Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Network Analysis through Big Data Platform Review

Social Network Analysis determines the relationships between people, organizations, groups or entities and the flow of information between them. A literature review is done for social network analysis and the prediction of links that could be done in future between the different entities within the network, many use cases are shown here for applying Social Network Analysis in different industries. This survey introduces different approaches to anticipating the connection between nodes in the social network. It will also support future research and development work as well as raising the awareness for the presented approaches.
An Adaptive Profile-Based Approach for Detecting Anomalous Traffic in Backbone

Anomaly detection is the first step with a challenging task of securing a communication network, as the anomalies may indicate suspicious behaviors, attacks, network malfunctions, or failures. In this paper, we address the problem of not only detecting different anomalies, such as volume based (e.g., DDoS or Flash crowd) and spatial based (e.g., network scan), that arise simultaneously in the wild but also of attributing the anomalous point to a single-anomaly event causing it. Besides, we also tackle the problem of low-detection accuracy caused by the phenomenon of traffic drift. To this end, a novel adaptive profile-based anomaly detection scheme is proposed. More specifically, a more comprehensive metrics set is defined from the dimensions of temporal, spatial, category, and intensity to compose IP traffic behavior characteristic spectrum for fine-grained traffic characterization. Then, the digital signature matrix obtained by using the ant colony optimization (ACO) algorithm is applied to construct the baseline profile of the normal traffic behavior. Anomalous points are identified and analyzed by using confidence bands and a generic clustering technique, respectively. Finally, a lightweight updating strategy is applied to reduce the number of false positives. Real-world data of China Education Research Network backbone and synthetic data are collected to verify our proposal. The experimental results demonstrate that our approach provides a fine-grained behavior description ability and has significantly increased the detection accuracy compared with other similar alternatives.
Comparison of ramosetron and granisetron for preventing postoperative nausea and vomiting after gynecologic surgery.

UNLABELLED In a prospective, randomized, double-blinded study, we evaluated the efficacy of granisetron and ramosetron for preventing postoperative nausea and vomiting (PONV) in major gynecologic surgery. One hundred twenty patients, ASA physical status I or II, aged 23-65 yr, received i.v. granisetron 2.5 mg or ramosetron 0.3 mg (n = 60 each) at the end of surgery. A standard general anesthetic technique and postoperative analgesia were used. The incidence of a complete response, defined as no PONV and no need for another rescue medication, 0-3 h after anesthesia was 87% with granisetron and 90% with ramosetron; the corresponding incidence 3-24 h after anesthesia was 85% and 90%; the corresponding incidence 24-48 h after anesthesia was 70% and 92% (P < 0.05). No clinically serious adverse events due to the drugs were observed in any of the groups. In conclusion, prophylactic therapy with ramosetron is more effective than granisetron for the longterm prevention of PONV after major gynecologic surgery. IMPLICATIONS We compared the efficacy of granisetron and ramosetron for preventing postoperative nausea and vomiting in major gynecologic surgery. Prophylactic therapy with ramosetron was more effective than granisetron for preventing postoperative nausea and vomiting 24-48 h after anesthesia.
Deep Learning Application in Mechatronics Systems’ Fault Diagnosis, a Case Study of the Demand-Controlled Ventilation and Heating System

Mechatronics systems include a vast range of interdisciplinary area of electrical and mechanical systems e.g. heating, ventilation, and air-conditioning systems in building automation systems that are responsible to provide occupants a comfortable and productive environment. The demand-controlled ventilation system as an advanced control approach in smart buildings is used for the main goal of energy saving. But, these kinds of systems because of their numerous components such as sensors and actuators are very prone to the faults. Arise of the faults, if they are not detected and diagnosed early, can lead to the system’s performance degradation or extra maintenance cost and effort. Nowadays, introducing a suitable generic technique for fault detection and diagnosis is an utmost challenge. The contribution of this paper is to present a novel fault detection and diagnosis framework based on deep learning method for a case study of mechatronics systems, the demand-controlled ventilation and heating system. This paper presents all the steps including data acquisition, data preprocessing, network model design, model optimization, and network model evaluation. Ten types of faults in different classes as well as the healthy data are used to train and evaluate the performance of the designed network model. The results describe a high accuracy (97.4% in confusion matrix) via the designed deep neural network. Also, this study describes the methodology of selecting the optimum parameters of the training process by analyzing the effect of each parameter on the training accuracy.
Mutual Coupling Reduction of a Circularly Polarized Four-Element Antenna Array Using Metamaterial Absorber for Unmanned Vehicles

