Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Spectrum Spatial Reuse Approach Based on Gibbs Sampling for Ultra Dense Networks

The ultra dense deployment of small cells is considered a key technology to achieve the requested capacity in future cellular networks. However, the interference pattern becomes more unpredictable and challenging in these networks. Therefore, a suitable trade-off between spectrum spatial reuse and interference level has to be pursued to achieve good performance in terms of provided throughput. This paper proposes a new method to maximize the achievable throughput of an UDN with a suitable level of spatial spectrum reuse. In particular, the focus is on the small cells tier where the available spectrum is divided into sub-bands and each cell can use some of these to communicate with its associated users. The goal is to find the sub-bands allocation among cells that maximizes the system throughput. However, to limit complexity and signaling overhead that could result unaffordable in an UDN, a new metric to approximate the cell throughput to be optimized is defined. Moreover, the newly defined problem is solved using the Gibbs Sampling approach. The method effectiveness is proven by comparing the achieved results with those of the maximization of the effective system throughput and the optimal solution.
EigenCloud: A Cooperation and Trust-Aware Dependable Cloud File-Sharing Network

There exist two severe challenges in cloud file-sharing networks: cooperation dilemma and trust dilemma. The mechanism designed to promote cooperation could suffer from malicious users, while the trust management that only considers the trust dilemma is subjected to denial-of-service attacks. To address these two dilemmas simultaneously, we present a dependable cloud file-sharing scheme—EigenCloud. The main contributions include the following. First, we propose a modified EigenTrust algorithm to calculate the global cooperation value and global trust value of each cloud user based on her/his past behaviors. Second, we propose cooperation and trust-aware worker recommendation mechanism by determining a Pareto front from all cloud users. Thus, a cloud user who adopts the recommendation mechanism by paying an additional fee could have a higher probability of receiving a valid file in one transaction. Last but not least, we use the evolutionary game theory (EGT) to study the acceptance and effectiveness of the proposed EigenCloud by strategically modeling cloud users. The Lyapunov stability theory is employed to mathematically investigate the stability of evolutionary equilibriums of our EigenCloud. Finally, both numerical simulations and simulator-driving experiments illustrate that our EigenCloud has an outstanding performance in promoting cooperation and inhibiting malicious activity.
Tensor Decompositions in Wireless Communications and MIMO Radar

The emergence of big data and the multidimensional nature of wireless communication signals present significant opportunities for exploiting the versatility of tensor decompositions in associated data analysis and signal processing. The uniqueness of tensor decompositions, unlike matrix-based methods, can be guaranteed under very mild and natural conditions. Harnessing the power of multilinear algebra through tensor analysis in wireless signal processing, channel modeling, and parametric channel estimation provides greater flexibility in the choice of constraints on data properties and permits extraction of more general latent data components than matrix-based methods. Tensor analysis has also found applications in Multiple-Input Multiple-Output (MIMO) radar because of its ability to exploit the inherent higher-dimensional signal structures therein. In this paper, we provide a broad overview of tensor analysis in wireless communications and MIMO radar. More specifically, we cover topics including basic tensor operations, common tensor decompositions via canonical polyadic and Tucker factorization models, wireless communications applications ranging from blind symbol recovery to channel parameter estimation, and transmit beamspace design and target parameter estimation in MIMO radar.
HSIGAN: A Conditional Hyperspectral Image Synthesis Method With Auxiliary Classifier

In this article, we explore a conditional hyperspectral image (HSI) synthesis method with generative adversarial networks (GAN). A new multistage and multipole generative adversarial network, which is suitable for conditional HSI generation and classification (HSIGAN), is proposed. For HSIs synthesis, it is crucial to learn a great deal of spatial–spectral distribution features from source data. The multistage progressive training makes the generator effectively imitate the real data by fully exploiting the high-dimension learning capability of GAN models. The coarse-to-fine information extraction method helps the discriminator to understand the semantic feature better while the multiscale classification prediction presents a positive impact on results. A spectral classifier joins the adversarial network, which offers a helping hand to stabilize and optimize the model. Moreover, we apply the 3-D DropBlock layer in the generator to remove semantic information in a contiguous spatial–spectral region and avoid model collapse. Experimental results of the quantitative and qualitative evaluation show that HSIGAN could generate high-fidelity, diverse hyperspectral cubes while achieving top-ranking accuracy for supervised classification. This result is encouraging for using GANs as a data augmentation strategy in the HSI vision task.
Energy-Efficiency Maximization in Downlink Clustered NOMA Networks with Energy-Harvesting Relays

