Miracle-dz/UniFAR-FTUs huggingface dataset:
cuBool: Bit-Parallel Boolean Matrix Factorization on CUDA-Enabled Accelerators

Boolean Matrix Factorization (BMF) is a commonly used technique in the field of unsupervised data analytics. The goal is to decompose a ground truth matrix C into a product of two matrices A and $B$ being either an exact or approximate rank k factorization of C. Both exact and approximate factorization are time-consuming tasks due to their combinatorial complexity. In this paper, we introduce a massively parallel implementation of BMF - namely cuBool - in order to significantly speed up factorization of huge Boolean matrices. Our approach is based on alternately adjusting rows and columns of A and B using thousands of lightweight CUDA threads. The massively parallel manipulation of entries enables full usage of all available cores on modern CUDA-enabled GPUs. Additionally, modelling up to 32 consecutive entries of the Boolean matrices A, Band C as 32-bit integer results in fewer data accesses and faster computation of inner products. This bit-parallel approach allows for a significant decrease of memory requirements in contrast to gradient-based continuous updates of entries on dense representations. cuBool is compared to other state-of-the-art matrix factorization algorithms. Experiments on a number of real-world data sets show highly competitive results at only a fraction of computation time. cuBool proves to be a good compromise between low run time and a high-quality factorization for the decomposition of large-scale Boolean matrices. It can freely be accessed under https://github.com/funatiq/cubool.
FBI: Friendship Learning-Based User Identification in Multiple Social Networks

Fast proliferation of mobile devices significantly promotes the development of mobile social networks. Users tend to interact with friends via multiple social networks. Multiple social networks identification is of great significance in terms of both attack and defense. Current methods either focus on the profile matching or network structure to re-identify a specific user. However, the accuracy are not satisfying with relative high error rate. In this paper, we propose a new Friendship learning-Based Identification (FBI) method to discriminate multiple pseudo identities of a real-world individual. We aim at providing potential attack mechanism to following privacy protection research. Firstly, we develop a new identification method based on friendship matching. Then, we implement a weighted mechanism which takes profile, network structure, and friendship into consideration. Furthermore, machine learning is leverage to further optimize the parameters and improve the accuracy. In addition, extensive experimental results show the superior of the FBI comparing to existing ones.
Reinforcement Learning-Based Interference Control for Ultra-Dense Small Cells

The densification deployment of small cells emerging into 5G cellular networks can achieve high capacity, but is faced with the challenge of how to manage energy consumption and inter-cell interference well in time-varying channels. In this paper, we propose a reinforcement learning based downlink power control algorithm to manage interference for the ultra-dense small cell networks. More specifically, base stations of the small cells use Q-learning to select the downlink transmit powers. A transfer learning method called hotbooting is applied to further accelerate the learning speed and save the energy consumption based on the estimated user density without being aware of the network and channel model of the other small cells. Simulation results demonstrate this scheme significantly improves the network throughput and saves the energy consumption compared with the benchmark, a data-driven based transmission power adaptation scheme.
Flexible Design of Millimeter-Wave Cache Enabled Fog Networks

Ultra-densification, millimeter wave (mmW) communications, and proactive network-edge caching, utilized within mmW fog networks (mmFNs), are foreseen to provide tangible gains for broadband access, network capacity, and latency. However, caching implementation in mmFN imposes high capital expenditure (CAPEX) due to the ultra-high density of base stations (BSs). For a given caching CAPEX, it may be more efficient to install higher capacity caches in a fraction of the BSs than installing smaller capacity caches in every BSs. In the former case, wireless self-backhauling of mmW systems can be exploited to share the cache contents stored in a given cache enabled BSs (CE-BSs) with other BSs in the network. In this regards, this paper develops a mathematical model, based on stochastic geometry, to study the tradeoff between the cache size and intensity of CE-BSs on the probability that requested popular contents are retrieved from the network edge, denoted as the hit probability. Assuming a power-law inverse relationship between the cache size and intensity of CE-BSs, an optimization problem is formulated and solved for the intensity of CE-BSs and probabilistic file placement in caches such that the hit probability is maximized. The results show that neither installing small caches in every BS nor having sufficiently high capacity caches (i.e., that confine all popular files) installed in small number of BSs exploit the full potential of mmFN. Instead, there exists an optimal balance between the cache size and intensity of CE-BSs, which depends on the network parameters such as the applied caching strategy, required rate, total intensity of BSs, popular content distribution, and cache size/intensity relationship.
Supporting k-Nearest Service Discoveries for Large-Scale Edge Computing Environments

