we refer to as differential chirp spread spectrum (DCSS) to cope with the requirements of LEO satellite communications. The latter modulation is naturally insensitive to any carrier frequency offset (CFO) induced by the Doppler effect and existing mismatches between local oscillators at transmission and reception. Hence we propose a time synchronization approach regardless of the CFO using non-chirp-based learning sequences. This processing makes our receiver have no limitation on the maximum CFO estimable as in the current LoRa receivers. In addition, in the presence of a Doppler shift variable in time, DCSS is much more efficient than CSS, making our proposed receiver a good candidate for the LEO satellite communications.
Towards 5G-ready Security Metrics

The fifth-generation (5G) mobile telecom network has been garnering interest in both academia and industry, with better flexibility and higher performance compared to previous generations. Along with functionality improvements, new attack vectors also made way. Network operators and regulatory organizations wish to have a more precise idea about the security posture of 5G environments. Meanwhile, various security metrics for IT environments have been around and attracted the community’s attention. However, 5G-specific factors are less taken into consideration.This paper considers such 5G-specific factors to identify potential gaps if existing security metrics are to be applied to the 5G environments. In light of the layered nature and multi-ownership, the paper proposes a new approach to the modular computation of security metrics based on cross-layer projection as a means of information sharing between layers. Finally, the proposed approach is evaluated through simulation.
Three-dimensional Mathematical Simulation of the Stator Slot of a Turbogenerator, Including Thermal Modeling and Flow Fluid Dynamics

In this article, the authors explain the principles of creating a three-dimensional symmetrical part of the stator slot of a turbo generator including thermal modeling and flow fluid dynamics. Various physics interfaces were used to create a complete numerical simulation model. To link variables in various physical interfaces, multiphysicists were used with their own differential equations. As a result of work in the COMSOL Multiphysics software, careful multiphysics mathematical models of the stator clot of the turbine generator were built. For the calculation in the program, the finite element method was used with the correct setting of the initial and boundary conditions. Building such complex models can not only improve the design process, but also enable analysis for various insulation systems. For example, air-cooled turbine generators initially use the Global VPI insulation system, but when replacing the stator winding, for economic reasons, a different insulation system is used - Resin Rich. For a correct transition to another insulation system, it is necessary to carry out complex calculations, including thermal ones. In practice, when changing the insulation system due to thermal constraints, it may be necessary to reduce the rated total power of the turbine generator for its further operation without overheating of the stator winding, which can be obtained on the basis of simulation. The equivalent coefficient of thermal conductivity, which can be preliminarily analyzed to select the necessary materials for thermal properties and their fill factor to maintain the nominal parameters of the turbine generator during rewinding, is very important for the calculation, as it allows you to optimize the model settings and the computation time.
Optimization of Base Station ON-Off Switching with a Machine Learning Approach

The next mobile generation is highly expected since it is supposed to increase the bit rate and reduce latency to allow multiple new services been offered. However, there is a big concern about energy efficiency, since, more base stations must be added, providing amplified coverage and higher spectral efficiency. There have been several algorithms designed to reduce the energy consumption of wireless networks by switching ON/OFF the small cells inside a Macro-cell without risking the bit rate received by the users. However, the resource allocation issues involved, e.g. base station selection and user association are NP-hard in general. In this paper we propose a novel solution, applying machine learning to train two neural networks to predict which base stations are not critical and can start their sleeping mode and also predict the associations between the users and base stations giving a complete solution for optimizing the energy efficiency in wireless networks. The outcome will provide similar results to the mathematical optimization but saving 99% of the time spent.
Network Flows-Based Malware Detection Using A Combined Approach of Crawling And Deep Learning

With society's increasing dependence on the Internet, more private data is transmitted through networks every day. Unfortunately, this traffic is susceptible to a wide range of threats and vulnerabilities, including phishing attacks that trick users into compromising their systems or revealing sensitive personal information. In this research, we proposed a deep learning approach to detect malware using data collected from a web crawler that systematically sent requests to benign and malicious websites on the Internet. After applying procedures to segment the network flows and extract features, we used these extracted high-level network traffic features to train a deep neural network to recognize benign and malicious flows. Finally, we evaluated our malware detection approach against various metrics, including precision, recall, and f1 score. The achieved f1 score was 0.924, validating the overall performance of the detection scheme.
FIRestarter: Practical Software Crash Recovery with Targeted Library-level Fault Injection

