Reviewerly/exHarmony huggingface dataset:
Research on the Accuracy and Speed of Three-Dimensional Reconstruction of Liver Surface Based on Binocular Structured Light

In view of the problem that the doctor cannot quickly obtain the three-dimensional information of the liver surface during the traditional open resection of liver tumor without radiation, a new three-dimensional reconstruction system of liver surface based on binocular structured light is proposed in this paper to obtain three-dimensional information of liver surface rapidly. The hardware construction and software module design of the three-dimensional reconstruction system of the liver surface are carried out. The multi-frequency heterodyne method is proposed to solve the problem of the lack of three-dimensional reconstruction of the liver surface. A GPU-CPU collaborative acceleration method is proposed to speed up the three-dimensional reconstruction of intraoperative liver surface. On this basis, a human respiratory platform is built to simulate the effect of human respiration on three-dimensional reconstruction of the liver surface in a real surgical environment. The accuracy and speed of the system are verified by the liver experiments: The errors of three-dimensional reconstruction of liver surface at expiratory peak and inspiratory peak are 0.78 ± 0.18mm and 0.92 ± 0.21 mm, respectively. The average time required for three-dimensional reconstruction of liver surface based on GPU-CPU collaborative calculation is 3.93s. The speed of three-dimensional reconstruction of liver surface based on GPU-CPU collaborative calculation is 52% faster than that of three-dimensional reconstruction of liver surface based on CPU. It can be seen that the system has high accuracy and fast speed, which can meet the basic needs of the doctor in surgery.
A Deep Learning Filter for Visual Drone Single Object Tracking

Object tracking is one of the most important topics in computer vision. In visual drone tracking, it is an extremely challenging due to various factors, such as camera motion, partial occlusion, and full occlusion. In this paper, we propose a deep learning filter method to relieve the above problems, which is to obtain a priori position of the object at the subsequent frame and predict its trajectory to follow up the object during occlusion. Our tracker adopts the geometric transformation of the surrounding of the object to prevent the bounding box of the object lost, and it uses context information to integrate its motion trend thereby tracking the object successfully when it reappears. Experiments on the VisDrone-SOT2018 test dataset and the VisDrone-SOT2020 val dataset illustrate the effectiveness of the proposed approach.
Massively-parallel column-level segmentation of depth images

Column-level segmentation of depth images is an energy-efficient strategy to perform 3D perception in autonomous-driving systems. These systems must perform 3D perception in real time through a pipeline of multiple tasks, which benefits from proposals that prioritize low complexity and short execution time over high levels of accuracy. For many years, column-level segmentation of depth images has been solved with the Stixels proposal, which uses an optimization algorithm with O(n2) computational complexity. This manuscript is an extended version of the ICCS paper “GPU-accelerated RDP Algorithm for Data Segmentation” (Cebrian and Moure, 2020). We present an alternative column-level segmentation proposal based on the RDP split-and-merge strategy, which has O(n⋅logn) computational complexity. The qualitative results obtained with the KITTI and Synthia image datasets evidence that our proposal can generate depth representations with greater compression and accuracy than the Stixels proposal. More importantly, we engineered a massively parallel design optimized for the low-power, GPU-accelerated embedded systems typically used for autonomous driving applications. For the datasets above, our proposal runs on a low-power NVIDIA Volta GPU 22 to 68 times faster than Stixels GPU-accelerated code. Additionally, our code achieves higher performance speedups as the computational capabilities and size of depth images increase.
Development and Modeling of Remote Laboratory Works for Engineering Education

Abstract The basic principles of finite-state machine synthesis and simulation on Proteus CAD and VHDL are discussed. The main steps of designing an electronic circuit of the control and operating finite-state machines that perform the calculation of a given algebraic expression were described. The main steps of consistent performance of a complex task for the development of cross-cutting knowledge and skills, which must be performed by the student in the framework of the normative discipline ware analysed in detail. It is shown that the developed cycle of laboratory works can be successfully implemented in various training courses.
Neural networks with finite-time convergence for solving time-varying linear complementarity problem

Time-varying linear complementarity problem (TLCP) has received a great deal of attention due to its broad variety of scientific and engineering applications. Several efficient Zhang neural networks are introduced for solving TLCP in this paper. Theoretical analysis shows that the related error function of the model proposed in this paper eventually tends to zero. The state convergence time periods of those Zhang neural networks with three types of activation functions are proved to be finite and can be quantitatively estimated by using some given parameters. Further, it is shown that the proposed neural network is of noise-tolerance, which means the neural network is more appropriate for a wider application. Moreover, in order to implement neural network numerically, a related discrete-time version is also studied. Finally, numerical simulations confirm the analysis of the proposed models concretely.
StyleGAN-LSRO Method for Person Re-Identification

