Reviewerly/exHarmony huggingface dataset:
StyleBERT: Chinese Pretraining by Font Style Information

With the success of down streaming task using English pre-trained language model, the pre-trained Chinese language model is also necessary to get better performance of Chinese NLP tasks. Unlike the English language, Chinese has its special characters such as glyph information. So in this article, we propose the Chinese pre-trained language model named StyleBERT which incorporate the following embedding information to enhance the savvy of language model, such as word, pinyin, five stroke and chaizi. The experiments show that the model achieves well performances on a wide range of Chinese NLP tasks.
Helicopter Health and Usage Monitoring System in China

With the great advantages of hovering and vertical take-off and landing, helicopters play irreplaceable roles in the field of civil aviation, including transport, search and rescue, firefighting, medical transport, tourism, etc. Collected from public information, by the end of 2019, the total number of the global helicopters had exceeded 52 000, in which the number of helicopters in China was only about 2000, nearly 4&#x0025; of the total. With the rapid development of the national economy and the opening of low-altitude airspace, there is an increasing demand for helicopters in China.
LRHW-AP: Using ranking-based metric as loss for Person Re-Identification

Optimizing a ranking-based metric as the loss function, such as Average Precision (AP), has been found very effective in image retrieval tasks, but it has received less attention in Person Re-Identification (Re-ID). In this paper, Low Rank High Weight (LRHW) AP is proposed to apply the AP-optimizing method on the Re-ID task. LRHW-AP employs high weight on the low rank positive instances, which provides more information for model optimization than high rank positive instances and distribute in high gradient area. We propose a new pooling method called Power Activation Weighted Mean (PAWM) pooling which can unify a set of pooling methods because of a changeable activation function and a trainable parameter. Thus one can adjust and train PAWM to adapt to the target task to improve the model performance. Besides, we incorporate Warmup and Exponentially Decay Scheduler with a delay period, called Warmup Delay Exponentially Decay Scheduler, which brings further improvement. Through an extensive set of ablation studies, we verify that all methods mentioned above contribute to the performance boosts on Re-ID and the model achieves 95.3% rank-1 and 88.4% mAP on Market1501 with ResNet50.
A Study on Importance of Blockchain Technology in Logistics Supply Chain Management

Blockchain is a decentralized data storage network where information is stored in a cryptographically secured block and maintained by a public ledger to protect against any alteration. This has been popularly used in financial systems but also has huge potential in other sectors such as health, smart city, and logistic. The supply chain logistic system is essential for whole management, starting from the raw material of industries until the last consumer delivery. This logistic management is faced with challenges such as transparency, delay in orders, lag in the information updating, multiple data entries. These can be diminished with this scattered technology. So, this article gives an overview of decentralized data storage networks by blockchain technology and their evolution and importance in logistics/supply-chain management. This article aims to traverse viable use of this scattered computing and its importance in supply chain logistics management.
Face Attribute Estimation Using Multi-Task Convolutional Neural Network

Face attribute estimation can be used for improving the accuracy of face recognition, customer analysis in marketing, image retrieval, video surveillance, and criminal investigation. The major methods for face attribute estimation are based on Convolutional Neural Networks (CNNs) that solve face attribute estimation as a multiple two-class classification problem. Although one feature extractor should be used for each attribute to explore the accuracy of attribute estimation, in most cases, one feature extractor is shared to estimate all face attributes for the parameter efficiency. This paper proposes a face attribute estimation method using Merged Multi-CNN (MM-CNN) to automatically optimize CNN structures for solving multiple binary classification problems to improve parameter efficiency and accuracy in face attribute estimation. We also propose a parameter reduction method called Convolutionalization for Parameter Reduction (CPR), which removes all fully connected layers from MM-CNNs. Through a set of experiments using the CelebA and LFW-a datasets, we demonstrate that MM-CNN with CPR exhibits higher efficiency of face attribute estimation in terms of estimation accuracy and the number of weight parameters than conventional methods.
DEMVSNet: Denoising and depth inference for unstructured multi‐view stereo on noised images

