Miracle-dz/UniFAR-FTUs huggingface dataset:
Redio: Accelerating Disk-Based Graph Processing by Reducing Disk I/Os

Disk-based graph systems store part or all of graph data on external devices like hard drives or SSDs, achieving scalability without excessive hardware. However, massive expensive disk I/Os remain the major performance bottleneck of disk-based graph processing. In this paper, we propose Redio, a new approach to accelerating disk-based graph processing by reducing disk I/Os. First, Redio observes that it is feasible to accommodate all vertex states in main memory and this can eliminate almost all vertex-related disk I/Os. Second, Redio introduces a dynamic selective scheduling scheme to identify inactive edges in each iteration and skip them when and only when such skipping can bring performance benefit. To improve its effectiveness, Redioin corporates a compact edge storage to improve data locality and an indexed bitmap to minimize its memory and computation overheads. We have implemented a single-node prototype for Redio under the edge-centric computation model. Extensive experiments show that Redio consistently outperforms well-known edge-centric disk-based systems in all experiments, delivering an average speedup of <inline-formula><tex-math notation="LaTeX">$4.33\times$</tex-math><alternatives><inline-graphic xlink:href="zhang-ieq1-2875458.gif"/></alternatives></inline-formula> on HDDs and <inline-formula><tex-math notation="LaTeX">$5.33\times$</tex-math><alternatives><inline-graphic xlink:href="zhang-ieq2-2875458.gif"/></alternatives></inline-formula> on SSDs over the fastest among them (i.e., GridGraph). Experimental results also show that Redio delivers an average speedup of <inline-formula><tex-math notation="LaTeX">$3.13\times$</tex-math><alternatives><inline-graphic xlink:href="zhang-ieq3-2875458.gif"/></alternatives></inline-formula> on HDDs and <inline-formula><tex-math notation="LaTeX">$1.28\times$</tex-math><alternatives><inline-graphic xlink:href="zhang-ieq4-2875458.gif"/></alternatives></inline-formula> on SSDs over the fastest among representative vertex-centric disk-based systems (i.e., FlashGraph).
Low Complexity Cooperative Positioning in Multipath Environment

In this paper, we propose a cooperative positioning technique based on time-of-arrival (TOA), angle-of-arrival (AOA), angle-of-departure (AOD), and received-signal-strength (RSS) collected from user equipment (UE) in single-bounce multipath environment, named as CPTAAR, to mitigate non-line-of-sight (NLOS) error due to single-bounce scattering. This technique can be further improved by a proposed weight function of variance of measurements. Then, a grouping strategy is integrated with the proposed work to reduce the running time of estimation progress, named as eCPTAAR. The system performance is verified by simulations and Cramer-Rao Lower Bound (CRLB). It is shown that the proposed techniques can outperform other approaches in terms of positioning accuracy and running time.
Brain-computer interface games based on consumer-grade electroencephalography devices: systematic review and controlled experiments

Brain-computer interfaces (BCIs) are specialized systems that allow users to control a computer or a machine using their brain waves. BCI systems allow patients with severe physical impairments, such as those su ering from amyotrophic lateral sclerosis, cerebral palsy and locked-in syndrome, to communicate and regain physical movements with the help of specialized equipment. With the development of BCI technology in the second half of the 20th century and the advent of consumer-grade BCI devices in the late 2000s, brain-controlled systems started to nd applications not only in the medical eld, but also in entertainment. One particular area that is gaining more evidence due to the arrival of consumer-grade devices is the eld of computer games, which has become increasingly popular in BCI research as it allows for more user-friendly applications of BCI technology in both healthy and unhealthy users. However, numerous challenges are yet to be overcome in order to advance in this eld, as the origins and mechanics of the brain waves and how they are a ected by external stimuli are not yet fully understood. In this sense, a systematic literature review of BCI games based on consumer-grade technology was performed with over 2,200 works investigated. Based on its results, two BCI games, one using attention and the other using meditation as control signals, were developed in order to investigate key aspects of player interaction: the in uence of graphical elements on attention and control; the in uence of auditory stimuli on meditation and work load; and the di erences both in performance and multiplayer game experience, all evaluated in the context of neurofeedback-based BCI games in three di erent controlled experiments, with a total of 40 subjects who played over 420 game matches.
Optimization of Virtual Mobility Management based on NFV and SFC