In this paper, the problem of joint relay assignment and energy-efficiency maximization (J-RA-EE-MAX) in energy-harvesting downlink clustered non-orthogonal multiple-access (NOMA) networks is considered. Specifically, the aim is to perform relay assignment to user clusters, while simultaneously maximizing energy-efficiency over each relay via multi-objective optimization, and satisfying users' quality-of-service (QoS) constraints. However, problem J-RA-EE-MAX happens to be nonconvex (i.e. computationally-prohibitive). Alternatively, a low-complexity solution procedure is devised to solve problem J-RA-EE-MA by: (1) optimally solving the energy-efficiency maximizing power allocation (EE-MAX-PA) for each (user cluster, relay) combination to construct the relays' preference profile, and (2) performing relay assignment via Gale's top trading cycles (TTC) matching mechanism. Simulation results are presented to validate the proposed solution procedure, which is shown to yield comparable energy-efficiency value per relay to the J-RA-EE-MAX scheme, while satisfying users' QoS constraints.
Space Pruning Based Time Minimization in Delay Constrained Multi-Task UAV-Based Sensing

Due to the inherent mobility and enhanced communication capability of the unmanned aerial vehicle (UAV), it is foreseen that effective UAV-based sensing schemes can achieve faster deployment and flexible observation as compared to the traditional fixed sensing systems. In many sensing applications, multiple geographically distributed tasks need to be executed while satisfying a stringent requirement on the transmission delay of the sensory data, especially for the time-critical sensing tasks, such as disaster rescue and environment monitoring. To minimize the overall mission completion time in such delay constrained multi-task UAV-based sensing applications, it is imperative to properly design the inter-task trajectory, the UAV-BS association, and the sensing order. Nonetheless, finding the optimal sensing scheme is highly non-trivial since these three sub-problems are tightly coupled and involve non-convex constraints. To overcome these challenges, firstly, a novel space pruning based trajectory search (SPTS) algorithm is proposed by exploiting the geometric characteristics of the optimal inter-task UAV trajectory, which can achieve a substantially faster convergence as compared to the Ployblock algorithm. Secondly, a novel optimal UAV-BS association (OUBA) algorithm is presented to quickly identify the best BS for each sensing task. Thirdly, by combining these two proposed algorithms with the Lin-Kernighan-Helsgaun (LKH) algorithm, a lower bound of the mission completion time can be efficiently computed and a near-optimal solution can be obtained. Numerical simulations demonstrate that significant performance improvement can be achieved by the proposed scheme.
Interference-Based QoS and Capacity Analysis of VANETs for Safety Applications

Whether the current IEEE 802.11p communication system meets the stringent quality of service (QoS) requirements for safety applications or not is still not very clear. Signal-to-interference-plus-noise ratio (SINR) distribution plays a primary role in quantifying the QoS as well as link capacity of IEEE 802.11p in one-dimensional (1-D) highway and two-dimensional (2-D) intersection road. Most of the analytical models based on stochastic geometry assumed ALOHA access to analyze the SINR distribution, while few of the works developed the stochastic geometry based model considering CSMA access but derived the SINR distribution utilizing statistical estimator. On the other hand, the current interference based probability analytic model for the SINR distribution limit the 1-D extension to 2-D, due to the very high numerical computational complexity at 2-D. In this paper, we propose an analytic model under more general non-homogeneous Poisson process (NHPP) node distribution, more general channel fading model (Nakagami) with path loss, and noise, for the study of QoS and capacity of VANET for BSM based safety applications in both 1-D highway and 2-D intersection road. The proposed model derives QoS and capacity of VANET BSM broadcast through evaluation of SINR distribution using probability theory and ordered statistics, which has much lower computational complexity compared with the unordered statistic model. The proposed model is validated by NS2 simulation and extended to the derivation of other QoS metrics such as packet reception probability, packet reception ratio, and broadcast link capacity, etc. The performance comparisons between 1-D and 2-D are implemented, and the QoS sensitivity analyses regarding the extension to multi-intersection as well as enlarging interference range are discussed.
Short-term effects of hardened wood ash and nitrogen fertilisation in a Norway spruce forest on soil solution chemistry and humus chemistry studied with different extraction methods

ABSTRACT A field trial with wood ash, nitrogen fertiliser, combined wood ash and nitrogen treatment and control was set up in a Norway spruce [Picea abies (L.) Karst. ] forest, and post-treatment changes and differences in humus chemistry and soil solution chemistry at 40 cm depth were studied from 2013 to 2015. Element concentrations in humus samples were compared using NH4NO3 extraction (M1) and nitric acid/perchloric acid digestion (M2). Nitrogen treatment led to significant increases in Mg determined after both M1 and M2. No significant effect of ash or ash + nitrogen treatment on soil C or N in the humus layer was found, while pH and concentrations of many elements after M2 increased in the same samples. Decreases after ash treatment for Al, Co, Fe, Ni, Pb and Zn in humus determined after M1 may be because of decreased mobility due to increased pH. Differences depending on analytical method suggest that methodology should be taken into account when evaluating ecological relevance of differences in element concentrations. In soil solution at 40 cm depth nitrogen treatment led to short-term increases in concentrations of , , Ca, Mg, Co, Ni and Zn and a reduction in pH, while ash treatment led to no clear effects.
A Quality of Experience Management Method For Intent-Based Software-Defined Networks