Most deep-learning-based multi-view stereo series studies are concerned with improving the depth prediction accuracy of noise-free images. However, it is difficult to obtain off-the-set clean images in practice and 3D convolutional neural networks require a lot of computing resources. To make full use of its computing power, different types of information can be processed simultaneously in the network. For these two issues, this paper proposes a novel multi-stage network architecture to address depth inference and denoising simultaneously. Specifically, 2D feature maps are first converted into 3D cost volumes containing pixel information and depth information through differentiable homography and Gaussian probability mapping. Then, the cost volume is input into the regularisation module in each network stage to obtain the predicted probability volumes. Furthermore, simple static weights lead to training failure, and it is necessary to dynamically adjust the loss function by gradient normalisation. The proposed method can dispose of pixel information and depth information simultaneously and both reach an excellent level. Extensive experimental results show that the authors’ work surpasses the state-of-the-art denoising on the DTU dataset (adding Gaussian–Poisson noise) and is more robust to noise images in depth inference.
The Indoor Space Layout of University Laboratories Based on Wireless Communication and Artificial Intelligence Decision-Making

In recent years, people have found that the use of contactless wireless communication can be more quickly interactive information transmission, which greatly facilitates our daily life. However, in the past, wireless communication is generally large-scale regional information dissemination. In recent years, with the further development of closed scenes, people are thinking about whether wireless communication can be used in closed scenes to do small and fast information transmission. The purpose of this paper is to study the indoor space layout of university laboratories based on wireless communication and artificial intelligence decision-making support. We are looking at the information about wireless communication to learn that wireless communication was often used in military transmission of signals and information; it has been vigorously developed and popularized, is now mainly used for civilian use, and can be combined with artificial intelligence to implement rapid information transmission transfer, in the data processing volume at a certain time basically achieving second transmission. Wireless communication and artificial intelligence decision-making can be used to conduct research on the indoor layout of college laboratories. In response, after data query and data comparison, we applied to the school for a computer lab and several idle laboratories to cooperate in experiments to facilitate the completion of the architecture of wireless communication and artificial intelligence systems, as well as subsequent data processing analysis. After a period of time of experiments, according to the results of the experiment, the use of wireless communication and artificial intelligence decision-making can help college laboratories according to their own functions, the installation of intelligent equipment, and the indoor layout of better arrangements.
FbHash-E: A time and memory efficient version of FbHash similarity hashing algorithm

With the rapid advancements in digital technologies and the exponential growth of digital artifacts, automated filtering of cybercrime data for digital investigation from a variety of resources has become the need of the hour. Many techniques primarily based on the “Approximate Matching” approach have been proposed in the literature to address this challenging task. In the year 2019, Chang et al. proposed one such algorithm - FbHash: A New Similarity Hashing Scheme for Digital Forensics that was shown to produce the best correlation results compared to other existing techniques and also resist active adversary attack, unlike others. However, no performance analysis of the tool was given. In this work, we show that the current design structure of FbHash is slower and memory intensive compared to its peers. We then propose a novel Bloom filter based efficient version, i.e., FbHash-E that has a much lower memory footprint and is computationally faster compared to FbHash. While the speed of FbHash-E is comparable to other state-of-the-art tools, it is resistant (like its predecessor) to “intentional/intelligent modifications that can fool the tool” attacks, unlike its peers. Our version thus renders FbHash-E fit for practical use-cases. We perform various modification tests to evaluate the security and correctness of FbHash-E. Our experiment results show that our scheme is secure against active attacks and detects similarity with 87% accuracy. Compared to FbHash, there is only 3% drop in accuracy results. We demonstrate the sensitivity and robustness of our proposed scheme by performing a variety of containment and resemblance tests. We show that FbHash-E can correlate files with up to 10% random-noise with 100% detection rate and is able to detect commonality as small as 1% between the two documents with an appropriate similarity score. We also show that our proposed scheme performs best to identify similarities between different versions of software or program files. We also introduce a new test, i.e., consistency test, and exhibit that our tool produces consistent results across all files under a fixed category with very low standard deviation, unlike other tools where standard deviation under a fixed test varies significantly. This shows that our tool is more robust and stable against different modifications.
Based on Arduino Design of Intelligent Exploration Vehicles

Abstract This paper details the design and implementation of the hardware circuit of the intelligent cart motion control system, and uses a variety of virtual reality and visual sensing technologies to design an environmental detection and search and rescue robot under complex conditions. Infrared technology is used to achieve the function of automatic obstacle avoidance of the trolley, ultrasonic technology is used to achieve the function of distance measurement, Bluetooth is used to achieve the function of wireless remote control of the trolley, GPS technology is used to display the geographical position of the trolley, a homemade bionic robotic arm is installed, and the robotic arm servo can be controlled by PC upper computer, Bluetooth and homemade body remote sensing controller to work to achieve the removal of obstacles and unknown environment and dangerous areas The information is collected through a PC, Bluetooth and a home-made body telemetry controller.
Image Captioning and Identification of Dangerous Situations using Transfer Learning

