Reviewerly/exHarmony huggingface dataset:
Multi-task model-based float flowmeter readout algorithm

In order to efficiently and quickly detect the float flowmeter indication, we propose a numerical reading algorithm based on the multi-task model named YOLO2R (You Only Look Once for Recognition of Reading). YOLO2R is composed of one encoder and two decoders. The improved CSPDarknet53 backbone and FPN feature pyramid network are used as encoders to extract the shared features. The two decoders are composed by object detection task and OCR text recognition task. The GRU recurrent neural network combined with spatial attention mechanism is used to decode the text in the target area. YOLO2R has achieved the state of the art in terms of accuracy and speed of multi-task learning models for high-precision real-time reading of float flowmeters.
Pyramid Structure-Based Context Aggregation Detail Restoration Low-illumination Image Enhancement Network

To address the shortcomings of existing low-illumination image enhancement methods in their ability to perceive and express features at different scales, we propose an image enhancement algorithm based on multi-scale pyramids and structural detail residual fusion blocks. This paper primarily exploits the feature pyramid as the pre-processing module to achieve feature extraction of low-illumination images. Subsequently, we use the backbone network to encode and decode the extracted shallow image features from the three-scale feature pyramid network. We also introduce a structural detail residual fusion block to enhance the network's ability to extract and characterize low-illumination image features. Finally, the extracted feature information is fused layer by layer to recover the normal light image. Experiments conducted on LOL dataset demonstrate that our network architecture achieves superior performance and outperforms the state-of-the-art enhancement approaches in qualitative and quantitative aspects.
Research on Self-Attention Image Description Technology Based on Object Detection

Image description is transform the image into a text description that tells about the image, but the characters in the image and their attributes and relationships will affect the one-to-one correspondence between the text description and the image. In response to the above problems, we design an encode-decoder structure based on a self-aware target detector to extract distinct feature and role information, and design a role integrity check module to supplement the description sentences with rich object information, which improves the accuracy of image description for role in the image. We have conducted many experiments on Flickr8k and Flickr30k and on the MS COCO dataset, this method can make the generated image description and images have better semantic consistency, and compared with traditional deep learning methods and the semantic diversity has a good effect.
Recognition of Junk Short Messages Based on Local Sensitive Hash KNN Algorithm

At present, the recognition and filtering of spam short messages mainly include the following technologies: black and white list recognition technology, transmission frequency limit recognition technology, keyword matching recognition technology, and machine learning based spam short message recognition technology. These commonly used spam short message recognition technologies have their own limitations. The K-Nearest Neighbor algorithm (KNN, K-Nearest Neighbor) based on locally sensitive hashing proposed in this paper can find the samples as close as possible when the eigenvectors are similar but not identical. First, define the features, and then use the local sensitive hash method(LSH). The task of calculating the similarity of the matrix is to find a measure, quantify the similarity of the matrix, compile and train the model, generate the matrix based on the text content of the short message, use TF-IDF algorithm, use Local Sensitive Hash to solve the most similar samples, record the category of the samples, import the training results to the KNN classifier, and obtain the best nearest neighbor through continuous experiments. The accuracy rate is evaluated on the test set or verification set. After KNN classifier, the classification accuracy rate reaches 98.7%.
A Object Detection Model with Multiple Data Enhancements

In the object detection task, it is worth studying the direction of generating models based on sequences to improve model training effect through data enhancement methods. In this paper, a variety of sequence data noise methods are proposed, and the noise is added after the real sequence by the enhancement method to form a new sequence. Experimental results show that the proposed data enhancement method can significantly improve the performance of the model.
Game Storyboard Script: A Fast Camera Placement Tool for Cinematographic Information Processing

Interactive narrative game is an important gene of digital games. To design an interactive narrative game, the placement of camera is essential to convey the narrative. There are two issues for placing camera. Firstly, the parameters of camera has no direct semantic relation to the staging performance. Secondly, some cameras share the same pattern but differ in parameters which results in a huge amount of repetitive work for designers. In this paper, we develop a tool call game storyboard language (GSS) to allow designer depicting the storyboard of the shot rather than directly place the camera in the virtual scene. We make an experiment to compare the result generated by GSS engine and those manual shots gathered in The Witcher 3. Our results given by the engine show the same effect as the manual shots while the configuration process is simpler.
Handwritten Signature Forgery Detection Using PCA and Boruta Feature Selection

