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681.
It has been acknowledged that both trait and state contribute to psychological measurements. However, existing structural equation models for disentangling these sources of variability are based on assumptions that are not tenable in the light of empirical results. A new model is presented, termed the integrated trait–state (ITS) model, which both decomposes state and trait variance and allows one to test the assumptions that underly existing approaches. This is illustrated with an empirical example. The relationship between the ITS model and other analytic approaches as well as conceptual models of traits and states are discussed. 相似文献
682.
近几年来,开放监控冥想(Open Monitoring Meditation,OM)受到越来越多的关注,许多研究者对它进行了系统研究。与其它冥想,特别是与聚焦注意冥想(Focus Attention Meditation,FA)相比较,OM具有明显的差异。OM似乎对某些认知过程(创造性思维)的影响以及疼痛和焦虑的缓解具有优势,而对注意力的影响结果并不一致。脑电波、功能定位以及脑功能关联性方面的研究结果初步揭示了开放监控冥想过程中的脑神经机制,但是这种冥想对于默认网络的影响效果表现出不一致性。另外,研究中使用的OM训练方法也存在明显的差异性,尤其是注意的起点是否从呼吸开始,以及是否需要保持注意两个方面。这些结果需要今后的研究使用更加标准化的方法进一步明确。 相似文献
683.
This article first introduced the current technology of the privacy protection model, and analyzed their characteristics and deficiencies. Afterwards, from the point of view of revenue, the shortcomings of the traditional privacy protection model have analyzed through the group intelligent computing method. Based on this, this paper proposes a research and application of virtual user information of security strategy based on group intelligent computing, through the collection of visitor's private information historical access data, intelligent calculation of the strategy group between the visitor and the interviewee. The setting of the threshold of the visited person can protect the privacy information of the user more effectively. In this paper, the implementation flow, algorithm implementation process, and specific architecture design of the proposed virtual user of privacy protection model based on group intelligent computing are introduced respectively. The specific algorithms include PCA, BP neural network, and genetic algorithm. Finally, the proposed privacy has verified through experiments. Protection model can protect user privacy more effectively than traditional privacy protection model. In the future, we will further expand and improve the privacy protection model of virtual users based on group intelligent computing, including considering the dynamic and inconsistency of access to the privacy information, that is, accessing different private information will produce different overlay effects and parallelism. We will also study how to apply this model to actual systems such as shopping websites and social platforms, and use commercial data to evaluate the performance of the model and further improve it. 相似文献
684.
Iliane Houle Frederick L. Philippe Serge Lecours Josiane Roulez 《Cognition & emotion》2018,32(2):363-370
Emotional openness is characterised by a capacity to tolerate threatening self-relevant material and an interest towards new emotional situations. We investigated how specific networks of memories could be an important contributing factor to emotional openness. At Phase 1, participants completed measures of personality traits and emotional intelligence, described a self-defining memory, provided other memories associated with it, and rated the valence of each of their memories. A score assessing the complexity of this memory network, comprising the number of memories reported and their valence diversity, was created. Two weeks later, in laboratory, participants watched an anxiety-inducing film and took part in an interview assessing their emotional openness to the film. They completed a cognitive task before and after the film to measure ego depletion. Controlling for traits and emotional intelligence, memory network complexity was positively associated with emotional openness and negatively with ego depletion. The mental organisation of self-defining memories thus appears to be a critical factor contributing to emotional openness. 相似文献
685.
Globally, motor vehicle crashes account for over 1.2 million fatalities per year and are the leading cause of death for people aged 15–29 years. The majority of road crashes are caused by human error, with risk heightened among young and novice drivers learning to negotiate the complexities of the road environment. Direct feedback has been shown to have a positive impact on driving behaviour. Methods that could detect behavioural changes and therefore, positively reinforce safer driving during the early stages of driver licensing could have considerable road safety benefit. A new methodology is presented combining in-vehicle telematics technology, providing measurements forming a personalised driver profile, with neural networks to identify changes in driving behaviour. Using Long Short-Term Memory (LSTM) recurrent neural networks, individual drivers are identified based on their pattern of acceleration, deceleration and exceeding the speed limit. After model calibration, new, real-time data of the driver is supplied to the LSTM and, by monitoring prediction performance, one can assess whether a (positive or negative) change in driving behaviour is occurring over time. The paper highlights that the approach is robust to different neural network structures, data selections, calibration settings, and methodologies to select benchmarks for safe and unsafe driving. Presented case studies show additional model applications for investigating changes in driving behaviour among individuals following or during specific events (e.g., receipt of insurance renewal letters) and time periods (e.g., driving during holiday periods). The application of the presented methodology shows potential to form the basis of timely provision of direct feedback to drivers by telematics-based insurers. Such feedback may prevent internalisation of new, risky driving habits contributing to crash risk, potentially reducing deaths and injuries among young drivers as a result. 相似文献
686.
