Creative Problem-Solving and Music: Analyzing the Correlation between Music and Divergent Thinking Abilities

This research paper delves into the connection, between problem-solving and music. It’s a topic that has piqued the interest of scholars in fields, including science and neuroscience. The study explores how music can influence our ability to think divergently which is an aspect of creative thinking. It builds upon advancements in methods to investigate the relationship between music and divergent thinking aiming to uncover potential correlations. Doing it offers insights into the interplay between artistic expression and cognitive innovation. This research combines an analysis of existing literature with data collected from a group of participants shedding light on how music impacts our capacity for creative thinking. It demonstrates that music plays a role as a catalyst, for stimulating and enhancing thinking abilities.

More Than Lightening:A Self-Supervised Low-Light Image Enhancement Method Capable for Multiple Degradations

Low-light images suffer from low quality due to poor lighting conditions,noise pollution,and improper settings of cameras.To enhance low-light images,most existing methods rely on normal-light images for guidance but the collection of suitable normal-light images is difficult.In contrast,a self-supervised method breaks free from the reliance on normal-light data,resulting in more convenience and better generalization.Existing self-supervised methods primarily focus on illumination adjustment and design pixel-based adjustment methods,resulting in remnants of other degradations,uneven brightness and artifacts.In response,this paper proposes a self-supervised enhancement method,termed as SLIE.It can handle multiple degradations including illumination attenuation,noise pollution,and color shift,all in a self-supervised manner.Illumination attenuation is estimated based on physical principles and local neighborhood information.The removal and correction of noise and color shift removal are solely realized with noisy images and images with color shifts.Finally,the comprehensive and fully self-supervised approach can achieve better adaptability and generalization.It is applicable to various low light conditions,and can reproduce the original color of scenes in natural light.Extensive experiments conducted on four public datasets demonstrate the superiority of SLIE to thirteen state-of-the-art methods.Our code is available at https://github.com/hanna-xu/SLIE.

Study on the Relationship Between College Students’ Self-Leadership and Career Preparation from the Perspective of Problem-Solving Skills

Based on the measurement of problem-solving skills,self-leadership,and career preparation,this paper collects 251 questionnaires from college students through online questionnaire survey.Through the analysis of the questionnaire results,this paper aims to clarify the relationship between college students’self-leadership and career preparation from the perspective of problem-solving skills,in order to provide ideas for college students’future career path and development.The results showed that from the perspective of problem-solving skills,there is a positive correlation between self-leadership and career preparation,and self-leadership has a positive impact on career preparation.

Edge-Federated Self-Supervised Communication Optimization Framework Based on Sparsification and Quantization Compression

The federated self-supervised framework is a distributed machine learning method that combines federated learning and self-supervised learning, which can effectively solve the problem of traditional federated learning being difficult to process large-scale unlabeled data. The existing federated self-supervision framework has problems with low communication efficiency and high communication delay between clients and central servers. Therefore, we added edge servers to the federated self-supervision framework to reduce the pressure on the central server caused by frequent communication between both ends. A communication compression scheme using gradient quantization and sparsification was proposed to optimize the communication of the entire framework, and the algorithm of the sparse communication compression module was improved. Experiments have proved that the learning rate changes of the improved sparse communication compression module are smoother and more stable. Our communication compression scheme effectively reduced the overall communication overhead.

Problem-Solving Based Intervention for Informal Caregivers: A Scoping Review

Background: Caregiving for someone is a huge task for informal caregivers. The caregiving role is often challenging and overwhelming for them. Stress, burden, and depression have been frequently identified as the negative caregiving experiences. This systematic review synthesized the available evidence for the problem-based intervention provided for caregivers as there is little insight about the effects of problem-based intervention on caregivers. Objectives: To describe: 1) the design of problem-solving intervention;2) the effects of problem-solving intervention for caregiver outcomes. Methods: This review followed Arksey and O’Malley’s methodological framework for conducting scoping reviews which entail setting research questions, selecting relevant studies, charting the data and synthesizing the results. FPRISMA guidelines were adopted and several databases were searched including MEDLINE;EMBASE;web of science. This review contains literature from 2012 to 2019 on problem-solving-based intervention which intended to caregivers. Results: 41 publications representing 27 unique problem-based interventions. Problem-solving-based intervention has different extent effects on caregiver emotion status, burden and quality of life. Conclusions: Problem-solving intervention is promising in caregiver intervention which could reduce caregiver depression, anxiety and burden.

