Subjects: Computer Science >> Computer Application Technology submitted time 2024-08-06
Abstract: Aiming at forecasting whether a student can graduate successfully or dropout, a stacking-based ensemble model is proposed to solve this issue. The model considers universal inherit and object factors, including student’s family background, academic performance and environment influence. We use BernoulliNB and HistGradientBoost to build base-learner, with Logistics regression as meta-learner to integrate classification, compared it with benchmark models after finished training. The experimental result shows that the accuracy of the model reaches 93.9%, which is 7.99%, 6.61%, 2.20% and 2.47% higher than that of K-proximity algorithm, decision tree model, random forest and lightgbm model, respectively. The F1 score have reach 0.95, indicating that the model is stable and has high application value in students’ academic prediction as same.
Subjects: Computer Science >> Computer Application Technology submitted time 2024-08-05
Abstract: The recent wave of foundation models has witnessed tremendous success in computer vision (CV) and beyond, with the segment anything model (SAM) having sparked a passion for exploring task-agnostic visual foundation models. Empowered by its remarkable zero-shot generalization, SAM is currently challenging numerous traditional paradigms in CV, delivering extraordinary performance not only in various image segmentation and multi-modal segmentation (e.g., text-to-mask) tasks, but also in the video domain. Additionally, the latest released SAM 2 is once again sparking research enthusiasm in the realm of promptable visual segmentation for both images and videos. However, existing surveys mainly focus on SAM in various image processing tasks, a comprehensive and in-depth review in the video domain is notably absent. To address this gap, this work conducts a systematic review on SAM for videos in the era of foundation models. As the first to review the progress of SAM for videos, this work focuses on its applications to various tasks by discussing its recent advances, and innovation opportunities of developing foundation models on broad applications. We begin with a brief introduction to the background of SAM and video-related research domains. Subsequently, we present a systematic taxonomy that categorizes existing methods into three key areas: video understanding, video generation, and video editing, analyzing and summarizing their advantages and limitations. Furthermore, comparative results of SAM-based and current state-of-the-art methods on representative benchmarks, as well as insightful analysis are offered. Finally, we discuss the challenges faced by current research and envision several future research directions in the field of SAM for video and beyond.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Cognitive Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2024-08-02
Abstract: Cognitive modeling involves instantiating theoretical or model-based knowledge of cognitive processes into computational models and validating these theories by fitting behavioral and/or neuroimaging data. It enhances our understanding of human cognition through quantitative analysis and aids in the study of individual differences. Despite the ability of theory-driven computational models to generate simulated data, their complexity makes it difficult to determine the likelihood function, hindering the estimation of model parameters and comparisons between models based on observed data. This challenge is known as the inverse problem of generative modeling, and in response to the dilemma that the likelihood function is impossible or difficult to compute, which in turn gave rise to likelihood-free inference.
likelihood-free inference, a case of simulation-based inference, which uses simulated data to approximate or circumvent the calculation of the likelihood function. This method enables the fitting and evaluation of the model. Approximate Bayesian Computation (ABC) and Probability Density Approximation (PDA) are two common techniques used in likelihood-free inference. ABC compares simulated data to observed data, while PDA uses a kernel density estimator to approximate the likelihood function. Both methods are powerful tools for model selection and parameter estimation, particularly when the likelihood function is intractable or unknown. However, these methods may suffer from the curse of dimensionality, as the number of model parameters increases, the computational cost and complexity of the simulation-based approaches grow exponentially, which can make the methods impractical for high-dimensional data and models.
Advances in deep learning and neural network technologies have led to the emergence of a new neural simulation-based inference (NSBI). NSBI leverages the power of deep learning to address the limitations of traditional simulation-based methods. NSBI possesses an amortization property, which allows the generation of a large amount of simulated data to be integrated into the training process. Consequently, once the neural network is trained, the inference step can be performed without the need for generating additional simulated data, significantly reducing computational overhead. Furthermore, with the advancement of neural conditional probability density estimation techniques, such as Normalizing Flows, NSBI can easily train models to estimate likelihood functions and posterior distribution functions. Neural posterior estimation methods, which offer a departure from conventional ABC techniques, directly sample from the posterior distribution using input data. Similarly, neural likelihood estimation methods, a substitute for PDA, compute likelihood values by leveraging both input data and prior parameters. These techniques enable the construction of flexible and complex cognitive models, making NSBI a promising approach for likelihood-free inference in high-dimensional settings.
