Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards QoS and Security in Software-Driven Heterogeneous Autonomous Networks

Autonomous Networks has a potential to solve complex and critical management issues in large scale multi- technological networks. Further, the novel paradigms, i.e., Software-Defined Networks (SDN) and Network Function Vir- tualization (NFV) offer unique and attractive solutions for Autonomous Networks or Systems (AS). However, despite of these attractive features, we observed two critical issues in this interlinked multi-technology domain. Firstly, the network externality and nodes heterogeneity seriously effected the flow specific Quality of Service (QoS). Secondly, it influenced se- curity adoption in an network of interconnected nodes. We observed that QoS and security both are non-negligible and inter-dependent factors. This motivates us to investigate solution towards a) alleviating the SDN network heterogeneity at control layer, and b) to strengthen the network security after alleviating the heterogeneity. In this research effort, we have attempted to alleviate the first issue. Firstly, significant and reasonable examples have been cited to motivate researchers to study QoS and security hand-to-hand. Secondly, a theoretical high level frame work has been proposed with the aim to transform the N heterogeneous controllers to n homogeneous controller groups. Following this, we have demonstrated that our approximation method to transform heterogeneous systems to homogeneous groups works well even at high degree of heterogeneity in the network. We have shown our theoretical analysis results using Matlab. Following this, we have shown the Proof of Concept (PoC) of our approach in SDN-NFV ecosystem using Mininet. This early analysis will help researchers to address heterogeneity and security in more effective ways.
A Self-Adaptable System for DDoS Attack Prediction Based on the Metastability Theory

Distributed Denial of Service (DDoS) attacks grow in volume, sophistication and impact. An example is the largest DDoS attack ever recorded against the developer platform GitHub, that reached 1.35 terabytes per second - an unprecedented volume of malicious traffic. DDoS attacks have been detected or mitigated only when they are in unrecoverable stages, being late to prevent their effects. Thus, differently from other works, we advocate for the early prediction of DDoS attacks to assist in reducing or avoiding costs and losses resulted from DDoS attacks. This paper presents STARK, a self- adaptable DDoS attack prediction system. STARK identifies signs of attack before it reaches an unrecoverable stage being founded on the metastability theory. Its evaluation follows a trace-driven approach, taking as input two databases containing records of DDoS attacks. Results show the prediction of DDoS attacks with minutes or hours in advance.
Optimizing Multicell Scheduling and Beamforming via Fractional Programming and Hungarian Algorithm

The problem of optimizing scheduling and beamforming to maximize the network weighted sum rate (WSR) for the downlink of multicell, multi-antenna networks is challenging due to its nonconvexity and NP-hardness. In this paper, we present a novel approach based on the Hungarian algorithm and fractional programming that allows us to converge to an effective solution of the WSR maximization problem. Through extensive simulations, we compare the performance of the proposed algorithm with state-of-the art coordinated and uncoordinated resource allocation schemes in the literature. The proposed algorithm is shown to provide higher sum-log-utility values than previously proposed schemes matched filtering (MF), zero-forcing (ZF) and weighted minimum-mean-squared error (WMMSE), while offering a substantial improvement in cell-edge user rates over WMMSE with proportionally fair scheduling. Furthermore, the proposed scheme has a considerably lower computational complexity than multi-cell WMMSE.
Kernel Based Estimation of Domain Parameters at IoT Proxy

In this paper we develop lightweight algorithms for monitoring and estimating data lifetime and round trip time at CoAP proxy. We deploy these algorithms in CoAP IoT domain with observe feature with random inter-observation times which can be a consequence of parameterized queries. Algorithms are based on kernel estimation of probability density distributions (pdf). As a result proxy maintains approximate pdfs of these parameters which can be used in congestion control and&#x002F;or anomaly detection in IoT domain. Results show that estimations with 400-500 samples render satisfactory tradeoff between accuracy and computational complexity even under skewed probability distributions such as exponential distribution.
A Versatile Markov Chain Model for the Performance Analysis of CCN Caching Systems

Beyond Content Delivery Networks (CDNs), Network Operators (NOs) are developing caching capabilities within their own network infrastructure, in order to face the rise in data consumption and to avoid the potential congestion at peering links. These factors explain the enthusiasm of industry and academics around the Content-Centric Networking (CCN) concept and its in-network caching feature. Many contributions focused these last years on improving the caching performance of CCN. In this paper, we propose a very versatile model capable of modeling the most efficient caching strategies. We first start by representing a single generic cache node. We then extend our model for the case of a network of caches. The obtained results are used to derive, in particular, the cache hit probability of a content in such caching systems. Using a discrete event simulator, we show the accuracy of the proposed model under different network configurations.
Resource Provisioning of MVNOs in a Virtualized Wireless Network: A Procurement Auction Approach

