Miracle-dz/UniFAR-FTUs huggingface dataset:
Research of The Influence of Technological Innovation on Business Performance Based on cluster analysis

With the advent of the era of big data, the data information processing ability of enterprises has been greatly improved. By using the innovative technology of big data, enterprises can realize the networked performance management. This paper adopts cluster analysis to make empirical analysis on listed companies, Cluster analysis is to divide samples into different categories according to similarity according to certain indexes. The results of SPSS show that there is a positive correlation between enterprise innovation capability and business performance. Moreover, technological innovation has a significant impact on the performance of labor-intensive and technology-intensive enterprises, but has no significant impact on capital-intensive enterprises. Managers should pay full attention to the impact of innovation ability on business performance of enterprises, take advantage of the progress of big data technology, and improve the competitiveness of technological advancement between countries.
Removing the Impulse Noise from Grayscaled and Colored Digital Images Using Fuzzy Image Filtering

Digital image is an important information source for various applications. Impulse noise may cause a blur to the image information that lead to lose its importance. Thus, the removal of this degradation is one of the areas that has faced a growing interest by researchers. This paper concerns to reduce Fixed and Random impulsive noises from a grayscaled and colored digital image. The fuzzy logic (FL) methods are applied to reduce these impulsive noises from the digital images. This method tries to detect the corrupted pixels before starting the filtration process and processes only the pixels which have been classified as a corrupted pixel. This is carried out by applying two algorithms: Impulse Noise Reduction for Colored Digital Images (INRC) and Fuzzy Random Impulse Noise Reduction (FRINR) algorithms in order to filter the grayscaled and colored images. To restore a digital image, a number of phases were performed where impulsive noise was added to the set of images in several ratios for each image (5%, 10%, …, 80%), afterwards the previously mentioned algorithms were applied through a series of experiments and monitoring results and observations of each results of each experiment. The fuzzy methods gave densities of up to 50% of the noise. These obtained results are comparable to the results rates that accessed in other research papers in the world. Also, Peak Signal to Noise Ratio (PSNR), Mean Squared Error (MSE) and universal image quality index (UIQ) measurements are used in this paper.
Banana Ripeness Classification Using Computer Vision-based Mobile Application

The integration of smartphone applications with the increasingly growing influence of artificial intelligence provides users with new ways to do about anything and allows users to be practical. In this paper, a mobile application to identify the ripeness of banana fruits is built by implementing a computer vision technique. Image classification is performed by adopting transfer learning to extract edges from a pre-trained model. Convolutional neural network (CNN) model is used to train the classifier. Banana is chosen as an instance due to its short shelf life and widely consumed by Malaysian. For this project, Google Colab is utilized for the code execution as it is run on cloud and well-suited for machine learning. TensorFlow Lite with Model Maker library simplified the process of adapting and converting a TensorFlow neuralnetwork model to particular input data before deploying to an Android application. The result emerged with an accuracy of 98.25%. The app can instantly recognize banana live image, display the ripeness level on the screen based on highest percentage matched and display the ripeness, enabling the users to identify the banana ripeness quickly and easily.
Observing Impression of User Experience for Learning Cranium Anatomy

3D technology has widely used as a learning media in education, including benefits for students' knowledge, so it is useful to complement learning techniques in the medical field. Mobile Augmented Reality (MAR) presents a dynamic way for users to interact with computers and digital information. However, the topic of mobile augmented reality for media learning anatomy is still lacking because the current study still focuses on developing and using it as a learning tool. So, there is a need to observe impressions of user experience using MAR. This study aims to observe the impression user experience to augmented reality-based learning media designed to support learning cranium anatomy. This study uses the User Experience Questionnaire (UEQ) to observe user experience. The experiment process includes 33 international program medical students first year at Gadjah Mada University. The results of this study indicate that the highest aspect in the student's impressions is perspicuity with a value of 0.974, dependability of 0.886, and novelty of 0.894 for using cranium anatomy learning media-based MAR. The value obtained for each scale is said to be positive if it is greater than 0.8. In comparison, the attractiveness value received the lowest scale average value with a value of 0.788. Users of Media Learning for cranium anatomy based on MAR welcomed the existence of such technology.
Application of Differential Equations to the Design of Deep Residual Networks

