Miracle-dz/UniFAR-FTUs huggingface dataset:
An Optimization Approach of Container Startup Times for Time-Sensitive Embedded Systems

Containers are a lightweight virtualization method that is widely adopted in Cloud Environments, Internet of Things (IoT) and embedded devices. Apart from virtualization, containers provide an efficient way of building, storing and distributing layered filesystems for applications. This is one reason why containers became state of the art for service development, deployment and administration in IT systems. Although containers are lightweight, the image layering and container creation add overhead to the application startup time causing a cold start problem in serverless computing. Therefore, the overhead must be kept as small as possible, especially for resource-constrained devices in time-sensitive systems. We present a concept to decrease container startup times and perform analysis on a resource constrained device, a Renesas RCar H3. The study shows that using our concept, the average container startup time can be reduced by up to 64% on target hardware. Furthermore, we evaluate how the solution scales on different hardware setups and determine the impact of container configuration parameters on startup times.
Predicting User Engagements using Graph Neural Networks on Online Social Networks

In today's world of attention economy, predicting user engagement is a crucial part for online content generators. Many different approaches have been applied to predict user engagement. However these methods rely solely on features based on the user and the content. A new class of deep learning methods based on graphs captures not only the content features but the graph structure as well. In this paper, we design a graph neural network to predict user engagements with tweets based on the features of the user, the tweet and the engagement graph of users and tweets. We first create features for the tweet text using the pre-trained GloVe embeddings and use GraphSAGE for embedding a user into a vector space. Then our GNN model combines the features and graph structure to predict the user engagement. Using a dataset officially released from Twitter, we are able to classify between like, retweet, reply and quote on a tweet. Our method is able to predict the final engagements with 94.22% precision score.
Link Quality Estimation in 6TiSCH Networks: A Review

The Industrial Internet of Things (IIoT) uses reliable wireless network solutions such as Time-Slotted Channel Hopping (TSCH) to integrate cyber-physical systems with industrial environments via the IPv6 over TSCH (6TiSCH) protocol stack. The quality of 6TiSCH radio links affects the network performance significantly in terms of energy consumption, latency, and reliability. Therefore, accurate link quality estimation (LQE) is an important feature influencing the design and optimization of higher-layer protocols. Indeed, accurate link quality estimation helps predict link failure and avoid packet loss, which ensures good routes and better quality of service. This paper surveys and discusses existing link quality estimators used in 6TiSCH networks and proposes a taxonomy that captures the fundamental differences among them. Besides, comparisons between different LQE solutions are performed to provide a comprehensive view of the state-of-the-art and useful insights. The paper ends by highlighting open problems in this active area of research.
Development of a Nanoprecision 3-DOF Vertical Positioning System With a Flexure Hinge

This paper describes the conceptual design of an ultraprecision 3-DOF (Z, Ox, Oy) vertical positioning system with nanometer precision. The vertical out-of-plane positioning system can be used for various nanoalignment applications, such as optical instrument alignment. The proposed vertical positioning system is driven by three piezoelectric (PZT) actuators and is guided by three rotationally symmetric hinges. Because the displacement generated by a PZT actuator is very small, the proposed system also includes an amplification hinge mechanism. Because the relationships between the variables of the hinge parameters and system performance levels are complicated, an optimization procedure to obtain optimal design parameters, which maximize the system bandwidth, is developed. Based on the solution to the optimization problem, the design of a vertical positioning system and finite-element-method simulation results are presented. Finally, the stage is manufactured and tested for verification. The stroke of the translational movement is 190 mm, and the stroke of the rotational movement is 0.5 mrad, whereas their in-position stability levels are ±2 nm and ±20 nrad and resolutions are 5 nm and 80 nrad, respectively. The settling time is less than 45 ms.
Web Attack Payload Identification and Interpretability Analysis Based on Graph Convolutional Network

Web attack payload identification is a significant part of the Web defense system. The current Web attack payload identification usually combines natural language processing and deep learning to automatically build a detection model to intercept malicious payloads. However, these detection methods ignore the bidirectional association between fields and is prone to the payload dilution problem for long strings. In addition, the weak interpretability of deep learning models makes it difficult for researchers to solve the problem of model pollution and adjust the model according to the prediction logic. Therefore, this paper proposes a new Web attack payload identification method based on Graph Convolutional Network (GCN), which can effectively extract Web payload features and help model interpretability analysis. The core of this method is to transform the text feature problem into a graph feature extraction problem and to understand the structure and content of the Web payload from the graph perspective. The method performs node embedding on the Web payload graph through GCN, then converts the embedding vector into a graph feature vector through a feature fusion method. The node ablation method is used to analyze malicious payloads' interpretability and calculate the predicted impact rate of nodes inside the graph structure. The experiments on the CSIC 2010 v2 HTTP dataset show that the method proposed in this paper has high accuracy for identifying Web attack payloads, and the node embedding of the Relational Graph Convolutional Network (RGCN) method is more suitable for identifying Web attack payloads than other GCN methods. The research results of the paper show that the model interpretability analysis based on the Web payload graph is reasonable and can effectively assist researchers in adjusting the model and preventing the problem of model pollution.
Transfer Route Recommendation for Metro Systems Based on Multi-source Data Fusion

