Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Soft Error Reliability of FPGA-based Deep Neural Networks with Reduced Approximate TMR

Deep Neural Networks (DNN) are recently used in different fields such as autonomous driving, cancer detection, and also space explorations. A certain level of reliability of DNN are indeed needed to be applied in these fields. More recently, FPGA devices have become a target platform for DNN applications due to their high flexibility and computational power. Unfortunately, the SRAM-based FPGAs are considered to be susceptible to soft errors which can lead to errors in execution. Triple Modular Redundancy (TMR) is one of the effective mitigation techniques for masking SEUs for FPGA but causes an increase in power consumption and area overhead. In this study, we evaluate the reliability of MNIST DNN implemented in Xilinx SRAM-based FPGA. Through fault injection simulation, we identified the most vulnerable parts of the design that SEU can generate errors. We improved the SEU reliability of DNN with our proposed hardening strategy named reduced approximate TMR. SEU reliability was enhanced by applying reduced approximate TMR to the most sensitive layers of MNIST DNN, achieving 40% improvement in reliability while recourses utilization and power consumption were increased by 8% and 4%, respectively.
High Dynamic Range Imaging for Dynamic Scenes With Large-Scale Motions and Severe Saturation

Two key challenges exist in high dynamic range (HDR) imaging from multiexposure low dynamic range (LDR) images for dynamic scenes: 1) aligning the input images with large-scale foreground motions and 2) recovering large saturated regions from a limited number of input LDR images. To tackle these challenges, several deep convolutional neural networks have been proposed that have made significant progress. However, these methods tend to suffer from ghosting and saturation artifacts when applied to some challenging scenes. In this article, we propose an end-to-end deformable HDR imaging network, called DHDRNet, which attempts to alleviate these problems by building an effective aligning module and adopting self-guided attention. First, we analyze the alignment process in the HDR imaging task and correspondingly design a pyramidal deformable module (PDM) that aligns LDR images at multiple scales and reconstructs aligned features in a coarse-to-fine manner. In this way, the proposed DHDRNet can handle large-scale complex motions and suppress ghosting artifacts caused by misalignments. Moreover, we adopt self-guided attention to reduce the influence of saturated regions during the aligning and merging processes, which helps suppress artifacts and retain fine details in the final HDR image. Extensive qualitative and quantitative comparisons demonstrate that the proposed model outperforms the existing start-of-the-art methods and that it is robust to challenging scenes with large-scale motions and severe saturation. The source code is available at: https://github.com/Tx000/DHDRNet.
Research on SDN-based data center network traffic management and optimization

Under the traditional network architecture, due to the close binding relationship between network management and network physical topology, network equipment increases, network complexity inevitably increases, and network management becomes more and more difficult, which seriously restricts the improvement of network service quality. This paper designs a multi-tenant oriented network resource allocation and management platform based on SDN (Software Defined Network), which provides different view interfaces for different tenant networks, and realizes the tenant resource allocation function and traffic isolation function. At the same time, the SDN-based bandwidth-aware energy-saving routing scheme focuses traffic on as few links as possible by sleeping suitable switches and links, thus achieving the energy-saving goal, balancing the network load and avoiding network congestion. Experimental results show that under different traffic models, compared with different algorithms, the proposed algorithm improves link bandwidth utilization and throughput, and reduces the average round-trip delay of traffic, thus improving the overall network performance of the data center.
Research on Mathematical Model of Relationship between Big Data Analysis Capability and Supply Chain Based on Artificial Intelligence

