Miracle-dz/UniFAR-FTUs huggingface dataset:
The Design and Implementation of Multi-Cloud Based Distributed Storage Platform with Random Linear Coding

Nowadays, distributed storage systems have been widely used in the world, e.g, Google Drive, OneDrive, DropBox, Baidu PCS, etc. Although these cloud storage services significantly facilitate the file storage for users, the performance of them varies widely when using in different countries. For example, Google Drive has high upload/download speed in America while it has very low upload/download speed in China. Therefore, to enhance the reliability and security of the cloud storage, in this paper, we firstly design a multi-cloud based distributed storage platform, named as, Normal Drive, by utilizing random linear coding and APIs provided by multiple cloud disks. We then implement the Normal Drive and conduct extensive evaluation from different aspects in five different locations around the world to test the performance of it. The experimental results show that the proposed Normal Drive can significantly improve the transmission performance and enhance the reliability of file storage when comparing with single-cloud based distributed storage system.
Efficient Task Scheduling for Applications on Clouds

Task scheduling is a central issue in the realm of parallel processing, and recently in cloud computing. The directed acyclic graph (DAG) is a well-known technique used to represent scheduling of computational tasks. Many researchers have studied task scheduling under various constraints and proposed alternative solutions. The purpose of this research is to edify task scheduling on the cloud using a recent bio-inspired optimization technique, the Grasshopper Optimization Algorithm (GOA). The proposed method is compared with the state-of-the art techniques in this area, where a reduction of 10% in the makespan is obtained.
Polarization-Insensitive Coherent Detection for Continuous, Low-Noise Tracking of Optical Beatnotes

We describe a novel approach to coherent detection which makes use of a dual polarization coherent receiver. The proposed technique, adapted from telecommunication technology, allows the analysis of the polarization state of an optical signal in addition to its phase. We demonstrate that, with proper digital signal processing, the optical phase between two lasers can be always extracted from a beatnote, regardless the polarization state of the two. In addition, birefringence-induced phase noise can be rejected from the measurement. Such capabilities cannot be achieved with traditional detection based on a single photodiode. This technique is particularly useful in frequency dissemination over fiber, where the polarization of received light changes randomly and on timescales that can be as fast as milliseconds. The proposed approach allows continuous tracking of the fiber-delivered signal as required in many scientific applications, and reduces the phase noise at detection.
Optimized FPGA Implementation of PWAM-Based Control of Three—Phase Nine—Level Quasi Impedance Source Inverter

Inherent buck–boost capability, reduced component count, controlled power injection and multilevel operation are some of the advantages which makes cascaded qZSI popular for integrating the generated solar energy with the utility grid. Phase–Shifted Carrier PWM (PSCPWM) and Pulse Width Amplitude Modulation (PWAM) are the most popular techniques for achieving multilevel qZSI operation. Generally, closed loop control implementation of three – phase qZSI system consists of large number of slave controllers (placed locally for voltage control) and one centralized master controller (for grid integration or load current control). Since the aim is to control single system with this highly distributed control structure, issues of clock pulse and interrupt signal synchronization, hardware and software redundancy are common in these implementations. This limits the utilization factor and step size of these control boards. To address these issues, either more optimized solutions must be suggested, or distribution of control structure must be reduced. In this paper, closed loop control of nine – level three – phase qZSI system is implemented using single FPGA control board thereby eliminating above said problems. Since, PWAM control algorithm is more complex than PSCPWM, FPGA based implementation for PWAM control is discussed. Critical implementation processes consisting of DAC – ADC interfacing, FPGA code per unitization, PI Controller realization and different clock pulse utilization are presented. For highlighting and comparing the resource consumption, PWAM and PSCPWM modulation are compared in terms of device utilization. Transient analysis and control algorithm are presented and validated during both starting and load transient conditions by means of simulation results. Finally, hardware results of these modulation methods are discussed and analyzed.
Generating Multiple OAM Based on a Nested Dual-Arm Spiral Antenna