A double-layer metamaterial absorber is proposed to suppress the mutual coupling within a circularly polarized (CP) four-element antenna array operating at the Chinese Beidou navigation satellite system (BDS) B3 band, wherein four miniaturized patch antenna elements are positioned in a rotationally symmetric distribution. The single-feed antenna element utilizes simultaneously a thick air substrate to widen the achievable CP bandwidth, four parasitic grounded strips to reduce the size occupation, and a capacitive probe feed to enhance the impedance matching. A wall of the double-layer metamaterial absorber using slotted cross patch structure is introduced in between the four CP antenna elements. An antenna array prototype with an overall footprint size of $0.81\lambda _{0}\,\,\times \,\, 0.81\lambda _{0} $ ( $\lambda _{0}$ denotes the free-space wavelength at 1268 MHz) is implemented and validated experimentally. The measured results in a good agreement with the simulated ones demonstrate a mutual coupling reduction of 11 and 8 dB, for the parallel-directed and orthogonal-directed antenna element pairs across the entire operation band, and meanwhile a good CP performance is also achieved. Details of the design considerations as well as the simulation and measurement results are presented and discussed. The proposed antenna array can be well suited for BDS anti-jam antenna applications in unmanned vehicles.
Lightweight Deep Residual CNN for Fault Diagnosis of Rotating Machinery Based on Depthwise Separable Convolutions

This paper proposes an efficient and noise-insensitive end-to-end lightweight deep learning method. The method synthesizes the characteristics of a frequency domain transform and a deep convolutional neural network. The former can extract multiscale information in vibration signal processing and the latter has a good classification performance, data-driven, and high transfer-learning ability. A vibration signal is decomposed into a pyramidal wavelet packet, and each sub-band coefficient is used as an input of a channel in the deep network. A deep residual convolutional network based on a separable convolution and concatenated rectified linear unit (CReLU) lightweight convolution technology is used for fault diagnosis. The proposed algorithm is compared with related deep learning algorithms using two bearing datasets produced by Case Western Reserve University (CWRU) and the Center for Intelligent Maintenance Systems (IMS), University of Cincinnati. Compared with the existing algorithms, the experimental results show that the comprehensive performance of the algorithm proposed in this paper is “small, light, and fast,” and satisfactory diagnostic results are obtained in the fault diagnosis of rotating machinery.
Oceanic Data Processing System Based on Multi-sensor Interaction through Internet of Things

With the rapid development of Internet of Things and data interactive processing technologies, the application of Internet of Things in marine field is emerging. In the face of big oceanic data from various sensors, a model of multi-sensor interaction and data processing is proposed in this paper. On the basis of this model, an oceanic data processing algorithm based on back propagation neural network data fitting is proposed, which can be used for various types of oceanic data. In order to make the data more intuitive and improve the efficiency of data processing, we design a basic model of oceanic data visualization concluding the graphical display and processing. In this model, the standard file will be generated finally through the effective data processing. Through the comparison and analysis of experimental results, it's shown that the algorithm achieves better optimization. And the model of data interaction and visualization are suitable and reasonable in practical application.
Resource Allocation Optimization for Hierarchical Cloud Data Centers

Ahstract- The current network infrastructure needs to adjust to meet the rapidly growing data demand generated by new emerging applications. Planning this infrastructure should be thought by operators so that the high number of applications that are emerging, with different types of requirements, can be efficiently met. In this work, a model of resource provisioning for hierarchically distributed data centers is proposed using Linear Programming (LP). The goal is to increase efficiency in the use of computational resources and decrease the overhead of network links. The results shows that the model is able to efficiently accommodate 48 % more applications when compared to First-Fit.
Multi-Agent SLAM Approaches for Low-Cost Platforms

Modern SLAM (Simultaneous Localization and Mapping) algorithms launched on a moving agent are bounded with its computation resources. The consistent way out is to add more computing agents that might explore the environment quicker than one and thus to decrease the load of each agent. This paper presents the state of art in area of Multi-agent SLAM algorithms and describes problems that are faced in front of a developer of such approach. The outstanding problem of Multiagent SLAM - merging of maps built by separate agent during algorithm is also considered in this paper. Moreover the algorithm that extends laser 2D single hypothesis SLAM for multiple agents is introduced with evaluation of its performance.
Developing a Network Attack Detection System Using Deep Learning

With the development of networks, the number of network attacks is increasing exponentially. The need for a network security system is becoming more and more important since there is a lot of sensitive information being stored and sent through the Internet. We need to find the best possible ways to protect our systems from any abnormal behaviors that attempt to violate the integrity, confidentiality or availability of useful information. Many different techniques have been used either to prevent or to detect attacks. In this paper, a Deep Learning (DL) approach is used that can create a better and more effective Intrusion Detection System (IDS). The intended approach is based on classifying normal behavior on the network from anomaly behavior. The proposed approach outperforms all the classical approaches with an accuracy of 99% for training and 91.28% for the testing phase, demonstrating its potential for real-time and practical applications.
Fully Dynamic Broadcasting under SINR