Boosting battery state of health estimation based on self-supervised learning

State of health(SoH) estimation plays a key role in smart battery health prognostic and management.However,poor generalization,lack of labeled data,and unused measurements during aging are still major challenges to accurate SoH estimation.Toward this end,this paper proposes a self-supervised learning framework to boost the performance of battery SoH estimation.Different from traditional data-driven methods which rely on a considerable training dataset obtained from numerous battery cells,the proposed method achieves accurate and robust estimations using limited labeled data.A filter-based data preprocessing technique,which enables the extraction of partial capacity-voltage curves under dynamic charging profiles,is applied at first.Unsupervised learning is then used to learn the aging characteristics from the unlabeled data through an auto-encoder-decoder.The learned network parameters are transferred to the downstream SoH estimation task and are fine-tuned with very few sparsely labeled data,which boosts the performance of the estimation framework.The proposed method has been validated under different battery chemistries,formats,operating conditions,and ambient.The estimation accuracy can be guaranteed by using only three labeled data from the initial 20% life cycles,with overall errors less than 1.14% and error distribution of all testing scenarios maintaining less than 4%,and robustness increases with aging.Comparisons with other pure supervised machine learning methods demonstrate the superiority of the proposed method.This simple and data-efficient estimation framework is promising in real-world applications under a variety of scenarios.

Impact of self-directed learning readiness and learning attitude on problem-solving ability among Chinese undergraduate nursing students

Objective: To explore the effects of self-directed learning readiness and learning attitude on problem-solving ability among Chinese undergraduate nursing students. Methods: A convenience sampling of 460 undergraduate nursing students was surveyed in Tianjin, China. Students who participated in the study completed a questionnaire that included social demographic questionnaire, Self-directed Learning Readiness Scale, Attitude to Learning Scale, and Social Problem-Solving Inventory. Pearson’s correlation analysis was performed to test the correlations among problem-solving ability, self-directed learning readiness, and learning attitude. Hierarchical linear regression analyses were performed to explore the mediating role of learning attitude. Results: The results showed that learning attitude (r=0.338, P<0.01) and self-directed learning readiness (r=0.493, P<0.01) were positively correlated with problem-solving ability. Learning attitude played a partial intermediary role between self-directed learning readiness and problem-solving ability (F=74.227, P<0.01). Conclusions: It is concluded that nursing educators should pay attention on students’ individual differences and take proper actions to inspire students’ self-directed learning readiness and learning attitude.

Method and Apparatus for Creating Problem-Solving Complexes from Individual Elements

Based on the biological key-lock-principle common in various biological systems such as the human brain, this paper relates to a method and device for creating problem-solving complexes from individual elements that can be coupled with one another and that have different properties to solve problems. The problem solution can be carried out either serially with a large computer, or with several independent, hierarchically joined computers. In this system, an independent control unit that assumes a multitude of tasks and also acts as an interface with access to all participating computers, is assigned to each problem or object class according to the amount of potential problem-oriented solutions. Such a unit prepares the partial solutions found in its computer for the totality of the solutions computed in the associated computers, finally leading to a total problem solution.

Role of academic self-efficacy in the relationship between self-directed learning readiness and problem-solving ability among nursing students

Objective: Problem-solving should be a fundamental component of nursing education because It is a core ability for professional nurses. For more effective learning, nursing students must understand the relationship between self-directed learning readiness and problem-solving ability. The aim of this study was to investigate the relationships among self-directed learning readiness, problemsolving ability, and academic self-efficacy among undergraduate nursing students.Methods: From November to December 2016, research was conducted among 500 nursing undergraduate students in Tianjin, China,using a self-directed learning readiness scale, an academic self-efficacy scale, a questionnaire related to problem-solving, and selfdesigned demographics. The response rate was 85.8%.Results: For Chinese nursing students, self-directed learning readiness and academic self-efficacy reached a medium-to-high level,while problem-solving abilities were at a low level. There were significant positive correlations among the students' self-directed learning readiness, academic self-efficacy, and problem-solving ability. Furthermore, academic self-efficacy demonstrated a mediating effect on the relationship between the students' self-directed learning readiness and problem-solving ability.Conclusions: To enhance students' problem-solving ability, nursing educators should pay more attention to the positive impact of self-directed learning readiness and self-efficacy in nursing students' education.