In this paper, a nested dual-arm spiral antenna (NDASA) is proposed to generate multiple orbital angular momentum (OAM) modes at the same operating frequency. The NDASA consists of the feeding network and three nested dual-arm spiral structures with different sizes. Meanwhile, a metal reflector is located between the spiral structures and the feeding network to enhance the radiation directivity, which can also reduce the impact of the radio frequency (RF) coaxial cables. Both the experimental and simulated results show OAM waves with topological charges of <inline-formula> <tex-math notation="LaTeX">$l = 1$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$l = 3$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$l = 5$ </tex-math></inline-formula> can be independently generated at <inline-formula> <tex-math notation="LaTeX">$f = 3$ </tex-math></inline-formula> GHz, and their maximum gains are 6.94dBi, 6.76dBi and 5.49dBi, respectively. In addition, the OAM modes multiplexing and demultiplexing technology are investigated in simulations and experiments. The isolations between the OAM modes of <inline-formula> <tex-math notation="LaTeX">$l = 1$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$l = 3$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$l = 5$ </tex-math></inline-formula> and the other different OAM modes are greater than 13.8dB, 13.2dB and 25.1dB, respectively, which proves the potential ability of the proposed NDASA for multiplexing and demultiplexing multiple OAM modes.
Similarity-Based Chained Transfer Learning for Energy Forecasting With Big Data

Smart meter popularity has resulted in the ability to collect big energy data and has created opportunities for large-scale energy forecasting. Machine Learning (ML) techniques commonly used for forecasting, such as neural networks, involve computationally intensive training typically with data from a single building or a single aggregated load to predict future consumption for that same building or aggregated load. With hundreds of thousands of meters, it becomes impractical or even infeasible to individually train a model for each meter. Consequently, this paper proposes Similarity-Based Chained Transfer Learning (SBCTL), an approach for building neural network-based models for many meters by taking advantage of already trained models through transfer learning. The first model is trained in a traditional way whereas all other models transfer knowledge from the existing models in a chain-like manner according to similarities between energy consumption profiles. A Recurrent Neural Network (RNN) was used as the base forecasting model, two initialization techniques were considered, and different similarity measures were explored. The experiments show that SBCTL achieves accuracy comparable to traditional ML training while taking only a fraction of time.
Generation of Bengali Voice Notifications Using Brain Machine Interface for Motor Disabled Patients

Brain Machine Interface (BMI) is a process where a communication link is established between a user and a device in which brain signals of the user is collected, processed and analyzed to recognize certain intention of the user. BMI helps patients to interact with others who are unable to communicate with the outside world due to motor disorder. In this paper, we have proposed a communication system that involves BMI where real-time brain signals are collected with the electrodes of EEG device placed at different lobes of the brain. Then the signals are transferred via Bluetooth to an Android smartphone. We have designed an app called “Chinta” which acts as an interface between the user and smartphone. The mobile app is trained for a user through performing different brain activities. A Boolean classifier has been used in this app, and it allows the smartphone to interpret and understand users intention. When the concentration level of an individual lies between 65% to 70%, the mobile app considers the user’s intention as true and responds accordingly. The output is in the form of voice in Bengali Language, and SMS is sent to designated persons whose contacts are configured in the app. We found that at least 14-channel electrodes are generally used in most of the BMI researches, however, in this research we have gathered the brain signals using a 5–channel EEG headset known as Emotiv Insight. Thus this research has demonstrated that with the help of 5– channel EEG headset, such communication system can also be established, which is economic and user–friendly.
Transmit Antenna Selection in Massive MIMO Systems: An Online Learning Framework

Antenna selection (AS) is a signal processing technique that activates a selected subset of available antennas in multi-antenna systems, based on which a performance-hardware tradeoff can be achieved by reducing the number of costly radio-frequency (RF) chains. The biggest challenge of AS is the combinatorial complexity that arises from the classic K-out-of-N problem, which makes it more challenging for massive MIMO systems equipped with large-scale antenna arrays. In addition, for massive MIMO systems with limited RF chains, the amount of radio resources dedicated to channel state information (CSI) measurement will increase tremendously, which may highly degrade the overall performance of AS. In this paper, we consider the transmit AS problem in time division duplexing (TDD) massive MIMO systems, where K out of N transmit antennas are selected to maximize the total throughput of M single-antenna users in the downlink. We propose an online learning scheme and introduce Thompson sampling techniques to update the set of active antennas with partial CSI. The idea behind is to find an efficient tradeoff between the exploitation of high-performance antennas and the exploration of antennas with uncertain CSI with low complexity. Our proposed scheme is validated by using COST 2100 channel model, and simulation results show that it greatly outperforms the conventional power-based and convex relaxation based schemes, in terms of the total downlink throughput.
Center-Less Single Sign-On With Privacy-Preserving Remote Biometric-Based ID-MAKA Scheme for Mobile Cloud Computing Services