NSBI has gained significant attention in the field of cognitive modeling and has been applied to various scenarios, including likelihood estimation, posterior estimation, and model comparison. For instance, neural likelihood networks such as LANs and MNLE are employed to estimate the likelihood function for intricate cognitive models, with MNLE being particularly adept at handling mixed data types with both continuous and discrete variables. In parameter inference for cognitive models, Bayesflow stands out as a neural posterior network capable of tackling a diverse array of complex models. For model comparison, Evidence Networks and Hierarchical Evidence Networks are utilized, with the Hierarchical variant being well-suited for nested data structures. Tools such as LANs, sbi, and Bayesflow are continuously optimizing the workflow of neural network-based simulation inference. These advancements have enabled NSBI to be applied to large-scale studies involving over a million data points, allowing for the construction of complex models that were previously difficult to build. This approach not only facilitates rapid validation of models and theories but also helps identify flaws and optimize performance, making NSBI a transformative technique with the potential to revolutionize the understanding and analysis of intricate systems.
While these new technologies show promise in cognitive modeling, this paper also discusses their limitations and offers constructive guidance for their use. The paper discusses the trade-offs of neural network training costs and model accuracy, the integration of NSBI methods for comprehensive analysis, and the importance of effective training for reliable models. It also emphasizes the potential for these technologies to enhance interdisciplinary collaboration, particularly between neuroscience, cognitive modeling, and other fields, to deepen our understanding of the mind and brain.
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Computer Application Technology submitted time 2024-06-28
Abstract: The early warning models of CET-4 scores are easily disturbed by the differences in students’ daily behavior patterns, affecting prediction accuracy. A gray image database of students’ modular learning is established using the CET-4 modular learning scores on a platform, and deep learning is integrated with early warning to form an early warning model of CET-4 scores based on deep convolution neural networks. An early prediction was made on whether students can achieve the expected results in CET-4. The results show that the prediction model based on the deep convolution neural networks has higher accuracy than the existing prediction models and can get an earlier optimal intervention time. It benefits teachers’ intervention with relevant students and can help improve their CET4 scores and English language competency.
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Computer Application Technology submitted time 2024-06-21
Abstract: With the development of pre-trained language models(PLMs), the methods have been immediately applied to the field classification of scientific and technological literature. They make the much more better performance than the traditional Natural Language Processing methods in similar tasks. The Scientific and Technological Achievements are similar to the literature. Both of them have the highly concise titles, and detailed introductions. At the same time, there are unique features of Scientific and Technological Achievements, e.g. the introduction will introduce the origination and background of the project, the application status, the awards, etc., while the literatures are usually highly focused on the research content. This feature makes it much more difficult to make correct predictions based on PLMs classification methods. Based on the pre-trained BERT model(RoBERTa), we have constructed an automatic Indexing system of Chinese Library Classification for the Scientific and Technological Achievements. We introduced a decoding method to enhance the prediction process, which is inspired by the generative Large Language Models. The method converts the original classification problem to a decoding problem, which not only improves the accuracy of prediction, but also enables dynamic predictions to the required levels. To meet the requirements of accuracy, thresholds can be set on the accumulated probability of predicted chain and its terminal node to make trade-offs between reliability and depth of the classification tree.
