Reviewerly/exHarmony huggingface dataset:
Imitation Learning Based on Visual-text Fusion for Robotic Sorting Tasks

In this paper, we propose an imitation learning method based visual-text fusion for manipulation task. Manipulation is predicted based on text instructions by abstracting the manipulation into text instructions, learning the semantic concepts in the text instructions, and combining them with spatial features for visual inference. The construction process and demonstration content of the expert demonstration dataset is described in detail, which is focused on the process of decomposing the operation task through text. In addition, we present the learning process and demonstrate the network structure of functional modules to highlight the fusion of text features with visual features. The effectiveness of this method is verified by a simulated learning experiment on a multi-step manipulation task. The results show that the behavioral strategy achieved a 92.19% task completion rate on known objects and 80.03% on unknown objects. It is proved that, owing to the introduction of text, the decomposition of the operational task in terms of abstract semantics is realized and the difficulty of learning is reduced. Meanwhile, the behavioral strategy can perform accurate spatial location inference based on text features, thereby achieving accurate action prediction.
A Blockchain-Enabled Location Privacy-preserving under Local Differential Privacy for Internet of Vehicles

Location and user information can be shared and interacted in the Internet of Vehicles (IoV), which bring many benefits to drivers and consumers. However, private issues become more acute as their data is outsourced to third parties. It is easy for sensitive information to be leaked in a big data environment. To solve these problems, a location data algorithm that satisfies Local Differential Privacy (LDP) is proposed to protect user privacy. In this paper, we use the randomized response mechanism to reconstruct the Laplace algorithm so that it satisfies LDP, perturbing the original location of each user from the client. The user location is clustered using k-means clustering algorithm, the perturbed data are noise reduced in the blockchain software development kit (SDK), and the noise reduced location data is uploaded to the blockchain network for storage through smart contracts. In addition, the effectiveness of the privacy protection mechanism is verified by comparative experiments. Compared with the existing privacy protection methods, the privacy protection mechanism not only can meet the privacy needs of users better, but also the noise reduction algorithm in the SDK can restore the original data and has higher data availability.
Development and Eevaluation of HMD with Internal Ventilation Function using Small Blower

In recent years, VR technology has developed remarkably and is drawing attention. HMD is indispensable for VR experience. When the HMD is worn, sweating causes the inside to get stuffy, causing VR sickness and heat stroke. In this research, we will develop an HMD with a ventilation function and aim to improve the comfort by blowing air into the HMD. We investigated the ventilation method and the ventilation route for sending wind into the HMD. Then, three types of wind, steady wind, rhythm wind, and 1/f fluctuation wind, were implemented, and the comfort feeling was evaluated using principal component analysis. When two types of evaluation experiments, an exercise task and a video task, were carried out, it was found that the rhythm wind was effective for the exercise task and the 1/f fluctuation wind was effective for the video task.
An Improved Multi-objective Evolutionary Algorithm for A Robot Detect Path Planning Problem

In this paper, we study the path planning problem of the detection robot after the coal mine disaster, and arrange a robot to detect multiple target points. The objective is to minimize the path length and the degree of danger simultaneously. First, the A-Star algorithm is used to obtain the path matrix of any two target points. Then an improved multi-objective optimization algorithm is proposed to find paths with Pareto optimal or near-optimal solutions. Finally, the multi-objective optimization algorithm is compared with other multi-objective optimization algorithms. The experimental results demonstrate that the algorithm can effectively obtain the detection path and minimize the length of the path and the degree of danger.
Target Tracking Algorithm Based on YOLOv3 and Feature Point Matching

In order to solve the problem that the multiple-target tracking algorithm is too storage intensive in memory and the detection is very time-consuming, a target tracking algorithm based on YOLOv3 and feature point matching is proposed. Compared with the mainstream algorithms in experiments, it is shown that the proposed algorithm can reduce memory significantly so as to be operated on portable devices and has the advantages of computational time-saving, anti-environment disturbance ability and high real-time performance. It also enables the tracking of multiple types of objects without training the cascade network each time for different types of targets.
Dynamic State Estimation of Generator Based on Amended Singular Value Decomposition Cubature Kalman Filter

