Reviewerly/exHarmony huggingface dataset:
RiIG Modeled Curvelet Coefficient Image-based Breast Tumor Classification Using deep CNN

Deep learning-based automatic classification of breast tumors using ultrasound (US) B-Mode images is still a challenging research area. A deep convolutional neural network employing parametric imaging is presented to categorize benign and malignant breast tumors from ultrasound B-Mode images. The Rician Inverse Gaussian (RiIG) distribution is used to represent the statistics of breast US imaging data in the domain of the Curvelet Transform. In different Curvelet sub-bands, locally estimated values of the RiIG distribution’s dispersion parameters produce parametric images. A publicly accessible dataset of 780 breast ultrasound images along with their ground truth images is used for classification with a specially designed convolutional neural network. The proposed technique yields 97.1%, 96.6%, 97.3%, 94.3%, and 98.4% accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), respectively, higher than those of several recently reported techniques.
Personalized Learning Resource Recommendation Based On Learner Profile

The online learning platform is overloaded with learning resources, and learners are prone to “information maze” when facing massive learning resources. An effective way to solve information maze is to recommend resources that conform to learners’ own characteristics from a large number of learning resources. Therefore, we proposes a personalized learning resource recommendation algorithm based on learner profile. Firstly, the learner portrait is modeled based on two characteristics of learner, learning interest preference and knowledge point mastery level. Secondly, K nearest neighbor learners who are most similar to the target learner are found according to the learner portrait. Thirdly, using the rating information of these K learners on the learning resources, the predicted scores of the target learner on the learning resources are calculated. Finally, the learning resources are sorted by predicted scores, the first N learning resources are the final recommended results. The algorithm we proposed is verified in EdNet, a data set published by Santa. The experimental results show that the algorithm has significantly improved the recommendation performance.
Teaching resource recommendation for OBE based on kernel canonical correlation analysis

Data mining is used to extract information from OBE teaching resources, then the teaching resources and user personality database are established to improve the effectiveness of resources recommendation on the online learning platform. The properties of teaching resources and user personality traits are analyzed using the kernel canonical correlation analysis algorithm, and teaching materials with a high correlation coefficient are chosen to recommend to users. The simulation results demonstrate that this proposed method has higher accuracy and more accurate recommended resources when compared to the widely used teaching resource recommendation techniques.
Low-light Image Enhancement via Unsupervised Progressive Fusion Network

To address the issue that unsupervised low-light image enhancement methods rely on finely selected training sets or contrast exposure stacks, this paper proposes a low-light image enhancement network with unsupervised progressive fusion driven jointly by knowledge and data. The network consists of low-light image pre-processing stage and progressive fusion stage. In the pre-processing stage, the input low-light image is first inverted. Next, we perform three methods of fixed-value gamma correction, adaptive gamma correction, and color-preserving adaptive histgram equalization (color-preserving AHE) to generate three images with different degrees of contrast enhancement. The progressive fusion stage performs a progressive fusion of the three images generated in the preprocessing stage and the inverted input image. The contrast and brightness of the fused result are improved by fusion while suppressing overexposure in the bright regions. It is shown experimentally that the proposed method outperforms the current state-of-the-art unsupervised low-light image enhancement networks in terms of both visual quality and technical specifications. In some low-light scenes, our method even outperforms the supervised learning-based low-light image enhancement network.
Research and design university campus as an effective environment for the development of a student-innovator

The relevance of the research is determined by the need to transform the educational environment of a modern university. Such environment is one of the conditions for the effective development of a student-innovator, who is motivated to carry out research and design, capable of searching for original ideas, innovative solutions, and project implementation. The purpose of the study is to reveal the essence of the concept of ″research and design campus″; to determine the structure of students’ readiness for innovation in the research and design campus; to identify the possibilities of the university research and design campus as an effective environment for the development of a student-innovator as illustrated by the example of Penza State Technological University (PenzSTU). The study shows that a research and design campus is an effective educational environment providing a system of opportunities for creativity, invention and personal self-development of a student-innovator at a modern university. The article reveals the essence of the concept of ″research and design campus″ from the standpoint of the environmental approach. The structure of the student’s readiness for innovation activity in a research and design campus is presented. This structure consists of four functional blocks. The personal-motivational block includes the student’s personal and professional qualities that determine his/her attitude to innovations of a career-oriented nature. The comprehension block contains ideas about the goals and content of the innovation activity. The implementation block includes qualities that ensure the creative implementation of an innovative project in practice. And the reflection block reflects the qualities associated with the student’s assessment of his/her own innovation activity. Based on the practical case of Penza State Technical University, the authors identified and characterized organizational, substantive and technological capabilities of the campus for the development of a student-innovator. Organizational capabilities include the resource potential and the structuring of the environment. Substantive capabilities are presented by the software provision of the innovative activity of students in the university research and design campus. The technological capabilities of the environment include diagnostics, forecasting, designing, production of the result, that assumes a change in the environment and personality according to the goals. The article makes the conclusion about the potential of the research and design campus in the fields of organizing and implementing theoretical and applied research and projects, of providing students’ collaborations and career guidance for student-innovators of a new formation.
A Study of Using Synthetic Data for Effective Association Knowledge Learning