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Information Security Subjects: Computer Science >> Computer Application Technology submitted time 2024-04-23
Abstract: Image steganography has become a focal point of interest for researchers due to its capacity for the covert transmission of sensitive data. Traditional diffusion models often struggle with image steganography tasks involving paired data, as their core principle of gradually removing noise is not directly suited for maintaining the correspondence between carrier and secret information. To address this challenge, this paper conducts an in-depth analysis of the principles behind diffusion models and proposes a novel framework for an image steganography diffusion model. The study begins by mathematically representing the steganography tasks of paired images, introducing two optimization objectives: minimizing the secrecy leakage function and embedding distortion function. Subsequently, it identifies three key issues that need to be addressed in paired image steganography tasks and, through specific constraint mechanisms and optimization strategies, enables the diffusion model to effectively handle paired data. This enhances the quality of the generated stego-images and resolves issues such as image clarity. Finally, on public datasets like CelebA, the proposed model is compared with existing generation model-based image steganography techniques, analyzing its implementation effects and performance parameters. Experimental results indicate that, compared to current technologies, the model framework proposed in this study not only improves image quality but also achieves significant enhancements in multiple performance metrics, including the imperceptibility and anti-detection capabilities of the images. Specifically, the PSNR of its stego-images reaches 93.14dB, and the extracted images’ PSNR reaches 91.23dB, an approximate improvement of 30% over existing technologies; the attack success rate is reduced to 2.4x10-38. These experimental outcomes validate the efficacy and superiority of the method in image steganography tasks.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2024-04-10
Abstract: Integrative complexity is a concept used in psychology to measure the structure of an individual’s thinking in two aspects: differentiation and integration. The measurement of integrative complexity relies primarily on manual analysis of textual content, which can be written materials, speeches, interview transcript large language models, or any other form of oral or written expression. To solve the problems of high cost of manual assessment methods, low accuracy of automated assessment methods, and the lack of Chinese text assessment scheme, this study designed an automated assessment scheme for integrative complexity on Chinese and English texts. We utilized text data enhancement technique of the large language model and the model migration technique for the assessment of integrative complexity, and explored the automated assessment methods for the two sub-structures of integrative complexity, namely, the fine integration complexity and the dialectical integration complexity. In this paper, two studies are designed and implemented. Firstly, a prediction model for the integration complexity of English text is implemented based on the text data enhancement technology of large language model; secondly, a prediction model for the integration complexity of Chinese text is implemented based on the model transfer technology. The results showed that: 1) We used GPT-3.5-Tubo for English text data enhancement, a pre-trained multilingual Roberta model for word vector extraction, and a text convolutional neural network model as a downstream model. The Spearman correlation coefficient between this model’s prediction of integration complexity and the manual scoring results was 0.62, with a dialectical integration complexity correlation coefficient of 0.51 and a fine integration complexity Spearman correlation coefficient of 0.60. It is superior to machine learning methods and neural network models without data enhancement. 2) In Study 2, a model with the same structure as the neural network in Study 1 was established, and the final model parameters in Study 1 were also transferred to the model in this study to train the integration complexity prediction model based on Chinese text. In the case of zero samples, the Spearman correlation coefficients of the transfer learning model for integrative complexity are 0.31, the Spearman correlation coefficient of dialectical integration complexity is 0.31, and the correlation coefficient of fine integration complexity is 0.33, all of which are better than the model in the case of random parameters (integrative complexity: 0.17, dialectical integrative complexity: 0.10, fine integrative complexity: 0.10). In the case of small samples, the Spearman correlation coefficient of the transfer learning model was 0.73, with a dialectical integration complexity correlation coefficient of 0.51 and a fine integration complexity correlation coefficient of 0.73.
