Reviewerly/exHarmony huggingface dataset:
Combined Weighted Whale Optimization and Attention-based Deep Learning Approach for Sentiment Analysis

The swift development of the Internet paved a vital contribution in the rocketing growth of the electronic commerce and online shopping portals to purchase products. Sentiment Analysis (SA) is a method of analyzing the user reviews posted on the e-commerce and shopping portals, for improving the user satisfaction. In the recent years, Deep learning models are applied for SA. This paper presents a combined attention-based deep model and weighted whale optimization for polarity detection in SA. Both the past and future contexts are extracted based on the flow of temporal information in the front and back directions. The Attention Mechanism (AM) is used at the output stage of the bi-directional layers in the deep network model for imposing either high or low emphasis on different words. Convolution and pooling mechanisms are used for reducing the feature dimensionality and extraction of the position-invariant features. word segmentation is performed on the input sequence for obtaining the etymological information about the words. The features are extracted after analyzing the data characteristics and the relevant features are ranked. The concept drift score is obtained to analyze the correlation relationship. Weighted whale optimization and deep attention based RNN are utilized to classify the posted reviews into three types: positive (affirmative) reviews, negative (contradictory) reviews and neutral review. From the comparative analysis with the existing classification approaches, the proposed method achieves high classification accuracy, precision, recall, F-score and low memory usage and time required for training.
Design and Implementation of Enterprise Industrial and Commercial Integrated Business System Based on Deep Learning

With the rapid development of the market economy and China’s accession to the World Trade Organization, the internationalization of China’s economy and trade and the modernization of economic information have been strengthened. Network technology is increasingly widely used in various fields of human economic and social life. E-government has also received widespread attention and promotion around the world, and has significantly changed the traditional organizational structure and management mode of commercial administrative management services. The purpose of this paper is to study the design and implementation of the enterprise integrated business system based on deep learning. Based on the analysis of the deep learning overview, the characteristics of the integrated industrial and commercial business system and the system design principles, the integrated business system of industry and commerce is designed and the system’s performance was tested. The test results show that the system has fewer defects and reached the goal of the system’s demand analysis stage.
Feature Pyramid Multi-View Stereo Network Based on Self-Attention Mechanism

Multi-view 3D reconstruction aims to recover the 3D geometry of an object from a set of 2D images. The traditional methods match a pair of the corresponding pixels by calculating the variance, which is primarily a pixel-based measure without consideration of the interdependence of pixels and is ineffective for matching texture-free or occluded regions. To address this issue, we investigate multi-view 3D reconstruction using deep learning and propose a feature pyramid multi-view stereo network based on self-attention mechanism (FPSA-MVSNet). We use a multi-scale feature pyramid network to extract deep image features and introduce the self-attention mechanism into the hierarchical feature extraction module to capture an extensive range of interdependencies between pixels. Meanwhile, we use the group correlation method and a cascade approach to construct lightweight cost volume, and employ a coarse-to-fine depth map inference strategy to predict the initial depth map at the coarsest level, then iteratively optimize it by progressive up-sampling to generate the high-resolution depth map at the finest level. We performed experiments on the DTU benchmark dataset and compared our method with the existing multi-view 3D reconstruction methods. Experimental results show that the proposed model outperforms the existing best network model CasMVSNet on the DTU benchmark dataset, with significant improvements in completeness and overall score, by 26.5% and 9.0%, respectively.
Bed-Leaving Action Recognition Based on YOLOv3 and AlphaPose

Considering a very few of research on bed-leaving action recognition, especially which can recognize the human actions with occlusion, we propose a bed-leaving action recognition algorithm based on YOLOv3 and AlphaPose. Six kinds of specific human actions are classified in the process of leaving the bed, which particularly include the normal bed-exit action(BEA) and the abnormal bed-fall action(BFA). First, YOLOv3 is used to extract the bed region and human region. Then, combined five skeleton key points extracted by AlphaPose with the angle and length measurements, the multi-layer neural network is constructed and trained for classification and recognition. The experiments on our datasets show that the accuracy of BEA and BFA could reach 98.82% and 95.27%, so our method can assist the medical staff to monitor the bed-leaving action.
Stochastic modeling of iron in coal seams using two-point and multiple-point geostatistics: A case study

