Miracle-dz/UniFAR-FTUs huggingface dataset:
Image Recognition from COVID-19 X-ray Images Utilizing Transfer Learning

The purpose of this study is to achieve an accurate classification of medical images of pneumonia using transfer learning. Pulmonary radiographs can provide physicians with an excellent aid in the diagnosis of pneumonia, and this paper collects images from patients with common bacterial pneumonia, COVID-19 disease, and normal incidents pulmonary x-rays are used as a dataset for transfer learning. The deep convolutional neural network framework proposed in recent years can be used to extract features from the dataset and classify them to obtain models with high accuracy. The transfer learning-based approach can not only reduce the dependence of the network model on the dataset but also significantly reduce the time and hardware costs, and the trained models usually produce significant results as well. A total of 4,500 x-ray images are collected in this paper, including 1,500 confirmed images of confirmed COVID-19 disease, 1,500 images of confirmed bacterial pneumonia, and 1,500 images of normal conditions. This paper, VGG19, ResNet-50, and GoogLeNet are used as the basis for transfer learning. The results show that using transfer learning can effectively extract advanced features of pneumonia and construct a pneumonia medical image recognizer, which is trained to recognize the models. The accuracy rates is 93.67%, 91.73%, and 90.80%, respectively, and the model shows excellent generalization ability. According to the results, this study can assist medical professionals in the diagnosis of pneumonia condition and effectively shorten the diagnosis time.
Distributed Data Center Failure Analysis Model Based on Deep Learning

Using deep neural network algorithms, application scenarios in a distributed multi-data center environment, in response to fault location requirements, a distributed multi-center fault model is established, operation and maintenance data is analyzed, and key infrastructure states are modeled and located, carry out the digital representation of common failure scenarios to accelerate the positioning process of automatic operation.
Community Detection Algorithm Based on Network Feature Vector Space

Community detection plays an important role in the research of complex networks. It is very convenient to map the elements of the network into vector space to study the community structure of networks. Aiming at the problems that the number of communities is difficult to determine and the accuracy is not high in many community partition algorithms, a community detection algorithm based on network eigenvector space is proposed based on graph partition algorithm. The results show that the algorithm has better accuracy and the result of community partition is closer to the real community.
Research on positioning algorithm of binocular camera based on multi-media

With the development of computer technology, image processing technology has become an increasing number of mature. However, the accuracy of target positioning has not reached the expectation in the case of multi-medium. Based on the binocular positioning algorithm, Aiming at the binocular positioning algorithm, this paper proposes a multi-medium binocular positioning algorithm. First, from the calibration of the camera, the internal parameters of the camera is obtained and the distortion caused by the camera is removed, and the picture is corrected. Secondly, the position of the target is detected by the YOLOv4 algorithm and the pixels of the target in the picture is obtained. Finally, through the binocular vision algorithm, camera imaging principle and the propagation characteristics of light on the interface of different medium, the spatial position of the target in the world coordinate system is obtained.
Trend Analysis of Research Direction in Computer Science Based on Microsoft Academic Graph

The field of computer science and its derived 34 research directions have developed dynamically and rapidly in recent decades, but the development trend and potential correlation of each research directions have not been well studied. Based on the Microsoft Academic Graph dataset, this paper obtained 13,126,576 pieces of academic data related to the computer science field from 1995 to 2019, and proposed an algorithm to identify the research directions of the paper. Finally, we obtained the number of papers in each research direction under the computer science over the years through bibliometrics and conducted trend analysis and correlation analysis.
Empowering Makers – A Design Science Approach

The advent of makerspaces has increased access and utilization of facilities for digital production. They can be of benefit for timely education, not only for qualifying existing workforce in production industries, but also for attracting students in production-related domains. We discuss the case of a regional makerspace and its first evaluation study to propagate additive manufacturing through a structured while individualized education scheme. Thereby, additive manufacturing capacity building starts with individual goal setting and structuring requirements for an envisioned solution which becomes part of a learning contract of a specific project. Learning steps are framed by design science stages and cycles, since artefacts in making can be of various kinds, stemming either from construction, modeling, material, or manufacturing exploration. The evaluation study revealed essential benefits in terms structured planning and individualization of capacity building processes.
Designing of New Global Sliding Mode Controller for Bridge Crane