Expeditious advancements in Artificial Intelligence have drawn the recognition of numerous researchers towards Image Captioning and its applications. Clubbing the concepts of computer vision and natural language processing, image captioning has become an integral aspect of interpreting a scenario by generating captions. In this paper, a model is proposed which generates a caption as output for images as an input and tells if the image input is an image of the dangerous situation or not by the means of transfer learning. First, a dataset of thousand dangerous images are built and meaningful captions are linked with them. Dangerous situation here includes various potentially dangerous situation for humans, such as vehicle accidents, fire accidents, injuries, burns, murders, etc. The novelty of this work is enhanced by the dataset containing 1000 dangerous images and their self-created captions. The dangerous images contain photos of injuries, accidents, blood, murders and kidnapping. The proposed Image Captioning model uses ResNet50 for encoding of images, RNN and LSTM for sentence generation. Apart from that, transfer learning concepts have been included. The deep learning model could achieve accuracy of 70 on the given metrics, recall of 84, F1 score of 77.8 and Meteor score of 27.6 on a combined datasets of the thousand dangerous images datasets and the Flickr8K datasets. Specificity value of 65.1 and area under ROC curve value of 70 have been obtained.
Breaking Fair Binary Classification with Optimal Flipping Attacks

Minimizing risk with fairness constraints is one of the popular approaches to learning a fair classifier. Recent works showed that this approach yields an unfair classifier if the training set is corrupted. In this work, we study the minimum amount of data corruption required for a successful flipping attack. First, we find lower/upper bounds on this quantity and show that these bounds are tight when the target model is the unique unconstrained risk minimizer. Second, we propose a computationally efficient data poisoning attack algorithm that can compromise the performance of fair learning algorithms.
An area-time efficient point-multiplication architecture for ECC over GF(2m) using polynomial basis

The modern age has seen an enormous progress in communications. Millions of devices communicate over the web transmitting confidential information which sometimes is of national importance. Securing these devices has become prime concern in this evolutionary digital world. Elliptic curve cryptography has grabbed quite attention for securing these devices primely because of its small-key size with relatively same-level of security when compared to other cryptosystems. The high performance of ECC relies on the finite-field arithmetic operations. Point-multiplication is the most resource consuming and time critical ECC operation. Many architectures and algorithms are presented in the literature to address the area complexity and time complexity of point-multiplication. In this paper, a point-multiplication architecture for generic irreducible polynomials is proposed based on the modified Montgomery-ladder algorithm. In addition, the FF-inversion operation of point multiplication is realized by employing modified Itoh–Tsujii algorithm to achieve reduction in the computation time. The hardware complexity and delay of the proposed point multiplication architecture are estimated, and a comparison with the corresponding point multiplication architectures available in the literature is presented. It is observed that the proposed architecture achieves area-time efficiency of around 17%–86% and 42%–98%, respectively, over GF(2163) and GF(2233) when compared to the architectures available in the literature.
A hybrid machine learning framework for e-commerce fraud detection

We currently see a large increase in e-commerce sector; it is becoming a central trend in the banking industry. Fraudsters keep up with modern technologies, and use weak points in human psychology and security systems to steal money from regular users. To ensure the required level of security, banks began to apply artificial intelligence in their anti-fraud systems. Fraud detection can be formulated as a classification problem with a case-based reasoning or knowledge extraction task with unbalanced classes. In this paper we present a framework of models based on various approaches of artificial intelligence, such as neural networks, decision trees, copula models and others to recognize payment behavior of fraudster. The considered framework is evaluated with different metrics and implemented in an actual anti-fraud system, which leads to an improvement of the system performance. Finally, the interrelation between the anti-fraud system indicators and banks operational risks is discussed in this paper.
Research on the Detection Technique of Situation Elements in Obscure Overlapping Scenes