Since the introduction of deep residual networks (ResNet) in 2015, there has been a wide range of research and applications. In recent years, due to the similarity between the network structure of ResNet and the forward Euler scheme of the numerical schemes of differential equations, researchers have started to focus on the relationship between numerical schemes of differential equations and the structures of deep learning models, and have proposed some new and improved structures and learning methods. In this paper, we focus on the application of compatibility analysis and zero stability analysis to the design of neural networks. Higher-order compatible and stable schemes are designed and applied to the cifar10 datasets with almost no change in the quantity of parameters, and higher accuracy than ResNet is obtained. Besides, we also study the schemes with different stability on small datasets, and demonstrate the superiority of stable linear multi-step schemes on small datasets. On between 20% and 40% of the cifar10 dataset, the stable scheme we propose gets significantly higher accuracy than other schemes including ResNet. We also find that as the number of categories increases, the stable scheme we propose shows great superiority to ResNet.
A Miniaturized Differential Microwave Microfluidic Sensor With High Decoupling

This letter presents a miniaturized differential microfluidic sensor by longitudinally integrating two substrate integrated waveguide (SIW) re-entrant cavity resonators (RECRs) for characterization of dielectric liquid materials. The mutual coupling between these two RECRs is prevented by the stacked metal wall between them. The feedline implemented by a microstrip-to-stripline-to-SIW transformer in the stacked metal wall is proposed to excite these two RECRs simultaneously. The dependence of complex permittivity of calibrated samples on the resonance frequency shift and quality factor of the sensor is used to build a predictive model of complex permittivity of liquid. The ability of this sensor to accurately measure liquid complex permittivity is verified by practical measurements.
Emotion recognition from facial expressions for 3D videos using siamese network

In this work, we have proposed STLEV (Siamese Neural Network with Triplet Loss and Long Short-Term Memory for Emotion recognition in Videos) a method for emotion recognition in videos. This method is beneficial for cognitive Human-Computer interaction. Emotion recognition from video is a challenging task as only few frames of a video will contain relevant information for recognizing emotions. Also identifying the apex frame is challenging. Emotion recognition from videos involves extracting the features from the frames of a video and classifying the sequence of features. For feature extraction we have used Siamese Neural Network (SNN) which is metric based meta-learning model. SNN is trained with triplet loss. For classification, Long Shot-Term Memory (LSTM) model is used. In this work we have used spatial and temporal aspects of video. Spatial features are extracted using SNN and temporal features using LSTM. Deep learning models need large amount of data for good accuracy. We have addressed this problem by leveraging few shot learning aspects of metric meta-learning so that fewer samples are needed for training the model used for feature extraction. Experiments done using BU-4DFE dataset achieved an accuracy of 87.5% which demonstrates the efficiency of the method STLEV.
X-Band Quasi-Elliptic Non-Reciprocal Bandpass Filters (NBPFs)

This article reports on the design and practical development of RF co-designed MMIC components that exhibit the function of a bandpass filter and an RF isolator. They are based on cascaded non-reciprocal frequency-selective stages (NFSs), one-pole/two-transmission zero (TZ) multi-resonant stages, and impedance inverters. The combination of these components results in a two-port network that exhibits a nonreciprocal quasi-elliptic bandpass filter (NBPF) response in the forward direction and full RF signal cancellation in the reverse one. The operating principles of the NBPF are presented through various circuit-based design examples of low- and high-order configurations. For proof-of-concept demonstration purposes, NBPF prototypes were designed on an MMIC GaAs process and were experimentally validated at $X$ -band. They include a two-pole/two-TZ NBPF and a three-pole/four-TZ NBPF.
Spatial Low-Rank Tensor Factorization and Unmixing of Hyperspectral Images

This work presents a method for hyperspectral image unmixing based on non-negative tensor factorization. While traditional approaches may process spectral information without regard for spatial structures in the dataset, tensor factorization preserves the spectral-spatial relationship which we intend to exploit. We used a rank-(L, L, 1) decomposition which approximates the original tensor as a sum of R components. Each component is a tensor resulting from the multiplication of a low-rank spatial representation and a spectral vector. Our approach uses the spatial factors, to identify high abundance areas where pure pixels (endmembers) may lie. Unmixing is done by applying Fully Constrained Least Squares such that abundance maps are produced for each inferred endmember. Results of this method are compared against other approaches based on non-negative matrix and tensor factorizations. We observed a significant reduction of spectral angle distance for extracted endmembers and equal or better RMSE for abundance maps as compared with existing benchmarks.
Comparison of Classification-based Methods for Network Traffic Anomaly Detection

With the development of technology, network technology is growing rapidly and there are more and more problems in the network, how to deal with anomaly traffic quickly and accurately in the network is a major problem that must be solved to achieve network security. This paper gives an overview of anomaly traffic and introduces three types of anomalies: point anomaly, contextual anomaly and collective anomaly, argues the relationship between anomalies, summarizes the latest publicly available datasets, focuses on the current classification approaches for handling network traffic anomalies, divides them into supervised multi-class classification approaches and unsupervised one-class classification approaches according to their learning types, and introduces some major development processes according to their respective. Finally, the advantages and shortcomings of multi-class classification and one-class classification approaches are summarized.
Temperature-dependent Lowering of Coercive Field in 300 nm Sputtered Ferroelectric Al0.70Sc0.30N

