bluuebunny/crossref_metadata_2025 huggingface dataset:
Closed-loop control of direct ink writing via reinforcement learning
Authors: Piovarči Michal , Foshey Michael , Xu Jie , Erps Timmothy , Babaei Vahid , Didyk Piotr , Rusinkiewicz Szymon , Matusik Wojciech , Bickel Bernd
Published: July 2022

Enabling additive manufacturing to employ a wide range of novel, functional materials can be a major boost to this technology. However, making such materials printable requires painstaking trial-and-error by an expert operator, as they typically tend to exhibit peculiar rheological or hysteresis properties. Even in the case of successfully finding the process parameters, there is no guarantee of print-to-print consistency due to material differences between batches. These challenges make closed-loop feedback an attractive option where the process parameters are adjusted on-the-fly. There are several challenges for designing an efficient controller: the deposition parameters are complex and highly coupled, artifacts occur after long time horizons, simulating the deposition is computationally costly, and learning on hardware is intractable. In this work, we demonstrate the feasibility of learning a closed-loop control policy for additive manufacturing using reinforcement learning. We show that approximate, but efficient, numerical simulation is sufficient as long as it allows learning the behavioral patterns of deposition that translate to real-world experiences. In combination with reinforcement learning, our model can be used to discover control policies that outperform baseline controllers. Furthermore, the recovered policies have a minimal sim-to-real gap. We showcase this by applying our control policy in-vivo on a single-layer printer using low and high viscosity materials.
Semantically supervised appearance decomposition for virtual staging from a single panorama
Authors: Zhi Tiancheng , Chen Bowei , Boyadzhiev Ivaylo , Kang Sing Bing , Hebert Martial , Narasimhan Srinivasa G.
Published: July 2022

We describe a novel approach to decompose a single panorama of an empty indoor environment into four appearance components: specular, direct sunlight, diffuse and diffuse ambient without direct sunlight. Our system is weakly supervised by automatically generated semantic maps (with floor, wall, ceiling, lamp, window and door labels) that have shown success on perspective views and are trained for panoramas using transfer learning without any further annotations. A GAN-based approach supervised by coarse information obtained from the semantic map extracts specular reflection and direct sunlight regions on the floor and walls. These lighting effects are removed via a similar GAN-based approach and a semantic-aware inpainting step. The appearance decomposition enables multiple applications including sun direction estimation, virtual furniture insertion, floor material replacement, and sun direction change, providing an effective tool for virtual home staging. We demonstrate the effectiveness of our approach on a large and recently released dataset of panoramas of empty homes.
TopoCut
Authors: Fang Xianzhong , Desbrun Mathieu , Bao Hujun , Huang Jin
Published: July 2022

Given a complex three-dimensional domain delimited by a closed and non-degenerate input triangle mesh without any self-intersection, a common geometry processing task consists in cutting up the domain into cells through a set of planar cuts, creating a "cut-cell mesh", i.e., a volumetric decomposition of the domain amenable to visualization (e.g., exploded views), animation (e.g., virtual surgery), or simulation (finite volume computations). A large number of methods have proposed either
efficient
or
robust
solutions, sometimes restricting the cuts to form a regular or adaptive grid for simplicity; yet, none can guarantee
both
properties, severely limiting their usefulness in practice. At the core of the difficulty is the determination of topological relationships among large numbers of vertices, edges, faces and cells in order to assemble a proper cut-cell mesh: while exact geometric computations provide a robust solution to this issue, their high computational cost has prompted a number of faster solutions based on, e.g., local floating-point angle sorting to significantly accelerate the process --- but losing robustness in doing so. In this paper, we introduce a new approach to planar cutting of 3D domains that substitutes topological inference for numerical ordering through a novel mesh data structure, and revert to exact numerical evaluations only in the few rare cases where it is strictly necessary. We show that our novel concept of
topological cuts
exploits the inherent structure of cut-cell mesh generation to save computational time while still guaranteeing exactness for, and robustness to, arbitrary cuts and surface geometry. We demonstrate the superiority of our approach over state-of-the-art methods on almost 10,000 meshes with a wide range of geometric and topological complexity. We also provide an open source implementation.
Alpha wrapping with an offset
Authors: Portaneri Cédric , Rouxel-Labbé Mael , Hemmer Michael , Cohen-Steiner David , Alliez Pierre
Published: July 2022