For the local robustness problem of the bridge crane sliding mode control method, a global back-stepping sliding mode control (GBSMC) law is proposed for 2D underactuated bridge crane systems. Different from the existing bridge crane control methods, the proposed controller not only achieves good control performance, but also ensures the global robustness of the control system. Specifically, we introduce a novel global sliding surface and construct the global back-stepping sliding mode controller with strong robustness in the whole process of system response by using back stepping method. The Lyapunov stability theory is employed in to analyze the stability and convergence of the closed-loop system. The simulation results are illustrated to demonstrate that the proposed controller in this paper can achieve better control effect than the existing methods and has strong robustness against external interference.
Electricity Load Forecasting Based on Sub-Item Data

Load forecasting is the basic work to carry out many power grid operations. With the improvement of living standards of residents, the total load of residential buildings increases, while the proportion of energy consumption of household appliances increases. Therefore, accurate prediction of household appliances energy consumption is of great significance for accurate prediction of residential load. The factors affecting the energy consumption mode of household appliances are complex, which are closely related to the user's use habits and their own control principles. Based on the electrical data obtained from power load monitoring, this paper presents a method of electrical energy consumption pattern analysis based on hierarchical clustering analysis and a method of home load forecasting based on Markov Chain Monte Carlo. Through the analysis method of electrical energy consumption mode, we can get the power consumption mode of different electrical appliances and the correlation degree between different electrical appliances. Then we can get the power consumption mode of a single family through the Monte Carlo-based household load forecasting method. Finally, different types of households are aggregated to the ground by bottom-up method to finish area load forecasting.
Editable 3D Model Based on Generative Adversarial Network

In recent years, 3D modeling technology has become more and more popular in emerging VR and AR applications. Due to the complexity and diversity of hair styles in the real world, hair modeling is still challenging, so it is very important to study hair modeling methods. This paper presents an interactive single - view 3D hair generation method based on generative adversarial network. We construct a parameter transformation by generative adversarial network to map the 2D hair to the 3D hair structure. The new 3D hair is generated by editing the 2D hair information and the 3D hair interactive operation is realized. Interactive editing mode mainly includes drawing mode. The results show that this method has good flexibility and superiority in 3d hair generation and user controllability.
Exploration of University Students’ Mobility Behavior on Campus

It is a subject worthy of being studied to predict human mobility through the big data of human movement trajectory. The prediction has been widely used in many fields. In this subject, we record and analyze the movement trajectory of students on campus during their university years with a purpose to make a prediction on mobility based on Markov chain models. We explore from the Campus Smart Card of the university, which records most of the activities of a university students on campus, and locks its position. Specifically, our data set include about 16.8 million consuming logs came from 4,741 students, they study at school from Sept, 2015 to June, 2019. The predictability differences among different students are smaller than those of individuals in Call Detail Records (CDR) data, which means that students seem more predictable. We also make predictions based on Markov chains model of different orders and find that high order Markov chains have better performance, although the inherent reason for this needs further research. Our work provides sufficient support in predicting human mobility area.
Quality Assessment of High-Voltage Transmission Lines Based on Multi-modal Data Fusion

Quality assessment is one of the essential aspects of the acceptance of transmission line project completion. To resolve the challenge of efficiency and accuracy, we presents a multi-modal data fusion approach established on two new technology, which are the 3D laser point cloud technology and the BIM technology. The scanned 3D laser point cloud data of transmission lines are firstly filtered and classified to build the laser point cloud model; The BIM based 3D model of transmission lines are then converted to the BIM point cloud model; the laser point cloud model and the BIM point cloud model are registered through application of both a coarse registration and a fine registration; Differences between the laser and the BIM point cloud models are assessed for the acceptance of transmission line project. Validation results showed that the multi-modal fusion algorithm had highly satisfactory performance.
MircoRV32: an open source RISC-V cross-level platform for education and research

In this paper we propose μRV32 (MicroRV32) an open source RISC-V platform for education and research. μRV32 integrates several peripherals alongside a 32 bit RISC-V core interconnected with a generic bus system. It supports bare-metal applications as well as the FreeRTOS operating system. Beside an RTL implementation in the modern SpinalHDL language (μRV32 RTL) we also provide a corresponding binary compatible Virtual Prototype (VP) that is implemented in standard compliant SystemC TLM (μRV32 VP). In combination the VP and RTL descriptions pave the way for advanced cross-level methodologies in the RISC-V context. Moreover, based on a readily available open source tool flow, μRV32 RTL can be exported into a Verilog description and simulated with the Verilator tool or synthesized onto an FPGA. The tool flow is very accessible and fully supported under Linux. As part of our experiments we provide a set of ready to use application benchmarks and report execution performance results of μRV32 at the RTL, VP and FPGA level together with a proof-of-concept FPGA synthesis statistic.
High-speed Dual Langmuir Probe Measurements of the Plasma Properties and EEDFs in a HET plume