Despite the development of identity detection using biometrics in the field of financial transactions, the handwritten signature remains the most commonly used to this day. The main challenge is that each person's signature may be distinctive, on the other hand, many difficulties aroused because two signatures created by the same individual may appear to be extremely identical. This similarity allows the imposters to claim a forged identity. In this paper, an off-line handwritten forgery detection method is introduced using traditional machine learning rather than deep learning methods to fulfill the need for a simpler model for saving both computation time and computation resources. The proposed method uses Histogram of Gradients (HOG) as a feature extraction method and Principal Component Analysis (PCA) to reduce the large extracted features number and Support Vector Machine (SVM) as a classifier. Another approach has been used by using Boruta feature selection for further reduction of feature numbers. CEDAR dataset has been used in this paper and the results were 99.24 % and 98.79 % in terms of accuracy for the two proposed methods respectively.
GPU Acceleration of Monte Carlo Tree Search Algorithm for Amazon chess and Its Evaluation Function

The evaluation function is a very important part of the Monte Carlo Tree Search (MCTS) algorithm for Amazon chess. This paper introduces the principle of Monte Carlo Tree Search algorithm, and realizes GPU accelerated calculation of Amazon chess situation's evaluation function, based on CUDA computing platform. Using the CPU code and CUDA code, two experiments were carried out to evaluate the function calculation accuracy and the effect of kernel function call parameters on the GPU acceleration performance. The results show that the evaluation function is feasible and effective for GPU acceleration, and the speedup ratio of GPU to CPU reaches 20.0.
A Computer Vision Technology Applied in the Task of Mechanical Parts Inspection

In this paper, based on the computer vision detection technology, the parts detection system is studied and discussed. This system mainly uses manual image segmentation and other methods, which can fully avoid the defects of image segmentation, improve the accuracy of image detection and ensure the detection speed. The system uses CCD or CMOS digital camera to capture the image of parts on the assembly line, which is roughly divided into three parts. First of all, it is necessary to take the template image that fully meets the quality requirements of the parts, and carry out preprocessing such as smooth filtering for the image taken. Second, during the test, the image of the component to be tested is taken on the assembly line, and the position of the detection target image and the standard template image is registered through the image registration algorithm. Finally, the image features of each partition area of the image to be detected are extracted and compared with the features of each partition area of the standard template image, so as to detect the processing quality of the assembly line parts, whether there is a component assembly error, error prompt and alarm, so as to achieve intelligent detection Purpose.
Target Tracking Algorithm of Radar and Infrared Sensor Based on Multi-Source Information Fusion

Nowadays, with the extensive application of interference technology, the accuracy and stability of target tracking system are facing severe challenges. In order to improve the tracking accuracy and anti-interference ability of UAV to ship targets, a multi-mode compound tracking algorithm based on multi-source information fusion is proposed. Firstly, the local estimation of radar and infrared sensor is given based on the interacting multi model unscented Kalman filter (IMM-UKF) algorithm. On this basis, the interference detection module is introduced to identify and reconstruct the interfered track. Finally, the global estimation of the track is given based on the hierarchical track fusion algorithm. The simulation results show that the algorithm can not only improve the target tracking accuracy, but also effectively improve the anti-interference ability and stability of the tracking system.
Data Augmentation Algorithm Based on Local Dynamic Transformation

This aimed at the problem of insufficient samples of training datasets for deep convolutional neural networks in computer vision tasks, which may easily lead to over-fitting, a Gaussian Adjusted Local Dynamic Transform (GA-LDT) data enhancement algorithm based on Gaussian adjustment was proposed. The distribution characteristics of the function perform a local dynamic transformation on the image, enhance the difference and diversity of image information, and alleviate the problem of overfitting. To verify the algorithm's effectiveness, comparative experiments on ResNet, Yolox, and Espnet networks suitable for different tasks are carried out on CIFAR-100, CIFAR-10, Pascal VOC, and Cityscapes data sets, respectively. After using the GA-LDT algorithm, the accuracy of the ResNet network on CIFAR-100 and CIFAR-10 data sets has increased by 3.86% and 7.61%, respectively, year-on-year. Yolox network uses GA-LDT algorithm, and the mAP on Pascal VOC dataset is increased by 1.86% year-on-year, while the mloU on Cityscapes dataset of Espnet network using GA-LDT algorithm is increased by 0.51% year-on-year.
Generating Masked Facial Datasets Using Dlib-Machine Learning Library