In recent years, with the continuous emergence of a series of emerging technologies on the Internet, technologies such as big data and artificial intelligence have had a greater impact on supply chain management. Among them, one of the important capabilities that supply chain companies should possess is the ability to analyze big data, which has a particularly significant impact on supply chain performance management. In enterprises of different sizes, how big data analysis capabilities play a role, what impact it has on supply chain performance, and how important its importance are are all questions worth exploring. Based on the theory of dynamic capabilities, this paper introduces supply chain strategy as an intermediate mechanism between big data capability analysis and supply chain performance, and studies the relationship between the three. Quantify sample data on the basis of questionnaire surveys and use SPSS analysis software to use hierarchical regression analysis on sample data to study the impact of big data analysis capabilities on supply chain performance. The results show that: 1. Big data analysis based on artificial intelligence has a positive and direct effect on supply chain performance. 2. Big data analysis ability not only has a direct positive effect on supply chain performance, but also has an indirect effect on supply chain performance through supply chain strategy.
Automated Software Test Data Generation With Generative Adversarial Networks

With the rapid increase of software scale and complexity, the cost of traditional software testing methods will increase faster than the scale of software. In order to improve test efficiency, it is particularly important to automatically generate high-quality test cases. This paper introduces a framework for automatic test data generation based on the generative adversarial network (GAN). GAN is employed to train a generative model over execution path information to learn the behavior of the software. Then we can use the trained generative model to produce new test data, and select the test data that can improve the branch coverage according to our proposed selection strategy. Compared to prior work, our proposed method is able to handle programs under test with large-scale branches without analyzing branch expressions. In the experiment, we exhibit the performance of our method by using two modules in GNU Scientific Library. In particular, we consider the application of our method in two testing scenarios; unit testing and integration testing, and conduct a series of experiments to compare the performance of three types of GAN models. Results indicate that the WGAN-GP shows the best performance in our framework. Compared with the random testing method, the WGAN-GP based framework improves the test coverage of five functions out of the seven in the unit testing.
A Review on Sign Language Recognition (SLR) System: ML and DL for SLR

Sign Language (SL) is a parallel communication medium to spoken language, mostly used by people who are not able to speak or hear. A human interpreter is also required to work as a mediator or to act like a bridge to reduce communication gap between deaf/dumb people and normal people. However a well-trained human interpreter is not easily available and is also not cost-effective. Besides, it also compromises confidentiality and accuracy. To overcome these problems Sign Language Recognition System (SLRS) has been developed as an automatic and intelligent mediator for the communication. For a decade different Machine Learning (ML) techniques have been being used to design SLRS. This paper highlights about SL, gives a general framework of SLRS and reports different ML approaches proposed for the design of SLRS. This paper also reports Deep Learning (DL) models proposed for SLRS and finally highlights the challenges encountered in the design of SLRS.
Precise Stock Price Prediction for Optimized Portfolio Design Using an LSTM Model

Accurate prediction of future prices of stocks is a difficult task to perform. Even more challenging is to design an optimized portfolio of stocks with the identification of proper weights of allocation to achieve the optimized values of return and risk. We present optimized portfolios based on the seven sectors of the Indian economy. The past prices of the stocks are extracted from the web from January 1,2016, to December 31, 2020. Optimum portfolios are designed on the selected seven sectors. An LSTM regression model is also designed for predicting future stock prices. Five months after the construction of the portfolios, i.e., on June 1, 2021, the actual and predicted returns and risks of each portfolio are computed. The predicted and the actual returns indicate the very high accuracy of the LS TM model.
An Enhanced Universal Droop Control for Load Sharing in AC Microgrid for Different Load Condition

The compatibility of integrating solar PV, fuel cells, batteries, ultra-capacitors, wind, and EV loads with the utility grid or standalone AC microgrid has improved with the evolution of power electronic topologies. This paper proposes a hybrid control scheme for precise load sharing of several voltage source inverters (VSIs) operated in parallel and improved AC microgrid voltage and frequency stabilization under the different load conditions. This paper presents the control of an AC microgrid using an enhanced universal droop control scheme for master VSI operated in a standalone. Further, the current reference for slave VSI is set independently to account for local load variations. Hence, local load variations do not impact AC microgrid voltage or frequency. Simulation results are presented for the proposed work.
Period Doubling and Subharmonic Generation in PIN Diode Control Circuits