Identity-based mutual authentication and key agreement (ID-MAKA) between a mobile user and cloud service provider is necessary for accessing any cloud services. In recent years, there are a large number of ID-MAKA schemes had been proposed on mobile cloud computing services to make authentication and accessing process more usability, security, and scalability. In this paper, we propose a new ID-MAKA scheme for mobile cloud computing, which firstly achieves remote biometric-based authentication(remote servers authenticate user’s biometrics), single sign-on (a single credential and single registration for accessing multiple servers) and center-less authentication(the registration center does not participate in the access procedure) in one scheme. In order to realize this target, we design a ZK-token based on ECC and cryptographic hash function, and then artfully employ it to introduce the fuzzy extractor technology and zero-knowledge technology into our scheme. Therefore, the user can access multiple cloud computing servers by registering only once in the registration center, and cloud computing servers can complete the biometric-based remote authentication and key agreement for the user without the registration center participating. In this way, our scheme greatly improves usability, scalability, and security compared to other existing solutions. We give a formal security proof for our scheme by using Real-Or-Random(RoR) model and Burrows-Abadi-Needham (BAN) logic to show that the present scheme is secure and security analysis for other known attacks. Finally, according to the experiment result, our scheme has lower computation and communication cost compared with most existing related schemes.
Unmanned Aerial Vehicles (UAVs) as on-demand QoS enabler for Multimedia Applications in Smart Cities

The evolution of drones and similar small wingspan UAVs has resulted in their use in many commercial applications. This has allowed investigating the potential use of drones in the context of Internet of Things. In the recent past, there is ample evidence indicating the use of UAVs as a means to supplement mobile infrastructure to extend it for surveillance, monitoring, data collection and providing on-demand network access capabilities. This paper explores the potential of UAVs to act as on-demand QoS enablers for TCP-based applications within Smart Cities, particularly those applications that require low connection delays, reliability and high throughputs such as multimedia streaming.Many multimedia rich applications, such as live streaming, multi-player online gaming are mostly tied down to fixed-line broadband infrastructure. Mobile cloud technologies and Mobile Edge Computing (MEC) address the challenge by bringing the computing, storage and networking resources to the edge and integrating with the base station, thereby providing better content delivery. The paper presents a concept of UAV-based aerial MEC, which hosts a TCP-proxy that acts as an ‘On-Demand QoS’ enabler to TCP-based applications in Smart Cities reducing the overall-connection delays and increasing the throughput thereby enhancing the end-user experience. With the technologies available in literature we demonstrate that a UAV-based aerial MEC with the capability to migrate QoS-enabling processes from the edge to the core and edge to the edge, to support mobile applications, is feasible.
A 3D Smooth Random Walk Mobility Model for FANETs

The number of Unmanned Aerial Vehicles (UAVs) applications has increased over the past few years. Among all scenarios, UAV group consisting multi-UAVs is normally used to provide extensible communications. As a networking solution, Flying Ad Hoc Networks (FANETs) routing with ideal routing performance is the prerequisite of the multi-UAV application. Regarding the high construction cost of devices for FANETs, it is infeasible to build the real experimental environment, hundreds of UAVs are needed. In this case, network simulation is the most common mean to study FANETs in most cases. For FANETs, the mobility of UAV nodes has an important impact on the simulation results. Thus, a mobility model which can fit into specific environments well is necessary. Traditional mobility models of FANETs are mainly designed for planar scenarios without considering the actual application of FANETs which is three-dimensional (3D). Therefore, in this paper, firstly, the characteristics of UAVs is analyzed and then the key points of 3D mobility model for FANETs are presented. In this context, we propose a 3D Smooth Random Walk (3DSRW) mobility model, which intends to mimic the mobility of UAVs to the greatest extent. Then, we conduct simulations to get the network performance of AODV to verify the performance gap between 2D and 3D environment, in order to demonstrate the validity of our proposal. To further present the usage of our proposal, we show the performance of four routing protocols, including AODV, DSDV, OLSR and GPSR under the 3DSRW-constucted 3D environment, and analyzed their applicability in 3D environment.
Task Scheduling in Mobile Edge Computing with Stochastic Requests and M/M/1 Servers

A multi-user mobile edge computing system with stochastic requests and M/M/1 queuing based servers is proposed in this paper. The problem of minimizing the total response time of all tasks is formulated, which is proved to be NP-complete. A greedy algorithm is proposed to solve the mentioned optimization problem, which prefers to assign task to the server with minimum response time for the task. Simulation results show that, the average response time of tasks in the proposed greedy algorithm is saved by 20%-30%, in comparison to the proposed random algorithm. Meanwhile, the average response time of tasks in customized tabu search is decreased by 8.5%, compared to the greedy algorithm.
Anybody (at) home? Communicative efficiency knocking on the Construction Grammar door