Abstract Association, aiming to link bounding boxes of the same identity in a video sequence, is a central component in multi-object tracking (MOT). To train association modules, e.g., parametric networks, real video data are usually used. However, annotating person tracks in consecutive video frames is expensive, and such real data, due to its inflexibility, offer us limited opportunities to evaluate the system performance w.r.t. changing tracking scenarios. In this paper, we study whether 3D synthetic data can replace real-world videos for association training. Specifically, we introduce a large-scale synthetic data engine named MOTX, where the motion characteristics of cameras and objects are manually configured to be similar to those of real-world datasets. We show that, compared with real data, association knowledge obtained from synthetic data can achieve very similar performance on real-world test sets without domain adaption techniques. Our intriguing observation is credited to two factors. First and foremost, 3D engines can well simulate motion factors such as camera movement, camera view, and object movement so that the simulated videos can provide association modules with effective motion features. Second, the experimental results show that the appearance domain gap hardly harms the learning of association knowledge. In addition, the strong customization ability of MOTX allows us to quantitatively assess the impact of motion factors on MOT, which brings new insights to the community.
Imaging and Vision Development Platform with Algorithm Library for Intelligent Vision Systems

Machine vision applications for intelligent vision systems in manufacturing industries were reported based on image processing and artificial intelligence technology. We propose the imaging and vision development platform in this research for creating vision applications using image processing, machine learning, and a deep learning algorithm library. An algorithm library, vision configurator, execution logic, display manager and deploy manager modules are all included in the proposed platform. This platform is based on an open-source software stack for machine learning and deep learning computer vision technologies including OpenCV, TensorFlow, CUDA, Keras, YOLO and PyTorch. To assess the performance of the suggested platform, real-time applications like vehicle identification, person detection, code scanner, and OCR vision application were developed, validated, and deployed in an embedded system utilizing this platform. The results of the experiments show that the suggested platform can be utilized to evaluate high resolution real-time images and construct vision applications.
Deep Neuroevolution Squeezes More Out of Small Neural Networks and Small Training Sets: Sample Application to MRI Brain Sequence Classification

Deep Neuroevolution (DNE) holds the promise of providing radiology artificial intelligence (AI) that performs well with small neural networks and small training sets. We seek to realize this potential via a proof-of-principle application to MRI brain sequence classification. We analyzed a training set of 20 patients, each with four sequences (weightings): T1, T1 post-contrast, T2, and T2-FLAIR. We trained the parameters of a relatively small convolutional neural network (CNN) as follows: first, we randomly mutated the CNN weights. We then measured the CNN training set accuracy, using the latter as the fitness evaluation metric. The fittest “child” CNNs were identified. We incorporated their mutations into the “parent” CNN. This selectively mutated parent became the next generation’s parent CNN. We repeated this process for approximately 50,000 generations. DNE achieved monotonic convergence to 100% training set accuracy. DNE also converged monotonically to 100% testing set accuracy. DNE can achieve perfect accuracy with small training sets and small CNNs. Particularly when combined with Deep Reinforcement Learning, DNE may provide a path forward in the quest to make radiology AI more human-like in its ability to learn. DNE may very well turn out to be a key component of the much-anticipated “meta-learning” regime of radiology AI; algorithms that can adapt to new tasks and new image types, similar to human radiologists.
Designing a Glyph-Based Polar Chart to Interpret the Results of Machine Learning Models

