Miracle-dz/UniFAR-FTUs huggingface dataset:
Empirical Study of Awareness towards Blended e-learning Gateways during Covid-19 Lockdown

One of the most remarkable changes in the academic Diaspora is the international creation of virtual platforms, which has given rise to a new edge system of learning. Covid-19 presents a unique and severe problem on every front. The nationwide shutdown by the administration aims to control the diffusion of Covid-19 at education institutions across the country. Many (local, national, and worldwide) institutions have implemented a reliable and beneficial contactless atmosphere for students and faculties to maintain the continuity of learning. As a result, teachers and students are greatly influenced by the new-age virtual teaching method adopted and implemented. The survey respondents were picked by a combination of online surveys and personality tests, and then the questionnaire they were given included both closed- and open-ended items. The numbers of university and secondary school portals have recently seen an upward trend. So, to better investigate the abilities of teachers and learners to identify the efficiency of dominating content delivery methods, a hybrid approach of the exploratory study was employed. Students and faculty, 140 each who have taken web-based learning at 25 Indian institutions, are sampled using a snowball sampling methodology. The results of the t-test demonstrated a considerable divergence in teaching-learning impressions between faculty and students on three manifests ($\mathrm{p} < 0.005$). Learners' responses differed from faculty responses, and statistically significant differences were found, such as scientific material can be taught effectively online, improved technocratic pedagogy is the core part of e-learning, reliance on computers/connectivity.
State Recognition of Isolating Switch in Traction Substation Based on Dual Network

Aiming at the problem that it is difficult to locate and identify the isolating switch in complex scenes, this paper studies a method of isolating switch state recognition based on dual network, which is divided into C-R2CNN isolating switch detection and SqueezeNet state recognition. Firstly, C-R2CNN is a rotating box detection network based on R2CNN, in which the channel attention module is utilized to effectively eliminate noise interference and extract features, improve the detection accuracy of the model, and realize the precise positioning of the isolating switch. Secondly, the isolating switch detection network is used to locate the isolating switch sub-region in the original image, then the target sub-region is cut out and sent to SqueezeNet network for state recognition, thus realizing the state recognition of the isolation switch. Experiments show that the method proposed in this paper can effectively identify the state of isolating switch.
A relationship-first approach to joint entity relationship extraction

Entity relationship extraction, which aims to extract semantic relationships between two entities from unstructured text, is a key step in building knowledge graphs. Existing works using entity-first phased joint extraction methods, which has achieved good results on the task of entity overlapping triad extraction, but all suffer from a large number of redundant operations severe exposure bias phenomenon. In response, this paper proposes a relationship-first joint extraction method that prioritizes the extraction of relationships between entities to avoid redundant computations, then uses conditional layer normalization to fuse a priori information to mitigate exposure bias, and finally uses the generation of entity expression vectors that fuse relationship information for relationship extraction. In the experimental part, the model performance is evaluated and analyzed, and the experimental results show that the method in this paper achieves 91.4% and 92.2% F1 values on both NYT and WebNLG datasets, which is better than the existing models.
Prediction of Birth rate in China under three-child Policy based on neural network

China’s birth rate has not increased since the introduction of the two-child policy on January 1, 2016, and has even been on a downward trend since 2018. The aging trend has already appeared in the age structure of Our country. In order to further improve the young population ratio, the three-child policy was put forward on May 31, 2021. Birth rates are influenced by a variety of factors, such as housing prices and the cost of education, and these factors are cross-correlated, making it relatively difficult to study comprehensively. But the neural network model overcomes this shortcoming better, it is mainly the law of data itself to train its own law model to predict. Therefore, this paper collects the birth rate data of China from 1980 to 2020, and uses neural network to predict it. In this paper, the experimental results show that the birth rate will not increase much in the next 2-3 years, but will increase a few points in the next few years.
Credit Decision System for Micro, Small and Medium Enterprises (MSMEs) Based on Neural Network Algorithm and Nonlinear Programming

Micro, Small and Medium Enterprises (MSMEs) play an important role in economic development However, due to the prevalence of information asymmetry, MSMEs are hard to borrow money and banks have difficulties in accurately assessing credit risks of MSMEs. In order to solve these problems in credit decision, we establish a credit risk assessment model for MSMEs based on the principle of back propagation (BP) neural network learning algorithm. After iteratively solve the nonlinear programming problem with the interior point method, a credit decision model is obtained. As the production and operation of enterprises and economic benefits may be affected by some unexpected factors, following that, the credit strategy is adjusted according to the enterprise's own business condition and market influencing factors.
Research on regional personnel monitoring technology based on channel state information

