Miracle-dz/UniFAR-FTUs huggingface dataset:
Observing Bandlimited Graph Processes from Subsampled Measurements

This work merges tools from graph signal processing and linear systems theory to propose sampling strategies for observing the initial state of a process evolving over a graph. The proposed method is ratified by a mathematical analysis that provides insights on the role played by the different actors, such as the graph topology, the process bandwidth, and the sampling strategy. Moreover, conditions when the graph process is observable from a few samples and (sub)optimal sampling strategies that jointly exploit the nature of the graph structure and graph process are proposed. Finally, numerical tests are conducted to illustrate the benefits of the proposed approach.
SAVED - Space Alternating Variational Estimation for Sparse Bayesian Learning with Parametric Dictionaries

In this paper, we address the fundamental problem of sparse signal recovery in a Bayesian framework, where the received signal is a multi-dimensional tensor. We further consider the problem of dictionary learning, where the tensor observations are assumed to be generated from a Khatri-Rao structured dictionary matrix multiplied by the sparse coefficients. We consider a Bayesian approach using variational Bayesian (VB) inference. VB allows one to obtain analytical approximations to the posterior distributions of interest even when an exact inference of these distributions is intractable. We propose a novel fast algorithm called space alternating variational estimation with dictionary learning (SAVED), which is a version of VB(-SBL) pushed to the scalar level. Similarly, as for SAGE (space-alternating generalized expectation maximization) compared to EM, the component-wise approach of SAVED compared to sparse Bayesian learning (SBL) renders it less likely to get stuck in bad local optima and its inherent damping (more cautious progression) also leads to typically faster convergence of the non-convex optimization process. Simulation results show that the proposed algorithm has a faster convergence rate and lower mean squared error (MSE) compared to the alternating least squares based method for tensor decomposition.
Achieving Adaptive Linkability for Cellular V2X Group Communications in 5G

Cellular vehicle-to-everything (C-V2X) in 5G enables device-to-device (D2D) group communications to support a variety of proximity-based services. While D2D group communication can improve mobile network spectrum efficiency and user experience by offloading local traffic and extending the coverage of cellular base stations, it has also raised new privacy challenges on balancing UE anonymity and message linkability. In this paper, we first identify the limitations of current standardization and literature on addressing this issue. Then, we design a Service-oriented Authentication Framework (SAF) that supports secure service delegation and anonymous message authentication. Specifically, a UE-controlled linking method is introduced to support adaptive linkability for different V2X use cases. Security analysis demonstrates the SAF is secure under cryptographic assumptions. Moreover, we conduct extensive simulations based on Vissim and NS3 to show that the SAF is more efficient compared with the state-or-art literature and is feasible for 3GPP standardized V2X group communications.
An Iterative Approach for Inter-Group Interference Management in Two-Stage Precoder Design

We consider a single cell downlink (DL) massive multiple-input multiple-output (MIMO) set up with user clustering based on statistical information. The problem is to design a fully digital two stage beamforming consisting of slow varying channel statistics based outer beamformer (OBF) and an inner beamformer (IBF) accounting for fast channel variations aiming to reduce the complexity involved in the conventional MIMO processing. Two different methods are considered to design the OBF matrix, so as to reduce the size of the effective channel used for IBF design. A group specific two-stage optimization problem with weighted sum rate maximization (WSRM) objective is formulated to find the IBF for fixed OBF. We begin by proposing centralized IBF design were the optimization is carried out for all sub group jointly with user specific inter-group interference constraints. In order to further reduce the complexity, we propose an iterative solution for group-specific beamformer design via the Karush-Kuhn-Tucker (KKT) conditions for fixed inter group interference (IGI) values with per group transmit power constraint. A low complexity heuristic iterative method is also proposed for managing the inter-group interference. In spite of incurring a small loss in performance, the computational complexity can be saved to a large extent with the group specific processing. The sum rate behavior of various proposed schemes are illustrated using numerical simulations.
Lightweight Group Authentication with Dynamic Vehicle-Clustering for 5G-Based V2X Communications

For 5G based Vehicle-to-Everything (V2X) communications, it is envisioned that the network latency will be dramatically decreased to the extent of 1msec. However, achieving such low latency with high mobility in vehicular networks is a challenge. In this paper, we propose a lightweight group authentication scheme for 5G- based V2X communications using three different vehicles' roles in each cluster: an ordinary vehicle, Cluster Head (CH), and gateway vehicle. To increase cluster connectivity time, our clustering algorithm is developed based on three metrics: cluster's relative velocity, relative position, and received signal strength. The proposed authentication scheme aims to mutually authenticate the vehicle and CH as well as create a shared group key to be used inside the cluster. The security analysis and performance evaluation show that our proposed scheme thwarts inside and outside authentication attacks, and achieves up to 50% less computation overhead than those achieved in the existing schemes.
TRUST: A TCP Throughput Prediction Method in Mobile Networks

