Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Application of Online Courses under the Threshold of Smart Innovation Education

— With the rapid development of the Internet and the growing demand for education, a new online teaching mode, massive open online courses (MOOC), emerged in 2012. To address the problems of sparse data and poor recommendation effect in online course recommendation, this paper introduces deep learning into course recommendation and proposes an auxiliary information-based neural network model (IUNeu), on the basis of which a collaborative neural network filtering model (FIUNeu) is obtained by improving it. Firstly, the principles and technical details of the deep learning base model are studied in depth to provide technical support for course recommendation models and online learning recommendation systems. In this paper, based on the existing neural matrix decomposition model (NeuMF), we combine user information and course information and consider the interaction relationship between them to improve the accuracy of the model to represent users and courses. The neural network model of auxiliary information (IUNeu) is incorporated into the online learning platform, and the system development is completed with the design of front and back-end separation, realizing the functions of the online learning module, course collection module, course recommendation module, and resource download module. Finally, the experimental results are analyzed: under the same experimental conditions, the test experiments are repeated 10 times, and the RMSE calculation results are averaged. The RMSE value of the neural network collaborative filtering model (FIUNeu) proposed in this paper based on deep learning is 0.85517, which is the best performance and has a high accuracy rate of rating prediction, and is useful for alleviating the data sparsity problem.
Treatment Effect Estimation to Guide Model Optimization in Continual Learning

Continual Learning systems are faced with a potentially large numbers of tasks to be learned while the models employed have only limited capacity available, which makes it potentially impossible to learn all required tasks within a single model. In order to detect on when a model might break we propose to use treatment effect estimation techniques to estimate the effect of training a model on a new task w.r. t. some suitable performance measure
TrendsHigh-Risk Plaque Features and Emerging Atherosclerosis: Updated Evidence on MR Imaging of Carotid Artery

SUMMARY: MR imaging is well-established as the criterion standard for carotid artery atherosclerosis imaging. The capability of MR imaging to differentiate numerous plaque components has been demonstrated, including those features that are associated with a high risk of sudden changes, thrombosis, or embolization. The ﬁ eld of carotid plaque MR imaging is constantly evolving, with continued insight into the imaging appearance and implications of various vulnerable plaque characteristics. This article will review the most up-to-date knowledge of these high-risk plaque features on MR imaging and will delve into 2 major emerging topics: the role of vulnerable plaques in cryptogenic strokes and the potential use of MR imaging to modify carotid endarterectomy treatment guidelines.
Deep Learning-Based Technique for The Perception of The Cervical Cancer

In third-world countries, cervical cancer is the most prevalent and leading cause of death. It is affected by a variety of factors, including smoking, poor nutritional status, immunological inadequacy, and prolonged use of contraception. The Pap smear test, which is intended to prevent cervical cancer, ﬁ nds preneoplastic changes in cervical epithelial cells. This study framework classi ﬁ ed cervical cancer cells from Pap smears into ﬁ ve speci ﬁ ed cell types using machine learning-based classi ﬁ cation algorithms. The SIPaKMeD database is used in this investigation. This public dataset, which was manually cropped from 966 cluster cell images taken from Pap smear slides, has 4045 isolated Pap smear cells. Depending on their cellular form and structure, experts categorize the cells into ﬁ ve distinct types which are super ﬁ cial-intermediate, Parabasal, Koilocytotic, Dyskeratotic, and Metaplastic cells. Introducing a pipeline to improve algorithm accuracy and easy implementation by using a speci ﬁ ed feature extractor and conducting a suitable preprocessing pipeline is the main contribution. The study reached the conclusion that machine learning could improve Pap smear screening classi ﬁ cation results. We concluded that the support vector machine (SVM) is the most suitable algorithm for this application. The SVM has the highest accuracy of 0.968, the Neural Network (NN) at 0.958, and the (K-Nearest Neighbor) KNN at 0.941. These results support the proposed framework as a reliable classi ﬁ cation diagnostic tool.
Fabrication and Investigation of Graphite-Flake-Composite-Based Non-Invasive Flex Multi-Functional Force, Acceleration, and Thermal Sensor