In 2020, the COVID-19 pandemic spread globally, leading to countries imposing health restrictions on people, including wearing masks, to prevent the spread of the disease. Wearing a mask significantly decreases distinguishing ability due to its concealment of the main facial features. After the outbreak of the pandemic, the existing datasets became unsuitable because they did not contain images of people wearing masks. To address the shortage of large-scale masked faces datasets, a developed method was proposed to generate artificial masks and place them on the faces in the unmasked faces dataset to generate the masked faces dataset. Following the proposed method, masked faces are generated in two steps. First, the face is detected in the unmasked image, and then the detected face image is aligned. The second step is to overlay the mask on the cropped face images using the dlib-ml library. Depending on the proposed method, two datasets of masked faces called masked-dataset-1 and masked-dataset-2 were created. Promising results were obtained when they were evaluated using the Labeled Faces in the Wild (LFW) dataset, and two of the state-of-the-art facial recognition systems for evaluation are FaceNet and ArcFace, where the accuracy of using the two systems was 96.1 and 97, respectively with masked-dataset-1 and 87.6 and 88.9, respectively with masked-dataset-2.
A multi-system collaborative data security and cross-domain authorization mechanism based on domestic cryptographic algorithms

This paper mainly discusses the multi-system collaborative data security and cross-domain authorization mechanism based on domestic cryptographic algorithms. This study is based on domestic cryptographic algorithms, combined with the technical characteristics of asymmetric encryption and symmetric encryption, uses symmetric encryption algorithm to encrypt plaintext data, and encrypts the session key of symmetric encryption using asymmetric encryption algorithm to protect data encryption. The classical data encryption ciphertext structure is optimized and adjusted to achieve the goal of tamper-proof and undeniable data security protection of multi-system collaborative business data.
Research on communication signal interference suppression based on deep learning

With the development of modern communication technology, people have higher and higher requirements for wireless communication. In practical applications, communication signals are vulnerable to noise and interference signals. Taking effective measures to suppress interference signals can ensure the communication quality. Deep learning technology has strong nonlinear mapping and data expression capabilities, so it can be applied to the field of communication interference suppression. Aiming at the problem that the interference reconstruction algorithm in the traditional interference suppression requires high accuracy of parameter estimation, this paper studies the interference suppression of communication signals combined with the deep learning U-Net network. Through BER simulation analysis, the traditional interference suppression method is compared with the communication signal interference suppression method based on deep learning. The results show that the communication signal interference suppression network based on deep learning can adaptively suppress interference.
A Study of User-item Graph Neural Network Recommendation Based on GCCGAT-LSTM

To solve the problem of high root mean square error (RMSE) caused by the Graph Convolution with Graph Attention Network for Matrix Completion (GCGAT), which cannot fully utilize the node's feature information and temporal information. In this paper, we propose an LSTM-based GCGAT algorithm (GCGAT based on LSTM, GCGAT-LSTM) to solve this problem: the linear processing part of the encoder in GCGAT is replaced by long short-term memory algorithm (LSTM). Two LSTM layers are used to process user's and item's feature information separately, named GCGAT -LSTM. This paper conducted several experiments on MovieLens-100K and MovieLens-1M datasets. The experimental results show that GCGAT -LSTM performs better than the GCGAT algorithm, effectively improving the performance of GCGAT and making more accurate recommendations. Compared with the RMSE in the previous GCGAT algorithm, the RMSE of GCGAT-LSTM is reduced by about 0.03 on both datasets. Therefore, the GCGAT-LSTM method proposed in this paper can better use node's feature information and temporal information to make more accurate predictions and thus obtain better recommendation results. However, the GCGAT-LSTM also has some limitations: the LSTM requires sequential processing over time and therefore has low efficiency and high time complexity for the input sequence.
2D Facial Images Attractiveness Assessment Based on Transfer Learning of Deep Convolutional Neural Networks

While beauty is subjective, it is not easy to quantify. Assessing facial beauty based on a computer perspective is an emerging research area with various applications. Different trainable models have been proposed to identify the attractiveness of facial beauty utilizing different types of features, machine learning techniques and lately, convolutional neural networks (CNNs) have proven their efficiency in image classification. The main objective of recent previous work is to enhance the performance of the existing trainable methods and make them suitable for beauty attractiveness identification. In this study, the accuracy and effectiveness of four affective pre-trained CNNs models (AlexNet, GoogleNet, ResNet-50, and VGG16) in assessing the attractiveness of human facial images using the CelebA dataset have been explored, evaluated, and analyzed. The results demonstrate that GoogleNet surpassed the investigated pre-trained networks with a performance accuracy of 82.8%.
Research on Data driven Communication Network Situation Awareness System

The rise of new technology makes the network structure increasingly complex, and the difficulty of management and maintenance increases exponentially. Situation awareness can evaluate the current situation of the whole network and predict the future change trend. Studying the acquisition implementation means, task scheduling model and hierarchical structure, and relying on the LUA script to customize the data acquisition, storage, transmission, and analysis strategies and rules, can build the communication network situation awareness system. Designing the data driven situation awareness pre-acquisition node and analysis center technology path, can adapt to the changing network needs, and provide important decision-making basis for network planning, operation assurance, problem handling, and early warning.
Design of Launch Vehicle Test Information Management System based on IETM

