Reviewerly/exHarmony huggingface dataset:
A comparative analysis between late fusion of features approach and ensemble of multiple classifiers approach for image classification

Summary In recent times the late fusion of features approach for high‐level features extracted by multiple deep convolutional neural networks (DCNNs) has proven to be very effective in the computer vision field, especially for object classification problems. Pretrained DCNNs DenseNet‐121 and ResNet‐18 are retrained, keeping the number of output nodes equal to the number of classes present in the dataset. The last fully connected layers of these networks thereby get adapted to transform the high‐level features to a low‐dimensional feature map. Then these maps are fused to improve the performance of the model. On the other hand, an ensemble of multiple classifiers reduces the overfitting problem by combining multiple models prediction matrices. In this work, the prediction matrices of two Logsoftmax multiclass classifiers are combined. The feature maps for these two classifiers are extracted using pretrained DenseNet and ResNet. This study compares the late fusion of high‐level features approach and ensemble of multiple classifiers approach for object classification problems. Experimentation has been carried out on two benchmark datasets, such as CIFAR‐10 and CIFAR‐100, and it achieves 96.48% and 83.33% of test accuracy for ensemble of multiple classifiers and the late feature fusion approach. The proposed method has been compared with other deep architectures and datasets.
WFN-PSC

Image dehazing is a fundamental task for the computer vision and multimedia and usually in the face of the challenge from two aspects, i) the uneven distribution of arbitrary haze and ii) the distortion of image pixels caused by the hazed image. In this paper, we propose an end-to-end trainable framework, named Weighted-Fusion Network with Poly-Scale Convolution (WFN-PSC), to address these dehazing issues. The proposed method is designed based on the Poly-Scale Convolution (PSConv). It can extract the image feature from different scales without upsampling and downsampled, which avoids the image distortion. Beyond this, we design the spatial and channel weighted-fusion modules to make the WFN-PSC model focus on the hard dehazing parts of image from two dimensions. Specifically, we design three Part Architectures followed by the channel weighted-fusion module. Each Part Architecture consists of three PSConv residual blocks and a spatial weighted-fusion module. The experiments on the benchmark demonstrate the dehazing effectiveness of the proposed method. Furthermore, considering that image dehazing is a low-level task in the computer vision, we evaluate the dehazed image on the object detection task and the results show that the proposed method can be a good pre-processing to assist the high-level computer vision task.
Attention Networks for the Quality Enhancement of Light Field Images

In this paper, we propose a novel filtering method based on deep attention networks for the quality enhancement of light field (LF) images captured by plenoptic cameras and compressed using the High Efficiency Video Coding (HEVC) standard. The proposed architecture was built using efficient complex processing blocks and novel attention-based residual blocks. The network takes advantage of the macro-pixel (MP) structure, specific to LF images, and processes each reconstructed MP in the luminance (Y) channel. The input patch is represented as a tensor that collects, from an MP neighbourhood, four Epipolar Plane Images (EPIs) at four different angles. The experimental results on a common LF image database showed high improvements over HEVC in terms of the structural similarity index (SSIM), with an average Y-Bjøntegaard Delta (BD)-rate savings of 36.57%, and an average Y-BD-PSNR improvement of 2.301 dB. Increased performance was achieved when the HEVC built-in filtering methods were skipped. The visual results illustrate that the enhanced image contains sharper edges and more texture details. The ablation study provides two robust solutions to reduce the inference time by 44.6% and the network complexity by 74.7%. The results demonstrate the potential of attention networks for the quality enhancement of LF images encoded by HEVC.
Conditional Classification: A Solution for Computational Energy Reduction

Deep convolutional neural networks have shown high efficiency in computer visions and other applications. However, with the increase in the depth of the networks, the computational complexity is growing exponentially. In this paper, we propose a novel solution to reduce the computational complexity of convolutional neural network models used for many class image classification. Our proposed technique breaks the classification task into two steps: 1) coarse-grain classification, in which the input samples are classified among a set of hyper-classes, 2) fine-grain classification, in which the final labels are predicted among those hyper-classes detected at the first step. We illustrate that our proposed classifier can reach the level of accuracy reported by the best in class classification models with less computational complexity (Flop Count) by only activating parts of the model that are needed for the image classification.
Semantic Image Synthesis from Inaccurate and Coarse Masks

