Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of hydro–alcoholic extract of Olea europaea on apoptosis–related genes and oxidative stress in a rat model of torsion/detorsion–induced ovarian damage

Objective: To evaluate the impact of Olea (O.) europaea extract on markers of oxidative stress and apoptosis of ovarian tissues in a rat model of torsion/detorsion-induced ovarian damage. Methods: A total of 28 Wistar female rats were randomly assigned into 4 groups, with 7 rats in each group. The sham group received a 2.5 cm longitudinal incision in the midline part of the abdomen which was then sutured with 5-0 nylon thread; the torsion/detorsion group underwent torsion induction for 3 h followed by reperfusion for 10 days; the torsion/detorsion+O. europaea group received 300 mg/kg hydro-alcoholic extract of O. europaea 30 min before detorsion, followed by reperfusion for 10 days; and the O. europaea group only received 300 mg/kg hydro-alcoholic extract of O. europaea for 10 days. After the treatment period, blood samples were taken; the levels of estrogen, glutathione peroxidase, superoxide dismutase, and malondialdehyde were assayed. The histological changes, as well as the rate of apoptosis in ovarian tissues, were also carried out by histomorphometric analysis at day 10 post-procedure. Results: Histological comparisons demonstrated a significant detrimental change in the torsion/ detorsion group as compared with other groups. The number of pre-antral and antral follicles and corpus luteum was significantly decreased in the torsion/detorsion group compared with the sham group, while treatment with O. europaea could enhance their numbers (P<0.05). The index of apoptosis and the number of atretic body in the ovarian tissue were significantly higher in the torsion/detorsion group compared with the sham group (P<0.05). The concentrations of glutathione peroxidase, estrogen, and superoxide dismutase as well as the mRNA expression of Bcl-2 were considerably diminished in the torsion/detorsion group while they were elevated in the torsion/detorsion+O. europaea group (P<0.05) compared with the torsion/detorsion group. The serum malondialdehyde level and the mRNA expression of Bax were markedly increased during ischemia, while treatment with O. europaea significantly diminished the increased concentrations of malondialdehyde and Bax level in the torsion/detorsion+O. europaea group (P<0.05). Conclusions: O. europaea extract can reduce the degree of tissue damage induced by oxidative stress and apoptosis in the ovary following ovarian ischemia/reperfusion.
Making Wideband Channel Estimation Feasible for mmWave Massive MIMO: A Doubly Sparse Approach

The widespread deployment of mmWave communication systems is an unstoppable trend, but its success will heavily rely on well-designed transceivers to combat the severe propagation loss. To acquire the accurate channel state information (CSI) so that the transceiver design can be facilitated, in this paper, we provide a new time-domain channel estimation scheme for hybrid mmWave massive multiple-input multiple-output (mMIMO) systems. In addition to utilizing the well-known angular sparsity, the delay-domain sparsity will also be exploited to accomplish the channel estimation with satisfactory accuracy yet affordable complexity. The successful combination of these two types of sparsity is attributed to a judiciously designed training pattern. Thanks to our innovative exploitation of the double sparsity, a satisfactory performance can be achieved together with largely reduced training overhead, storage demand, and computational complexity.
A Mean-Field-Type Game Approach to Computation Offloading in Mobile Edge Computing Networks

Mobile edge computing has been proposed to reduce latency inherent in traditional cloud computing. One of the services offered in a mobile edge computing network is computation offloading in which computing nodes with limited capabilities and performance can offload a computation-intensive task to other computing nodes in the network. Recently, mean-field-type game (MFTG) has been applied in engineering applications in which the number of decision makers is finite and where a decision maker can be distinguishable and have a non-negligible effect on the total utility of the network. Since mobile edge computing networks have a finite number of computing nodes where the computing capability of a computing node can affect the state (i.e., the amount of computation task) of the network, we propose a MFTG approach to formulate and solve a computation offloading problem. In this scenario, the goal of each computing node is to compute the portion of the aggregate computation task it can offload from the network that minimizes its cost. Then, we utilize a direct approach to solve for the optimal portion of the aggregate computation task that minimizes the cost incurred by a computing node. Finally, we conclude the paper with simulations to show the significance of the approach.
Resource Allocation Optimization in the NFV-Enabled MEC Network Based on Game Theory

