Reviewerly/exHarmony huggingface dataset:
Enhancement of cone beam CT image registration by super-resolution pre-processing algorithm

In order to enhance cone-beam computed tomography (CBCT) image information and improve the registration accuracy for image-guided radiation therapy, we propose a super-resolution (SR) image enhancement method. This method uses super-resolution techniques to pre-process the CBCT prior to registration. Three rigid registration methods (rigid transformation, affine transformation, and similarity transformation) and a deep learning deformed registration (DLDR) method with and without SR were compared. The five evaluation indices, the mean squared error (MSE), mutual information, Pearson correlation coefficient (PCC), structural similarity index (SSIM), and PCC + SSIM, were used to validate the results of registration with SR. Moreover, the proposed method SR-DLDR was also compared with the VoxelMorph (VM) method. In rigid registration with SR, the registration accuracy improved by up to 6% in the PCC metric. In DLDR with SR, the registration accuracy was improved by up to 5% in PCC + SSIM. When taking the MSE as the loss function, the accuracy of SR-DLDR is equivalent to that of the VM method. In addition, when taking the SSIM as the loss function, the registration accuracy of SR-DLDR is 6% higher than that of VM. SR is a feasible method to be used in medical image registration for planning CT (pCT) and CBCT. The experimental results show that the SR algorithm can improve the accuracy and efficiency of CBCT image alignment regardless of which alignment algorithm is used.
Design finned electronic curtain for control of solar collector using Arduino with smart phone

This paper pursuits to learn about and control of evacuated tube solar collector the usage of Finned Electronic Curtain to enhance performance. The starting of the fins work is to function in accordance to the times listed in the time table and start shifting every 1/2 hour period till it follows the sun. It shall be closed at the time of closure, As for the control, it will become by means of the temperature sensor from the contact eighty. it closes and opens from the return 65 automatically, also, the same programmed algorithm and matlab by programming the Arduino, And it became control, so its work without programs and made a simulation of the Android program, For the phone until they control and give me data about the temperature and the ability to open and close. The improvement percentages of Cu(25nm)+PW) at without fins machine are (12.28%, 20.52% and 25.18%) while the percentages with TiO2(50nm)+PW)are (3.906 percent, 5.34% and 7.407%) due to thermal houses and nanoparticles measurement of first kind greater than 2d kind at distinctive drift fee and concentration. Finally, the charge of enchancment of effectivity share at case of distilled water only is (2.34% and 3.81%). on the other hand the percentages of enhancement of Cu(25nm)+(Al(25nm)+DW) at with shadow gadget are (19.53%, 24.42% and 28.14%) at the sametime as the percentages with TiO2(50nm)+PW)are (7.03%, 9.16% and 11.89%). The results show Finned Electronic Curtain controller is good controlled and a superior performance of evacuated Tube Solar., Finned Electronic Curtain has been applied evacuated tube solar collector in Baghdad. There controller has been extensively utilized in many industrial applications.
Using Deep Learning to Predict Mortality in Myocardial Infarction Patients

Myocardial infarction is a serious risk for patients with cardiovascular-related diseases. This study aims to predict the mortality in myocardial infarction patients during hospitalization. We used the Mimic III database from 2001 to 2012 to build the learning models. Four sampling methods, Over-Sampling, Under-Sampling, KMeansSMOTE, and Repeated Edited Nearest Neighbors were used for data scaling in the experiments. A variety of classification algorithms and feature analysis tools such as SHAP were adopted to adjust the suitable model. After a series of experiments, we obtain 91.3% accuracy based on the XGBoost model with different settings and processes on the dataset.
SCAN: Stronger Collaborative Attention Network for Vehicle Re-Identification

Extracting robust feature representation is one of the key challenges in person/vehicle re-identification (ReID). Although approaches based on convolutional neural networks (CNN) for local feature learning have been very successful in person ReID, they have not been discussed deeply about effective ways to obtain local features for slicing. The horizontal slicing is commonly used for person ReID, which is more likely to cause fine-grained information to be overlooked in vehicle ReID with diverse orientations and a discriminative information distribution different from that of the person. To overcome these limitations, we propose the stronger collaborative attention network (SCAN). Specifically, we first perform cross slicing of backbone features in each scale branches to ensure that different fine-grained information can be divided into different parts with higher probability, and fuse different directional features of the same order of parts in each local scale branch to gradually strengthen the global nature of local vehicle features by associating other regions with common regions of different directions. To further mine the useful information, we design three modules carefully. (i) The collaborative feature refinement module (CFR) is proposed to further explore the collaborative relationship of local features and filter out the redundancy of collaborative information in local scale branches by superposition fusion strategy. (ii) The discriminative edge information learning module (DEL) is proposed to learn reliable discriminative edge information by adding edge parts in each local scale branch. (iii) The nonvisual information embedding module (NIE) is introduced to mitigate the feature bias caused by background and vehicle shape by embedding learnable explicit nonvisual camera and view information. Experimental results of our proposed method are superior, which achieve state-of-the-art performance on vehicle ReID benchmarks.
A Novel Video Coding Framework with GAN-based Face Generation for Videoconferencing

