Miracle-dz/UniFAR-FTUs huggingface dataset:
Live Video Multicast for Dynamic Users via Segment Routing in 5G Networks

Live video streaming applications are expected to proliferate very rapidly in the next generation network, i.e., 5G networks. To efficiently serve all users watching the same live video in 5G networks, the multicast technique plays an important role in providing scalable and high-performance services. Software-Defined Networking (SDN), contained in 5G networks, further facilitates the development of multicast techniques due to its flexibility of updating routing rules. However, few of conventional multicast mechanisms in SDN take into account the rule update overhead resulting from handovers of mobile users, which leads to tremendous network overhead. In this paper, we adopt Segment Routing (SR) as the first step to alleviate the rule update overhead, and then consider the rule update cost while maintaining the multicast tree to deal with user handovers. Thus, we first formulate Handover-aware Multicast Tree (HMT) problem and then show that HMT is NP-hard and does not admit any approximation algorithm. We then propose a heuristic algorithm called Mobility Aware Multicast Tree Algorithm (MAMTA). MAMTA takes advantage of user movement prediction to assign a base station that could provide longer service to a user, which leads to infrequent rule updates. Simulation results show that MAMTA significantly outperforms the shortest path tree and Steiner tree algorithms.
Efficient Privacy-Preserving Query Processing on Outsourced Geographic Databases

Location-based services (LBS) enables a user to query on spatial-temporal data with certain query criteria. With the popularity of mobile devices such as mobile phones and navigators, LBS has shown increasingly importance in various real world applications. However, in a traditional LBS system, the service provider maintains a geographic database (Geo-DB) and acquires the users' real-time locations. This raises the concerns of users privacy. This paper presents a privacy-preserving LBS scheme. In our scheme, the data owner outsources Geo-DB to the server in encrypted form, and the user enjoys the LBS without disclosing its location to the server. We construct a secure index to data objects in the Geo-DB by using the Encrypted Coded Quad-tree (ECQtree), such that user's queries can be efficiently located over the encrypted data. We also propose a Bloom filter based method for users to generate query trapdoors by hashing user's query regions into a Bloom filter vector, which is able to hide users' locations and query regions from the server. We also give security proof and analysis. Extensive simulations have been conducted and the results have shown the superior performance of our proposed scheme.
QoS-Aware Adaptive Flow-Rule Aggregation in Software-Defined IoT

In this paper, we propose a QoS-aware adaptive flow-rule aggregation scheme in software-defined IoT (SDIoT) network with an aim to address flow-table overflow problem in SDN switches. The proposed scheme uses a key-based mechanism that is capable of fast aggregation and provides sufficient reduction in the number of flow rules, while having minimal impact on the QoS of IoT traffic. Further, we observe that it is necessary to adequately select a QoS path from multiple candidate paths, while considering the flow-table utilization at the switches. Accordingly, we present the Best-fit heuristic which takes into account the number of flow-rule insertions along with the bottleneck rule-capacity switch on a path, in order to minimize the total number of flow-rules in the network. Experimental results show that the proposed scheme is capable of reducing the average delay and packet drop by 35% and 12%, respectively, and improving the average throughput by 20% compared to the existing delay-based flow-aggregation scheme, while having comparable performance in terms of rule-aggregation.
A Cost-Efficient and Fair Multi-Resource Allocation Mechanism for Self-Organizing Servers

In this paper, we study cost-efficient and fair allocation of multiple types of resources in an environment of heterogeneous and self-organizing servers. To address this problem, we formulate an optimization problem which aims at minimizing the operational costs for all servers, while providing fairness across different users. We propose a fully distributed implementation to solve this problem. The proposed mechanism is shown to achieve envy-freeness among different users. Furthermore, we show how it captures the trade-off between cost-efficiency and fairness. We employ numerical experiments to show the effectiveness of our proposed mechanism in reducing operational costs for a geo-distributed data-center.
Classifying the Order of Higher Derivative Gaussian Pulses in Terahertz Wireless Communications

The terahertz band is considered the last frontier for wireless communications and expected to play a significant role in beyond 5G networks. Besides supporting extremely high data rates for existing devices, the terahertz band is also expected to connect future nanoscale devices using graphene-based nano-antenna, which happens to radiate in 0.1-10 THz band. In this band, higher order derivatives of Gaussian pulses can provide energy-efficient communication for nanodevices. In this paper, we propose a metric, called root mean square (RMS) frequency spread, to detect the derivative order of the pulse at the receiving base station using uniform linear array antennas. Simulation experiments demonstrate that RMS frequency spread can be used to detect the derivative order of Gaussian pulses with 99% accuracy for distances up to 50 cm. This finding opens up a new design space for nanoscale terahertz communication, which can encode information in time derivative order of the transmitted pulse.
Virtualization of Table Resources in Programmable Data Plane with Global Consideration