In recent years, some scholars have proposed to apply single-stage target detection algorithms such as YOLO (You Only Look Once) to situational element detection, but the traditional YOLO algorithm is to treat the target detection process as a regression problem, which cannot distinguish well between overlapping objects and has defects such as less accurate bounding boxes and hard to distinguish objects from the background, and it is difficult to cope with problems such as the higher overlap of targets to be detected and stronger target camouflage ability in obscured overlapping scenes. In this paper, we propose to add the attention module CBAM to the backbone network of the YOLOv3 model, to construct a SEDNet with high accuracy and good robustness for situational element detection, and to apply it to the situational element detection in occlusion overlapping scenes. We use SEDNet to classify and localize ten elemental targets, respectively. The analysis of experimental results shows that the SEDNet target detection model can complete element detection in complex environments with strong target camouflage, achieve end-to-end detection, and lay the technical foundation for the formation of complete situational awareness.
Non-Linear Non-Gaussian Gaussian and Seventh-Order Volume Kalman Filter Algorithm and Its Modeling Application

In this paper, the Gaussian sum recursive algorithm of the nonlinear non-Gaussian state space model is derived by expressing the state noise and observation noise of the model in the form of Gaussian sum. The algorithm uses the form of Gaussian sum to approximate the non-Gaussian posterior probability density, and uses SCKF as a sub-filter to update the time and measurement of each Gaussian component to effectively solve the problem of nonlinear non-Gaussian filtering. The simulation results verify the effectiveness and correctness of the algorithm. While ensuring accuracy, EKSF and GHSF greatly reduce the amount of calculation. The simulation time is about 6.5% and 6.7% of GSPF, respectively.
Design of Information System for Bookstore support Student paper

The paper presents techniques, methods and tools for web application development using MERN stack. A real system that is modeled and improved is described, as well as an analysis of user requirements, design, implementation, testing and presentation of the web application, developed in order to support the business of bookstores. The architecture of the developed web application is explained in detail and a description of its work is presented. Conclusions were drawn based on the results of work and performances of the developed application. This work also includes possible ways of improvement and further development, as well as the advantages in relation to other, similar solutions.
Exam Fraud Prevention and Class Concentration Improvement System for an Online Environment

This paper presents exam fraud prevention and class concentration improvement system for an online environment. The proposed system consists of deep learning-based face analysis, video analysis, and action recognition modules. The face analysis module first detects a face and performs face recognition for identification. Then, face spoofing detection is performed to determine if it is a fraud face, and gaze estimation and facial expression recognition are conducted to inspect the student's condition. And the video analysis module identifies whether the current image is the correct online class or exam state and detects inappropriate objects around it. Finally, the action recognition module determines abnormal online class or exam behavior. For performing on client devices., all deep learning models are optimized and converted through ONNX to be operated in an internet browser environment. The proposed system required about 300ms to analyze an image in a typical laptop computer.
A new method for automated driving image defogging based on improved dark channel prior

Abstract To tackle the problem of blurred image subjects due to haze in the images captured by the automatic driving system, which affects the safety of automated driving, a new, improved dark channel image defogging method based on adaptive domain dark channel calculation, fast bilateral filtering to optimize transmittance and automatic color equalization is proposed. First, map the original image based on the haze image degradation model and evaluate the atmospheric light intensity and transmittance based on the adaptive domain. Then, the atmospheric transmittance is optimized by combining the powerful value filtering with the fast bilateral filtering method. Finally, the image is further optimized by using the multi-channel automatic color gradation equalization method to solve the phenomenon of oversaturated color and dark brightness in the filtered image. The results show that the algorithm of this paper has high clarity and contrast, reduces the computation and running time, preserves the image edge information, has an excellent fog removal effect, and is highly adaptive for automatic driving image processing.
A Method of Monitoring and Evaluating the Quality of English Classroom Teaching Based on Big Data

To improve the reliability and accuracy of quality of English instruction in the classroom monitoring and assessment, as well as to reduce the time spent monitoring and evaluating the quality of English instruction in the classroom, a big data-based approach to monitor and evaluate the quality of English instruction in the classroom is proposed. A frequent itemset mining technique for quality of English instruction in the classroom monitoring data is built based on a study of the theoretical basis of big data and the features of quality of English instruction in the classroom monitoring data. To complete the data transformation of quality of English instruction in the classroom monitoring, the multivalued continuous attribute is quantified and turned into a two-dimensional Boolean data matrix. To mine the frequent itemsets of quality of English instruction in the classroom monitoring data, a frequent itemset mining algorithm based on the compressed matrix is applied. This work creates a quality of English instruction in the classroom evaluation model using the gray correlation method of multiobjective decision-making and weighted gray correlation analysis to realize the monitoring and evaluation of quality of English instruction in the classroom. The experimental results suggest that the proposed method is highly reliable and accurate and that it can significantly reduce the time spent monitoring and evaluating the quality of English classroom instruction.