Explainable artificial intelligence practices can support data scientists in interpreting the results of machine learning (ML) models. However, current practices require effortful and time-consuming coding to compare explanations that either relate to different ML models of the same data, or that are generated with different computational methods. We report the development of a glyph-based polar chart (GPC), designed to support a more comprehensive interpretation of the results of ML models by allowing these comparisons. The results from our user experience evaluation indicated that the proposed GPC supported data scientists in identifying the most relevant model variables, comparing different explanation methods, and performing logic reviews.
Implementation of Web-Based Case Midwifery Notes Documentation System (CMNotes) for Intra Natal Care

Case Midwifery Notes (CMNotes) is a digital-based documentation system designed to develop learning methods and interactive consultations between students and lecturers. CMNotes facilitates digital and comprehensive recording of patient data starting from assessment, diagnosis, plan of care, implementation to evaluation by applying Varney's management principles and SOAP documenta-tion. This study aims to design produce a documentation system for midwifery care emphasized on the Intranatal care. The app was developed using php and html followed by hosting via wordpress. The data was collected using pssuq instrument (usability assessment), need analysis instrument and student responses questionnaire. 63 students from Midwifery student of Health Polytechnic Aceh were involved in the implementation of the system. The implementation results show that overall score of the system usability measured using PSSUQ is 2.87 indicating that the CMNotes system provides convenience and satisfaction for students in making documentation of childbirth care. Due to comments of users regarding the practi-cality of system, it is necessary to develop an Android-based CMNotes application to be able to fill in patient data more easily and effectively without the need of internet access.
Image Generation Method Based on Improved Generative Adversarial Network

Background: The image generation model based on generative adversarial network (GAN) has achieved remarkable achievements. However, traditional GAN has the disadvantage of unstable training, which affects the quality of the generated image. Objective: This method is to solve the GAN image generation problems of poor image quality, single image category, and slow model convergence. Methods: An improved image generation method is proposed based on GAN. Firstly, the attention mechanism is introduced into the convolution layer of the generator and discriminator and a batch normalization layer is added after each convolution layer. Secondly, the ReLU and leaky ReLU are used as the active layer of the generator and discriminator, respectively. Thirdly, the transposed convolution is used in the generator while the small step convolution is used in the discriminator, respectively. Fourthly, a new discarding method is applied in the dropout layer. Results: The experiments were carried out on Caltech 101 dataset. The experimental results showed that the image quality generated by the proposed method is better than that generated by GAN with attention mechanism (AM-GAN) and GAN with stable training strategy (STS-GAN) and the stability was improved. Conclusion: The proposed method is effectiveness for image generation with high quality.
Deep Learning Optical Flow with Compound Loss for Dense Fluid Motion Estimation

Flow motion with complex patterns, such as vortex, stagnant flow, and seepage, put forward higher spatial resolution requirements for particle image velocimetry (PIV). With the development of deep learning technology in optical flow estimation, many attempts have been made to introduce deep learning-based optical flow (DLOF) into PIV. Compared with the traditional optical flow method, the DLOF method has the advantages of higher precision, faster calculation speed, and avoiding manual parameter adjustment. However, DLOF research is generally developed based on the basic characteristics of rigid body motion, and its key loss function part still generally uses the L1 (mean absolute error, MAE, L1) or L2 (mean square error, MSE, L2) loss functions, which lack consideration of fluid motion characteristics. Therefore, the current DLOF research has the problems of large angular error and serious curl-divergence loss in fluid motion estimation scenarios with smaller spatial scales than rigid bodies. Based on the prior knowledge of the traditional fluid motion characteristics, this study proposes a fluid loss function for describing the fluid motion characteristics, and combines this loss function with Flownet2. The compound loss (CL) function is combined with the displacement error, angular error, and div-curl smooth loss. The method combined with the loss function in this paper is called FlowNet2-CL-PIV. In order to verify that the compound loss function proposed in this study has a positive impact on the model training results, this paper uses the cosine similarity measure to demonstrate its effectiveness. In addition, the parameter selection of the compound loss function is analyzed and compared, and it is verified that the best training effect can be achieved by adjusting the parameter so that the order of magnitude of each part of the compound loss function is consistent. In order to test the calculation effect of the Flownet2-CL-PIV method proposed in this study, synthetic particle images are used for model training and performance analysis. Simulation results in various flow fields show that the root mean square error (RMSE) and average angular error (AAE) of Flownet2-CL-PIV reach 0.182 pixels and 1.7°, which are 10% and 54% higher than the original model, respectively.
Progress of Deep Resources Exploration and Mining (DREAM) program in China: Critical minerals