This paper reports the temperature-dependent ferroelectric properties of sputtered ferroelectric Al0.70Sc0. 30N. The coercive field is experimentally demonstrated to decrease by 1.5 MV/cm in 300 nm films in the temperature range of 20– 193 0C, corresponding to an average linear coefficient of decrease of 8.8 kV/0C. In the same range, the remnant polarization varies by 23.5% between 90-114 µC/cm2 without a significant drop-off at higher temperatures. Thermally cycling the chip between room temperature and the highest achievable temperature of 193 0C shows a reversible tuning of the coercive field, paving the path towards on-chip ovenization-based control of ferroelectric properties. The dielectric breakdown under AC and DC testing are driven by filamentary and thermally driven breakdown processes respectively. These breakdown processes suggest the potential for high frequency operation of sputtered Al0.70Sc0. 30N as a ferroelectric thin film at elevated temperatures with low leakage.
Refining Fitness Functions for Search-Based Program Repair

Debugging is a time-consuming task for software engineers. Automated Program Repair (APR) has proved successful in automatically fixing bugs for many real-world applications. Search-based APR generates program variants that are then evaluated on the test suite of the original program, using a fitness function. In the vast majority of search-based APR work only the Boolean test case result is taken into account when evaluating the fitness of a program variant. We pose that more fine-grained fitness functions could lead to a more diverse fitness landscape, and thus provide better guidance for the APR search algorithms. We thus present 2Phase, a fitness function that also incorporates the output of test case failures, and compare it with ARJAe, that shares the same principles, and the standard fitness, that only takes the Boolean test case result into consideration. We conduct the comparison on 16 buggy programs from the QuixBugs benchmark using the Gin genetic improvement framework. The results show no significant difference in the performance of all three fitness functions considered. However, Gin was able to find 8 correct fixes, more than any of the APR tools in the recent QuixBugs study.
A Review on Intrusion Drone Target Detection Based on Deep Learning

In order to solve the problems of disrupting aviation order, stealing private information by the abuse of commercial small drones, even terrorist attacks caused by drones, many drone target detection algorithms based on deep learning have been proposed in recent years, but due to the lack of large-scale public datasets, most of the existing researches are based on self-made datasets, the algorithms lack comparative analysis. In this paper, we summarize and analyze the current research status of intrusion drone target detection based on deep learning. The paper summarizes the existing target detection algorithms based on deep learning, aiming at the problem that public drone target datasets are insufficient and difficult to obtain, it summarizes the currently publicly available drone datasets, then summarizes the intrusion drone target detection algorithms based on deep learning in recent years. Finally, the detection performance of some algorithms under different public datasets is compared, the difficulties in current research and direction of next research are analyzed.
A Defect Model for Simulating Suspended Potential Discharge in GIS

GIS is an important power equipment in power system, which is the basis of ensuring power supply reliability. Although the reliability of GIS operation is very high, there are inevitable defects in GIS, such as partial discharge, insulation deterioration and equipment failure. A defect model is designed to simulate the suspended potential discharge in GIS, which can be used to simulate the common suspended potential discharge defects in GIS. It can be used to measure the partial discharge by combining the pulse current, UHF and decomposition component analysis methods, so as to promote the development of GIS partial discharge detection technology. The defect model consists of high voltage electrode, suspended potential body, ground electrode, fixture and SF6 gas. The results of partial discharge test show that the defect model can well simulate the suspended potential discharge in GIS equipment.
"Machine Learning applied to credit analysis: a Systematic Literature Review"

Machine Learning (ML) has been increasingly used in credit analysis, also known as credit scoring. However, the vast majority of articles focus on ML techniques and do not delve into what are the most relevant variables to define good and bad payers. The objective of this research is to identify published works that study the variables that define the customer as a default or not, as well as to identify what leads the consumer to take credit even though he / she does not have the resources to (re-)pay. To achieve the objective of this study, a systematic literature review was carried out. The combination of automatic searches resulted in 36,639 articles, of which 17 were relevant. The studies found about the credit score present similar rating analysis methods, and only the variables used in the models changed.
Synthesis, Properties, and Applications of Transition Metal-Doped Layered Transition Metal Dichalcogenides

