Miracle-dz/UniFAR-FTUs huggingface dataset:
A Task Allocation Framework for Large-Scale Mobile Edge Computing

We consider the problem of intelligent and efficient task allocation mechanism in large-scale mobile edge computing (MEC), which can reduce delay and energy consumption in a parallel and distributed optimization. In this paper, we study the joint optimization model to consider cooperative task management mechanism among mobile terminals (MT), macro cell base station (MBS), and multiple small cell base station (SBS) for large-scale MEC applications. We propose a parallel multi-block Alternating Direction Method of Multipliers (ADMM) based method to model both requirements of low delay and low energy consumption in the MEC system which formulates the task allocation under those requirements as a nonlinear 0-1 integer programming problem. To solve the optimization problem, we develop an efficient combination of conjugate gradient, Newton and linear search techniques based algorithm with Logarithmic Smoothing (for global variables updating) and the Cyclic Block coordinate Gradient Projection (CBGP, for local variables updating) methods, which can guarantee convergence and reduce computational complexity with a good scalability. Numerical results demonstrate the effectiveness of the proposed mechanism and it can effectively reduce delay and energy consumption for a large-scale MEC system.
Evaluating the Structural Model using Internet Interdomain Topological Datasets

Recently we proposed a structural model of the Internet interdomain topology. The model partitioned the nodes of the topology into seven categories, which is helpful for the deep cognition of Internet structure. However, the correctness of the model was only confirmed on some local geographical topologies obtained by a certain measurement method, and neglects the verification on global topologies and the characteristic analysis on evolution process. This paper chooses two global topological datasets, explored by BGP (Border Gateway Protocol) routing table and traceroute-based measurement respectively, to verify the effectiveness of the model, and applies the structural properties described by the model to analyze the differences and the evolutionary behaviors of the topological datasets obtained by the two commonly-used measurement methods. Using the datasets explored from September 1999 to March 2018, we determine that the seven-node-category structure always exist but has distinct node partition percentages for different measurement methods, and most of the node categories remain stable percentages under the evolution (scale growth) of the Internet, namely this paper shows that the model captures plenty of size-indepe-ndent structural characteristics, which is effective for different commonly-used measurement methods.
A Novel Criterion of Reconstruction-based Anomaly Detection for Sparse-binary Data

Computer usage behaviour information can be used by anomaly detection algorithms to identify the current user of the computer system for security reasons. However, the data collected in this setup can be binary and very sparse, resulting in poor performance for some widely used anomaly detection methods. In this study, we propose a novel reconstruction criterion inspired by the F1 score and the cross-entropy loss, that tackles the class imbalance problem introduced by binary and sparse data distribution with effectively merging reconstruction criterion calculated from vector elements of both positive and negative classes. Our experiments show that the proposed criterion can effectively improve the performance of reconstruction based anomaly detection methods, including both the PCA and the autoencoder.
An Empirical Study of Counterexample-Guided Fuzzing for Neural Networks Verification

The uncertainty of intelligence software makes it impossible for even developers to determine the output corresponding to each input. Therefore, it is particularly important to find a way to find intelligence software errors efficiently. Fuzzing testing technology is considered to be an effective technology for discovering software vulnerabilities, The selected test case generation strategy will affect its testing efficiency. In this article, we propose a counterexample-guided fuzzing method. According to the generated counterexamples, the scope of the sample space is continuously reduced, so as to find more counterexamples in a limited number of samples and improve the ability to uncover mistakes. In our research, The ability about revealing errors of test object has increased by 20%.
Air Gap Inductance Calculation for Transformer Transient Model

Zero sequence characteristic and parameters play a vital role in some transformer transients involving unbalance or saturation conditions such as inrush, ferroresonance, and Geomagnetically Induced Current (GIC). An important parameter of the zero-sequence characteristic is the inductance of the air gap between the core and the tank. Based on a Finite-Element Method (FEM), this letter determines the air gap inductance, considering fringing flux effects. The comparison of the simulation results with an analytical method shows a good agreement for small air gaps but reveals a significant difference for relatively large air gaps. Thus, an inductance enhancement factor (IEF) is introduced to correct the analytical results and facilitate the transformer modeling parameter estimation from the design data. Furthermore, the significance of the accurate calculation of the air-gap inductance is demonstrated in the time-domain simulation of different saturation levels of the transformer core, when the transformer is subjected to GIC.
Frequency Reconfigurable Antenna Inspired by Tri-SRR Metamaterial