Given an input 3D geometry such as a triangle soup or a point set, we address the problem of generating a watertight and orientable surface triangle mesh that strictly encloses the input. The output mesh is obtained by greedily refining and carving a 3D Delaunay triangulation on an offset surface of the input, while carving with empty balls of radius alpha. The proposed algorithm is controlled via two user-defined parameters: alpha and offset. Alpha controls the size of cavities or holes that cannot be traversed during carving, while offset controls the distance between the vertices of the output mesh and the input. Our algorithm is guaranteed to terminate and to yield a valid and strictly enclosing mesh, even for defect-laden inputs. Genericity is achieved using an abstract interface probing the input, enabling any geometry to be used, provided a few basic geometric queries can be answered. We benchmark the algorithm on large public datasets such as Thingi10k, and compare it to state-of-the-art approaches in terms of robustness, approximation, output complexity, speed, and peak memory consumption. Our implementation is available through the CGAL library.
Blending camera and 77 GHz radar sensing for equitable, robust plethysmography
Authors: Vilesov Alexander , Chari Pradyumna , Armouti Adnan , Harish Anirudh Bindiganavale , Kulkarni Kimaya , Deoghare Ananya , Jalilian Laleh , Kadambi Achuta
Published: July 2022

With the resurgence of non-contact vital sign sensing due to the COVID-19 pandemic, remote heart-rate monitoring has gained significant prominence. Many existing methods use cameras; however previous work shows a performance loss for darker skin tones. In this paper, we show through light transport analysis that the camera modality is fundamentally biased against darker skin tones. We propose to reduce this bias through multi-modal fusion with a complementary and fairer modality - radar. Through a novel debiasing oriented fusion framework, we achieve performance gains over all tested baselines and achieve skin tone fairness improvements over the RGB modality. That is, the associated Pareto frontier between performance and fairness is improved when compared to the RGB modality. In addition, performance improvements are obtained over the radar-based method, with small trade-offs in fairness. We also open-source the largest multi-modal remote heart-rate estimation dataset of paired camera and radar measurements with a focus on skin tone representation.
Neural rendering in a room
Authors: Yang Bangbang , Zhang Yinda , Li Yijin , Cui Zhaopeng , Fanello Sean , Bao Hujun , Zhang Guofeng
Published: July 2022

We, as human beings, can understand and picture a familiar scene from arbitrary viewpoints given a single image, whereas this is still a grand challenge for computers. We hereby present a novel solution to mimic such human perception capability based on a new paradigm of amodal 3D scene understanding with neural rendering for a closed scene. Specifically, we first learn the prior knowledge of the objects in a closed scene via an offline stage, which facilitates an online stage to understand the room with unseen furniture arrangement. During the online stage, given a panoramic image of the scene in different layouts, we utilize a holistic neural-rendering-based optimization framework to efficiently estimate the correct 3D scene layout and deliver realistic free-viewpoint rendering. In order to handle the domain gap between the offline and online stage, our method exploits compositional neural rendering techniques for data augmentation in the offline training. The experiments on both synthetic and real datasets demonstrate that our two-stage design achieves robust 3D scene understanding and outperforms competing methods by a large margin, and we also show that our realistic free-viewpoint rendering enables various applications, including scene touring and editing. Code and data are available on the project webpage: https://zju3dv.github.io/nr_in_a_room/.
Physics informed neural fields for smoke reconstruction with sparse data
Authors: Chu Mengyu , Liu Lingjie , Zheng Quan , Franz Erik , Seidel Hans-Peter , Theobalt Christian , Zayer Rhaleb
Published: July 2022

High-fidelity reconstruction of dynamic fluids from sparse multiview RGB videos remains a formidable challenge, due to the complexity of the underlying physics as well as the severe occlusion and complex lighting in the captured data. Existing solutions either assume knowledge of obstacles and lighting, or only focus on simple fluid scenes without obstacles or complex lighting, and thus are unsuitable for real-world scenes with unknown lighting conditions or arbitrary obstacles. We present the first method to reconstruct dynamic fluid phenomena by leveraging the governing physics (ie, Navier -Stokes equations) in an end-to-end optimization from a mere set of sparse video frames without taking lighting conditions, geometry information, or boundary conditions as input. Our method provides a continuous spatio-temporal scene representation using neural networks as the ansatz of density and velocity solution functions for fluids as well as the radiance field for static objects. With a hybrid architecture that separates static and dynamic contents apart, fluid interactions with static obstacles are reconstructed for the first time without additional geometry input or human labeling. By augmenting time-varying neural radiance fields with physics-informed deep learning, our method benefits from the supervision of images and physical priors. Our progressively growing model with regularization further disentangles the density-color ambiguity in the radiance field, which allows for a more robust optimization from the given input of sparse views. A pretrained density-to-velocity fluid model is leveraged in addition as the data prior to avoid suboptimal velocity solutions which underestimate vorticity but trivially fulfill physical equations. Our method exhibits high-quality results with relaxed constraints and strong flexibility on a representative set of synthetic and real flow captures. Code and sample tests are at https://people.mpi-inf.mpg.de/~mchu/projects/PI-NeRF/.
A moving eulerian-lagrangian particle method for thin film and foam simulation
Authors: Deng Yitong , Wang Mengdi , Kong Xiangxin , Xiong Shiying , Xian Zangyueyang , Zhu Bo
Published: July 2022