This work examines the physics of a non-invasive multi-functional elastic thin-film graphite flake–isoprene sulfone composite sensor. The strain design and electrical characterization of the stretching force, acceleration, and temperature were performed. The rub-in technique was used to fabricate graphite flakes and isoprene sulfone into sensors, which were then analyzed for their morphology using methods such as SEM, AFM, X-ray diffraction, and Fourier transform infrared spectroscopy to examine the device’s surface and structure. Sensor impedance was measured from DC to 200 kHz at up to 20 gf, 20 m/s2, and 26–60 °C. Sensor resistance and impedance to stretching force and acceleration at DC and 200 Hz rose 2.4- and 2.6-fold and 2.01- and 2.06-fold, respectively. Temperature-measuring devices demonstrated 2.65- and 2.8-fold decreases in resistance and impedance at DC and 200 kHz, respectively. First, altering the graphite flake composite particle spacing may modify electronic parameters in the suggested multi-functional sensors under stress and acceleration. Second, the temperature impacts particle and isoprene sulfone properties. Due to their fabrication using an inexpensive deposition technique, these devices are environmentally friendly, are simple to build, and may be used in university research in international poverty-line nations. In scientific laboratories, such devices can be used to teach students how various materials respond to varying environmental circumstances. They may also monitor individuals undergoing physiotherapy and vibrating surfaces in a controlled setting to prevent public health risks.
A Novel Standardized Method for Aiding to Determine Left Atrial Enlargement on Lateral Thoracic Radiographs in Dogs

Simple Summary Measuring left atrial size has important prognostic and therapeutic consequences in cardiac diseases. An enlarged left atrium indicates that the cardiac disease is severe. Though cardiac ultrasound examination is the best way to determine left atrial size, this technique is often unavailable in veterinary practices, as it is expensive and requires specific expertise. Therefore, chest X-rays are usually used to decide whether the left atrium is enlarged. With that, practicing veterinarians aim to differentiate cardiac from respiratory diseases, which both can lead to similar clinical signs. However, determining left atrial enlargement on X-rays can be challenging. The authors of this study came up with a simple and reproducible method that might make determining left atrial size on X-rays easier. The study aimed to compare two methods. Dogs with and without cardiac disease were included. The left atrial size of the included dogs had also been determined with ultrasonography to serve as a reference. First, 39 veterinarians and veterinary students interpreted 90 canine chest X-rays subjectively. At least two weeks later, the same observers applied the new method on the same radiographs. The new standardized method did not give a more accurate diagnosis than subjective assessment. Abstract Background: Left atrial enlargement indicates severe cardiac disease. Although the gold standard for determining left atrial size is echocardiography, many veterinary practices lack the necessary equipment and expertise. Therefore, thoracic radiography is often used to differentiate cardiogenic pulmonary edema from primary respiratory diseases and to facilitate distinguishing dogs with stage B1 and B2 mitral valve degeneration. Methods: The goal was to test a new standardized method for identifying radiographic left atrial enlargement. On a lateral radiograph, a straight line was drawn from the dorsal border of the tracheal bifurcation to the crossing point of the dorsal border of the caudal vena cava and the most cranial crus of the diaphragm. If a part of the left atrium extended this line dorsally, it was considered enlarged. Echocardiographic left atrial to aortic ratio (LA:Ao) was used as a reference. Thirty-nine observers with various levels of experience evaluated 90 radiographs, first subjectively, then applying the new method. Results: The new method moderately correlated with LA:Ao (r = 0.56–0.66) in all groups. The diagnostic accuracy (72–74%) of the subjective assessment and the new method showed no difference. Conclusions: Though the new method was not superior to subjective assessment, it may facilitate learning and subjective interpretation.
User2Vec: A Novel Representation for the Information of the Social Networks for Stock Market Prediction Using Convolutional and Recurrent Neural Networks