As the network-structure complexity and the passenger volume continuously grow, how to accurately perform passenger flow guidance is a crucial issue for metro operators. Transfer route recommendation is a core approach for passenger flow guidance. Existing works are limited in obtaining route attributes (such as transfer walking time and waiting time, etc.). Furthermore, most of existing methods may not work well in the scenario of sudden passenger flow, because of their limited capability to accurately measure the real-time crowding. To address these issues, a data fusion based transfer route recommendation method (DFTRR) is proposed. Specifically, we first propose a spatio-temporal diversity-based method (STD) to measure the trajectory similarity. Based on that, we fuse two kinds of heterogeneous data, i.e., swiping records of smart cards and Wi-Fi signal records of mobile devices to extract a passenger's complete trip. And then we present the historical median travel time to alleviate the computation issue of route attributes. Meanwhile the mean crowding level is introduced to accurately measure the real-time crowding of routes. Finally, a generalized travel cost based on median travel time, number of transfers and mean crowding level is constructed for transfer route recommendation. The experimental results illustrate that the accuracy of STD algorithm is higher than the baseline 34.92% at maximum; DFTRR can offer the optimal transfer route according to passengers' preferences under sudden passenger flow.
THperf: Enabling Accurate Network Latency Measurement for Tianhe-2 System

To prevent the network from becoming a potential performance bottleneck, two approaches commonly adopted in recent High-Performance Computing systems are offloading the network stack and Remote Direct Memory Access. Notably, these technologies require precise measurement of network latency due to the increasing granularity of congestion control mechanisms. This demand is more urgent for the Tianhe system because of the huge volume and performance requirements. However, the Tianhe system lacks an accurate network latency measurement tool due to its customizable nature. In this paper, we propose THperf, a network round-trip latency measurement tool for the Tianhe network architecture that can accurately obtain network latency information without the need for hardware support. We perform accurate network latency measurement at various traffic sizes with THperf and analyze the obtained the latency as a percentage of all latency. In addition, we compare THperf to other measurement tools and discover that THperf can eliminate a significant amount of latency. Using THperf, we further measure and compare the performance of different Submission Queues in the Tianhe network, demonstrating that the hardware descriptor submission method outperforms the software descriptor submission method in terms of latency. We believe that THperf can be used as a controlled benchmark for more granular congestion control algorithms.
The microwave surface impedance of high-T/sub c/ superconductors

The available data on the microwave surface impedance Z/sub s/ of high-T/sub c/ superconductors from more than 30 laboratories around the world are reviewed to show the frequency, temperature, and field dependence of the surface resistance R/sub s/ and the temperature dependence of the field penetration depth lambda . The data are discussed on the basis of a two-fluid model and are compared with results from the classical superconductors Nb and Nb/sub 3/Sn. At temperatures above 0.8 T/sub c/, Z/sub s/ values of classical and high-T/sub c/ superconductors show strong similarities and are in close agreement with the Mattis-Bardeen theory. The comparatively very high R/sub s/ of the cuprates at 4.2 K, however, is indicative of a high density of unpaired charge carriers. The lowest values for R/sub s/ and lambda obtained until now with epitaxially grown YBa/sub 2/Cu/sub 3/O/sub 7- delta / films are 16 mu Omega at 4.2 K and 10 GHz and 140 nm, respectively. Such films are required for high RF field applications. Polycrystalline samples show a strong field dependence of R/sub s/. The highest magnetic surface field without R/sub s/ deterioration achieved so far on single crystals of YBa/sub 2/Cu/sub 3/O/sub 7- delta / is about 100 G.
Degradation of the Recoloring Specific Degree Protan CVD Image From inserted Watermark

