Reviewerly/exHarmony huggingface dataset:
Neural network based visual servo for quick-change device alignment in context of fusion reactor remote maintenance

The remote maintenance process for fusion reactor is complex, which can be very time-consuming and labor-consuming. This paper proposes a modified neural network based visual servo method to align a quick-change device with robot camera system. A classical position-based visual servo control law is used to guide the robot to reach the desired position. The key target for the above visual servo controller is to obtain a robust pose estimator to calculate the quick-change device pose with respect to the camera. This pose estimator is trained using RepVGG model with a self built image samples. An attention mechanism is added to the neural network to enhance the stability of pose prediction for reflective metal objects. The robot joint speed is also smoothed to reduce the image motion blur effect and make the visual servo process stable. The performance of the proposed visual servo controller was verified on an UR5 robot, and the results show that the stable and rough alignment of the quick-change device can be realized.
Using the Technology of Collective Peer Learning in Foreign Language Classes at a Non-Linguistic University

The study aims to identify ways to improve students’ communicative skills in foreign language classes at a non-linguistic university using the technology of collective peer learning. The paper describes the technology of collective peer learning, discusses its differences from similar pedagogical technologies of group work, advantages and disadvantages. Scientific novelty of the study lies in determining a number of communicative skills that students develop when the technology of collective peer learning is used in the process of foreign language teaching at a non-linguistic university. As a result of the study, the authors have developed an algorithm and techniques for organising work through this technology, which can help students to learn new foreign language information faster and more efficiently, as well as revise a large amount of material; have identified the advantages and disadvantages of teaching students a foreign language at a non-linguistic university based on the technology of collective peer learning.
Improve Security Using Steganography and Cryptography Based on Smartphone Users Locations

Smartphones have become one of the most important used devices worldwide. This is, in fact, due to the significant advances in: communication technology, mobility, and the development of various types of these devices. They have been used for communication, entertainment, navigation, etc. Smartphones are usually integrated with navigation systems such as The Global Positioning System (GPS). As a result, the location of smartphone users can be determined via such systems. The security of transmitting information over mobile phone networks is an issue by itself. In this paper, proposed a system that integrates the location of the smartphone obtained via GPS with steganography for transmitting confidential information, and an encryption method to improve the security of the transmitted information. The proposed system has implemented the Twofish Algorithm to encrypt confidential information, and the RSA algorithm to encrypt the GPS coordinates that will serve as keys. A number of measures of image quality were used to evaluate the proposed system such as MSE, PSNR, SSIM and NCC. As a result of this integration, the proposed system showed more flexibility and a high level of security.
Research on Neural Network Construction Method Based on Approximate Computational Test Data

In some applications of the power grid, there are problems that the volume of real data is small and the security of real data is difficult to guarantee, which poses a challenge to the data governance model. This paper proposes a parallel convolutional neural network structure based on approximate calculation of test data, constructs test data through approximate calculation, and uses parallel convolutional neural network structure to learn the corresponding data model, which can solve the problems of data resources, computing resources and problems in data governance. Calculate the cost problem. Experiments based on existing data sets show the unique advantages of this network structure for approximately computing test data.
Modeling User Strategies on Interactive Information Dashboards

Interacting with and making sense of information dashboards is often problematic. Typically, users develop strategies to go around and overcome these problems. These strategies can be conceived as behavioural markers of cognitive processes that indicate problematic interactions. Consequently, if we were able to computationally model these strategies, we could detect if users are encountering problems in real time (and act accordingly). We conducted an experiment (N=63) to identify the interaction strategies users employ on problematic dashboards. We found that while existing challenges impact significantly on user performance, interventions to mitigate such challenges were especially beneficial for those with lower graph literacy. We identified the strategies employed by users when encountering problems: extensive page exploration as a reaction to information overload and use of customisation functionalities when understanding data is problematic. We also found that some strategies are indicators of performance in terms of task completion time and effectiveness: extensive exploration strategies were indicators of lower performance, while the exhibition of customisation strategies is associated with higher effectiveness.
Local Edge Structure Guided Depth Up-Sampling

