Miracle-dz/UniFAR-FTUs huggingface dataset:
QoS-Based Robust Transceiver Design for Coexistence of MIMO Radar and FD MU-MIMO Cellular System

In this paper, spectrum sharing between a multiple-input-multiple-output (MIMO) radar and a full duplex (FD) multi-user MIMO (MU-MIMO) cellular system is studied. While a joint transceiver design technique at a hardware impaired FD cellular system considering imperfect channel state information is developed to maximize the detection probability of the MIMO radar, null-space based waveform projection is proposed to mitigate the interference from the radar towards the cellular system. The proposed technique exploits the monotonically increasing relationship of detection probability and its non-centrality parameter and accordingly optimizes the performance of detection probability of the radar, while also providing the cellular users with specific quality-of-service (QoS). Numerical results demonstrate the feasibleness of spectrum sharing between both systems, albeit certain trade-offs in radar transmit power, detection probability and QoS of users in the cellular system.
Keyboard Side Channel Attacks on Smartphones Using Sensor Fusion

Smartphones are equipped with a range of highly responsive sensors. From gyroscopes to accelerometers, providing mobile applications a wide variety of ways to interact with the environment. Unfortunately, some applications may be able to use these sensors to monitor their surroundings in unintended ways. Acoustic emanations are the most commonly used side channel in attacking computer keyboards, ATMs, etc. However, if multiple uncorrelated sensors are used, then the attack can be much more effective. In this paper, we explore how data from various sensors can be fused to improve the accuracy in recovering characters typed in a nearby mechanical keyboard from a smartphone. Unlike conventional approaches wherein acoustic emanations or other sensor reading are used as side-channel vectors, we combine emanations recorded from multiple sensors available in a smartphone and fuse them to achieve higher accuracy than any single sensor. Experiments reveal that the use of fusion could achieve an accuracy of 80 percent which is an improvement of over 6 percent using only audio emanations.
Provably Efficient Algorithms for VNF Routing Optimization

Previous researches on (Virtualized Network Function)VNF deployment mainly focus on resource and VNF distribution given several candidate paths, whereas routing, though more practical, is not under consideration. Especially, resource distribution does not remain optimal given continuously coming flows with different characteristics. No quantitative approach is proposed to determine the timing to redistribute resources and functions while real-time adjusting network distribution is too expensive. In this paper, we claim an alternative approach to handle the resource-and-flow matching problem using the method of routing. We find a routing and evaluate its costs using our VNFs Routing Evaluation Model. We prove the NP-hardness of VNF - RE. While previous works focus on heuristic algorithms, whose performance cannot be proven, we propose a two phase approach and prove the approximation ratio. The problem is formulated as an optimization problem which jointly considers VNF covering, multi-resource consumption, and routing overhead. We prove that the problem is NP-hard and our solution 0 ((1 + (kD fmax / $D$ −1)β)) log3 $N$ log m) approximates the optimal where k, Dfrnax and β are variables that depend on the input flow. The simulation compares our work with several representative heuristic algorithms and shows that VNF - RE has better performance.
Parallelizing Machine Learning Optimization Algorithms on Distributed Data-Parallel Platforms with Parameter Server

In the big data era, machine learning optimization algorithms usually need to be designed and implemented on widely-used distributed computing platforms, such as Apache Hadoop, Spark, and Flink. However, these general distributed computing platforms themselves do not focus on parallelizing machine learning optimization algorithms. In this paper, we present a parallel optimization algorithm framework for scalable machine learning, and empirically evaluate the synchronous Elastic Averaging SGD (EASGD) and other distributed SGD-based optimization algorithms. First, we design a distributed machine learning optimization algorithm framework based on Apache Spark by adopting the parameter server. Then, we design and implement the widely-used distributed synchronous EASGD and several other popular SGD-based optimization algorithms, such as Adadelta and Adam, on top of the framework. In addition, we evaluate the performance of synchronous distributed EASGD compared with other distributed optimization algorithms based on the same framework. Finally, to explore the optimal settings of mini-batch size in large-scale distributed optimization, we further analyze the empirical linear scaling rule originally proposed in the single-node environment. Experimental results show that our parallel optimization algorithm framework achieves good flexibility and scalability. And, the distributed synchronous EASGD runs over the proposed framework gains a competitive convergence performance and is about 5.7% faster than other distributed SGD-based optimization algorithms. Experimental results also verified that the empirical linear scaling rule only holds well before the mini-batch size exceeds certain threshold on large-scale benchmarks in the distributed environment.
Oinput: A Bone-Conductive QWERTY Keyboard Recognition for Wearable Device