In this work, a novel video coding framework dedicated to videoconferencing is proposed. The proposed framework can improve coding efficiency by extracting facial landmarks and reconstructing the frame using the faces generated by GAN from the landmarks. By only transmitting the facial landmarks and key frames, the proposed coding structure achieves a very high coding efficiency, comparable to that of VVC down to QP52.
Comparison of Dark Channel Prior and Contrast Limited Histogram Equalization for the Enhancement of Underwater Fish Image

The application of artificial intelligence (AI) in aquaculture may improve the efficiency of fish farming management. Computer vision is one of the fields in AI beneficial for aquaculture. However, the underwater image quality is usually low due to light scattering through the water. Therefore, image enhancement is necessary before any further processing can be done. There are numerous image enhancement techniques for underwater images reported in the literature. In this paper, the comparison of the two most common image enhancement techniques for underwater images, the Dark Channel Prior (DCP) and Histogram Equalization (HE), is presented. The strength and weaknesses of each technique pertaining to the underwater images are also described.
Re-identification Loss in Combination Spaces for Multiple Object Tracking

The joint-detection-and-tracking framework shares network features of detection and re-identification (re-ID), which drives multi-object tracking (MOT) to be simple, fast and accurate. Most of the existing algorithms utilize global information of image to optimize re-ID feature, which result in weak representation of features and a large number of ID switches in the association phase. To solve the above problems, we present a loss function for re-ID task based on cosine space and angle space. Specifically, the algorithm maximizes the decision boundary distance between different categories in cosine space and angle space, respectively, so as to improve the compactness of feature distribution in the same category and expand the variability of feature between different categories. For the problem of small number of pedestrian samples with the same ID, a mixed data augmentation method is introduced based on statistical information of object location and motion distribution. Experimental results of MOTChallenge benchmark show that the proposed method obtains different degrees of improvement in MOTA and IDF1 scores compared with the baseline model, and the IDs metric decreases significantly, while it can cope with tracking scenarios of different complexity.
Generation of Quality Web Services: A Survey

Business is expanding through the internet these days and web services are becoming the main components of the internet for business. Thus, web services should be faster and secure enough to facilitate business. Still, the area of quality web service generation is very new and there are lots of scopes to pursue research in it. The generation of quality web services is a requisite to get a better response from business users. There are three main attributes of quality web services, web service selection and composition, security of web services and performance of web services. The expected quality of web services can be achieved by providing these three attributes. Hence In this paper, we have reviewed the literature available for these three quality domains of web services and acknowledged the issues that can upset the quality of web services.
Object Manipulation via Visual Target Localization

Object manipulation is a critical skill required for Embodied AI agents interacting with the world around them. Training agents to manipulate objects, poses many challenges. These include occlusion of the target object by the agent's arm, noisy object detection and localization, and the target frequently going out of view as the agent moves around in the scene. We propose Manipulation via Visual Object Location Estimation (m-VOLE), an approach that explores the environment in search for target objects, computes their 3D coordinates once they are located, and then continues to estimate their 3D locations even when the objects are not visible, thus robustly aiding the task of manipulating these objects throughout the episode. Our evaluations show a massive 3x improvement in success rate over a model that has access to the same sensory suite but is trained without the object location estimator, and our analysis shows that our agent is robust to noise in depth perception and agent localization. Importantly, our proposed approach relaxes several assumptions about idealized localization and perception that are commonly employed by recent works in navigation and manipulation – an important step towards training agents for object manipulation in the real world. Our code and data are available at prior.allenai.org/projects/m-vole .
Requirements Analysis and a Design Model for Educational VR Prototyping