In view of the shortcomings of traditional camera and sensor type monitoring, such as blind spots, limited recognition distance and sensitive scene limitations, this paper proposes a human activity detection and monitoring method based on channel-state-information (CSI). The CSI information of the WiFi signal in the monitored area. Next, use the Butterworth low-pass filter to detect and remove abnormal data. And then use the principal component analysis (PCA) to extract the features of the human body posture, gait information, and number of people model; Learn to build a number recognition model for CSI data; because everyone is different, gait information can be used as an ID for human identification to identify different identities, and the human gait information based on Dynamic Time Warping (DTW) can be Effective identification, so as to play the effect of regional environmental monitoring. In the experiment, this method can achieve 92% capture performance for human gesture recognition, more than 93% error in indoor area recognition is less than 1, and the correct rate of gait recognition is up to 95.2%.
Functional Analysis of MOSFETs and HEMTs with Laser Stimulation and Photonemission

v Abstract Die moderne Mikroelektronikindustrie ist an der Ver fügbarkeit effektiver Verfahren zur Lokalisierung von Fehlern in Schaltkreisen und/oder inzelnen Bauelementen stark interessiert. In den letzten Jahren diesbezüglich e ntwickelte Analyseverfahren wie Laserstimulationstechniken und Photonemissionsmikro skopie (PEM) gelten mittlerweile als hocheffektive, nichtdestruktive und zeitsparende Ve rfahrenstechniken bei der Fehleranalyse mikroelektronischer Bauelemente und Schaltkreise. U m das Potenzial dieser Analyseverfahren auszuschöpfen und weiterzuentwickeln ist ein tiefer es Verständnis der das Analysesignal determinierenden physikalischen Effekte notwendig. In dieser Arbeit werden Untersuchungen vorgestellt, die einen Beitrag zum Verständnis und der Charakterisierung verschiedener Effekte sowohl bei der Wechsel-wirkung von Laserstr ahlung mit MOS Transistoren als auch der Photonemission von AlGaN/GaN HEMTs liefern soll en. Das Hauptaugenmerk dieser Arbeit betrifft Untersuch ungen zur Laser-stimulation von MOS Transistoren. Effekte der Wechselwirkung zwischen L aser und dem Bauelement werden systematisch charakterisiert. In Anwendung von Lase rlicht mit einer Wellenlänge von 1300 nm werden Effekte der thermischen Laserstimulation von Silizium MOS Transistoren untersucht. Überdimensionierte Bauelemente sind ver wendet für die Untersuchungen, um die Signalbeiträge der diversen Schaltungskomponenten z u unterscheiden. Thermische Laser Stimulationseffekte (TLS) sind unt ersucht und mit den Temperaturabhängigkeit der Transistor Parametern ko rreliert. Die Stimulationsergebnisse bestätigt die Tatsache, dass die thermische Effekte an MOSFET infolge der Absorption sowohl in dem hoch dotierten Silizium als auch in M etall Regionen in Erscheinung treten. Indirekte Erwärmung des Transistorkanals mittels Wä rmediffusion über die in der Umgebung liegende metallische Strukturen scheint als der dom inante TLS-Effekt der MOSFET. Außer der Untersuchungen der thermischen Effekte si nd auch photo-elektrische Effekte an dem MOSFET charakterisiert. Die Stimulationsergebni sse werden bestätigen, dass die Anwendung einer 1064 nm Laser in dem Bauelementen n icht ur photoelektrische Effekte mittels Elektron-Loch Paar Generation induzieren ka nn sondern auch in der Lage ist,
Anti-multi-jammer forwarding deceptive jamming based on distributed SAR

Aiming at the problem that deceptive jamming generated by multiple jammers can effectively generate high-fidelity false targets in SAR images, a distributed SAR anti-deceptive jamming method based on co-location and spatial suppression is proposed in this paper. Firstly, the SAR observes the target from different angles, and recognizes jamming based on the difference of target position in different images. Secondly, there is a certain difference in the interference phase between real target and false target through analyzing the principle of deceptive jamming, and then the false target generated by different jammers can be classified by cross-orbit SAR. Thirdly, the positions of multiple jammers are co-located by the spatial distribution characteristics of false targets and radars. Finally, the jamming signal is suppressed by the adaptive beamforming method of jammer positioning. The effectiveness of the proposed method is verified by simulation.
SSD Drive Failure Prediction on Alibaba Data Center Using Machine Learning

Flash-based Solid-State Drives (SSDs) have become a critical storage tier in data centers and enterprise storage systems. Cloud companies are very interested in predicting drive failures. Drive failure prediction enables managing drive replacement and backup data beforehand and helps planning drive purchase strategies. Solidigm and Alibaba collaborate to collect and analyze Self-Monitoring, Analysis, and Reporting Technology (SMART) data and predict SSD failures 30 days ahead of time using machine learning techniques. In this paper, we use group k-fold cross-validation to select the best parameters for machine learning models and avoid overfitting. After obtaining the prediction score of each sample from the model, a post-processing with neural network is applied on those prediction scores to get the drive-level prediction. A modified ensemble learning method is designed and implemented by majority voting on different models of Light GBM and Random Forest to further improve prediction results. This paper is the first work in both academia and the storage industry to design a drive failure prediction system for deploying in data centers by optimizing models with the highest Precision instead of the highest F1-score to minimize false positive rate. We advance to get drive failure prediction with 100% Precision and 21% Recall, enabling us to avoid the high cost of false positives.
Migration and Tuning of Software Prefetching for Sunway Multi-Core Processor

