Reviewerly/exHarmony huggingface dataset:
Adaptive Batch Size CGP: Improving Accuracy and Runtime for CGP Logic Optimization Flow

With the recent advances in the Machine Learning field, alongside digital circuits becoming more complex each day, machine learning based methods are being used in error-tolerant applications to solve the challenges imposed by large integrated circuits, where the designer can obtain a better overall circuit while relaxing its accuracy requirement. One of these methods is the Cartesian Genetic Programming (CGP), a subclass of Evolutionary Algorithms that uses concepts from biological evolution applied in electronic design automation. CGP-based approaches show advantages in the logic learning and logic optimization processes. However, the main challenge of CGP-based flows is the extensive runtime compared to other logic synthesis strategies. We propose a new strategy to tackle this challenge, called Adaptive Batch Size (ABS) CGP, in which the CGP algorithm incrementally improves the fitness estimation of the candidate solutions by using more terms of the truth table for evaluating them along the evolutionary process. The proposed approach was evaluated in nine exemplars from the IWLS 2020 contest, in which 3 exemplars are from the arithmetic domain, and six are from image recognition domain, specifically three from the CIFAR-10 dataset and three from the MNIST dataset. The results show that ABS presented an accuracy increase of up to 8.19% and decreased the number of candidate solutions evaluations required by up to 84.56%, in which directly affects the runtime of the algorithm. Furthermore, for all circuits, no significant accuracy reduction was observed while a significant reduction in the number of evaluations was achieved.
Research of Improved Deep Transfer Learning Algorithm in Image Classification

Due to the problem of low efficiency and overfitting of the algorithm with too little test data when the deep learning algorithm classifies images, an improved deep transfer learning algorithm is proposed. First, the model parameters trained in the source domain transfer to the target domain network model to speed up the optimization of model parameters. Then, what the improvement of Dropout in the network structure, can avoid overfitting and affect the accuracy of the model due to the small amount of sample data in the target domain. Finally, the improved algorithm is used on target domain task to realize the classification function of the DogsVSCats and MNIST dataset. The experimental results show that the improved deep transfer learning algorithm is significantly better than the deep transfer learning algorithm in terms of time efficiency, loss function convergence and accuracy.
An Object Detection Method for Remote Sensing Images Based on DA-YOLO

Aiming at the difficulty of small-scale objects in high-resolution remote sensing images, this paper proposes a new detector DA-YOLO (dilation and attention YOLO) to locate objects quickly and accurately. Firstly, during the data preprocessing, the remote sensing images processed by “quadruple cropping” to adjust the original image size and enlarge the number of data instance. Then, the CSPDarknet53 backbone network is optimized: the dilated separable convolution (DSC) module is applied to enlarge the receptive range of feature maps without losing the resolution of feature maps. Then, the convolutional block attention module (CBAM) is introduced for feature enhancement, and finally, the last four stages of feature maps are used instead of three stages to obtain more contour details of small-scale objects. Extensive experiments show that DA-YOLO has good performance in DOTA, with a 2.36% increase in mAP compared to the original YOLOv4 without a significant decrease in detection speed.
Research on Master-Slave Coordinated Control Method of Dual-Arm Robot

The mechanical structure of the dual-arm robot is analyzed in this paper, according to the characteristics and redundancy of human arm joints. First, the positive and inverse kinematics model of dual-arm robot is verified based on the DH method. For the problem of coordinate motion of dual-arm robot handling object, master-slave coordinated control strategy is adopted. Second, the dual-arm system is decomposed into a master-arm system and a slave-arm system. The trajectory of master-arm is planned. Last, the trajectory of slave-arm is calculated according to the constraint between the two arm ends. Finally, the solution of inverse kinematics of dual-arm system is obtained. This method is proved to have better performance in constraint handling and greatly simplifies the amount of calculation. The simulation results display that the algorithm designed makes the trajectory of the arms natural and smooth. The Movement consistency of the dual-arm robot is also ensured.
Multiple Object Detection Based Semen Microscopic Video Analysis

Sperm concentration and sperm motility are essential indicators for evaluating male reproductive health and sperm fertilization ability. As the quality of male sperm decreases year by year, people put forward higher requirements for semen quality analysis. The efficiency and accuracy of traditional manual observation and analysis methods have long been unable to meet the needs of hospitals. Compared with manual analysis, the computer-assisted semen analysis system has faster analysis speed and higher accuracy, reducing the subjectivity of diagnosis and the burden on doctors. According to the comparative experimental results, the difference rate between the method studied in this paper and the hospital inspection results is within an acceptable range. The analysis data obtained can effectively reflect sperm activity, an important reference value for auxiliary diagnosis.
Shadow Removal Network with Error Map Prediction