The current depth up-sampling cannot effectively using the pixel correlation and the edge structure between depth map and color map. In this paper, we proposes a new depth up-sampling method based on the local edge structure. Firstly, the non-robust pixel refinement in the low resolution (LR) depth image is obtained from the LR sobel-based edge map. Then, the structural consistency judgment within the depth map and the color map and the pixels classification is implemented with the guidance of the gradient matrix of the high resolution (HR) color image. Finally, according to the influence between pixels and spatial location constraints, the depth map is refined by the effective depth. Extensive experiments demonstrate that the proposed method outperforms conventional interpolation algorithms and some other edge-based depth up-sampling methods.
<scp>ALGERIA</scp>: Corruption Sentences

Africa Research Bulletin: Economic, Financial and Technical SeriesVolume 59, Issue 5 p. 23864A-23865B Policy and Practice ALGERIA: Corruption Sentences First published: 02 July 2022 https://doi.org/10.1111/j.1467-6346.2022.10601.xRead the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume59, Issue5July 2022Pages 23864A-23865B RelatedInformation
KA-Recsys: Patient Focused Knowledge Appropriate Health Recommender System

Patients with chronic diseases, such as diabetes, cancer, and heart disease, actively participate in disease management and seek health information on a constant basis for decision-making and self management. Patient focused health recommender systems (PHRSs) that suggest health information relevant to patients’ changing information needs across their disease trajectory can provide significant help to patients as they manage their disease on a day-to-day basis. A unique requirement of the PHRS would be to suggest health information in line with patients’ changing knowledge about the disease. It is crucial to recognize that patient knowledge of the disease may change as they become more actively involved in understanding and self-managing the illness. By providing patients with appropriate information, they are more likely to not only understand and engage, but also learn. Hence, the purpose of this doctoral thesis is to explore technologies in the field of recommender systems and personalized learning for the purpose of suggesting health information that accounts for patients’ dynamic information needs and level of knowledge about disease. We will explore these ideas in the context of developing a knowledge-appropriate PHRS (KA-Recsys). As a case study, a recommender will be integrated to an existing ovarian cancer patients’ information access portal. To assess the utility of KA-Recsys, the system will be evaluated based on expert-based and patient-based feedback. The expectation is that health information suggested by KA-Recsys will increase as well as benefit patients’ involvement in self management and treatment decisions.
Training of Basic Skills of Writing Scientific Using Mendeley Software and Pharaprase Techniques

Community Service is a form of response from a lecturer to Contribute to Improving the quality of the community. Community services be wrong one obligation a lecturer that Stated of deep Tri Dharma College Tall. Lecturer Required can Increase quality self and customize with development science knowledge. Activities community services be container Distribution science knowledge. Lecturer transfers knowledge that is Owned through Activities that are Compiled automatically methodical. Harapanya with the existence of Activities that are already Structured with good Expected transfer of science knowledge get executed automatically optimally. Community services that already Implemented be enter Into Devotion regkognisi. Community services this Implemented at the University Halu Oleo, STIE Pertiwi, and STIAB Lampung which already have Collaborate with Faculty Economics State University of Jakarta. Community services that Themed regkognisi be wrong one proof that there is Follow further from collaborated that already Done. As for the Activities that are Done be given Training Form Skills Use software mandelay and Engineering phrase with method Talk, practice and ask and answer. The aforementioned training Expected will be able to Increase the quality or ability of lecturer deep write scientific work.
An Approach for Cloud Security Using TPA- and Role-Based Hybrid Concept

In the cloud environment, enormous amount of the data is shared on the server for the availability of access to the employees or customers related to the organization. Two main issues which are generally faced—when data is shared in cloud environment, first is authenticating the user who can access the data, and secondly, to secure the data itself. Seeing the concern, we proposed the hybrid concept which involves the role-based security as well as TPA-based security. By making use of role-based security, first we have authenticated the users using the graphical authentication in which first the image requires to be selected, then the image gets segmented into image blocks, which when selected then the pattern is formed which is used for the authentication purpose, after that when the user shares the file, then he/she specifies the role who can access the file.
Joint Memory with Distance Recalculation for Unsupervised Person Re-Identification