Research into layered transition metal dichalcogenides (TMDCs), most notably those of molybdenum and tungsten disulfides, has become extensive, involving fields as diverse as optoelectronics, spintronics, energy storage, lubrication, and catalysis. The modification of TMDCs by transition metal doping can improve their performance in such applications and hence extend their potential for technological applications. This review concerns the synthetic strategies that have been used to incorporate transition metals into TMDCs and the applications of the resultant materials and relevant computational studies on the predicted properties of the doped materials.
Tunable Photoluminescence Properties of Fluorescein in a Layered Double Hydroxide Matrix by Changing the Interlayer Microenvironment

This paper reports a novel method to tune the fluorescence properties of fluorescein (FLU) in a 2D matrix of layered double hydroxide (LDH) by changing the interlayer microenvironment. FLU and surfactants with different alkyl chain lengths were cointercalated in the galleries of a Zn2Al LDH by the anion exchange method. Thin films of FLU-CnH2n+1SO3/LDH (n = 5, 6, 7, 10, and 12, respectively; n stands for the number of carbon in the alkyl chain), which possess a well c-orientation revealed by XRD and SEM, were obtained by the solvent evaporation method on Si substrates. It was found that the orientation of FLU and its anisotropy, fluorescence wavelength, fluorescence quantum yield, and lifetime correlate with the microenvironment of the LDH gallery, which can be tuned by simply changing the alkyl chain length of the surfactant. The optimal fluorescence quantum yield, anisotropy, the longest fluorescence lifetime and the strongest photostability of the FLU-CnH2n+1SO3/LDH film can be obtained with n = 7, due.
Online Learning during the COVID-19 Pandemic: Issues and Challenges

Covid-19 has been declared by the World Health Organization a pandemic that poses a new hazard to humankind. This has resulted in a global close-down of several operations, including education events. This led universities to migrate their education systems from traditional to online systems using e-learning educational platforms. The goal of this paper is to recognize the most relevant challenges and issues the associated with using e-learning as seen through the perspective of university students. It also observers their opinions and measures their level of readiness and satisfaction to adopt the e-learning methods existing in Libya. A descriptive method has been conducted and an online questionnaire was sent to collect data from students to explore the challenges facing them in e-learning during coronavirus pandemic lockdown. The research community consists of students from six faculties at the Libyan International Medical University (LIMU). The statistical analysis of the data was executed by means of SPSS. The mean and standard deviation of all questionnaire items are recorded and analyzed. Through the analysis of the results, the social challenge comes at the forefront with a large percentage, Then there's the issue of students' problems as they relate to e-learning. The challenges that arise from the lecturers are not as effective as social and student challenges. In general, an average of more than or equal to 3.44 was obtained through the four challenges, which are associated with a high degree of acceptance and thus each of them has a substantial influence on e-learning.
Value Sensitive Design and Environmental Impact Potential Assessment for Enhanced Sustainability in Unmanned Aerial Systems

Value sensitive design (VSD) is an approach that facilitates the pro-active incorporation of human values into technological design. The VSD literature, as well as empirical studies, identify environmental sustainability as a human value with importance in design, and therefore importance in Unmanned Aerial Systems (UAS) design. UAS have begun to spark significant public interest and environmental changes. However, there are few studies that address how to design UAS for these changes, and none that take VSD as their point of departure. In this work, the environmental sustainability of UAS are analyzed using VSD and environmental impact potential assessment (EIPA) approaches. VSD envisioning cards are used as design prompts to identify relevant social and environmental impacts for two case studies to illustrate the approach: a healthcare application, and a powerline inspection application. The environmental impact potential is assessed, along with consideration of the drone’s materials and manufacturing processes which have an effect on toxicity to humans, water depletion, and acidification. Then, general insights into how UAS can be designed for enhanced environmental sustainability are discussed. The results show high sensitivity to changes in defining the system boundaries and in defining relevant UAS scenarios, as a direct comparison of drone and non-drone scenarios is not possible. Thus, VSD and EIPA approaches can provide a nuanced way to analyze UAS applications, leading to positive social impacts and enhanced environmental sustainability in UAS in the future.
Sequential Interactive Biased Network for Context-Aware Emotion Recognition

Emotion context information is crucial yet complicated for emotion recognition. How to process it is a challenging problem. Existing works mainly extract context representations of the face, body and scene independently. These strategies may be limited in the understanding of emotional context relation. To address this problem, we propose Sequential Interactive Biased Network (SIB-Net), which is motivated by the studies that the context contains sequential, interactive and biased relation. Specifically, SIB-Net captures and utilizes the context relation by three modules: i) a Sequential Context Module captures consecutive relation with a GRU-like architecture, ii) an Interactive Context Module acquires cooperative context with global correlated linear fusion, and iii) a Biased Context Module benefits from the biased relation with distribution labels and the L1 loss. Extensive experiments on EMOTIC and CAER datasets show that our SIB-Net improves baseline significantly and achieves comparable results to the state-of-the-art methods.