Identification and Modeling of College Students’ Psychological Stress Indicators for Deep Learning

Aiming at the problems of low accuracy of recognition results, long recognition time, and easy interference in traditional methods, a deep learning-oriented recognition modeling method of college students' psychological stress indicators is proposed. First, the ECG signal is collected by the ECG signal acquisition system, and the wavelet transform method is used to denoise the collected ECG signal. Then, the sequential backward selection algorithm is used to select the features of psychological stress indicators to reduce the feature dimension. Finally, based on the convolutional neural network in deep learning technology, a mental pressure indicator recognition model is established and the model parameters are optimized to realize the recognition of college students’ mental pressure indicators. Experimental results show that the method in this paper has high recognition accuracy, has high recognition efficiency, is not susceptible to interference, and has certain feasibility and effectiveness.

Simulating the Influence of Supporting Electrolyte Concentration on Copper Electrodeposition in Microvias

Robust, void-free Cu electrodeposition in high-aspect ratio features relies on careful tuning of electrolyte additives, concentrations, and electrochemical parameters for a given feature dimension or wafer pattern. Typically, Cu electrodeposition in electronics manufacturing of microscale or larger features (i.e., microvias, through-holes, and high-density interconnects) employs a CuSO4 – H2SO4 electrolyte containing millimolar levels of chloride and, at a minimum, micromolar levels of a polyether suppressor. Research and optimization efforts have largely focused on the relationship between electrolyte additives and growth morphology, with less attention given to the impact of supporting electrolyte. Accordingly, a computational study exploring the influence of supporting electrolyte on Cu electrodeposition in microvias is presented herein. The model builds upon prior experimental and computational research on localized Cu deposition by incorporating the full charge conservation equation with electroneutrality to describe potential variation in the presence of ionic gradients. In accord with experimental observations, simulations predict enhanced current localization to the microvia bottom as H2SO4 concentration is decreased. This phenomenon derives from enhanced electromigration within recessed features that accompanies the decrease of conductivity with local metal ion depletion. This beneficial aspect of low acid electrolytes is also impacted by feature density, CuSO4 concentration, and the extent of convection.

Prediction of cell penetrating peptides and their uptake efficiency using random forest-based feature selections

Cell penetrating peptides (CPPs) are short peptides that can carry biomolecules of varying sizes across the cell membrane into the cytoplasm. Correctly identifying CPPs is the basis for studying their functions and mechanisms. Here, we propose a novel CPP predictor that is able to predict CPPs and their uptake efficiency. In our method, five feature descriptors are applied to encode the sequence and compose a hybrid feature vector. Afterward, the wrapper + random forest algorithm is employed, which combines feature selection with the prediction process to find features that are crucial for identifying CPPs. The jackknife cross validation result shows that our predictor is comparable to state-of-the-art CPP predictors, and our method reduces the feature dimension, which improves computational efficiency and avoids overfitting, allowing our predictor to be adopted to identify large-scale CPP data.

Automatic Feature Engineering-Based Optimization Method for Car Loan Fraud Detection

Fraud detection is one of the core issues of loan risk control, which aims to detect fraudulent loan applications and safeguard the property of both individuals and organizations. Because of its close relevance to the security of financial operations, fraud detection has received widespread attention from industry. In recent years, with the rapid development of artificial intelligence technology, an automatic feature engineering method that can help to generate features has been applied to fraud detection with good results. However, in car loan fraud detection, the existing methods do not satisfy the requirements because of overreliance on behavioral features. To tackle this issue, this paper proposed an optimized deep feature synthesis (DFS) method in the automatic feature engineering scheme to improve the car loan fraud detection. Problems like feature dimension explosion, low interpretability, long training time, and low detection accuracy are solved by compressing abstract and uninterpretable features to limit the depth of DFS algorithm. Experiments are developed based on actual car loan credit database to evaluate the performance of the proposed scheme. Compared with traditional automatic feature engineering methods, the number of features and training time are reduced by 92.5% and 54.3%, respectively, whereas accuracy is improved by 23%. The experiment demonstrates that our scheme effectively improved the existing automatic feature engineering car loan fraud detection methods.

Analysis of Feature Dimension Reduction Techniques Applied on the Prediction of Impact Force in Sports Climbing Based on IMU Data

Sports climbing has grown as a competitive sport over the last decades. This has leading to an increasing interest in guaranteeing the safety of the climber. In particular, operational errors, caused by the belayer, are one of the major issues leading to severe injuries. The objective of this study is to analyze and predict the severity of a pendulum fall based on the movement information from the belayer alone. Therefore, the impact force served as a reference. It was extracted using an Inertial Measurement Unit (IMU) on the climber. Additionally, another IMU was attached to the belayer, from which several hand-crafted features were explored. As this led to a high dimensional feature space, dimension reduction techniques were required to improve the performance. We were able to predict the impact force with a median error of about 4.96%. Pre-defined windows as well as the applied feature dimension reduction techniques allowed for a meaningful interpretation of the results. The belayer was able to reduce the impact force, which is acting onto the climber, by over 30%. So, a monitoring system in a training center could improve the skills of a belayer and hence alleviate the severity of the injuries.

