Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalable Statistical Inference Using Distributed Bootstrapping And Iterative ℓ1-Norm Minimization

In this paper we address the problem of performing statistical inference for large-scale data sets where volume and dimensionality of the data may be so high that it cannot be processed or stored in a single unit. In particular, we focus on bootstrapping based methods that can provide quantitative information on the accuracy of the inference such as confidence intervals without explicit assumptions on the probability models. We propose a scalable distributed boot- strap method that uses iterative estimation equations favoring sparse solution. Scalability is achieved by applying bootstrapping to multiple smaller distinct subsets generated by resampling the full data without replacement similarly to BLB method [1]. An iteratively reweighted $\ell_{1}$-norm minimizing estimation equations are applied to each bootstrap sample. Such estimators allow for parameter estimation and inference even for moderately underdetermined systems as well as performing variable selection by promoting a sparse parameter vector. Estimation problems may become underdetermined for the distinct subsets of data even if the full large scale problem would be overdetermined. The performance of the presented approach is studied in extensive simulations. It is demonstrated that the method gives smaller Root MSE and significantly lower bias than bootstrap employing widely used sparse estimator BPDN. Moreover, better performance is obtained in variable selection in terms of classification error rate (CER) and recovery rate (RER) in identifying sparse parameters. Estimated confidence intervals are also highly concentrated about the true parameter values.
Construction D Lattice Decoding and Its Application to BCH Code Lattices

The decoding of Construction D lattices is described. While similar to the multistage decoding of Code Formula codes, modification is required so that lattice components are subtracted in a process called reencoding. A generator matrix for Construction D lattices is given. Construction D lattices obtained from BCH codes were described by Barnes and Sloane. In this paper, we consider some practical issues of encoding and decoding these lattices. Using ordered statistics decoding, dimension 128 BCH code lattices outperform turbo lattices and low-density lattice codes of similar dimension. These results show relatively good performance of lattices based on algebraic constructions, compared to lattices typically designed for high dimensions. The performance over power-constrained channel is also evaluated, where the near-optimal performance is demonstrated.
NOMA-Assisted Small-Packet Transmissions in Mission-Critical MTCs for Industrial Automation

In industrial automation, monitoring information is critical and expected to be received with ultra- high reliability and low latency. On the other hand, different industrial monitoring applications usually have diverse requirements on the transmission quality. This paper investigates the deadline aware reliable transmission in mission-critical machine-type communications (MTCs) to satisfy the service requirements for different monitoring applications in terms of reliability and latency. Specifically, a hybrid non-orthogonal multiple access (NOMA) framework is firstly introduced for improving spectrum utilization as well as meeting the diverse requirements of different applications. Under this framework, a NOMA-assisted small-packet transmission scheme is proposed for mission- critical MTCs, and the related performance is then mathematically formulated as a constrained optimization problem with the objective to maximize the network-wide revenue. The formulated non- trivial problem is effectively solved by considering the diverse requirements of different applications. Simulation results are provided to demonstrate both the impact of small packet size and the superiority of NOMA technique on the network-wide revenue improvement.
Progress on Transition Metal-Doped ZnO Nanoparticles and Its Application

ZnO nanoparticles are still a hot area for research even after ample work has been done by researchers across the world. It is a versatile material for doping of different transition metals among other metal oxides. Having a large family of morphological structures, high electron mobility, and n-type carrier defects, it is suitable for a number of applications such as memory devices, spintronics, optoelectronic devices, solar cells, and sensors. Here, we have emphasized a review of the effects of transition metal (TM) doping on the different physical properties of ZnO nanoparticles and their applications. We have gone through some theoretical models which were used by most of the researchers for explaining the variations in physical properties. Explanation of the sensing, photocatalytic, and optoelectronic processes in different classes of devices is briefly described. Lastly, the comparative studies of different devices are also made available for clear understanding.
A Topological Approach to Understanding Location-Based Data

Location-based services have seen a boon in data production recently which has simultaneously stoked the research community to better understand this type of information. Traditional methods in analyzing such data require significant a priori understanding of the organization of the data. We submit that the nascent field of topological data analysis (TDA) may be able to contribute new insights to analysis of such data without the aforementioned requirement. To this end, we propose two novel methods of embedding such data in order to leverage the expressive power of TDA. To demonstrate its effectiveness we apply the embeddings to maritime automated information system data.
Joint User Scheduling and Hybrid Precoding for Multi-User mmWave Systems with Two-Layer PS Network