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Computer Application Technology submitted time 2024-04-10
Abstract: In the context of artificial intelligence and big data technology, the use of large models and the construction of standard literature knowledge bases are of great value for scientific research innovation, knowledge mining, and information retrieval. The standard literature knowledge base provides solid support for the standardization and standardization of various industries. This study first explores the current status of standard literature, then builds a framework for integrating large models and standard literature knowledge bases based on retrieval enhancement, and proposes exploration of enhancement optimization in each stage. Finally, it looks forward to future research directions and application prospects.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2024-04-09
Abstract: In recent years, the Linguistic Inquiry and Word Count (LIWC) tool has garnered increasing attention, offering the promise of objective, automated, and transparent psychological text analysis. This resurgence has reignited enthusiasm among psychologists for language analysis research. The recent revision of the LIWC-22 dictionary has introduced numerous variables aimed at assessing various socio-psychological structures, thus expanding the application potential of the LIWC tool. To further promote the cultural adaptation of the LIWC tool, we have revised and validated the Simplified Chinese Linguistic Inquiry and Word Count Dictionary 2024 (SCLIWC2024) to better align with the features of LIWC-22. In Study One, building upon the SCLIWC dictionary, we revised SCLIWC2024 by comparing it with the LIWC-22 and CLIWC2015 dictionaries. In Study Two, we conducted two experiments to validate the efficacy of SCLIWC2024 in detecting different psychological semantics in online texts, addressing crucial questions regarding how to more effectively utilize SCLIWC2024 for detecting the psychological semantics of short texts on social networking platforms.
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Computer Application Technology submitted time 2024-04-07
Abstract: Physical function monitoring (PFM) plays a crucial role in healthcare especially for the elderly. Traditional assessment methods such as the Short Physical Performance Battery (SPPB) have failed to capture the full dynamic characteristics of physical function. Wearable sensors such as smart wristbands offer a promising solution to this issue. However, challenges exist, such as the computational complexity of machine learning methods and inadequate information capture. This paper proposes a multi-modal PFM framework based on an improved TimeMAE, which compresses time-series data into a low-dimensional latent space and integrates a self-enhanced attention module. This framework achieves effective monitoring of physical health, providing a solution for real-time and personalized assessment. The method is validated using the NHATS dataset, and the results demonstrate an accuracy of 70.6% and an AUC of 82.20%, surpassing other state-of-the-art time-series classification models.
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Computer Application Technology submitted time 2024-04-05
Abstract: The rapid development of 3D acquisition technology has made it possible to obtain point clouds of real-world terrains. However, due to limitations in sensor acquisition technology or specific requirements, point clouds often contain defects such as holes with missing data. Inpainting algorithms are widely used to patch these holes. However, existing traditional inpainting algorithms rely on precise hole boundaries, which limits their ability to handle cases where the boundaries are not well-defined. On the other hand, learning-based completion methods often prioritize reconstructing the entire point cloud instead of solely focusing on hole filling. Based on the fact that real-world terrain exhibits both global smoothness and rich local detail, we propose a novel representation for terrain point clouds. This representation can help to repair the holes without clear boundaries. Specifically, it decomposes terrains into low-frequency and high-frequency components, which are represented by B-spline surfaces and relative height maps respectively. In this way, the terrain point cloud inpainting problem is transformed into a B-spline surface fitting and 2D image inpainting problem. By solving the two problems, the highly complex and irregular holes on the terrain point clouds can be well-filled, which not only satisfies the global terrain undulation but also exhibits rich geometric details. The experimental results also demonstrate the effectiveness of our method.