In this study, the StyleGAN-LSRO method has been developed for person re-identification (re-ID) tasks.This method applies the style-based generative adversarial network (StyleGAN) to generate new synthetic images from existing person re-ID datasets and the label smoothing regularization for outliers (LSRO) algorithm to process those newly produced unlabeled images by assigning them a uniform label distribution along with the definition of a loss function for the training process.A baseline model based on a convolutional neural network (CNN) was developed to learn the discriminative features to recognize a person's identity.The developed method has been tested on three datasets.These datasets are Market-1501, DukeMTMC-reID, and MSMT17.The experimental results show that the StyleGAN model achieved a Fréchet inception distance score of 12.67 and structural similarity score of 0.387, outperforming all the previous generative methods and demonstrating that the images generated by StyleGAN are of superior quality.Adding these StyleGAN-generated data significantly improves the person re-ID accuracy.The StyleGAN-LSRO person re-ID method achieved 98.5% rank-1 accuracy and 91.8% mean average precision (mAP) on Market-1501, 87.0% rank-1 accuracy and 83.8% mAP on DukeMTMC-reID, and 81.5% rank-1 accuracy and 60.9% mAP on MSMT17, respectively.These results show that the StyleGAN-LSRO method significantly outperforms most of the state-of-the-art person re-ID methods.The success rate for person re-ID increases when the images used are of high resolution and square matrix form.In other cases, the success rate decreases.
Online Signature Verification Based on Signer Dependent Sampling Frequency and Dynamic Time Warping

Previous studies have shown that choosing a different sample rate or signature point count provides better accuracy in online signature verification systems. However, the sampling rate that minimizes the error may vary on the database and signer levels. In this work, we studied the effect of choosing individual sampling frequencies for each signer and proposed a signature verification system based on signer-dependent sampling frequency. The system was tested on five different databases, using several features and preprocessing methods. Results showed accuracy improvement in 70% of the overall 500 tests and 92% in the chosen system where z-normalization and six samples used in the preprocessing phase.
Deep Learning Based Ancient Asian Character Recognition

Ancient documents record a lot of information such as politics, economy, culture and so on, which may help people know the history and culture. However, a lot of ancient documents have not be organized yet. One of the reasons is some kinds of character are not be used now and only few of specialists can understand them, especially in Asian. These ancient Asian characters include Oracle Bone Inscriptions, Kuzushi characters, etc. Furthermore, these ancient documents are found one after another, letting document organization becomes a hard work, and aging processing increases the difficult of organization. This paper aims to recognize some kinds ancient character for helping people organize the ancient Asian documents by deep learning method. For achieving better accuracy, we applied the most state-of-the-art deep learning model -self-attention, which is different with current convolutional neural network. The experimental results shows the self-attention also achieves good accuracy in Oracle Bone Inscriptions and Kuzushi characters recognition, which also prove the effectiveness self-attention in ancient character recognition.
Learning Low Resource Consumption CNN through Pruning and Quantization

Deep learning models have evolved into powerful tools that can be used for many artificial intelligence tasks. However, deploying deep neural networks into real-world applications is still challenging due to their high computational complexity and storage overhead. Fortunately, a densely connected neural network can be converted into a sparsely connected network with low resource demand by the neural network compression. Since deep neural networks are complicated, compression mechanism should find a tradeoff between compression ratio and model accuracy. In this article, by analyzing the statistics of channel connection, we propose an interactive neural network compression mechanism including out-in-channel pruning and neural network quantization. Many channel pruning works apply structured sparsity regularization on each layer separately. We consider correlations between successive layers to retain predictive power of the compact network. A global greedy pruning algorithm is designed to remove redundant out-in-channels in an iterative way. Moreover, in order to solve the shortcomings of the one-shot quantization, we propose the incremental quantization algorithm in the dimension of the output channel, which can smooth network fluctuations and recover accuracy better during retraining. Our mechanism is comprehensively evaluated with various Convolutional Neural Networks (CNN) architectures on popular datasets. Notably, on ImageNet-1K, the out-in-channel pruning reduce 54.0 percent FLOPS on AlexNet and 50.0 percent FLOPs on ResNet-50 with only 0.15 and 0.37 percent top-1 accuracy drop respectively. On classification and style transfer tasks, the superiority of incremental quantization increases with the decrease of the number of quantization bits.
Quadratic Programs for High Relative Degree Spatial Constraints and Spatiotemporal Specifications with Spacecraft Applications