The existence of shadows affects many computer vision tasks. Recovering image information in shadowed regions by removing shadows has been proven to improve the performance of various tasks. However, shadow removal is a challenging task. The complex presence pattern makes it difficult for us to com succinct pletely shadows. In this paper, we study the problem of shadow removal, which aims to obtain traceless shadow removal results and preserve the image information under the original shadow area. To more adapt to the shadow removal task in complex backgrounds flexibly, we propose an Error Map mechanism to pre-estimate the feature error generated by the fusion network and use it to guide the refinement work to obtain better shadow removal results. In addition, we redesigned the higher performance network structure. We evaluate the performance of our method on multiple large-scale shadow removal datasets, our method achieves better performance than the original scheme.
Domain-Aware Feature Learning with Grammar-Guided Genetic Programming

Feature Learning (FL) is key to well-performing machine learning models. However, the most popular FL methods lack interpretability, which is becoming a critical requirement of Machine Learning. We propose to incorporate information from the problem domain in the structure of programs on top of the existing M3GP approach. This technique, named Domain-Knowledge M3GP, works by defining the possible feature transformations using a grammar through Grammar-Guided Genetic Programming. While requiring the user to specify the domain knowledge, this approach has the advantage of limiting the search space, excluding programs that make no sense to humans. We extend this approach with the possibility of introducing complex, aggregating queries over historic data. This extension allows to expand the search space to include relevant programs that were not possible before. We evaluate our methods on performance and interpretability in 6 use cases, showing promising results in both areas. We conclude that performance and interpretability of FL methods can benefit from domain-knowledge incorporation and aggregation, and give guidelines on when to use them.
SPPFU-Net: A Fusion Downsampling Network Focusing on Multiscale Features

Semantic segmentation is a pixel-level classification. As a classic image segmentation network, U-Net is a common strategy for image segmentation. However, the traditional U-Net structure has limitations such as limited receptive field, poor modeling ability, and limited feature extraction ability. To deal with these problems, we proposed an improved U-Net with SPP attention and fusion downsampling (SPPFU-Net). A new multi-scale attention mechanism is added to strengthen features in different scales to learn richer semantic information, and a fusion downsampling module is designed to avoid the information loss caused by the downsampling. It shows good robustness to boundary information in the experiment. On the medical segmentation ACDC dataset, our model achieved good performance on DICE, HD95, and ASD coefficients metrics of 88.97%, 1.4303, and 0.3584, respectively, which are 0.41%, 0.85, and 0.24 higher than the classical U-Net. This demonstrates that the SPPFU-Net structure proposed in this paper can increase image segmentation accuracy.
An Overview of Robust Secret Image Sharing

Secret image sharing encrypts the secret image into n shadow images. The original secret image can be disclosed in case of k or more shadows are obtained; in contrast, when gathering no more than k shadow images, failed to restore secret, where k ≤ n. Since the shadow image will be maliciously tampered with or processed in practical application, researchers began to study robust secret image sharing schemes. At present, there is no overview of robust secret image sharing. This paper gives an extensive overview of this field to help new researchers quickly understand robust secret image sharing and identify research problems. First, this paper highly summarizes the robust secret image sharing schemes that have emerged in recent years and introduces the robust secret image sharing into two categories: one is the robust secret image sharing scheme resist to fake shadows, and the other is the robust secret image sharing scheme resist to image processing. Then, this paper compares the characteristics, performance indicators, advantages, and disadvantages of each type of scheme. Besides, this paper gives the definitions of robust secret image sharing schemes for these two types. Finally, we discuss these problems to be solved and the future research direction.
Design and Research on Valve Train of Automobile Engine

In the process of modern automobile manufacturing, automatic assembly is the inevitable trend of future development. Due to the flexible action and large working space, industrial robot has been widely used in the field of automobile manufacturing. Its programmability determines that it will eventually replace many human physical labor, especially in complex working environment. Industrial robots are widely used in the four major processes of automobile manufacturing. In the stamping process of automobile manufacturing, industrial robots cooperate with stamping machines to carry out workpiece handling and stamping. In the welding process, industrial robots carry out automatic welding with spot welding or arc welding equipment. In the coating process, industrial robots carry out spraying operations. In this paper, the intelligent assembly system of automobile engine valve train is designed by SolidWorks, Robotstudio and TIA software. The industrial robot tool hand and fixture are designed by SolidWorks software. The automobile engine valve station is simulated by Robotstudio software. The control program is programmed by TIA software. Through the simulation operation of engine valve station, it can be seen that the engine valve mechanism designed in this paper is reasonable and can be used for automatic assembly of automobile engine valve train.
Sentiment Analysis Based on CNN and BILSTM Model