The networking architecture of 5G and various service requirements brings a huge challenge to the existing centralized mobility management due to the deployment of core functional modules in the core network. vMME is proposed as an alternative solution to meet different application demands. Among vMME mapping solutions, 1:N mapping is a prospective way for distributed mobility management. In this paper, the optimization of virtualized mobility management is investigated by using network function virtualization and service function chain for 1:3 mapping of vMME. A general signaling processing flow of vMME based on service function chain is analyzed. The optimization of vMME is formulated as an optimization problem to place chained VNFs of vMME and optimize the performance of vMME, by choosing the total signaling communication overhead cost and migration cost of state data as optimization objectives. Two heuristic algorithms including TSCMA and DMCMA are proposed and evaluated by simulation, and the performance of different VNF composition solutions under various application scenario parameters are presented.
An Ultralow Phase Noise Eight-Core Fundamental 62-to-67-GHz VCO in 65-nm CMOS

An eight-core fundamental VCO with ultralow phase noise (PN) performance is presented. Eight VCOs are in-phase coupled for low PN, and a 9-dB PN improvement is achieved. A scalable layout methodology is presented to solve the layout difficulties in multicore VCOs. Two VCO cores are connected together directly to form a VCO cell. By simply connecting <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>/2 VCO cells, the PN of the entire multicore VCO will achieve a 10 logN dB PN improvement compared with the single VCO core. The proposed eight-core VCO is fabricated in 65-nm CMOS and occupies 0.15 mm<sup>2</sup>. The measured PN is −105.5 dBc/Hz at 1-MHz offset, with the tuning range from 62.2 to 67.3 GHz (7.9%). The VCO consumes 61.2-mW power with the figure-of-merit of −183.5 dBc/Hz at 1-MHz offset.
Scalable Model of Planar Square One-turn Inductors for Wireless Power Transfer Applications

This paper describes an scalable Planar-Square One-turn Inductor model valid for estimating the quality factor and the input impedance of the inductor up to its self-resonance frequency (SRF). Therefore it can be used to guide the optimized process of the inductive link and the power stage of the WPT system without full-wave electromagnetic simulation. The proposed scalar model is analytic, its circuit components have physical meaning, and its model parameters can be extracted from empirical or simulated data. This model was assessed by full-wave EM simulation, the results was consistent the values predicted by the lumped model.
Role of HVDC Interconnection for Improving Frequency Response in a Low Inertia Power System

The target of this research work is to investigate the role of High Voltage DC (HVDC) interconnection for enhancing frequency response in a low inertia grid. In recent times, installed capacity of wind and photovoltaic (PV) sources has significantly increased in South Australian power system. Most of these wind power plants deploy variable speed wind machines, whereas PV sources are predominantly distributed at consumer end. Therefore, wind power plants and PV generators in South Australia do not participate in frequency regulation. Under prolific penetration of wind and PV and considerable power ingress from adjoining system, an insignificant number of synchronous machines may be present in the South Australian network. During such condition, a big contingency (trip of an interconnection/a major generator) may result in unacceptable frequency response. Consequently, the network might face substantial under frequency load shedding. To mitigate this challenge, a solution for improving frequency response by taking into account the frequency control support from an HVDC interconnection is explored in this paper. To this end, a low inertia network, which mimics the South Australian grid, is analysed under various operating conditions.
Data Analytics Enhanced Data Visualization and Interrogation with Parallel Coordinates Plots