China launched the Deep Resources Exploration and Mining (DREAM) program in 2016. Since then, the program has made significant progress in the exploration of critical minerals, such as rare earth, rare, and rare scattered metals. A “five-in-one” model, based on climate, landform, parent rocks, carrier minerals, and pH values of weathering crust, has been established for rare earth prospecting in South China. It has led to a major breakthrough in the discovery of a new type of ion-adsorption rare earth deposit in the weathered crust of low-grade metamorphic rocks in southern Jiangxi, South China. A pegmatite beryllium (Be), skarn beryllium-tungsten (Be-W), cassiterite sulfide tin-tungsten-beryllium (Sn-W-Be), independent fluorite, and lead-zinc (Pb-Zn) vein five-in-one model was developed for the prospecting of rare metals such as Be and W and polymetals in the Zhaxikang-Cuonadong ore-concentrated area of the eastern Himalayas. A series of metallogenic models has been proposed for the investigation of lithium (Li) resources, leading to important breakthroughs in the prospecting of large to superlarge Li deposits in the Jiajika (western Sichuan), Dahongliutan (southwestern Xinjiang), and Xiaoshiqiao (central Yunnan) ore fields. Meanwhile, the DREAM program has achieved significant advancements in its knowledge of the ultranormal enrichment of indium, germanium, gallium, niobium, and rare earth elements in the western Yangtze block, Southwest China.
GEO 2023 Learning Conference

Nonprofit Business AdvisorVolume 2023, Issue 404 p. 4-4 Conferences & Events GEO 2023 Learning Conference First published: 04 April 2023 https://doi.org/10.1002/nba.31450Read 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 onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume2023, Issue404May 2023Pages 4-4 RelatedInformation
Domain-oriented masked bit-parallel finite-field multiplier against side-channel attacks

Side-Channel Analysis (SCA) constitutes a serious threat to the security of implemented cryptosystems. In SCA, the attacker can obtain information leakage from a device executing cryptographic algorithms by means of the measure of side-channels such as power consumption, electromagnetic radiation and execution time. For this reason, effective countermeasures against SCA are indispensable in implemented cryptographic devices. The use of masking schemes (in which intermediate computations are independent from the sensible input data) constitutes the most effective approach to achieve resistance against physical attacks. Among the different masking methods proposed for hardware, domain-oriented masking is one of the most promising due to its lower implementation costs, level of security and glitch resistance. In this paper, a new bit-parallel first-order domain-oriented masked finite field multiplier is presented which incorporates the addition of fresh random values without increasing the computation delay. Explicit expressions for the computation of the new masked multiplier for the binary extension field used in the Advanced Encryption Standard (AES) are also given.
IoT based Illness Prediction System using Machine Learning

The adoption of wearable technology will increase and its integration into daily life will improve, particularly in the healthcare sector. The emergence of mobile medicine, the development of new technologies like smart sensing, and the adoption of customised health ideas have all contributed to the rapid growth of smart wearable technology in recent years. The study was primarily focused on the use of wearable technology in office situations with the goal of daily health and safety monitoring of employees. In order to perform data classification and data labeling, a machine learning model is constructed. This research work has proposed a novel framework for processing data with text-related properties using machine learning techniques. Further a data analysis process has been carried out by using a Machine Learning (ML) framework. In the proposed study, machine learning classifiers are used. This study has analyzed the outcomes by considering accuracy as a performance indicator after applying the algorithms to the datasets. After analyzing the accuracy, it is evident that the machine learning algorithms like K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) are effective on processing the text datasets.
An Enhanced and Interactive Training Model for Underground Coal Mines Using Virtual Reality

To solve the problems that occur during the training of coal mining, this study has developed a VR-based user interactive solution with which immersive virtual training will be provided to the miners before going into real fieldwork, and workers can get the in-handed experience of safety and precautions in coal mines. VR solutions for underground coal mining training can help the workers to skill up their workforce, especially for the young miners and better life-saving training. Maximum of the present VR-based education structures are lacking in a learning enjoy. But these structures adopt training and mastering functions via player interatcion with the assist of 3D coal mine VR schooling models. The system can be efficiently implemented in a laboratory environment. The proposed model provides four types of training - Coal digging and Loading, Fire rescue operation, Disaster scenario, and mechanical operations. In all these operations, after wearing the VR headset instructions will be provided to the users step by step on what they must do accordingly. With the help of this solution, miners can be trained to save their lives and handle the disastrous situation in coal mines. This will be a complete training for new miners.