Using an advanced High-speed Dual Langmuir Probe (HDLP) with a 1-µs temporal resolution, the plume of an 11 kW nested-channel Hall Effect Thruster (nested-channel HET or NHET) is investigated. The HDLP employs a single active Langmuir probe that is rapidly swept from -30 V to +30 V over a time as short as 1 µs (an insulated null probe is driven in parallel to compensate for adverse high-frequency effects). The resulting properties of electron (and ion) density, electron temperature, plasma potential, floating potential, and electron energy distribution function (EEDF) are all acquired simultaneously with excellent temporal and spatial resolution. A planar region measuring 2.5 mean channel diameters radially and 2.25 mean channel diameters axially is mapped beginning 2.5 mean channel diameters downstream from the thruster exit plane (410 total grid points). Large collections (0.25 seconds at 0.4 MHz for all plasma properties at each grid point) of the acquired high-speed probe data are combined together using a method of spatio-temporal data fusion that enables the synchronization of all the spatial grid-points to a common temporal evolution. The resulting visualizations of the various plasma transients observed show large amplitude bursts of plasma corresponding to the Hall thruster breathing mode as well as inter-channel coupling between the simultaneously discharging nested channels. The time-resolved EEDFs in the plume of the thruster display distributions that are predominantly Maxwellian and contain transient bursts of higher energy electrons that occur with breathing mode oscillations observed in the total discharge current signal. Overall, these spatially and temporally resolved measurements reveal the strongly electrodynamic nature of HET discharges that permeates all plasma properties at all plume positions investigated in this study with relative fluctuations averaging approximately 10% to 15% .
Legal judgment prediction based on computer topology learning and fact elements

Legal Judgment Prediction (LJP) is an application of artificial intelligence techniques to predict the judgment results of criminal cases. In the real word, the criminal fact of cases is complex, leading to the diversity and flexibility of the judgment results. This paper presents a new approach to complete the Legal judgment prediction based on topology learning and fact elements. We concentrate on cases with the same charge to improve the accuracy of prediction of judgment on term of imprison. Considering about the peculiarity of LJP contrasting with common text classification, we try to select some key elements from the description of fact in legal case texts, and put them into the process of text classification learning by combining these fact elements with Topological Learning. The experiments are demonstrated to illustrate the effectiveness of our proposed method.
Research on Comprehensive Potato Classification Based on Support Vector Machine

In order to identify the potato accurately and quickly, this paper uses saturation and support vector machine to classify potatoes. Firstly, we collect 90 potato image samples, and then use the method of extracting the S component to filter out the unusable potato targets due to budding. Secondly, the R, G and B components are extracted and to perform R+G which is gray-scaled. The point noise is removed through corrosion expansion, then we use the boundary function for finding the image boundary. Finally, according to the rectangle degree and other parameters as the shape feature, the support vector machine is trained to classify the potatoes. The experimental results show that this method can classify potatoes effectively in potato shape and budding, it meets the demand for potato commercial production.
Research on Grid Voltage Feed-forward Control of LCL Inverter consider Capacitor Current Estimate

The three-phase grid-connected inverter system with LCL filter bears lower stability due to the characteristic of gaining peak at its resonance, and the distortion of grid voltage will cause grid-connected current. To address these problem, this paper adopts the method of applying capacitor current feedback to increase system damping and restrain the peak of resonance, in which the value of the capacitor current can be obtained through estimation, aiming to avoid increasing the extra sensors, then adopts the grid voltages feedforward scheme to eliminate the influences of the distorted grid voltage, and finally proposes the feedforward function of the grid voltage of a three-phase grid-connected inverter with an LCL filter in the stationary α-β frame. Simulation and experimental results show that it is effective that the method described in this paper.
Internalizing psychopathology and all‐cause mortality: a comparison of transdiagnostic vs. diagnosis‐based risk prediction