Most of the purely unsupervised person Re-Identification (Re-ID) methods use memory dictionary to calculate loss, and use clustering to create memory dictionary and generate pseudo labels. But most of these methods neglect the camera style of the training images, which will severely affect the results of clustering. This paper aims at this challenge, proposes the Joint Memory mechanism with Distance Recalculation. We make full use of and group the feature vectors according to the camera IDs of the training images, and use the clustering algorithm to generate pseudo labels and create memory dictionary inside the same camera. Further, we take advantage of this memory dictionary to recalculate the distances between the training images across all cameras and second time use clustering algorithm to generate pseudo labels and create a new memory dictionary. By jointly utilizing these two kinds of memory dictionary, we can train the network more robustly. Our method accomplishes excellent performance compared to most of state-of-the-art unsupervised Re-ID methods on many datasets, e.g., 91.7%, 84.2%, 59.1% rank-1 accuracy and 80.9%, 71.1%, 32.4% mAP on the Market, Duke and MSMT17 datasets. We achieve this performance when we train the network with a very small batch size, and it is very possible that we can reach a better, maybe surpass state-of-the-art, performance when using a bigger batch size.
30‐3: A Moving Camera and Synthetic Calibration Target Solution for Non‐Planar Scene Estimation and Projector Calibration

The automatic calibration of projector systems improves the possible environments available to this technology which relies on exact scene calibration. This paper explores the challenge of projector calibration and non‐planar scene estimation. This formulation assumes no prior information on the moving camera or fixed projector. Limited scene understanding provides the basis for constructing synthetic calibration targets to perform a geometric recovery through bundle adjustment. Synthetic data are generated from ground‐truth camera and projector parameters to explore the method's performance over varying properties of the synthetic calibration targets.
Object detection and estimation: A hybrid image segmentation technique using convolutional neural network model

SUMMARY Object detection from image is more challenging and integral part in the inter‐discipline area of computer vision. The computer vision is highly attractive in many applications like human pose estimation, instance segmentation, recognizing action, disease predictions object prediction and many more applications. The traditional method of detecting objects from the images is done using bounding boxes with labels. It suffers from the overlapping of the boxes with various smaller objects, which leads to accuracy issues in detection problems. Hence, machine learning techniques are used to detect the relevant objects from the image using center point to avoid the nonmaximal suppression in bounding box. To accurately identify images, an U‐Net architecture based object detection method is proposed. In this model, it effectively uses semantic level segmentation and instance segmentation. This system effectively identifies all the objects present in the given image using the efficient hybrid segmentation models and Gromov Hausdroff distance measure. For experimentation, two data sets are used for evaluation of the model to identify all categories of objects from the image. The proposed model achieves an accuracy of 91.8% and reliable when compared to existing effective object detection algorithms like fully convolution network (FCN), YOLO (you only look once) and mask region based‐convolutional neural network (mask R‐CNN) model.
A Unified and Semantic Model Approach for Histopathologic Cancer Detection Based on Deep Double Transfer Learning

Accurately predicting the risk of cancer recurrence and metastasis is very important for individual cancer treatment. Currently, doctors usually use a histological grade that pathologists determine by performing a semi-quantitative analysis of the three histopathological and cytological features of hematoxylin-eosin (HE) stained histopathological images. Evaluate the prognosis and treatment options of patients with breast cancer. In order to efficiently and objectively fully utilize the valuable information underlying HE-stained histopathological images, this work has potential as a feature for constructing a classification model of cancer prognosis. So, a calculation method is proposed to extract morphological information. Breast cancer is not a single disease, but it is composed of many different biological entities with different pathological features and clinical significance. With the advent of personalized medicine, pathologists are facing a significant increase in the workload and complexity of digital pathology in cancer diagnosis, and diagnostic protocols need to focus on equal efficiency and accuracy. Computer-aided image processing techniques have been shown to be able to improve the efficiency, accuracy, and consistency of histopathological assessments and provide decision support to ensure diagnostic consistency. First, a method for segmenting tumor lesions based on a pixel-by-pixel deep learning classifier is proposed and a method for segmenting cell nuclei based on marker-driven watersheds. It then subdivides all image objects and extracts a rich set of predefined quantitative morphological object feature. Then a classification model based on these measurements is used to predict disease-free survival in binary patients. Finally, the predictive model is tested in two independent cohorts of breast cancer patients.
User Control in Adaptive Information Access

