Miracle-dz/UniFAR-FTUs huggingface dataset:
MMNR: A Network Representation Framework Based on Multi-View Motif Fusion

Learning network representation has a variety of application scenarios, such as network classification and clustering. It is a promising direction that introducing the novel idea of higher-order motif into network representation. However, most existing motif-based network representation methods do not fully integrate external attributes, namely, the attributes on edges and nodes, including temporal, edge frequency, and node importance. In this paper, we propose a multi-view motif network representation framework (MMNR). Our framework fuses the static, temporal, edge-labeled and node-labeled motifs of 3 rd -order and 4 th -order to achieve the full integration of static, temporal, frequency and importance attributes in the network, and ultimately to realize unsupervised graph-level and node-level embedding simultaneously. Graph-level embedding takes advantage of the distribution of motifs, while node-level embedding further considers the location of nodes in motifs, and both of them fully incorporate external attributes. We analyze and explain the network behavior patterns in detail from four views of motif, and demonstrate the efficacy of MMNR over existing state-of-the-art methods on classification, clustering, and visualization. The classification accuracy of MMNR in graph-level embedding achieves 17% relative gains over the best-performing methods, and the silhouette coefficient of node-level embedding clustering is 0.21 higher than baseline. MMNR also shows clear boundaries and outperforms other methods in terms of visualization.
Deep Learning Decoder for MIMO Communications with Impulsive Noise

In this paper we consider signal detection in multiple-input-multiple-output (MIMO) systems with an impulsive noise channel. The existing, near optimal, sphere decoder (SD) achieves good performance, however, the computational complexity is directly related to the number of nodes visited during the tree search and the signal-to-noise ratio (SNR). Using neural network techniques, a Deep Learning Detector (DLD) is proposed. The DLD method detects signals transmitted in an impulsive noise channel, after an off-line training phase. The detection process of DLD has lower complexity than the average SD complexity, while exhibiting good performance. What is even more interesting is that the computational complexity of DLD is constant across SNR, in contrast to the SD detectors, which have an exponential complexity across the SNR. This constant complexity could be very helpful when implementing a detector in practice because it could allow for better optimization of resources. To evaluate the performance of our proposed method we have used a low level simulator that generates a fairly accurate model of a MIMO system with an impulsive noise channel. The complexity analysis and simulation results validate the arguments presented in this paper.
A Lightweight Strategy for Reliability of Consensus Mechanisms based on Software Defined Networks

Private blockchain applications simplify the network consensus while relying on voting-based consensus mechanisms because these mechanisms present lower computational cost than proof-of-work. The voting-based consensus is adequate to networks where all nodes are known, although it requires a high amount of exchanged messages, proportional to the number of participant nodes. In this paper, we propose a strategy for applying Quality of Service (QoS) techniques to ensure the reliable operation of voting-based consensus mechanisms. The proposed strategy is based on Software Defined Networking (SDN) and aims at reducing the time for the termination of the consensus protocols. The proposal evaluation leans on an emulated environment, using Mininet, where we run the Raft and BFT-SMaRt consensus mechanisms. Results show that the proposal ensures the termination of the protocols in a reduced time and the reduction of the control load for the election of leaders since we assure a minimum quality of service on the throughput rate for the consensus network flows. The proposed strategy enhances up to 100% of the consensus terminations on Raft and, for BFT-SMaRt, ensured the operation even in contention scenarios where the consensus was previously unfeasible. Results also show 80% reduction in the exchanged messages for achieving consensus.
Exploring Deep Learning based Object Detection Architectures: A Review

As one of the hottest fields of computer vision, object detection is employed in many useful applications, including face recognition, autonomous cars and human behaviour analysis. CNN's have played a vital role in the progression of computer vision, especially object detection. In this paper, a review is provided on object detection methods which utilize deep learning to render outstanding results, and different object detection frameworks are discussed and analyzed with respect to their differences amongst each other. The comparisons are carried out based upon the criterion of accuracy and frames per second. The review starts with the explanation of CNNs and their advantages. Then we focus our attention on two types of object detection architectures and dive deep into their respective details. In the end, we draw a conclusion based on the facts detailed in the review.
Low Cost C-RAN and Fronthaul Design with WDM-PON and Multi-hopping Wireless Link