A frequency reconfigurable metamaterial-based microstrip antenna is presented in this paper. The proposed antenna consists of three split-ring resonators (SRR) and a tapered feeder placed on the top layer of a 30 × 25 × 1.6 mm3 substrate. Nine PIN-Diode switches are distributed around the three rings, each ring contains 3 switches with 120-degree spacing. Reconfigurability is achieved by varying the condition of these switches. The suggested antenna is simulated using Ansys Electronics Desktop simulator (HFSS), and MATLAB software is used to analyze the characteristics of the metamaterial unit-cell. Single and dual bands are produced at 2.8, 3.025, (2.45 and 4.75) and (2.93 and 4.85) GHz. Radiation patterns, gain, input impedance and radiation efficiency are simulated to have a better observation of the performance of the antenna.
Enhanced Low Dose Rate Sensitivity of PNP Transistor at Extreme-Low Dose Rates

The ELDRS of PNP transistors were investigated at dose rates of 10, 5, 1, and 0.1mrad(Si)/s and dose rate 100rad(Si)/s, while the evaluation of the ELDRS under the high and dose rate are performed with the Temperature-Switching-Approach Irradiation. The estimated results using Temperature-Switching-Approach were in good agreement with the experimental data.
Oversampling Algorithm based on Reinforcement Learning in Imbalanced Problems

The imbalanced problem indicates that the data set is unevenly distributed, resulting in sub-optimal classifiers to recognize the minority class. Traditional solutions try to design new classifiers to solve this problem or balance the skewed data sets, the former is too costly while the latter has an uncertain effect on different combinations of classifiers and measurements. In this paper, we propose a reinforcement learning-based oversampling method, which can directly produce targeted samples according to the downstream classifiers and measurements. During training, our learning procedure introduces the classification information to the generation process. Moreover, as opposed to oversampling approaches, we have no assumption of the downstream classifiers and performance metrics, and the proposed has a wider application. We carry out experiments on 17 UCI and KEEL data sets, experimental results demonstrate the superior performance of our proposed method.
Linear MMSE Precoder Combiner Designs for Decentralized Estimation in Wireless Sensor Networks

This work considers the design of linear minimum mean square error (MMSE) precoders and combiners for the estimation of an unknown vector parameter in a coherent multiple access channel (MAC)-based multiple-input multiple-output (MIMO) wireless sensor network. The proposed designs that minimize the mean squared error (MSE) of the parameter estimate at the fusion center are based on majorization theory, which leads to non-iterative closed-form solutions for the precoders and combiners. Various scenarios are considered for parameter estimation such as networks with ideal high precision sensors as well as noisy non-ideal sensors. Moreover, inter parameter correlation is also incorporated, which makes the analysis comprehensive. The Bayesian Cramer-Rao bound (BCRB) and centralized MMSE bound are determined to characterize the estimation performance. Simulation results demonstrate the improved performance and also corroborate our analytical formulations.
An Electric Vehicle Charging Reservation Approach Based on Blockchain

The popularity of electric vehicles depends on convenient and efficient charging services. At present, none of existing charging services allow users to reach charging stations at desirable time and charge immediately when they arrive without waiting. This paper proposes a charging reservation service approach based on the consortium blockchain and smart contract technology. Users can choose the charging station and charging time period with no charging congestion, which is based on the charging information recorded in the consortium blockchain composed of stations located in distributed regions in a city. To ensure a user arrives at the charging station on time and charge within due time as he/she has reserved, a personalized pricing scheme for reward and punishment by utilizing smart contract is proposed. We take the past charging behavior into consideration when deciding current charging price of each user, which can provide individualized prices for different users. This approach can not only greatly reduce the user’s waiting time, but also offer high cost-effective charging services for good behavior users. We carry out experimental verification under multiple sets of parameter settings, illustrate the variations in three aspects including user’s initial score, violation rate and intensity of reward and punishment, thus the feasibility of our approach is proved. Our work is a credible charging paradigm based on trust mechanism via blockchain, which has the potential to become an industry service standard for electric vehicle charging.
A Time-aware Similarity-based Trust Computational Model for Social Internet of Things

The recent integration of social networking concepts with the emerging yet promising paradigm of Internet of Things (IoT) has facilitated the emergence of a new domain named Social Internet of Things (SIoT) to socialize smart objects by emulating the human behavior. The interaction between these smart objects depend on social relations amongst them, wherein each object establishes these relationships autonomously via rules set out by its owners for creating virtual communities and working environments with shared interests. Trust is a concept that quantifies the reliability of these autonomous relationships and hence enriches cooperation between the SIoT objects. This paper explores the concept of time-aware similarity-based trust to address the issue of trust while establishing a social relationship concerning virtual communities, work environment, and friendship similarity. The proposed trust model segregates the trust in two types, i.e., direct trust and indirect trust, wherein direct trust signifies the direct perception of a trustor towards a trustee, whereas, indirect trust depends on recommendations from the neighboring objects of a trustor. Moreover, a dynamic weighted sum method is employed to aggregate all the similarities so as to obtain a single trust score at any given time. We conducted a comprehensive experimental study based on a real-world data set and the simulation results demonstrate that our method can efficiently classify trustworthy and untrustworthy objects.
AUV-Aided Hierarchical Information Acquisition System for Underwater Sensor Networks