The so-called IBN (Intent-based networking) is founded on the well-known SDN (Software-Defined Network) and represents one of the most important emerging capabilities of network infrastructure. IBN is offering network administrators a simple way to express their business goals, such as providing the necessary QoE, by allowing network software to automatically achieve those QoE goals. The goal is to rely on the programmability of SDN for simple and flexible network planning, design and operation, thereby abstracting from the complexity of networks. This paper presents the development and implementation of a QoE (Quality of Expirience) monitoring system for future Intent-Based Software-Defined Networks (IBSDN) that will improve the quality of end-user experience and enable more efficient use of network resources. The paper also presents methods for measuring network parameters: delay and packet loss. A carried investigation to assess the performance of the proposed monitoring system by generating audio and video traffic in the Mininet network and graphically presented the results.
A Data-Driven Analysis of Employee Development Based on Working Expertise

Employees’ expertise is the basic component of human capital of organizations. As the role of human capital and the understanding of employee development become increasingly vital, research works about the effects of working expertise on development are necessary. This article aims to confirm the effect of expertise and find out how expertise affects development. In this article, we analyze employee development and working expertise through data-driven methods, using a data set of a Chinese state-owned enterprise. In addition to statistical analysis, expertise networks are constructed to discover more insights about the effect of expertise on employee development. Moreover, to further validate and exploit the effect, a prediction model of development potential is proposed based on machine learning. Results of the experiment show that the random forests model with network embedding (RFNE) is effective in identifying excellent employees. Finally, with the help of data-driven analysis of expertise and development, we find that the appropriate post, the right choice, the distinctive competency, as well as the interdisciplinary transfer contribute to employee development.
Research on lithology identification based on two-stage genetic algorithm

A two-stage genetic evolution algorithm based on gene fragments optimization (GF-GA) is proposed, which optimizes multi-parameter weights in logging lithology identification. First, gene fragments composed of multivariate weights corresponding to certain category are preselected to build the gene fragment library; then gene recombination and genetic evolution are carried out on the basis of the gene fragment library. GF-GA greatly reduces the search space redundancy of the genetic algorithm and improves the optimization efficiency by retaining the genetic diversity. The experiments of the KNN and fuzzy recognition methods of multivariable weighting in the lithology identification of complex carbonate rocks show that the GF-GA outperforms the traditional genetic algorithm (GA) in accuracy and time-consuming. Specifically, the GF-GA weighted model has an 2.5% to 3.5% accuracy improvement compared with the traditional GA weighted model, and the convergence speed is doubled. Moreover, compared with the conventional equal weight model, the accuracy of lithology identification is increased by 10% to 20%. Therefore, GF-GA is an optimization model with wide prospects in application.
Superpixel-Based Convolutional Neural Network for Georeferencing the Drone Images

Information extracted from aerial photographs has been used for many practical applications such as urban planning, forest management, disaster relief, and climate modeling. In many cases, labeling of information in the photo is still performed by human experts, making the process slow, costly, and error-prone. This article shows how a convolutional neural network can be used to determine the location of ground control points (GCPs) in aerial photos, which significantly reduces the amount of human labor in identifying GCP locations. Two convolutional neural network (CNN) methods, sliding-window CNN with superpixel-level majority voting, and superpixel-based CNN are evaluated and analyzed. The results of the classification and segmentation show that both of these methods can quickly extract the locations of objects from aerial photographs, but only superpixel-based CNN can unambiguously locate the GCPs.
Research on Application of Classification Model Based on Stack Generalization in Staging of Cervical Tissue Pathological Images

Cervical cancer is a malignant tumor that threatens women’s health and life. Cervical pathology examination, as the gold standard for cervical cancer diagnosis, provides an important basis for the surgical plan and postoperative follow-up strategy for cervical cancer. Cervical biopsy diagnosis includes normal, low-grade squamous intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL) and squamous cell carcinoma (SCC). At present, cervical pathology examination still relies on the doctor’s personal clinical experience and subjective judgment, which is time-consuming and may cause misdiagnosis or missed diagnosis. In addition, the current intelligent classification of cervical pathological images still has disadvantages such as imperfect classification system and low classification accuracy. Therefore, this experiment uses the ResNet50 model of the convolutional neural network as the feature extractor, and selects the K-Nearest Neighbour (KNN), Random Forest (RF), Support Vector Machine (SVM) classifiers in Machine Learning to perform cervical tissue pathological images Discrimination, the accuracy of the classification results were 85.83%, 80.33%, and 86.67%. In order to further improve the accuracy of the model and enhance the applicability and stability of the model, this experiment proposes the Stacked Generalization (SK) classification model. The first-layer base learner of the SK classification model selects CNN-KNN, CNN-RF, CNN-SVM, and the second-layer classifier selects Multilayer Perceptron (MLP). Among them, MLP makes the final result by learning the classification performance of the base learner for label discrimination, the accuracy of the classification model after ensemble learning is 90.00%. In addition, this experiment uses the Synthetic Minority Oversampling Technique (SMOTE) algorithm to amplify the training samples, and the amplified data set has a classification accuracy of 89.17% under the training of the SK classification model. The results show that the SK classification model in this experiment has a high classification ability for cervical histopathological images, and has good generalization ability and robustness.
Secrecy Analysis of Relay-User Selection in AS-AF Systems Over Nakagami Fading Channels