In this work, we try to address the problem of memory fragmentation in ternary content addressable memory (TCAM) in programmable data plane (PDP), by designing and implementing a novel network hypervisor for PDP, namely, TPVX. TPVX realizes the virtualization of table resources in PDP with global consideration, i.e., when mapping tenant flow tables to physical switches, TPVX considers their table sizes and the pre-formatted sub-tables in the physical network to improve TCAM utilization and avoid memory fragmentation. Our experimental results verify that with TPVX, the utilization of the table resources in PDP can be improved dramatically and the extra processing latency due to the newly-introduced overheads can be maintained well simultaneously.
Robust Motion Planning and Safety Benchmarking in Human Workspaces

It is becoming increasingly feasible for robots to share a workspace with humans. However, for them to do so robustly, they need to be able to smoothly handle the dynamism and uncertainty caused by human motions, and efficiently adapt to newly observed event. While Markov Decision Processes (MDPs) serve as a common model for formulating cost-based approaches for robot planning, other agents are often modeled as part of the environment for the purpose of collision avoidance. This practice, however, has been shown to generate plans that are too inconsistent for humans to confidently interact with. In this work, we show how modeling other agents as part of the environment makes the problem ill-posed, and propose to instead model robot planning in human workspaces as a Stochastic Game. We thus propose a planner with safety guarantees while avoiding overly conservative behavior. Finally, we benchmark the evaluation process in the face of pedestrian modeling error, which has been identified as a major concern in state-of-the-art approaches for robot planning in human workspaces. We evaluate our approach with diverse pedestrian models based on real-world observations, and show that our approach is collision-safe when encountering various pedestrian behaviors, even when given inaccurate
Information Distance based Photoshop Metric

Objective predictor of perceived modifications to images, or the so-called Photoshop metric, finds many applications and is particularly desirable in the fashion industry. A promising solution to the problem is information distance, which measures the minimal number of bits required to transform one object to another. The application of information distance measures to photo editing assessment, however, is perplexed by human visual system characteristics, such as the difference in attention levels to different parts of an image, and the varying sensitivity at detecting different types of image distortions. Here we make one of the first attempts to develop an information distance based measure for Photoshop metric, where information distance-based features are used to train a Support Vector Regressor (SVR). Experimental results show that the proposed metric is well correlated with mean human observer ratings.
Power Allocation for Reliable Smart Grid Communication Employing Neighborhood Area Networks

Smart grid is a next generation electricity network that transmits electricity to end users and enable two way digital communication for providing remote reading and other advanced metering functions. For Smart grid domains to interact with each other, a reliable communication is required. This reliable two-way communication can be achieved with the use of small cells technology (femto, pico, etc), relay, or any of the transmission scheme proposed in 5G systems. With the use of femto cells and relay nodes in the network, the allocation of resources for the purpose of smart grid communication needs to be done efficiently. Power being one of the resource has been studied for its allocation in a femto cell network for Smart grid communication, however, transmission reliability and interference is generally ignored. In this paper, we investigate the problem of power allocation in a Neighborhood Area Network (NAN) with femto cell and relay technology as a communication mechanism. We formulate the problem as a power minimization problem such that the transmission reliability is ensured. Our performance evaluation shows that the average transmit power required for transmission through femto cell is 99% less as compared with cooperative transmission.
Deep Reinforcement Learning for User Association and Resource Allocation in Heterogeneous Networks

Heterogeneous networks (HetNets) can offload the traffic and reduce the deployment cost, which is regarded as a promising technique in next-generation cellular networks. Because of the non-convex and combinatorial features of the joint issue of user association and resource allocation, it is challenging to achieve an optimal solution. In this paper, a novel method is proposed to maximize the long-term overall network utility while ensuring the user equipments' quality of service requirements in the downlink of HetNets. Multi-agent reinforcement learning approach is developed to obtain the distributed optimal strategy. To solve the computationally expensive issue with the large action space, the multi-user deep reinforcement learning is presented. Double deep Q-network (DDQN) approach is introduced to achieve an optimal policy. Simulation results clearly indicate the better performance of DDQN than that of other reinforcement learning methods.
Computation Efficiency Maximization for Wireless-Powered Mobile Edge Computing

Energy-efficient computation is an inevitable trend for mobile edge computing (MEC) networks. However, resource allocation strategies for maximizing the computation efficiency have not been fully investigated. In this paper a computation efficiency maximization problem is formulated in the wireless-powered MEC network under a practical non-linear energy harvesting model. The energy harvesting time, the local computing frequency, the offtoading time, and power are all jointly optimized to maximize the computation efficiency under the max-min fairness criterion. The problem is non-convex and challenging to solve. An iterative algorithm is proposed to solve this problem. Simulation results show that our proposed resource allocation scheme outperforms the benchmark schemes in terms of the computation efficiency and verify the efficiency of our proposed algorithm. A tradeoff is elucidated between the achievable computation efficiency and the computation bits.
Joint Power Allocation and Caching for SVC Videos in Heterogeneous Networks