Previous research has indicated the potential of Virtual Reality (VR) in education, and there is a growing need in vocational education to apply VR in teaching and learning. Helping secondary vocational teachers join in the creation process of VR applications is of great significance. However, VR prototyping tools are lacking for teachers. To address this issue, we collect information through interviews and questionnaires and analyze the requirements for educational VR prototyping. Then based on these requirements collected from secondary vocational teachers, we propose a design model with recommendations for educational VR fast prototyping. This model comprises four modules: learning resources, shared resources database, built-in templates, and scanning modeling. Additionally, it suggests the prototyping should have functions including no programming, low fidelity, interactive and immersive, offline download, team collaboration, and multi-platform operation.
An Unmanned Cleaning Robot for the Inner Wall of Large LNG Tanks

A floating-type auto-cleaning robot was designed to solve the cleaning conundrum of the inner wall of large liquefied natural gas (LNG) tanks. Such a cleaning robot was designed based on the modularization, wherein all modules could enter the LNG tank through the slinging hatch at its top and were assembled on the tank’s inner wall before performing cleaning work. On the base of an introduction to the makeup and operating principle of the cleaning robot of the inner wall of LNG tanks, this paper analyzes its navigation control, autonomous navigation, stability design, system integration, and other key technologies. The robot’s control performance was tested by a navigational stability experiment; the cleaning-related technological parameters of the cleaning robot were verified by a cleaning effectiveness experiment. Both experiments demonstrated the cleaning robot could perform safe, reliable, and high-efficient cleaning of the inner wall of LNG tanks in a harsh environment.
MTAP-DK: Multivariate Time-Series Anomaly Prediction with Domain Knowledge

Predicting anomalies of mobile equipment plays an important role in performing preventive maintenance, alleviating major economic losses and personal safety issues. Previous studies basically adopted data-driven models for anomaly prediction or detection of industrial equipment, ignoring the importance of domain knowledge. The domain knowledge can more accurately and theoretically capture the complex relationship among features. However, building the deep learning models incorporating domain knowledge is very difficult due to the following challenges. First, the domain knowledge is often different from the actual state of the equipment, so it is difficult to obtain knowledge information that conforms to the real situation. Second, domain knowledge is difficult to directly and effectively be applied to deep learning models due to its diverse representations. In this paper, we propose a Multivariate Time-Series Anomaly Prediction with Domain Knowledge (MTAP-DK) to address these issues. Specifically, we firstly propose a knowledge extraction module, which can extract the domain equations that conform to the actual situation with the domain knowledge and historical data. Secondly, we design a domain guidance module to guide and constrain the graph neural network from the knowledge level, to improve its capabilities to express the relationship among features. Thirdly, we predict future data based on the graph incorporating knowledge information. Finally, the prediction is reconstructed by the multi-scale convolution reconstruction method, and the abnormal information is inferred according to the reconstruction error.
Implementation of Server Based Authentication Security System using Augmented Reality

In the current digital world of the security domain, wherein technology focuses on either software or hardware implementation, which can sometimes compromise security. This paper introduces an idea to enhance the security through a two-factor authentication using Augmented Reality and the server-client architecture, enabling the user to have a mixed reality experience. This paper highlights the significance of future advanced devices used in server-client safety frameworks.
THE STUDENT’S RESPONSES TO THE USE OF COMIC STRIPS AS A LEARNING MEDIA

The purpose of this study is to describe the students' responses to the use of comic strips as a learning medium in class VIII MTs N1 Morotai. This research used a quantitative method and included 38 eighth-grade students. This research employs a genuine experimental procedure, using random sampling as the sampling strategy. In the learning process, this instrument is utilized as a questioner following the comic strip treatment. As a result of this study, seven things are right or legitimate, while the other three are incorrect. Valid items ranging from 0.374 to 0.545 with an r-table of 5% were 0.320. Out of the 10 things tested, only seven were correct. The indicator selection revealed that the lowest score was 28 and the highest score was 45. The indicator of learning independence was assigned a score of 37.45, while the final one was assigned a score of 38.32 as an indicator of media presentation. Although comic strip media has limits, many other respondents think that they outweigh these drawbacks. The majority of responders believe it helps pupils enhance their writing skills.
Augmented Reality System with a 3DCG Character Running on a Spinning Analog Record

A new type of augmented reality (AR) using smartphones has been developed that uses a label attached to the center of a real spinning analog record as a marker. The Vuforia Augmented Reality SDK and the Unity game engine are used to develop applications for smartphones. The smartphone screen shows an animation of a 3D character running on a real record, which can be enjoyed visually and aurally. With the use of a Unity script to rotate the 3D character in the opposite direction of the record, the 3D character can be displayed as if it were running on a treadmill and always in the same place on the screen.
Research of Scene Text Detection Algorithms