Aiming at the problems that the traditional convolutional neural network is easy to cause gradient disappearance and gradient explosion, and it is easy to ignore the context syntax and semantics, this paper proposes a method to integrate CNN and BILSTM. CNN is good at local feature extraction but cannot obtain the meaning of the context. In order to obtain the time information of the data, BILSTM solves the disadvantage that LSTM cannot process the information in reverse, and is used to extract the local structure of the data. In this paper, the local feature representation extracted by CNN is introduced into the emotional feature representation of BILSTM module, which effectively enhances the ability of BILSTM to capture emotional semantic information. Experimental results show that the ensemble model outperforms the two separate models. We are also able to achieve very high accuracy compared to previous work.
FocalPoint

We propose FocalPoint, a direct manipulation technique in smartphone augmented reality (AR) for selecting small densely-packed objects within reach, a fundamental yet challenging task in AR due to the required accuracy and precision. FocalPoint adaptively and continuously updates a cylindrical geometry for selection disambiguation based on the user's selection history and hand movements. This design is informed by a preliminary study which revealed that participants preferred selecting objects appearing in particular regions of the screen. We evaluate FocalPoint against a baseline direct manipulation technique in a 12-participant study with two tasks: selecting a 3 mm wide target from a pile of cubes and virtually decorating a house with LEGO pieces. FocalPoint was three times as accurate for selecting the correct object and 5.5 seconds faster on average; participants using FocalPoint decorated their houses more and were more satisfied with the result. We further demonstrate the finer control enabled by FocalPoint in example applications of robot repair, 3D modeling, and neural network visualizations.
Joint Learning Based on Discriminant Representation and Group Collaborative Measurement for Image Set Classification

In daily life, it is easy to obtain a large number of images in the form of sets. How to extract discriminative information from a wide range of images become very important. Most of the existing image set classification methods based on collaborative representation assume that the images in the same set can affect each other, ignoring the interaction between sets. In order to solve the above problems, a joint learning based on discriminative representation and group collaborative measurement (DRGCM) is proposed. Through the deep subspace clustering algorithm, the set is accurately divided, and multiple local models are found to form a dictionary. In the training stage, a global projection matrix learning model is designed to make the dictionary have stronger representation ability of prototype points in the embedded space. In the testing phase, a class-specific projection matrix model is constructed around the test set to make the dictionary more discriminative to the test set in a specific embedding space. Finally, the excellent performance on Honda, Mobo and other database proves the effectiveness of the algorithm.
Designing Low-Cost Open-Hardware Electromechanical Scientific Equipment: A Geological Analogue Modeling Sandbox

Scientific experimentation often requires building custom apparatus. However, published results usually focus on the experiment, disregarding technical details of the scientific equipment. Lacking enough information about these custom devices prevents their accurate replication, hindering the experiment reproducibility, which is a fundamental requirement for Open Science. In the field of Geology, custom electromechanical devices with low-speed moving elements are required to analyze scaled-down models of the tectonic deformation processes. In these experiments, the earth crust is modeled with materials whose properties and setup are scrupulously specified to comply with the scale model theory and to have standard and reproducible procedures. Notwithstanding this rigorous characterization, we believe that the moving apparatus has received little attention, implicitly assuming an ideal behavior despite the difficulties of moving uniformly at such slow speeds, which could produce disparities with the natural model. In this paper we address this issue by presenting a device for scientific analogue modeling of contractional and extensional tectonics. We analyze the challenges and implications of moving at such low speeds, demonstrate its satisfactory performance and provide suggestions for improvement. In addition, the proposed apparatus is not only affordable and relatively easy to build, but also is an open-hardware project that can be replicated, improved or customized, even in other research fields. We hope that this contribution will be beneficial for the scientific and educational community, facilitating the replicability of experiments, the exchange of ideas, and thereby the promotion of Open Science.
Research on Plane-Space Algorithm of Binocular Stereo Vision Under Zoom Condition

In the paper, a zoom-plane-space algorithm based on binocular stereo vision is presented to expand the range of the plane-space algorithm. On the basis of the plane-space algorithm, a mathematical model of the zoom-plane-space algorithm is established by introducing the zoom function. According to the optical imaging principle, the imaging model of the zoom image sensor is established and the input point coordinates of the left image sensor without zoom are calculated with this model. Finally, the position coordinates of the target point can be obtained by bringing the converted input point coordinates into the plane-space algorithm. The algorithm is simulated by using the Matplotlib library based on Python3.6. The simulation data show that the error of the algorithm is about 0.04 m when the horizontal coordinates of the target point are moved in steps of 0.1 for 100 steps.
FFP-MVSNet: Feature Fusion Based Patchmatch for Multi-view Stereo