This paper describes work done in sub-harmonic signal generation through period doubling in a PIN diode switch circuit. The work compares simulations using a SPICE PIN diode model of period doubling with measurements in the high frequency range and shows good agreement between the measurements and simulations. Of note is the observation that the peak sub-harmonic signal is approximately 15 dBc the fundamental over an octave change in frequency.
A Mathematical Analysis of Discrete Waveform Relaxation Algorithms for Transmission Line Type Circuits

Convergence rate is greatly improved by using the new waveform relaxation (WR) methods, known as optimized waveform relaxation (OWR) algorithms, over the classical ones due to the new transmission conditions that exchange more information between subsystems when solving in parallel using multi processors. The new transmission conditions include a free parameter whose value should be chosen in an appropriate way in order to get the best convergence result. In all previous work on OWR for transmission line type circuits, the analysis for finding such value of the free parameter was performed at the continuous level in which any impact of the temporal discretization is neglected. However, it is more realistic to perform the analysis at the discrete level for real computations in a computer when solving time dependant problems. Therefore, choosing transmission line type circuits as our model problem, we perform in this paper the analysis for finding the value of the free parameter appears in the transmission conditions at the discrete level and we study the resulting convergence behavior of the discrete OWR algorithm. We focus on revealing the influence of the time discretization parameter $\triangle t$ on the convergence rate of the OWR algorithm.
Studying Transfer of Learning using a Brain-Inspired Spiking Neural Network in the Context of Learning a New Programming Language

Transfer of learning (TL) has been an important research area for scholars, educators, and cognitive psychologists for over a century. However, it is not yet understood why applying existing knowledge and skills in a new context does not always follow expectations, and how to facilitate the activation of prior knowledge to enable TL. This research uses cognitive load theory (CLT) and a neuroscience approach in order to investigate the relationship between cognitive load and prior knowledge in the context of learning a new programming language. According to CLT, reducing cognitive load improves memory performance and may lead to better retention and transfer performance. A number of different frequency-based features of EEG data may be used for measuring cognitive load. This study focuses on analysing spatio-temporal brain data (STBD) gathered experimentally using an EEG device. An SNN based computational architecture, NeuCube, was used to create a brain-like computation model and visualise the neural connectivity and spike activity patterns formed when an individual is learning a new programming language. The results indicate that cognitive load and the associated Theta and Alpha band frequencies can be used as a measure of the TL process and, more specifically, that the neuronal connectivity and spike activity patterns visualised in the NeuCube model can be interpreted with reference to the brain activities associated with the TL process.
Critical success factors and challenges for cloud ERP system implementations in SMEs: A vendors’ perspective

To reduce costs and enhance the efficiency and effectiveness of enterprises, implementation of enterprise resource planning (ERP) systems is required in a highly competitive market. ERP systems have evolved from on-premise to cloud-based, which employ cloud computing features through an application-oriented software for storing data on remote servers. However, there have been few studies that have investigated the critical success factors (CSFs) and challenges for cloud ERP systems, particularly from a vendors’ perspective in the New Zealand (NZ) environment. This paper evaluates the CSFs and challenges which influence implementation of cloud ERP systems in NZ SMEs, based on vendors’ perspectives. Indepth interviews are conducted from six cloud ERP vendors in NZ to provide a comprehensive understanding of cloud ERP implementations. Findings reveal technology-enhanced skillsets, good governance, and innovative culture as the key CSFs and lack of information compatibility between departments, failure in governance, and cost overruns as the major challenges for cloud ERP implementations in NZ SMEs. Findings from this study provide practical insights to vendors and company managers that can assist in implementing cloud ERP systems.
Three-dimensional finite element analysis based electromechanical impedance model and its application