Semantic image synthesis is an image-to-image translation problem where the goal is to learn mapping from semantic segmentation masks to corresponding photorealistic images. However, conventional semantic image synthesis methods require numerous pairs of correct semantic masks and real images, and collecting these pairs is not always possible. To address this issue, we propose a smoothing method, which we call local label smoothing (LLS), that incorporates label smoothing per small patch of an input mask to learn mapping from masks to images even when semantic masks are inaccurate. Furthermore, we also propose an extended method for coarse masks. We demonstrate the advantage of the proposed methods over existing methods to deal with noisy masks on several datasets.
Digitalization of educational technologies of higher education institutions as a means of achieving a competitive advantage in the market of educational services

Digitalization is becoming an integral part of the development of all spheres of society, including the education system. The purpose of this research is to analyze trends in the development of digitalization in modern higher education institutions, the prospects for their interaction and mutual influence. The dialectical method, instrumental and functional approaches are used. The author concludes that in Russia, according to modern needs, requests and interests of the population, there is a qualitative process of development of digitalization of higher education.
Residual Fractal Network for Single Image Super Resolution by Widening and Deepening

The architecture of the convolutional neural network (CNN) plays an important role in single image super-resolution (SISR). However, most models proposed in recent years usually transplant methods or architectures that perform well in other vision fields. Thence they do not combine the characteristics of super-resolution (SR) and ignore the key information brought by the recurring texture feature in the image. To utilize patch-recurrence in SR and the high correlation of texture, we propose a residual fractal convolutional block (RFCB) and expand its depth and width to obtain residual fractal network (RFN), which contains two variations, deep residual fractal network (DRFN) and wide residual fractal network (WRFN). RFCB is recursive with multiple branches of magnified receptive field. Through the phased feature fusion module, the network focuses on extracting high-frequency texture feature that repeatedly appear in the image. We also introduce residual in residual (RIR) structure to RFCB that enables abundant low-frequency feature feed into deeper layers and reduce the difficulties of network training. RFN is the first supervised learning method to combine the patch-recurrence characteristic in SISR into network design. Extensive experiments demonstrate that RFN outperforms state-of-the-art SISR methods in terms of both quantitative metrics and visual quality, while the amount of parameters has been greatly optimized. The source code and pre-trained models are released in https://github.com/JiahangGu/RFN.
Real-World Simulation Through Obstacle Avoiding Robot

Technology advancements are now reaching new levels for instance, Tesla’s auto-drive, auto-mobile, and so on. It can identify obstacles on its own and thus can automatically avoid them. Now scientists and engineers are working more towards the advancement of this technology. Autonomous robots are used everywhere nowadays and robotics is additionally gaining popularity all around the world. This motivates us to make an autonomous Robot. Similarly, our project is also related to self-obstacles avoiding technology. It also includes the des ign and development of an autonomous robot that is s mart enough to sense and avoid hindrance of its path. The machine is set up using electronic sensors and programming languages, so that the s mart robot can be navigated to monitor in real-time. This model can be implemented using a python programming language. As a result, an autonomous robot is designed to monitor the objects using a virtual screen.
A novel scheme of domain transfer in document-level cross-domain sentiment classification

The sentiment classification aims to learn sentiment features from the annotated corpus and automatically predict the sentiment polarity of new sentiment text. However, people have different ways of expressing feelings in different domains. Thus, there are important differences in the characteristics of sentimental distribution across different domains. At the same time, in certain specific domains, due to the high cost of corpus collection, there is no annotated corpus available for the classification of sentiment. Therefore, it is necessary to leverage or reuse existing annotated corpus for training. In this article, we proposed a new algorithm for extracting central sentiment sentences in product reviews, and improved the pre-trained language model Bidirectional Encoder Representations from Transformers (BERT) to achieve the domain transfer for cross-domain sentiment classification. We used various pre-training language models to prove the effectiveness of the newly proposed joint algorithm for text-ranking and emotional words extraction, and utilised Amazon product reviews data set to demonstrate the effectiveness of our proposed domain-transfer framework. The experimental results of 12 different cross-domain pairs showed that the new cross-domain classification method was significantly better than several popular cross-domain sentiment classification methods.
The errors analysis of natural language generation — A case study of Topic-to-Essay generation

Although natural language generation (NLG) has achieved great success, there are still many problems with the generated text, if humans carefully examine it. To analyze the problems of NLG, we use manual evaluation methods to annotate and analyze the text generated by NLG. According to the analysis results, we can understand the defects of NLG in-depth, comprehensively, and accurately. Further, these provide cues for future improvement. In this paper, we first use a state-of-the-art Topic-to-Essay generation model to generate texts conditional on some topic words. Then, by analyzing the generated text, we propose an annotation framework, and then quantify the main drawbacks of current NLG, including poor semantic coherence, content duplication, logic errors, and repetition. It shows that the text generated by the current sequence-to-sequence model is still far from human expectation.
Walking-posture Classification from Single-acceleration-sensor Data using Deep Learning