Parallel coordinates plots (PCPs) suffer from curse of dimensionality when used with larger multidimensional datasets. Curse of dimentionality results in clutter which hides important visual data trends among coordinates. A number of solutions to address this problem have been proposed including filtering, aggregation, and dimension reordering. These solutions, however, have their own limitations with regard to exploring relationships and trends among the coordinates in PCPs. Correlation based coordinates reordering techniques are among the most popular and have been widely used in PCPs to reduce clutter, though based on the conducted experiments, this research has identified some of their limitations. To achieve better visualization with reduced clutter, we have proposed and evaluated dimensions reordering approach based on minimization of the number of crossing pairs. In the last step, k-means clustering is combined with reordered coordinates to highlight key trends and patterns. The conducted comparative analysis have shown that minimum crossings pairs approach performed much better than other applied techniques for coordinates reordering, and when combined with k-means clustering, resulted in better visualization with significantly reduced clutter.
A Cluster Compositional Algorithm for Incorporation of Multiple Sets of Clusters of Identical Data

Cluster analysis has become a foremost practice in innumerable sectors. When different techniques produce multiple set of clusters from the same problem domain, these sets demand to be incorporated together to produce a better solution. In this paper, a novel cluster compositional algorithm is proposed to address this issue. The proposed two phased approach intends to assimilate two sets of clusters into one. Firstly, two sets of clusters are compared thoroughly to generate a cumulative set of clusters. Secondly, the approach checks the newly identified set to find out the single element clusters and merges those with other clusters from the set. A case study demonstrates how the approach successfully compares and merges two sets of clusters from a same dataset and results into a more convenient solution.
Real-Time Geometry-Based Wireless Channel Emulation

Connected autonomous vehicles and industry 4.0 production scenarios require ultrareliable low-latency communication links. The varying positions of transmitter, reflecting objects, and receiver cause a nonstationary time- and frequency-selective fading process. In this paper, we present the necessary hardware architecture and signal processing algorithms for a real-time geometry-based channel emulator, that is needed for testing of wireless control systems. We partition the nonstationary fading process into a sequence of local stationarity regions and model the channel impulse response as sum of propagation paths with time-varying attenuation, delay, and Doppler shift. We implement a subspace projection of the propagation path parameters, to compress the time-variant channel impulse response. This enables a low data-rate link from the host computer, which computes the geometry-based propagation paths, to the software defined radio unit, that implements the convolution on a field programmable gate array (FPGA). With our new architecture, the complexity of the FPGA implementation becomes independent of the number of propagation paths. Our channel emulator can be parametrized by all known channel models. Without loss of generality, we use a parameterization by a geometry-based stochastic channel model, due to its nonstationary nature. We provide channel impulse response measurements of the channel emulator, using the RUSK Lund channel sounder for a vehicular scenario with 617 propagation paths. A comparison of the time-variant power delay profile and Doppler spectral density of simulated and emulated channel impulse response showed a close match with an error smaller than $-35$dB. The results demonstrate that our channel emulator is able to accurately emulate nonstationary fading channels with continuously changing path delays and Doppler shifts.
DeepPet: A Pet Animal Tracking System in Internet of Things using Deep Neural Networks

Pets animal monitoring in smart cities is a challenging problem. classic approach to identify the animal and surveillance methods like air tags, GPS, and RFID has its disadvantage to provide the full required level of monitoring and tracking of the pets. Such devices have many limitations and costly. The massive development in the area of Internet of things (IOT) in smart cities can be used to provide a human control and interaction with pets by using the Internet of Things and its technologies. This paper will present an approach of pet animal tracking on the video stream using deep learning capabilities with the goal to detect and classify the object of interest.
Content-Based Brain Tumor Retrieval for MR Images Using Transfer Learning