An accurate electromechanical impedance(EMI)finite element model was established for structural health monitoring,in order to check the existing structure theories for high-frequency impedance analysis and to verify the effectiveness of the damage detection method based on high-frequency electric impedance signals.The coupling interaction among piezoelectric patches,adhesive layer,and main structure was considered and the effect of adhesive layers on EMI signals was further taken into account.A three-dimensional finite element model was developed to simulate this coupled structural system using the software ANSYS and the correlative experimental study was conducted.Results showed that the harmonic analysis based on the three-dimensional finite element model could predict the vibration modes of the structure at high frequency,which agreed well with the experimental results,and the existing structure theories for high-frequency impedance analysis were effectively checked.Further study also showed that the EMI technique can feasibly detect small and local damages in structures.In addition,the location of structural damage can be determined through reasonable distribution of piezoelectric-ceramic patches.
A Novel STT–SOT MTJ-Based Nonvolatile SRAM for Power Gating Applications

This article proposes novel nonvolatile (NV) static random access memory (SRAM) circuits based on spin transfer torque (STT) and spin orbit torque (SOT) operated magnetic tunnel junction (MTJ) device for energy-efficient power gating circuits. In which, two different designs of 9T-2MTJs and 10T-2MTJ-based NV SRAM circuits are proposed for low power, high noise margin, and faster switching speed with less area overhead. The proposed architecture uses a smaller number of transistor combinations to accommodate low-power operation with compact cell size when compared with available pieces of literature. Also, the combination of nMOS and pMOS transistors in the proposed architecture enhances the driving capability of the circuit for both low and high input operations. Here, the electrical characteristics of the proposed designs have been compared and contrasted with the available 11T-2MTJ and 7T-2D-2MTJ non volatile static random access memory (NVSRAM) and 6T SRAM circuits using a UMC-40 nm design kit and physics-based STT–SOT MTJ Verilog-A Model. The proposed 9T-2MTJ-based NVSRAM was found to be a better alternative and offers improved performance in terms of higher restore noise margin and lower power consumption than other circuits reported at this node. Moreover, the studied 10T-2MTJ exhibits an improved write and hold noise margin than the proposed/available 9T/7T-2D/11T-2MTJ circuits.
Design of Beam-Steering Kirigami Loop Antennas

In this paper, a dual-band beam-steering loop antenna based on the kirigami technique is presented. The proposed design combines two concentrical loop antennas working at 1.35 GHz and 3.45 GHz. With the rotation of the inner loop antenna, the radiation pattern can be steered simultaneously at the frequency of 3.45 GHz. To obtain better matching impedance, a stand-alone core ring is placed inside the loop. The proposed design is made of polypropylene sheet and adhesive copper film. The proposed design is portable and easy to be fabricated. The measured results agree well with the simulation data.
Ecophysiological Responses of Five Mangrove Species (Bruguiera gymnorrhiza, Rhizophora stylosa, Aegiceras corniculatum, Avicennia marina, and Kandelia obovata) to Chilling Stress

Although the low temperature is a critical growth constraint on plants, the physiological mechanism remains unclear, especially in mangrove plants. Hence, the morphological characteristics of five mangrove plants (Bruguiera gymnorrhiza, Rhizophora stylosa, Aegiceras corniculatum, Avicennia marina, and Kandelia obovata) were compared under chilling stress. The contents of hydrogen peroxide (H2O2), malondialdehyde (MDA), and proline were tested. Activities of reactive oxygen species (ROS)-scavenging enzyme [superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT)] were also measured after chilling stress. It was concluded that K. obovata can well tolerate chilling stress, and B. gymnorrhiza suffered the most severe chilling damage. Leaf-morphology observation exhibited that K. obovata and A. corniculatum can sustain chilling stress, while B. gymnorrhiza wilted and A. marina turned brown. The content of H2O2 increased at first and subsequently decreased in all plants. MDA increased instantaneously in B. gymnorrhiza and R. stylosa but changed slowly in K. obovata and A. corniculatum. The high content of proline accumulated in B. gymnorrhiza and K. obovata. The activities of the SOD, POD, and CAT increased at first and then decreased in all mangrove species. The antioxidants maintained high activity in K. obovata while decreasing earliest in A. marina exposed to the long-term chilling stress. Principal component analysis (PCA) indicated that high antioxidant enzyme activities play key roles in chilling tolerance for mangrove plants. The longer-term chilling tolerance of K. obovata may be related to the high antioxidant enzyme activities and proline accumulation. Lower H2O2 and MDA contents strengthen the anti-chilling ability of A. corniculatum. Further investigation on the molecular mechanisms will facilitate the understanding of the anti-chilling ability of mangrove plants.
Alzheimer's Disease Classification Through Imaging Genetic Data With IGnet

