Reviewerly/exHarmony huggingface dataset:
Research on the Application of Binocular Vision-Based Pose Estimation Technology in Live-Line Working

To engage robots in live-line working and reduce the cost of electric power companies, this study proposes a binocular vision-based pose estimation method that can automatically identify the target's pose, guide the robotic arms to grasp the tools and fulfill live-line working tasks. First, this method matches and calculates the deep information of images via the binocular camera, and converts the images into the cloud of points; then, the advanced image segmentation technology is used to recognize the tools, and 3D modelling is conducted for the objects to accurately match the target image and the real target; the iterative closest point (ICP) method is used to solve the spatial pose of the target corresponding to the robot and thus guide the robots in tasks. Meanwhile, experiments are conducted to simulate the actual work process, and the result shows that the proposed method can accurately estimate the pose of targets and has good robustness and application value.
BERT-Based Neural Collaborative Filtering and Fixed-Length Contiguous Tokens Explanation

We propose a novel, accurate, and explainable recommender model (BENEFICT) that addresses two drawbacks that most review-based recommender systems face. First is their utilization of traditional word embeddings that could influence prediction performance due to their inability to model the word semantics’ dynamic characteristic. Second is their black-box nature that makes the explanations behind every prediction obscure. Our model uniquely integrates three key elements: BERT, multilayer perceptron, and maximum subarray problem to derive contextualized review features, model user-item interactions, and generate explanations, respectively. Our experiments show that BENEFICT consistently outperforms other state-of-the-art models by an average improvement gain of nearly 7%. Based on the human judges’ assessment, the BENEFICT-produced explanations can capture the essence of the customer’s preference and help future customers make purchasing decisions. To the best of our knowledge, our model is one of the first recommender models to utilize BERT for neural collaborative filtering.
A systematic review on the developments and evaluation of twin learning algorithms

There are fruitful advancements in Machine Learning Algorithms as per the demand of the digital world. The degree of performance of Twin Algorithms should have an upper hand over its parents. The improvements in the running time of Twin Extreme Learning Machine (TELM) and Twin Support Vector Machine (TWSVM) are commendable and have given confidence to the researchers. This paper contributes a detailed study on the developments of twin learning algorithms that are happened on top of the TWSVM, TELM and Twin Random Vector Functional Link (TRVFL). The single-hidden-layer learning paradigms and the twin models that have come after Extreme Learning Machine (ELM) tried to address a series of pitfalls of their parents. The iterative versions of single-hidden-layer models and their robust performances will catalyst growth in artificial intelligence. In this paper, some of the works in twin learning algorithms that are either technically sound or better in the performances are taken for the study. The working principle and performance of the algorithms are detailed with the help of published works and findings.
Catalogo De Datos Covid-19 Transparencia, Innovación Y Compromisso Social (Cataloging COVID-19 Data: Transparency, Innovation and Public Good)

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Transformer-based Summarization by Exploiting Social Information

Information from social media, e.g. user comments and tweets, provides an additional channel that enriches the content of Web documents. This paper introduces a model by exploiting relevant social information to enhance web document summarization. Different from prior studies using feature engineering, we empower our model by utilizing transformers. To do that, relevant user posts are paired with sentences for utilizing the support from social information. The paired information is fed into transformers for taking full advantage of the contextual aspect. The model is then adapted by stacking an additional convolution neural network layer for classification. Experimental results on two English datasets show that our model achieves promising results for summarizing single documents.
Investigating the Lack of Consensus Among Sentiment Analysis Tools

Sentiment analysis, the classification of human emotion expressed in text, has the potential to enhance our ability to analyse the ever growing amount of information published each day on social media. Thus, we compare here seven of the most well-regarded sentiment analysis tools, and conclude that none of them is sufficiently reliable to be used on its own. Combining them and relying on their results only when various tools reach an agreement seems to be a better option. The pros and cons of such an approach are discussed in this paper, while providing recommendations related to the usability of the tools in question. Our work is of particular relevance to small and medium-sized enterprises (SMEs), which constitute a large and integral part of the economy. SMEs seem to be ideal candidates to turn data derived from sentiment analysis into business opportunities.
An Implementation of NNEF-Darknet Neural Network Model Converter

There are various neural network engines that perform AI functions, and each neural network engine uses its own format for storing neural network graph information and weight data. To provide interoperability among various neural network engines, Khronos Group proposed Neural Network Exchange Format. In this paper, we described the NNEF-Darknet neural network model converter that enables the neural network model stored in the NNEF format to be executed in Darknet that is one of neural network engines. This paper shows the architecture and operation flow of the developed neural network model converter, and shows that this converter works normally with Yolo v3 neural network example.
PrTaurus: An Availability-Enhanced EMR Service on Preemptible Cloud Instances