This work addresses the problem of quantifying iron content in a coal deposit in the Republic of Kazakhstan. The process of resource estimation in the mining industry usually involves building geological domains and then estimating the grade of interest within them. In coal deposits, the seam layers usually define the estimation domains. However, the main issue with the coal deposit in this study is that the iron dataset is solely based on data from three newly drilled drill holes located a significant distance apart and additional rock samples from stopes. A massive amount of geological information comes from legacy drill hole data sampled a long time ago, but there is no evidence of proper QA/QC being performed on those samples. For this reason, a workflow was introduced to construct a representative training image from legacy data and stochastically model geological domains within these three drill holes using a multiple-point geostatistics technique. Once the geological model was obtained, a two-point geostatistics algorithm was applied to model the iron inside each geological domain. The results showed that direct sampling (DeeSse) is a suitable multiple-point geostatistics algorithm that can reproduce the long-range connectivity and curvilinear features of seam layers. Furthermore, a sequential Gaussian simulation was used to model the iron in the corresponding domains. Both methods were extensively evaluated using different statistical tools and analyses.
EDGE DETECTION AND OBJECT IDENTIFICATION FOR VISION ENHANCEMENT AT RESCUE OPERATIONS USING DEEP LEARNING TECHNIQUES

In this modern world the rate of occurrence of accidents and military operation in dark and unpleasant goes on increasing. At this situation the rescue squad faces difficulty in navigation. The visual detection and object clarity is difficult in risky situations like rescue operations in dark environment, secret armed rescue and military based operations. Due to this difficulty the failure rate of the operation is high. The proposed idea based on AI -Deep Learning technology provides better clarification on edge detection and enhances the vision with the help of OpenCV (Computer Vision Library) and other frameworks. Data transmission and Image processing is implemented with the help of raspberry pi. The object identification is implemented in the dark environment. This makes the navigation of the rescue quad inside the operation make easier. To provide visual enhancement for the structures of buildings and objects during rescue operation and also the visual projection of object structures in secret operations at military. The real time object edges and classification of objects and internal structures of environment is projected based on the user comfortless.
Improvement of Japanese Text Emotion Analysis by Active Learning Using Transformers Language Model

With the increasing popularity of Twitter in recent years, many people are using Twitter to communicate on a daily basis. Recent research suggests that a large part of the texts posted by Twitter users contain various emotions, which can further be used for the Web marketing and information recommendation systems. However, because the language usage habits differ among Twitter users, the research of emotion analysis on Twitter texts has been considered to be a difficult problem. In addition, the number of Twitter texts is so large that it is impossible to analyze all of them. In this paper we propose to create a high-quality Japanese Tweet Emotion corpus by using active learning and employ a Transformers language model to improve the emotion analysis. Experiment results suggest that a fine-tuned Transformer language model tuns better than the previous model in both Twitter emotion analysis and Twitter emotion corpus creation.
Knowledge-Based Intelligent Education Recommendation System with IoT Networks

The intelligent education recommendation system can recommend knowledge suitable for students' personal learning. However, the traditional recommendation algorithm has generality problems, which lead to poor knowledge recommendation effects. In order to improve the performance of the education recommendation system, based on the machine learning algorithm, this paper combines the knowledge graph algorithm to improve the recommendation algorithm and decomposes the matrix with a higher dimension into several matrices with relatively small dimensions through matrix transformation. Moreover, this paper conducts in-depth mining of the potential attributes of users and items and improves the matrix decomposition formula based on knowledge recommendation requirements. In addition, this paper constructs the framework of the intelligent education recommendation system with IoT networks based on the analysis of functional requirements. Finally, this paper designs experiments to verify and analyze the model from the perspective of model performance and user satisfaction. The research results show that the algorithm model constructed in this paper is effective.
Risk-Based Dynamic Identity Authentication Method Based on the UCON Model

With the continuous progress of computer technology, static identity authentication technology has encountered challenges in practical applications; in addition, it has deficiencies in continuity and mutability. For these reasons, we propose a risk-based dynamic identity authentication method based on the Usage Control (UCON) model. When authenticating a user, we consider their access rights based on the degree of risk, except in case of password authentication. We propose a risk assessment method, and blockchain technology is used in the scheme to provide a reliable process for risk assessment and authorization. Our scheme represents an improvement in traditional identity authentication, resulting in higher continuity and mutability. We also prove that the scheme has high security and scalability.
AnkleSens: Foot Posture Prediction Using Photo Reflective Sensors on Ankle