Previous studies have documented the utility of a transdiagnostic internalizing factor in predicting important future outcomes (e.g., subsequent mental disorder diagnoses). To date, however, no study has investigated whether an internalizing factor predicts mortality risk. Also, while previous studies of mortality risk have emphasized its associations with particular internalizing disorders, no study has assessed how the transdiagnostic internalizing factor vs. disorder‐specific variance differently predict that risk. The primary aims of this study were to explore: a) whether the internalizing factor predicts mortality risk, b) whether particular internalizing psychopathologies uniquely predict mortality risk over and beyond the transdiagnostic internalizing factor, and c) whether there is a significant interaction of internalizing with self‐reported health in the prediction of mortality risk. We utilized a large national sample of American adults from the Midlife in the United States (MIDUS), a longitudinal study that examined midlife development of individuals across multiple waves between 1995 and 2015. Data were analyzed for the 6,329 participants who completed the phone interview and self‐administered questionnaire in MIDUS 1 (1995‐1996) and were then followed up until October 31, 2015 or until death. To investigate the association between internalizing and mortality risk, we used the semi‐parametric proportional hazards Cox model, where survival time was regressed on a latent internalizing factor. Overall findings indicate that a transdiagnostic internalizing factor significantly predicts mortality risk over a 20‐year period (hazard ratio, HR=1.12, 95% CI: 1.05‐1.16, p<0.01) and that internalizing outperforms disorder‐specific variance (e.g., depression‐specific variance) in the prediction of that risk. Further, there was a significant interaction between transdiagnostic internalizing and self‐reported health, whereby internalizing psychopathology had a specific association with early death for individuals with excellent self‐reported health condition (HR=1.50, 95% CI: 1.17‐1.84, p<0.05). This highlights the clinical utility of using the transdiagnostic internalizing factor for prediction of an important future outcome, and supports the argument that internalizing psychopathology can be a meaningful liability to explore in public health practice.
The promise of machine learning in predicting treatment outcomes in psychiatry

For many years, psychiatrists have tried to understand factors involved in response to medications or psychotherapies, in order to personalize their treatment choices. There is now a broad and growing interest in the idea that we can develop models to personalize treatment decisions using new statistical approaches from the field of machine learning and applying them to larger volumes of data. In this pursuit, there has been a paradigm shift away from experimental studies to confirm or refute specific hypotheses towards a focus on the overall explanatory power of a predictive model when tested on new, unseen datasets. In this paper, we review key studies using machine learning to predict treatment outcomes in psychiatry, ranging from medications and psychotherapies to digital interventions and neurobiological treatments. Next, we focus on some new sources of data that are being used for the development of predictive models based on machine learning, such as electronic health records, smartphone and social media data, and on the potential utility of data from genetics, electrophysiology, neuroimaging and cognitive testing. Finally, we discuss how far the field has come towards implementing prediction tools in real‐world clinical practice. Relatively few retrospective studies to‐date include appropriate external validation procedures, and there are even fewer prospective studies testing the clinical feasibility and effectiveness of predictive models. Applications of machine learning in psychiatry face some of the same ethical challenges posed by these techniques in other areas of medicine or computer science, which we discuss here. In short, machine learning is a nascent but important approach to improve the effectiveness of mental health care, and several prospective clinical studies suggest that it may be working already.
Comparison of Four Extraction Methods for Essential Oil from Thymus daenensis Subsp. Lancifolius and Chemical Analysis of Extracted Essential Oil

The air dried aerial parts of Thymus daenensis were subjected to extract the essential oil (EO) using hydrodistillation (HD), ohmic (O), ultrasound-assisted hydrodistillation (UAHD) and ultrasound-assisted ohmic (UAO) extraction methods. The influence of applied different extraction methods on extraction parameters, microstructure of oil glands and chemical properties of extracted EO were investigated. Hydrodistillation and ultrasound-assisted ohmic extraction methods showed the longest and the shortest extraction onset time, and ultrasound-assisted hydrodistillation and ohmic extraction methods showed the most and the least extraction efficiency (W/W), respectively. Moreover, hydrodistillation and ultrasound-assisted ohmic extraction produced the least and the most essential oils yield, respectively. According to obtained gas chromatography-mass spectrometry (GC-MS) analysis of extracted T. daenensis EO based on the method of extraction, quantitatively different, among 37 detected, compounds were observed. Extraction methods showed significant effects on different extraction parameters while ultrasound treatment had no significant effect except on extraction efficiency of hydrodistillation. Practical Application Among the novel extraction methods application of ultrasound and ohmic attracted lots of attentions due to a short time and enough extraction efficiency. Comparison the novel methods and their possible effects of chemical structure of extracted essential oil could provide useful information in the term of possibility of approach about other similar essential oil. Application of the novel proposed methods in combination of conventional or as alternative in order to increase the extraction rate in industrial scale could be recommended.