Abstract The present study focuses on the locative adverbials home and at home, which are interchangeable in some contexts, e.g. She decided to stay (at) home today. Using data from the spoken component of COCA and different multivariate statistical techniques, such as conditional inference trees and dichotomous logistic regression, I investigate the differences between home and at home with regard to several contextual variables, such as the syntactic function of (at) home, the presence of particular adverbs, e.g. back (at) home, figurativeness of semantics and the presence of presupposed arrival in the context. Moreover, special attention is paid to the variables that represent predictability of Verb + (at) home given a verbal predicate and the other way round, as well as linguistic distance between the predicate and the locative adjunct. The effects of these variables are interpreted as a manifestation of the universal tendency to maximize communicative efficiency and minimize cognitive complexity. I also argue that these effects represent an important social aspect of language use that should be taken into account by contemporary Cognitive Linguistics and Construction Grammar.
Fast Convergence Average Consensus-Based Time Synchronization with Innovative Implementation

The robust average consistency theory is ideal for building time synchronization in the dynamic and distributed networks, however it remains the major challenge due to the iteration. Existing consensus-based approaches converge slowly, although they are reliable even in dynamic networks. It is thus unreasonable to implement these algorithms in a large-scale wireless network. Motivated by this consideration, we note that the graph theory-based theory suggests that the greater the algebraic connectivity, the faster the convergence, thereby leading to the proposal of multi-hop average consensus time synchronization (MACTS) with innovative implementation in consideration of the features of wireless sensor network. To accelerate the synchronization convergence without changing the network topology, we present a packet fast forwarding model to generate virtual communication links among multi-hop nodes, meanwhile, an attenuation methods for the forwarding model is designed to balance the accuracy and cost of synchronization. We compensate relative clock offset estimates by transmitting delay estimates to achieve accurate synchronization. Moreover, based on the popular one-way broadcast model, the proposed MACTS is easy to implement. The experimental results indicate that MACTS is more accurate and converges much faster than the typical average consistency synchronization algorithm.
A 3D-Printed Compact High-Efficiency Magneto-Electric Dipole Antenna Sub-Array for Millimeter-Wave Multi-Beam Applications

A novel three-dimensional (3D) printed $2\times 2$ multi-beam magneto-electric (ME) dipole antenna subarray with a metallic configuration is investigated in the Ka-band. A modified topology of the beamforming network consisting of four air-filled 3-dB couplers is proposed, whose miniaturized size in the horizontal plane is equal to the radiating aperture of the subarray. By combining the beamforming network with the 3D printed waveguide-fed ME-dipoles, the two-dimensional multi-beam subarray is designed, printed and tested. A wide impedance bandwidth of 20%, four stable tilted radiation beams with almost identical radiation patterns in the two orthogonal planes, high radiation efficiency of 87% and high gain performance are achieved successfully. The millimeter-wave subarray-level beam-scanning array with a large size can be implemented based on the presented design. With the compact geometry fabricated in a whole piece and the good operating characteristics, this work is valuable to the emerging millimeter-wave multiple-input multiple-output (MIMO) applications.
Design and Implementation of GPU Accelerated Active Storage in FastDFS

With the rapid increase of the data, the network bandwidth of the distributed storage system should be full utilized. Existing distributed storage models have higher network delay which leads to less efficiency in data-intensive services. Therefore, we propose a GPU-accelerated Active Storage (GAS) model for distributed file systems and implement it on FastDFS. GAS decreases the data transfer by executing the data clustering and filtering operations on storage nodes, and promotes the computing capacity of the storage server by calling GPU. For ensuring the active storage service commands be transmitted correctly, a communication protocol, called active storage communication protocol (ASCP) is designed. The experimental results show that, compared with traditional storage (TS) and active storage (AS), GAS reduces the execution time of SUM by up to 73.9% and 2.7%, lower the run time of AES by up to 91.9% and 70.3%, and shortens the time consumed of Mean-filter to 59.5% and 59.0% respectively.
3D Structure Determination and Validation of mTORC2 using Computational Modeling Techniques

As the mammalian target of rapamycin (mTOR) regulated pathway has been found most commonly activated and deregulated in cancer, so there is a need to understand the underlying mechanism of its key interacting proteins. Structural insights of proteins so far undiscovered will unravel these phenomenons. 3D structure of mTORC2 is built using several ab - initio protein structure prediction tools and homology modelling algorithm of Modeller. The quality and validation of the obtained models were accessed using Errat, PROSA and Qmean softwares while the Ramachandran plot was used to access the overall stereochemical properties of the proteins. The individual protein subunits were finally docked using HADDOCK webserver. This approach will aid in understanding the function of individual proteins and a step forward towards drug designing against cancer.