In wireless network virtualization, by separating services and infrastructure, and due to new network components, some new challenges appear. In this paper, we consider the competition among multiple mobile virtual network operators (MVNOs) for provisioning the required rates for users subscribed to a service provider (SP). To realize this competition, we consider an SP as a beneficiary decision maker component, who can choose which users to be serviced by each MVNO. Since the states between each user and different channels of each MVNO are distinct, MVNOs compete to serve the users with good channel conditions to minimize their costs. Using a multiple non-identical object procurement (reverse) auction, we propose a new resource provisioning for SP's subscribers. Our simulation results show the efficiency of the proposed approach.
An alternating optimization algorithm for two-channel factor analysis with common and uncommon factors

An alternating optimization algorithm is presented and analyzed for identifying low-rank signal components, known in factor analysis terminology as common factors, that are correlated across two multiple-input multiple-output (MIMO) channels. The additive noise model at each of the MIMO channels consists of white uncorrelated noises of unequal variances plus a low-rank structured interference that is not correlated across the two channels. The low-rank components at each channel represent uncommon or channel-specific factors.
Adaptive Multi-Trace Carving Based on Dynamic Programming

In this paper, we study the problem of tracking multiple frequency components in a noisy signal using a spectrogram-based method. Previous approaches such as image processing based or hidden Markov model based methods may not be capable of tracking multiple frequency components, may require extensive training, and may be time-consuming. To address these issues, we propose an accurate and efficient method named Adaptive Multi-Trace Carving (AMTC) for tracking multiple frequency traces by iterative forward and backward dynamic programming and adaptive trace compensation. Experimental results reveal that the proposed method outperforms the state-of-the-art methods especially under low SNR conditions.
Modeling the Effect of Non-Linear Distortion in a Centralized RAN with Analog Optical Fronthaul

The Centralized Radio Access Network (C-RAN) architecture has potential to achieve the target goals that have been set for 5G. However, as the number of radio signals to be transported from the Centralized Base Station (CBS) to the distributed Remote Antenna Units (RAUs) grows, the use of contemporary digital fronthaul technologies (such as CPRI) complicates. One simple way to address this problem consists in multiplexing the baseband signals intended to the different RAUs in the frequency domain and, after that, use the resulting analog signal to modulate the intensity of the optical carrier. This way, there is no bandwidth expansion and the processing delay is kept low; however, as the ideal fronthaul assumption does not hold any more in this case, the impairments that the analog optical fronthaul introduces should be taken into account when performing the digital signal processing in transmission. In this paper, we focus on the non-linear distortion that the external Mach-Zehnder (optical) modulator introduces and, through it, estimate the End-to-End (E2E) performance when different intensity modulation indexes (or clipping ratios) are used. As expected, moderate levels of non-linear distortion are beneficial if properly selected according to the C-RAN configuration.
Practical Nonlinear Energy Harvesting Model in MIMO DF Relay System with Channel Uncertainty

In this paper, we aim to maximize the end-to-end achievable rate of multiple-input multiple-output (MIMO) decode-and-forward (DF) where the relay is an energy harvesting (EH) node using the time switching (TS) scheme. The relay first harvests the energy from the source, then uses its harvested energy to forward the information carrying signal from the source to the destination. The EH model at the relay is a nonlinear model. Also, we assume that the channel knowledge is imperfect at the relay and destination. We propose the structure of the optimal covariance matrices at the source (during EH and information decoding periods), the optimal covariance matrix at the relay and the optimal EH time ratio. Through the simulation results, we compare between different linear&#x002F;nonlinear EH models and we show the gain&#x002F;loss performance of the linear model compared to other nonlinear EH models.
Towards Self-Organizing UAV Ad-Hoc Networks Through Collaborative Sensing and Deployment

In this paper, we consider an aerial ad-hoc network construction problem using UAVs in a disaster scenario. We aim to reconnect the communication-wise isolated urban area with the outside communication infrastructure. Our main goal is to perform both network exploration and relay deployment tasks at the same time by taking a progressive optimization toward a self-organizing network construction. We propose a novel UAV exploration-and-deployment algorithm that gradually explores the region of interests and achieves full network coverage in a fast manner. Then, we present an effective network refinement algorithm based on clustering that minimizes the number of UAVs for deployment by finding out essential UAVs, while keeping the similar network coverage performance. Simulation results demonstrate that our proposed scheme significantly reduces the execution time for network exploration and deployment compared to a baseline counterpart. Also, our cluster-based network refinement algorithm provides a very lightweight yet effective solution, well-balancing between UAV resource and computation overhead.
User-Centric Location Prediction in Mobile Social Networks: A Factor Graph Learning Approach