Compared with the conventional mobile edge cloud (MEC) network, the network function virtualization (NFV)-enabled MEC network provides new flexibility on the MEC service deployment. Resource wastage owing to dynamic workloads in traditional MEC networks can be overcome through adaptive resource allocation. In this paper, we investigate the resource allocation problem to minimize the operational cost (e.g., energy consumption, capital expenditure) and the average response time in the NFV-enabled MEC network. We consider the problem from the perspective of MEC service deployment, assignment, and routing among the access points (APs) and MEC servers. We propose an user-network cooperation-based algorithm with low-complexity. In the proposed algorithm, the network announces a path-switching rule (i.e., α-approximate deviation) with proportionally shared operational cost, while the APs selfishly choose their paths with the least cost accordingly. We analyze the selfish behaviors of APs with game theory. We prove existence and convergence of α-approximate equilibriums. Also, we evaluate the efficiency of the equilibriums with the price of stability (POS). Furthermore, an enhanced algorithm based on public service advertising (PSA) is proposed to improve the convergence performance and equilibriums efficiency. Through simulations, we show the superiority of the proposed algorithms over existing algorithms (e.g., BnB-SD and greedy routing) on the accuracy and convergence performance (measured by the overall path switching).
Joint User Association, Grouping and Power Allocation in Uplink NOMA Systems with QoS Constraints

To alleviate severe inter-user interference in Non-orthogonal multiple access (NOMA) systems, in practice, several users are grouped together for NOMA transmission and orthogonal resources are allocated among different groups. In this paper, we investigate uplink NOMA scenarios with multiple base stations, where user association is jointly considered with user grouping. It is worth noting that a user prefers to be associated with the base station where it can be assigned into a group with less inter-user interference. Furthermore, different users have different quality-of-service (QoS) requirements. Based on these observations, we firstly quantitatively analyze the extra transmit power that a user brings to the others in the same group under QoS constraints, i.e., externalities, and derive an externality function to indicate interference among users in uplink NOMA. Then, we obtain a game model for the joint user association, grouping and power allocation (JAGP) problem in uplink NOMA systems and prove that it is an exact potential game, with the goal to minimize total transmit power. Furthermore, we prove the existence of Nash Equilibrium (NE) as well as the finite improvement property (FIP) of this game. In addition, the NE of this game is proved to be Pareto-optimal. A heuristic algorithm is proposed to find the solution to the JAGP problem by achieving the NE of the proposed game, which convergence is guaranteed by FIP. Simulation results show that the proposed algorithm can considerably reduce the total transmit power compared with existing schemes.
TranGAN: Generative Adversarial Network Based Transfer Learning for Social Tie Prediction

Social tie prediction is an important issue in social network analysis. Transfer learning is often used for social tie prediction to address the problem of insufficient labeled training data, since few users manually annotate their social relationships. In this paper, we propose TranGAN, a novel generative adversarial network (GAN) based transfer learning framework for social tie prediction, which leverages social theories as the common knowledge to bridge the source network and the target network. GAN helps augment the original data set by generating data samples that have a similar probability distribution to that of the original data, and the training of TranGAN converges faster compared to existing transfer learning models. We evaluate the performance of TranGAN with extensive experiments, and show that TranGAN outperforms traditional learning algorithms and existing transfer learning algorithm on several metrics, and is efficient for large-scale social networks.
Overprivileged Permission Detection for Android Applications

Android applications (Apps) have penetrated almost every aspect of our lives, bring users great convenience as well as security concerns. Even though Android system adopts permission mechanism to restrict Apps from accessing important resources of a smartphone, such as telephony, camera and GPS location, users face still significant risk of privacy leakage due to the overprivileged permissions. The overprivileged permission means the extra permission declared by the App but has nothing to do with its function. Unfortunately, there doesn't exist any tool for ordinary users to detect the overprivileged permission of an App, hence most users grant any permission declared by the App, intensifying the risk of private information leakage. Although some previous studies tried to solve the problem of permission overprivilege, their methods are not applicable nowadays because of the progress of App protection technology and the update of Android system. Towards this end, we develop a user-friendly tool based on frequent item set mining for the detection of overprivileged permissions of Android Apps, which is named Droidtector. Droidtector can operate in online or offline mode and users can choose any mode according to their situation. Finally, we run Droidtector on 1000 Apps crawled from Google Play and find that 479 of them are overprivileged, accounting for about 48% of all the sample Apps.
Probabilistic Caching Policy for Categorized Contents and Consecutive User Demands

In wireless caching networks, each user generally consumes more than one content in a row, and the number of consecutive demands could vary for different users. In addition, popular contents are usually classified into several categories. In this case for consecutive user demands, the content popularity model largely depends on the previously consumed contents, i.e., contents that belong to the same category as the previously consumed content would be highly popular. Based on this observation, this paper proposes an optimal probabilistic caching policy for consecutive user demands in categorized contents. The proposed caching scheme maximizes the minimum of the success probabilities for content delivery of all users when individual users request different numbers of contents in a row. Comparing with the content placement optimized for one-shot request, intensive numerical results verify the impacts of categorized contents and consecutive user demands on the caching policy.
Dynamic RAT Selection and Pricing for Efficient Traffic Allocation in 5G HetNets

