Miracle-dz/UniFAR-FTUs huggingface dataset:
On Enhancing Location Privacy and QoS for Video Streaming Over Wireless Networks

In wireless networks based on the IEEE 802.11 standard, mobile stations communicate with access points through the exchange of packets transmitted over the air. These packets can be captured by any listener in the vicinity which can extract the MAC address of the transmitter and thus be able to record the movements of the transmitter over time even when the system uses encryption as layer 2 headers, particularly the MAC address which uniquely identifies a mobile station is not encrypted. Existing solutions to this privacy breach aim at using pseudonyms to avoid using the same MAC address and also introduce silent periods to make it difficult for an attacker to track users. The introduction of silent periods may have negative effects on some applications particularly for those with constraints on bandwidth and delay such as video streaming. In this paper, we propose an enhanced silent period-based solution that allows to maximize the perceived QoS for a preset privacy. Our solution is elaborated from our mathematical model that allows to: (i) quantify the desired user location privacy and QoS, (ii) compute the silent and active periods that cope with these two objectives, and (iii) offer users the best trade-off between privacy and QoS. We experimented our proposal with a set of numerical simulations. The obtained results demonstrated the efficiency of the proposed solution.
Energy-Efficient Min-Max Planning of Heterogeneous Tasks with Multiple UAVs

Unmanned Aerial Vehicles (UAVs) have been widely used in various applications such as inspection, security surveillance, and aerial photography, in which the cooperation of multiple UAVs is significantly important for better accomplishing complex tasks due to the limited capability for individual UAV s. Task planning is the primary issue for the cooperation of multiple UAV s, and has attracted extensive research interests. However, most research fails to account adequately for limited energy on each UAV, which involves in many factors such as different operations for performing a task and various movement patterns besides the distance and turns that have been commonly considered. By contrast, we conduct a series of experiments to obtain the energy model of UAV s. Furthermore, we focus on the energy-efficient min-max task planning (E2M2TP) problem by considering the heterogeneity of tasks and integrating various energy factors, which is beneficial for balancing the workload and energy consumption among UAV s and thus reducing the number of required UAVs. We show that E2M2TP is NP-hard, and propose an energy-aware variable neighbor search (EVNS) algorithm to iteratively optimize both task allocation and path planning. Extensive simulations are conducted to validate that EVNS outperforms the other state-of-the-art algorithms.
Universal Language Model Fine-tuning for Patent Classification

This paper describes the methods used for the 2018 ALTA Shared Task. The task this year was to automatically classify Australian patents into their main International Patent Classification section. Our final submission used a Support Vector Machine (SVM) and Universal Language Model with Fine-tuning (ULMFiT). Our system achieved the best results in the student category.
Enabling Adaptive Data Prefetching in 5G Mobile Networks with Edge Caching

The exponential growth of data traffic volume dominates the demand for the next generation mobile networks (5G). The consistent and satisfied quality of experience (QoE) is one of the leading challenges of provisioning services in 5G mobile networks. Thus, in this paper, we propose a novel adaptive prefetching scheme to compensate the undesired transmission conditions in the 5G mobile network by extending the content prefetching concept from the users to the network. Specifically, an optimization problem is proposed for a prefetching scheme that adaptively retrieves users' data to access nodes and (or) user equipments (UEs) before the actual requests according to the network status, QoE status, predicted data rates, and mobility patterns of users. For the sake of tractability, the prefetching problem is transferred to a convex problem that can be solved efficiently. Accordingly, to implement the proposed schemes in the 5G network, system interactions among entities in the network are designed to realize prefetching-related functions. A signaling protocol to support the adaptive prefetching scheme is also presented. Simulation results show that an adaptive prefetching scheme can improve the network performance significantly.
On the Capacity and Optimal Signal Constellations for SISO-VLC Systems

In this paper, we investigate the capacity of the SISO-VLC system. The capacity of SISO-VLC system without considering the average intensity constraint is firstly analyzed. It can be written as a functional optimization problem in a normed linear space and the objective function is proved to be concave with convex feasible region. Thus, a unique optimal probability density function (PDF) of the input signal exists and its corresponding necessary and sufficient condition is deduced according to the optimization theorem. It is further proved that the capacity-reaching PDF of the input signal is combined of a finite number of scaled impluse function. Therefore, the capacity-reaching constellation optimization problem is formulated and an algorithm is proposed to solve it. Thereafter, the capacity of the SISO-VLC system with average intensity constraint is considered as a functional optimization problem with an equality constraint and is transformed into a new unconstrained functional optimization problem by method of Lagrange multipliers. The existence and uniqueness of the capacity-reaching PDF still hold. The necessary and sufficient condition of the capacity-reaching PDF is obtained in a similar way. The capacity-reaching PDF is proved to be finite and discrete, too. In addition, the algorithm to solve the capacity-reaching constellation optimization problem is proposed as well.
An Analysis of Content Sharing Hops for Dual-Structural Network Based on General Random Graph