Throughput prediction is essential for ensuring high quality of service for video streaming transmissions. However, current methods are incapable of accurately predicting throughput in mobile networks, especially for moving user scenarios. Therefore, we propose a TCP throughput prediction method TRUST using machine learning for mobile networks. TRUST has two stages: user movement pattern identification and throughput prediction. In the prediction stage, the long short-term memory (LSTM) model is employed for TCP throughput prediction. TRUST takes all the communication quality factors, sensor data and scenario information into consideration. Field experiments are conducted to evaluate TRUST in various scenarios. The results indicate that TRUST can predict future throughput with higher accuracy than the conventional methods, which decreases the throughput prediction error by maximum 44% under the moving bus scenario.
A Knowledge-Based Path Optimization Technique for Cognitive Nodes in Smart Grid

The cognitive network uses cognitive processes to record data transmission rate among nodes and applies self-learning methods to trace data load points for finding optimal transmission path in the distributed computing environment. Several industrial systems, e.g., data centers, smart grids, etc., have adopted this cognitive paradigm and retrieved the least HOP count paths for processing huge datasets with minimum resource consumption. Therefore, this technique works well in transmitting structured data such as `XML', however, if the data is in unstructured format i.e. `RDF', the transmission technique wraps it with the same layout of payload and eventually returns inaccuracy in calculating traces of data load points due to the abnormal payload layout. In this paper, we propose a knowledge-based optimal routing path analyzer (RORP) that resolves the transmission wrapping issue of the payload by introducing a novel RDF-aware payload-layout. The proposed analyzer uses the enhanced payload layout to transmit unstructured RDF triples with an append pheromone (footsteps) value through cognitive nodes towards the semantic reservoir. The grid performs analytics and returns least HOP count path for processing huge RDF datasets in the cognitive network. The simulation results show that the proposed approach effectively returns the least HOP count path, enhances network performance by minimizing the resource consumption at each of the cognitive nodes and reduces traffic congestion through knowledge-based HOP count analytics technique in the cognitive environment of the smart grid.
Joint Compression of EEG and EMG Signals for Wireless Biometrics

In this paper, we propose a new method for jointly compressing EEG and EMG biosignals based on the so-called cortico-muscular coherence, a function that takes into account the simultaneous frequency changes of the brain and the muscles activity, and can be used, e.g., to classify different kinds of movement. It is shown that this method increases the achievable compression rate compared to transmitting EEG and EMG samples separately, while trading-off with the accuracy of the classification. This can be exploited in several kinds of life and health applications e.g., motor rehabilitation and drivers attention monitoring; it could be especially useful for low-power wireless technologies, such as Bluetooth Low Energy or IEEE 802.15. 6, whose transmission resources are limited.
Learning Based Mobility Management Under Uncertainties for Mobile Edge Computing

Mobile edge computing (MEC) offloads computation-intensive applications and overcomes the long latency by pushing data traffic towards the network edges. With base stations (BSs) densely deployed in a hot-spot area to improve user experience, mobile user equipments (UEs) have multiple choices to offload tasks to edge servers by jointly considering both the channel condition and the computing capacity. However, precise full system information is hard to be synchronized between BSs and UEs for mobility management decision making. In this paper, a Q-Iearning based mobility management scheme is proposed to handle the system information uncertainties. Each UE observes the task delay as an experience and automatically learns the optimal mobility management strategy through trial and error. Simulations show that the proposed scheme manifests the superiority in dealing with the uncertainties. Compared with the traditional received signal strength-based handover scheme, the proposed scheme reduces the task delay by about 30%.
SNR Equalization of Multicarrier Frequency-Selective Channels Using GMD

Geometric mean decomposition (GMD) was originally proposed for MIMO (multiple-input multiple-output) systems as an alternative to singular value decomposition (SVD)to convert the MIMO channels into identical and parallel independent channels to minimize bit error rate (BER). In this paper novel applications of GMD in multicarrier com- munication system using a frequency selective channel are investigated. With the implementation of GMD to matrices comprised of subcarriers of a frequency selective channel, frequency dependencies of subcarriers' SNRs are equalized resulting in performance enhancement.
Secure and Efficient Data Accessibility in Blockchain Based Healthcare Systems

