Reviewerly/exHarmony huggingface dataset:
Innovative Application of Python in Data Crawling —Chinese Version of Movie Recommendation Platform

The rapid development of computer hardware and Internet makes the information transmission speed and storage capacity of the network explode. The development of basic technology has led to the advance of many multimedia and rich text technologies that serve the public. In order to solve the problem that users and companies need to process and retrieve massive amounts of information, web crawler technology extracts the specified information from web pages and then analyzes it, providing a directional function of crawling information from large amounts of information. This article explains the web crawler that uses Python language to extract specific movie information from the website in detail and analyzes the detailed process of web crawler written in Python language.
Semantic Correspondence in the Wild

Semantic correspondence estimation where the object instances depicted are deformed extensively from one instance to the next is a challenging problem in computer vision that has received much attention. Unfortunately, all existing approaches require prior knowledge of the object classes which are present in the image environment. This is an unwanted restriction as it can prevent the establishment of semantic correspondence across object classes in wild conditions when it is uncertain which classes will be of interest. In contrast, in this paper we formulate the semantic correspondence estimation task as a key point detection process in which image-to-class classification and image-to-image correspondence are solved simultaneously. Identifying object classes within the same framework to establish correspondence, increases this approach's applicability in real world scenarios. The use of object regions in the process also enhances the accuracy while constraining the search space, thus improving overall efficiency. This new approach is compared with the state-of-the-art on publicly available datasets to validate its capability for improved semantic correspondence estimation in wild conditions.
Pixel-wise binary classification network for salient object detection

Deep convolutional neural networks have led to significant improvement over the previous salient object detection systems. The existing deep models are trained end-to-end and predicts salient objects by calculating pixel values, which results saliency maps are typically blurry. Our Pixel-wise Binary Classification Network (PBCN) focuses on binary classification in pixel level for salient object detection: saliency and background. In order to increase the resolution of output feature maps and get denser feature maps, Hybrid dilation convolution (HDC) is employed into PBCN. Then, Hybrid Dilation Spatial Pyramid Pooling (HDSPP) is proposed to extract denser multi-scale image representations. In HDSPP, it contains one 1&times;1 convolution and several dilated convolutions, with different rates and the output feature maps of the convolutions will be fused. Finally, softmax is introduced to implement the binary classification instead of sigmoid. Experiment, on four datasets, show that PBCN significantly improves the state-of-the-art.
Intelligent control of a large underwater tunnel sinking pipe hoisting manipulator

To improve the intelligence of sinking pipe hoisting in the underwater tunnel, an intelligent mechanical arm is used for hoisting control. This paper presents an intelligent control technique based on loading of load transfer mechanism and state feedback regulation without joint for lifting and hoisting manipulator of large underwater tunnel. This paper analyzes the technical preparation flow and key technology of the lifting of large underwater tunnel pipe sinking, and constructs the constraint parameter model of the pressure control of the pipe sinking pipe hoisting arm of the large underwater tunnel. The load-transfer mechanism of large underwater tunnel sinking pipe hoisting manipulator is analyzed, and the moving platform and power driving unit of hoisting arm are established. Combined with the compensation method of end position and attitude parameters of multi-joint tracking, the pressure bearing mechanical control of large underwater tunnel sinking pipe hoisting mechanical arm is carried out, and the control law of large underwater tunnel sinking pipe hoisting robot arm is constructed by using finite element dynamic analysis method. The pressure torque control of large underwater tunnel pipe hoisting mechanism arm is realized, and the position and pose adjustment ability of large underwater tunnel sinking pipe hoisting is improved, and the docking error of sinking pipe hoisting is reduced. The simulation results show that this method has good stability for intelligent control of large underwater tunnel sinking pipe hoisting robot arm, and the accuracy of position and pose tracking for large underwater tunnel sinking pipe hoisting process is high. The safety and intelligence of sinking pipe hoisting in the underwater tunnel are improved.
Electromagnetically tracked partial nephrectomy navigation: demonstration of concept