In the first part of the paper the color vision deficiency (CVD), as a consequence of the deformity in the L cones photoreceptors, was described. After that, an algorithm for the color transformation in the LMS color space is described. The color transformation algorithm aims to expand the range of colors (recoloring) in images intended for protan CVD people. The described algorithm is adapted to the degree of protanomaly for a specific protan CVD person. The second part of the paper presents the results of the Experiment, which was realized with the aim of analyzing the effect of the inserted watermark on the recoloring of the image with the degree of protanomaly. The watermark is inserted using the Block SVD algorithm. Some degrees of protanomaly are included in the analysis of the experimental results. The Experimental results are presented in the form of recoloring images, extracted watermarks and graphics. The analysis of the results was carried out by: a) subjective and b) objective methods. The subjective analysis was realized by visual inspection of the recoloring images and extracted watermarks. The objective analysis was carried out by comparative analysis of mean square error (MSE). Based on subjective and objective analysis, the threshold for the insertion factor, αc, where the watermark and the RC image are of satisfactory quality was determined.
TONGUES, BOTTLES, AND DISCONNECTED LOOPS: THE OPENING AND CLOSING OF THE INTERPLANETARY MAGNETIC FIELD

For years the field of Space Physics has had a problem, a really big problem for it occurs on the largest spatial scales in Space Physics - across the entire region under the Sun's influence, the heliosphere. The problem is that the Sun appears to keep opening new magnetic fields into interplanetary space with no obvious way to close them back off again. This state of affairs, without some method for closing the open interplanetary magnetic field (IMF), would lead to an ever growing IMF magnitude in interplanetary space - a catastrophe that is clearly not observed. Figure 1 displays a composite picture of the Sun's outermost atmosphere or corona from 30 June 1973. The outer portion was taken in ordinary white light from the ground during a solar eclipse. The superposed soft X-ray image of the denser, near-Sun corona was taken from Skylab on the same day. The structure evident in the images is a consequence of the solar magnetic field that permeates the corona; more and less populated magnetic fields are mapped out as density structures in these images. Coronal holes are low-density regions where the magnetic fields open out into interplanetary space; a large coronal hole is displayed as the dark region near the top of Figure 1. In contrast, bright loop-like structures indicate closed field regions on the Sun that contain high-density plasma. The centers of helmet streamers (closed field loops overlaid by nearly radial high density streamer structures), such as those seen extending outwards to the left and right sides in Figure 1, map out to large reversals of the magnetic field or current sheets in interplanetary space. The solar wind flow from streamers is slower and higher density than it is from coronal holes [e.g., Borrini et al., 1981]. Coronagraph and soft X-ray observations over the past several decades have shown that the solar corona is highly dynamic with open and closed regions evolving over time scales as short as minutes and as long as the 22-year solar cycle.
Network Intrusion Detection System using ML

Technologies are making our life easier and simple, but it has both positive and negative effect. Many new methods of cybercrimes are coming which cannot be solved using earlier conventional method like using firewalls, antivirus, old ML algorithms. In recent years every device whether it’s hardware or software is being connected with IOT. Hence, there is huge growth in data also their privacy is huge concern for industries. In this model, we are implementing Network Intrusion detection system using Machine learning algorithms which would resolve security problems using KNN, SVM, LR, RF, DT and Gaussian NB with greater efficiency. Our system uses both supervised and unsupervised machine learning techniques. Both misuse and Anomaly based detection to detect malware and viruses, our system is capable to detect both known and unknown attacks. In case of misuse detection system known attacks are being easily identified using a database where list of all known attacks is available. If any attack happens on network system, then NIDS checks whether the attack is listed in dataset or not. If attack is known, then system administrator gets notified. If attack is unknown, then NIDS uses outlier detection to identify attack using several machine learning algorithms like clustering and other techniques. So, with the help of above-mentioned techniques attack is being detected. Our model improves the attack detection mechanism with high accuracy and less prediction time. It is better than previous conventional machine learning algorithms. Our model is broadly accepted in companies and organization. it is fulfilling the cyber security issue also threat prediction time of our model is quite improved and the prediction time is reduced as compared to previous model.
Machine Learning Based Feature Selection for DDoS Detection in Named Data Networking

With the growing recognition that current Internet protocols have significant security flaws; several ongoing research projects are attempting to design potential next-generation Internet architectures to eliminate flaws made in the past. These projects are attempting to address privacy and security as their essential parameters. NDN (Named Data Networking) is a new networking paradigm that is being investigated as a potential alternative for the present host-centric IP-based Internet architecture. It concentrates on content delivery, which is probably underserved by IP, and it prioritizes security and privacy. NDN must be resistant to present and upcoming threats in order to become a feasible Internet framework. DDoS (Distributed Denial of Service) attacks are serious attacks that have the potential to interrupt servers, systems, or application layers. Due to the probability of this attack, the network security environment is made susceptible. The resilience of any new architecture against the DDoS attacks which afflict today's Internet is a critical concern that demands comprehensive consideration. As a result, research on feature selection approaches was conducted in order to use machine learning techniques to identify DDoS attacks in NDN. In this research, features were chosen using the Information Gain and Data Reduction approach with the aid of the WEKA machine learning tool to identify DDoS attacks. The dataset was tested using K-Nearest Neighbor (KNN), Decision Table, and Artificial Neural Network (ANN) algorithms to categorize the selected features. Experimental results shows that Decision Table classifier outperforms well when compared to other classification algorithms with the with the accuracy of 85.42% and obtained highest precision and recall score with 0.876 and 0.854 respectively when compared to the other classification techniques.
Intelligent Control System of Construction Machinery Based on PLC and Machine Vision