We present the Moving Eulerian-Lagrangian Particles (MELP), a novel mesh-free method for simulating incompressible fluid on thin films and foams. Employing a bi-layer particle structure, MELP jointly simulates detailed, vigorous flow and large surface deformation at high stability and efficiency. In addition, we design multi-MELP: a mechanism that facilitates the physically-based interaction between multiple MELP systems, to simulate bubble clusters and foams with non-manifold topological evolution. We showcase the efficacy of our method with a broad range of challenging thin film phenomena, including the Rayleigh-Taylor instability across double-bubbles, foam fragmentation with rim surface tension, recovery of the Plateau borders, Newton black films, as well as cyclones on bubble clusters.
DeepPhase
Authors: Starke Sebastian , Mason Ian , Komura Taku
Published: July 2022

Learning the spatial-temporal structure of body movements is a fundamental problem for character motion synthesis. In this work, we propose a novel neural network architecture called the Periodic Autoencoder that can learn periodic features from large unstructured motion datasets in an unsupervised manner. The character movements are decomposed into multiple latent channels that capture the non-linear periodicity of different body segments while progressing forward in time. Our method extracts a multi-dimensional phase space from full-body motion data, which effectively clusters animations and produces a manifold in which computed feature distances provide a better similarity measure than in the original motion space to achieve better temporal and spatial alignment. We demonstrate that the learned periodic embedding can significantly help to improve neural motion synthesis in a number of tasks, including diverse locomotion skills, style-based movements, dance motion synthesis from music, synthesis of dribbling motions in football, and motion query for matching poses within large animation databases.
High dynamic range and super-resolution from raw image bursts
Authors: Lecouat Bruno , Eboli Thomas , Ponce Jean , Mairal Julien
Published: July 2022

Photographs captured by smartphones and mid-range cameras have limited spatial resolution and dynamic range, with noisy response in underexposed regions and color artefacts in saturated areas. This paper introduces the first approach (to the best of our knowledge) to the reconstruction of highresolution, high-dynamic range color images from raw photographic bursts captured by a handheld camera with exposure bracketing. This method uses a physically-accurate model of image formation to combine an iterative optimization algorithm for solving the corresponding inverse problem with a learned image representation for robust alignment and a learned natural image prior. The proposed algorithm is fast, with low memory requirements compared to state-of-the-art learning-based approaches to image restoration, and features that are learned end to end from synthetic yet realistic data. Extensive experiments demonstrate its excellent performance with super-resolution factors of up to ×4 on real photographs taken in the wild with hand-held cameras, and high robustness to low-light conditions, noise, camera shake, and moderate object motion.
Parallel Weighted Random Sampling
Authors: Hübschle-Schneider Lorenz , Sanders Peter
Published: July 2022

Data structures for efficient sampling from a set of weighted items are an important building block of many applications. However, few parallel solutions are known. We close many of these gaps. We give efficient, fast, and practicable parallel and distributed algorithms for building data structures that support sampling single items (alias tables, compressed data structures). This also yields a simplified and more space-efficient sequential algorithm for alias table construction. Our approaches to sampling
k
out of
n
items with/without replacement and to subset (Poisson) sampling are
output-sensitive
, i.e., the sampling algorithms use work linear in the number of
different
samples. This is also interesting in the sequential case. Weighted random permutation can be done by sorting appropriate random deviates. We show that this is possible with linear work. Finally, we give a communication-efficient, highly scalable approach to (weighted and unweighted) reservoir sampling. This algorithm is based on a fully distributed model of streaming algorithms that might be of independent interest. Experiments for alias tables and sampling with replacement show near linear speedups using up to 158 threads of shared-memory machines. An experimental evaluation of distributed weighted reservoir sampling on up to 5,120 cores also shows good speedups.
Evaluating Alarm Classifiers with High-confidence Data Programming
Authors: Pugh Sydney , Ruchkin Ivan , Bonafide Christopher , Demauro Sara , Sokolsky Oleg , Lee Insup , Weimer James
Published: July 2022

Classification of clinical alarms is at the heart of prioritization, suppression, integration, postponement, and other methods of mitigating alarm fatigue. Since these methods directly affect clinical care, alarm classifiers, such as intelligent suppression systems, need to be evaluated in terms of their sensitivity and specificity, which is typically calculated on a labeled dataset of alarms. Unfortunately, the collection and particularly labeling of such datasets requires substantial effort and time, thus deterring hospitals from investigating mitigations of alarm fatigue. This article develops a lightweight method for evaluating alarm classifiers without perfect alarm labels. The method relies on probabilistic labels obtained from data programming—a labeling paradigm based on combining noisy and cheap-to-obtain labeling heuristics. Based on these labels, the method produces confidence bounds for the sensitivity/specificity values from a hypothetical evaluation with manual labeling. Our experiments on five alarm datasets collected at Children’s Hospital of Philadelphia show that the proposed method provides accurate bounds on the classifier’s sensitivity/specificity, appropriately reflecting the uncertainty from noisy labeling and limited sample sizes.
Parameter Coverage for Testing of Autonomous Driving Systems under Uncertainty
Authors: Laurent Thomas , Klikovits Stefan , Arcaini Paolo , Ishikawa Fuyuki , Ventresque Anthony
Published: July 2022