The noise inaccuracy issue in nonlinear dynamic systems leads to a reduction in the accuracy of the filtering algorithm, and the cubature Kalman filter (CKF) can effectively deal with this problem. Singular value decomposition (SVD) is used to replace Cholesky decomposition can avoid the case of negative definite prior covariance matrix and improve the stability of the filtering. The measurement innovation and noise covariance matrix are used to set the amending rules, and the rules directly amend the state prediction value or Kalman gain and adjust the proportion of the measured value in the filter. It can reduce the influence of the abnormal measured value on the state estimation value. Simulation results show that the amended singular value decomposition cubature Kalman filter algorithm proposed in this paper has higher accuracy and robustness.
Improving Abstractive Multi-document Summarization with Predicate-Argument Structure Extraction

Multi-Document Summarization (MDS) aims to generate a concise summary for a collection of documents on the same topic. However, the fixed input length and a large number of redundancies in source documents make the pre-trained models less effective in MDS. In this paper, we propose a two-stage abstractive MDS model based on Predicate-Argument Structure (PAS). In the first stage, we divide the redundancy of documents into intra-sentence redundancy and inter-sentence redundancy. For intra-sentence redundancy, our model utilizes Semantic Role Labeling (SRL) to covert each sentence to a PAS. Benefiting from PAS, we can filter out redundant contents while preserving the salient information. For inter-sentence redundancy, we introduce a novel similarity calculation method that incorporates semantic and syntactic knowledge to identify and remove duplicate information. The above two steps significantly shorten the input length and eliminate documents redundancies, which is crucial for MDS. In the second stage, we sort the filtered PASs to ensure important contents appear at the beginning and concatenate them into a new document. We employ a pre-trained model ProphetNet to generate an abstractive summary from the new document. Our model combines the advantages of ProphetNet and PAS on global information to generate comprehensive summaries. We conduct extensive experiments on three standard MDS datasets. All experiments demonstrate that our model outperforms the abstractive MDS baselines measured by ROUGE scores. Furthermore, the first stage of our model can improve the performance of other pre-trained models in abstractive MDS.
Image Recognition Method of Educational Scene Based on Machine Learning

The traditional image recognition method mainly relies on the similarity expansion calculation of the prominent features of the image to realize the image recognition. This method not only reduces the recognition accuracy of the image, but also makes the recognition efficiency of the image low due to the complex calculation process. In response to the above problems, this research designed an image recognition method for educational scenes based on machine learning. After performing normalization, denoising, and enhancement preprocessing on the educational scene image, the HOG, SIFT, and Haar features in the image are extracted. Then use the convolutional neural network model in machine learning technology to complete the recognition of educational scene images. Experimental results show that the effective recognition rate of this method is higher than 92%, and compared with traditional methods, the recognition efficiency of this method is significantly improved.
The Classroom System Design of College English Teaching Reform Under the Genetic Algorithm

The English curriculum is already an important part of the university education system. To change the traditional ‘pioneer’ educational model, the English education reform of the university is an important project for the construction of higher education in China. The classroom model has changed considerably in the course of deepening teaching reform, such as catechism and flipped classroom, which provide an online learning platform for students to study online, thus effectively improving the quality of university English teaching. Based on this, This paper analyzes the effect of the question answering method on the classroom model in the English education reform of the University, then proposes a concrete reform strategy and provides valuable reference.
Hybrid-Spatial Transformer for Image Captioning

Recent years, the transformer-based model has achieved great success in many tasks such as machine translation. This encoder-decoder architecture is proved to be useful for image captioning tasks as well. We propose a novel Hybrid-Spatial Transformer model for image captioning. In this work, we combine the Global information and Local information of image as input of encoder which extracted by VGG16 and Faster R-CNN respectively. To further improve the performance of model, we add spatial information to attention layer by incorporating geometry features to attention weight. What’s more, queries Q, keys K, values V are a bit different from standard transformer, which is reflected in theses aspects. The positional encoding or embedding is not added to values V both encoder and decoder, the positional embedding is added to keys K on cross-attention. The experimental results illustrate that our model can achieve state-of-the art performance on CIDEr-D, METEROR and BLEU-1 on MS-COCO dataset.
A Unified Model for Line Projections in Catadioptric Cameras with Rotationally Symmetric Mirrors

Lines are among the most used computer vision features, in applications such as camera calibration to object detection. Catadioptric cameras with rotationally symmetric mirrors are omnidirectional imaging devices, capturing up to a 360 degrees field of view. These are used in many applications ranging from robotics to panoramic vision. Although known for some specific configurations, the modeling of line projection was never fully solved for general central and non-central catadioptric cameras. We start by taking some general point reflection assumptions and derive a line reflection constraint. This constraint is then used to define a line projection into the image. Next, we compare our model with previous methods, showing that our general approach outputs the same polynomial degrees as previous configuration-specific systems. We run several experiments using synthetic and real-world data, validating our line projection model. Lastly, we show an application of our methods to an absolute camera pose problem.
Data-Efficient Deep Reinforcement Learning with Symmetric Consistency