EMR (Elastic Map Reduce) is a service provided by mainstream cloud vendors for data processing users to directly obtain well-managed Hadoop YARN clusters on the cloud. Preemptible instance is a kind of cloud server that is cheap but is likely to be reclaimed by cloud vendors suddenly. Running EMR clusters on preemptible instances relies on YARN's own fault-tolerance, which is limited. In this paper, we present PrTaurus as an availability-enhanced EMR service on preemptible instances. PrTaurus integrates a system-level checkpoint capability based on Docker into YARN to further improve its fault-tolerance. In addition, PrTaurus's scheduling strategy takes advantage of Alibaba Cloud's one-hour protection policy. Furthermore, a new method that comprehensively considers cost-efficiency, preemption risk and overhead is proposed to select cluster instances. We evaluated PrTaurus through simulations on real-world workload and instance price traces. Experimental results show that compared with the existing EMR clusters running on preemptible instances, PrTaurus significantly reduces cost (13.0%-74.6%), instance preemptions (60.3%-88.9%), and task preemptions (86.0 % - 98.6 %).
Deep Learning Methods for Image Decomposition of Cervical Cells

One way to solve under-determined image decomposition is to use statistical information about the type of data to be decomposed. This information can be obtained by a deep learning where convolutional neural networks (CNN) are a subset recently used widely in image processing. In this paper, we have designed a two-stage CNN that takes cytology images of overlapped cervical cells and attempts to separate the cell images. In the first stage, we designed a CNN to segment overlapping cells. In the second stage, we designed a CNN that uses this segmentation and the original image to separate the regions. We implemented a CNN similar to U-Net for image segmentation and implemented a new network for the image separation. To train and test the proposed networks, we simulated 50000 cervical cell cytology images by overlaying individual images of real cervical cells using the Beer-Lambert law. Of these 50000 images, we used 49000 images for training and evaluated the method with 1000 test images. Results on these synthetic images give more than 97% segmentation accuracy and gives decomposition SSIM scores of more than 0.99 and PSNR score of more than 30 dB. Despite these positive results, the permutation problem that commonly effects signal separation occasionally occurred resulting in some cell structure mis-separation (for example, one cell given two nucleoli and the other given none). In addition, when the segmentation was poor from the first stage, the resulting separation was poor.
Adaptive Visualization of Gas Distribution Using Augmented Reality Glasses

Monitoring gas distribution is a method to supervise and validate the stabilization and safety of landfill sites. Recent technologies that use sophisticated sensors confuse workers' understanding of estimated gas distribution due to mismatch between the gas position in a scene and the one in a monitor. In this paper, we propose a method for spatially visualizing gas distribution using augmented reality glasses. Furthermore, we discuss the initial step toward the adaptive visualization of gas distribution based on workers' knowledge and monitoring purposes using real-time AR.
Consumer-Oriented Multi-Party Matching Recommendation System Based Deep Learning

In this paper, we propose the consumer-oriented multi-party matching recommendation system based deep learning that recommends the most suitable participants to consumers when producing a work. The proposed method uses a deep learning model that takes the characteristics and constraints of the consumer as input. The deep learning model uses the Candidate Generation Model, which selects candidate candidates, and the Ranking Model, which recalculates the recommendation scores of selected candidate candidates. Therefore, a group of candidates suitable for a large candidate group is recommended.
A Novel Complex SAR Spatial De-Speckling Filter Evaluation Based On Time-Frequency Domain Formulation Of Texture Information

The adverse effects analysis of high-frequency embedded speckle under implementation of spatial de-speckling filters on a reference SAR image is investigated in this paper. The investigation including the speckle probability distribution function (PDF) formulation and the power spectral density (PSD) function modeling as well as quality metrics evaluations. As a novel approach, an objective de-speckling filter classification has been proposed to further categorize the speckle suppression on the basis of their performance. The proposed evaluation method as well as the filter categorization are completely helpful for radar remote sensing and data preservation while dealing with low-resolution image restoration and spatial de-speckling filter application. The spatial resolution simulations are carried out with the help of a hybrid-domain processing algorithm.
Teaching Effect and Reform Strategies on Graduation Design for Undergraduates in Information Related Majors in Independent College Based on Psychological Identity