Effects of Sex Differences and Problem-Solving Strategies on Digital Game Performance

Problem-solving strategy i,,; a critical skill in inquiry-based learning. Several studies have investigated how to use learning games to improve inquiry abilities. However, not every learner favors this kind of approach. Thus, we need to examine how human factors affect learners＇ reactions to the use of a digital game to support inquiry-based learning. This study addressed this issue by developing a digital game, ＂Baking Town＂, and using the game to examine the effects of two central human factors, sex differences and problem-solving strategies, on students＇ performance. The results demonstrate that students＇ inquiry abilities were significantly improved after they participated in the digital game. The results also demonstrate that the digital game may be a feasible way to reduce differences between boys and girls in this domain. Finally, we used the findings to develop a framework that can be used to enhance our understanding of sex differences and the use of problem-solving strategies in the context of digital games.

Circumstance Leading to Pregnancy and Problem-Solving Process of Unwanted Teenage Pregnancy in the Phetchaburi Province, Thailand

This descriptive qualitative study aims to understand circumstance leading to pregnancy and problem-solving process of unwanted teenage pregnancy in the Phetchaburi Province. A qualitative research design was used. Participants were 15 unwanted pregnant teenagers, aged between 15 and 19 years. All participants were primiparous and interviewed at postpartum stage. A semi-structured in-depth interview was the main technique of data collection. Data was analyzed using the process of manifest content analysis. Five themes, nine categories, 19 subcategories emerged from the data analysis. The themes were： （1） circumstance leading to pregnancy was the failure of contraceptive use and lack of knowledge about reproductive health; （2） negative emotional reactions included worry about parents＇ rejection and being blamed by others; （3） boyfriend＇s reaction to the pregnancy had a big impact on teen girl＇s feelings toward the situation. The majority of the teenagers＇ pregnancies accepted a child in the womb. The couples did not consider termination of the pregnancy; （4） parent＇s seeking a solution to serve the family dignity by organizing wedding ceremony and collaboratively plans for the future; and （5） life changes after the pregnancy due to the strong concerns about being blamed by the society. Findings from this study suggest that teens should particiPate in sex education classes that are specific for teenagers to prevent becoming pregnant. Moreover, the educational program specific for the teenagers＇ needs should be established so that they can continue their pregnancy without quitting school.

Enhancing the Problem-Solving Skills of Vocational Students Through Skills Competition

Unlike general education,vocational education aims to nurture skilled people for various sectors in the society.The development of students’problem analysis and problem-solving skills is crucial.As an important part of the vocational education system,skills competitions are considered a“booster”for improving the quality of teaching in vocational institutions.This paper examines the problem-solving skills of students in preparation for skills competition.First of all,we introduce China’s education policies and conduct a review of early scholars’views,followed by a discussion on the specific problems faced in the design of the entry for skills competition and an exploration of the process of enhancing students’problem-solving skills around these problems;we then propose several suggestions for vocational institutions to enhance their participation in skills competitions.Skills competitions provide a“special stage”for students in vocational institutions to show their abilities.The question of how this“stage”can be utilized to better improve students’abilities is worth exploring in different fields.

The enlightenment of artificial intelligence large-scale model on the research of intelligent eye diagnosis in traditional Chinese medicine

Eye diagnosis is a method for inspecting systemic diseases and syndromes by observing the eyes.With the development of intelligent diagnosis in traditional Chinese medicine(TCM);artificial intelligence(AI)can improve the accuracy and efficiency of eye diagnosis.However;the research on intelligent eye diagnosis still faces many challenges;including the lack of standardized and precisely labeled data;multi-modal information analysis;and artificial in-telligence models for syndrome differentiation.The widespread application of AI models in medicine provides new insights and opportunities for the research of eye diagnosis intelli-gence.This study elaborates on the three key technologies of AI models in the intelligent ap-plication of TCM eye diagnosis;and explores the implications for the research of eye diagno-sis intelligence.First;a database concerning eye diagnosis was established based on self-su-pervised learning so as to solve the issues related to the lack of standardized and precisely la-beled data.Next;the cross-modal understanding and generation of deep neural network models to address the problem of lacking multi-modal information analysis.Last;the build-ing of data-driven models for eye diagnosis to tackle the issue of the absence of syndrome dif-ferentiation models.In summary;research on intelligent eye diagnosis has great potential to be applied the surge of AI model applications.