Dual-Structural Network (DSN), pioneered by our group for content sharing, is a networking paradigm with the Internet as primary structure and the broadcast-storage network (BSN) as secondary structure. In order to quantitatively evaluate its content sharing capability, in this paper, we generally adopt a deductive methodology, namely that DSN is formalized as a complex network and then its sharing capability is derived according to graph theory. In specific, according to bipartite graph theory, we first construct a bipartite dual-structural network model to obtain an abstract content sharing graph through top-down projection, and then content sharing hops (CSH) in the graph is capitalized as a metric to evaluate the sharing capability between any two content nodes. Furthermore, we leverage the general random graph theory to generate the sharing graph for deriving quantitative upper bounds on average content sharing hops (ACSH) and maximum content sharing hops (MCSH) of DSN. Lastly, the theoretical derivations are validated by numerical simulation. Moreover, compared with content delivery network (CDN), content centric network (CCN) and information-centric mobile ad hoc networks (ICMANET), DSN is demonstrated to be superior in terms of the sharing capability.
Caching as a Service in 5G Networks: Intelligent Transport and Video on Demand Scenarios

With the explosive growth of mobile multimedia traffic, the problem of allocating computing and spectral resources for very low latency services become a challenge for next generation mobile networks. We exploit all the benefits of a completely virtualized environment, where mobile virtual network operators (MVNOs) and virtual service providers (VSPs) are connected in the Cloud through network as a service (NaaS) using distributed infrastructure as a service (IaaS). VSPs provide services according to Internet of things (IoT) devices requests including software as a service (SaaS) and emerging caching techniques Cache as a Service (CaaS) to satisfy the quality of service (QoS) requirements. Thus, we propose a many-to-many matching game between the sets of IoT devices and the set of virtual service providers (VSPs). To solve this game, we exploit the deferred acceptance algorithm that enables the players to self-organize into a stable matching and a reasonable number of algorithm iterations. To highlight the effectiveness of our approach for the on-demand services, we applied it on two typical services requiring ultra- reliability and low-latency communications (uRLLC): The intelligent transport and the video on-demand services. Simulation results has demonstrated that our proposed matching strategy coupled to CaaS caching capabilities on distributed F-RAN significantly outperforms the traditional strategies in terms of latency and network traffic load.
LDL Decomposition-based FPGA Real-time Implementation of DOA Estimation

—A n FPGA implementation and real-time experimental verification of proposed direction of arrival (DOA) estimation algorithm employing LDL factorization are presented in this paper. The proposed algorithm is implemented on a Xilinx FPGA using LabVIEW software and its real-time experimental verification is performed using National Instruments (NI) PXI platform. The proposed method has several advantages over well-known methods which are based on either eigen value decomposition (EVD) or singular value decomposition (SVD). It provides faster execution since LDL factorization requires O(n 3 /6) number of operations whereas EVD requires O(n 3 ) . Results from Matlab simulations and real-time experiments demonstrate the effectiveness of the proposed method. Successful FPGA compilation reports show low resource usage and faster computation time for LDL-based method compared with QR-based implementations. Performance comparison is done in terms of estimation accuracy, FPGA processing time and resource utilization.
Co- Location Resistant Virtual Machine Placement in Cloud Data Centers

Due to increasing number of avenues for conducting cross-virtual machine (VM) side-channel attacks, the security of public IaaS cloud data centers is a growing concern. These attacks allow an adversary to steal private information from a target user whose VM instance is co-located with that of the adversary. To reduce the probability of malicious co-location, we propose a novel VM placement algorithm called “Previously Co-Located Users First”. We perform a theoretical and empirical analysis of our proposed algorithm to evaluate its resource efficiency and security. Our results, obtained using real-world cloud traces containing millions of VM requests and thousands of actual users, indicate that the proposed algorithm provides a significant increase in the cloud's co-location resistance with little compromise in resource utilization, compared to existing approaches.
Data-Driven Caching with Users' Local Differential Privacy in Information-Centric Networks

Information-centric networking (ICN) is developed for the future Internet because of the tremendous increase of content demands in the Internet. In the ICN architecture, in-network storage for caching plays an important role in improving content delivery efficiency, scalability and availability. To enjoy the benefits of caching users' preferable contents without disclosing the users' privacy, in this paper, we aim to integrate local differential privacy (LDP) techniques into data-driven optimization, and propose a novel scheme to allow content provider (CP) to collect the locally differentially private content preferences of a selected group of users, exploit data-driven approach to predict the content popularity, and offer the cache-enabled access points (APs) economic incentives to cache the selected preferable content. Here, optimized local hashing (OLH) is employed to locally add differential private noise to the users' preference content information and the noisy data is sent to the CP. Besides, we leverage data-driven methodology to predict the content popularity according to the constructed reference distribution of the given noisy preference content data from users. We formulate a data-driven caching revenue optimization, provide feasible solutions, and conduct simulations to show the effectiveness of the proposed scheme.
ReFeR: Resource Critical Flow Monitoring in Software-Defined Networks