Data prefetching is a widely used technique to alleviate "memory wall" problem by fetching the data that may be touched in the near future in advance. Generally, data prefetching is classified into hardware prefetching and software prefetching. Compared to hardware prefetching, software prefetching is more flexible, and typically achieves higher prefetch accuracy. Currently, Sunway multiple-core processor only supports hardware prefetching. To study how software prefetching perform on Sunway processor, in this paper, we first migrate the software data prefetching in the GCC complier to Sunway processor. Then we tune the loop-level prefetching cost model according to Sunway processor’s hardware features. Finally, we conduct experiments to evaluate the effectiveness of the tuned software prefetching. Results show that, compared to the baseline where no prefetching is applied, software prefetching delivers an average speedup of 1.08x (up to 2.46x) and 1.16x (up to 1.88x) for SPECint 2006 and SPECfp 2006 benchmark suite, respectively. Moreover, software prefetching outperforms hardware prefetching for both benchmark suites. This demonstrates the efficacy of software data prefetching.
Leveraging the Layered Architecture for Microservice Recovery

The microservice-oriented architecture (MSA) is an architectural style which involves organizing an application as of small independent services, each oriented towards one business functionality while being data autonomous. In pursuit of modernizing their software to take advantage of the Cloud, companies have been eager to migrate their monolithic legacy software towards an MSA. This migration necessitates an identification phase to reorganize classes around the monolith’s functionalities as a set of microservice candidates. However, most identification approaches fail to utilize the monolith’s internal multilayered architecture to identify those functionalities, and thus the microservices. As a consequence, ignoring the internal multilayered architecture increases the risk of identifying microservice by their technical layer which is recognized as a conceptual anti-pattern. In this paper, we explore the impact of the multi-layer architecture in monolithic applications during the identification to develop a semi-automatic approach that relies on it to identify an MSA. Particularly, we analyze the presentation layer to determine the endpoints of each business functionality of the monolith. From these endpoints, we apply a vertical decomposition to identify the necessary classes to implement each feature as a microservice. In the process, we also define the bounded context of each microservice during the vertical decomposition of the data-access layer. For the evaluation, we implemented a model-driven process and applied it on a set of varying open-source applications commonly used in the literature. We compared the results of approach with and without the reverse-engineering of the internal architecture to measure the impact of our approach on the identification of quality microservices. Using decomposition metrics (e.g., MoJoFM, c2ccvg), we were able to measure a significant positive impact.
Magnetic Type Classification in Sunspot Group Based on Semi-supervised Learning and Knowledge Distillation

Sunspot group, known as active solar regions, is the main sources of solar storms. The morphological and magnetic characteristics of solar active regions play a very important role in solar storm forecasting and is well described by Mount Wilson Sunspot Classification Scheme. The development of convolutional neural network methods in the field of image processing makes efficient magnetic type classification possible. In this paper, we propose a method based on semi-supervised learning and knowledge distillation for magnetic type classification in sunspot group. On the sunspot magnetic type classification dataset, our method achieves 95.14% total classification accuracy, 97.4%, 94.43% and 85.71% F1-scores of Alpha, Beta, and Beta-x types respectively.
An introduction to the design of small-scale embedded systems

This book offers a comprehensive and balanced introduction to the design of small embedded systems. Important topics covered include microcontroller architectures, memory technologies, data conversion, serial protocols, program design, low power design, and design for the real time environment. The final chapter ingeniously applies systematic engineering design principles to embedded system design. While the Microchip PIC 16F84 is used extensively to illustrate the early material, examples elsewhere are drawn from a range of microcontroller families, leading to a broad view of device capabilities.
Carrot Cure: A CNN based Application to Detect Carrot Disease

Carrot is a famous nutritional vegetable and developed all over the world. Different diseases of Carrot has become a massive issue in the carrot production circle which leads to a tremendous effect on the economic growth in the agricultural sector. An automatic carrot disease detection system can help to identify malicious carrots and can provide a guide to cure carrot disease in an earlier stage, resulting in a less economical loss in the carrot production system. The proposed research study has developed a web application “Carrot Cure” based on Convolutional Neural Network (CNN), which can identify a defective carrot and provide a proper curative solution. Images of carrots affected by cavity spot and leaf bright as well as healthy images were collected. Further, this research work has employed Convolutional Neural Network to include birth neural purposes and a Fully Convolutional Neural Network model (FCNN) for infection order. Different avenues regarding different convolutional models with colorful layers are explored and the proposed Convolutional model has achieved the perfection of 99.8%, which will be useful for the drovers to distinguish carrot illness and boost their advantage,
Reynolds-Averaged Navier–Stokes Simulations on NACA0012 and ONERA-M6 Wing with the ONERA elsA Solver