Research on Self-Supervised Comparative Learning for Computer Vision

In recent years,self-supervised learning which does not require a large number of manual labels generate supervised signals through the data itself to attain the characterization learning of samples.Self-supervised learning solves the problem of learning semantic features from unlabeled data,and realizes pre-training of models in large data sets.Its significant advantages have been extensively studied by scholars in recent years.There are usually three types of self-supervised learning:"Generative,Contrastive,and GeneTative-Contrastive."The model of the comparative learning method is relatively simple,and the performance of the current downstream task is comparable to that of the supervised learning method.Therefore,we propose a conceptual analysis framework:data augmentation pipeline,architectures,pretext tasks,comparison methods,semisupervised fine-tuning.Based on this conceptual framework,we qualitatively analyze the existing comparative self-supervised learning methods for computer vision,and then further analyze its performance at different stages,and finally summarize the research status of sei supervised comparative learning methods in other fields.

A Multi-Level Circulant Cross-Modal Transformer for Multimodal Speech Emotion Recognition

Speech emotion recognition,as an important component of humancomputer interaction technology,has received increasing attention.Recent studies have treated emotion recognition of speech signals as a multimodal task,due to its inclusion of the semantic features of two different modalities,i.e.,audio and text.However,existing methods often fail in effectively represent features and capture correlations.This paper presents a multi-level circulant cross-modal Transformer(MLCCT)formultimodal speech emotion recognition.The proposed model can be divided into three steps,feature extraction,interaction and fusion.Self-supervised embedding models are introduced for feature extraction,which give a more powerful representation of the original data than those using spectrograms or audio features such as Mel-frequency cepstral coefficients(MFCCs)and low-level descriptors(LLDs).In particular,MLCCT contains two types of feature interaction processes,where a bidirectional Long Short-term Memory(Bi-LSTM)with circulant interaction mechanism is proposed for low-level features,while a two-stream residual cross-modal Transformer block is appliedwhen high-level features are involved.Finally,we choose self-attention blocks for fusion and a fully connected layer to make predictions.To evaluate the performance of our proposed model,comprehensive experiments are conducted on three widely used benchmark datasets including IEMOCAP,MELD and CMU-MOSEI.The competitive results verify the effectiveness of our approach.

SSL Depth: self-supervised learning enables 16× speedup in confocal microscopy-based 3D surface imaging [Invited]

In scientific and industrial research, three-dimensional (3D) imaging, or depth measurement, is a critical tool that provides detailed insight into surface properties. Confocal microscopy, known for its precision in surface measurements, plays a key role in this field. However, 3D imaging based on confocal microscopy is often challenged by significant data requirements and slow measurement speeds. In this paper, we present a novel self-supervised learning algorithm called SSL Depth that overcomes these challenges. Specifically, our method exploits the feature learning capabilities of neural networks while avoiding the need for labeled data sets typically associated with supervised learning approaches. Through practical demonstrations on a commercially available confocal microscope, we find that our method not only maintains higher quality, but also significantly reduces the frequency of the z-axis sampling required for 3D imaging. This reduction results in a remarkable 16×measurement speed, with the potential for further acceleration in the future. Our methodological advance enables highly efficient and accurate 3D surface reconstructions, thereby expanding the potential applications of confocal microscopy in various scientific and industrial fields.

Defining Computational Thinking as an Evident Tool in Problem-Solving:Comparative Research on Chinese and Canadian Mathematics Textbooks

Purpose:To analyze mathematics problem-solving(PS)procedures in Chinese(CH)and Canadian(CA)elementary mathematics textbooks that leverage computational thinking(CT)as a cognitive tool,which have evidently existed and been implemented.Design/Approach/Methods:In this study,an analysis framework was developed to investigate the characteristics of CT tools for three PS steps—understand the problem,devise and conduct plans,and look back into textbooks—in four contexts:data practices,modeling and simulation practices,computational tools practices,and systemic thinking practices.Findings:Our results demonstrate the tools(CT)employed in the PS process in CH and CA mathematics textbooks.The strong connections between the“look back”stage and CT tools were explored.During the“look back”stage,both countries required students to transfer their knowledge and perform generalization.In addition,CT is regarded as a basic skill analysis for students in mathematics education and has received significant attention at every stage of the PS process.Originality/Value:This study brings a new perspective to CTresearch in education by regarding CT as a cognitive tool for students in mathematics PS.