Predicting stock market trends is an intriguing and complex problem, which has drawn considerable attention from the research community. In recent years, researchers have employed machine learning techniques to develop prediction models by using numerical market data and textual messages on social networks as their primary sources of information. In this article, we propose User2Vec, a novel approach to improve stock market prediction accuracy, which contributes to more informed investment decision making. User2Vec is a unique method that recognizes the unequal impact of different user opinions on specific stocks, and it assigns weights to these opinions based on the accuracy of their associated social metrics. The User2Vec model begins by encoding each message as a vector. These vectors are then fed into a convolutional neural network (CNN) to generate an aggregated feature vector. Following this, a stacked bi-directional long short-term memory (LSTM) model provides the final representation of the input data over a period. LSTM-based models have shown promising results by effectively capturing the temporal patterns in time series market data. Finally, the output is fed into a classifier that predicts the trend of the target stock price for the next day. In contrast to previous attempts, User2Vec considers not only the sentiment of the messages, but also the social information associated with the users and the text content of the messages. It has been empirically proven that this inclusion provides valuable information for predicting stock direction, thereby significantly enhancing prediction accuracy. The proposed model was rigorously evaluated using various combinations of market data, encoded messages, and social features. The empirical studies conducted on the Dow Jones 30 stock market showed the model’s superiority over existing state-of-the-art models. The findings of these experiments reveal that including social information about users and their tweets, in addition to the sentiment and textual content of their messages, significantly improves the accuracy of stock market prediction.
Nanoparticle-Based Adjuvants and Delivery Systems for Modern Vaccines

Ever since the development of the first vaccine, vaccination has had the great impact on global health, leading to the decrease in the burden of numerous infectious diseases. However, there is a constant need to improve existing vaccines and develop new vaccination strategies and vaccine platforms that induce a broader immune response compared to traditional vaccines. Modern vaccines tend to rely on certain nanotechnology platforms but are still expected to be readily available and easy for large-scale manufacturing and to induce a durable immune response. In this review, we present an overview of the most promising nanoadjuvants and nanoparticulate delivery systems and discuss their benefits from tehchnological and immunological standpoints as well as their objective drawbacks and possible side effects. The presented nano alums, silica and clay nanoparticles, nanoemulsions, adenoviral-vectored systems, adeno-associated viral vectors, vesicular stomatitis viral vectors, lentiviral vectors, virus-like particles (including bacteriophage-based ones) and virosomes indicate that vaccine developers can now choose different adjuvants and/or delivery systems as per the requirement, specific to combatting different infectious diseases.
MOTO: Offline to Online Fine-tuning for Model-Based Reinforcement Learning

We study the problem of offline-to-online reinforcement learning from high-dimensional pixel observations. While recent model-free approaches successfully use offline pre-training with online fine-tuning to either improve the performance of the data-collection policy or adapt to novel tasks, model-based approaches still remain underutilized in this setting. In this work, we argue that existing methods for high-dimensional model-based offline RL are not suitable for offline-to-online fine-tuning due to issues with representation learning shifts, off-dynamics data, and non-stationary rewards. We propose a simple on-policy model-based method with adaptive behavior regularization. In our simulation experiments, we find that our approach successfully solves long-horizon robot manipulation tasks completely from images by using a combination of offline data and online interactions.
An GNSS/INS Integrated Navigation Algorithm Based on PSO-LSTM in Satellite Rejection