Hybrid analog&#x002F;digital precoding provides an energy- efficient solution for multi-user millimeter-wave (mmWave) systems by utilizing a small number of radio frequency (RF) chains for digital precoding and a phase shifter (PS) network for analog precoding. However, the conventional fully-connected and partially-connected architectures exist the problem of terrible trade-off between performance and hardware complexity. In this paper, we propose a joint user scheduling and hybrid precoding scheme for multi-user mmWave systems with two-layer PS network at the base station. Specifically, we propose a rank-one approximation based hybrid precoding algorithm to handle the hardware constraints on analog precoder. Moreover, a vertical user grouping scheduling algorithm is proposed to improve the achievable-rate performance by simultaneously serving users with similar vertical angle of departures (AoDs) but different horizontal AoDs. Simulation results demonstrate that the proposed scheme approaches the performance of fully- connected architecture with much lower hardware complexity.
Preferred Path Routing - A Next-Generation Routing Framework beyond Segment Routing

Segment Routing (SR) reintroduces source routing to networking. While SR has been defined for MPLS and IPv6 data planes, there are considerable problems with respect to increased path overhead in various deployments. This paper presents a new framework that is designed to overcome the SR challenges with a new routing paradigm, PPR (Preferred Path Routing). PPR is intended as enabler for next generation source routing by minimizing the data plane overhead caused in SR, which includes the network layer tax and processing overhead that is imposed on packets, critical specifically for small packets that are characteristic to many 5G applications. PPR extends SR for IP data planes without needing to replace existing hardware or even to upgrade the data plane. In addition, PPR allows dynamic path QoS reservations viz., bandwidth, and resources for providing deterministic queuing latency.
Low-Complexity Approximation to the Kalman Filter Using the Dichotomous Coordinate Descent Algorithm

The Kalman Filter is known to be the optimal solution to tracking and data prediction tasks under linear Gaussian models. However, depending on the application, the Kalman filter is very costly to implement, since $\mathcal {O}(M^{2.376})$ operations are required for each time update. On the other hand, the RLS algorithm (for which $\mathcal {O}(M)$ versions are available) is known to be equivalent to the Kalman filter for a certain model for the evolution of the unknown weight vector. This article seeks to extend the class of models for which RLS-like $\mathcal {O}(M)$ algorithms exist, proposing new low complexity versions of the Kalman Filter which use the Dichotomous Coordinate Descent (DCD) technique.
A Novel IoT-Based Architecture for Self-Adaptive Aerodynamic Flow Control System for Motorcycle

In this paper, we introduce an autonomous system for aerodynamic flow control for motorcycle based on the Internet of Things (IoT) paradigm. The architecture we propose adapts dynamically the flows at the traveling conditions, in order to obtain an improvement of performance and vehicle stability. In our architecture, we deploy a group of sensors on the top surface of the wings to sense the air pressure. We design a centralized on-board unit that computes a new wing angle of attack according to the data received from the sensors. The on-board unit includes a local information database which represents its knowledge: it stores both the data gathered by the sensors and the fluid dynamics model used to compute and adjust the angle of attack. The on-board database is periodically updated transmitting all the measurements gathered from the sensors to a High-Performance Cloud Data Center (DC) which executes a parallel version of Computational Fluid Dynamics (CDF) algorithms, computes the updated model, and transmits the processed information back to the on-board unit. We perform preliminary tests in the wind tunnel, and we show how the cooperation between IoT devices and DC can reduce the on-board unit computational effort.
Text-dependent Forensic Voice Comparison: Likelihood Ratio Estimation with the Hidden Markov Model (HMM) and Gaussian Mixture Model

Among the more typical forensic voice comparison (FVC) approaches, the acoustic-phonetic statistical approach is suitable for text-dependent FVC, but it does not fully exploit available time-varying information of speech in its modelling. The automatic approach, on the other hand, essentially deals with text-independent cases, which means temporal information is not explicitly incorporated in the modelling. Text-dependent likelihood ratio (LR)-based FVC studies, in particular those that adopt the automatic approach, are few. This preliminary LR-based FVC study compares two statistical models, the Hidden Markov Model (HMM) and the Gaussian Mixture Model (GMM), for the calculation of forensic LRs using the same speech data. FVC experiments were carried out using different lengths of Japanese short words under a forensically realistic, but challenging condition: only two speech tokens for model training and LR estimation. Log-likelihood-ratio cost (Cllr) was used as the assessment metric. The study demonstrates that the HMM system constantly outperforms the GMM system in terms of average Cllr values. However, words longer than three mora are needed if the advantage of the HMM is to become evident. With a seven-mora word, for example, the HMM outperformed the GMM by a Cllr value of 0.073.
Energy-Efficient Resource Allocation Based on Hypergraph 3D Matching for D2D-Assisted mMTC Networks