Recently, location prediction has attracted considerable research effort because of the popularity of location- based services, such as mobile advertising and recommendations. With the unprecedented proliferation of mobile social networks, we are able to use location service to bridge the online and offline worlds. Different from existing studies, in this paper we promote a user-centric location prediction approach by leveraging a user's local mobile social information without involving other users' location privacy. We propose a factor graph learning model that integrates not only user's social and network information, but also the correlations between user's locations into a unified framework. Furthermore, we use ReliefF algorithm to select user-specific significant features for location prediction and define the measure of location entropy to study the similarity between location, network status and social behavior. We conduct extensive experiments using a real-world dataset, which shows that our model performs much better in location prediction compared with several classic methods.
Clustered Cell-Free Massive MIMO

This paper introduces a novel technique, clustered cell-free massive-MIMO (C2F-M-MIMO), that generalizes the recently proposed cell-free massive MIMO (CF-M-MIMO) concept by optimizing the connectivity pattern among access points (APs) and mobile stations (MSs). Relying on the popular K-means clustering algorithm, APs and MSs are grouped together in clusters in such a way that strong interferers arising due to pilot contamination are minimized. The clustering pattern varies in accordance to the large-scale fading parameters and is therefore able to respond to macroscopic changes in the network (user mobility, network load variations). Numerical results show that the proposed architecture is able to support a large number of users even with very low-complexity processing at the AP side while greatly reducing the fronthaul capacity requirements.
On the Spectral Efficiency of Orthogonal Frequency-Division Multiplexing with Index Modulation

Orthogonal frequency-division multiplexing with index modulation (OFDM-IM) promises superior bit error rate (BER) performance and transmit power savings compared to classical OFDM. The concept consists in dividing the available subcarriers into groups and activating a subset of subcarriers in each group for transmitting $M$ -QAMlPSK symbols. The remaining subcarriers are kept idle. The transmitted symbols and the indexes of the active subcarriers both carry useful information. In the recent literature, OFDM-IM is conceived by activating half of the subcarriers in each group for transmission of $M$-QAM/PSK symbols, which results in low spectral efficiency (SE). This paper addresses the low SE problem in two ways. First, an inequality rule is developed by comparing the SE of classical OFDM and OFDM-IM. The rule is used to derive three limits on the group size and it is shown that OFDM-IM can overcome the SE limitation of classical OFDM for a group size of 2M or greater. Secondly, a new OFDM-IM scheme with a variable number of active subcarriers per group, termed OFDM-IM-VAS, is proposed. The optimal group size for a given $M$ is obtained, and it is shown that OFDM-IM-VAS can achieve higher SE than classical OFDM with low BER and high power savings. The BER, bit level throughput, transmit power saving, and complexity of OFDM-IM and OFDM-IM-VAS are analyzed considering transmissions over Rayleigh fading channels and compared with classical OFDM. A closed-form BER expression is derived to support the simulation results.
Effect of adding the Limiting Amino Acids to Low – Crude Protein Diets on Performance of Broiler Chicks.

This study was conducted to study the effect of decreasing the dietary crude protein level while supplementing the essential amino acids on growth performance, carcass, some blood constitutes of broiler chicks. An experiment was done using 252 one day-old broiler chicks Arbor-acres, randomly allotted to seven dietary treatment groups; the first group positive control with optimum CP level (PC), the second group negative control (NC) low 3 points than requirements, and groups 3 up 7 were fed the NC diet but supplemented incrementally with the essential amino acids; lysine, lysine+ methionine, lysine+ methionine+ tryptophan, lysine+ methionine+ tryptophan +threonine and lysine+ methionine+ tryptophan+ threonine+ valine, respectively to cover the dietary requirements for each amino acid during starter, grower and finisher periods. Results show that there were no significant differences between treatments (P=0.242) on starter live weight. For grower period the control group with optimum CP % level, followed by low CP % diet supplemented with all amino acids had significantly the highest live body weight compared with the low CP group without supplemental amino acids (P= 0.006). The gains in body weight were found to be significant during grower and finisher phases the highest values were observed in the positive control and low CP supplemental with all AA diets. The same two treatments the best FCR for the overall experimental period compared to the other treatments. Carcass traits were not significantly affected by dietry treatments. Plasma total protein (P=0.034), albumin (P=0.002), and triglycerides (P=0.002), were significantly affected by dietary treatments. The lowest feed cost per kg live weight gain of chickens were found in the experimental group (T7) containing all amino acids supplemented to the low CP diets. It could be recommended to decrease the dietary CP level of broiler chicks by 3 points without affecting the growth performance up on supplementing the essential AA.
Wet Peroxide Oxidation of Paracetamol Using Acid Activated and Fe/Co-Pillared Clay Catalysts Prepared from Natural Clays