In this paper, we focus on 5G heterogeneous networks, considering the existence of multiple Distributed Units (DUs) that can provide access to end users implementing several access technologies, managed by a Central Unit (CU) responsible for the allocation of network resources. Based on a distributed dynamic pricing scheme, that gives to User Equipment (UE) the ability to select the appropriate Radio Access Technology (RAT) depending on its sensitivity to congestion, we investigate a scheme of greater granularity, where UEs are able to allocate each of their traffic classes to the appropriate RAT, exploiting their multi-homing features. As UEs are sequentially polled to request for network resources, we develop a centrally controlled proportionally fair ranking as a benchmark policy. We then propose a dynamic polling policy that presents close performance to the benchmark policy, while maintaining a distributed nature. We evaluate our framework for a variety of traffic classes in terms of Quality of Service (QoS) requirements, and we provide results on capacity utilization and load distribution over available RATs, as well as access price variations.
Towards Multi-Controller Placement for SDN Based on Density Peaks Clustering

As a novel network architecture, software-defined networking (SDN) decouples control and data planes, which brings a lot of flexibility to the network. But the separation arises some problems, such as controller placement problem(CPP). Controller placement problem is an important task especially in wide area networks, which would directly affect latency and performance of entire network. In this paper, we propose a method based on modified density peaks clustering(MDPC) algorithm to solve controller placement problem, aiming to minimize the average propagation latency between switches and controllers. The entire network is divided into several sub-networks, each of sub-networks is managed by a controller. We conduct the experiments on the Internet2 OS3E topology to evaluate the performance of our method. The experimental results show that our controller placement strategy can significantly reduce latency. Specifically, the average latency can be reduced by 35% compared to K-means and 10% compared to optimized K-means.
Interference-Aware Co-Channel Transmission Over DTV Bands via Partial Frequency and Time Overlaps

This paper studies transmission performance in a coexistence scenario when a secondary communication network is deployed in co-channel mode within TV broadcast bands. Keeping in view the differences in timing and spectral characteristics between the primary and secondary transmissions, subcarrier-level interference at the primary is computed in presence of time-and-frequency overlapped secondary transmission. This estimation is validated experimentally as well as via simulations. Specifically, the possibility of coexistent secondary transmission near the primary broadcast receivers is experimentally evaluated in an emulated DVB-T2 transmission environment. The analytical and experimental studies demonstrate that the effects of interference at the primary receiver are within tolerable range even when the secondary transmitter transmits at high power with appropriately chosen time-frequency occupancy overlaps. The benefits are further enhanced when the agility of frequency and temporal overlaps are combined with power control in the secondary network.
Design and Coverage Analysis of Elliptical Cell Based Beamforming for HSR Communication Systems

In this paper, beamforming design based on a novel coverage model of high-speed railway (HSR) to improve the coverage in HSR communication systems is investigated. A dedicated coverage model, where the coverage cell is ellipse rather than the traditional circular is considered. Based on the elliptical coverage cell, an optimization problem for beamforming design is formulated to maximize the percentage of rail coverage for a given railway length, which is defined as the percentage of coverage distance that the signal-to-noise ratio (SNR) exceeds a desired SNR threshold for the given railway length, subject to the equal average received power on the elliptical line constraint. Considering that the percentage of coverage can be improved by increasing the received power, the problem can be recast to maximizing the expected average received power. A semidefinite relaxation (SDR) method is developed to solve the optimization problem. Through numerical results, the proposed elliptical coverage cell based beamforming can increase the covering railway distance and reduce the transmission power consumption compared with the circular coverage cell. It is also found that the larger eccentricity of the ellipse with appropriately chosen BS location can provide further coverage distance.
An Intelligent Packet Forwarding Approach for Disaster Recovery Networks

Disasters, such as earthquakes, typhoons, and tsunamis, usually cause extreme damages to the communication infrastructures, which results in a heavy recovery workload and seriously affects people's life. The disaster recovery networks play a critical role to reduce the loss caused by the disasters. However, the suddenly varying traffic demand and limited resources after disasters may lead to the repetitive reconfigurations for running the existing packet forwarding strategies, such as the shortest path algorithms. To handle this problem, it is necessary to adopt the deep learning technique to develop a disaster-resilient solution. In this paper, we utilize the deep reinforcement learning technique to propose a self-adaptive routing method for the Movable and Deployable Resource Unit (MDRU) based backbone network. Compared with existing deep learning based routing strategy, our proposal can adapt to the sudden network errors. Moreover, we also analyze the deployment manner and consider a centralized control structure to significantly balance the traffic.
A New Method for Sparse Signals Reconstruction in Fusion Center with Incomplete Measurements