Autonomous Driving Systems (ADSs) are promising, but must show they are secure and trustworthy before adoption. Simulation-based testing is a widely adopted approach, where the ADS is run in a simulated environment over specific scenarios. Coverage criteria specify what needs to be covered to consider the ADS sufficiently tested. However, existing criteria do not guarantee to exercise the different decisions that the ADS can make, which is essential to assess its correctness. ADSs usually compute their decisions using parameterised rule-based systems and cost functions, such as cost components or decision thresholds. In this article, we argue that the parameters characterise the decision process, as their values affect the ADS’s final decisions. Therefore, we propose parameter coverage, a criterion requiring to cover the ADS’s parameters. A scenario covers a parameter if changing its value leads to different simulation results, meaning it is relevant for the driving decisions made in the scenario. Since ADS simulators are slightly uncertain, we employ statistical methods to assess multiple simulation runs for execution difference and coverage. Experiments using the Autonomoose ADS show that the criterion discriminates between different scenarios and that the cost of computing coverage can be managed with suitable heuristics.
Black-box Safety Analysis and Retraining of DNNs based on Feature Extraction and Clustering
Authors: Attaoui Mohammed , Fahmy Hazem , Pastore Fabrizio , Briand Lionel
Published: July 2022

Deep neural networks (DNNs) have demonstrated superior performance over classical machine learning to support many features in safety-critical systems. Although DNNs are now widely used in such systems (e.g., self driving cars), there is limited progress regarding automated support for functional safety analysis in DNN-based systems. For example, the identification of root causes of errors, to enable both risk analysis and DNN retraining, remains an open problem. In this article, we propose SAFE, a black-box approach to automatically characterize the root causes of DNN errors. SAFE relies on a transfer learning model pre-trained on ImageNet to extract the features from error-inducing images. It then applies a density-based clustering algorithm to detect arbitrary shaped clusters of images modeling plausible causes of error. Last, clusters are used to effectively retrain and improve the DNN. The black-box nature of SAFE is motivated by our objective not to require changes or even access to the DNN internals to facilitate adoption.

Experimental results show the superior ability of SAFE in identifying different root causes of DNN errors based on case studies in the automotive domain. It also yields significant improvements in DNN accuracy after retraining, while saving significant execution time and memory when compared to alternatives.
A Decoupled Kernel Prediction Network Guided by Soft Mask for Single Image HDR Reconstruction
Authors: Cao Gaofeng , Zhou Fei , Liu Kanglin , Wang Anjie , Fan Leidong
Published: July 2022

Recent works on single image high dynamic range (HDR) reconstruction fail to hallucinate plausible textures, resulting in information missing and artifacts in large-scale under/over-exposed regions. In this article, a decoupled kernel prediction network is proposed to infer an HDR image from a low dynamic range (LDR) image. Specifically, we first adopt a simple module to generate a preliminary result, which can precisely estimate well-exposed HDR regions. Meanwhile, an encoder-decoder backbone network with a soft mask guidance module is presented to predict pixel-wise kernels, which is further convolved with the preliminary result to obtain the final HDR output. Instead of traditional kernels, our predicted kernels are decoupled along the spatial and channel dimensions. The advantages of our method are threefold at least. First, our model is guided by the soft mask so that it can focus on the most relevant information for under/over-exposed regions. Second, pixel-wise kernels are able to adaptively solve the different degradations for differently exposed regions. Third, decoupled kernels can avoid information redundancy across channels and reduce the solution space of our model. Thus, our method is able to hallucinate fine details in the under/over-exposed regions and renders visually pleasing results. Extensive experiments demonstrate that our model outperforms state-of-the-art ones.
The effects of biological sex and cardiovascular disease on COVID-19 mortality
Authors: Hernandez-Hernandez Maria Elena , Zee Robert Y. L. , Pulido-Perez Patricia , Torres-Rasgado Enrique , Romero Jose R.
Published: July 2022

CVD’s effect on COVID-19 mortality is dependent on biological sex and age. CVD in women but not men with COVID-19 is associated with significantly unfavorable outcomes.
Nanoscale Features of Gambogic Acid Induced ROS-Dependent Apoptosis in Esophageal Cancer Cells Imaged by Atomic Force Microscopy
Authors: Liu Jianxin , Fan Shuhao , Xiang Yinhong , Xia Jiaojiao , Jin Hua , Xu Jun-fa , Yang Fen , Cai Jiye , Pi Jiang
Published: July 2022