Recognizing foot gestures can be useful for subtle inputs to appliances and machines in everyday life, but for a system to be useful, it must allow users to assume various postures and work in different spaces. Camera-based and pressure-based systems have limitations in these areas. In this paper, we introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AnkleSens</i> , a novel ankle-worn foot sensing device that estimates a variety of foot postures using photo reflective sensors. Since our device is not placed between the foot and the floor, it can predict foot posture, even if we keep the foot floating in the air. We developed a band prototype with 16 sensors that can be wrapped around the leg above the ankle. To evaluate the performance of the proposed method, we used eight foot postures and four foot states as preliminary classes. After assessing a test dataset with the preliminary classes, we integrated the eight foot postures into five. Finally, we classified the dataset with five postures in four foot states. For the resulting 20 classes, the average classification accuracy with our proposed method was 79.57% with user-dependent training. This study showed the potential of foot posture sensing as a new subtle input method in daily life.
Skeleton-Based Spatio-Temporal U-Network for 3D Human Pose Estimation in Video

Despite the great progress in 3D pose estimation from videos, there is still a lack of effective means to extract spatio-temporal features of different granularity from complex dynamic skeleton sequences. To tackle this problem, we propose a novel, skeleton-based spatio-temporal U-Net(STUNet) scheme to deal with spatio-temporal features in multiple scales for 3D human pose estimation in video. The proposed STUNet architecture consists of a cascade structure of semantic graph convolution layers and structural temporal dilated convolution layers, progressively extracting and fusing the spatio-temporal semantic features from fine-grained to coarse-grained. This U-shaped network achieves scale compression and feature squeezing by downscaling and upscaling, while abstracting multi-resolution spatio-temporal dependencies through skip connections. Experiments demonstrate that our model effectively captures comprehensive spatio-temporal features in multiple scales and achieves substantial improvements over mainstream methods on real-world datasets.
An Augmented Reality based Web App To Learn Guitar

Guitar is one of the most common instruments and the interest in learning guitar is at an all-time high. A web app which helps users learn guitar using augmented reality has been proposed. It teaches users to learn guitar by projecting the strings that are to be pressed on the guitar in the user’s camera feed. Now, all the user has to do is to press the highlighted strings to play a particular song. The app uses AR markers to get the position of the guitar strings to be highlighted. This method of learning guitar provides real time visual feedback to the user and therefore helps users learn guitar by the same speed as traditional methods but without the need of an instructor therefore making musical education accessible to more people.
The Architecture of Cognition as a Generalization of Adaptive Problem-Solving in Biological Systems

The emerging science of Human-Centric Functional Modeling has recently been used to develop what has been suggested to be the first model of human cognition with the potential to represent all of the functionality of human intelligence, a significant milestone critical to the goal of creating a real-life computational equivalent of the human mind that is central to the field of Biologically Inspired Cognitive Architectures. This paper provides an overview of this modeling technique and why it can potentially be used to represent all biological processes as generalizations of the same adaptive problem-solving process, as well as providing an overview of why this approach has the potential to solve fundamentally different problems than any preceding one, such as increasing our collective capacity to understand and make use of each of the different cognitive models to the point we are able to collectively to converge on a single understanding that is most “fit” at solving the problem of cognition.
Methodology and application of GAN algorithms

GAN, proposed in 2014, has received high attention and extensive research from industry and academia. It is an important node in the development of generative model. With the development of research, GAN has been improved in theory and application. The basic idea of GAN is derived from two-person zero-sum game, which consists of a generator and discriminator, and is iteratively trained through confrontation. The purpose is to learn the distribution of sample data and generate new samples. Firstly, the theoretical framework of GAN is introduced. Secondly, some typical GAN models are discussed. Then four applications of GAN in data generation, image super resolution and so on is shown. Then the advantages and disadvantages of GAN are discussed and the future development direction of GAN is pointed out. Finally, the future prospect is summarized.
People Removal Using Edge and Depth Information