This paper presents an automatic content-based image retrieval (CBIR) system for brain tumors on T1-weighted contrast-enhanced magnetic resonance images (CE-MRI). The key challenge in CBIR systems for MR images is the semantic gap between the low-level visual information captured by the MRI machine and the high-level information perceived by the human evaluator. The traditional feature extraction methods focus only on low-level or high-level features and use some handcrafted features to reduce this gap. It is necessary to design a feature extraction framework to reduce this gap without using handcrafted features by encoding/combining low-level and high-level features. Deep learning is very powerful for feature representation that can depict low-level and high-level information completely and embed the phase of feature extraction in self-learning. Therefore, we propose a deep convolutional neural network VGG19-based novel feature extraction framework and apply closed-form metric learning to measure the similarity between the query image and database images. Furthermore, we adopt transfer learning and propose a block-wise fine-tuning strategy to enhance the retrieval performance. The extensive experiments are performed on a publicly available CE-MRI dataset that consists of three types of brain tumors (i.e., glioma, meningioma, and pituitary tumor) collected from 233 patients with a total of 3064 images across the axial, coronal, and sagittal views. Our method is more generic, as we do not use any handcrafted features; it requires minimal preprocessing, tested as robust on fivefold cross-validation, can achieve a fivefold mean average precision of 96.13%, and outperforms the state-of-the-art CBIR systems on the CE-MRI dataset.
Exploring Urban Spatial Hotspots’ Properties Using Inter-Connected User-Location Networks

The rapid urbanization in recent years booms our daily life. However, great challenges still exist when it comes to resource allocation and urban planning. While there are lots of works on sensing urban spatial hotspots and exploring their properties, little attention has been given to users’ interactions with them from an inter-connected way. Users’ visits on places always reveal the places’ ability of grouping people together. We call it users and places are inter-connected. Benefiting from the development of information technology, we have observed a dramatic increase of the mobile big data. How to convert the data into actionable insights are some of the biggest impediments to unlock its value. Fully mining the mobile big dataset collected from one northern city in China, we first present a density-based method to recognize hotspots in a city, which are usually significant places with specific functions. Then a user-location inter-connected network is constructed based on the detected spatial hotspots. Different properties are shown in different places. Our analysis sheds light on the relationship between the prosperity of people and places, distinguishing various urban functions. Besides, it will benefit the next generation of location-based services and applications.
Online Data Traffic Steering in Software-Defined Autonomous Vehicle Networks

In the past decade, autonomous driving technologies have experienced a significant growth. In order to meet the increasing data transmission demands from autonomous vehicles (AVs), a novel network paradigm connecting AVs with the Internet is needed. In this paper, we first present the Software-Defined Autonomous Vehicle Networks (SD-AVN) framework to bridge the gap by introducing Software Defined Networking (SDN) and fog computing technologies. With SDN, we focus on a centralized routing problem in SD-AVN, and our intent is to minimize the overall transmission cost by reducing the usage of 5G base stations (BSs). Motivated by this, we formulate the global routing problem as a mixed integer programming (MIP) problem and develop an online log-competitive approximation algorithm to solve it. After that, we also explain that the computation-intensive routing tasks can be distributed to different fog controllers to reduce the scheduling time and end-to-end delay. Experimental results validate the effectiveness of the proposed algorithm in comparison with other two routing heuristics.
Ensemble of Two-Stage Regression Based Detectors for Accurate Vehicle Detection in Traffic Surveillance Data

The growing amount of traffic surveillance data results in an increased need for automatic detection systems to analyze the data. For this purpose, deep learning based detection frameworks like Faster R-CNN and SSD have been employed in recent years. Though the detection accuracy is clearly improved compared to conventional detection methods, there exists large potential for further improvements especially in case of adverse weather conditions. In this paper, we employ the RefineDet detection framework as it combines advantages of several detection frameworks including Faster R-CNN and SSD. We use an ensemble of two detectors with different base networks to generate detections that are more robust. For this, SENets – the winner of the ImageNet2017 classification challenge – are used in addition to ResNet-50. To account for small vehicles in the background and strong variation in vehicle scale, we apply multi-scale testing. Our proposed detector achieves top-performing results on the UA-DETRAC dataset especially in case of rainy and nighttime scenarios.
Analytical Methodology for Loss Calculation of SiC MOSFETs