This paper presents the secrecy outage probability of relay and user (RU) selection in the antenna selection (AS)-amplify and forward (AF) system over integer-valued $m$ Nakagami fading channels for the physical layer security. This work derives the lower bound (LB) and asymptotic cumulative density functions (CDFs) of the signal to noise ratio (SNR) for the RU selection in the AS-AF system. This study also considers the impact of the two types of receivers, i.e., maximal ratio combining (MRC), selection combining (SC) for the authorized system and eavesdropper. Using the lower bound CDFs, the LB expressions of the secrecy outage probability are derived for various scenarios. Applying the asymptotic CDFs, the diversity order of the secrecy performance is also quantified for each case. From analysis results, we show that the diversity order of the secrecy outage probability in the proposed system is only affected by authorized system's parameters, not by eavesdropper's parameters. Further, the relay and user nodes with the MRC receiver provide the SNR gain compared to the SC receiver regardless of eavesdropper's receiver type. Finally, the secrecy outage probability of the proposed RU system with the SC eavesdropper has the SNR gain compared to the MRC eavesdropper, while the diversity order is identical to each other.
Hyperspectral image recognition based on lightweight causal convolutional network

Due to the phenomenon of mixed pixels in hyperspectral images, the spatial relationship between pixels in feature recognition is more important than in common image classification. Traditional convolutional neural networks can extract spatial structure features, but it cannot accurately express the spatial causality between image pixels. A lightweight deep learning network architecture is proposed based on causal convolution, which can effectively mine the intrinsic causality between pixels in hyperspectral images. Through comparative experiments in three public datasets, it is shown that compared with the traditional convolutional neural network, this network not only maintains the accuracy in ground objects recognition but also greatly reduces the model parameters. The structure proposed in this paper has high practical value in the recognition task of hyperspectral image.
Donald J. Trump’s Presidency in Cyberspace: A Case Study of Social Perception and Social Influence in Digital Oligarchy Era

In the past few years, with the rapid growth of digital technologies, Facebook, Twitter, and other social media platforms have become the digital oligarchies, which have the enormous capabilities to potentially control what is discussed in cyberspace. In the digital oligarchy era, social perception and social influence in different complex social systems have evolved quickly. In this article, we conducted large-scale empirical studies on social perception and social influence regarding the Trump phenomenon from personal perception, media, and public attention perspectives. We found that there exist obvious correlations between the posting behavior of Trump and the attention of news media. By constructing public attention networks using complex networks based on Google search information, we further reveal that digital platforms could affect social perception and social influence significantly. Especially, we obtained that the public attention can always be influenced by the political moments.
Secure Multiuser Scheduling for Hybrid Relay-Assisted Wireless Powered Cooperative Communication Networks With Full-Duplex Destination-Based Jamming

In this paper, we investigate secure communication in a hybrid relay (HR)-assisted wireless powered cooperative communication network (WPCCN), where an eavesdropper tries to intercept the data transmitted by a source user and the HR simultaneously. The HR has the ability to wireless powered the energy-constrained multiple users in the downlink and to relay the received confidential information in the uplink. In particular, the full-duplex destination-based jamming (FD-DBJ) strategy is exploited to improve the secrecy performance. Moreover, in order to conduct effective secure communications, we design two multiuser scheduling schemes, i.e., random user selection (RUS), and best user selection (BUS) based on the main channel quality. In order to evaluate the secrecy performance, we analytically derive the energy outage probability (EOP), the secrecy outage probability (SOP), and the hybrid outage probability (HOP), respectively. As such, we formulate the secrecy throughput (ST) maximization problem to optimize the predetermined transmission rate. Our analysis and numerical results reveal that: 1) The BUS scheme has better security performance. 2) Both of the energy conversion efficiency and the number of users have a positive effect on secrecy performance. 3) The FD-DBJ scheme is an effective method to achieve secure communications. 4) The time-switching factor and predetermined transmission rate have a crucial influence on the secrecy performance, which should be considered carefully in system design.