Traditional launch vehicle tests are recorded on paper, which is not convenient for the effective management of information. In order to improve the informationization level of launch vehicle test management and realize the secure share of integrative test information, a launch vehicle test information management system based on IETM (interactive electronic technology manual) is designed. The overall architecture of the system is constructed, and the working principle of the system is introduced. The crucial technology issues are discussed, such as the reliability design, interface design, secrecy and security design of the system, and the solutions are put forward. The practice demonstrates that the application of the system can improve the information level of the launch vehicle electric test management and the integrative test capability, and the efficiency of searching information is increased by more than quadrupled.
Computer network system security assessment method

With the rapid development and popular utilization of computer network system, the proportion of attacks against computer network system has been increasing year by year. The traditional security vulnerability assessment method only evaluates individual vulnerabilities in isolation, and the assessment results do not reflect the degree of impact of vulnerabilities on the whole network. To address this problem this paper proposes a computer network system security assessment method, which uses the CVSSv3 evaluation index to assess vulnerabilities based on vulnerability association graph and risk matrix, while taking into account the association relationship between the fore-order vulnerability nodes, back-order vulnerability nodes and the vulnerability itself. The vulnerability correlation hazard calculated by the method can accurately reflect the degree of impact of the vulnerability on the whole computer network system, and the accuracy and effectiveness of the method is proved through experiments.
Blockchain for Big Data Security, Issues, Challenges and Future Directions

Over the last decade, worldwide data traffic has risen at an unprecedented rate, prompting a surge in interest in big data. manufacturing, entertainment, and media. With this interest, Blockchain Technology, appeared as a promising technology that enables the transaction record to be continuously stored, protected with the digital signature, and validated by consensus. It operates under the concept of a digital ledger that is distributed. In this article, recent growth in blockchain interest as an alternative to traditional centralized systems has been presented and considered the emerging implementations thereof. In particular, the key approaches needed for the introduction of the blockchain and security issues. This includes the general issue behind the blockchain, description of the component, and the blockchain importance and connection with the big data. Thus, the paper focuses on reviewing the research in blockchain applications in securing big data. The paper compares big data security techniques and mechanisms provided by the blockchain approach considering security attacks that might shed light on Blockchain enthusiasts and researchers. Finally, the paper evaluates the various challenges of blockchain and put some recommendations for future research.
Anomaly Detection based on Teacher-Student Network in the field of Security Document Production

Our paper proposes an unsupervised learning framework for knowledge distillation based on teacher-student networks for anomaly detection and classification in secure document images. The teacher network uses Wide-ResNet-50 as the backbone network and is pre-trained on large datasets of secure document images and ImageNet. We use a multi-scale feature pyramid matching strategy, so that the student network can receive multi-scale feature maps to better detect anomalies of various sizes. We introduce an attention mechanism to transfer the attention map of the middle layer of the teacher model to the student model as knowledge, hoping that the student model will focus on the region that the teacher model focuses on, thereby distinguishing the difference between normal regions and abnormal regions, and improving the accuracy of detecting anomalies. Our method is applied in the field of Security document to achieve accurate and fast anomaly detection.
Preliminary Scaling Characterization of TPCx-AI

TPCx-AI is the latest TPC benchmark addressing some of the numerous challenges in AI benchmarking. It has the ability to scale datasets, to emulate machine learning and deep learning end-to-end pipelines, and to provide solutions that are commercially available with pricing and support. With TPC benchmarks it can be difficult to get started as the nature of system benchmarks require larger server configurations and software solutions. TPCx-AI is a TPC Express Benchmark and thus provides an executable kit that simplifies the setup and execution of the benchmark. Moreover, the TPCx-AI kit includes two reference implementations: one for single-node environments—using scikit-learn as its primary ML processing library—and the other targeted for multi-node environments—centered around the use of Spark—. This paper provides insights into the TPCx-AI kit installation and setup steps. It also shares preliminary scaling data of the key phases of the benchmark (e.g. pre-processing, training, serving, etc.), distribution of the phases for each of the use cases, and finally an example of how one can look at resource utilization for a specific use case. We show that for the single-node implementation each of the use cases show a unique runtime distribution for the time spent in preprocessing, training and serving phases. In addition, we also show how the processing runtimes scale with dataset sizes as they would in real world, with some use cases scaling poorly. We anticipate that optimizations in the platform as well as software stack can lead to overall reductions in runtime and expect this work to inspire others to investigate the runtime characteristics of the ML/DL workloads included in TPCx-AI and their possible optimizations.