Despite advances in software testing, many bugs still plague deployed software, leading to crashes and thus service disruption in high-availability production applications. Existing crash recovery solutions are either limited to transient faults or require manual annotations to target predetermined persistent bugs. Moreover, existing solutions are generally inefficient, hindering practical deployment.In this paper, we present FIRestarter (Fault Injection-based Restarter), an efficient and automatic crash recovery solution for commodity user applications. To eliminate the need for manual annotations, FIRestarter injects targeted software faults at the library interface to automatically trigger error handling code for standard library calls already part of the application. In particular, when a crash occurs, we roll back the application state before the last recoverable library call, inject a fault, and restart execution forcing the call to immediately return a predetermined error code. This strategy allows the application to automatically bypass the crashing code upon such a restart and exploits existing error-handling code to recover from even persistent bugs. Moreover, since library calls lie pervasively throughout the code, our design provides a large recovery surface despite the automated approach. Finally, FIRestarter’s recovery windows are small and frequent compared to traditional checkpoint-restart, which enables new optimizations such as the ability to support rollback by means of hybrid hardware/software transactional memory instrumentation and improve performance. We apply FIRestarter to a number of event-driven server applications and show our solution achieves near-instantaneous, state-preserving crash recovery in the face of even persistent crashes. On popular web servers, our evaluation results show a recovery surface of at least 77%, with low performance overhead of at most 17%.
Asteria: Deep Learning-based AST-Encoding for Cross-platform Binary Code Similarity Detection

Binary code similarity detection is a fundamental technique for many security applications such as vulnerability search, patch analysis, and malware detection. There is an increasing need to detect similar code for vulnerability search across architectures with the increase of critical vulnerabilities in IoT devices. The variety of IoT hardware architectures and software platforms requires to capture semantic equivalence of code fragments in the similarity detection. However, existing approaches are insufficient in capturing the semantic similarity. We notice that the abstract syntax tree (AST) of a function contains rich semantic information. Inspired by successful applications of natural language processing technologies in sentence semantic understanding, we propose a deep learning-based AST-encoding method, named ASTERIA, to measure the semantic equivalence of functions in different platforms. Our method leverages the Tree-LSTM network to learn the semantic representation of a function from its AST. Then the similarity detection can be conducted efficiently and accurately by measuring the similarity between two representation vectors. We have implemented an open-source prototype of ASTERIA. The Tree-LSTM model is trained on a dataset with 1,022,616 function pairs and evaluated on a dataset with 95,078 function pairs. Evaluation results show that our method outperforms the AST-based tool Diaphora and the-state-of-art method Gemini by large margins with respect to the binary similarity detection. And our method is several orders of magnitude faster than Diaphora and Gemini for the similarity calculation. In the application of vulnerability search, our tool successfully identified 75 vulnerable functions in 5,979 IoT firmware images.
Optimal Control for Full-Duplex Communications with Reconfigurable Intelligent Surface

In this paper, the problem of optimal passive beamforming design is studied for a reconfigurable intelligent surface (RIS) assisted full-duplex (FD) communication system. In the studied model, two devices communicate with each other using one RIS under the FD mode. Each of the device will receive not only the message from the other device but also the self-interference. The main problem of this work is to minimize the sum transmit power by jointly optimizing the reflection coefficients matrix and the transmit power of devices. To solve this problem, a dual method is proposed, where the dual problem is formulated as a semidefinite programming problem. After solving the dual problem, the phase beamforming of the RIS is obtained in the closed form. Simulation results show that the proposed scheme can reduce up to 66% sum transmit power compared to a conventional RIS assisted half-duplex mode.
High-dimensional Multivariate Time Series Forecasting using Self-Organizing Maps and Fuzzy Time Series

Machine learning models that follow the FTS (Fuzzy Time Series) approach stand out as data-driven non-parametric models of easy implementation and high accuracy, which can be applied to uni-variate and multivariate time series. However, this approach encounters difficulties when dealing with databases of many variables, given the explosion of rules that are generated for the construction of models. Usually filter and wrapper techniques (e.g. Boruta test) and data projection techniques (e.g. Principal Component Analysis) are used. The present work proposes a methodology for tackling this issue by projecting the original high-dimensional data into a low dimensional embedding space using self-organizing Kohonnen maps and later using the Weighted Multivariate FTS method (WMVFTS) for rule discovery and forecasting. The results obtained showed good values of RMSE and MAPE, illustrating the validity and potential of the method.
A Remote Surgery Use Case for the IEEE P1918.1 Tactile Internet Standard

Remote interaction and manipulation of physical objects is one of the promising directions in networked services across the connected world. In formalizing an enabling architecture, the IEEE P1918.1 Standard for the Tactile Internet (TI) is being developed to outline tactile interactions and democratizing skills. For the Tactile Internet to become a reality, advancements in network technologies, orchestrated with haptic-technologies and artificial intelligence, are necessary. The standardization of 5G, rolling out as the next generation of mobile communications, is paving the way for supporting the Tactile Internet vision. The IEEE P1918.1 Working Group (WG) has been developing the architecture, objectives, reasoning and future use cases of the Tactile Internet. This paper focuses on the potential for enabling TI-based remote surgery, and introduces a specific use case (cholecystectomy) that is mapped to the IEEE P1918.1 standard reference architecture. Challenging issues such as network design, Quality of Service (QoS), and multiplexing are taken into account and discussed. This work also emphasizes the role of deterministic message exchanges that would support Tactile Internet in carrying out a remote operation.
IoDAGR: An Airway-based Geocast Routing Protocol for Internet of Drones