We described a walking-posture classification method from a single accelerator attached to a human waist using a deep learning technique. We considered deep learning architectures for a single accelerator based on previous human activity recognition studies and investigated the classification accuracy of the proposed method using the walking-posture dataset. The results demonstrate that a deep learning approach to walking-posture classification using a single accelerator is more useful than the conventional SVM approach. Additionally, we also confirmed that a hybrid network architecture with three convolutional neural layers, two pooling layers between the convolutional layers, and a long short-term memory layer achieved the best accuracy of 0.9803 compared to other network architectures. We also confirmed the deep learning approach yielded more correct classification for each walking-posture category in spite of the difficulty to detect the classification by the SVM approach.
Context Visual Information-based Deliberation Network for Video Captioning

Video captioning automatically and accurately generates a textual description for a video. The typical methods following the encoder-decoder architecture directly utilize hidden states to predict words. Nevertheless, these methods do not amend the inaccurate hidden states before feeding those states into word prediction. This leads to a cascade of errors in generating word by word. In this paper, the context visual information-based deliberation network is proposed, abbreviated as CVI-DeINet. Its key idea is to introduce a deliberator into the encoder-decoder framework. The encoder-decoder first generates a raw hidden state sequence. Unlike the existing methods, the raw hidden state is no longer directly used for word prediction but is fed into the deliberator to generate the refined hidden state. The words are then predicted according to the refined hidden states and the contextual visual features. The results on two datasets show that the proposed method significantly outperforms the state-of-the-art methods.
Construction Method of Seepage Control and Leakage Stoppage in Water Conservancy Project Construction

Abstract In order to promote the development of water conservancy and improve the quality of water conservancy project, this paper expounds the causes of leakage in water conservancy project construction through theoretical analysis, and expounds two basic construction methods of seepage prevention and plugging in water conservancy project, and puts forward guarantee measures for seepage prevention and plugging construction in water conservancy project, for reference of relevant personnel.
Learning Attention-Based Translational Knowledge Graph Embedding via Nonlinear Dynamic Mapping

Knowledge graph embedding has become a promising method for knowledge graph completion. It aims to learn low-dimensional embeddings in continuous vector space for each entity and relation. It remains challenging to learn accurate embeddings for complex multi-relational facts. In this paper, we propose a new translation-based embedding method named ATransD-NL to address the following two observations. First, most existing translational methods do not consider contextual information that have been proved useful for improving performance of link prediction. Our method learns attention-based embeddings for each triplet taking into account influence of one-hop or potentially multi-hop neighbourhood entities. Second, we apply nonlinear dynamic projection of head and tail entities to relational space, to capture nonlinear correlations among entities and relations due to complex multi-relational facts. As an extension of TransD, our model only introduces one more extra parameter, giving a good tradeoff between model complexity and the state-of-the-art predictive accuracy. Compared with state-of-the-art translation-based methods and the neural-network based methods, experiment results show that our method delivers substantial improvements over baselines on the MeanRank metric of link prediction, e.g., an improvement of 35.6% over the attention-based graph embedding method KBGAT and an improvement of 64% over the translational method TransMS on WN18 database, with comparable performance on the Hits@10 metric.
Soft and self constrained clustering for group-based labeling

When using deep neural networks in medical image classification tasks, it is mandatory to prepare a large-scale labeled image set, and this often requires significant effort by medical experts. One strategy to reduce the labeling cost is group-based labeling, where image samples are clustered and then a label is attached to each cluster. The efficiency of this strategy depends on the purity of the clusters. Constrained clustering is an effective way to improve the purity of the clusters if we can give appropriate must-links and cannot-links as constraints. However, for medical image clustering, the conventional constrained clustering methods encounter two issues. The first issue is that constraints are not always appropriate due to the gap between semantic and visual similarities. The second issue is that attaching constraints requires extra effort from medical experts. To deal with the first issue, we propose a novel soft-constrained clustering method, which has the ability to ignore inappropriate constraints. To deal with the second issue, we propose a self-constrained clustering method that utilizes prior knowledge about the target images to set the constraints automatically. Experiments with the endoscopic image datasets demonstrated that the proposed methods give clustering results with higher purity.
On the Possibility of Creating Smart Contracts on Bitcoin by MPC-based Approaches