A mobile base station (MBS) comprises a central unit (CU), a distributed unit (DU), and a remote unit (RU) for efficient deployment in 5th-generation mobile communications system and beyond. The link between a DU and an RU is established using an optical fiber and is renowned as fronthaul (FH). Our study aims to reduce the cost of this FH link. Networking and wirelessly connecting the FH link have been studied to suppress the FH link cost and to flexibly deploy RU1s. Wireless fronthauling is an important solution because it can dispense with optical fiber laying. The location of MBSs is also an important issue. RUs are densely deployed to gain high wireless throughput per area. A DU should be located at a place where RUs can perform cooperative operation and the latency requirement is satisfied. To reduce the optical fiber deployment cost, we studied the DU placement design using wireless multihop links and point-to-point (PtP) optical links. It determines the DU locations to increase the wireless links for reducing the deployment cost. The problem of this scheme is that most of the RUs are still connected to the DU via PtP optical links. This paper proposes a novel FH designing scheme that employs a passive optical network (PON) and wireless multi-hop as well as PtP optical links. We propose a separate designing algorithm for the wireless and PON links. The optical fiber link cost is observed to reduce when compared with that of the previously proposed scheme using a simulation.
A 10-GS/s Sample and Hold System for High-Speed Time-Interleaved ADC

This paper presents a 10-GS/s sample and hold system, which is designed for a 16-way time-interleaved analog to digital converter (ADC). The sample and hold system is composed of three stage circuits. The multi-stage structure could make the sampling rate of 10GS/s down to the rate of sub-ADCs (625MS/s). To extend the bandwidth of the stage-1, a differential source-degenerated common-source amplifier with peaking inductance is adopted as an input buffer. A switched source follower samples the differential input signal at 10GHz. Moreover, the stage-2 is based on a wideband fully differential source-follower configuration. With the calibration technique, the source follower could eliminate the channel length modulation and the body effect. Therefore, the linearity of the proposed source follower is superior to the conventional ones.
Design of Imperceptible Metamer Markers and Application Systems

This paper presents a application system using imperceptible metamer markers. The proposed system is based on metamer markers that are invisible to the human eye but visible to infrared (IR) cameras. The metamer means the indistinguishable colors with different spectrums. RGB cameras cannot capture the different spectrums of metamer markers, but IR cameras can detect the metamer markers printed under the color image. This paper describes how to create metamer markers using a ordinary color printer. First, uniformly select color samples from the RGB color space, analyze the visibility of RGB colors and metamer color printed by the printer, and then design a color mapping model between color printers to produce the effective metamer color. After confirming successful detection of a metamer marker under various infrared lightings and filters, the exact color differences were analyzed and the validity of the proposed method was clarified. To verify its applicability, a prototype of fairytale book was published and demonstrated as an AR-book. The metamer markers are expected to be very useful for AR applications, projector-camera systems, human-computer interaction systems, and so on.
Using Data Mining Techniques to Predict Student Performance to Support Decision Making in University Admission Systems

An admissions system based on valid and reliable admissions criteria is very important to select candidates likely to perform well academically at institutions of higher education. This study focuses on ways to support universities in admissions decision making using data mining techniques to predict applicants’ academic performance at university. A data set of 2,039 students enrolled in a Computer Science and Information College of a Saudi public university from 2016 to 2019 was used to validate the proposed methodology. The results demonstrate that applicants’ early university performance can be predicted before admission based on certain pre-admission criteria (high school grade average, Scholastic Achievement Admission Test score, and General Aptitude Test score). The results also show that Scholastic Achievement Admission Test score is the pre-admission criterion that most accurately predicts future student performance. Therefore, this score should be assigned more weight in admissions systems. We also found that the Artificial Neural Network technique has an accuracy rate above 79%, making it superior to other classification techniques considered (Decision Trees, Support Vector Machines, and Naïve Bayes).
Current and Future Research of Machine Learning Based Vulnerability Detection

The quantity and volume of software increase rapidly, more vulnerabilities are hidden there, and thus vulnerability detection becomes more important. This paper reviews works about machine learning based vulnerability detection methods for source code, especially on program representation and vectorization, and Machine Learning based Methods. However, current machine learning methods detect vulnerabilities in coarse-grained level, and locating the vulnerabilities still needs much additional work. This problem can be solved by vulnerability detection in fine-grained level. This paper assumes that vulnerability detection in fine-grained level will be a future research trend, and proposes several possible solutions.
A Flexible Processing-in-Memory Accelerator for Dynamic Channel-Adaptive Deep Neural Networks