In this paper, we propose a hierarchical information acquisition system composed of a marine stationary sensor layer and an autonomous underwater vehicle (AUV) motion layer. Specifically, in the sensor layer, we design an energy-efficient clustering protocol based on the improved K-Means algorithm (ECBIK), which can implement uniform classification and select the cluster head dynamically according to energy awareness. Compared with the traditional K-Means and LEACH algorithm, our method achieves lower energy consumption and higher node survival rate, which can balance the energy load effectively to extend the life of the network. Additionally, in the AUV motion layer, we define the rotation-angle of AUV and analyze its influence quantitatively for the AUV information collection. Meanwhile, a novel Ant Colony (ACO) algorithm based on Markov Reward Process (MRP) is proposed for AUV path planning. As the simulation experiments indicate, our algorithm can achieve shorter distance, smaller angle, and faster convergence speed in path optimization.
SEARCH: An SDN-Enabled Approach for Vehicle Path-Planning

With increasing vehicle density and the growth of accidents in urban areas, navigation management becomes a serious problem. Even though there is a multitude of navigation systems, ambulances, taxis, or even ordinary vehicles, sometimes find it challenging to reach their destinations on time. There are two main reasons for this difficulty: (i) lack of local knowledge of the area of navigation solutions and (ii) their inflexibility against unforeseeable situations that may occur on the roads. Indeed, the majority of navigation solutions are based only on the distance, the journey time, or even statistics related to the density of vehicles to plan the full paths, while neglecting the dynamic nature of the vehicle traffic. Also, their respective centralized architectures are unable to monitor both the traffic and unexpected events continuously and in real-time without not being overloaded by the flow of message exchanges between the road entities and the central processing entity. To address these issues, we propose in this paper a novel three-tier architecture, called SDN-enabled Approach for Vehicle Path-Planning (SEARCH), to enhance the situation awareness on urban roads, efficiently collect traffic information in real-time, and decide the best navigation strategy. The proposed architecture exploits Unmanned Aerial Vehicles (UAVs), Vehicular Ad hoc Networks (VANETs), 5G based cellular systems, and Software-Defined Networking (SDN) to provide better and faster communication to changing road conditions. Based on these technologies, some parameters related to vehicles and driving environments, such as speed, distance, traffic jams, incidents, and travel flow, are efficiently collected and dynamically exploited to achieve faster paths between any existing pairs of locations. Furthermore, the deployed architecture of SEARCH can provide sufficient bandwidth to support all data traffic needs to update vehicles during their journey efficiently and in real-time. To evaluate the performance of our architecture, we conduct a series of simulations and perform a set of comparisons with relevant route planning algorithms. We found that the proposed architecture works effectively in terms of saving on driving time to reach any target destinations.
HPNet: A Compressed Neural Network for Robust Hybrid Precoding in Multi-User Massive MIMO Systems

In multi-user millimeter wave (mmWave) communications, massive multiple-input multiple-output (MIMO) systems can achieve high gain and spectral efficiency significantly. To reduce the hardware complexity and energy consumption of massive MIMO systems, hybrid precoding as a crucial technique has attracted extensive attention. Most previous works for hybrid precoding developed algorithms based on optimization or exhaustive search approaches that either lead to sub-optimal performance or have high computational complexity. Motivated by the thought of cross-fertilization between Data-driven and Model-driven approaches, we consider applying deep learning approach and introduce the Hybrid Precoding Network(HPNet), which is a compressed deep neural network exploiting the feature extracting (thanks to convolutional kernels) and generalization ability of neural networks and the natural sparsity of mmWave channels. The HPNet takes imperfect channel state information (CSI) as the input and predicts the analog precoder and baseband precoder for multi-user massive MIMO systems. Moreover, in order to make the approach more practical in real scenarios, we further introduce a model compression algorithm, using network pruning, to greatly reduce the computational complexity and memory usage of the neural network while almost retaining the model performance and then assess the influence of pruned parameters in the network. Numerical experiments demonstrate that HPNet outperforms state-of-the-art hybrid precoding schemes with higher performance and stronger robustness. Finally, we analyze and compare the computational complexity of different schemes.
Follow the User: A Framework for Dynamically Placing Content Using 5G-Enablers