With the explosive increment in mobile users and mobile devices, nowadays network infrastructures are foreseen to be much more struggle in handling the vast mobile video traffic in the network. To meet this challenge, small-cell base stations (SBSs) are introduced in heterogeneous networks (HetNets) to support high data rate services in the fronthaul. However, HetNets still suffer from backhaul bandwidth shortage. Caching is a promising way to relieve the bandwidth burden on the backhaul. However it may fail in improving user's quality of experience (QoE) in video services. The reason is that, without a corresponding power allocation strategy in the fronthaul, users are unlikely to be allocated sufficient power to receive the higher quality version of videos, which in return leads to the waste of the limited caching space. On the other hand, the distinct layered feature of scalable video encoding (SVC) videos allows us to do layer-wise caching to further promote the cache utilization efficiency. Thereupon, a nature idea is to combine the power allocation strategy and the layer-wise caching strategy to effectively utilize the limited power and cache space of SBSs, so that the limited bandwidth of backhaul can be saved while user's QoE can be improved. In this work, we take the layered feature of SVC videos into account to jointly optimize these two strategies, aiming to maximize user's QoE. A simulated annealing based algorithm is proposed to solve the formulated problem. Simulation results show that, by doing so, the cache-hit ratio is increased while the limited backhaul bandwidth can be saved. Moreover, user's QoE has been significantly improved compared to traditional caching strategies,
Block Sparse Recovery for Wideband Channel Estimation in Hybrid mmWave MIMO Systems

Channel state information (CSI) is essential to achieve the optimal configuration of hybrid precoders and combiners in millimeter wave (mmWave) communication system. Exploiting the sparsity of mmWave channel enables to improve the CSI quality with small training overhead. In this paper, to further reduce the training overhead for channel estimation, the joint sparsity of wideband mmWave channel in angular-delay domain is exploited, where the mmWave channel estimation is formulated as a block sparse recovery problem. Accordingly, the block coherence of equivalent sensing matrix is smaller than the coherence of original sensing matrix, and decreases with the length of channel delay taps in the lower bound. The lower block coherence in turn elevates the recovery probability of unknown sparse channel. Finally, we proposed to employ the block orthogonal matching pursuit to exploit the derived block sparsity of wideband mmWave channel. The simulation results verify the analysis and demonstrate that the proposed block sparse recovery scheme outperforms the existing wideband mmWave channel estimators in terms of both the estimation accuracy and required training overhead.
Performance Analysis of 3D XPoint SSDs in Virtualized and Non-Virtualized Environments

Intel's Optane SSD recently came to the market as the pioneer of 3D XPoint based commercial devices. They have much lower latency (about 14 μs) and better parallelism properties than traditional SSDs, and as such are set to replace NAND flash SSD in commercial settings. To best serve cloud and enterprise data centers' higher performing storage demands, it is necessary to know the performance characteristics of the new devices in both virtualized cloud environments and traditional non-virtualized environments. In this paper, we present an analysis of Optane SSDs based on a large number of experiments. We use several micro-benchmarks to gain knowledge of Optane's basic performance metrics. We also discuss the impact of state-of-the-art storage stacks on the performance of Optane SSDs. By analyzing the test results, we provide configuration suggestions for storage I/O applications using Optane SSDs. Lastly, we evaluate the real-world performance of Optane SDDs by running MySQL database based experiments. All the experiments are performed in non-virtualized and virtualized environments (Linux and QEMU) with a comparison study between the Optane SSD and a SATA NAND flash-based SSD.
Modeling Rumor Spreading with Repeated Propagations in Multilayer Online Social Networks

Online social networks have been getting closely cooperation and increasingly dependent on each other to achieve better popularity and efficiency. Unfortunately, novel security threats are emerging as the cooperation and interdependence contribute to forming the multilayer online social networks. In multilayer networks, the rumor also takes the opportunity to spread more efficiently than that in single-layer network. Rumor spreading in single- layer networks has drawn considerable attention. However, spreading processes in multilayer networks were developed only recently. In this paper, concerning the public will confront more repeated rumors in multilayer networks, the SIHR rumor spreading model, which considered a ``hesitate'' psychological state, is proposed to study the dynamical process of rumor. The mean-field equations are derived to describe the rumor spreading process. After theoretical and experimental analysis, the performances of rumor spreading with various parameters in SIHR model are illustrated. As a result, some feasible security strategies are drawn.
Backscatter Communications Over Ambient OFDM Signals Using Null Subcarriers