With the success of deep neural networks (DNN), many recent works have been focusing on developing hardware accelerator for power and resource-limited embedded system via model compression techniques, such as quantization, pruning, low-rank approximation, etc. However, almost all existing DNN structure is fixed after deployment, which lacks runtime adaptive DNN structure to adapt to its dynamic hardware resource, power budget, throughput requirement, as well as dynamic workload. Correspondingly, there is no runtime adaptive hardware platform to support dynamic DNN structure. To address this problem, we first propose a dynamic channel-adaptive deep neural network (CA-DNN) which can adjust the involved convolution channel (i.e. model size, computing load) at run-time (i.e. at inference stage without retraining) to dynamically trade off between power, speed, computing load and accuracy. Further, we utilize knowledge distillation method to optimize the model and quantize the model to 8-bits and 16-bits, respectively, for hardware friendly mapping. We test the proposed model on CIFAR-10 and ImageNet dataset by using ResNet. Comparing with the same model size of individual model, our CA-DNN achieves better accuracy. Moreover, as far as we know, we are the first to propose a Processing-in-Memory accelerator for such adaptive neural networks structure based on Spin Orbit Torque Magnetic Random Access Memory(SOT-MRAM) computational adaptive sub-arrays. Then, we comprehensively analyze the trade-off of the model with different channel-width between the accuracy and the hardware parameters, eg. , energy, memory, and area overhead.
Thermal properties and space charge behavior of thermally aged polypropylene/elastomer blends nanocomposite

Polypropylene (PP)-based composites including PP/thermoplastic polyolefin (TPO) elastomer blends and PP/TPO-based nanocomposite are rather promising insulation materials for HVDC cables. Aging is an inevitable issue in practical operation and needs to be investigated thoroughly. In this paper, PP/TPO blends and PP/TPO/ZnO nanocomposite were prepared and thermally aged at 130 °C for various periods (28 days at longest). The micro-morphology, thermal properties and high-temperature space charge behavior were investigated. The results show that PP and TPO still exhibited excellent compatibility without phase separation after thermally aging. The crystallinity was not influenced after thermally aging, but a new endothermic peak at about 140 °C appeared in the melting process of thermally aged composites. Space charges accumulated in the two thermally aged composites, but dissipated slowly in PP/TPO, while dissipated rapidly first and then reduced in PP/TPO/ZnO nanocomposite. The calculated apparent charge mobility and trap level distribution based on space charge dissipation show that deep traps increased in aged PP/TPO, but decreased in aged PP/TPO/ZnO nanocomposite, which may be correlated with the chemical variation of thermally aged polymer matrix. The work may give a reference on the aging research of PP-based composite material for recyclable HVDC cable insulation.
Multilevel Image Dehazing Algorithm Using Conditional Generative Adversarial Networks

In recent years, the hazy weather in China occurs frequently, and image dehazing has gradually become a research hotspot. To improve the dehazing effect of the hazy images, this paper has proposed a multilevel image dehazing algorithm using conditional generative adversarial networks (CGAN). The hazy image is used to generate the composed image $K$ jointly estimated by a transmission map and atmospheric light value through a generator network, and a dehazed image is calculated through an improved atmospheric scattering model. The generator network and the joint discriminator network are subjected to adversarial training and reconstruction constraints. The experimental results show that the proposed method achieved good dehazing effect in synthetic hazy images and real hazy images, and is ahead of other advanced dehazing methods in subjective evaluation and objective evaluation.
Online Gait Generation For NAO Humanoid Robot Using Model Predictive Control

This manuscript presents an effective method for online real time walking pattern generation using non-linear optimization of the model predictive control cost function under given constraints and applies this method to the NAO humanoid robot to achieve stable dynamic walking. This paper explains how the data generated is used to create a balanced robot center of mass trajectory along with smooth feet trajectories. The paper introduces a computationally efficient method for joint angle trajectory generation using inverse kinematics combined with iterative optimization to achieve desired center of mass trajectory. Finally the above mentioned modules were integrated together in MATLAB/SIMULINK environment to construct a NAO gait generation engine based on desired robot walking path input and the overall ZMP root mean square error in steady state is around 0.002 m which is very small relative to the support polygon area.
Exploring the Hand and Finger-Issued Behaviors Toward Natural Authentication

As wearables, such as smartwatches and data gloves, become ubiquitous in our daily lives, it is possible to capture users’ behaviors as they go, and then to authenticate them using biometrics. A variety of behaviors have been exploited to authenticate users in a plethora of scenarios. However, there is still a lack of systematic investigations on what types of behaviors present better performance for authentication, and therefore, limiting our understanding of the custom design of behavioral “passwords” to fit people’s needs. This paper is designed to help answer this question. First, we design a collection of behaviors, which is used to explore the effect of different types of behaviors on authentication. This collection involves hand and finger behaviors since they are common and natural human actions; it also reasonably covers both basic movements and complex behaviors, two major categories of behaviors. We evaluated authentication performance along the dimensions of behaviors, users (sensitivity) and classification models (independence). A valuable finding shows that finger-issued movements present almost equivalent performance with complex behaviors. Therefore, it suggests applying them to the wearable and mobile devices as an alternative to complex behaviors, and could considerably reduce requirements for computational resources compared to complex behaviors. Furthermore, they extend the applications of behavioral authentication to a wider array of scenarios, like performing behavioral authentication such as moving fingers in one’s pocket, which is common for wearable and mobile applications.
Research on Intelligent Portrait of Chinese Elderly Based on Big Data and Deep Learning