Flow monitoring is widely applied in software-defined networks for monitoring network performance. Especially, the detection on heavy hitters can prevent the Distributed Denial of Service attack. However, many existing approaches fall in one of two undesirable extremes: (i) inefficient collection where only accuracy is concerned in the method; (ii) low accuracy caused by the sacrifice with fast detection. As a result, we aim to find a balance between the accuracy and efficiency of flow monitoring, where the network resources can be saved and the error rate can also be confined simultaneously. In this paper, we present ReFeR, a novel "Report- Feedback-Report" scheme to improve the detection efficiency of heavy item detecting while ensuring low error rate of the measurement. ReFeR leverages the binary order of magnitude of item measurement to replace the long statistical information shared between switches and controller; after the items are analyzed with the magnitude, only fewer uncertain items are involved in further detection, where their information (i.e., significant digits) is provided for final judgment. Theoretical analysis and simulated evaluation have proved the effectiveness of our solution. ReFeR keeps the error rate under $1\%$ and the saving rate larger than $20\%$ in most cases as the selecting fraction $\alpha&#62; \frac{5}{24}$, which guarantees both high efficiency and low error rate compared with existing methods.
Recycling Cellular Downlink Energy for Overlay Self-Sustainable IoT Networks

This paper investigates the self-sustainability of an overlay Internet of Things (IoT) network that relies on harvesting energy from a downlink cellular network. Using stochastic geometry and queueing theory, we develop a spatiotemporal model to derive the steady state distribution of the number of packets in the buffers and energy levels in the batteries of IoT devices given that the IoT and cellular communications are allocated disjoint spectrum. Particularly, each IoT device is modeled via a two- dimensional discrete-time Markov Chain (DTMC) that jointly tracks the evolution of data buffer and energy battery. In this context, stochastic geometry is used to derive the energy generation at the batteries and the packet transmission probability from buffers taking into account the mutual interference from other active IoT devices. To this end, we show the Pareto-Frontiers of the sustainability region, which defines the network parameters that ensure stable network operation and finite packet delay. The results provide several insights to design self-sustainable IoT networks.
Elastic Network Service Chain with Fine-Grained Vertical Scaling

By moving network functions from dedicated hardware to software, Network Function Virtualization (NFV) is expected to bring the advantages of cloud computing to network management. Frequent workload changes require the underlying infrastructure to be dynamic and agile to cope with the changes. Some existing studies have investigated elastic virtual machine (VM) positioning solutions by dynamically creating and destroying VM replicas, while maintaining balanced workload among VMs. However, those solutions are coarse-grained which may cause unnecessary resource over-provisioning and low resource utilization. In this paper, we propose ElasticNFV, a dynamic solution that achieves fine-grained cloud resource provisioning for Virtual Network Functions (VNFs). ElasticNFV analyzes realtime resource demand of multiple service chains and allocates resource through an elastic provision mechanism. When a scaling conflict occurs, ElasticNFV provides a Two-Phase Minimal Migration (TPMM) algorithm to optimize migration time and embedding cost of VNFs based on prediction. We implemented ElasticNFV on top of a KVM virtualization platform and Open vSwitch. Through simulation and testbed evaluation, we show that ElasticNFV can achieve high resource utilization and short migration time with low cost.
Decentralized Online Nonparametric Learning

We consider decentralized online supervised learning where estimators are chosen from a reproducing kernel Hilbert space (RKHS). Here a multi-agent network aims to learn nonlinear statistical models that are optimal in terms of a global convex functional that aggregates data across the network, while only having access to locally observed streaming data. We address this problem by allowing each agent to learn a local copy of the global regression function while enforcing consensus constraints. We use a penalized variant of functional stochastic gradient descent operating simultaneously with low-dimensional subspace projections. The resulting algorithm allows each individual agent to learn, based upon its locally observed data stream and message passing with its neighbors, a function that is provably close to globally optimal and satisfies the consensus constraints. Moreover, the complexity of the learned regression functions is guaranteed to be finite. We then validate this approach on the Brodatz textures dataset for the case of decentralized online multi-class kernel logistic regression.
A Cloud-Based Energy Efficient Hosting Model for Malware Detection Framework