The annual graduation design in universities is the final cultivation process of the comprehensive professional competencies for undergraduates before they end their college career, and is also an important component in the tasks of undergraduate education. Teaching effect of graduation design on one hand depends on the foundation and application ability of professional knowledge of each student, and also depends on the psychological identity on advisor and the topic chosen from the student point of view on the other hand. In this paper, undergraduates who have all finished their graduation design in Department of Electronic and Information Engineering in Independent College T from the year 2017 to 2019 have been set as study objects, by deeply investigating students’ attitudes toward advisors and topics chosen from questionnaire survey, and by analyzing the grade point averages (GPA) of students, the relationship between psychological identity on advisor as well as topic chosen and the teaching effect of graduation design for each student has been found out, and related reform strategies on teaching methods has been discussed.
Segmentation and Registration of the Liver in Dynamic Contrast-Enhanced Computed Tomography Images

Dynamic contrast-enhanced computed tomography (DCE-CT) is the main auxiliary diagnostic tool for liver diseases. Liver segmentation and registration in all stages of DCE-CT images are the key technology for big data analysis of liver disease diagnosis. The change of imaging conditions in different stages of DCE-CT brings enormous challenges to the segmentation of liver CT images. This study proposes an automatic model for liver segmentation from abdominal CT images in different stages of DCE on the basis of U-Net. The skip connection in U-Net can improve the ability of complex feature recognition. A total of 4863 CT slices from 16 patients with hepatocellular carcinoma (HCC) were selected as the training set, and 1754 CT slices from 6 patients with HCC were selected as the test set. The training and test sets included plain scan, hepatic arterial-dominant phase, and portal venous-dominant phase CT scans. Results showed that the Dice value of the proposed method was significantly higher than those of the full convolutional network and region-growing method. Then, 3D reconstruction and registration were performed on the segmentation results of the liver region of DCE-CT images. The proposed method obtained the best performance, which can provide technical support for the big data analysis of liver diseases.
A fast filter enhancement method for the infrared image

Recent advances in image enhancement explored the power of convolutional neural network (CNN) to achieve a better performance. Despite the great success of CNN-based methods, it is not easy to apply these methods to edge devices (such as FPGA, ASIC) due to the requirement of heavy computation, and the CNN-based methods heavily rely on the training datasets. In this paper, we propose a simple method for image enhancement, without any training steps.
Pedagogical conditions for achieving the quality of distance education in higher education institutions

Based on the analysis of the concepts of “distance education” and “distance learning”, we proposed components of the quality of the remote education system (availability of educational standards and distance learning programs, high efficiency of remote learning technologies, evaluation of administrative and management decisions on the creation and functioning of the system, degree of regulatory and legal regulation, assessment of the level of technical and resource equipment, personnel readiness to organize and monitor the educational process in digital format) as well as the quality of distance learning (assessment of the content of digital educational content, the degree of innovation of the distance learning course, the effectiveness of feedback pedagogical communication, the degree of student training, the strength of residual knowledge, the effectiveness of digital educational means, the intensity of pedagogical interaction, the level of digital competencies of teachers and the readiness of students for independent educational activities in the digital environment). On the basis of these conditions, we formulated external (administrative, regulatory, material and technical, personnel) and internal (didactic and personal) factors affecting the effectiveness of distance learning. We also specifiedAspects related to internal factors, such as determining the pace of completing a distance course, requirements for academic support, reliance on internal motivation, managing student activity, using tools for measuring learning, substantiating methods of feedback pedagogical communication, studying student satisfaction with a distance course.
Orthogonal Coded Multi-view Structured Light for Inter-view Interference Elimination

Rapid 3D reconstruction of dynamic scenes is very useful in 3D object structure analysis, accident avoidance for UAV, and other visual applications. Against dynamic scenes, coded structured light methods have been proposed to obtain the depth information of an object in 3D world, and most of them are based on spatial codification. A brutal truth is that two or more cameras and projectors from different viewpoints are needed to measure the dynamic scene simultaneously for rapid 3D reconstruction. However, when two traditional patterns, especially the binaries, are mutually overlapped, interference between them arises to a new challenge to 3D reconstruction. Traditional patterns can hardly be separated from each other, which surely influence the quality of the 3D reconstruction. To eliminate the interference problem, we propose a scheme of orthogonal coded multi-view structured light systems, which can obtain accurate of depth maps for a scene. Besides, we also test the stability of the orthogonal patterns by establishing three different scenes and making a comparisons to traditional patterns. New state-of-the-art results can be obtained by our scheme in the experiments.
An occlusion compensation model for improving the reconstruction quality of light field

