Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Power of Combiner Optimizations in MapReduce Over MPI Workflows

Analyzing large volumes of data is becoming more and more important in various scientific computing domains. MapReduce over MPI frameworks are an appealing solution to enable scalable big data analytics on supercomputing systems. These systems can further leverage features of MapReduce applications by merging (key/value) pairs before the reduce function in combiner optimizations. In this paper, we propose a pipeline combiner workflow and integrate it into Mimir, a cutting-edge implementation of Map Reduce over MPI. Our results with real datasets on the Tianhe-2 supercomputer prove that our pipeline combiner workflow can reduce memory usage up to 51 % and improve the overall performance up to 61 %.
Resilient Controller Placement in Hybrid SDN&#x002F;Legacy Networks

Given the rate at which communication technologies and protocols evolve, network operators are often cautious of fully migrating to a new technology like Software Defined Networking (SDN) at one go, and prefer to do so in phases. Consequently, the number of SDN switches and in turn the amount of SDN control traffic in the network increases gradually. This control traffic processing occurs at the SDN controller(s), which are, hence, required incrementally in such hybrid SDN&#x002F;legacy networks. The placement of these controllers significantly affects several aspects such as control latency, resiliency, and load balancing. All existing controller placement strategies place controller(s) in a pure SDN network at a point in time, which is contrary to the SDN predominant migration scenario described above. Even when suitably adapted for hybrid networks, the existing controller placement strategies lead to inefficient placements. In this paper, we introduce and formulate the controller placement problem for hybrid SDN&#x002F;legacy networks over a period of time, with an aim to maximize the switch-controller control channel resilience. We consider an SDN migration trajectory and deduce a resilient controller placement schedule using an optimization approach. Based on 138 real network topologies, we comprehensively evaluate our approach against a well-known existing resilient controller placement strategy (suitably adapted herein for hybrid SDN&#x002F;legacy networks for a fair comparison), and demonstrate that our approach is more effective with up to 77% higher resiliency, while requiring up to 33% fewer controllers.
A Heuristic Approach for Website Classification with Mixed Feature Extractors

We proposed an intelligent website classification schema based on deep neural networks using mixed featured extractors. With the guidance of supervised learning methods and iterative training, we use the gradient descent algorithm to model the website classification. This novel model is composed of four components, which includes a Website Encoder, a Text CNN Feature Extractor, a Bidirectional GRU Feature Extractor and a Fully Connected Classifier. It can extract multiple features at different granularities of a website. By using the concatenated mixed features taken from mixed feature extractors, our model can easily choose a suitable website class. We make extensive experiments on the realistic collected website dataset. The dataset is collected using domains extracted from DNS records of Telecom Operator. Compared the multiple widely used machine learning models and our novel model, results demonstrate the proposed classification schema outperforms the current models with the metrics precision, recall, F1, and accuracy. All of this can contribute to various web applications, such as malicious website detection, online advertising, etc.
Performance Improvement of M-QAM OFDM-NOMA Visible Light Communication Systems

Visible light communication (VLC) system is a great candidate for indoor downlink access. Due to the limitation of light-emitting diode (LED)'s bandwidth, orthogonal frequency division multiplexing (OFDM) is used to enhance the transmission capacity of VLC system. Non-orthogonal multiple access (NOMA) can support multiple users in VLC system by sharing time and spectrum resources. The combination of OFDM and NOMA improves the overall channel capacity and spectral efficiency of a multi-user VLC system. In order to increase the data rate further, high order modulation formats are often applied. However, for the superposition of multiple high order modulation signals such as 16-quadrature amplitude modulation (QAM) and 64-QAM, it is hard to recover each user's signal under traditional successive interference cancellation (SIC) method, which is usually used in NOMA scheme. In this paper, we proposed a novel method to decode the superposed signals. This method is called ergodicity and comparison (EAC). By applying the EAC method, the bit error rate (BER) performance of two- user OFDM-NOMA VLC system is investigated under different power allocation conditions. In this system, the modulation formats for both the two users' signals are 4-QAM, 16-QAM and 64-QAM. Peak clipping effect on the received superposed signals is also considered. Numerical results demonstrate that the EAC method can improve significantly the BER performance of OFDM-NOMA VLC system, when high order M-QAM (M&#62; 4) signals are applied to the two users.
Non-Cooperative Game Theory Based Congestion Control in Lossy WSN