Protection strategy against malware attacks, whether in enterprise networks or on personal computers is generally divided into two main aspects; the detection rate and the resource consumption. Having a better detection rate is not the solution unless it is complimented by energy efficiency with respect to its CPU resource consumption of the host machine. This paper presents a cloud-based energy efficient hosting model for an intelligent malware detection framework, which amalgamates different components of Amazon's cloud services to develop a unique and dynamically scalable hosting model. The hosting model is then evaluated separately for its CPU utilization and in comparison with the conventional security software. The experimental results demonstrate significant energy efficiency in terms of CPU utilization by the hosting model.
Fast Charging Station Placement with Elastic Demand

The scarcity of efficient charging infrastructures has suppressed penetration rate of Electric Vehicles (EVs). This paper mainly focuses on the Fast Charging Station (FCS) placement problem, especially with the elastic demand. We first propose the distance preference and waiting time preference to capture the elastic EV charging demand. Moreover, a fixed-point equation is proposed to illustrate the relationship between serving demand rate and waiting time at station. We further formulate the problem of FCS placement with elastic demand as a bi-level optimization problem. In the upper level, we propose a heuristic algorithm to determine the optimal location of charging stations, with the goal of maximizing overall system profit. In the lower level, queueing theory is utilized to analyze the optimal capacity of each charging station. The sufficient condition for the existence of optimal station capacity is given. Simulation results demonstrate the effectiveness of our approach in improving system profit and reducing demand loss rate.
Mining Magnitude-Oblivious Periodical Patterns of Dockless Shared Bike Demands

In spite of their convenience and popularity, dockless shared bikes pose several challenges to operations and city management, e.g., unbalanced supply and demand across a city, and harmful congestions in pedestrian walkways. A step towards addressing these challenges is to find whether there exist periodical patterns in dockless shared bike demands. A periodical pattern of dockless shared bike demand is a time-dependent demand profile that repeats over a time period and frequently appear in different areas across a city. Mining periodical patterns of dockless shared bike demands, however, is very challenging because of data noises and different demand scales in different areas. In this paper, we propose a two-stage method for mining the periodical patterns of dockless shared bike demands. In the first stage, we determine the areas which have periodical dockless shared bike demands. And in the second stage, we introduce the concept of magnitude-oblivious demand to handle the issue on the different scales in different areas and propose an algorithm to compute the magnitude-oblivious periodic patterns. We conduct experiments with real data in Shanghai, China, which show that 12.4% of spatial areas in Shanghai have periodicity in bike demands, and we report 157 magnitude-oblivious periodical patterns in Shanghai. We also present several case studies which show our approach produces meaningful and useful patterns. Interestingly, we find that the areas with a similar geography often share the same periodical patterns of dockless shared bike demands.
Efficient Identity Spoofing Attack Detection for IoT in mm-Wave and Massive MIMO 5G Communication

In many IoT (Internet-of-Things) applications, a large number of low-cost IoT devices are connected to the Internet through an access point (AP) or gateway via wireless communication. Due to the resource constraints on IoT devices and broadcast nature of wireless medium, identity spoofing attacks are easy to launch but hard to defend in an IoT wireless access network. In this paper, under the context of 5G communication, we propose an efficient physical layer identity spoofing attack detection scheme for IoT. By harnessing the sparsity of the virtual channel in mmWave and Massive MIMO 5G communication, we propose a two- step detection scheme. In the first step, our scheme detects anomalies by examining the virtual angles of arrival (AoA) and path gains of all the IoT devices simultaneously in a virtual channel space (VCS). In the second step, we introduce a machine learning based detection scheme to detect the actual attack. Simulation results evaluate and confirm the effectiveness of the proposed detection scheme. The minimum Bayes risk of the proposed scheme can be less than 0.5\% even in the presence of 100 IoT devices.
Efficient Algorithms for Graph Coloring on GPU

Graph coloring is an important problem in computer science and engineering with numerous applications. As the size of data increases today, graphs with millions of nodes are becoming commonplace. Parallel graph coloring algorithms on high throughput graphics processing units (G PU s) have recently been proposed to color such large graphs efficiently. We present two new graph coloring algorithms for GPUs which improve upon existing algorithms both in coloring speed and quality. The first algorithm, counting-based Iones-Plassmann (CJP), uses counters to implement the classic Jones-Plassmann parallel coloring heuristic in a work-efficient manner. The second algorithm, conflict coloring (CC) achieves higher parallelism than CJP, and is based on optimistically coloring the graph using estimates of the chromatic number. We compared CC and CJP with two state-of-the-art GPU coloring algorithms, csrcolor [1] and Deveci et al's [2] vertex/edge-based algorithms (which we call VEB), as well as the sequential CPU algorithm ColPack [3]. In terms of coloring quality, CJP and CC are both far better than csrcolor, while CJP uses 10% fewer colors than VEB on average and CC uses 10% more. Compared to ColPack, CJP and CC use 1.3× and 1.5× more colors on nonbipartite graphs, resp. In terms of speed, CJP is on average 1.5–2× faster than the other algorithms, while CC is 2.7–4.3× faster.