Occlusion lack compensation (OLC) is a multi-plexing gain optimization data acquisition and novel views rendering strategy for light field rendering (LFR). While the achieved OLC is much higher than previously thought possible, the improvement comes at the cost of requiring more scene information. This can capture more detailed scene information, including geometric information, texture information and depth information, by learning and training methods. In this paper, we develop an occlusion compensation (OCC) model based on restricted boltzmann machine (RBM) to compensate for lack scene information caused by occlusion. We show that occlusion will cause the lack of captured scene information, which will lead to the decline of view rendering quality. The OCC model can estimate and compensate the lack information of occlusion edge by learning. We present experimental results to demonstrate the performance of OCC model with analog training, verify our theoretical analysis, and extend our conclusions on optimal rendering quality of light field.
Evaluation of Enhanced Password Authentication Using Device Stored Random Number

Password-based authentication is the most commonly used form of a user authentication, however incidents of unauthorized access continue to occur. In order to enhance the security, the authors have proposed the enhanced password authentication method using a random number stored in the user device. This paper evaluates the security of the proposed method in comparison with each authentication factor combined with a password.
Evaluation factors in semantic mechanisms of manipulation (in italian)

The aim of the article is to analyze the manipulative potential of the modern Italian-speaking media space in terms of creating a negative image of Russia as an international player. The sources of language material for the analysis are two authoritative Italian publications − Internazionale newspaper and Formiche.net Internet portal − characterized by the exclusively analytical content of articles and expert comments in the field of international relations and geopolitics, the authors of which are able to significantly influence the readership. The methodological apparatus includes a descriptive method and a method of semantic and syntactic analysis of linguistic units with a manipulative potential. From a pragmatic point of view, examples of evaluation predicates used by the Italian mass media to inform the reader about the actions of the Russian Federation are given. The article may be of interest to linguistic researchers dealing with romance studies, political linguistics, as well as to political scientists interested in Russian-Italian relations.
An algorithm for rule-based layout pattern matching

As feature size continues to shrink and design complexity continues to increase, a circuit layout has become more difficult to verify than before. Rule-based pattern matching is considered as a practical approach for layout verification, but unlike traditional ones, this paper focuses on three kinds of rules and presents an efficient pattern matching algorithm. Given a layout and a set of rules, our algorithm first adopts a line sweep method to scan the layout twice such that for each given rule, it collects all pairs of polygons satisfying that rule. It then bases on the collected polygon pairs to find all layout patterns each of which meets the given set of all rules. Experimental results show that our algorithm is able to find the correct set of matched patterns efficiently for each test case.
Deep Learning approach for text, image, and GIF multimodal sentiment analysis

Recently, social media users are increasingly using visual media like GIFs, videos and images as powerful means of expressing their emotions and sentiments. We created a multimodal sentiment analysis tool for different forms of tweets in Python to compute the sentiment score not only base on the tweet's text, but also to consider GIFs and images to gain better accuracy for the tweet's overall sentiment score. For image sentiment, we use fine-tuned CNN, for texts we use VADER, and for the GIFs we apply both image sentiment and facial expression analysis in every frame of the file. In our work, we demonstrate that by utilizing both textual and image features we can achieve better results compared to other models that only rely on either images or text features. The final sentiment score for the incoming tweets will be calculated by aggregating the output scores obtained from each of our text, image, and GIF modules.
Order Dependency in Sequential Correlation

Data analytics is a core part of the fast-moving world in the modern era, and its origin is data mining. SSequential pattern mining is a cutting-edge studies location of facts mining. An easy sample mining technique is inadequate to apprehend the connection among the objects in a dataset. A correlation degree may be extra beneficial to decide the dependency among the entities. We suggest a method in this paper that can evaluate the relationship's strengths and the degree of order. While assessing the ranking of dependency, we use one way and reverse connections. Moreover, To determine the order of dependency, we use one method and contrary relationships. Sequential correlations are widely applicable in the sector of diagnosing disease, optimize the internet, and develop active retail strategies.
Fine-Tuning Tree-LSTM for Phrase-Level Sentiment Classification on a Polish Dependency Treebank

We describe a variant of Child-Sum Tree-LSTM deep neural network [16] fine-tuned for working with dependency trees and morphologically rich languages using the example of Polish. Fine-tuning included applying a custom regularization technique (zoneout, described by Krueger et al. [9], and further adapted for Tree-LSTMs) as well as using pre-trained word embeddings enhanced with sub-word information [2]. The system was implemented in PyTorch and evaluated on phrase-level sentiment labeling task as part of the PolEval competition.