The healthcare industry is constantly reforming and adopting new shapes with respect to the technological evolutions and transitions. One of the crucial requirements in the current smart healthcare systems is the protection of patients sensitive data against the potential adversaries. Therefore, it is vital to have secure data access mechanisms that can ensure only authorized entities can access the patients medical information. Hence, this paper considers blockchain technology as a distributed approach protect the data in healthcare systems. This research proposes a blockchain based secure and efficient data accessibility mechanism for the patient and the doctor in a given healthcare system. Proposed system able to protect the privacy of the patients as well. The security analysis of our scheme shows that it can resist to well-known attacks along with maintaining the integrity of the system. Moreover, an Ethereum based implementation has used to verify the feasibility of our proposed system.
Seal: Efficient Training Large Scale Statistical Machine Translation Models on Spark

Statistical machine translation (SMT) is an important research branch in natural language processing (NLP). Similar to many other NLP applications, large scale training data can potentially bring higher translation accuracy for SMT models. However, the traditional single-node SMT model training systems can hardly cope with the fast-growing amount of large scale training corpus in the big data era, which makes the urgent requirement of efficient large scale machine translation model training systems. In this paper, we propose Seal, an efficient, scalable, and end-to-end offline SMT model training toolkit based on Apache Spark which is a widely-used distributed data-parallel platform. Seal parallelizes the training process of the entire three key SMT models that are the word alignment model, the translation model, and the N -Gram language model, respectively. To further improve the performance of the model training in Seal, we also propose a number of system optimization methods. In word alignment model training, by optimizing the block size tuning, the overhead of IO operation and communication is greatly reduced. In translation model training, by well encoding the training corpus, the data size transferred over the network can be reduced significantly, thus improving the overall training efficiency. We also optimize the maximum likelihood estimation (MLE) algorithm to solve the data skew issue on the join operation which is adopted both in the translation model training and the language model training. The experiment results show that Seal outperforms the well-known SMT training system Chaski with about 5x speedup for word alignment model training. For the syntactic translation model and language model training, Seal outperforms the existing cutting-edge tools with about 9~18x speedup and 8~9x speedup on average, respectively. On the whole, Seal outperforms the existing distributed system with 4~6x speedup and the single-node system with 9~60x speedup on average respectively. Besides, Seal achieves near-linear data and node scalability.
Online-Based Learning for Predictive Network Latency in Software-Defined Networks

In Software Defined Networking, due to the significant bandwidth and latency requirements, predicting available resources (i.e., latency, bandwidth) is crucial for enhancing performances, resource utilization and power consumption of the data plane. In this paper, we propose an efficient rules placement algorithm based on predictive network latency using online learning. Our proposal aims to dynamically predict the latency for updating the flow rules in network devices. To do so, we first formulate the flow rules placement as an Integer Linear Program (ILP) that aims to minimize the total network delay. Then, we propose a simple yet efficient heuristic algorithm to solve the formulated ILP problem with low time complexity. Experimental results using ONOS controller and Mininet show the efficiency of our proposal in decreasing network latency, packet loss and rising the network throughput.
JSNVM: Supporting Data Persistence in JavaScript Using Non-Volatile Memory

Data persistence refers to an characteristic that data needs to outlive its creator. In recent years, NVM (Non-volatile Memory), a new type of computer memory supporting memory-like byte-addressable access and disk-like persistence, became a promising technique for facilitating data persistence in memory and its management. So far NVM has been supported in many programming languages such as C, C++, and Java. Meanwhile, it has not yet been well supported in JavaScript, a popular scripting language for software development. This paper presents JSNVM, an extension to JavaScript's runtime and execution engine, to support data persistence in JavaScript using NVM. JSNVM consists of (1) $a$ JavaScript persistent object pool that serves as a persistent heap in which persistent objects are created and managed. This pool is also enhanced for guaranteeing data safety and consistency; and (2) a set of JavaScript persistent APIs that provide programmers with a support in creating, managing, and accessing persistent data in an easy-to-use and safe manner. We have implemented JSNVM and evaluated it against two database-supported data persistence styles (i.e., MongoDB's object store and indexedDB's binary store) on micro-benchmarks and real-world applications. The evaluation results show that compared with MongoDB's object store, JSNVM can achieve a 1.6× speedup; when applied to access binary data, JSNVM can achieve a 24.6× speedup over indexedDB's binary store, denoting that JSNVM can enhance performance of JavaScript applications in practice. JSNVM is planned to be publicly available by the end of 2018.
Packet-in-Packet: Concatenation with Concurrent Transmission for Data Collection in Low-Power Wireless Sensor Networks