This paper presents an overlay network protocol called “LASK: Locality-Aware Service discovery protocol for K-nearest search”, for supporting scalable and locality-aware distributed k-Nearest Service Discovery (kNSD). The kNSD provides a lookup function to find $k$ service nodes located close to the requester. Importance of such function increases under the situation of growth in size and the diversity of the resources in the so-called edge computing environments. In LASK, efficient cooperative routing algorithm is implemented utilizing the two-dimensional structure of key-order preserving structured overlay networks that are constructed according to the network topology and the name of the service. This structure achieves scalable and locality-aware routing of the lookup messages to the matched nodes avoiding redundant data transmissions across the edge networks. Extensive simulation evaluations show that LASK could achieve small discovery latency in the large-scale edge computing environments; a typical kNSD took less than 5 ms in a data center network model and less than 100 ms in the Internet model even when there are 100,000 nodes in the entire network.
H-Scheduler: Storage-Aware Task Scheduling for Heterogeneous-Storage Spark Clusters

A trend in nowadays data centers is that heterogeneous storage devices are deployed to meet different storage demands of various big data workloads. For example, many nodes are equipped with both SSDs and HDDs. And HDFS has introduced the heterogeneous-storage-aware feature to adapt to such hybrid storage clusters. However, current task scheduler on big data processing platforms (such as Hadoop and Spark) only considers the overhead of network data transmission by exploiting the data locality principle. On heterogeneous storage clusters, task completion time is also affected by the speed of storage devices (SSDs and HDDs) where the data are stored. Ignoring the different speed of storage devices results in poor utilization of high speed devices such as SSD. In this paper, we propose a task scheduling strategy for heterogeneous storage clusters called H-Scheduler. The key idea of H-Scheduler is to differentiate speeds of storage devices by storage types. It classifies the tasks by both data locality and storage types, and redefines the priorities of different classes of tasks by both storage device speed and data locality to reduce job execution time. We implemented H-Scheduler in Spark, and the experiment results show that H-Scheduler can reduce job execution time by up to 73.6 %, depending on the workload characteristics and data distribution among different types of storage devices.
Revisiting the Linear Prediction Analysis-by-Synthesis Speech Coding Paradigm Using Real-Time Convex Optimization

In this work, we propose a novel approach to speech coding by rewriting the nonlinear analysis-by-synthesis linear prediction scheme as a convex problem. This allows for determining trade-offs between, on one hand, the reconstruction error and, on the other, the sparsity of the predictor and the residual used to parametrize the speech signal. Differently from traditional coding schemes where the parameters are chosen throughout multiple optimization stages, our scheme produces a one-shot parametrization of a speech segment that intrinsically takes into consideration the voiced or unvoiced nature of a speech segment providing a better balance between residual and predictor and, consequently, a more appropriate bit allocation.
Context-Aware Decoupled Multiple Association in Ultra-Dense Networks

The new trends in network denisification, heterogeneity, and the introduction of new techniques (e.g., full-duplex) introduce new challenges for user association. For most existing user association schemes, the uplink (UL) and downlink (DL) access are coupled. That is, a user equipment (UE) is associated with the same BS for UL and DL transmissions. However, in ultra-dense heterogeneous networks (UDNs), due to the large disparities among base stations in different tiers and among uplink and downlink, the coupled UL-DL user association will limit the system performance. In this paper, we propose a novel concept of decoupled multiple association for user association in UDNs, which allows a UE to be associated with multiple base stations (BSs) for UL and DL in a decoupled manner. Furthermore, the context information of UEs is considered when making association decisions. Specifically, a decoupled multiple association matching game is formulated and a context-aware swap matching algorithm is proposed. The proposed scheme could attain higher data rates and could satisfy the quality of service (QoS) requirements of different UEs. Additionally, it could overcome the back-haul limitation of individual BSs. We compare the proposed scheme with three other association schemes, and the simulation results show significant performance gains of our proposed scheme in UDNs.
Explorations of Data Swapping on Burst Buffer

Burst buffers have been widely deployed in many supercomputers to absorb bursty I/O and accelerate I/O performance. Previous work has shown that with burst buffer systems, I/O operations from computer nodes can be greatly accelerated. While the lack of data swapping supports on burst buffer leads to under-utilization and application failure issues. In addition, the effects of data replacement algorithms on application performance and the suitability of each algorithm for the target application are unclear. In this paper, we address these challenges by simulating data swapping on burst buffers with different data replacement strategies. Trace logs from a set of real-world HPC applications are used with different data replacement algorithms to show the behavior of representative HPC applications. From the results, we found that most HPC applications can still achieve full performance when using a buffer size that is far less than the total access space of the application, which can lead to a huge reduction on the required capacity for burst buffer. Moreover, we found that data replacement algorithms can have significant impact on the application performance. Our finding show the importance of having data swapping in reducing the required capacity and guide the future design of the usage of burst buffer.
Mobility-Driven BLE Transmit-Power Adaptation for Participatory Data Muling