Dynamicity is one of the critical characteristics and a major challenge in designing communication protocols in wireless networks. Most of the previous works had focused on the internal node changes (e.g., mobility, arrival, or departure) and not considered the effect of external environmental change. However, the external environmental change, in general, is a more complex phenomenon that can impede nodes from successful communication, implying the protocols of the previous dynamic models do not work well in practice. In this paper, we give an algorithm for distributed broadcasting in a more general model with fully dynamic wireless networks, called FD-Broadcast. Specifically, we present a fully dynamic model which allows node mobility and churns (due to node arrivals/departure) and external environmental change. In contrast to the previous works on dynamic networks, our model defines the full dynamicity in terms of localized topological changes of each node and can tolerate some external environmental change. The external environment changes are captured by the random jamming method. We show that FD-Broadcast can achieve broadcasting in $O(D_{S})$ rounds with a high probability guarantee under the assumption of constant dynamic rate in the SINR model, where $D_{S}$ is the dynamic diameter, a parameter proposed to depict the complexity of dynamic broadcasting. Moreover, the lower bound of dynamic broadcasting is proved to be $\Omega(D_{S})$, thus, FD-Broadcast is asymptotically optimal with high probability.
Purine Biosynthesis, Riboflavin Production, and Trophic-Phase Span Are Controlled by a Myb-Related Transcription Factor in the Fungus Ashbya gossypii

ABSTRACT Ashbya gossypii is a natural riboflavin overproducer used in the industrial production of the vitamin. We have isolated an insertional mutant exhibiting higher levels of riboflavin production than the wild type. DNA analysis of the targeted locus in the mutant strain revealed that a syntenic homolog of the Saccharomyces cerevisiae BAS1 gene, a member of the Myb family of transcription factors, was inactivated. Directed gene disruption of AgBAS1 confirmed the phenotype observed for the insertional mutant, and the Δbas1 mutant also showed auxotrophy for adenine and several growth defects, such as a delay in the germination of the spores and an abnormally prolonged trophic phase. Additionally, we demonstrate that the DNA-binding domain of AgBas1p is able to bind to the Bas1-binding motifs in the AgADE4 promoter; we also show a clear nuclear localization of a green fluorescent protein-Bas1 fusion protein. Real-time quantitative PCR analyses comparing the wild type and the Δbas1 mutant revealed that AgBAS1 was responsible for the adenine-mediated regulation of the purine and glycine pathways, since the transcription of the ADE4 and SHM2 genes was virtually abolished in the Δbas1 mutant. Furthermore, the transcription of ADE4 and SHM2 in the Δbas1 mutant did not diminish during the transition from the trophic to the productive phase did not diminish, in contrast to what occurred in the wild-type strain. A C-terminal deletion in the AgBAS1 gene, comprising a hypothetical regulatory domain, caused constitutive activation of the purine and glycine pathways, enhanced riboflavin overproduction, and prolonged the trophic phase. Taking these results together, we propose that in A. gossypii, AgBAS1 is an important transcription factor that is involved in the regulation of different physiological processes, such as purine and glycine biosynthesis, riboflavin overproduction, and growth.
Radar imaging of building interiors using sparse reconstruction

At TNO an innovative concept to obtain inside building awareness with stand-off, through-the-wall radar has been developed: SAPPHIRE. The system concept exploits particular phase behavior in the 3D radar data to extract dominant scatterers inside a building. These scatterers can be reconstructed from the data using an overcomplete dictionary and sparse reconstruction. In this paper, the reconstruction algorithm and some results will be presented.
Optimal Mode Selection for Full-Duplex Enabled D2D Cognitive Networks

Full-Duplex (FD) and Device-to-Device (D2D) communications have been recognized as one of the successful solutions of spectrum scarcity in 5G networks. Significant advancements in self-interference-to-power-ratio (SIPR) reduction have paved the way for FD use to double the data rates and reduce the latency. This advantage can now be exploited to optimize dynamic spectrum sharing among different radio access technologies in cognitive networks. However, protecting the primary user communication has been a challenging problem in such coexistence. In this paper, we provide an abstract level analysis of protecting primary users reception based on secondary users FD enabled communication. We also propose optimal mode selection (Half-duplex, Full-duplex, or silent) for secondary D2D users depending on its impact on primary users. Our analysis presents the significant advantage of D2D mode selection in terms of efficient spectrum utilization while protecting the primary user transmission, thus, leading the way for FD enabled D2D setup. Depending on the location and transmit power of D2D users, the induced aggregate interference should not violate the interference threshold of primary users. For this, we characterize the interference from D2D links and derive the probability for successful D2D users for half-duplex and full-duplex modes. The analyses are further supported by theoretical and extensive simulation results.
Removal of PFASs from water with ionic liquids: Quantum-mechanical and experimental studies