PURPOSE: Partial nephrectomy is the preferred method for managing small renal masses. This procedure has significant advantages over radical nephrectomy. However, partial nephrectomy is under-used due to its difficulty. We propose a navigation system for laparoscopic partial nephrectomy. In this study, we evaluate the usability and accuracy of the navigation system. METHODS: An electromagnetically tracked navigation system for partial nephrectomy was developed. This system tracks the positions of the laparoscopic scissors, ultrasound probe, tumor, and calyces and vasculature. Phantom kidneys were created using mixtures of plastisol and cellulose. To test the system, the navigation display quality was measured through measurement of lag and frames per second displayed. The accuracy of the system was determined through fiducial registration. Finally, a study consisting of ten participants was conducted to assess the usability of the navigation system using the System Usability Survey. RESULTS: The mean System Usability Score of the navigation system was 82.5. The navigation display had an average lag of 243 milliseconds and showed 5 frames per second. The accuracy was measured with fiducial registration and found to have an RMS error of 2.84 mm. CONCLUSION: The results of this study suggest that the partial nephrectomy navigation system developed is both usable and accurate. Future work will include the conversion of the laparoscopic scissor tool tracking to optical. Further studies will be conducted to determine the effectiveness of this technology in tumor resection and avoidance of calyx and vasculature damage. We will additionally explore this system as a training tool.
Holonovel: Perspective on Enactive Narrative Intelligence

The paper outlines conceptual framework for Holonovel as an intelligent system that operates on the basis of narrative interaction. Under the idea of interactive interface that assumes narrative as a sense-making tool, embodied and situated aspects of engagement with the world are discussed. Theoretical framework based on enactive model of perception and cognition here underlines features of human-level narrative comprehension on which computational models of narrative can draw in their designs.
Trigonometric Wavelet Methods for the Plane Elasticity Problem

In this paper, we apply interpolatory Hermite-type trigonometric wavelet to investigate the numerical solution of the natural boundary integral equation of plane elasticity problem in the outer domain of a disk by Galerkin method. In our fast algorithm, the computational formulae of the stiffness matrix yield simple close-form and the stiffness matrix is sparse. The error estimates of the approximate solution are given and the test examples are presented in the end.
An Efficient Two-Server Password-only User Authentication for Consumer Electronic Devices

We propose an efficient and secure two-server password-only remote user authentication protocol for consumer electronic devices, such as smartphones and laptops. Our protocol works on-top of any existing trust model, like Secure Sockets Layer protocol (SSL). The proposed protocol is secure against dictionary and impersonation attacks.
Workspace layouts for communication quality in Scrum teams

Purpose of study to show the influence of workspace design on the quality of communication, especially on the Scrum team. The motivation is to find out the effect of changes in layout of the workspace on the quality of communication. Communication quality is an important concern about the performance of software development in the Scrum team. Problem: How does the layout change affect the Scrum team's workspace on the quality of communication between members on a team; team members with other teams, and differences in the effect of changing layouts with old layouts. Methodology: Research steps use Participatory Action Research (PAR). This method has four main steps, namely planning; acting; observing; and reflecting. Data retrieval uses survey method by distributing questionnaires, conducting daily observations, and Forum Group Discussion (FGD). Findings: The Development Team Cell (DTC) workspace model is better than the Conference Cell (CC) model. This finding strengthens the previous research conducted by Scott and Pawel Role. Conclusion: There are three conclusions. a) changes in layout can improve the quality of individual communication. b) changes in layout have a positive effect on the quality of communication between team members and outside parties. c) changes in layout have a positive effect on team members' understanding of the work and methods used.
Twitter Activity at Recent LIS Academic Conferences

The present paper reports on different Twitter activities throughout several library and information science (LIS) focused research conferences which took place over the summer of 2018. Current findings show levels of activity and engagement, both overall and at different time-points throughout the conferences. The study provides descriptive findings about Twitter use and ways in which researchers can analyze social media activities as measures of scholarly communication at academic conferences. Opportunities remain for future in-depth studies of social media and its broader implications for scholarly communications from academic conferences.
Taylor Convolutional Networks for Image Classification

This paper provides a new perspective to understand CNNs based on the Taylor expansion, leading to new Taylor Convolutional Networks (TaylorNets) for image classification. We introduce a principled combination of the high frequency information (i.e., detailed information) and low frequency information in the end-to-end TaylorNets, based on a nonlinear combination of the convolutional feature maps. The steerable module developed in TaylorNets is generic, which can be easily integrated into well-known deep architectures and learned within the same pipeline of the back propagation algorithm, yielding a higher representation capacity for CNNs. Extensive experimental results demonstrate the super capability of our TaylorNets which improve widely used CNNs architectures, such as conventional CNNs and ResNet, in terms of object classification accuracy on well-known benchmarks. The code will be publicly available.
The extraction of altered mineral information based on WorldView-3 data