Congestion control is considered to be an important issue in wireless sensor networks as congestion can cause packet loss and degrade the throughput of the network. In this work, we have proposed a congestion control mechanism for a tree-based network by using non-cooperative game theory where each leaf node tries to maximize its data sending rate. The objective of the proposed work is to find a unique Nash equilibrium point to get the optimal data sending rate of each leaf node so that no congestion will occur at the destination parent node as long as the service rate of the destination node does not change. This also helps in maximizing the overall throughput of the network. In the proposed work, we consider both the buffer overflow and the channel congestion as the major causes of packet loss. The proposed congestion control method named Non-cooperative Game Theory based Congestion Control (NGTCC) has been simulated and compared with an existing algorithm for both single and multi-path tree networks. Simulation results show that the proposed method performs better than the existing method in terms of data sending rate and throughput.
Improved Localization Accuracy Using Machine Learning: Predicting and Refining RSS Measurements

Wireless localization methods are often subject to errors due to radio signal fluctuations that are used to estimate inter-device separation distances. We propose a novel method called MLRefine to counter these effects by refining RSS measurement data to obtain more accurate values that can enhance ranging and localization accuracies. MLRefine uses machine learning methods to model the relationship between accurate values and features extracted from in silico RSS values. MLRefine then applies the trained model to features extracted from real RSS measurement values to return a predicted set of refined RSS values. The refined RSS values are shown through computer simulations and real experiments to improve localization accuracy.
Federated Learning Based Proactive Content Caching in Edge Computing

Content caching is a promising approach in edge computing to cope with the explosive growth of mobile data on 5G networks, where contents are typically placed on local caches for fast and repetitive data access. Due to the capacity limit of caches, it is essential to predict the popularity of files and cache those popular ones. However, the fluctuated popularity of files makes the prediction a highly challenging task. To tackle this challenge, many recent works propose learning based approaches which gather the users' data centrally for training, but they bring a significant issue: users may not trust the central server and thus hesitate to upload their private data. In order to address this issue, we propose a Federated learning based Proactive Content Caching (FPCC) scheme, which does not require to gather users' data centrally for training. The FPCC is based on a hierarchical architecture in which the server aggregates the users' updates using federated averaging, and each user performs training on its local data using hybrid filtering on stacked autoencoders. The experimental results demonstrate that, without gathering user's private data, our scheme still outperforms other learning-based caching algorithms such as m-epsilon-greedy and Thompson sampling in terms of cache efficiency.
Mathematical Model and Framework of Physical Layer Encryption for Wireless Communications

As a response to serious transmission security problems in wireless communications, physical layer encryption (PLE) provides an effective security measure which is very different from upper layer cryptography technologies. PLE can take advantage of the effects of channel and noise and the processing objects are complex vector signals, which are essentially different from Boolean algebra based traditional cryptography. This paper establishes mathematical models, design frameworks and cryptographic primitives for PLE. Two design frameworks are proposed: stream PLE and block PLE. For stream PLE, a new 3D security constellation mapping is derived. For block PLE, two types of sub-transforms are defined: isometry transformations and stochastic transformations. The proposed PLE framework has a large cipher signal space and key space; it provides more freedom in design and can resist known plaintext attacks and chosen-plaintext attacks.
A Quick Survey on Large Scale Distributed Deep Learning Systems

Deep learning have been widely used in various fields and has worked very well as a major role. While the gradual penetration into various fields, data quantity of each applications is increasing tremendously, and so as the computation complexity and model parameters. As an obvious result, the training and inference is time consuming. For example, a classic Resnet50 classification model will be trained in 14 days on a NVIDIA M40 GPU with ImageNet data set. Thus, distributed acceleration is a very useful way to dispatch the computation of training and even inference to scale of nodes in parallel and accelerate the whole process. Facebook's work and UC Berkeley's acceleration can training the Resnet-50 model within hour and minutes by distributed deep learning algorithm and system, representatively. As other distributed accelerations, it gives a possibility to accelerate large models on large data sets from weeks to minutes, which gives researchers and developers more space to explore and search. However, besides acceleration, what other issues will be confronted of the distributed deep learning system? Where is the upper limit of acceleration? What application will acceleration be used for? What is the price and cost of acceleration? In this paper, we will take a simple and quick survey on the distributed deep learning system from algorithm perspective, distributed system perspective and applications perspective. We will present several recent excellent works, and bring analysis on the restricts and prospects of the distributed methods.
Mutual Information Maximization for Collaborative Mobile Sensing with Calibration Constraint