Bitcoin, as the first and the most adopted cryptocurrency, offers many features one of which is contingent payment, that is, the owner of money can programmatically describe the condition upon which his/her money is spent. The condition is determined using a set of instructions written in the Bitcoin scripting language. Unfortunately, this scripting language is not sophisticated enough to create complex conditions or smart contracts in general. Many admirable efforts have been made to build a smart contract infrastructure on top of the Bitcoin platform. In this paper, given the inherent limitations of the Bitcoin scripting language, we critically analyze the practical effectiveness of these methods. Afterwards, we formally define what a smart contract is and introduce seven requirements that if are satisfied, can make creation of smart contracts for Bitcoin possible. Based on the introduced requirements, we examine the ability of the current methods that use secure Multi-party Computation (MPC) to create smart contracts for Bitcoin and show where they fall short. We additionally compare their pros and cons and give clues on how a comprehensive smart contract platform can be possibly built for Bitcoin.
Rank-based ordinal classification

Differently from the regular classification task, in ordinal classification there is an order in the classes. As a consequence not all classification errors matter the same: a predicted class close to the groundtruth one is better than predicting a farther away class. To account for this, most previous works employ loss functions based on the absolute difference between the predicted and groundtruth class labels. We argue that there are many cases in ordinal classification where label values are arbitrary (for instance 1 ...C, being C the number of classes) and thus such loss functions may not be the best choice. We instead propose a network architecture that produces not a single class prediction but an ordered vector, or ranking, of all the possible classes from most to less likely. This is thanks to a loss function that compares groundtruth and predicted rankings of these class labels, not the labels themselves. Another advantage of this new formulation is that we can enforce consistency in the predictions, namely, predicted rankings come from some unimodal vector of scores with mode at the groundtruth class. We compare with the state of the art ordinal classification methods, showing that ours attains equal or better performance, as measured by common ordinal classification metrics, on three benchmark datasets. Furthermore, it is also suitable for a new task on image aesthetics assessment, i.e. most voted score prediction. Finally, we also apply it to building damage assessment from satellite images, providing an analysis of its performance depending on the degree of imbalance of the dataset.
Tamper-Proofing Imagery from Distributed Sensors Using Learned Blockchain Consensus

Area monitoring using wireless sensor networks that collect imagery and multimodal data from multiple vantage points, while requiring only limited local bandwidth and compute resources, promises improved resilience and scalability over single-camera imagery. However, the distributed nature of such networks can also increase their relative vulnerability to subversion via physical tampering. Here we address that nascent vulnerability by introducing a blockchain application that (1) learns correlations between low-dimensional projections of observed sequences captured by pairs of sensors, and (2) uses those correlations as a baseline for a soft consensus mechanism that identifies potentially compromised sensors with the strongest pairwise statistical anomalies. We then demonstrate our approach in a simulated environment in which a network of virtual cameras are aimed at a common dynamical scene from different vantage points, and show that after a training period of observing baseline behavior followed by the subversion of various numbers of the cameras, our application can correctly identify the cameras that have been compromised. Finally, we explore automated responses to such compromised sensors, including denying them shared resources and services.
Application of Improved Interactive Multimodel Algorithm in Player Trajectory Feature Matching

The original target tracking algorithm based on a single model has long been unable to meet the complex and changeable characteristics of the target, and then there are problems such as poor tracking accuracy, target loss, and model mismatch. The interactive multimodel algorithm uses multiple motion models to track the target, obtains the degree of adaptation between the actual motion state of the target and each model according to the calculated likelihood function, and then combines the updated weight values of each filter to obtain a weighted sum. Therefore, the interactive multimodel algorithm can achieve better performance. This paper proposes an improved interactive multimodel algorithm that can achieve player tracking and trajectory feature matching. First, this paper proposes an improved Kalman filtering (IKF) algorithm. This method is developed from the unbiased conversion measurement Kalman filter, which can obtain more accurate target state and covariance estimation. Secondly, using the parallel processing mode of the IMM algorithm to efficiently solve the data association between multiple filters, the IMM-IKF model is proposed. Finally, in order to solve the problem of low computational efficiency and high mismatch rate in image feature point matching, a method of introducing a minimum spanning tree in two-view matching is proposed. Experimental results show that the improved IMM-IKF algorithm can quickly respond to changes in the target state and can find the matching path with the lowest matching cost. In the case of ensuring the matching accuracy, the real-time performance of image matching is ensured.