In recent years, ambient backscatter communications have gained a lot of interest as a promising enabling technology for Internet-of-Things and green communications. In ambient backscatter communication systems, battery-less devices are able to transmit information by backscattering ambient RF signals generated by legacy communication systems such as digital TV broadcasting, Wi-Fi, or cellular. This paper is concerned with ambient backscatter communications over legacy cellular OFDM signals. We propose a novel modulation scheme that allows backscattering devices to take advantage of the spectrum structure of ambient OFDM symbols to transmit information. We analyze the error performance of the proposed scheme, provide an exact expression for the error probability, and validate our analysis using Monte-Carlo simulation. We investigate the effects of varying the OFDM symbol size and maximum channel delay spread on the error performance. Our numerical results show that the proposed technique outperforms other techniques available in the literature for backscatter communication over ambient OFDM signals in different scenarios.
OFDM-Based Spatial Modulation for Optical Wireless Communications

Spatial modulation is a power and complexity efficient multiple-input-multiple-output (MIMO) method which uses the transmit unit indexes to encode information. This paper investigates two different spatial modulation (SM) methods for optical wireless communications (OWC) systems based on orthogonal frequency division multiplexing (OFDM). Moreover, a novel method is proposed to enhance the spectral efficiency of SM systems by mapping the data symbols in time domain rather than subcarrier. The bit error rate (BER) performance of the proposed method is studied and compared with conventional OFDM-based SM and repetition coding (RC) as a benchmark. Maximum-a-posteriori-probability (MAP) detection and zero-forcing (ZF) are considered which affect the BER performance under different channel correlation conditions. Computer simulations have shown that the proposed method outperforms both the conventional OFDM-based SM and RC. It is also confirmed that the proposed method performs better than the reference systems under highly correlated channel conditions.
MmWave Vehicular Beam Training with Situational Awareness by Machine Learning

Configuring beams in millimeter-wave (mmWave) vehicular communication is a challenging task. Large antenna arrays and narrow beams are deployed at transceivers to exploit beamforming gain, which leads to significant system overhead if an exhaustive beam search is adopted. In this paper, we propose to learn the optimal beam pair index by exploiting the locations and sizes of the receiver and its neighboring vehicles (parts of the situational awareness for automated driving), leveraging machine learning tools with the past beam training records. MmWave beam selection is formulated as a classification problem based on situational awareness. We provide a comprehensive comparison of different classification models and various levels of situational awareness. Practical issues are considered in realistic implementations, including GPS inaccuracies, out-dated locations due to fixed location reporting frequencies and missing features with limited connected vehicles penetration rate. The result shows that we can achieve up to 86% of alignment probability with ideal assumptions.
Semi-Supervised Spectral Clustering

In this article, we propose a semi-supervised version of spectral clustering, a widespread graph-based unsupervised learning method. The semi-supervised spectral clustering has the advantage of producing consistent classification of data with sufficiently large number of labelled or unlabelled data, un-like classical graph-based semi-supervised methods which are only consistent on labelled data. Theoretical arguments are provided to support the proposition of this novel approach, as well as empirical evidence to confirm the theoretical claims and demonstrate its superiority over other graph-based semi-supervised methods.
Reinforcement Learning-Based Computing and Transmission Scheduling for LTE-U-Enabled IoT

To facilitate the private deployment of industrial Internet-of-Things (IoT), applying LTE in unlicensed spectrum (LTE-U) is a promising approach, which both tackles the problem of lacking licensed spectrum and leverages an LTE protocol to meet stringent quality-of- service (QoS) requirements via centralized control. In this paper, we investigate the computing offloading problem in an LTE-U-enabled network, where the task on an IoT device is carried out either locally or is offloaded to the LTE-U base station (BS). The offloading policy is formulated as an optimization problem to maximize the long term discounted reward, considering both task completion profit and the task completion delay. Due to the stochastic task arrival process at each device and the Wi-Fi's contention-based random access, we reformulate the computing offloading problem into a Q-learning problem and solve it by a deep learning network-based approximation method. Simulation results show that the proposed scheme considerably enhances the system performance.
Extracting structured data from invoices

Business documents encode a wealth of information in a format tailored to human consumption – i.e. aesthetically disbursed natural language text, graphics and tables. We address the task of extracting key fields (e.g. the amount due on an invoice) from a wide-variety of potentially unseen document formats. In contrast to traditional template driven extraction systems, we introduce a content-driven machine-learning approach which is both robust to noise and generalises to unseen document formats. In a comparison of our approach with alternative invoice extraction systems, we observe an absolute accuracy gain of 20\% across compared fields, and a 25\%–94\% reduction in extraction latency.