To promote uranium exploration in Chaganhada and provide remote sensing alteration anomaly information, this paper extracted chlorite alteration and iron-strained alteration by principal component analysis on the basis of analysis of wall rocks related to uranium mineralization and spectral characteristics of the typical alteration minerals in WorldView-3(shorthand for WV-3 in the following) bands. Then, the alteration information extracted was made processing on the ArcGIS platform, and an integrated anomalous information figure was compiled. The results of field verification show that the alteration anomaly information which extracted from WV-3 data is consistent with the reality. And the alteration anomaly information should be taken as one of the important clues in the uranium exploration in this area.
Event Pattern Constructing Technique for Prediction of Violations in Technological Processes

The problem of the prediction of violations in business processes is considered in the paper. The authors suggest application of the frequent item sets mining technique for analysis of the event streams. This approach allows investigation of the event sequences and revealing of the event sequences which lead to the violations. The detected violation patterns then could be used for monitoring data in the real time mode. The proposed technique could be applied when analyzing data from sensors of different type and used in distributed systems. The method is able to predict a violation before it occurs avoiding thus dangerous situations.
Locally Adaptive Contrast Enhancement Using a Modified 2D Smooth Histogram

In this paper, we propose an locally adaptive contrast enhancement using a modified 2D smooth histogram method that allows an input image to expand the dynamic range and improve contrast and image detail information for Advanced Driver Assistance System (ADAS). We uses the relationship between each pixel's grayscale and its adjacent grayscale to construct a two-dimensional probability density of the input image grayscale distribute and masking operations are used to build the coefficient of determination. This coefficient is used to calculate the appropriate area window size for each pixel's neighborhood to reduce calculation time. A method for weighting the neighborhoods information and smooth two-dimensional histogram method are proposed. Experimental results show that the proposed method has well result and lower computational complexity.
Real-time Strategy Game Tactical Recommendation Based on Bayesian Network

Real-time strategy (RTS) games simulate battles between large numbers of units and pose significant challenges to artificial intelligence which are complex areas of confrontation. In the field of RTS games, confrontation planning under uncertainty remains an unsolved challenge. There are two main types of uncertainty in RTS games. The first is the partial observability of the game, causing uncertainty. Second, there is uncertainty as the game is against the game and the player cannot predict what the opponent will do. This paper uses the Bayesian model as a logical alternative to solve the uncertainty caused by the confrontation game. The experimental results show that the proposed model can effectively solve the tactical recommendation problem in real-time strategy games.
An Improvement Method to Mining Outliers Based on Social and Spatial Information

In this paper, a method for finding outliers is proposed for communication data. By extracting the social and spatial information as the eigenvalues from the user's call list. We use clustering method to aggregate users with the same attributes, and then analyze the clusters to find outliers. We use the real communication data of a communication company as the data set, and the experimental results show that our method can quickly and accurately find the abnormal communication user.
Recognition of thermal infrared oil tank targets based on blotch detection and clustering and SVM classification

This paper proposes a method for detecting oil tank targets that combines improved blotch feature detection and SVM sample learning in order to address deficiencies in the recognition of oil tank targets in thermal infrared remote sensing images due to indistinct edge information, high noise level, and small dimensions. First of all, initial detection was conducted on the basis of blotch features and mass features. Then the features of oil tank targets and an optimal combination of classification features were generated from sample learning. Finally oil tank targets were detected through classification based on SVM sample learning. The results of the experiment show that: 1) by setting appropriate parameters, this method combines blotch features and textural features for the extraction of a more comprehensive range of TIR oil tank features that can achieve the effective detection of oil tank targets; 2) based on the detection of blotch targets, this method can filter out false targets with relatively high accuracy and is capable of more stable and efficient recognition of Type #1 and Type #2 targets.
Associations between mammographic phenotypes and histopathologic features in ductal carcinoma in situ