When the satellite signal is lost or interfered with, the traditional GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System) integrated navigation will degenerate into INS, which results in the decrease in navigation accuracy. To solve these problems, this paper mainly established the PSO (particle swarm optimization) -LSTM (Long Short-Term Memory) neural network model to predict the increment of GNSS position under the condition of satellite rejection and accumulation to obtain the pseudo-GNSS signal. The signal is used to compensate for the observed value in the integrated system. The model takes the advantages of LSTM, which is good at processing time series, and uses PSO to obtain the optimal value of important hyperparameters efficiently. Meanwhile, the improved threshold function is used to denoise the IMU (inertial measurement unit) data, which improves the SNR (signal-to-noise ratio) of IMU outputs effectively. Finally, the performance of the algorithm is proved by actual road test. Compared with INS, the method can reduce the maximum errors of latitude and longitude by at least 98.78% and 99.10% while the satellite is lost for 60 s, effectively improving the accuracy of the GNSS/INS system in satellite rejection.
Focus-MOT: Multi-Target Tracking Detection Algorithm with Fine-Grained Feature Extraction Aggregation

This work proposes a multi-target tracking and detection algorithm Focus-MOT based on feature refinement extraction fusion, t through the designed Field Enhancement Refinement Module and Information Aggregation Module, which effectively reduces the number of target ID switching. Jointly learns the Detector and Embedding model method becomes the mainstream of multi-target tracking and detection due to its fast detection speed, its Re-ID branch needs to use low-dimensional features and high-dimensional features to accommodate both large and small targets, however, its insufficient feature extraction leads to high ID_SW. Therefor this work aims to extract features of different levels for aggregation as a way to reduce the number of ID switching. The experimental results show a 2.7% improvement in MOTA and a 2300 times decrease in ID_SW relative to the results of the FairMOT algorithm on the MOT17 dataset.
Cell Segmenter: A General Framework for Multi-modality Cell Segmentation

Cell Segmentation is an initial and fundamental step in biomedical image analysis, which strongly affects the experimental results of this analysis. Recently, deep learning based segmentation methods have shown great power in segmentation accuracy and efficiency. However, these data-driven methods still face many challenges, such as lack of annotations, multi-modality, and complex morphology , where morphological complexity significantly limits model performance. In this paper, we propose a new all-purpose framework with high morphological adaptability for multi-modality cell segmentation, termed Cell Segmenter (CS). For high convex cells with an arbitrary size, the Anchor-based Watershed Framework (AWF) precisely locates well-defined cell centers and generates segmentation based on these markers. For those elongated or non-convex cells, the center-independent segmentation method Omnipose [1] is adopted to obtain satisfying masks. In the inference time, confidence-based quality estimation is conducted on the branch predictions if needed, and then the better result is chosen as the final segmentation. The F1-score of the proposed method reaches 0.8537 on TuningSet and 0.6216 on the final test set of the NeurIPS 2022 Cell Segmentation Challenge.
Hierarchical Convolutional Neural Networks using CCP-3 Block Architecture for Apparel Image Classification

—In fashion applications, deep learning has been applied automatically to recognize and classify the apparel images under the massive visual data, emerged on social networks. To classify the apparel correctly and quickly is challenging due to a variety of apparel features and complexity of the classification. Recently, the hierarchical convolutional neural networks (H–CNN) with the VGGNet architecture was proposed to classify the fashion-MNIST datasets. However, the VGGNet (many layers) required many filters (in the convolution layer) and many neurons (in the fully connected layer), leading to computational complexity and long training-time. Therefore, this paper proposes to classify the apparel images by the H–CNN in cooperated with the new shallow-layer CCP-3-Block architecture, where each building block consists of two convolutional layers (CC) and one pooling layer (P). In the CCP-3-Block, the number of layers can be reduced (in the network), the number of filters (in the convolution layer), and the number of neurons (in the fully connected layer), while adding a new connection between the convolution layer and the pooling layer plus a batch-normalization technique before passing the activation so that networks can learn independently and train quickly. Moreover, dropout techniques were utilized in the feature mapping and fully connected to reduce overfitting, and the optimizer adaptive moment estimation was utilized to solve the decaying of gradients, which can improve the network-performance. The experimental results showed that the improved H–CNN model with our CCP-3-Block outperformed the recent H–CNN model with the VGGNet in terms of decreased loss, increased accuracy, and faster training
End-to-End Real-time Architecture for Fraud Detection in Online Digital Transactions