Energy efficiency is essential for massive machine-type communication (mMTC), because of the limited energy in internet of things (IoT) devices. We consider a two-hop amplify-and-forward (AF) relay communication, and allow IoT devices with inferior channel conditions to connect relays by using device-to-device (D2D) technology. This paper proposes to jointly optimize relay selection, channel allocation and power control, so that the total energy efficiency is maximized while guaranteeing the signal to interference plus noise ratio (SINR) requirements of relays and BSs. The formulated joint optimization problem involves a nonlinear fractional programming (NFP) problem and a user-relay-channel matching problem which is NP-hard. Therefore, we propose a two-stage approach composed of the Dinkelbach method and a hypergraph-based 3D matching (HGM). Simulation results show that the total energy efficiency under the HGM is 8.41% and 59.85% higher than the iterative Hungarian method (IHM) and the minimum zero surface prioritized allocation (MZPA), respectively.
Independent Component Analysis Based on Non-polynomial Approximation of Negentropy: Application to MRS Source Separation

In this paper, a new ICA algorithm based on non-polynomial approximation of negentropy that captures both the asymmetry of the sources’ PDF and the $sub/super$-Gaussianity of this latter is proposed. A gradient-ascent iteration with quasi-optimal stepsize is used to maximize the considered cost function. With this quasi-optimal computation in the case of highly non-linear objective function, the main advantages of the proposed algorithm are 1) its robustness to outliers compared to kurtosis-based ICA method especially for situations of small data size, and 2) its ability to capture sources’ asymmetric probability density functions which is a property that can’t be fulfilled in classic ICA algorithms like FastICA. Numerical results reported in the context of source separation of brain magnetic resonance spectroscopy show the superiority of the proposed algorithm over the FastICA algorithm in terms of both source separation accuracy and the number of iterations required for convergence.
Performance Analysis for Passive Radar Estimation Using Non-Cooperative Illuminators

In this paper, we derive Cramer-Rao Lower Bound (CRLB) expressions for two-channel radar target delay-Doppler estimation with unknown transmit waveforms. We have access to these unknown waveforms only via the noisy reference measurements. Further, we assume that the waveform source transmits periodic pulses as is common for most pulsed radar illuminators. Recent CRLB literature for radar has focussed on specific types of illuminators and derived the CRLB assuming these specific transmitted waveforms are known and incorporating them directly into the measurement model. Further, some papers while assuming the waveform itself to be unknown, make simplifying statistical assumptions on the waveforms or the waveform structure. Contrary to this, we compute a more general result that is not restricted to any specific waveform and we also include the noisy reference channel measurements in our derivation of the CRLB to obtain a more accurate performance analysis. The only assumption we make is on the periodicity of the transmit signal, which is true in practice.
Satisfaction Based Channel Allocation Scheme for Self-Organization in Heterogeneous Networks

The next-generation wireless networks are expected to become denser and more heterogeneous in order to boost the network capacity. However, densely deployed base stations (BSs) in heterogeneous networks (HetNets) can give rise to interference. On the other hand, a limited number of channels is allocated within the HetNets. Therefore, the efficient assignment of channels among BSs is considered to be an important issue. Furthermore, the density and heterogeneity of the networks motivate self-organizing resource management techniques. In this paper, we address the problem of channel allocation in HetNets, and propose a satisfaction based channel allocation algorithm. The problem is modeled as a game in satisfaction form, in which BSs act as the players with the constraint given by the loads at the BSs. The objective is to meet the data rate requirements of user equipments. In this regard, the BSs aim at seeking a satisfaction solution rather than the optimal one. In order to learn the satisfaction equilibrium, a fully distributed algorithm based on the individual utility is applied. Simulation results show that the proposed approach can increase the average BS's throughput compared to the benchmark algorithms.
Performance Analysis for Multi-Layer Unmanned Aerial Vehicle Networks

In this paper, we provide the model of the multi-layer aerial network (MAN), composed unmanned aerial vehicles (UAVs) that distributed in Poisson point process (PPP) with different transmission power, heights, and densities. In our model, we consider the line of sight (LoS) and non-line of sight (NLoS) channels, which is probabilistically formed. We first derive the probability distribution function (PDF) of the main link distance and the Laplace transform of interference of MAN by considering a transmitter/receiver association based on the strongest average received power. We then analyze the successful transmission probability (STP) of the MAN, and provide the upper bounds of the optimal UAV densities in each layer that maximize the STP of the MAN. Through the numerical results, we show the existence of the optimal height of the aerial network (AN) after exploring the performance tradeoff caused by the height. We also show both the optimal UAV density as well as its upper bound decrease with the height of the ANs.
Task Selection and Scheduling for Food Delivery: A Game-Theoretic Approach