This paper analyzes a human-centric framework, called SmartABLE, for easy retrieval of the sensor values from pervasively deployed smart objects in a campus-like environment. In this framework, smartphones carried by campus occupants act as data mules, opportunistically retrieving data from nearby BLE (Bluetooth Low Energy) equipped smart object sensors and relaying them to a backend repository. We focus specifically on dynamically varying the transmission power of the deployed BLE beacons, so as to extend their operational lifetime without sacrificing the frequency of sensor data retrieval. We propose a memetic algorithm-based power adaptation strategy that can handle deployments of thousands of beacons and tackles two distinct objectives: (1) maximizing BLE beacon lifetime, and (2) reducing the BLE scanning energy of the mules. Using real-world movement traces on the Singapore Management University campus, we show that the benefit of such mule movement-aware power adaptation: it provides reliably frequent retrieval of BLE sensor data, while achieving a significant (5-fold) increase in the sensor lifetime, compared to a traditional fixed-power approach.
LBT Switching Procedures for New Radio-Based Access to Unlicensed Spectrum

Two channel access procedures are being considered for New Radio-based access to unlicensed spectrum (NR-U) in millimeter-wave (mmWave) bands: omnidirectional Listen-Before-Talk (omniLBT) and directional LBT (dirLBT), which differ on the type of physical carrier sense. Under beam-based transmissions, omniLBT is overprotective and reduces spatial reuse due to exposed node situations, while dirLBT enables spatial reuse, but may create hidden node problems. To address the omniLBT-dirLBT trade-off, this paper proposes LBT switching procedures to dynamically adapt the type of physical carrier sense for LBT in NR-U devices. Switching from dirLBT to omniLBT can be implemented by exploiting already available control messages in NR. Differently, to switch from omniLBT to dirLBT, no information in NR specifications is still available at the time of writing. We define new procedures to measure the overprotection level of spatial reuse provided by omniLBT, and send the switching indication accordingly. Results demonstrate the effectiveness of LBT switching, which enables an independent and dynamic adaptation according to the density and activity of neighboring nodes.
Cloud Distributed File Systems: A Benchmark of HDFS, Ceph, GlusterFS, and XtremeFS

Cloud computing nowadays is the cornerstone for all the business applications, mainly because of its high fault tolerance characteristic. High resilience and availability typical of cloud-native applications are achieved using different technologies. Regarding the file system, the main fault tolerant application examples are distributed file systems, such as HDFS, Ceph, GlusterFS, and XtremeFS. These file systems have different architectures and deployment models than the Traditional Distributed File Systems (TDFSs), such as NFS. The primary goal of this work is to analyze and compare different Cloud Distributed File Systems (CDFSs) in terms of characteristics, architecture, reliability, and components. As a key feature, the paper benchmarks them considering as use case an IaaS platform.
5G mm Wave Downlink Vehicular Positioning

5G new radio (NR) provides new opportunities for accurate positioning from a single reference station: large bandwidth combined with multiple antennas, at both the base station and user sides, allows for unparalleled angle and delay resolution. Nevertheless, positioning quality is affected by multipath and clock biases. We study, in terms of performance bounds and algorithms, the ability to localize a vehicle in the presence of multipath and unknown user clock bias. We find that when a sufficient number of paths is present, a vehicle can still be localized thanks to redundancy in the geometric constraints. Moreover, the 5G NR signals enable a vehicle to build up a map of the environment.
Understanding Base Stations' Behaviors and Activities with Big Data Analysis

This paper uses big data technologies to study base stations' behaviors and activities and their predictability in mobile cellular networks. With new technologies quickly appearing, current cellular networks have become more larger, more heterogeneous, and more complex. This provides network managements and designs with larger challenges. How to use network big data to capture cellular network behavior and activity patterns and perform accurate predictions is recently one of main problems. To the end, firstly we exploit big data platform and technologies to analyze cellular network big data, i.e. Call Detail Records (CDRs). Our CDRs data set, which includes more than 1000 cellular towers, more than million lines of CDRs, and several million users and sustains for more than 100 days, is collected from a national cellular network. Secondly, we propose our methodology to analyze these big data. The data pre-handling and cleaning approach is proposed to obtain the valuable big data sets for our further studies. The feature extraction and call predictability methods are presented to capture base stations' behaviors and dissect their predictability. Thirdly, based on our method, we perform the detailed activity pattern analysis, including call distributions, cross correlation features, call behavior patterns, and daily activities. The detailed analysis approaches are also proposed to dig out base stations' activities. A series of findings are found and observed in the analysis process. Finally, a study case is proposed to validate the predictability of base stations' behaviors and activities. Our studies demonstrates that big data technologies can indeed be utilized to effectively capture network behaviors and predict network activities so that they can help perform highly effective network managements.
AQ-DP: A New Differential Privacy Scheme Based on Quasi-Identifier Classifying in Big Data