—The banking sector is witnessing a fierce concurrence characterized by changing business models, new entrants such as FinTechs, and new customer behaviors. Financial institutions try to adapt to this trend and invent new ways and channels to reach and interact with their customers. While banks are opening their services to avoid missing this shift, they become naturally exposed to fraud attempts through their digital banking platforms. Therefore, fraud prevention and detection are considered must-have capabilities. Detecting fraud at an optimal time requires developing and deploying scalable learning systems capable of ingesting and analyzing vast volumes of streaming records. Current improvements in data analytics algorithms and the advent of open-source technologies for big data processing and storage bring up novel avenues for fraud identification. In this article, we provide a real-time architecture for detecting transactional fraud via behavioral analysis that incorporates big data analysis techniques such as Spark, Kafka, and h2o with an unsupervised machine learning (ML) algorithm named Isolation Forest. The results of experiments on a significant dataset of digital transactions indicate that this architecture is robust, effective, and reliable across a large set of transactions yielding 99% of accuracy, and a precision of 87%.
Q-factors of CVD monolayer graphene and graphite inductors

A carbon-based inductor may serve as an important passive component in a carbon-based radio-frequency (RF) integrated circuit (IC). In this work, chemical vapor deposition (CVD) synthesized monolayer graphene and graphite inductors are fabricated and their Q-factors are investigated. We find that the large series resistance of signal path (including coil resistance and contact resistance) in monolayer graphene inductors causes negative Q-factors at the whole frequency range in measurement. Comparatively, some of the graphite inductors have all of their Q-factors above zero, due to their small signal path resistance. We also note that some other graphite inductors have negative Q-factor values at low frequency regions, but positive Q-factor values at high frequency regions. With an equivalent circuit model, we confirm that the negative Q-factors of some graphite inductors at low frequency regions are related to their relatively large contact resistances, and we are able to eliminate these negative Q-factors by improving the graphite-metal contact. Furthermore, the peak Q-factor (Qp) can be enhanced by lowering down the resistance of graphite coil. For an optimized 3/4-turn graphite inductor, the measured maximum Q-factor (Qm) can reach 2.36 and the peak Q-factor is theoretically predicted by the equivalent circuit to be as high as 6.46 at a high resonant frequency, which is beyond the testing frequency range. This research indicates that CVD synthesized graphite thin film is more suitable than graphene for fabricating inductors in carbon-based RF IC in the future.
Out-of-the-loop Autotuning of Metropolis Light Transport with Reciprocal Probability Binning

The performance of Markov Chain Monte Carlo (MCMC) rendering methods depends heavily on the mutation strategies and their parameters. We treat the underlying mutation strategies as black-boxes and focus on their parameters. This avoids the need for tedious manual parameter tuning and enables automatic adaptation to the actual scene. We propose a framework for out-of-the-loop autotuning of these parameters. As a pilot example, we demonstrate our tuning strategy for small-step mutations in Primary Sample Space Metropolis Light Transport. Our σ -binning strategy introduces a set of mutation parameters chosen by a heuristic: the inverse probability of the local direction sampling, which captures some characteristics of the local sampling. We show that our approach can successfully control the parameters and achieve better performance compared to non-adaptive mutation strategies.
Fast Procedural Noise By Monte Carlo Sampling

Procedural noise functions are widely used in computer graphics as a way to add texture detail to surfaces and volumes. Many noise functions are based on weighted sums that can be expressed in terms of random variables, which makes it possible to compute Monte Carlo estimates of their values at lower cost. Such stochastic noise functions ﬁt naturally into many Monte Carlo estimators already used in rendering. Leveraging the dense image-plane sampling in modern path tracing renderers, we show that stochastic evaluation allows the use of procedural noise at a fraction of its full cost with little additional error