Miracle-dz/UniFAR-FTUs huggingface dataset:
A Blockchain-based SDN East/West Interface

Software-Defined Networking (SDN) architecture was developed to address the shortcomings of traditional network architectures. It allows system administrators to easily manage and configure the network by separating and abstracting the control plane from the data plane. All the knowledge and intelligence of SDN is concentrated in a software entity called the SDN controller, making the network programmable. However, a large-scale SDN architecture, particularly in the IoT domain, requires the implementation of a physically distributed control mechanism. Such a mechanism, based on the East/West interface raises many challenges in terms of scalability, reliability, security, consistency, and traceability. The development of the Blockchain allows addressing some of these challenges. In this paper, we present a design using Blockchain technology to improve SDNs in terms of trackability and discuss the adaptations required for large-scale deployment. Experimental results clearly show that the use of a proof-of-authority consensus algorithm in combination with a Merkle tree approach reduces the impact in terms of latency as well as in terms of Gas consumption.
Fake news detection method based on multi-feature fusion of entity and structured text

Internet technology has facilitated the development of social media, which has become the main way for people to get news. The low cost of information generation has allowed unconfirmed fake news to be noticed and reposted, which can easily cause social and economic harm. Much of the existing research on fake news detection has focused solely on the accuracy of detection, but has neglected the time efficiency of detection. We propose a fake news detection method called MFEAST, which simplifies article content and speeds up model training by extracting key information from articles. Compared with existing real fake news detection methods, our method substantially reduces the training time and achieves high detection accuracy.
In-domain damping assignment of a Timoshenko-beam using state feedback boundary control

In this paper, we combine the backstepping methodology and the port Hamiltonian framework to design a boundary full-state feedback controller that modifies the closed-loop in-domain damping of a Timoshenko beam. The beam under consideration is clamped in one end of its spatial domain and actuated at the opposite one. The port Hamiltonian formulation is used to derive several boundedly invertible transformations that map the original system into an exponentially stable closed-loop target system with additional in-domain damping terms. The proposed methodology allows the introduction of tuning parameters with clear physical interpretations for achievable closed-loop behavior. Simulations illustrate the performances of the controller.
Fast Feature Matching in Visual-Inertial SLAM

Feature matching is an important step for SLAM with high real-time requirements. Currently, most feature matching methods only rely on visual information, even in visual-inertial SLAM. In this paper, we propose an efficient feature matching method for point and line features by fusing IMU information with visual information. The key ideas are utilizing IMU pre-integration to estimate the relative pose change of two consecutive frames and narrow the feature search area to accelerate feature matching. Experiment results show that our method shortens the feature matching time significantly compared with other feature matching methods while ensuring high matching accuracy.
Satellite Relay Task Scheduling Based on Dynamic Antenna Setup Time and Splittable Task

The demand for satellite relay service is increasing, while the satellite network resources are limited and unevenly distributed, which pose a great challenge to task scheduling of tracking and data relay satellites. Most existing relay scheduling models are based on static antenna setup time, which has limitations in practical applications and leads to ineffective utilization of satellite resources. This paper models the task scheduling problem based on dynamic antenna setup time and splittable tasks, which maximizes the total scheduled task number and minimizes the total antenna setup time. We also propose a two-stage insertion heuristic to solve the problem. The experimental results show that the proposed algorithm can significantly improve the total scheduled task number, total antenna setup time and effective time window utilization compared with traditional methods.
Simulation study of the role of ion kinetics in low‐ frequency wave train evolution

The evolution of uniform, parallel propagating, low-frequency (≲ion cyclotron) wave trains is followed with a one-dimensional hybrid numerical code with fluid electrons and particle ions. We show that moderate amplitude (δB/B 1 and instability exists for wavenumbers both below and above the wavenumber of an initial, left-handed wave train or pump wave. For corresponding parameters a fluid theory gives only a narrow range of instability above the pump wavenumber where decay and beat instabilities can occur. In simulations wave energy inverse cascades to smaller wavenumbers and into a greater number of forward than backward going waves. In fluids energy by decay goes mostly to backward ones of smaller wavenumber, and energy by beat goes mostly to forward ones of larger wavenumber. Neither fluid instability explains simulation results. The instability is saturated by thermalizing ions and sometimes exciting small wavenumber electrostatic or acoustic modes. In contrast, saturation in fluids first occurs by generating the harmonics of the growing linear modes. Harmonic generation is mostly absent in simulations. Simulations are carried out to long times and mostly reach a limit beyond which no further significant evolution can occur. Application to Alfvenic fluctuations in the solar wind is discussed.
Sensor Selection for Minimizing the Condition Number with Guaranteed E-Efficiency