With the advent of regular breast screening, ductal carcinoma in situ (DCIS) diagnoses have risen in number, now making up almost 20% of all detected breast cancers at screening. Women diagnosed with DCIS are almost universally treated. However, recent studies suggest that up to 70% of DCIS lesions will never become life-threatening, which emphasizes the need for better risk stratification strategies. Considering that histopathologic features have been shown to be predictive of DCIS aggressiveness, our aim was to study associations between DCIS histopathologic features and mammographic phenotypes towards identifying readily-available mammography-based prognostic biomarkers. To this end, breast density and parenchymal texture features were extracted from screening digital mammograms and principal component analysis was used to capture the dominant textural components. Primary analyses included statistical tests to compare feature distributions between histopathologic subgroups. Logistic regression models were, then, applied to evaluate trends in DCIS histopathologic characteristics among mammographic features, after adjustment for risk factors known to affect mammographic phenotypes. We found that HER2 had a significant association with breast percent density (p = 0.006) and the first principal component (PC1) of texture features (p = 0.034). Our risk-factor-adjusted logistic regression analyses showed that breast percent density was predictive of HER2 status (AUC = 0.71), while prediction performance was further increased when PC1 was added to the model (AUC = 0.74). These findings provide preliminary evidence about the potential value of mammographic phenotypes in prediction of DCIS aggressiveness and could ultimately contribute to identifying patients who do not require treatment.
Medical Data Are Safe

In this internet era, transferring and preservation of medical diagnostic reports and images across the globe have become inevitable for the collaborative tele-diagnosis and tele-surgery. Consequently, it is of prime importance to protect it from unauthorized users and to confirm integrity and privacy of the user. Quantum image processing (QIP) paves a way by integrating security algorithms in protecting and safeguarding medical images. This chapter proposes a quantum-assisted encryption scheme by making use of quantum gates, chaotic maps, and hash function to provide reversibility, ergodicity, and integrity, respectively. The first step in any quantum-related image communication is the representation of the classical image into quantum. It has been carried out using novel enhanced quantum representation (NEQR) format, where it uses two entangled qubit sequences to hoard the location and its pixel values of an image. The second step is performing transformations like confusion, diffusion, and permutation to provide an uncorrelated encrypted image.
New Bargaining Game Model for Collaborative Vehicular Network Services

The wireless industry’s evolution from fourth generation (4G) to fifth generation (5G) will lead to extensive progress in new vehicular network environments, such as crowdsensing, cloud computing, and routing. Vehicular crowdsensing exploits the mobility of vehicles to provide location-based services, whereas vehicular cloud computing is a new hybrid technology that instantly uses vehicular network resources, such as computing, storage, and Internet for decision-making. In this study, novel crowdsensing, cloud computing, and routing algorithms are developed for a next-generation vehicular network, and they are combined into a hybrid scheme. To provide an effective solution toward an appropriate interactive vehicular network process, we focus on the paradigm of learning algorithms and game theory. Based on individual cooperation, our proposed scheme is designed as a triple-plane game model to adapt to the dynamics of a vehicular network system. The primary advantage of our game-based approach is to provide self-adaptability and responsiveness to current network environments. The performance of our hybrid scheme is evaluated and analyzed using simulation experiments in terms of the cloud service success ratio, normalized dissemination throughput, and crowdsensing success probability.
SOCIONOMICS: The way business transforms with social media

Objectives:To develop an enhanced web application, using web services for interconnecting social network, E-Commerce application and News channels.Products will be recommended based on customers' preferences.Methods/Statistical Analysis: A three-tier methodology in which social network is the decision-making layer and E-commerce and news channel portals are the application layers.The social media layer will be analyzed using a method called as Text categorization which generates data for gathering user preferences.This information will be made as micro-blogging information.This will be passed on to other networks via Artificial Neural Network (ANN).Findings: The micro-blogging details are created at the tier one, Social network.This is then connected to e-commerce website and news portals.The micro-blogging information created will be purely based on users' preferences, hobbies and other user data provided in the social network.These are further passed on to the e-commerce website to accurately recommend the products to individual users.Also, on the news portal, individual users can get notified about local news about their localities.Application/Improvements: This serves as a three-in-one application since there are three different tiers.Further improvements could be made by adding offline features.This could be improved by developing this as a separate mobile application.
Investigation on the Cultivation of Physical Education Teachers’ Teaching Ability

The teaching and learning ability of physical education (PE) teachers is an important part of the process of PE.It determines the success or failure of the whole PE.The physical and mental health of students, the cultivation of good living habits and the continuous deepening reform of education, which bring higher demands on PE teachers.Based on the current situation of the cultivation of PE teachers' teaching ability, this paper analyzes its connotation, constructs the PE teacher's education and teaching ability from the perspective of PE teaching process, psychological process in PE and effective concept of PE teaching, and finally finds the key aspect in the growth of PE teacher's education and teaching ability.