Data augmentation methods have proven highly effective in supervised learning domains where semantic-invariant perturbations can be easily applied to the labeled input examples. However, some used in deep reinforcement learning cannot guarantee the consistent semantics in the transition samples, leading to wrong value estimation and optimization during training. In this paper, we focus on the symmetric consistency in states and actions, and propose the Symmetric DQN to maintain it for better performance and data efficiency on small data regimes. Specifically, we adopt a consistent state flip and action projection on the original interaction transitions to construct the corresponding symmetric ones. The symmetric ones have similar middle rewards and terminal signals as the original ones but reverse states and actions. Therefore, by optimizing both the original and the symmetric losses, Symmetric DQN facilitates the value estimation from two directions per transition. We illustrate the promise of Symmetric DQN by conducting experiments on the Atari 100K benchmark. Symmetric DQN achieves a median HNS of 27.6% and a mean HNS of 51.1%, which surpasses most of the previous deep reinforcement learning methods with data augmentation.
Semantic-Aware Non-local Network for Handwritten Mathematical Expression Recognition

Handwritten mathematical expression recognition (HMER) is a challenging task due to its complex two-dimensional structure of mathematical expressions and the high similarity between handwritten texts. Most existing encoder-decoder approaches for HMER mainly depend on local visual features but are seldom studied in explicit global semantic information. Besides, existing works for HMER primarily focus on local information. However, this obtained information is difficult to transmit between distant locations. In this paper, we propose a semantic-aware non-local network to tackle the above problems for HMER. Specifically, we propose to adopt the non-local network to capture long-term dependencies while integrating local and non-local features. Moreover, we customized the FastText language model to our backbone to learn the semantic-aware information. The experimental results illustrate that our design consistently outperforms the state-of-the-art methods on the Competition on Recognition of Online Handwritten Mathematical Expressions (CROHME) 2014 and 2016 datasets.
Video recognition algorithm of fake face based on SVM model

In response to more and more fake face videos in the network, we propose three feature detection methods based on image color histogram, SURF (Speeded Up Robust Features), and ELA (Error Level Analysis), SVM(Support Vector Machine) model training and verification methods, compared with the current common detection methods, this paper starts from the mechanism of fake face tampering, converts the processing of video streams into image features recognition, and analyzes various image features of faces. The recognition rate of face-swapping videos is significantly improved and the machine learning problem in the case of small samples is solved by using svm.
Molecular dynamics method as an additional educational tool in the course of solid state physics

The article demonstrates the possibility of using atomistic modeling, using the example of the molecular dynamics method, as an additional educational tool in the study of the properties of crystalline solids in laboratory work. The research was carried out with the participation of 5th year bachelor students studying in a double specialty "physics and computer science" in a pedagogical direction. A range of software is considered, which allows a flexible approach to the choice of topics for laboratory work, ensuring their implementation without significant time expenditures both on the part of the teacher and the students. A positive response from students was received, which was also expressed in a higher average score according to the results of mastering the discipline. The opinion of students is expressed that additional means of visualizing the results when carrying out molecular dynamics modeling are not required.
Based on The Improved BertForSequence-Classification Method Social Text Classification Topic Analysis

Text classification based on Bert Model has recently attracted much attention from researchers. Many adversarial training methods (e.g., PGM and PGD) also exist to increase the model robustness to improve the accuracy of the corresponding task. In this paper, We improve the traditional BertForSequence-Classification model and use freelb adversarial training to make it more robust. In contrast to the BertForSequence-Classification model, we add three linear layers and two softmax layers after the Bert Model. We chose among the datasets for two Twitter text classification tasks. The improvement of our improved model for this task is more obvious. In particular, we steadily improve the accuracy of the task again after using adversarial training. From the experimental results, we find that the more labels we classify, the greater the improvement in accuracy we have over the traditional model. This process do not only improve the task for our chosen dataset, but can also be extended to the whole task of text classification.
Fast Semantic Segmentation for Vectorization of Line Drawings Based on Deep Neural Networks

Much research has been done on pattern recognition in line drawings. Converting raster graphics into vector graphics is one such examples. Vector graphics are composed of meaningful basic components such as lines, curves, and parabolas etc. However, converting raster graphic to a vector graphic is difficult because the structures of the basic components must be recognized. Therefore, we propose a semantic segmentation method for converting line drawings in raster format into vector format and verify the accuracy of the extraction of basic components and the processing time through computer experiments.
Differentiable Channel Sparsity Search via Weight Sharing within Filters