In the last few years, new scenarios have emerged for drone applications including those of risk scenarios. In this case, it is essential to have communication among drones whenever they perform tasks that need some sort of coordination, management or synchronization, for instance. In this work, we propose IoDAGR – Internet of Drones Airway-based Geocast Routing, a geocast protocol to transmit messages, such as alerts, to a specific geographical region in a drone network. IoDAGR advances the state of the art of geocast protocols for drones by proposing an efficient solution to this scenario. Simulation results revealed that IoDAGR presents promising results when compared to a baseline protocol.
Online Trajectory and Radio Resource Optimization for Cache-enabled Multi-UAV Networks

In this paper, we propose a novel joint trajectory and communication scheduling scheme for multiple unmanned aerial vehicles (UAVs) enabled wireless caching networks. To exploit the favorable propagation of air-to-ground channels and spatial multiplexing gains, we consider an ultra dense UAVs enabled content-centric wireless transmission network, where massive UAVs are deployed to transmit cached contents to a group of random distributed ground users. We formulate this problem as a infinite horizon ergodic stochastic differential game (SDG) for optimizing the users’ quality-of-experience (QoE) on the concept of request queues for cached contents. By means of mean-field game (MFG) analysis, we derive a reduced-complexity control solution. In addition, we further propose a novel model-specific deep neural network (DNN) to learn the PDEs numerically by exploiting a homotopy perturbation method (HPM).
Finite-Set Model Predictive Current Control of Induction Motors by Direct Use of Total Disturbance

Disturbance rejection strategies are very useful for the robustness improvement of the predictive control method. But they can only be used in the modulated-based predictive control methods such as continuous set model predictive control (CS-MPC) and deadbeat control. This paper presents a robust current prediction model based on total disturbance observer (TDO), which is applicable in the finite set model predictive current control (FS-MPCC). In the proposed method, the disturbance is directly used as a part of the prediction model instead of the disturbance rejection loop. So, the proposed method has two advantages over the disturbance rejection-based CS-MPC schemes. The first advantage is no need for a controller, which is an essential part of the disturbance rejection-based CS-MPC. Therefore, the proposed method is simpler and has fewer control parameters. The second feature is that the proposed model is in the stationary frame. In this way, the frame transformation is avoided in the prediction model. Moreover, to guarantee zero steady-state error in the current prediction model, this paper proposes a complete designing process for TDO based on the convergence analysis. The performance of the proposed control system is evaluated through simulations and experimental tests.
Polymeric Composite Insulators for Overhead Power Lines. A Review of In-Service Damages and Diagnostic Approaches

Polymeric composite insulators have been used worldwide from decades. Various cases of their failures attracted attention to the disadvantages of the design and to the necessity of tools and methods for diagnostics of their condition and remaining service life. The most important thing for the personal responsible for the maintenance is to have effective tools and knowledge to determine the insulators condition. The paper presents a review of the current experience on the maintenance of polymeric composite insulators and aims to highlight the existing diagnostic methods that are suitable for field inspection. Another purpose of the publication is to outline guidelines for development in the field of in-service diagnostics. The types of damages, the causes and the factors leading to their occurrence are presented. Various methods for diagnostics and assessment of their applicability are considered. The corresponding conclusions have been made.
A Novel Ferrofluid Rolling Robot: Design, Manufacturing, and Experimental Analysis

With the increasing applications of magnetic robots in medical instruments, the research on different structures and locomotion approaches of magnetic robots has become a hotspot in recent years. A ferrofluid rolling robot (FRR) with magnetic actuation is proposed and enabled to realize a novel locomotion approach in this article. The drive performance of ferrofluid is elaborated, which is characterized by the magnetic torque of a rectangle-shaped object filled with ferrofluid under magnetic field. First, the proposed structure and locomotion mechanism of the FRR are detailed. Moreover, based on the established mathematical models of the FRR, the simulations with straight and turning locomotion are carried out, respectively. Finally, the FRR prototype is manufactured by 3-D printing, and experimental results demonstrate that the feasibility of straight and turning locomotion is verified. The locomotion performance of the FRR is in good agreement with the theoretical models where the root mean square (rms) value of displacement for experiments and simulations is 1.2 mm. In this work, the proposed FRR can automatically switch from straight to turning locomotion with a fast response in an external magnetic field, and does not has magnetism when without a magnetic field.
Learnability in “Learning from Fuzzy Labels”

Learning from fuzzy labels (LFL) refers to a generalization of supervised learning in which supervision is represented as a (epistemic) fuzzy set over the collection of possible classification labels: this represents the uncertainty of the annotating agent with respect to the true class label to be assigned to the data instances, using a possibility distribution. The two most popular LFL algorithmic approaches are either based on generalized risk minimization (GRM) or nearest-neighbor (NN) methods: while both methods have been applied successfully with promising empirical results, theoretical characterizations of these approaches, in the framework of learning theory, have been lacking. In this article we address this gap and study the LFL problem from the perspective of statistical learning theory, providing a theoretical analysis of both GRM and NN, in terms of sample complexity and risk bounds.