The design of a network of observation locations is addressed in the setting of estimating unknown parameters of a spatiotemporal system modelled by a partial differential equation. The resulting measurements are supposed to make the Hessian of the least-squares criterion well-conditioned, which amounts to the design yielding a minimal condition number of the corresponding Fisher information matrix (FIM). To prevent this matrix from being close to singular, an additional constraint is imposed on the lowest allowable value of its largest eigenvalue. In order to make selection of a best subset of gauged sites from a possibly very large set of candidate sites computationally tractable, its relaxed version is introduced in the form of a fractional programming problem. Using the Charnes-Cooper variable transformation, it is converted to an equivalent convex problem. Its nonsmoothness is then circumvented by expressing the problem as a semi-infinite programming problem. It is then solved by an exchange method, which reduces to an extremely simple algorithm that alternates between finding the eigenvalues and eigenvectors of the current FIM and solving a linear-programming problem. The excellent performance of the proposed technique is illustrated by a nontrivial simulation example.
Effect of roughness in nondiffusive regions within diffusive media

Recently, it has been shown that clear regions within diffusive media can be accurately modelled within the diffusion approximation by means of a novel boundary condition or by an approximation to it. This can be directly applied to the study of light propagation in brain tissue, in which there exist clear regions, and in particular, the cerebro spinal fluid (CSF) layer under the skull. In this work, we present the effect that roughness in the boundary of non-diffusive regions has on the measured signal, since, in practice, the CSF layer is quite rough. The same conclusions can be extended to any diffusive medium which encloses rough non-diffusive regions. We will demonstrate with numerical calculations that the roughness statistics of the interfaces although not their actual profile must be known a priori in order to correctly predict the shape of the measured signal. We show that as the roughness increases, the effect of the non-diffusive region diminishes until it disappears, thus yielding data similar to those of a fully diffusive region.
Design of an Underwater Robot Propelled by a Novel Class of Bio-Inspired Thrusters

In the last three decades, bio-inspired solutions have been investigated by researchers worldwide as a source of improvement for Autonomous Underwater Vehicles. In order to replace the screw propellers traditionally employed on marine vessels with more efficient bio-inspired thrusters, the authors of this paper have developed, manufactured, and tested a series of swimming robots in the last six years. The main design novelty, conceived and fine-tuned through these projects and prototypes, is the replacement of multiple inefficient servomotors, employed to drive the tails and fins of the robotic fish in the literature, with a single, efficient rotary actuator to solve control, encumbrance, and waterproofing issues. In this work, the single-motor design has been exploited to develop a swimming robot while pursuing the highest possible propulsive efficiency and the capability to perform low-speed and stationary turnabouts: the simulation performed on the multiphysics platform developed by the authors through their research have shown that the proposed solution can be applied to any robotic fish embodying a multi-joint tail mechanism regardless of the adopted swimming mode.
Equilibrium-driven Dynamic Pricing for Shared Access C-RAN

Given the scarcity of wireless resources, demand-side management for cellular networks is indispensable. In this paper, we investigate base station (BS) level dynamic pricing in the shared access C-RAN, such that the overall demand-supply equilibrium can be achieved. The main challenge is how to promptly and accurately determine the price for utilizing the resources of each BS, without knowing how users will behave when price changes. To this end, we adopt the deep reinforcement learning (DRL) method, which can learn from the environment and make decisions accordingly. We propose a domain-specific DRL-based dynamic pricing scheme and elaborately design the state, action and reward, as well as utilize other techniques for implementation of the dueling double deep Q-network (D3QN) algorithm. Simulations are conducted to test the trained network, and the results demonstrate the stability and performance improvement over baseline methods.
EEG-based Engagement Index for Video Game Players

Modern era has changed the lifestyle of the people with technological advancement. Video games have become an integral part of daily entertainment for society. This study proposes an engagement index for a video game using electroencephalography (EEG) and compares its result with existing indices available in the literature. This study employs the use of a 14-channel Emotiv EPOC headset for evaluating the engagement of the players in a video game. The study utilizes the dataset of 10 volunteer participants available on Kaggle. Previously available engagement index calculation techniques utilized three or more features while we propose the use of only two features i.e., theta AF3 and alpha P7 for the calculation of the player's engagement index. Results depict that our proposed index is statistically similar to previous indices, while it needs only two electrodes to gauge player engagement. Additionally, these indices can also differentiate between an expert and a novice player. Thus, it is a step towards the improvement of player experience using dynamic difficulty adjustment (DDA).
Predicting the Urban Water Demand Based on Transfer Learning Method With Multi-head Attention