Optimizing Network Anomaly Detection Based on Network Traffic

Cyber-attack is a very hot topic today. Nowadays, systems must always be connected to the internet, and network infrastructure keeps growing in both scale and complexity. Therefore, the problem of detecting and warning cyber-attacks is now very urgent. To improve the effectiveness of detecting cyber-attacks, many methods and techniques were applied. In this paper, we propose to apply two methods of optimizing cyber-attack detection based on the IDS 2018 dataset using Principal Component Analysis (PCA) and machine learning algorithms. In the experimental section, we compare and evaluate the efficiency of the algorithm through 2 parameters: detection and processing time, and the accuracy of the algorithm. The experimental results show that the model using optimized features has brought an apparent and better effect than models that have not reduced the feature dimension. Keywords— PCA; Network traffic; Anomaly; Cyberattack detection.

EXPRESS: Object-based Encoding in Visual Working Memory: A Critical Revisit

Visual working memory (VWM) is responsible for the temporal retention and manipulation of visual information. It has been suggested that VWM employs an object-based encoding (OBE) manner to extract highly-discriminable information from visual perception: Whenever one feature dimension of the objects is selected for entry into VWM, the other task-irrelevant highly-discriminable dimension is also extracted into VWM involuntarily. However, the task-irrelevant feature in OBE studies might reflect a high capacity fragile VWM trace (FVWM for short) that stores maskable sensory representations. To directly compare the VWM storage hypothesis and the FVWM storage hypothesis, we used a unique characteristic of FVWM that the representations in FVWM could be erased by backward masks presented at the original locations of the memory array. We required participants to memorize the orientations of three colored bars while ignoring their colors, and presented backward masks during the VWM maintenance interval. In four experiments, we consistently observed that the OBE occurs regardless of the presentation of the backward masks, except when even the task-relevant features in VWM were significantly interrupted by immediate backward masks, suggesting that the task-irrelevant features of objects are stored in VWM rather than in FVWM.

Impact of Kernel-PCA on Different Features for Person Re-Identification

In the driving field of computer vision, re-identification of an individual in a camera network is very challenging task. Existing methods mainly focus on strategies based on feature learning, which provide feature space and force the same person to be closer than separate individuals. These methods rely to a large extent on high-dimensional feature vectors to achieve high re-identification accuracy. Due to computational cost and efficiency, they are difficult to achieve in practical applications. We comprehensively analyzed the effect of kernel-based principal component analysis (PCA) on some existing high-dimensional person re-identification feature extractors to solve these problems. We initially formulate a kernel function on the extracted features and then apply PCA, significantly reducing the feature dimension. After that, we have proved that the kernel is very effective on different state-of-the-art high-dimensional feature descriptors. Finally, a thorough experimental evaluation of the reference person re-identification data set determined that the prediction method was significantly superior to more advanced techniques and computationally feasible.

Prediction Poverty Levels of College Students Using a Machine Learning Model

Nowadays, poverty-stricken college students have become a special group among the college students and occupied higher proportion in it. How to accurately identify poverty levels of college students and provide funding is a new problem for universities. In this manuscript, a novel model that combined Random Forest with Principle Components Analysis (RF-PCA) is proposed prediction poverty levels of college students. To build this model, data was firstly collected to establish datasets including 4 classed of poverty levels and 21 features of poverty-stricken college students. Then, feature dimension reduction includes two steps: the first step we selected the top 16 features with the ranking of feature, according to the Gini importance and Shapley Additive explanations (SHAP) values of features based on Random Forest (RF); the second step of feature extraction through Principle Components Analysis (PCA) extracted 11 dimensions. Finally, confusion metrics and receiver operating characteristic (ROC) curves were used to evaluate the performance of the proposed model, the accuracy of the model achieved 78.61%. Furthermore, compared with seven different classification algorithms, the model has a higher prediction accuracy, the result has great potential to identify the poverty levels of college students.

Gain-Loss Evaluation-Based Generic Selection for Steganalysis Feature

Fewer contribution feature components in the image high-dimensional steganalysis feature are able to increase the spatio-temporal complexity of detecting the stego images, and even reduce the detection accuracy. In order to maintain or even improve the detection accuracy while effectively reducing the dimension of the DCTR steganalysis feature, this paper proposes a new selection approach for DCTR feature. First, the asymmetric distortion factor and information gain ratio of each feature component are improved to measure the difference between the symmetric cover and stego features, which provides the theoretical basis for selecting the feature components that contribute to a great degree to detecting the stego images. Additionally, the feature components are arranged in descending order rely on the two measurement criteria, which provides the basis for deleting the components. Based on the above, removing feature components that are ranked larger differently according to two criteria. Ultimately, the preserved feature components are used as the final selected feature for training and detection. Comparison experiments with existing classical approaches indicate that this approach can effectively reduce the feature dimension while maintaining or even improving the detection accuracy. At the same time, it can reduce the detection spatio-temporal complexity of the stego images.