The application of deep learning techniques to the detection and automated classification of Alzheimer's disease (AD) has recently gained considerable attention. The rapid progress in neuroimaging and sequencing techniques has enabled the generation of large-scale imaging genetic data for AD research. In this study, we developed a deep learning approach, IGnet, for automated AD classification using both magnetic resonance imaging (MRI) data and genetic sequencing data. The proposed approach integrates computer vision (CV) and natural language processing (NLP) techniques, with a deep three-dimensional convolutional network (3D CNN) being used to handle the three-dimensional MRI input and a Transformer encoder being used to manage the genetic sequence input. The proposed approach has been applied to the Alzheimer's Disease Neuroimaging Initiative (ADNI) data set. Using baseline MRI scans and selected single-nucleotide polymorphisms on chromosome 19, it achieved a classification accuracy of 83.78% and an area under the receiver operating characteristic curve (AUC-ROC) of 0.924 with the test set. The results demonstrate the great potential of using multi-disciplinary AI approaches to integrate imaging genetic data for the automated classification of AD.
Cognitive Impairment of Patient With Neurological Cerebrovascular Disease Using the Artificial Intelligence Technology Guided by MRI

This study was to explore the application of MRI based on artificial intelligence technology combined with neuropsychological assessment to the cognitive impairment of patients with neurological cerebrovascular diseases. A total of 176 patients were divided into a control group, a vascular cognitive impairment non-dementia (VCIND) group, a vascular dementia (VD) group, and an Alzheimer's disease (AD) group. All patients underwent MRI and neuropsychological evaluation and examination, and an improved fuzzy C-means (FCM) clustering algorithm was proposed for MRI processing. It was found that the segmentation accuracy (SA) and similarity (KI) data of the improved FCM algorithm used in this study were higher than those of the standard FCM algorithm, bias-corrected FCM (BCFCM) algorithm, and rough FCM (RFCM) algorithm (p < 0.05). In the activities of daily living (ADL), the values in the VCIND group (23.55 ± 6.12) and the VD group (28.56 ± 3.1) were higher than that in the control group (19.17 ± 3.67), so the hippocampal volume was negatively correlated with the ADL (r = −0.872, p < 0.01). In the VCIND group (52.4%), VD group (31%), and AD group (26.1%), the proportion of patients with the lacunar infarction distributed on both sides of the brain and the number of multiple cerebral infarction lesions (76.2, 71.4, and 71.7%, respectively) were significantly higher than those in the control group (23.9 and 50%). In short, the improved FCM algorithm showed a higher segmentation effect and SA for MRI of neurological cerebrovascular disease. In addition, the distribution, number, white matter lesions, and hippocampal volume of lacunar cerebral infarction were related to the cognitive impairment of patients with cerebrovascular diseases.
Editorial: The CRISPR/Cas System in Pathogen Resistance, Virulence, Diagnosis and Typing

Department of Epidemiology, College of Public Health, Zhengzhou University, Zhengzhou, China, National Institute for Communicable Diseases Prevention and Control, Chinese Center for Disease Control and Prevention, Beijing, China, Department of Cell and Molecular Biology, QIMR Berghofer Medical Research Institute, Herston, QLD, Australia, Center for Disease Control and Prevention of PLA, Beijing, China