The emergence of wearable devices has brought great simplicity and convenience to people's daily lives. However, due to the small form-factor, low-profile hardware interfaces, the input scheme for such wearable devices becomes a bottleneck and even sabotages their functionalities. The state-of-the-art interaction schemes, including voice input, inertial measurement unit (IMU)based input, or acoustic based input, all require a stable environment, which is critical for wearable device. To break this stalemate, we propose a stable QWERTY keyboard input for wearable devices based on bone-conduction models. Using the characteristics of human anatomy, we achieve a low-cost and high precision text input system, named Osteoacusis input (Oinput), with the help of human bones. To be specific, we first investigate a new set of bone-conduction theories. Through this set of theories, we combine a strong anti-noise cyclic neural network to achieve a high-precision QWERTY keyboard recognition for text input. Furthermore, in order to improve the user experience, we leverage slightly keyboard layout changing, dimensionality and feature selection to reduce the power consumption while preserving the convenience and stability. We have conducted experiments on 30 volunteers. The results show that Oinput has superior robustness with a high recognition accuracy of 93.3% in average. Moreover, Oinput's calibration mechanism increases the accuracy by more than 99%.
A 4-Step Tool-Chain for IoT Business Model and Sustainability Recommendations

Today many different technologies and products are available for the application field of Internet of Things (IoT) and the development continues. Before launching a new product on the market, a market analysis may be used to develop a specific business model (BM) recommendation. This includes not only the classical analysis according to the BM CANVAS (e.g., stakeholders involved, key resources, revenue streams, and cost), but also the analysis of socio-economic aspects up to the identification of sustainability aspects and measures from a Business-to-Business and Business-to-Customer view. Thus, this paper proposes a generic-applicable 4-step tool-chain combining well-established methods with new ones to develop a strong and valid BM for IoT products including the identification of essential sustainability items. This tool-chain is applied to an IoT scenario EduCampus, which forms as a concrete instantiation of the methodology developed.
A Cross-Layer MAC Aware Pseudonym (MAP) Scheme for the VANET

In vehicular ad hoc networks (VANETs), safety messages must be protected for location privacy. Pseudonym schemes have provided a promising solution. However, attacks could still be carried out from the medium access control (MAC) layer. In this paper, we present a new MAC semantic linking attack that links the new and old pseudonyms by analyzing the vehicles' transmission patterns in the MAC layer, even if they change pseudonyms simultaneously. To deal with the attack, a MAC layer aware pseudonym (MAP) scheme is proposed. The MAP scheme is compatible with the standard and coordinates each vehicle to access the wireless medium in a time- slotted manner. In MAP scheme, vehicles change pseudonyms and slot utilization pattern consistently. The interactive influence between the pseudonym changing and safety message transmission is evaluated. Taking the pseudonym age, anonymity set size, time-toconfusion and packet delivery ratio as the performance metrics, extensive simulation results have verified that the MAP scheme can improve the location privacy and enhance transmission efficiency in VANETs.
Online Energy Management for Smart Communities with Heterogeneous Demands

With the development of renewable energy technology and communication technology in recent years, many residents utilize renewable energy devices in their residences with energy storage systems. However, it is a great challenge to share residents' energy with others in the smart community for minimizing the total cost of all residents. In this paper, we investigate the problem of energy management and task scheduling for a smart community with residential combined heat and power system (resCHP) and renewable energy to pay the least bill. We take heterogeneous task arrival into consideration, which widely exists in the community. We formulate the minimum cost problem of a non-cooperative community as a random non-convex optimization problem with physical constraints. Our objective is to minimize the community time-average cost, including the cost of the external grid and natural gas. We adopt the Lyapunov optimization theory to tackle this problem, which needs no future data and has low computational complexity. Furthermore, we design a cooperative renewable energy sharing algorithm based on Sarsa Algorithm. Finally, we present extensive simulations to validate the proposed algorithms by using real trace data.
Examining the Perceptual Effect of Alternative Objective Functions for Deep Learning Based Music Source Separation

In this study, we examine the effect of various objective functions used to optimize the recently proposed deep learning architecture for singing voice separation MaD - Masker and Denoiser. The parameters of the MaD architecture are optimized using an objective function that contains a reconstruction criterion between predicted and true magnitude spectra of the singing voice, and a regularization term. We examine various reconstruction criteria such as the generalized Kullback-Leibler, mean squared error, and noise to mask ratio. We also explore recently proposed, for optimizing MaD, regularization terms such as sparsity and TwinNetwork regularization. Results from both objective assessment and listening tests suggest that the TwinNetwork regularization results in improved singing voice separation quality.
Joint Optimization of Cache Allocation and Content Placement in Urban Vehicular Networks