Gambogic acid (GA), a kind of polyprenylated xanthone derived from Garcinia hanburyi tree, has showed spectrum anticancer effects both in vitro and in vivo with low toxicity. However, up to now, there is little information about the effects of GA on esophageal cancer. In this study, we aim to test the anticancer effects of GA on esophageal cancer EC9706 cells. We established a nanoscale imaging method based on AFM to evaluate the reactive oxygen species- (ROS-) mediated anticancer effects of GA on esophageal cancer regarding the morphological and ultrastructural changes of esophageal cancer cells. The obtained results demonstrated that GA could inhibit cell proliferation, induce apoptosis, induce cell cycle arrest, and induce mitochondria membrane potential disruption in a ROS-dependent way. And using AFM imaging, we also found that GA could induce the damage of cellular morphology and increase of membrane height distribution and membrane roughness in EC9706 cells, which could be reversed by the removal of GA-induced excessive intracellular ROS. Our results not only demonstrated the anticancer effects of GA on EC9706 cells in ROS-dependent mechanism but also strongly suggested AFM as a powerful tool for the detection of ROS-mediated cancer cell apoptosis on the basis of imaging.
[Retracted] Segmentation of Oral Leukoplakia (OL) and Proliferative Verrucous Leukoplakia (PVL) Using Artificial Intelligence Techniques
Authors: Alshawwa Samar Zuhair , Saleh Asmaa , Hasan Malek , Shah Mohd Asif
Published: July 2022

PVL (proliferative verrucous leukoplakia) has distinct clinical characteristics. They have a proclivity for multifocality, a high recurrence rate after treatment, and malignant transformation, and they can progress to verrucous or squamous cell carcinoma. AI can aid in the diagnosis and prognosis of cancers and other diseases. Computational algorithms can spot tissue changes that a pathologist might overlook. This method is only used in a few studies to diagnose LB and PVL. To see if their cellular nuclei differed and if this cellular compartment could classify them, researchers used a computational system and a polynomial classifier to compare OLs and PVLs. 161 OL and 3 PVL specimens in the lab were grown, photographed, and used for training and computation. Exam orders revealed patients’ sociodemographics and clinical pathologies. The nucleus was segmented using Mask R‐CNN, and LB and PVL were classified using a polynomial classifier based on nucleus area, perimeter, eccentricity, orientation, solidity, entropies, and Moran Index (a measure of disorderliness). The majority of OL patients were male smokers; most PVL patients were female, with a third having malignant transformation. The neural network correctly identified cell nuclei 92.95% of the time. Except for solidity, 11 of the 13 nuclear characteristics compared between the PVL and the LB showed significant differences. The 97.6% under the curve of the polynomial classifier was used to classify the two lesions. These results demonstrate that computational methods can aid in diagnosing these two lesions.
A Deep Learning-Based Framework for Human Resource Recommendation
Authors: Ming Li
Published: July 2022

Recently, small- and medium-scale organizations have gained favorable responses due to the dramatic economic growth in China. This rise is due to international collaborations and social-economic growth by providing good quality and quantity services. However, when compared with other developing countries, small- and medium-scale organizations in China face many restrictions considering size and contributions. Few organizations are still facing a challenge because of the difficulties and lesser quality and also with lack of human resources. The prime objective of this study is to identify the current scenario of human resources for small- and medium-scale organizations, the factors affecting it, and the steps that can be effective in overcoming these challenges. In this study, human resource data is analyzed and managed using deep learning. The functionalities of human resources are realized by the deep learning approach, and further, business volume is reduced for enhancing the efficiency of human resources. The forecasting model is proposed and tested in human resource data by implementing a gradient descent process. Additionally, a deep neural network is implemented to enhance the accuracy of the proposed model. Experimental analysis is conducted by considering several neurons at the hidden layer, iteration count, and different types of decent gradient processes. The training accuracy and validation accuracy of the proposed model by implementing a deep neural network are observed as 95.67% and 94.53%. The experimental observations reveal the potential and significance of the proposed model.
Evaluating and Exploring the Effectiveness of Journalism and Communication Discipline Construction in the Context of Smart Era
Authors: Li Yang
Published: July 2022

In today’s intelligent era, the talent training mode of traditional journalism and communication discipline cannot keep up with the pace of the times. Under the background of intelligent era, the cultivation of compound talents in journalism and communication discipline is not only in line with the direction of education reform in journalism and communication discipline, but also in line with the objective requirements of economic and social development. Based on this, this paper puts forward three suggestions: improve the discipline layout of “smart age + journalism and communication discipline” and strengthen professional construction; enrich the textbook resources in the era of wisdom and promote smart teaching.
Bloom’s Classification of Educational Objectives Based on Deep Learning Theory Teaching Design of Nursing Specialty
Authors: zheng Jia , Tayag Jarrent , Cui Yingai , Chen Jiajun
Published: July 2022