This paper presents a new methodology for ensuring forward invariance of sublevel sets of high relative degree functions, and convergence of the state to these sets. We introduce the notion of the boundary layer of a set defined by multiple constraints, and develop polynomially-derived trajectory constraints as means to enforce set invariance by redirecting trajectories that enter this boundary layer. This strategy is then extended to achieve convergence to and invariance of goal sets that are specified using Signal Temporal Logic. A quadratic program computes control inputs online that yield trajectories that achieve high level objectives specified by these sets, such as obstacle avoidance and target observation. We present a case study utilizing this controller for a spacecraft position and attitude control problem requiring observation of targets on the surface of a small body.
Homogenized ORB Algorithm Using Dynamic Threshold and Improved Quadtree

The Oriented FAST and Rotated BRIEF (ORB) algorithm has the problem that the extracted feature points are overconcentrated or even overlapped, leading to information loss of local image features. A homogenized ORB algorithm using dynamic thresholds and improved quadtree method is proposed in this paper, named Quadtree ORB (QTORB). In the feature point extraction stage, a new dynamic local threshold calculation method is proposed to enhance the algorithm’s ability to extract feature points at homogeneous regions. Then, a quadtree method is improved and adopted to manage and optimize feature points to eliminate those excessively concentrated and overlapping feature points. Meanwhile, in the feature points optimization process, different quadtree depths are set at different image pyramid levels to prevent excessive splitting of the quadtree and increase calculation speed. In the feature point description stage, local gray difference value information is introduced to enhance the saliency of the feature description. Finally, the Hamming distance is used to match points and RANSAC is used to avoid mismatches. Two datasets, namely, the optical image dataset and SAR image dataset, are used in the experiment. The experimental result shows that, considering accuracy and real-time efficiency, the QTORB can effectively improve the distribution uniformity of feature points.
Sign language recognition through Leap Motion controller and input prediction algorithm

Abstract The sign recognition systems are aimed to help deaf people communicate with society. In this paper we proposed our own concept of sign language recognition, which is based on a co-operative deep learning neural network, a text input prediction algorithm and a feedback from the user. We have pointed out the complexity of the Russian sign language and conceived the fingerspelling recognition. The method utilizes the natural properties of fingerspelling in order to increase the accuracy and recognition performance by predicting the ongoing letter. We also provide a detailed review of data acquisition in the related works. From a hardware perspective, we suggest using Leap Motion controller.
Evaluating the Impact of Software Security Tactics: A Design Perspective

Design architecture is the edifice that strengthens the functionalities as well as the security of web applications.In order to facilitate architectural security from the web application's design phase itself, practitioners are now adopting the novel mechanism of security tactics.With the intent to conduct a research from the perspective of security tactics, the present study employs a hybrid multi-criteria decision-making approach named fuzzy analytic hierarchy process-technique for order preference by similarity ideal solution (AHP-TOPSIS) method for selecting and assessing multi-criteria decisions.The adopted methodology is a blend of fuzzy analytic hierarchy process (fuzzy AHP) and fuzzy technique for order preference by similarity ideal solution (fuzzy TOPSIS).To establish the efficacy of this methodology, the results are obtained after the evaluation have been tested on fifteen different web application projects (Online Quiz competition, Entrance Test, and others) of the Babasaheb Bhimrao Ambedkar University, Lucknow, India.The tabulated outcomes demonstrate that the methodology of the Multi-Level Fuzzy Hybrid system is highly effective in providing accurate estimation for strengthening the security of web applications.The proposed study will help experts and developers in developing and managing security from any web application design phase for better accuracy and higher security.
Sentence similarity evaluation using Sent2Vec and siamese neural network with parallel structure

Sentence similarity evaluation is a significant task used in machine translation, classification, and information extraction in the field of natural language processing. When two sentences are given, an accurate judgment should be made whether the meaning of the sentences is equivalent even if the words and contexts of the sentences are different. To this end, existing studies have measured the similarity of sentences by focusing on the analysis of words, morphemes, and letters. To measure sentence similarity, this study uses Sent2Vec, a sentence embedding, as well as morpheme word embedding. Vectors representing words are input to the 1-dimension convolutional neural network (1D-CNN) with various sizes of kernels and bidirectional long short-term memory (Bi-LSTM). Self-attention is applied to the features transformed through Bi-LSTM. Subsequently, vectors undergoing 1D-CNN and self-attention are converted through global max pooling and global average pooling to extract specific values, respectively. The vectors generated through the above process are concatenated to the vector generated through Sent2Vec and are represented as a single vector. The vector is input to softmax layer, and finally, the similarity between the two sentences is determined. The proposed model can improve the accuracy by up to 5.42% point compared with the conventional sentence similarity estimation models.
Set Augmented Triplet Loss for Video Person Re-Identification