Adaptive information access systems is one of the most popular types of adaptive systems. It includes adaptive search, recommender system, adaptive navigation support, and adaptive information visualization. This is also one of the oldest types of adaptive systems where the ideas of user control were explored. Motivated by a timely selection of user control as the theme of UMAP 2022, this tutorial provides a systematic review of approaches focused on adding various kinds of user control to adaptive information access systems and discuss lessons learned, prospects, and challenges of this direction of research.
Beyond-Accuracy Perspectives on Graph Neural Network-Based Models for Behavioural User Profiling

The presented doctoral research aims to develop a behavioural user profiling framework focusing simultaneously on three beyond-accuracy perspectives: privacy, to study how to intervene on graph data structures of specific contexts and provide methods to make the data available in a meaningful manner without neither exposing personal user information nor corrupting the profiles creation and system performances; fairness, to provide user representations that are free of any inherited discrimination which could affect a downstream recommender by developing debiasing approaches to be applied on state-of-the-art GNN-based user profiling models; explainability, to produce understandable descriptions of the framework results, both for user profiles and recommendations, mainly in terms of interaction importance, by designing an adaptive and personalised user interface which provides tailored explanations to the end-users, depending on their specific user profiles.
Optical Character Recognition Based Webapp

As the use of computers in our daily lives increases, so has the need for a natural procedure to interact with the computers. The ultimate aim of human computer interaction is to bring the change that there is always a natural way of interacting with computers coupled with ease and flexibility. Printed and textual media such as prescriptions, invoices, receipts, etc. occupies a large segment of our day-to-day activities and given their volume, it is inefficient to manage them physically as there’s always an associated risk of fading, damage, misplacing, etc. and hence a medium is required for their digital conversion. In this project, we have developed a robust, cross-platform web application that can process the images using PyTesseract based algorithms that can efficiently extract the textual data to facilitate the storage and retrieval of the same. The extracted text can be downloaded as a text file and can also be translated into the desired language. This is an active field of research and thus this paper also discusses various current implementations of the mentioned concept. The Optical Character Recognition framework finds applications in a variety of fields such as business process activities, number plate recognition, KYC and banking processes to name a few.
cube strategy effect On the ability of fourth-grade literary students to analyze literary texts

The current research aimed to identify the effect of the cube strategy on the ability of fourth-grade literary students to analyze literary texts among fourth-grade literary students.The research sample consisted of students of the fourth literary grade at Granada Secondary School for Girls, in the city of Kirkuk, for the academic year (2020-2021).It was chosen intentionally, as the total number of its members is (66) students.The sample of the experimental group consisted of (33) female students, who studied according to the cube strategy, and the sample of the control group consisted of (33) female students, who studied in the usual way.To achieve the goal of the research, a test was built to analyze literary texts for the fourth-grade literary students in the Arabic language, and after preparing the teaching plans, the research experiment was carried out by the researcher to teach Arabic in the second course of the academic year (2020-2021) on (4/25/2021).It continued for a full academic course, with two lessons per week, and the post-test was applied on (27/6/2021).The data was statistically treated by t-test for two independent samples.The result was that the members of the experimental group who were taught by the cube strategy outperformed the members of the control group ( ‫لعدد‬ ‫ا‬ ، 1
Media Performance of American Zionist lobby and its Role in Strengthening Hatred Speech for Afmerican Public Opinion

Modern media plays a very important and dangerous role of affecting local and international public opinion changing their role from presenting and alalysing news to amajor player.It deform deeply destroys and the rooted values, replaces them with different values on conceptions, beliefs, ideas, behaviour individ towards individuals.The tracker of the performance of American media intitufions finds that they present a very fake and deformed image of Arabs, islam, and their main issues about Palestine because of Zionist lobby and its influence over American media, specially after 9/11.Media has exaggerated in degrading Arabs, calling them with worst names, mock their civilization, scientific contributions, and injustice of their case Now the image inside the mind of common American and public American opinion about Arabic and Muslim society is attached to terrorism, violence and radicality.In contrast,such media presents on the other the zionist Hebrew state with sophistication, and democracy which needs support and opprove.Thus, this research addresses the following question: comes the proplem of this research.Do the great roles of American media affirm values of peace, forgiveness and co existence among people and states, or and because of the strong influence of American Zionist lobby strengthen hatred speech of American public opinion through this negative, deformed and fake image of Arabic and Islamic societies in general and their central cases in particular.Thus the American media would not be an honest mediator to give the night and objective media information.