With the development of artificial intelligence, obtaining textual information from natural scenes has become a hot topic. There are still huge challenges for curved text and arbitrary orientation text detection in real-world scenes. In this work, we briefly introduce the development of text detection algorithms in recent years and study a typical lightweight detection algorithm. We have improved the algorithm to different degrees to make it more suitable for detection of the curved text. Finally, we select the three most representative public text datasets for experiments. Experiments conducted on CTW1500, ICDAR2015, and MSRA-TD500 have demonstrated the superiority of this model.
The Research on University English Classroom Teaching with Production-Oriented Approach Based on the Computer Data Analysis

With the development of science technology, computer big data has had a great impact on people’s life, while it is a new attempt applying computer big data in college English teaching under the theory of Production-oriented approach. The computer big data provides us more new teaching and learning methods. Production-oriented approach is a foreign language teaching theory proposed by Professor Wen Qiufang, which aims to improve the time-consuming and inefficient teaching effect of English in Chinese universities. While this paper takes the English teaching of Jingchu University of Technology as an applied undergraduate, the author tries to take the training of applied talents, apply the computer big data analysis to the teaching practice, which aims to cultivate more English practical talents for the society through the college English classroom, and develop towards the goal of practice and application. The research shows students’ learning motivation is greatly improved and their English output capacity is promoted as well with Production-oriented approach under the background of computer big data. The computer big data makes Production-oriented approach available and efficient.
Feature Fusion and Graph Convolution Correction Network for Human Parsing

In human parsing research, using a multitasking approach i.e., parsing task assisted by other tasks is an effective method to improve parsing accuracy. However, such methods do not enable the effective fusion of different types of features generated by the two tasks, so a feature fusion and graph convolution correction network (FGNet) for human parsing is proposed in the paper. The proposed network follows a multitasking architecture while a non-local feature fusion module is designed inspired by the idea of attention mechanism to keep the consistency of edge features and parsing features. Moreover, by taking the pixels in images as graph nodes, the graph convolution correction module is proposed to learn the relationship between nodes to further correct and refine the wrong annotations in the parsing results obtained from the non-local feature fusion module. The final experimental results show that the method we proposed performs well in both quantitative and qualitative aspects.
Object Detector with Multi-head Self-attention and Multi-scale Fusion

Aiming at the problems that the existing object detection network cannot fully extract the internal correlation of features and the multi-scale feature fusion is insufficient, an object detector that introduces multi-head self-attention and multi-scale fusion is proposed. Firstly, the multi-head self-attention mechanism is introduced, and the internal correlation of the extracted features is strengthened through the multi-head self-attention mechanism, and the dependence of the features on the external information is reduced. Secondly, the DenseASPP module is integrated in the designed network, and the ability of multi-scale feature fusion is improved through dilated convolution with different dilatation rate. The experiments of the proposed object detection algorithm on the PASCAL VOC 2007 dataset show that the overall accuracy is greatly improved compared with other advanced algorithms.
Adaptive Sequential Composition for Robot Behaviours

Autonomous robots are prone to fail in real world environments, where unknown factors can cause their world model to be inaccurate or incomplete. This causes robots to become stuck or repeatedly perform unsuccessful actions believing it is the best option. Static switching frameworks such as sequential composition guarantee stability of the overall system if its constituents are also stable. However, due to unknown factors, robot actions may fail when the robot senses the environment inaccurately. We propose Adaptive Sequential Composition, a novel framework that dynamically selects robot behaviours based on their utility to achieving success. We show the usefulness of the framework in a simulated second order system task as well as its application in navigating a robot through narrow gaps, where the clearance with the gap is smaller than position accuracy. Simulated results show adaptive sequential composition outperforms sequential composition by up to 30 % when presented with unknown factors leading to behaviour failure. For navigating through a narrow gap, adaptive sequential composition improved success by 65 %.
Fast Two-View Motion Segmentation Using Christoffel Polynomials

We address the problem of segmenting moving rigid objects based on two-view image correspondences under a perspective camera model. While this is a well understood problem, existing methods scale poorly with the number of correspondences. In this paper we propose a fast segmentation algorithm that scales linearly with the number of correspondences and show that on benchmark datasets it offers the best trade-off between error and computational time: it is at least one order of magnitude faster than the best method (with comparable or better accuracy), with the ratio growing up to three orders of magnitude for larger number of correspondences. We approach the problem from an algebraic perspective by exploiting the fact that all points belonging to a given object lie in the same quadratic surface. The proposed method is based on a characterization of each surface in terms of the Christoffel polynomial associated with the probability that a given point belongs to the surface. This allows for efficiently segmenting points “one surface at a time” in $$\mathcal {O}(\text {number of points)}$$ .