Many pharmaceuticals have been recently identified at trace levels worldwide in the aquatic environment. Among them, the highly consumed paracetamol (PCM), an analgesic and antipyretic drug, is largely being accumulated in the aquatic environment due to inefficient removal by conventional sewage treatment plants. This work deals with the treatment of PCM, used as a model pharmaceutical contaminant of emerging concern, by catalytic wet peroxide oxidation using clay-based materials as catalysts. The catalysts were prepared from natural clays, extracted from four different deposits using acid-activated treatment, calcination, and pillarization with Fe and Co. Pillared clays show the highest catalytic activity owing to the presence of metals, allowing to remove completely the PCM after 6 h under the following operating conditions: CPCM = 100 mg L−1, CH2O2 = 472 mg L−1, Ccat = 2.5 g L−1, initial pH = 3.5 and T = 80 °C. The prepared materials presented high stability since leached iron was measured at the end of reaction and found to be lower than 0.1 mg L−1.
Discriminative Dictionary Learning for Mixture Component Detection with Application to RF Signal Recognition

Pattern classification algorithms based on sparse dictionary learning are derived. After training a discriminative dictionary and a linear classifier using the samples of the individual classes, the aim is to apply the dictionary and classifier for recognizing the component signals in a mixture of different class signals. A key issue is to prevent the “leakage” of strong signal components to weaker components in the classifier. We tackled this issue by encouraging orthogonality among the discriminants during the training, applying the algorithms to RF signal recognition verified the efficacy of the approach.
Improved Implementation Approaches for 512-tap 60 GSa/s Chromatic Dispersion FIR Filters

In optical communication the non-ideal properties of the fibers lead to pulse widening from chromatic dispersion. One way to compensate for this is through digital signal processing. In this work, two architectures for compensation are compared. Both are designed for 60 Gsa/s and 512 filter taps and implemented in the frequency domain using FFTs. It is shown that the high-speed requirements introduce constraints on possible architectural choices. In this work, it is shown that it is not required to use two overlapping FFTs to obtain continuous filtering. In addition, efficient highly parallel implementation of FFTs is discussed and an improved FFT compared to our earlier work is proposed. The results are compared to using an approach with a shorter FFT and FIR filters.
Cluster Labeling by Word Embeddings and WordNet's Hypernymy

Cluster labeling is the assignment of representative labels to clusters obtained from the organization of a document collection. Once assigned, the labels can play an important role in applications such as navigation, search and document classification. However, finding appropriately descriptive labels is still a challenging task. In this paper, we propose various approaches for assigning labels to word clusters by leveraging word embeddings and the synonymity and hypernymy relations in the WordNet lexical ontology. Experiments carried out using the WebAP document dataset have shown that one of the approaches stand out in the comparison and is capable of selecting labels that are reasonably aligned with those chosen by a pool of four human annotators.
Two-Level MPPM-MDPSK Modulation for Free-Space Optical Channels

A hybrid two-level multipulse pulse-position modulation-M-ary differential phase-shift keying (2L-MPPM- MDPSK) technique is proposed which achieves high power and spectral efficiencies. The proposed 2L-MPPM-MDPSK scheme is shown to have the highest spectral efficiency in comparison with other hybrid modulation schemes. Additionally, the proposed technique has the advantage of removing the complex variable delay-line required for the direct- detection of other hybrid MPPM-MDPSK systems. The performance of 2L-MPPM-MDPSK is quantified in free-space optical (FSO) channels modeled with an exponentiated Weibull (EW) fading distribution. Closed-from expressions for both bit-error rate (BER) and outage probability upper-bound are derived and verified using Monte Carlo (MC) simulation. At the same data rate and bandwidth, 2L-MPPM-MDPSK techniques outperform traditional MPPM-MDPSK scheme by approximately 2dB at a BER of 10^{&#8722;4}.
User Reactions Prediction Using Embedding Features

By the massive available people data in social media, many digital service providers exploit widely this information to improve their services by predicting future requirements of their customers. This prediction mainly needs to study users' previous behavior and interactions and identify their preferences to provide rigorous recommendations that fulfill their requirements more favorably. Meanwhile, experiments show the prediction methods which exploit representation learning instead of traditional hand-crafted features accomplish better results and more precise predictions. In this study, we take advantage of representation learning method to predict user's future interactions by extracting users embeddings from their reactions history and exploit them in predicting future reactions. In this approach, users embeddings are used in a neural network designed with one-hidden layer and a softmax function in the end layer in order to predict users reactions. The proposed method is evaluated when user embeddings come from two different sources; users reactions history and random walks on the user network. The performance of the method has been evaluated by using a large Flickr dataset including more than 2M users and 11M users reactions sequences. The results show outperforming of the prediction method when it uses the history of user reactions to derive user embeddings.