With the development of embedded sensors in smartphones and the ever-growing number of mobile users, more and more spatial crowdsourcing applications come into our daily lives. Food delivery, a specific application of spatial crowdsourcing, emerged and quickly proliferates in recent years. However, task selection and scheduling remains a challenging problem in food delivery. In this paper, we aim to address the task selection and scheduling problem from a game-theoretic perspective. Specifically, the riders are allowed to select a set of tasks as well as the task completion order, by taking account of their locations, speed, traveling costs and capacities. The objective of each rider is to maximize his own utility. We formulate a food delivery game and prove the existence of a Nash equilibrium. Experimental results show that our scheme outperforms existing solutions in terms of the social welfare, average utility, task completion ratio, and fairness. Moreover, the social welfare attained by our scheme comes close to that of the centralized optimal solution.
Byzantine Failures and Security: Arbitrary is not (always) Random

The Byzantine failure model allows arbitrary behavior of a certain fraction of network nodes in a distributed system. It was introduced to model and analyse the effects of very severe hardware faults in aircraft control systems. Lately, the Byzantine failure model has been used in the area of network security where Byzantine-tolerance is equated with resilience against malicious attackers. We discuss two reasons why one should be careful in doing so. Firstly, Byzantine-tolerance is not concerned with secrecy and so special means have to be employed if secrecy is a desired system property. Secondly, in contrast to the domain of hardware faults, in a security setting it is difficult to compute the assumption coverage of the Byzantine failure model, i.e., the probability that the failure assumption holds in practice. For this latter point we develop a methodology which allows to estimate the reliability of a Byzantine-tolerant solution exposed to attackers of different strengths.
An Efficient Embedding Algorithm for Energy Multi-Domain Virtual Network Embedding

Network virtualization (NV) is one important attribute of the next generation network (e.g, upcoming 5G virtualized networking). Virtual network embedding (VNE) is about the resource allocation problem in NV. Multiple VNE algorithms have been proposed over the past decade. Prior algorithms focus on accepting more virtual networks (VNs) in one shared substrate network (SN) in order to maximize the embedding revenue. As energy cost is approaching half of the SN operating cost, it is crucial for infrastructure providers to minimize energy cost. In addition, in real networking environment, it is vital to embed each VN among multiple SNs, geographically distributed in different regions. To address both issues, we firstly propose the formal VNE problem model and VNE energy cost model. Then an energy efficient heuristic algorithm (EEMD-VNE) is detailed, aiming at maximizing the net profit. Meanwhile, the EEMD-VNE is able to embed each requested VN among multiple SNs in polynomial time. Numerical simulation results reveal that the EEMD-VNE significantly reduces the energy cost by approximately 8.5% over the existing energy related algorithms for accommodating the same amount of VNs.
xStream: A New Platform Enabling Communication Between Applications and the 5G Network

Satisfying heterogeneous Quality of Service requirements of different applications in 5G systems requires tight interaction between the network and the applications. Although being pushed to 3GPP, SAND, the most mature solution enabling such interaction and cross-layer optimization for video streaming, has many limitations and drawbacks. This paper fills the gaps found in the SAND specification and enriches its applicability, by introducing a new general platform called xStream. xStream enables communication between various applications and the 5G system and opens the door for many innovative solutions for various types of traffic as well as optimal resource slicing (not only video ones). With extensive simulation, it is shown that xStream provides benefits for both OTT service providers and network operators significantly improving QoE for their customers.
Toward Fast Regex Pattern Matching Using Simple Patterns

Nowadays network solutions employ pattern matching methods to apply protocol identification, traffic billing, load balancing, or detecting network malicious activities. Although regular expression (regex) patterns provide a powerful option to express signatures more effectively, they make the matching procedure more challenging. Dozens of finite automata based methods have been proposed to deal with regex patterns and boost the matching procedure. However, they suffer from high spatial or temporal complexity. Recently, we proposed HES as a practical novel method to match thousands of regex patterns in a reasonable time. Although HES was extremely faster than DFA, it was unable to support regex patterns without simple pattern (SP-Free regex patterns). In this paper we developed Enhanced HES (E-HES) to handle these kinds of patterns. Experimental results revealed that E-HES not only supported SP-Free regex patterns, but also it significantly optimized the regex handler check procedure. It leads us to match any kind of regex patterns in high bandwidth networks without spatial or temporal limitation.
On Opportunities for Mobile Offloading

Since the number of Wi-Fi-capable devices existing nowadays has increased drastically in the last few years, traffic offloading between mobile broadband and Wi-Fi has been gaining steady traction. When performed correctly, offloading should pro- vide users with a seamless experience when they use applications on their devices, while optimizing resources to improve user experience. For this reason, in order to analyze the situations when offloading can be performed and how it should best be done, in this paper we perform an in-depth analysis of the behavior of devices with mobile broadband interfaces in various conditions, such as mobility, different times of day, varying types of traffic (audio and video streaming, HTTP). Using this information as input, we propose a device-to- device offloading method and show that using other devices as an offloading tool brings improvements in terms of processing time and server usage.