Results of the elsA software package, the ONERA multipurpose tool for applied aerodynamics and multiphysics, are presented for a well-known NACA0012 configuration proposed by the turbulence modeling resource website of NASA Langley Research Center and on the ONERA-M6 wing. ONERA proposed new computer-aided design models for the turbulence modeling resources to make ONERA-M6 a reference test case. Results and solver performance are presented and discussed.
An Analysis on the Evolutionary Characteristics of Ubiquitous City through Evolutionary Map of Ubiquitous City

This study aims to analyse the U-City characteristics through the U-City historical mapping. The U-City characteristics were analysed by building the U-City historical map in terms of STIM model which consists of service, technology, infrastructure and management. The data for analysis is the National Informatization White Paper published by the NIA (National Information Society Agency) from 2002 to 2013. As a result, first, the U-City service were evolved from administration informatization, enterprise informatization, administration/living informatization and administration/space/private informatization through the intelligence facilities and space. Second, the U-City technology were changed through wire network, sensor/network, processing/super-highway network, convergence of network/security. Third, the U-City infrastructure have had evolutionary process such as wire computer network, wire/wireless network, intellectualization facility and intelligent facility space. Forth, the U-City management were carried out with making the unit network/infrastructure management, information connection/operating management and information integration/participation management. Therefore, the history of U-City has been making rapid development in government computerization, computer oriented society, information city and ubiquitous city.
A Study On the Design of a Servo Controller for a Tracking Antenna System between Moving Vehicles by the Fuzzy-PID Controller

For continuous communication between moving vehicles such as satellites and unmanned aerial vehicles, an antenna system having at least more than 2-axes is needed. When the antenna is mounted on a moving vehicle such as ground vehicle, ship and so on, a stabilization and tracking system must be equipped to compensate the roll, pitch and yaw motion of the vehicle. The performance of stabilization and tracking system mainly depends on the servo control system that driving the antenna pedestal. Therefore, in this paper, a Fuzzy-PID controller for a stabilization and tracking system of a 2-axes antenna was designed and the performance was verified. To verify the verification of designed servo control system, the performance of the conventional PID controller and that of the Fuzzy-PID controller, designed by the same PID control gains, was compared.
Layout Optimization for Agriculture or Small Scale Agrarian Industry

As a developing country, India is gradually developing at a good level in terms of industries, machinery, infrastructure, and so on, and it is highly believed that by increasing the efficiency of different areas, the country can be extended to the next advanced level. In any case, very less attention is given to modern agriculture, which occupies one of the major areas of our Gross Domestic Product (GDP). A more specified research works are required to further enhance the agricultural sector with novel farming sector layouts. This design improvement will benefit the farmer by making their work easier and increasing productivity, and the technology will also allow the farmers to recycle the unwanted garbage. The proposed research has thoroughly analyzed some of the primary challenges encountered by farmers in order to reduce the further damage in agricultural field.
A multi-controller deployment method for satellite networks based on network delay

Software-defined network (SDN) has the advantages of decoupling the data plane and control plane and flexible centralized control. It is considered to introduce SDN technology into satellite networks. In this paper, a satellite network architecture based on SDN is proposed. Aiming at the delay problem of satellite networks, a multi-controller deployment method of improved Marine Predator Algorithm (IMPA) is proposed, which improves the updating strategy in the initial predation stage and introduces a simulated annealing algorithm to further optimize the convergence speed and accuracy of the algorithm. Simulation results show that IMPA can achieve load balancing while reducing network delay compared with MPA and HPSO.
Child Mortality Prediction using Machine Learning

Children under the age of five are considered to be mortal in this context. The death rate for children under the age of five, or the under-five death rate, refers to the likelihood of dying between the ages of birth and the age of five. The death of a fetus is just as common as the death of a kid. The goal is to study AI-based strategies for determining the mortality fetal well-being arrangement that provides the best precision. It will be necessary to examine the entire dataset using the SMLT regulated AI strategy in order to identify the few data points that are similar to variable identification, univariate investigation, bivariate investigation, and multivariate investigation, as well as missing worth medicines and dissect information approval, cleaning/getting ready, and information perception. Using the results of this study, a complete approach has been developed to sensitivity analysis for model parameters that affect fetal health categorization. This paper proposes a machine learning-based approach for predicting child mortality and compares various machine learning methods against the provided dataset.