This article presents a framework for improved and efficient video delivery in scenarios featuring users moving at high speed (e.g., trains), leveraging on dynamic Multi-access Edge Computing (MEC) enabled 5G network capabilities. The framework is location-aware and it allows the content to efficiently follow the users, conserving load usage on network and computational resources, by placing virtualized Content Delivery Network (vCDN) nodes at edge sites. The nodes are controlled by the framework’s centralized control unit, which is able to dynamically and preemptively deploy virtulized resources, as the train moves. The framework is capable of segmenting video content and placing the specific portion of content that a user is likely to consume across a set of dynamically deployed vCDN nodes, associated to the coverage section the train is currently passing. A proof of concept was implemented and evaluated, where the benefits of using this framework are assessed. Results showed that the proposed system was able to reduce the load on the core network by 10.9 percent and maximize the cache hit ratio to a value of 99.8 percent.
Capillary Pick-and-Place of Glass Microfibers

Microfibers are key components in construction of fiber-based materials, biomimetic materials, microsensors, and other fiber-based microstructures. Due to the scaling law, the adhesion forces such as van der Waals force or electrostatic force in the micro world play a more dominant role than the gravity, causing difficulties in precise pick-and-place of the micro objects. In this paper, we propose a capillary force-based pick-and-place method for handling microfibers. The method combines the typical robotic transportation technique and capillary gripping method to achieve fast and accurate pick-and-place of microfibers. We quantitatively analyzed the critical conditions for capillary pick-up and placement of microfibers and validated the technique experimentally. The theoretical analysis indicates that both pick-up and the placement of microfibers are largely dependent on the contact length on the fiber or the contact angle of the meniscus on the substrate. The experimental results show that the microfibers can be reliably picked up from and placed on the substrate of different materials including paper tissue, glass, silicon, stainless steel, copper with droplet volume of 0.1 nL and 0.3 nL. We further applied the method to the placement of the microfibers on super hydrophilic-super hydrophobic grooves and studied its placement speed and accuracy. We demonstrated that microfiber can be placed in such grooves in less than 0.1 seconds, with linear placement accuracy of $10~\mu \text{m}$ and angular placement accuracy of 0.5°. The proposed method is fast and simple, and it is especially suitable for handling fragile and flexible micro sized objects and construction of fiber-based materials.
BF2FA: Beacon Frame Two-factor Authentication

Beacon frame is a type of management frame that is transmitted periodically by Wi-Fi access points (APs) for the purpose of establishing connections; also, it announces the existence of a network, allowing devices to find and identify the network and subsequently to request access. It contains information about the network needed to make the request. Within a range of different radio frequency (RF) technologies, WiFi-capable devices can sense beacon frames. The primary advantage of a broadcast message is that devices can read and process the enclosed data with no need to be connected to the corresponding network. Our research uses data within these transmitted messages to perform two-factor authentication (2FA) system that, unlike existing methods, with minimal user involvement. We can ensure that both devices belong to the original user by determining if both of a user’s devices are in the same physical area. The presented method relies on an object that a user knows, an object that a user owns, and – a significant contribution of this system – an object in the user’s environment. The newly designed method presented in this study is empirically evaluated based on real login attempts.
Cluster based Deep Reinforcement Learning for Wireless Caching with Social Connection Awareness

Coded caching can improve the robustness of wireless caching networks. This paper investigates the joint caching and communication optimization in terminal based wireless coded caching networks. Social characteristics of private terminals are considered to further harvest more performance gain in terms of hit ratio. To tackle the problem of unknown popularity distribution, we adopt deep reinforcement learning. Since the joint caching and communication optimization of all Content Providers (CP) results in larger-scale action and state space, we propose a novel cluster based deep reinforcement learning (CB-DRL) scheme. We present simulation results to demonstrate complexity reduction and effectiveness of the proposed algorithm.
Phased-MIMO Antenna Arrays of Radar System in Detection Performance

The use of multiple antennas in radar systems that establish antenna arrays in transmitter (Tx) and receiver (Rx) has become of current interest. The Phased-MIMO radar (PMIMO) combines simultaneously the high coherent directional gain capabilities of the phased array (PA) radar and the high multi-target detection capabilities of the MIMO radar. This can be realized because of the use of overlapped subarrays in Tx so that it improves the detection performance flexibly such as the detection and false alarm probabilities. In this paper, the detection performance of this radar for the optimal detector has been derived and validated and then compared with the performance of other multi-antenna radars based on the effect of the threshold, SNR, and the number of subarrays in Tx. The results of the evaluation show that the PMIMO radar is more flexible through its subarrays variations than the PA and the MIMO radars for its performance capabilities under various target environmental conditions. For example, to achieve a detection performance that fulfil the tolerance of a certain threshold, which is above 35, the resulting false alarm probability is above 10e-4 with a detection probability of more than 99%.