Distributing popular contents, e.g., high precision digital maps and latest road conditions, through roadside units (RSUs) is a promising way to provide better driving safety and support for autonomous driving. Successful content download probability can be improved by allocating cache to RSUs and caching popular contents therein. In this paper, we consider a joint cache allocation and content placement problem in vehicular networks to maximize the overall successful content download probability, exploiting the moving information of vehicles. We first prove that the original problem is NP-hard, and then propose a low-complexity approximate algorithm which performs within a bounded gap (a multiplicity factor of 1 −1/ e) to the optimum. The expression of the cache size allocated to each RSU is derived for the proposed algorithm when the content popularity obeys Zipf distribution. Extensive numerical experiments show that, the proposed strategy can significantly increase the successful content download probability as compared to existing solutions.
Efficient Indexing of Hashtags Using Bitmap Indices

The enormous amounts of data being generated regularly means that rapidly accessing relevant data from data stores is just as important as its storage. This study focuses on the use of a distributed bitmap indexing framework to accelerate query execution times in distributed data warehouses. Previous solutions for bitmap indexing at a distributed scale are rigid in their implementation, use a single compression algorithm, and provide their own mechanisms to store, distribute and retrieve the indices. Users are locked to their implementations even when other alternatives for compression and index storage are available or desirable. We provide an open source, lightweight, and flexible distributed bitmap indexing framework, where the mechanisms to search for keywords to index, the bitmap compression algorithm used, and the key-value store used for the indices are easily interchangeable. We demonstrate using Roaring bitmaps for compression, HBase for storing key-values, and adding an updated version of Apache Orc that uses bitmap indices to Apache Hive that although there is some runtime overhead due to index creation, the search of hashtags and their combinations in tweets can be greatly accelerated.
The cost-effectiveness of a steel tube or a buffer zone for mitigating blast effects on a building spanning an underpass with transport of LPG

The use of space is being intensified near and above transport routes of hazardous materials. In The Netherlands, some buildings are even realized above infrastructure with transport of hazardous materials like LPG. An accident with an LPG-tank may result in a BLEVE, causing injuries and large structural damage to the spanning building and the vicinity. Fortunately, such disasters are scarce up to now. However, one should be aware of that such accidents may occur and escalation from accident to disaster should be prevented. On one hand, this paper presents the modelling and the analysis of the explosion effects and the dynamic response of the structural elements. On the other, an analysis of structural measures to control the consequences for the spanning building when the explosion occurs in the underpass is given: two mitigating measures to minimize the effects of a gas explosion or a BLEVE are analyzed, i.e. a steel tube for packing the infrastructure in which the LPG is transported or a buffer zone by adding two extra stories to the building, in which the first two lower floors are designed to be severely damaged under explosive loading forming a buffer zone between the infrastructure and the building above. Subsequently, the cost-effectiveness of these safety measures is determined.
Improved Neural Machine Translation using Side Information

In this work, we investigate whether side information is helpful in neural machine translation (NMT). We study various kinds of side information, including topical information, personal trait, then propose different ways of incorporating them into the existing NMT models. Our experimental results show the benefits of side information in improving the NMT models.
VPad: Virtual Writing Tablet for Laptops Leveraging Acoustic Signals

Human-computer interaction based on touch screens plays an increasing role in our daily lives. Besides smartphones and tablets, laptops are the most popular mobile devices used in both work and leisure. To satisfy requirements of many emerging applications, it becomes desirable to equip both writing and drawing functions directly on laptop screens. In this paper, we design a virtual writing tablet system, VPad, for traditional laptops without touch screens. VPad leverages two speakers and one microphone, which are available in most commodity laptops, for trajectory tracking without additional hardware. It employs acoustic signals to accurately track hand movements and recognize characters user writes in the air. Specifically, VPad emits inaudible acoustic signals from two speakers in a laptop. Then VPad applies Sliding-window Overlap Fourier Transformation technique to find Doppler frequency shift with higher resolution and accuracy in real time. Furthermore, we analyze frequency shifts and energy features of acoustic signals received by the microphone to track the trajectory of hand movements. Finally, we employ a stroke direction sequence model based on possibility estimation to recognize characters users write in the air. Our experimental results show that VPad achieves the average trajectory tracking error of only 1.55cm and the character recognition accuracy of above 90% merely through two speakers and one microphone on a laptop,
Hybrid VLC&#x002F;RF Networks with Non-Orthogonal Multiple Access

Recently, visible light communication (VLC) networks have emerged as a possible alternative for data access, primarily indoors. The very high data rates, low implementation cost and free from radio frequency (RF) interference property make them particularly attractive for the next generation of indoor networking. Furthermore, non-orthogonal multiple access (NOMA) is a very promising candidate technique for the next generation of wireless networks, mainly due to its increased spectrum efficiency, compared to orthogonal access techniques. In this paper, we investigate, for the first time in existing literature, the practical indoor scenario of a hybrid VLC&#x002F;RF network, where both VLC and RF subsystems perform NOMA. More specifically, we study the user grouping through the coalitional game theory, where each coalition is assigned to a specific access point, VLC or RF. Note that due to NOMA's particularities, optimal user grouping is still an open problem of research. Computer simulations illustrate the accuracy of the analysis and reveal the effectiveness of the proposed scheme compared to the standard opportunistic one, as well as its robustness with respect to the number of users.
Query Processing on OpenCL-Based FPGAs: Challenges and Opportunities