In this paper, we focus on a specific problem in joint sparse signals recovery, where not all of the measurements are fully received (observed) in the data fusion center (FC), but some of them are missing. Most of the previous work focused on the sparse signals recovery problem that the information of the measurements in FC is complete (fully observed). However, in practice, usually it is not easy for the data center to receive complete measurements, due to limited sampling rate, noise corruption, or transmission error, etc. Hence, how to reconstruct the sparse signals under the incomplete measurements is an important problem for the fusion center. Based on Regularized M-FOCUSS (FOCal Underdetermined System Solver) algorithm, this paper proposes a new method to efficiently reconstruct the sparse signals from the incomplete measurements. Different to the widely used matrix completion based method, our method obtains the solution from the received measurements directly, instead of running the matrix completion algorithm in advance. Furthermore, our method shows excellent recovery performance compared to the matrix completion based method, and also the independent vector recovery method, and the block-sparse based recovery method. Experimental results are also provided to show the effectiveness of our proposed scheme.
Sum Rate Maximization for Frame-Based Multigateway Satellite Systems with Feeder Link Interference

This paper studies the multicast precoding problem in frame-based multigateway multibeam satellite communications with feeder link interference. We formulate a sum rate maximization problem that incorporates the minimum signal-to-interference-and-noise-ratio (SINR) requirement for each user, the sum power constraint at each gateway as well as the per feed power constraints at the satellite. We propose two algorithms to solve the formulated problem. In the first algorithm, by employing the successive convex approximation (SCA) approach, we iteratively approximate the original nonconvex problem to a second-order cone program (SOCP) which can be solved by modern solvers efficiently. For the second, we propose a modified joint power control and beamforming algorithm which computes QoS beamforming and geometric programming (GP) based power allocation iteratively. Compared to the traditional method [5] which uses a subgradient and projection based approach for the power control, the GP based solution is more implementation friendly and practical appealing which also achieves a slightly better sum rate performance. Finally, numerical results are presented to validate the efficiency of the proposed schemes.
Multi-Layer-Mesh: A Novel Topology and SDN-Based Path Switching for Big Data Cluster Networks

Big Data technologies and tools have being used for the past decade to solve several scientific and industry problems, with Hadoop/YARN becoming the “de facto” standard for these applications, although other technologies run on top of it. As any other distributed application, those big data technologies rely heavily on the network infrastructure to read and move data from hundreds or thousands of cluster nodes. Although these technologies are based on reliable and efficient distributed algorithms, there are scenarios and conditions that can generate bottlenecks and inefficiencies, i.e., when a high number of concurrent users creates data access contention. In this paper, we propose a novel network topology called Multi-Layer-Mesh and a path switching algorithm based on SDN, that can increase the performance of a big data cluster while reducing the amount of utilized resources (network equipment), in turn reducing the energy and cooling consumption. A thorough simulation-based evaluation of our algorithms shows an average improvement in performance of 31.77% and an average decrease in resource utilization of 36.03% compared to a traditional Spine-Leaf topology, in the selected test scenarios.
A Multi-User ACK-Aggregation Method for Large-Scale Reliable LoRaWAN Service

In this paper, we analyze the throughput of LoRaWAN Class-A data transfer with acknowledgement (ACK) under duty-cycle restriction which is imposed on gateways and end-devices. We show that ACK traffic limits the throughput of data traffic. We propose a method for aggregating ACKs from multiple users to improve the throughput and reliability of data transfer. The proposed method cumulates ACKs for multiple packets for multiple user-nodes. It also has schedules receive windows of user-nodes'. We show via performance analysis and simulation results that our method improves data transfer throughput by ten to thirty times.
Deep Reinforcement Learning for Multi-User Access Control in UAV Networks

Unmanned Aerial Vehicles (UAVs) have recently been proposed as flying base stations, called UAV-BSs, to provide reliable connections and extend the coverage of the existing wireless networks. The mobility of UAV-BSs leads to a dynamic network environment, in which the global network information is hard to be obtained. Since frequent information exchanges cause huge signaling overheads, it is difficult to deploy centralized algorithms in UAV networks. Hence, we propose a distributed deep reinforcement learning (DRL) framework for multi-user access control in UAV networks. In particular, each user makes its own access decisions independently based on the local network information, and maximizes the long-term throughput while avoiding frequent handovers. Simulation results have validated the effectiveness of the proposed algorithm and shown the superiority of the proposed DRL framework over the state of arts.