Through the effective integration of various teaching resources, carry out the teaching design of “basic nursing technology” for the nursing specialty in the information environment based on Bloom’s education objective classification. Select 200 nursing students from two natural classes in the third year of undergraduate nursing major as the research objects with100 students in the experimental group and 100 students in the control group. The experimental group adopted Bloom’s objective classification teaching mode, and the control group adopted the traditional teaching model. They have carried out the teaching intervention to ensure the consistency of teachers, textbook use, teaching hours, and teaching progress between the two groups. The evaluation model of the teaching design results is the RBF neural network. The model improves the convergence ability of the RBF network by dynamically changing the control parameters of the L-M algorithm. RBF realizes the goal of the comprehensive analysis of course teaching effects evaluation and various influencing factors. The results showed that the differences in self-management ability, information ability, cooperation score, and total score of the experimental group were significantly higher than those of the control group, with statistical significance

P
<
0.01

. The teaching of Bloom’s educational objective model can enhance students’ subjective consciousness and effectively improve their independent learning ability. The learning effect is improved obviously. This paper studies the teaching approaches to improve nursing students’ comprehensive ability and humanistic care quality and provides a reference for deepening nursing undergraduate teaching reform.
Multivariate Statistical Analysis of Quality Improvement Effect of Innovation and Entrepreneurship Education Based on Random Matrix Theory
Authors: Wu Xiaohang , Qin Zhihua
Published: July 2022

The reform can better improve students’ innovation and entrepreneurial skills and comprehensively improve the innovation and entrepreneurial competitiveness of college students, which is conducive to their survival and development in the market. Therefore, this paper analyzes the data generated in the educational process from the perspective of educational data mining and studies students’ entrepreneurial interests and motivations so as to improve the quality of students’ entrepreneurial guidance. The knowledge map, an important visualization tool in personalized learning systems, is used as a research object to analyze students’ online evaluation data. We develop a prototype system for improving teaching quality—a knowledge map generation and knowledge point recommendation path analysis system. We hope to further explore new ways and methods to promote the reform of innovation and entrepreneurship education in colleges and universities, giving full play to the proper role and value of innovation and entrepreneurship education.
Identification of Six Genes as Diagnostic Markers for Colorectal Cancer Detection by Integrating Multiple Expression Profiles
Authors: Wu Peijie , Yang Xiuqing , Qiao Ling , Gong Yanju
Published: July 2022

Background. Many studies have demonstrated the promising utility of DNA methylation and miRNA as biomarkers for colorectal cancer (CRC) early detection. However, mRNA is rarely reported. This study aimed to identify novel fecal-based mRNA signatures. Methods. The differentially expressed genes (DEGs) were first determined between CRCs and matched normal samples by integrating multiple datasets. Then, Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to reduce the number of candidates of aberrantly expressed genes. Next, the potential functions were investigated for the candidate signatures and their ability to detect CRC and pan-cancers was comprehensively evaluated. Results. We identified 1841 common DEGs in two independent datasets. Functional enrichment analysis revealed they were mainly related to extracellular structure, biosynthesis, and cell adhesion. The CRC classifier was established based on six genes screened by LASSO regression. Sensitivity, specificity, and area under the ROC curve (AUC) for CRC detection were 79.30%, 80.40%, and 0.85 (0.76–0.92) in the training set, and these indexes achieved 93.20%, 41.80%, and 0.73 (0.65–0.83) in the testing set. For validation set, the sensitivity, specificity, and AUC were 98.90%, 98.00%, and 0.97 (0.94–0.99). The average sensitivities exceeded 90.00% for CRCs with different clinical features. For adenomas detection, the sensitivity and specificity were 74.50% and 64.00%. Besides, the six genes obtained an average AUC of 0.855 for pan-cancer detection. Conclusion. The six-gene signatures showed ability to detect CRC and pan-cancer samples, which could be served as potential diagnostic markers.
Cloud Computing to Tourism Economic Data Scheduling Algorithm under the Background of Image and Video
Authors: Zhang Heqing , Feng Yilin , Wang Leilei
Published: July 2022

With the rapid development of image video and tourism economy, tourism economic data are gradually becoming big data. Therefore, how to schedule between data has become a hot topic. This paper first summarizes the research results on image video, cloud computing, tourism economy, and data scheduling algorithms. Secondly, the origin, structure, development, and service types of cloud computing are expounded in detail. And in order to solve the problem of tourism economic data scheduling, this paper regards the completion time and cross-node transmission delay as the constraints of tourism economic data scheduling. The constraint model of data scheduling is established, the fitness function is improved on the basis of an artificial immune algorithm combined with the constraint model, and the directional recombination of excellent antibodies is carried out by using the advantages of gene recombination so as to obtain the optimal solution to the problem more appropriately. When the resource node scale is 100, the response time of EDSA is 107.92 seconds.
High-Capacity Data Hiding Method Based on Two Subgroup Pixels-Value Adjustment Using Encoding Function
Authors: Sun Jie , Yang ZhaoFang , Zhang Yu , Li Teng , Wang Sha
Published: July 2022