Modern video person re-identification (re-ID) machines are often trained using a metric learning approach, supervised by a triplet loss. The triplet loss used in video re-ID is usually based on so-called clip features, each aggregated from a few frame features. In this paper, we propose to model the video clip as a set and instead study the distance between sets in the corresponding triplet loss. In contrast to the distance between clip representations, the distance between clip sets considers the pair-wise similarity of each element (i.e., frame representation) between two sets. This allows the network to directly optimize the feature representation at a frame level. Apart from the commonly-used set distance metrics (e.g., ordinary distance and Hausdorff distance), we further propose a hybrid distance metric, tailored for the set-aware triplet loss. Also, we propose a hard positive set construction strategy using the learned class prototypes in a batch. Our proposed method achieves state-of-the-art results across several standard benchmarks, demonstrating the advantages of the proposed method.
The challenge of incorporating digital skills in the classroom: perceptions and attitudes of Spanish Salesian teachers

Skills enabling the integration of information and communications technology (ICT) in classrooms have become necessary for teachers' professional practice. Using an applied approach, this study conducts an analysis of the use of and attitudes and perceptions towards digital skills among Spanish teachers in Salesian schools. Given the relevance of technologies in society, the study aims to understand the viewpoint of teachers in relation to the use and assessment of digital means appropriate for school tasks. The objective is to provide an accurate depiction of teachers' perspectives, opinions, and attitudes towards ICT use in the educational sphere. Specifically, the study addresses aspects related to their perceptions of the areas proposed to be focused on by the Spanish Education Ministry in the Common Framework for Digital Competence of Teachers (2017). Thus, the study identifies key shortcomings to address necessary and appropriate training of children in the current society of information.
Multi-Category Image Super-Resolution with Convolutional Neural Network and Multi-Task Learning

This paper presents an image super-resolution technique using a convolutional neural network (CNN) and multi-task learning for multiple image categories. The image categories include natural, manga, and text images. Their features differ from each other. However, several CNNs for super-resolution are trained with a single category. If the input image category is different from that of the training images, the performance of super-resolution is degraded. There are two possible solutions to manage multi-categories with conventional CNNs. The first involves the preparation of the CNNs for every category. This solution, however, requires a category classifier to select an appropriate CNN. The second is to learn all categories with a single CNN. In this solution, the CNN cannot optimize its internal behavior for each category. Therefore, this paper presents a super-resolution CNN architecture for multiple image categories. The proposed CNN has two parallel outputs for a high-resolution image and a category label. The main CNN for the high-resolution image is a normal three convolutional layer-architecture, and the sub neural network for the category label is branched out from its middle layer and consists of two fully-connected layers. This architecture can simultaneously learn the high-resolution image and its category using multi-task learning. The category information is used for optimizing the super-resolution. In an applied setting, the proposed CNN can automatically estimate the input image category and change the internal behavior. Experimental results of 2× image magnification have shown that the average peak signal-to-noise ratio for the proposed method is approximately 0.22 dB higher than that for the conventional super-resolution with no difference in processing time and parameters. We have ensured that the proposed method is useful when the input image category is varying.
S3-Net: A Fast and Lightweight Video Scene Understanding Network by Single-shot Segmentation

Real-time understanding in video is crucial in various AI applications such as autonomous driving. This work presents a fast single-shot segmentation strategy for video scene understanding. The proposed net, called S3-Net, quickly locates and segments target sub-scenes, meanwhile extracts structured time-series semantic features as inputs to an LSTM-based spatio-temporal model. Utilizing ten-sorization and quantization techniques, S3-Net is intended to be lightweight for edge computing. Experiments using CityScapes, UCF11, HMDB51 and MOMENTS datasets demonstrate that the proposed S3-Net achieves an accuracy improvement of 8.1% versus the 3D-CNN based approach on UCF11, a storage reduction of 6.9× and an inference speed of 22.8 FPS on CityScapes with a GTX1080Ti GPU.