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Computer Application Technology submitted time 2024-04-04
Abstract: Text analysis and processing have become increasingly important topics, and there are many examples of Chinese word segmentation in jieba. However, there is little research on Japanese language word segmentation. This article aims to introduce the MeCab library’s function of segmenting Japanese words in Python, and provide relevant case codes to implement Japanese word segmentation as needed.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2024-03-23
Abstract: In recent years, researchers have recognized the impact of Zhong-yong Thinking Style on mental health. However, it is not clear how Zhong-yong thinking style affects mental health through internal psychological mechanisms. Previous studies found that individuals with a better ability to coordinate agency (a motivation representing self-interest) and communion (a motivation representing altruism) tend to have a higher level of moral centrality. Moral centrality reflects the balance of internal motivation system, which can reduce the conflict between agency and communion, helping individuals reach a state that the opposing motivations support and energies each other. Moral centrality may play a potential mediating role in the impact of Zhong-yong thinking style on mental health. Although there are relatively mature methods for measuring individual moral centrality, it involves the complex task of coding values in personal strivings, making the measurement of moral centrality particularly complicated and labor-intensive. However, with the development of large language models(LLM) like ChatGPT, they have demonstrated excellent contextual comprehension skills and offered new possibilities for text analysis and coding work. Accordingly, this study intends to apply large language models to the coding work of psychological research, reduce the time and labor cost required in the process of measuring individual moral centrality, and explore how Zhong-yong thinking style affects individual mental health through moral centrality. Study 1 involves training GPT-3.5 Turbo to recognize values contained in personal strivings (achievement / power / universalism / benevolence) using differentiated prompts and evaluating its accuracy, precision, and recall rates, in order to obtain a model that meets the requirements for application. Study 2 applies above GPT-3.5 Turbo models in the process of measuring moral centrality, exploring how moral centrality mediates the impact of Zhong-yong thinking style on depression and anxiety. The findings are as follows: (1) The GPT-3.5 Turbo demonstrated an accuracy rate of not less than 0.80 in recognizing values of power, achievement, universlaism, and benevolence, showing the potential application of ChatGPT in psychological research; (2) Moral centrality played a mediating role in the impact of Zhong-yong thinking style on depression/anxiety. Specifically, individuals with a higher level of Zhong-yong thinking style could better integrate agency and communion, enhancing their moral centrality, and thereby reducing levels of depression/anxiety. In summary, this study utilized large language models to break through the technical limitations of traditional psychological research, exploring the mechanisms through which Zhong-yong thinking style affects mental health and verifying the mediating role of moral centrality. On the one hand, it proves the application potential of large language models in the field of psychological research. On the other hand, it deepens our understanding of the mechanisms through which Zhong-yong thinking style influence mental health, enriching the theoretical foundation of this field. It suggests that policymakers could use the advantages of Zhongyong thinking culture, advocating for values that emphasize individual development while also focusing on collective well-being, helping people improve moral centrality, thereby mitigating the negative impact of economic inequality on mental health.
Peer Review Status:Awaiting Review
Subjects: Psychology >> Applied Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2024-03-23
Abstract: In recent years, researchers have increasingly recognized the impact of unequal income distribution on individual mental health. However, it is not clear how it affects mental health through internal psychological mechanisms. As the macro environment in which individuals live, economy shape people’s different values and make individuals have different levels of motivation orientation. Previous studies have indicated that individuals with a better ability to coordinate agency and communion tend to have a relatively high level of moral centrality. Moral centrality reflects the balance of internal motivation system, which can reduce the conflict between agency and communion, helping individuals reach a state that the opposing motivations support and energies each other. Thus, individuals are not only able to efficiently realize their personal values but also more easily allow for the attainment of eudaimonic well-being, thereby reducing the risk of mental health problems. Therefore, moral centrality may play a potential mediating role in the impact of income distribution inequality on mental health. Overall, with income distribution inequality as independent variables, this study aims to explore the mechanisms through which it affects mental health, by examining how income distribution influences individual moral centrality and, in turn, affect mental health. Our research not only enriches the theoretical foundation of the mental health field, but also provides a theoretical basis for interventions, and helps to formulate targeted strategies to improve the psychological well-being of the public. With the help of social media big data and natural language processing technology, we use posts made by regional microblogs to extract word frequency features representing the group’s moral centrality and group’s mental health level through the psychosemantic lexicon, and use panel data analysis to examine how the inequality in income distribution affects the negative emotions and suicide risk of the regional group through moral centrality. The results confirm that moral centrality plays a mediating role in the effect of regional income distribution inequality on group negative emotions/suicide risk, and that regions with higher income distribution inequality tend to be accompanied by lower levels of group moral centrality, which in turn leads to an increase in negative emotions/suicide risk among groups in the region.