This PFASs are man-made chemicals with vast number of useful applications. Wide use of the chemicals during last decades resulted in their common widespread in natural environment, especially in water. Recent studies revealed toxic and bio-accumulative character of PFASs for human and wildlife. Many remediation techniques for removal of PFASs from water were performed, however, due to the high stability of C-F bonds, PFASs exhibit exceptional resistance to most of the conventional removal methods. The most common solution for the removal of PFASs remains, so far, adsorption using activated carbon. However, the major problem of this technique is expensive regeneration of the carbonaceous material. New technological solutions for removal of the emerging contaminants from water environment are in high demand. The paper presents recent developments in separation of persistent organic pollutants with a usage of ionic liquids and introduces theoretical approach for identification and design of ionic liquids useful for efficient separation of PFASs from water environment.
5G Ultra-Reliable Low-Latency Communications in Factory Automation Leveraging Licensed and Unlicensed Bands

Factory automation (FA) applies ultra-reliable low latency communications (URLLC) to support closed-loop control systems with millisecond cycle times. Presently, many of these applications rely on maintenance-prone wireline bus systems since existing wireless technologies cannot meet the stringent latency and outage requirements. The cellular standards body, the Third Generation Partnership Project (3GPP), aims to change this predicament by incorporating support for FA and URLLC into the fifth generation (5G) communication networks. We investigate how combining operation in the unlicensed and licensed band can appropriately balance the technical, regulatory and economic constraints to enable large-scale deployments of bandwidth-hungry FA applications. We present 5G physical layer/MAC layer (PHY/MAC) designs, which carry the bulk of traffic in the cost-effective unlicensed band but switch to the licensed band within the available latency budget to circumvent unlicensed-band interference. We discuss design trade-offs between licensed band occupation, link outage and transmission power level.
Fog-Assisted Aggregated Synchronization Scheme for Mobile Cloud Storage Applications

Cloud storage applications, such as Dropbox and Google Drive, have recently become very popular among mobile users. In these applications, a cloud server is responsible for synchronizing updates to files among mobile users, and thus if files are shared by many mobile users and are frequently updated then the resulting synchronization traffic can be significant. In order to reduce the synchronization traffic with providing acceptable access latency, we propose a fog-assisted aggregated synchronization (FAS) scheme in which the fog computing server and the cloud server conduct localized and aggregated synchronizations, respectively. We develop an analytical model of the FAS scheme based on renewal-reward theory and use it for model-based adjustment of the timer that controls the trade-off between access latency and synchronization traffic. We use analytical and simulation results to give insight into the effects of the timer, the update-to-access ratio, the number of mobile users, and the sensitivity to the arrival process. The analytical and simulation results demonstrate that the FAS scheme can reduce the synchronization traffic significantly with acceptable access latency compared to conventional schemes.
RAON: a P2P network for MANET

Overlay network has been effectively used in Internet for application deployment. We believe that it is also a good abstraction for application deployment over ad hoc networks because it requires the nodes running an application to maintain the related states while all other nodes only perform networking functions. We propose a P2P system for MANET, RAON, which performs query forwarding decision based on link instability and power constraints. Our simulation results show that RAON improves the success rate and delay of query search as compared to Gia, a competitive system for wired network.
Outdoor 5G Radio Access Experiments on Downlink Distributed MIMO Separation Distance with Beamforming in 28 GHz Frequency Band

Based on a 5G cellular radio access system, this paper presents experimental results of line-of-sight (LoS) outdoor field measurements clarifying the 4-by-8 multiple-input multiple-output (MIMO) throughput performance when applying distributed MIMO with narrow antenna-beam tracking in the 28-GHz frequency band in the downlink. To investigate the influence of the separation distance between distributed transmission points (TPs), deployments of distributed MIMO with various TP separation distances from 0.115 m to 20 m are investigated. The throughput and average rank selectivity increases throughout the area as the separation distance increases indicating that the channel correlation decreases as the distance increases. When the separation distance is 20 m, throughput performance exceeding 11 Gbps is observed with a high average rank of nearly 4. For the distributed MIMO gain in terms of throughput, the throughput gain of 50% is confirmed in the downlink at the cumulative distribution function of 60% compared to that without separation. In addition, the degree of additional improvement in the throughput performance is smaller when the distance between TPs becomes greater than 4 m, indicating that 4 m is sufficient to support a 100 m LoS distance with good throughput performance.