Highly resolved and accurate air pollution maps are valuable resources for many issues related to air quality including exposure modeling and urban planning. Due to the high equipment costs, there are limited high quality monitoring stations (HQMS) in cities. In order to achieve high resolution air pollution maps, a large number of mobile sensors are required. Besides, mobile sensors require frequent calibrations with the HQMS to maintain data accuracy. Existing work on route design for mobile sensors largely focuses on data reconstruction, which either ignores calibration or views it as an independent problem. To improve the accuracy of data reconstruction, this paper proposes a novel scheme that jointly considers sensor calibration and data reconstruction in route design for mobile sensors. We formulate a novel sensor route planning problem (SRPP) which aims to maximize the mutual information and guarantee the accuracy of measurements through sensor calibration. A heuristic algorithm is proposed to solve the SRPP, which supports calibration between mobile sensors and HQMS in route planning. Simulation results show that, compared with traditional approach, our approach can reduce 83% root mean square error (RMSE) on average.
Characterizing and Adapting to the Structure of Millimeter Wave Channel Covariance Matrices

We consider the classical estimation-theoretic frame-work of a channel estimation phase followed by a data transmission phase for a class of widely-used millimeter wave (mmW) channel models. We derive the structure of optimal transmissions/sounding beams during the channel estimation phase and show that this structure depends on the transmit covariance matrix of the channel. To further understand this structure, we derive exact and approximate expressions for the transmit and receive covariance matrices of the channel. We conclude the paper with numerical studies on the accuracy of our approximations and apply the results of our analysis to the problem of the energy trade-off between the channel estimation and data transmission phases.
Parallel Processing SPARQL Theta Join on Large Scale RDF Graphs

Theta join is commonly employed in real practices. Although SPARQL is a popular RDF query language, SPARQL does not define the specification of theta join until 2013. After that, few RDF stores except the stores based on RDBMS or key-value stores can process theta join. However, processing theta join on RDBMS or key-value stores is costly due to lack of RDF-native optimization. Efficient solutions to process theta join queries on RDF graph directly have not been fully explored. Here, we present ThetaStore to parallel process SPARQL theta join queries on large RDF graphs. First, a uniform SPARQL query graph model for theta join queries and equi-join queries is defined. Second, each subject, predicate, and object of RDF triples are mapped into order-preserving IDs instead of an random integer as most of RDF stores do. By this way, the engine does not need to translate theta join into a set of equi-joins. Finally, the engine employs decomposition and segment-oriented parallelization to execute SPARQL queries. The engine decomposes query into several star sub-queries and the intermediate results matching each pattern are divided into segments. Extensive experiments are conducted on large RDF data sets and experimental results show that ThetaStore outperforms state-of-the-art systems in both theta joins and equi-joins.
Predictive Intra-Edge Packet-Source Mapping in Agricultural Internet of Things

Dense IoT implementations incur heavy data load on the implemented networks. In this paper, we propose and evaluate a low-latency method of increasing the packet throughput in agricultural IoT implementations. The proposed method envisions removal of node identifiers from packets before transmission and predictive packet-source mapping method within the edge layer of an agrarian Internet of Things (IoT) implementation. The edge layer following a master-slave architecture. Pre-trained lightweight machine learning models at the edge identify the origin of the incoming packets based on the long-term learned collective variations of the sensorial values from the slave node. This reduction in packets significantly frees up time-slots at the receiving master node, allowing for more simultaneous connections to it. This intra-edge packet origin mapping scheme is further compared with the approach of edge node identification at a remote server to adjudge the tradeoffs between accuracy and latency of transmission. The proposed method doubles the amount of sensor data transmitted between the slave to master nodes with significant energy savings over longer duration and increases the data throughput by approximately 1.5 times between the master node and the remote server for our implementation. The proposed method estimates energy savings in the order of 20 watts for a deployment setup of 100 nodes over a year. The energy savings over densely deployed IoT networks can be utilized to accommodate more nodes and increase the lifetime of the network.
Full System Emulation of Embedded Heterogeneous Multicores Based on QEMU

The emerging edge computing is poised to move computing and intelligence to the network's edge so as to be close to the data sources for fast responses and reduced network traffic. In edge computing, edge devices need to encompass a wide variety of applications or services, from data preprocessing, intelligence inference, to multimedia human interface. Many such applications are well suited for special-purpose hardware accelerators. With the increasing number of accelerators on the edge devices, a promising architecture for edge devices is an asymmetric heterogeneous multicore that incorporates one or more microcontrollers to offload accelerator scheduling and interrupt handling from the main CPU, as exemplified in the NVIDIA Deep Learning Accelerator (NVDLA). To develop such computing systems, virtual platforms such as QEMU are often used. Unfortunately, QEMU only supports symmetric homogeneous multicore systems. In this paper, we tackle the challenging problem of supporting asymmetric heterogeneous multicore systems on QEMU by considering two possible implementation strategies: one-process and multi-process. The challenges are discussed and our implementations are presented. The two approaches are then compared qualitatively and quantitatively.
Towards Careless Wireless Mobile Networks