Confidential information can be hidden in digital images through data hiding technology. This has practical application value for copyright, intellectual property protection, public information protection, and so on. In recent years, researchers have proposed many schemes of data hiding. However, existed data hiding schemes suffer from low hiding capacity or poor stego-image quality. This paper uses a new method of multiple pixels-value adjustment with encoding function (MPA) to further improve the comprehensive performance, which is well in both hiding capacity and stego-image quality. The main idea is to divide n adjacent cover pixels into two sub-groups and implement multi-bit-based modulus operations in each group, respectively. The efficacy of this proposed is evaluated by peak signal-to-noise ratio (PSNR), embedding payload, structural similarity index (SSIM), and quality index (QI). The recorded PSNR value is 30.01 dB, and embedding payload is 5 bpp (bits per pixel). In addition, the steganalysis tests do not detect this steganography technique.
Vibration and Trajectory Tracking Control of Engineering Mechanical Arm Based on Neural Network
Authors: Lei Xinjun , Wu Yunxin
Published: July 2022

We offer a neural network-based control method to control the vibration of the engineering mechanical arm and the trajectory in order to solve the problem of large errors in tracking the path when the engineering mechanical arm is unstable and under the influence of the outside world. A mechanical arm network is used to perform tasks related to learning the unknown dynamic properties of a engineering mechanical arms keyboard without the need for prior learning. Given the dynamic equations of the engineering mechanical arm, the dynamic properties of the mechanical arm were studied using a positive feedback network. The adaptive neural network management system was developed, and the stability and integrity of the closed-loop system were proved by Lyapunov’s function. Engineering mechanical arm motion trajectory control errors were modeled and validated in the Matlab/Simulink environment. The simulation results show that the management of the adaptive neural network is able to better control the desired path of the engineering mechanical arm in the presence of external interference, and the fluctuation range of input torque is small. The PID control has a large error in the expected trajectory tracking of the engineering mechanical arm, the fluctuation range of the input torque is as high as 20, and the jitter phenomenon is more serious. The use of detailed comparisons and adaptive neural network monitoring can perform well in manipulating the trajectory of the engineering mechanical arm. The engineering mechanical arm uses an adaptive neural network control method, in which the control precision of engineering mechanical arm motion trajectory can be improved and the out-of-control phenomenon of mechanical arm motion can be reduced.
Adaptive Learning Model of English Vocabulary Based on Blockchain and Deep Learning
Authors: Li Jie
Published: July 2022

As one of the three basic elements of language, vocabulary is particularly important when learning English. Nowadays, many students are faced with problems such as boring vocabulary teaching, forgetting quickly after reciting, inefficient vocabulary learning, and lack of initiative. Therefore, it is imperative to seek a scientific and effective teaching mode to improve students’ vocabulary achievement. It builds an adaptive learning model of English vocabulary based on blockchain and deep learning, and tests the model. According to the experimental data, 46.8% of the learners will often read English newspapers or magazines to expand their English vocabulary. 50.4% of the students classified and memorized English vocabulary according to different categories. 59% of students often use context to memorize English vocabulary. The results show that before using the English vocabulary adaptive learning model based on blockchain and moderate learning, the average score of students’ methods and strategies is in the middle and lower levels. After using the adaptive learning model, some students have been able to use some learning strategies, and a small number of students have expanded their vocabulary through extracurricular reading.
Hip and Knee Joint Angles Determine Fatigue Onset during Quadriceps Neuromuscular Electrical Stimulation
Authors: Cavalcante Jonathan Galvão Tenório , de Almeida Ventura Álvaro , de Jesus Ferreira Leandro Gomes , de Sousa Alessandra Martins Melo , de Sousa Neto Ivo Vieira , de Cássia Marqueti Rita , Babault Nicolas , Durigan João Luiz Quagliotti
Published: July 2022

Neuromuscular electrical stimulation (NMES) has been used to increase muscle strength and physical function. However, NMES induces rapid fatigue, limiting its application. To date, the effect of quadriceps femoris (QF) muscle length by knee and hip joint manipulation on NMES-induced contraction fatigability is not clear. We aimed to quantify the effects of different muscle lengths on NMES-induced contraction fatigability, fatigue index, and electromyographic (EMG) activity for QF muscle. QF maximum evoked contraction (QMEC) was applied in a 26 min protocol (10 s on; 120 s off; 12 contractions) in 20 healthy participants (