In the field of industrial automation, the application of machine vision(MV) technology in industrial production positioning detection is developing rapidly, and the positioning task can still be accomplished by image detection and recognition in the harsh environment of industrial sites. And construction machinery(CM) also towards the direction of intelligence and automation, people also pay more and more attention to the design of CM control system(CS) research. This paper develops an intelligent CS for CM suitable for industrial production process. The system processes the image information by MV system, and then the processed data is calculated by PLC, which combines MV technology and PLC technology to realize the detection of pulse signal frequency and the monitoring of CM and equipment status. The test results of the intelligent CS module in this paper also show that the system meets the design requirements and can be put into engineering production applications.
Defending Software Defined Network Against DDoS Attack Using Machine Learning

Unlike the traditional networks, Software Defined Network (SDN) networks are based on attributes. The traditional networks employ networking tool to take the decision related to handle an arriving packet on the location of its IP (Internet Protocol) destination. This tool assigns a label "malevolent" to the node having least trust. The number of packets, whose transmission is done under this period, is considered to compute the trust. The node responsible for transmitting the maximum packets and consuming few resources is considered as malevolent node in the network. NS2 (network simulator 2) is applied to simulate the introduced framework and diverse metrics are employed to analyse the outcomes.
Survey and Evaluation of Extreme Learning Machine on TF- IDF Feature for Sentiment Analysis

Sentiment analysis is a tool to understand the emotion of the statement given by customers. This understanding helps the service provider to in improving the quality of service. Machine learning models are one of the popular choices for designing sentiment analysis systems. However, hyper-parameter tuning is one of the important concerns in most of these models. Moreover, the gradient-based training models are prone to the local-minima problem. In such a case one-pass learning model like Extreme Learning Machine (ELM), gives generalization performance with minimal hyper-parameter tunning. This work studies in depth the ability of ELM to learn a generalization model for the sentiment analysis problem. Here the study uses the airline Twitter review dataset to empirically analyze the ELM model and the required hyper-parameter setting.
Scheduling Performance Evaluation Framework for ROS 2 Applications

Recently, performance analysis research on self-driving software has attracted considerable attention because the development of self-driving software has increased performance needs. ROS 2 is an open-source application framework for robotics. Simultaneously, as the Executor is responsible for scheduling callbacks (the minimal schedulable entity) in ROS 2, the Executor's scheduling performance is one of the most crucial factors affecting the real-time performance of ROS 2. A performance evaluation tool for Executor scheduling is required to evaluate its performance. This paper proposes a tracing and performance analysis framework with instrumentation and tools for ROS 2 Applications, with the visualization of Executor scheduling results and analysis of Executor deadline misses. The proposed framework can help researchers better understand and develop ROS 2 Executor by providing deadline miss analysis of the end-to-end path and the scheduling data. Furthermore, this paper verifies the proposed approach to the Autoware Reference System, which can simulate a real-world scenario to evaluate the performance of Executor.
A Reference-Free Phase Noise Measurement Circuit Achieving 24.2-fs Periodic Jitter Sensitivity and 275-fsrms Resolution With Background Self-Calibration

This article presents an ON-chip jitter/phase noise measurement (PNM) circuit that is reference-free and self-calibrated in situ in the background. <inline-formula> <tex-math notation="LaTeX">$\Delta \Sigma $ </tex-math></inline-formula> time to digital converters (TDCs) are employed to measure cycle jitters of the signal under test and to digitize the power spectral density of phase noise. As a clean reference clock is not required for the PNM, the sensitivity of PNM is not limited by the reference source. The signal bandwidth of the TDC ranges from 100 kHz to 3.125 MHz, and the measured errors are less than 1 dB by a single-tone phase modulation (PM) test across the frequency range. Compared to the measurement results using a spectrum analyzer (Keysight N9030B), it demonstrates a jitter resolution of 275 fsrms with only 4.8% error, which is at least 3<inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> finer compared to the prior art. This PNM is fabricated via a Taiwan Semiconductor Manufacturing Company (TSMC) 28-nm CMOS process, and the core area is 450 <inline-formula> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 453 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>. The total power consumption is 15.83 mW.