Accurate prediction of urban water demand is very important for efficient use of water resources and optimal management of urban water supply systems. However, it is usually difficult to achieve satisfactory water demand prediction due to the differences in the characteristics of different water use regions and the limitation of sampling frequency caused by the power consumption control of Internet-of-Things (IoT) data sampling terminals. In this paper, a transfer learning prediction model is proposed by combining the multi-head attention mechanism. Firstly, the attention features are extracted from the water demand of the source domain and the target domain simultaneously by the encoder, and then the maximum mean difference is adopted to make the features of the source domain match the feature distribution of the target domain. Finally, the decoder infers the predicted value based on the multi-head attention mechanism. During the training process of the model, the overall loss function is used to continuously reduce the error between the predicted value and the actual value. We compare the proposed model with other representative models on the real datasets of a water supply company, and the experimental results show that the proposed model reduces Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) by about 50% and about 30%, respectively. It reflects that the model proposed in this paper can achieve a more accurate prediction of the water demand of the divided regional water supply cities.
Adaptive Finite Blocklength for Low Access Delay in 6G Wireless Networks

As the number of real-time applications with ultra-low delay requirements quickly grows, massive ultra-reliable and low-latency communication (mURLLC) has been proposed to provide a wide range of delay-sensitive services for the sixth generation (6G) wireless networks. However, it is difficult to meet the stringent delay demand of massive connectivity with existing grant-based (GB) random access and fixed frame structure in long-term evolution (LTE) and the fifth generation (5G) new radio (NR) systems. To solve this problem, in this paper we propose the new grant-free (GF) based adaptive blocklength scheme for short packet transmission to reduce the access delay. We develop the adaptive blocklength framework where the blocklength can be adaptively changed according to the real-time load, to revise the traditional non-flexible frame structure which impacts the delay performance. Taking the features of mURLLC into consideration, we analyze the GF random access procedure, packet arrival behavior, packet collision, and packet transmission error in the finite blocklength (FB) regime. On this basis, we derive the closed-form expression of successful access and transmission probability and give the GF-based status update model. Then, we propose the access delay minimization problem that jointly considers queuing delay and transmission delay to reduce the overall access delay. With the alternating optimization algorithm, we obtain the optimal blocklength of each packet, thus forming the corresponding adaptive blocklength scheme for mURLLC. Simulation results verify the correctness of theoretical results and show that our proposed adaptive blocklength scheme can significantly reduce the access delay compared with that of LTE and 5G NR systems.
An Efficient DMO Task Scheduling Technique for Wearable Biomedical Devices

The popularity of wearable devices has grown as they improve the quality of life in many applications. In particular, for medical devices, energy harvesters are the dominating source of energy for wearable devices. However, their power budget is limited. Thus, power-saving techniques are essential components in the whole technology stack of those devices. That is, choosing the optimal schedule for different tasks running on the wearable device can help to reduce energy consumption. This paper presents a sensor task scheduling technique for optimizing energy consumption for energy harvesting-based wearable biomedical devices. The proposed technique is based on Dwarf Mongoose optimization (DMO) to compute Hamming-based Tikhonov regularization. The proposed DMO-based scheduling algorithm is assessed against the state-of-the-art technique and compares the results with our prior study’s FPA-based algorithm. The data used in the experiments are collected from an in-lab prototype.
Efficient and Privacy-Preserving Fusion Based Multi-Biometric Recognition

The advance of artificial intelligence has stimulated the rapid development of multi-biometric recognition that recognizes users based on their multiple biometric features. Since biometric features are private information of users, multi-biometric recognition has to be processed in a privacy-preserving way. Although many approaches have been proposed for privacy-preserving multi-biometric recognition, they have some limitations in security and performance. Aiming at this challenge, we propose a privacy-preserving multi-biometric recognition scheme. In this scheme, we first utilize a neural network to train a face and voiceprint fusion model, which obtains better fusion layer parameters and improves the accuracy of fusion recognition. Then, we design an efficient and privacy-preserving multi-biometric recognition scheme based on the MK-CKKS cryptosystem in the decentralized model, which does not rely on a trusted third party to increase its universality. The scheme can guarante recognition accuracy by utilizing a feature-level fusion mechanism, reaching EERs as low as 0.66%. Security analysis shows that the privacy of biometric information and recognition results can be guaranteed. Experimental results validates the efficiency of our scheme.
Enabling Anticipatory Response in Multi-Stage MPC Formulation for Fully Automated Artificial Pancreas System