The growth rate of the elderly population in China has accelerated. According to UN standards, China has fully entered the aging society. In the future, the trend of population aging is irreversible. At the same time, Internet and mobile technologies are developing rapidly. We can use it to actively and effectively respond to the aging of China's population. Based on the data source under the health media of the elderly, this paper constructs the portraits of the elderly, focusing on the different aspects of the elderly, including clothing, food, housing and other aspects, and classifying them. This paper, extracts the local features of the text by constructing a word embedding sequence and imports it into the bidirectional Long Short-Term Memory (LSTM) model. Through the attention mechanism and the LSTM model combined with the text importance calculation, the model is obtained by classifying the overall characteristics of the text. At the same time, a deep convolutional neural network model based on word embedding and multiple convolution kernels is proposed to deal with text multi-classification problem. In order to verify the accuracy of the model, 8000 old people's state and behavioral habit data were used in the experiment. The experimental results show that the model constructed in this paper is more accurate than the traditional machine learning model and the standard deep learning model.
A Unified Storage System for Whole-Time-Range Data Analytics over Unbounded Data

Nowadays, enterprises rely on acquiring and analysis over huge amounts of data to earn profits on the markets. The data are produced from many sources in forms of event streams and unbounded by nature. In order to efficiently analyze these unbounded data, unified analytic frameworks such as Flink and Spark are introduced recently, which provide both real-time stream processing as well as batch processing. However, the unbounded data is still stored in isolated systems: one for real-time data and another for historical data. Such isolation brings additional complications for both application developers and platform operators. Moreover, multiple storage systems also introduce data migration overhead and increase total system cost. In this paper, we present CStream, a unified storage system for unbounded data streams. CStream uses row format for newly arrived data and columnar format for historical data. In addition, CStream transforms row format data into columnar format data asynchronously and efficiently. As a result, CStream can provide both low-latency real-time data access and high-bandwidth historical data access. We conduct extensive experiments, and the results show that CStream outperforms Kafka by up to 50% in write performance and outperforms HDFS by up to 30% in columnar read performance.
Vehicular Fog Resource Allocation Scheme: A Multi-Objective Optimization based Approach

Vehicular fog computing is an extension of fog computing in the context of vehicular networks. Vehicles are used to serve mobile users at the edge of networks. The vehicular fog is characterized by its dynamicity due to the mobility of vehicles. The change of vehicular fog members over time causes the variation of the available resources. So, the selection of the suitable fog node (i.e. vehicle) to execute a task is challenged. In some cases, the users' requests can be redirected to the cloud to be processed if no suitable vehicle is found to fulfill local processing. To this aim, we suggest a multi-objective based vehicular fog resource allocation scheme. A software-defined networking (SDN) controller is deployed to manage the allocation of the vehicular fog resources based on the information gathered from vehicles and the received requests. The proposed problem is solved using non-dominated sorting genetic algorithm (NSGA-II).
Analytical model for homocharge accumulation in LDPE — role of conduction, injection and diffusion

Homocharge, particularly, positive charge near anode has been observed inside low-density polyethylene (LDPE) by many researchers. However, the phenomenon is yet to be understood theoretically and mathematical analysis is pending, although, simulations have been reported. Simulation of evolution of space charge in LDPE, with time and space, using bipolar charge transport model considered only drift component of charge carriers inside the dielectric. The experimental results suggest significant concentration gradient in space charge distribution. The authors derived a comprehensive drift and diffusion based analytical models, for the time/space evolution of space charge and electric field in dielectrics using Maxwell's equations, current continuity equation, conduction and injection mechanisms. The proposed analytic models of electric field and space charge distribution entwines charge injection at electrodes and charge transport inside the medium, which seem to have not been reported until now. Remarkable, believed to be unprecedented, agreement between the analytical and experimental results has been observed. Using the model, the role of conduction, injection, diffusion on homocharge accumulation has been investigated. Pulsed electroacoustic method is used for the measurements of the space charge dynamics.
Optimal Power Allocation and Signal Phase Selection in NOMA Wireless Relay Networks

In this paper, we propose a novel approach of optimal power allocation and signal phase selection for NOMA wireless relay networks. Specifically, to optimize the network throughput, the base station and the stronger relay optimally allocate their transmission power, while the weaker relay selects an optimal signal phase. To maximize the end-to-end sum rate, we formulate a non-convex optimization problem and derive novel analytical results. In addition, we propose the four-candidate greedy algorithm for optimal power allocation and signal phase selection in NOMA wireless relay networks. Simulation results show that the proposed greedy algorithm could significantly improve the system performance.