Combining two recent trends in wireless mobile systems, the small cells and the mobile edge computing, in this paper, we first identify new technical requirements for the implementation of the future generation of the mobile systems and then present architecture, modules and protocols complementing the current 3GPP Release 15 Reference Model to enable self-organized, wireless network of the future, in principle able to operate without any topological core network and seamlessly supporting hybrid mode operations.
Policy Gradient for Observer Trajectory Planning with Application in Multi-target Tracking Problems

Tracking multiple moving targets with bearing-only measurement is a challenging task, due to the inherent difficulties in determining the correct trajectory of the observer that will meet observability conditions. The work presented here formulates Observer Trajectory Planning (OTP) as a continuous control problem, and proposes reinforcement learning as a solution. The proposed architecture in this work constitutes a model-independent framework that allows for the estimation of the states of targets, and that allows multiple targets to be tracked in a realistic scenario, where the agent has no prior information about the initial locations and velocities of the targets.
Energy Efficient Resource Allocation and Caching in Fog Radio Access Networks

The combination of resource allocation and fog computing based radio access network (Fog-RAN) have great potential for future wireless networks. However, the cross-tier interference in the spectrum-sharing deployment of Fog BSs could affect the network performance seriously and most of the solutions focus on the spectral efficiency optimization. In this paper, the user association, caching strategy, and power allocation are investigated in Fog-RAN with consideration of energy efficiency and cross-tier interference mitigation. The user association, caching, and power allocation are formulated as a non-convex optimization problem and then transformed into a convex problem, which is solved by Alternating Direction Method of Multipliers (ADMM). Then ADMM-based resource allocation algorithms are proposed to improve the energy efficiency of Fog-RAN. Simulation results demonstrate the proposed algorithms's convergence and effectiveness by comparing with existing method.
A Novel Hierarchical Architecture Design for Secure Wireless Fieldbus Systems

The secure wireless fieldbus system faces error diffusion problem. When Advanced Encryption Standard (AES) adopted, even few errors in the channel spread, causing half of the data corrupted in an AES block. It significantly degrades the system reliability. In this work, we first design a new secure communication architecture to tackle the error diffusion effect, without compromising the system latency. Our novel hierarchical architecture is implemented in two dimensions. At the transmitting end, the first dimension performs encryption followed by short code encoding. Accompanying, a long code encoding is performed in parallel in the second dimension. While at the receiving end, the first dimension of the short code decoding and decryption, and the second dimension of the long code decoding are performed serially. Then, we conduct theoretical and practical analyses of error performance. Both theoretical and practical simulations show that our hierarchical architecture achieves a steeper bit error rate (BER) curve than the traditional architecture. At the BER of 10^(-6), the hierarchical architecture using (252,128) BCH in first dimension and (8128,7296) BCH &#x002F; (8060,7040) LDPC in the second dimension achieves 0.25 &#x002F; 1.3 dB performance gain than the traditional architecture only using (252,128) BCH code.
Optimal Power Control for Superimposed Pilots in Uplink Massive MIMO Systems

In Massive MIMO, the pilot contamination effect reduces the spectral efficiency (SE) gains and superimposed pilot (SP) transmission has been proposed to mitigate this effect. SP is based on transmitting pilot and data symbols simultaneously to allow for longer pilots and no pilot overhead. This work studies the optimal power control strategies in the uplink of a Massive MIMO system with SP and detection based on maximum ratio combining. The optimization objectives are maximum product of SINRs and max-min fairness, and these problems are reformulated as geometric programs which allow for efficient implementations. The numerical results indicate that the SE gains from the optimal power control with respect to the heuristic statistical channel inversion power control, are more significant when the interference from pilot symbols is subtracted before data detection.
Physical-Layer Security Enhancement for SIMO-MBM Systems

Media-based modulation (MBM) has become a promising technology for wireless communications, which offers significant throughput enhancement and superior performance due to the channel diversity gain achieved by RF mirrors. In this paper, the physical-layer security (PLS) issue is investigated for the single-input-multiple-output MBM (SIMO-MBM) system, and a secure SIMO-MBM system is proposed. Especially, by considering time-division duplexing (TDD) mode, based on its channel reciprocity property, both the data symbol bits and the index bits are protected from the eavesdropper by using the amplitude and phase information of the legitimate channel states as the secret key. For performance evaluation, the bit error rate (BER) of the eavesdropper as well as the system secrecy mutual information are investigated, and Monte Carlo simulations are carried out. Simulation results demonstrate that, the proposed secure SIMO-MBM system is capable of efficiently preventing data leakage whilst ensuring reliable data transmission.