The rapid development of big data has brought great convenience to human's lives. The circulation and sharing of information are two main characteristics of the big data era. However, the risk of privacy leakage is also greatly increased when we enjoy the various services of big data. Therefore, how to protect the data privacy in the complex context of big data has become a research hotspot in academic circles. Most of the current researches on privacy protection are divided into two research fields: k-anonymity and differential privacy. Some existing research shows that traditional methods of privacy protection, such as k-anonymity and its extension, cannot achieve absolutely security. The emergence of differential privacy provides a new solution for privacy protection. We draw the lessons from exiting work and propose a new privacy method based on differential privacy: AQ-DP. We propose the first method for classifying quasi-identifiers based on sensitive attributes, which divide quasi-identifiers into associated quasi-identifiers (AQI) and non-associated quasi-identifiers (NAQI). The purpose is not to lose the correlation between quasi-identifiers and sensitive attributes. Our model AQ-DP carries out random shuffling of NAQls. , generalizes the AQIs. , and adds random noise that satisfies the laplacian distribution to the statistics. We have conducted extensive experiments, confirming that our model can achieve a satisfying privacy level and data utility.
DACSC: Dynamic and Fine-Grained Access Control for Secure Data Collaboration in Cloud Computing

Data collaboration is more and more popular in cloud computing. In a typical collaboration scenario, data owner outsources the data to cloud platforms, and users can access and re-upload the data. In consideration of the semi-trusted cloud platform, attribute-based encryption (ABE) has been utilized to guarantee data confidentiality and fine-grained access control. However, how to allow the collaborative data to be accessed only by authorized users in a flexible and dynamic manner is a challenging problem. In this paper, we propose DACSC, a dynamic and fine-grained access control scheme for secure data collaboration in cloud computing. First of all, we adopt ciphertext-policy ABE technique to define the original access policy of outsourced data. Second, we introduce a tree-based policy extending framework which allows users who satisfy the original access policy to customize a new access policy and add it to current access policies in a non-restrictive or restrictive way. Furthermore, we achieve integrity checking during the policy extending procedure based on ABE with equality test algorithm, which ensures that the added access policy comes from authorized user. The security analysis and experimental results indicate that DACSC is secure and efficient, and is suitable for the data collaboration scenario in cloud computing.
Extremely Large Aperture Massive MIMO: Low Complexity Receiver Architectures

This paper focuses on new communication paradigms arising in massive multiple-input-multiple-output systems where the antenna array at the base station is of extremely large dimension (xMaMIMO). Due to the extreme dimension of the array, xMaMIMO is characterized by spatial non-stationary field properties along the array; this calls for a multi-antenna transceiver design that is adapted to the array dimension but also its non-stationary properties. We address implementation aspects of xMaMIMO, with computational efficiency as our primary objective. To reduce the computational burden of centralized schemes, we distribute the processing into smaller, disjoint sub-arrays. Then, we consider several low-complexity data detection algorithms as candidates for uplink communication in crowded xMaMIMO systems. Drawing inspiration from coded random access, one of the main contributions of the paper is the design of low complexity scheme that exploits the non-stationary nature of xMaMIMO systems and where the data processing is decentralized. We evaluate the bit-error-rate performance of the transceivers in crowded xMaMIMO scenarios. The results confirm their practical potential.
Modeling of MTC Energy Consumption for D2D Communications with Chase Combining HARQ Scheme

Device-to-Device (D2D) communication for Massive Machine-Type Communication (mMTC) applications is a promising approach to reduce the energy consumption of the battery-limited Machine-Type Devices (MTDs) located in poor coverage areas. In this paper, we analyze the energy consumption of the MTDs when Automatic Repeat reQuest (ARQ) and Hybrid ARQ with Chase Combining (CC-HARQ) mechanisms are used to improve the reliability of D2D communications. By using the tools of stochastic geometry, we derive analytical expressions for the global success probability, the average number of transmissions, and the average MTD energy consumption. Numerical results show that CC-HARQ outperforms ARQ in terms of energy consumption, especially for low relay density when the distance between an MTD and its relay increases or when the density of MTDs sharing the same sub-channel increases.