Unsupervised Domain Adaptation on Sentence Matching Through Self-Supervision

Although neural approaches have yielded state-of-the-art results in the sentence matching task,their perfor-mance inevitably drops dramatically when applied to unseen domains.To tackle this cross-domain challenge,we address unsupervised domain adaptation on sentence matching,in which the goal is to have good performance on a target domain with only unlabeled target domain data as well as labeled source domain data.Specifically,we propose to perform self-su-pervised tasks to achieve it.Different from previous unsupervised domain adaptation methods,self-supervision can not on-ly flexibly suit the characteristics of sentence matching with a special design,but also be much easier to optimize.When training,each self-supervised task is performed on both domains simultaneously in an easy-to-hard curriculum,which gradually brings the two domains closer together along the direction relevant to the task.As a result,the classifier trained on the source domain is able to generalize to the unlabeled target domain.In total,we present three types of self-super-vised tasks and the results demonstrate their superiority.In addition,we further study the performance of different usages of self-supervised tasks,which would inspire how to effectively utilize self-supervision for cross-domain scenarios.

Solving Algebraic Problems with Geometry Diagrams Using Syntax-Semantics Diagram Understanding

Solving Algebraic Problems with Geometry Diagrams(APGDs)poses a significant challenge in artificial intelligence due to the complex and diverse geometric relations among geometric objects.Problems typically involve both textual descriptions and geometry diagrams,requiring a joint understanding of these modalities.Although considerable progress has been made in solving math word problems,research on solving APGDs still cannot discover implicit geometry knowledge for solving APGDs,which limits their ability to effectively solve problems.In this study,a systematic and modular three-phase scheme is proposed to design an algorithm for solving APGDs that involve textual and diagrammatic information.The three-phase scheme begins with the application of the statetransformer paradigm,modeling the problem-solving process and effectively representing the intermediate states and transformations during the process.Next,a generalized APGD-solving approach is introduced to effectively extract geometric knowledge from the problem’s textual descriptions and diagrams.Finally,a specific algorithm is designed focusing on diagram understanding,which utilizes the vectorized syntax-semantics model to extract basic geometric relations from the diagram.A method for generating derived relations,which are essential for solving APGDs,is also introduced.Experiments on real-world datasets,including geometry calculation problems and shaded area problems,demonstrate that the proposed diagram understanding method significantly improves problem-solving accuracy compared to methods relying solely on simple diagram parsing.

On Robust Cross-view Consistency in Self-supervised Monocular Depth Estimation

Remarkable progress has been made in self-supervised monocular depth estimation (SS-MDE) by exploring cross-view consistency, e.g., photometric consistency and 3D point cloud consistency. However, they are very vulnerable to illumination variance, occlusions, texture-less regions, as well as moving objects, making them not robust enough to deal with various scenes. To address this challenge, we study two kinds of robust cross-view consistency in this paper. Firstly, the spatial offset field between adjacent frames is obtained by reconstructing the reference frame from its neighbors via deformable alignment, which is used to align the temporal depth features via a depth feature alignment (DFA) loss. Secondly, the 3D point clouds of each reference frame and its nearby frames are calculated and transformed into voxel space, where the point density in each voxel is calculated and aligned via a voxel density alignment (VDA) loss. In this way, we exploit the temporal coherence in both depth feature space and 3D voxel space for SS-MDE, shifting the “point-to-point” alignment paradigm to the “region-to-region” one. Compared with the photometric consistency loss as well as the rigid point cloud alignment loss, the proposed DFA and VDA losses are more robust owing to the strong representation power of deep features as well as the high tolerance of voxel density to the aforementioned challenges. Experimental results on several outdoor benchmarks show that our method outperforms current state-of-the-art techniques. Extensive ablation study and analysis validate the effectiveness of the proposed losses, especially in challenging scenes. The code and models are available at https://github.com/sunnyHelen/RCVC-depth.