For evaluating and optimizing the efficiency of power converters, the loss model of the power semiconductor devices is needed. The analytical loss model is favorable for its simplicity. However, the model accuracy needs to be improved. This paper proposes an analytical loss model for the commutation pair of silicon carbide (SiC) MOSFETs and SiC Schottky barrier diodes. Compared to the conventional loss calculation method, the proposed model is derived based on the conservation of energy, which considers the impact of the displacement currents on estimating the turn-on and turn-off losses. The modeling and extraction methodology of the model parameters are given, and the impact of operational conditions on the model parameters is studied. Using the proposed model, the losses can be calculated analytically and also the switching trajectories can be predicted with good consistency. A double pulse tester is built to verify the proposed model. The results show that an average modeling error is reduced from 20% using the conventional piecewise linear model, to around 10% using the proposed model under various operational conditions, and the time cost is at least 3000 times smaller compared to the existing SiC MOSFETs models.
Real-life Implementation of Internet of Robotic Things Using 5 DoF Heterogeneous Robotic Arm

Establishing a communication bridge by transferring data driven from different embedded sensors via internet or reconcilable network protocols between enormous number of distinctively addressable objects or "things", is known as the Internet of Things (IoT). IoT can be amalgamated with multitudinous objects such as thermostats, cars, lights, refrigerators, and many more appliances which will be able to build a connection via internet. Where objects of our diurnal life can establish a network connection and get smarter with IoT, robotics can be another aspect which will get beneficial to be brought under the concept of IoT and is able to add a new perception in robotics having "Mechanical Smart Intelligence" which is generally called "Internet of Robotic Things" (IoRT). A robotic arm is a part of robotics where it is usually a programmable mechanical arm which has human arm like functionalities. In this paper, IoRT will be represented by a 5 DoF (degree of freedoms) Robotic Arm which will be able to communicate as an IoRT device, controlled with heterogeneous devices using Social IoT and "Cloud Robotics".
Real-time maritime situation awareness based on deep learning with dynamic anchors*

Situation awareness in maritime environment entails early detection and classification of maritime targets of varying sizes, depths, shapes, textures, and contrasts. Thus, this paper describes a novel deep learning based maritime situation awareness approach using high-definition video. Maritime object detection is achieved in three main steps. At first, a key region based tracking algorithm allows to, dynamically and parsimoniously, extract high-quality region proposals mainly focalized around rigid (i.e.; potential object) video locations. The latter are, further, fed into a Fast-RCNN for carrying out objectness detection and box regression. Finally, a mere box post-regression operation enables to extract maritime objects. Furthermore, the found object detections are fed into a second classification RCNN, specifically, trained to recognize up to 40 vessel classes. Our experiments have shown that the proposed approach achieves state of the art speed and accuracy.
Onthology-based Masking Loss for Improved Generalization in Remote Sensing Semantic Image Retrieval

Semantic image retrieval can significantly reduce the time required to process the vast amounts of remote sensing image data and support applications such as detection of illegal fishing or logging or analysis of growth and change in residential and industrial areas. In this work we propose a novel method for remote sensing semantic retrieval based on a masking loss for convolutional neural networks which allows multi-dataset training despite different and incompatible semantic classes in different datasets. We achieve improved generalization when the trained model is applied in a realistic cross-dataset setting. In addition to this we perform a thorough evaluation of several design choices which are popular in other retrieval tasks, most notably the impact of specialized ranking losses, and formulate guidelines for future research. Our trained models are evaluated on the recent PatternNet dataset and the established WHU-RS19 and UCM dataset. We outperform the state-of-the-art on PatternNet, UCM, and WHU-RS19 by 29.3%, 17.2%, and 4.5%, respectively.