Concurrent transmission, a novel communication paradigm, has been shown to effectively achieve reliable and energy-efficient flooding in low-power wireless networks. With multiple nodes exploiting a receive-and-forward scheme, this technique works effectively in flooding-based networks, i.e., in one-to-many scenarios. However, application-level scheduling has to be introduced for data collection in wireless sensor networks (WSNs). In this paper, we propose Packet-in-Packet (PiP), an energy-efficient paradigm requiring no application-level scheduling for timely data collection in low-power WSNs. PiP builds on concurrent transmissions, exploiting constructive interference and the capture effect to achieve high reliability and low latency. Moreover, PiP uses a packet concatenation capability to gather single-hop information in a best-effort manner. As a result, PiP significantly reduces collection time. We further compare PiP with a state-of-the-art protocol by extensive experiments in a 30-node FlockLab testbed. Experimental results show that PiP highly reduces collection time (i.e., rounds) and achieves good performance in terms of high reliability of approximately 98.7% and high energy efficiency in all experimental scenarios of the real-world testbed.
Adaptive Bayesian Channel Estimation for Millimeter-Wave MIMO Systems with Hybrid Architecture

The hybrid multiple-input-multiple-output (MIMO) transceiver architecture is a promising solution to reducing hardware and power cost at millimeter-wave (mmWave) frequencies. However, channel estimation is a major issue for the deployment such system models. Thus, this work shows that by exploiting the sparse scattering nature of the mmWave channel, an efficent channel estimation algorithm can be achieved by utilizing state-of-the-art compressive sensing (CS) techniques. In general previous CS-based channel estimation methods consider an on-grid sparse signal representation problem, however this is not truly realistic to the scenario of mmWave massive MIMO systems. To achieve a more realistic channel estimation algorithm for the mmWave MIMO system, this work considers an off-grid signal model approach, i.e., the directions of sparse channels are not confined on the angular grid for sparse signal formulation. A new adaptive channel estimation method is proposed by using Bayesian CS (BCS) to accurately and efficiently sense channels in terms of an off-grid signal model. A measurement of recovery uncertainty output by BCS is exploited to adaptively design the sensing matrix, thereby improving its estimation performance.
Monitoring User-Intent of Cloud-Based Networked Applications in Cognitive Networks

The cognitive network system learns from the past (situations, plans, decisions, actions) and uses this knowledge to improve the decisions in the future. Scenarios in which data resources for configuring radio-system parameters are stored in the cloud for easily sharing and exchanging between nodes are foreseeable. Due to the physical inaccessibility and limited control, it is always a tough topic to formulate appropriate access control strategies for the cloud data and data access requests submitted by applications are not always correct and credible. Cloud servers cannot clearly confirm that these requests are consistent with a user's original intent. In this paper, we propose a new access control method and forensic framework for user-intent monitoring of cloud-based networked applications in cognitive networks. Our framework has two main functions. Firstly, it makes sure that every data access request submitted by applications is correct. This means that it accurately shows what it wants. Monitoring user-intent can also help the cognitive engine to make decisions in turn. Secondly, it can offer adequate details to help forensic analysts reconstruct a precise view of user interaction with applications and understand system conditions. Our framework can function correctly in untrusted environments and is transparent to applications, systems and communication environments. It incurs no discernible performance overhead.
Dynamic Controller&#x002F;Switch Mapping in Virtual Networks Service Chains

Accelerated Software Defined Networking (SDN) adoption makes SDN paradigm emerging in the state of the art. Especially, the combination of SDN and network functions virtualization (NFV) becomes a promising trend in deploying virtual network services for network operators. Although SDN can decouple networks into the control plane and the data plane to obtain flexible operation and programmability, there are many open issues that need to be addressed for SDN deployments, such as i) where to place SDN controllers in a given network, ii) how to assign connections from controllers to switches in terms of satisfying multiple objectives (e.g., resource utilization, failure, quality of services, etc.). In this work, we focus on the efficient assignment between SDN controllers and switches to guarantee a low operational cost, quality of services, and fault tolerance, in which the complexity of virtual links in network services, an omitted factor in current works, is considered and addressed. We formulate an optimization problem for dynamic controller&#x002F;switch mapping (DCSM) in the network virtualization. We then proposed approximation algorithms in terms of relaxing the binary variables to solve the NP-hard problem, DCSM, in both centralized and distributed mechanisms. We also create various simulation schemes to evaluate our methods where they outperform state-of-the-art methods.
Energy-Efficient D2D Underlaid MIMO Cellular Networks with Energy Harvesting

This paper considers the precoder design for energy- efficient data transmissions in energy harvesting (EH)-aided device-to-device (D2D) communications underlaid multiple-input multiple-output (MIMO) cellular networks. We aim to maximize the energy efficiency (EE) of the network, defined as the ratio of the system sum rate to the system power consumption, under EH and transmit power constraints for both cellular and D2D users. The considered problem is nonconvex due to the concave-convex and fractional form of the objective. We propose to apply the concave-convex procedure (CCCP) and the Dinkelbach method to find tractable, approximate solutions. Numerical results demonstrate the performance of the proposed method from various perspectives.