Peer Review Status:Awaiting Review
Subjects: Astronomy >> Astronomical Instruments and Techniques Subjects: Physics >> Nuclear Physics Subjects: Computer Science >> Computer Application Technology submitted time 2024-03-10
Abstract: The HADAR experiment, which will be constructed in Tibet, China, combines the wide-angle advantages of traditional EAS array detectors with the high sensitivity advantages of focused Cherenkov detectors. Its physics objective is to observe transient sources such as gamma-ray bursts and counterparts of gravitational waves. The aim of this study is to utilize the latest AI technology to enhance the sensitivity of the HADAR experiment. We have built training datasets and models with distinctive creativity by incorporating relevant physical theories for various applications. They are able to determine the kind, energy, and direction of incident particles after careful design. We have obtained a background identification accuracy of 98.6 %, a relative energy reconstruction error of 10.0 %, and an angular resolution of 0.22-degrees in a test dataset at 10 TeV. These findings demonstrate the enormous potential for enhancing the precision and dependability of detector data analysis in astrophysical research. Thanks to deep learning techniques, the HADAR experiment’s observational sensitivity to the Crab Nebula has surpassed that of MAGIC and H.E.S.S. at energies below 0.5 TeV and remains competitive with conventional narrow-field Cherenkov telescopes at higher energies. Additionally, our experiment offers a fresh approach to dealing with strongly connected scattered data.
Subjects: Psychology >> Applied Psychology Subjects: Computer Science >> Computer Application Technology submitted time 2024-02-29
Abstract: Objective With the development of network big data and machine learning, more and more studies starting to combine text analysis and machine learning algorithms to predict individual satisfaction. In the studies focused on building life satisfaction prediction models, it is often difficult to obtain large amounts of valid and labeled data. This study aims at solving this problem using data augmentation and optimizing the prediction model of life satisfaction. Method Using 357 life status descriptions annotated by self-rating life satisfaction scale scores as original text data. After preprocessing using DLUT-Emotionontology, EAD and back-translation method was applied and the prediction model was built using traditional machine learning algorithms. Results Results showed that (1) the prediction accuracy was largely enhanced after using the adapted version of DLUT-Emotionontology; (2) only linear regression model was enhanced after data augmentation; (3) rigid regression model showed the greatest prediction accuracy when trained by original data (r = 0.4131). Conclusion The improvement of feature extraction accuracy can optimize the current life satisfaction prediction model, but the text data augmentation methods, such as back translation and EDA may not be applicable for the life satisfaction prediction model based on word frequency.
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Computer Application Technology submitted time 2024-01-09
Abstract: Object detection under low illumination conditions is an important task in image processing. The current research pay attention to reduce image noise by image enhancement, improve network structure and data sets to adapt to object detection under low illumination conditions. However, few people have studied the specific influence of low illumination conditions on object detection. Therefore, in this paper, we generate data sets that simulate low illumination conditions through algorithms. Then, we conduct object detection under different noise conditions and collect results, research the impact on object detection.
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Computer Application Technology submitted time 2024-01-07
Abstract: Aiming at the exponential growth of solution scale in multiple hypothesis tracking (MHT), a continuous consistency model (CCM) is proposed. The key to improve MHT performance is to improve the effi#2;ciency of branch management. However, due to the inevitable detector failure, when the tree is expanded and each detection is organized as the root node of the new tree, a large number of virtual nodes are used. This leads to rapid growth of branches. Different from previous MHT implementations, CCM divides detection into four categories, in#2;cluding continuous, left continuous, right continuous and discontinuous. Comparative experiments show that CCM has significantly improved the computational efficiency and obtained the most advanced results on MOT challenge benchmark.
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Computer Application Technology submitted time 2024-01-07
Abstract: In this paper, the classification of feature-level data fusion algorithms is summarized, and the distribution is summarized from the fusion algorithm based on probability and statistics, the fusion algorithm based on logical reasoning, the fusion algorithm based on feature extraction, the fusion algorithm based on search and the fusion algorithm based on neural network, and the future research direction of data fusion is summarized.
Peer Review Status:Awaiting Review
Subjects: Computer Science >> Computer Application Technology submitted time 2024-01-07
Abstract: The present study utilizes the YOLO visual algorithm for the recognition and detection of labels, providing a discourse on the experimental process and results.
Peer Review Status:Awaiting Review