In this paper, we propose a people removal method from a single image for privacy and other reasons using a three-stage network of depth estimation, semantic segmentation, and inpainting, as shown in Fig. 1. In this three-stage network, we improve semantic segmentation for detecting people. We focus on a special situation of a person and construct a network. It is known that the accuracy of conventional methods can be improved by using edge information. The accuracy of segmentation can be further improved by increasing the accuracy of the edge map. In addition, edge detection does not work well when the person and the background are of the similar color, because edge detects the brightness change of the image. Therefore, in this paper, an adversarial loss function for edge maps is proposed. In addition, since an image with people is expected to have a depth difference from the background image, we use a trained depth estimation network to include the depth image in the input. In this way, it is possible to construct a network for people removal with a high accuracy both quantitatively and qualitatively.
[Planning of ecologically suitable areas for Ligusticum chuanxiong under background of soil cadmium pollution].

Chuanxiong Rhizoma is a traditional Chinese medicinal material mainly produced in Sichuan and Chongqing of China. In recent years, the cadmium content in Chuanxiong Rhizoma produced in most of the genuine producing areas has exceeded the standard, which makes Chuanxiong Rhizoma difficult to be exported. To solve the problem of excessive cadmium content in soil, this study employed the MaxEnt model to simulate the potential geographic distribution of Ligusticum chuanxiong and evaluate important environmental factors, and re-plan its ecologically suitable areas based on the mineral distribution characteristics and soil cadmium pollution status. The results showed that the places suitable for L. chuanxiong growing covered an area of 335 523.69 km~2, mainly in central and eastern Sichuan, southern Shaanxi and most parts of Chongqing. Among them, the highly suitable areas of L. chuanxiong were mainly concentrated in Chengdu, Ya'an, Deyang, and Mianyang. Solar radiation, annual precipitation, and annual range of temperature were evaluated as important variables affecting the distribution of L. chuanxiong, with the contribution rates of 62.3%, 13.3%, and 6.8%, respectively. In addition, Qionglai county, Chongqing county, Mianyang city(Youxian district and Fucheng district), Qingchuan county, and Xinjin county were classified into the first-class ecologically suitable zone, covering a total area of 2 768.87 km~2. The se-cond-class ecologically suitable zone was even wider, involving such counties as Tongjiang county, Renshou county, Jianyang county, and Nanjiang county, and the total area reached 43 616.92 km~2. The re-planning of the ecologically suitable areas for L. chuanxiong has provided strong data support for the cultivation and resource development of L. chuanxiong and also new ideas for solving the problem of excessive cadmium content in L. chuanxiong.
Confluence of left-linear higher-order rewrite theories by checking their nested critical pairs

Abstract User-defined higher-order rewrite rules are becoming a standard in proof assistants based on intuitionistic type theory. This raises the question of proving that they preserve the properties of beta-reductions for the corresponding type systems. In a series of papers, we develop techniques based on van Oostrom’s decreasing diagrams that reduce confluence proofs to the checking of various forms of critical pairs for higher-order rewrite rules extending beta-reduction on pure lambda-terms. As shown in a previous paper of the two middle authors, confluence of a terminating set of left-linear rewrite rules is obtained when their critical pairs are joinable, beta-rewrite steps being disallowed. The present paper concentrates on the case where arbitrary beta-rewrite steps are allowed for joining critical pairs. The rewrite relation used for analyzing confluence may rewrite arbitrarily many non-overlapping redexes in a single step. This relation gives rise to critical pairs that overlap both horizontally, as with parallel rewriting, but also vertically, forming chains of successive overlaps. Practical examples of use of this technique are analyzed.
Effect of data preprocessing on ensemble learning for classification in disease diagnosis

In recent years, supervised machine learning methods have increased attention to extracting clinically relevant information from complex health data. Ensemble learning methods enable the establishment of more successful models by training multiple learners jointly to solve the same problem. Herein, we aimed to compare the performance of classification algorithms after data preprocessing to problems such as missing data, class noise, and class imbalance that may be encountered in the datasets used to make an accurate disease diagnosis. To this end, we used random forest and weighted subspace random forest as bagging algorithms while additive logistic regression and gradient boosted machines algorithms were used as boosting algorithms. The performance and running time of the algorithms were also calculated. Our findings indicated that the performance of algorithms increased after data preprocessing and the performance of boosting algorithms yielded higher results than the bagging algorithms. We also observed that the boosting algorithms were the longest-running ones. In conclusion, complementing existing studies, our work highlights the importance and effect of using multiple data preprocessing methods together.