In this paper, we propose the differentiable channel sparsity search (DCSS) for convolutional neural networks. Unlike traditional channel pruning algorithms which require users to manually set prune ratios for each convolutional layer, DCSS automatically searches the optimal combination of sparsities. Inspired by the differentiable architecture search (DARTS), we draw lessons from the continuous relaxation and leverage the gradient information to balance the computational cost and metrics. Since directly applying the scheme of DARTS causes shape mismatching and excessive memory consumption, we introduce a novel technique called weight sharing within filters. This technique elegantly eliminates the problem of shape mismatching with negligible additional resources. We conduct comprehensive experiments on not only image classification but also find-grained tasks including semantic segmentation and image super resolution to verify the effectiveness of DCSS. Compared with previous network pruning approaches, DCSS achieves state-of-the-art results for image classification. Experimental results of semantic segmentation and image super resolution indicate that task-specific search achieves better performance than transferring slim models, demonstrating the wide applicability and high efficiency of DCSS. The codes of DCSS are publicly available at: https://github.com/zhaoyu775885/DCSS.
A Robust Watermarking Method For Image Processing Models

Sharing neural network models in some platforms and communities has become a popular trend, but they will inevitably suffer from malicious theft of models, If the attacker knows the structure and weights of the model, the model can be easily modified, how to protect the intellectual property rights of the deep models has become a serious problem. The appearance of model watermarking provides a technical possibility for the intellectual property protection of neural network models, and it provides us a reliable method of verifying model ownership when the model's intellectual property rights are infringed. Based on previous work, this paper proposes a new watermarking method for image processing models, which embeds the watermark information into the two chroma channels of the YCbCr color space of image processing model's output images. The proposed method can verify the model ownership in black-box case, which has strong robustness and resistance to common model compression and model fine-tuning attacks.
Airflow Pattern Simulation in Open Type Wind Tunnel with Test Section 40 cm X 40 cm X 80 cm

This study aims to determine the pattern of wind flow in the open type wind tunnel and compare actual measurements and simulation results with software. The actual method used is testing using a wind tunnel, anemometer, dimmer, axial suction fan with parameters RPM 615, 1932, 2400,2860, and the simulation used using solidwork flow simulation software with parameters Inlet Velocity 1.4 m/s, 2.6 m /s, 3.9 m/s, 4.2 m/s. From the results of the study, it was found that the velocity of the air flowing through the wind tunnel channel obtained the largest value of 14.05 m/s and the smallest value of 0.5 m/s. has a different speed in each section which is influenced by the shape and dimensions of each section. in each section if the velocity is low the flow pattern is laminar, if the velocity is medium the flow pattern is transitional and if the velocity is high the flow pattern is turbulent. And the results of the comparison of actual and simulated velocity occur in the form of a similar flow pattern
An Adaptive Classification and Enhancement Method for Different Genetic Types of Low-light Images

Low-light ambient condition is an important external factor of image degradation. In order to improve the different genetic types of low-light degradation image enhancement effect, in this paper we combine the artificial intelligence method and image processing algorithm, using a degradation reason based image classifier to classify images into two types: dark image and underwater image. Different enhancement algorithms are designed for the two types of lowlight images with different causes. Dark images are enhanced by brightness adjustment based on Retinex model, while underwater images are enhanced by adaptive contrast enhancement and color correction to eliminate the influence of underwater factors on the imaging effect. The experiment result show that the proposed algorithm can effectively correct the color and adjust the brightness of low-light images and underwater images, and meet the requirements of image enhancement applications.
Active Learning for Deterministic Bottom-Up Nominal Tree Automata

Nominal set plays a central role in a group-theoretic extension of finite automata to those over an infinite set of data values. Moerman et al. proposed an active learning algorithm for nominal word automata with the equality symmetry. In this paper, we introduce deterministic bottom-up nominal tree automata (DBNTA), which operate on trees whose nodes are labelled with elements of an orbit finite nominal set. We then prove a Myhill-Nerode theorem for the class of languages recognized by DBNTA and propose an active learning algorithm for DBNTA. The algorithm can deal with any data symmetry that admits least support, not restricted to the equality symmetry and/or the total order symmetry. To prove the termination of the algorithm, we define a partial order on nominal sets and show that there is no infinite chain of orbit finite nominal sets with respect to this partial order between any two orbit finite sets.