Traditionally, FPGAs were programmed using low-level Hardware Description Languages (HDLs) like Verilog or VHDL, which made it extremely difficult to design, build and maintain systems for FPGAs. However, the recent release of OpenCL SDKs by FPGA vendors like Xilinx and Altera have significantly improved the programmability of FPGAs and have brought new research opportunities for query processing systems on FPGAs. It remains an open question whether and how we can optimize OpenCL based database engines for FPGAs. There is a gap on optimizations and tuning between OpenCL and FPGA, since OpenCL is mainly designed for parallel multi-/many-core architectures. In this paper, we attempt to answer this question under the context of pipelined query execution. For this, we first perform a detailed study of database engines on the latest generation of FPGAs. We then design an FPGA based shared pipeline query execution system (FADE) which exploits the hardware features of FPGAs and minimizes inefficiencies like the high communication reconfiguration overhead. Our experiments show that our design achieves significant performance speedup over existing approaches for pipelined query executions on FPGA. Finally, we also present the challenges and opportunities for query processing on the latest generation FPGAs.
Take Your Cell a Walk for Ultra-Low e2eDeiay in Software Defined Vehicular Networks

Recently, to handle end to end mobility and to reduce the OPEX/CAPEX costs of vehicular networks, Handover management is executed with network service chain by virtual Evolved Packet Core (vEPC) thanks to Network Function Virtualization (NFV). However; as the number of Handover requests has increased in an ultra-dense topology with a high number of road side units (RSUs), the SDN/NFV controller has become a bottleneck with an increased response time to a vehicle. In this paper, we investigate the following research question: How to reach few milliseconds end-to-end Delay (e2eDelay) for Ultra-Reliable and Low Latency (URLLC) services in 5G vehicular networks? Therefore, we propose a new SDN/NFV based Handover management without disrupting the centralized manner of SDN controller. To do this, we define a logical and physical coverage area of RSUs with two newly redesigned VNFs named as Smallcell and Macrocell virtual Network Functions (SvNF, MvNF). The former one is for serving vehicle on RSU as a smallcell via physical coverage; whereas the latter one is for forwarding data packets over the same RSU that is a macrocell via logical coverage without any Handover request. E2eDelay is monitored by the proposed queuing theoretic formula in a novel Handover Triggering Algorithm which checks the length of the service chain and determines the optimal time to run Handover network function. According to performance results, the proposed SDN/NFV architecture offers 12 milliseconds reduced e2eDelay, by keeping it under 5G requirements (a few milliseconds) with the service chain up to 7 lengths and keeping the centralized manner of SDN.
APPR: Additive Personalized Point-of-Interest Recommendation

Providing location recommendations has become an essential feature for location-based social networks (LBSNs), as it helps the users to explore new places and makes LBSNs more prevalent to them. Existing studies mostly focus on introducing the new features that affect users' check-in behaviours in LBSNs. However, despite the difference in the type of the features exploited, they mostly follow the same principle - characterizing dependencies between the probability of a user visiting a point-of-interest (POI) and each feature separately. The decision of a user on where to go in an LBSN, however, is driven by multiple features that act simultaneously. On the other hand, applying a full model which considers all the features jointly suffers from overfitting, as for each user there is limited available data. In this paper, we propose an intermediate solution by fragmenting the model into multiple partial models which each takes the subset of the features as the input. The proposed approach focuses on building the personalized partial models (PRMs) which are further combined by applying an additive approach. Experiments on two datasets from Foursquare show that our proposed method outperforms the state-of-the-art approaches in POI recommendation.
Single-Anchor, Multipath-Assisted Indoor Positioning with Aliased Antenna Arrays (Invited Paper)

Highly accurate indoor positioning is still a hard problem due to interference caused by multipath propagation and the resulting high complexity of the infrastructure. We focus on the possibility of exploiting information contained in specular multipath components (SMCs) to increase the positioning accuracy of the system and to reduce the required infrastructure, using a-priori information in form of a floor plan. The system utilizes a single anchor equipped with array antennas and wideband signals to separate the SMCs. We derive a closed form of the Cramér-Rao lower bound for array-based multipath-assisted positioning and examine the beneficial effect of spatial aliasing of antenna arrays on the achievable angular resolution and as a direct consequence onto the positioning accuracy. It is shown that ambiguities, arising due to the aliasing, can be resolved by exploiting the information contained in SMCs. The theoretic results are validated by simulations.