24.0
±
4.6

years old), over 4 sessions on different days to test different conditions. The tested conditions were as follows: supine with knee flexion of 60° (SUP60), seated with knee flexion of 60° (SIT60), supine with knee flexion of 20° (SUP20), and seated with knee flexion of 20° (SIT20). Contraction fatigability (torque decline assessed by maximal voluntary contraction [MVC] and during NMES), fatigue index (percentage reduction in MVC), and EMG activity (root mean square [RMS] and median frequency) of the superficial QF’ constituents were assessed. After NMES, all positions except SUP20 had an absolute reduction in MVC (

p
<
.001

). Fatigue index was greater in SIT20 than in SIT60 (

p
<
.001

) and SUP20 (

p
=
.01

). There was significant torque reduction across the 12 QMEC in SUP60 and SIT60, up to 10.5% (

p
<
.001
–
.005

) and 9.49% (

p
<
.001
–
.033

), respectively. There was no torque reduction during NMES in SUP20 and SIT20. Fatigue was accompanied by an increase in RMS (

p
=
.032

) and a decrease in median frequency for SUP60 (

p
<
.001

). Median frequency increased only in the SUP20 condition (

p
=
.021

). We concluded that QF NMES-induced contraction fatigability is greater when the knee is flexed at 60° compared to 20°. In addition, a supine position promotes earlier fatigue for a 60° knee flexion, but it delays fatigue onset for a 20° knee flexion compared to the seated position. These results provide a rationale for lower limb positioning during NMES, which depends on training objectives, e.g., strengthening or task-specific functionality training.
Deep Collaborative Filtering: A Recommendation Method for Crowdfunding Project Based on the Integration of Deep Neural Network and Collaborative Filtering
Authors: Yin Pei , Wang Jing , Zhao Jun , Wang Huan , Gan Hongcheng
Published: July 2022

In real recommendation systems, implicit feedback data is more common and easier to obtain, and recommendation algorithms based on such data will be more applicable. However, implicit feedback data cannot directly express user preferences. Meanwhile, data sparsity caused by massive data is still an urgent problem to be solved in recommendation system. In response to this phenomenon, this paper proposes a deep collaborative filtering algorithm. In the perspective of implicit feedback, this method uses the advantages of convolutional neural network for effective learning of the nonlinear interaction of users and items and the characteristics of collaborative filtering algorithm for modeling the linear interaction of users and items and combines the two methods for recommendation. Finally, the baseline method is set up and the comparative experiment and parameter adjustment is carried out. The experimental results show that the proposed algorithm has significantly improved the recommendation accuracy on public dataset (Yahoo! Movie). The parameter adjustment results show that, under the condition of uniformly collecting negative feedback data and setting a certain number of convolution layers, the sparser the data is, the better the recommendation performs. As a result, this paper has made some progress in solving the problem of data sparsity and enriching the research of recommendation system.
Analysis of Improving Effect of Running APP on College Students’ Physique by Using Student Data Mining Technology
Authors: Zhuo Ni
Published: July 2022

With the continuous development of sports software, college students, as the most advanced social new ideological group, running APP gradually enter the study and life of college students. Based on students’ DM (data mining), this paper analyzes the influence of running apps on the improvement of college students’ physique, chooses DT (decision tree) algorithm to construct the structure according to the characteristics of the data used, obtains students’ DM model, and prunes it by using substitution error rate and PEP (pessimistic error pruning). The results show that the results of intra-group comparison among boys show that the scores of 1000 m in the intervention group have no obvious change compared with those before the intervention, and the difference is not statistically significant (

P
=
0.516

). The 800 m scores of girls in the intervention group were better than those in the control group, and the difference was statistically significant (

P
=
0.03

). The results of intra-group comparison showed that there was no significant difference in the scores of the intervention group before and after the intervention, and the difference was not statistically significant (

P
=
0.32

). After the experiment, the vital capacity scores of boys and girls in APP intervention group and control group were improved, with statistical significance (

P
<
0.05

). The conclusion shows that running APP can improve students’ speed level, cultivate students’ endurance level, and improve students’ physical health.
Optimization and Quality Evaluation of Online Teaching Courses Based on Machine Learning
Authors: Li Siwen , Shi Mufan
Published: July 2022

Due to the epidemic, online courses have become an important form of school courses, so it is very necessary to optimize online teaching courses. This paper analyzes the survey data by using factor analysis and ANP model and finally determines 18 evaluation indicators by scoring the evaluation indicators by the respondents. The analysis of these 18 indicators shows that the average score of goal setting is the highest, and the average score of interface design is the highest. Lowest: the low-scoring portion of the course interaction is the most important aspect of developing a suggested strategy, and setting the environment, stimulating interest, interface design, and performance evaluation are also important factors in improving the quality of online courses. It can be seen that teacher-student interaction, media presentation, and interest stimulation are three more important factors, and these three factors are relatively less important for performance evaluation, course duration, and language level. It can be seen that learners pay relatively low attention to performance evaluation and pay the highest attention to intelligent learning. These indicators with high attention are improved to optimize online teaching courses.