Controlling postprandial glucose excursions in type 1 diabetes (T1D) still remains a challenge for most automated insulin delivery (AID) algorithms. The main limiting factor has been the slow absorption of current insulin analogs that prevents the controller from responding aggressively enough to significant glucose excursions without increasing the risk for insulin stacking and, consequently, late hypoglycemia. Faster insulin analogs represent an appealing solution to this problem, because they enable better alignment between meal and insulin rates of appearance. In this paper, we discuss about another way to overcome this fundamental performance limitation that consists in allowing the controller to properly anticipate likely disturbances through a robust model predictive control (MPC) strategy.
An Accurate & Efficient Approach for Traffic Classification Inside Programmable Data Plane

In-network traffic classification is a class of in-network computing that brings significant benefits to the network, i.e., the first line of defence, classification at line rate and fast reaction time. However, it is still challenging to accurately and efficiently classify Internet traffic at an early stage due to a clear trade-off between flow identification time and classification accuracy - both are competing objectives. To this end, $w$e introduce a framework that focuses on deploying an accurate network traffic classifier inside a programmable data plane that can classify the traffic at maximal speed while considering the underlying constraints of the device. Notably, $w$e move from statistical feature-based traffic analysis and argue that traffic flow can be classified using a single feature called sequential packet size information as input. We evaluate our approach by identifying different types of IoT traffic inside a programmable data plane. Our findings demonstrate that accurate and early-stage network traffic classification is achievable with minor use of networking device resources.
Non-Destructive Diagnosis of FRPM Pipelines Using Microwave Guided-Mode and Optical Fiber Link

We have proposed and developed a new non-destructive inspection method for underground fiber-reinforced plastic mortal (FRPM) pipelines utilizing microwave guided-modes propagating along the FRPM pipe-wall. In this paper, we report remote measurement of FRPM pipelines using microwave transfer over an optical fiber link. The optical fiber link used was composed of a DFB laser diode and a high-speed photodiode connected by a silica optical cable. This method is useful for measuring underground pipelines from the ground nondestructively and remotely.
Center Keypoint for Parking Slot Detection with Self-Calibrated Convolutions Network

Available parking slot detection is the first step for autonomous parking systems. In this paper, we propose a novel parking slot detection method that uses the center information regression and the occupancy classification of the parking slot. We design a self-calibrated convolutions network (SCCN) to obtain the position, length, occupancy and direction, which can also infer the parking slot type according to the prediction results. The method divides an around view monitor (AVM) image into the 16 × 16 grid cells and performs a SCCN detector for feature extraction. Subsequently, the whole parking slot can be easily inferred via prior geometric information and detection results. We adopt the heatmap, MultiBins, and midline to detect the center keypoint, direction and occupancy, respectively. And we quantitatively evaluate the performance of the proposed method on the public PS2.0 datasets. The experimental results show the outperformance by a precision rate of 99.35%, a recall rate of 99.17%, an occupancy classification accuracy of 99.12% and all correctly inferred types of parking slots on the datasets.
Time-Series Forecasting of Ethereum Price Using Long Short-Term Memory (LSTM) Networks

The cryptocurrency, ether (ETH), often sees its price go through rapid fluctuations due to its popularity for being the foundation for various decentralized applications and the fuel of the Ethereum network which has simplified commerce and trade between both anonymous and recognized parties, unfavourable circumstances like political conflicts, natural disasters, and so on, causing the market to become extremely volatile and risky for the crypto investors and the developers. So, from the urge to have a specialized ether price forecasting system, this research aimed to find an accurate price prediction model for ether using the long short-term memory (LSTM) network. For this, ether time-series price data were fitted into multiple basic and hybrid LSTM network variants, with future prices predicted using both univariate and multivariate time-series analysis. Furthermore, a comparative analysis was conducted among the models and also some popular existing forecasting techniques like autoregressive integrated moving average (ARIMA) as the baseline forecast to understand the effectiveness of the LSTM networks, especially, the hybrid variant, in the prediction of future market behaviour.
OAM-based Holographic MIMO using Large Intelligent Surfaces

This paper addresses the exploitation of the orbital angular momentum (OAM) property of the electromagnetic waves with large intelligent surfaces (LISs) in near-field and line-of-sight propagation conditions for holographic multiple-input multiple-output (MIMO) communications. The relation-ship between OAM-carrying waves and communication modes established between intelligent surfaces is discussed, and practical strategies for exploiting OAM with LISs are then presented. Nu-merical results characterize the effectiveness of OAM compared to alternative strategies, though highlighting that OAM-based techniques constitute a particular choice of basis set that is sub-optimum with respect to the basis sets corresponding to optimal communication modes, albeit ensuring lower complexity.