With the development of technology, 3D reconstruction has been widely used in many fields. In this paper, we propose a learnable 3D reconstruction method using a cascaded Patchmatch approach to form a new network. By introducing a dual-channel attention module, point clouds reconstruction has been improved in accuracy and completion This network has high computational speed and low memory requirements, which allows it to handle higher-resolution images. The network is more suitable for running on resource-constrained devices than competitors that employ 3D cost volume regularization. We introduce the feature fusion module to an end-to-end trainable framework for the first time. The weight parameters of the multi-scale network output can be adaptively learned in each calculation, which can reduce the feature dispersion caused by the multi-scale output. This method has good performance on DTU.
Survey on Decoding Schemes of the Polar Code Using the Deep Learning

In the era of 5G Internet of everything, communications between different machines have strict requirements on latency and reliability. Due to the large amount of computations of the decoding algorithm of the polar code, the decoding time is too long, which cannot meet the low-latency system requirements. Deep learning has the ability of parallel computing, associative memory, self-organization and self-adaptation. Therefore, it is of great theoretical significance to apply deep learning to the polar code decoding algorithm. This paper reviews the development of the deep learning, and indicate that deep learning algorithms have been widely used in various fields. The traditional decoding method of the polar code is introduced, and the advantages and disadvantages of different decoding methods are compared.
Neural Network for Arabic Text Diacritization on a New Dataset

Arabic language is one of the most spoken languages in the world, it’s the official language of many countries and the fourth most used language on the internet. Arabic texts are often written without diacritic marks. However, those marks are important to clarify the sense and meaning of words. Automatic diacritization is the process of assigning diacritics to letters, and it’s an important field in Arabic Natural Language Processing (ANLP). In this work, we try to find the effect of increasing the training dataset on the diacritization error rate (DER) by building a new dataset and concatenating it with the Tashkeela dataset. We trained a deep learning model based on bidirectional long short-term memory BLSTM that transcribes undiacritized sequences of Arabic letters and produces an output sequence of the same length fully diacritized. Our model shows significant results on the new dataset in terms of DER and validation loss.
Anchor-Free RetinaNet: Anchor-Free Based Insulator Status Detection Method

When UAVs are used to detect the status of insulators, the appearance of insulators may change greatly because of the great variation of shooting angle and distance. Thus, due to the inefficient hand-craft anchor, the existing anchor-based object detection methods are difficult to perform robust detection of the status of insulators. Anchor-free object detection methods, in which samples needed for training are automatically allocated to each feature pyramid layer, can overcomes the disadvantage of poor detection with large morphological changes by hand-craft anchor. In this paper, we propose anchor-free RetinaNet to improve the accuracy of insulators status detection with different morphological changes. Specifically, anchor-free RetinaNet uses sample generation of spatial dimension to automatically generate positive samples and uses unified focal loss to introduce regression task into classification to enhance the model’s detection accuracy. Experiments on the public insulator status detection dataset show that the proposed method can improve the training efficiency and performance obviously.
A Knowledge Reasoning Model Based on Non-Factoid Information Enhancement

Q&A system plays an increasingly important role in the modern society with information explosion, and knowledge reasoning model (KRM) is the main research content of Q&A system. Existing knowledge reasoning models are mainly divided into text-based information retrieval (IR) and knowledge graph embedding (KGE). KGE is superior to IR in terms of storage and reasoning capabilities for massive factoid information, but lacks the ability to reason non-factoid information, merely focus on the mining of structural information without the semantic information. We proposed a knowledge reasoning model based on non-factoid information enhancement (NFE-KRM) in scenic Q&A. It realizes the KGE integrates semantic information (SIKGE) and the unified semantic embedding space (USES), so that NFE-KRM has the ability to answer both factoid and non-factoid questions. We have used a large number of experiments to prove that SIKGE gets a better performance on Mean Rank and [email protected] NFE-KRM's F1 score and accuracy on the mixed dataset are both competitive.
Performance Comparison of Deep Learning Text Embeddings in Sentiment Analysis Tasks with Online Consumer Reviews

In order to investigate the effect of various natural language processing models on different data processing, this paper adopted the consumer reviews of two well-known Internet retailing websites: Yelp and Zappos, and used four text embedding methods: word2vec, Glove, BERT, and GPT-2 and two text classification methods: SVM and Neural Network (NN) for text classification, in order to compare the performance of the combinations of these text mining techniques. The result shows that BERT is the best-performing text embedding method overall in both datasets when used with both SVM and NN. It is also found that NN is better than SVM for overall text classification. As an exploratory experiment, we aim to provide a three-dimensional comparison to find the most suitable algorithm for consumer review data, and the implication is that BERT and NN can achieve satisfactory results in most of the scenarios.