Application of artificial intelligence in Bio-Medical image processing is gaining more and more importance in the field of Medical Science. The bio medical images, has to go through several steps before the diagnosis of the disease. Firstly, the images has to be acquired and preprocessing has to be done and the data has to be stored in memory. It requires huge amount of memory and processing time. Among the preprocessing steps, edge detection is one of the major step. Edge detection filters the unwanted details in the image, and preserves the edges of the image, which describe the boundary of the image. In biomedical application, for the detection of the diseases, it is very essential to have the boundary detail of the acquired image of the organ under observation. Thus it is very essential to extract the edges of the images. Power is one of the main parameters that have to be considered while dealing with biomedical instruments. The biomedical signal processing instruments should be capable of operating at low power and also at high speed. In order to segregate the images into different levels or stage, we use convolutional neural networks for classification. By having a hardware architecture for image edge detection, the computational time for pre-processing of the image can be reduced, and the hardware can be a part of acquisition device itself. In this paper a low-power architecture for edge detection to detect the biomedical images are presented. The edge detection output are given to the system, which will diagnose the diseases using image classification using convolutional neural network. In this paper, Sobel and Prewitt, algorithms are used for edge detection using 180 nm technology. The edge detection algorithms are implemented using VLSI, and digital IC design of the architecture is presented. The algorithms for edge detection is co-simulated using MATLAB and Modelsim. The architecture is first simulated using CMOS logic and new method using domino logic is presented for low power consumption. 相似文献
687.
Facial expression recognition in a wild situation is a challenging problem in computer vision research due to different circumstances, such as pose dissimilarity, age, lighting conditions, occlusions, etc. Numerous methods, such as point tracking, piecewise affine transformation, compact Euclidean space, modified local directional pattern, and dictionary-based component separation have been applied to solve this problem. In this paper, we have proposed a deep learning–based automatic wild facial expression recognition system where we have implemented an incremental active learning framework using the VGG16 model developed by the Visual Geometry Group. We have gathered a large amount of unlabeled facial expression data from Intelligent Technology Lab (ITLab) members at Inha University, Republic of Korea, to train our incremental active learning framework. We have collected these data under five different lighting conditions: good lighting, average lighting, close to the camera, far from the camera, and natural lighting and with seven facial expressions: happy, disgusted, sad, angry, surprised, fear, and neutral. Our facial recognition framework has been adapted from a multi-task cascaded convolutional network detector. Repeating the entire process helps obtain better performance. Our experimental results have demonstrated that incremental active learning improves the starting baseline accuracy from 63% to average 88% on ITLab dataset on wild environment. We also present extensive results on face expression benchmark such as Extended Cohn-Kanade Dataset, as well as ITLab face dataset captured in wild environment and obtained better performance than state-of-the-art approaches. 相似文献
688.
Lukas Fellmann; 《Personal Relationships》2024,31(4):966-986
Studies in the field of child welfare services have shown that children's externalizing problems are linked to the immediate social relationships in which they are embedded. However, most studies that examined this association focused on specific dyads or general social support, ignoring more diverse and complex patterns of relationships in which children are embedded. Therefore, this study used Social Network Analysis to analyze how compositional and structural properties of parents' personal networks predict externalizing problems of children in vulnerable family contexts. The sample consists of 70 parents who were enrolled in a home-based family intervention in Switzerland. A hierarchical cluster analysis revealed three clusters based on the network composition: mixed, child-oriented, and family of orientation. Child behavior problems were associated with the network clusters; school-related problems, on the other hand, were additionally linked with support density in the personal networks of parents. The results show that children in vulnerable families are embedded in diverse network compositions and relational structures, which influence their externalizing problems in different ways. 相似文献
689.
The search for new ideas often frustratingly cycles back to old ones, a phenomenon known as fixation. Recent research has shown ways to kick-start finding new uses for familiar objects, a prototypical creativity task: wandering in the mind or the world or working on a messy desk. Those techniques seem to succeed by helping break fixation, but do not guide the search for new ideas. The perspective-taking or human-centric or empathic mindset championed by many in HCI and in design firms does provide a search strategy. We compared the mind-wandering mindset to a perspective-taking mindset, the latter priming thinking of ways that people in different roles (gardener, artist, etc.) might use the objects. In two studies, the Perspective-Taking mindset yielded more ideas and more original ideas than Mind-Wandering, which did not differ from a No-Mindset control. Original ideas came late, rewarding persistence. The perspective-taking mindset is productive for problem-solving, forecasting, and social interactions as well as innovation. 相似文献
690.
Network theories have been put forward for psychopathology (in which mental disorders originate from causal relations between symptoms) and for personality (in which personality factors originate from coupled equilibria of cognitions, affect states, behaviours, and environments). Here, we connect these theoretical strands in an overarching personality–resilience–psychopathology model. In this model, factors in personality networks control the shape of the dynamical landscape in which symptom networks evolve; for example, the neuroticism item ‘I often feel blue’ measures a general tendency to experience negative affect, which is hypothesized to influence the threshold parameter of the symptom ‘depressed mood’ in the psychopathology network. Conversely, events at the level of the fast-evolving psychopathology network (e.g. a depressive episode) can influence the slow-evolving personality variables (e.g. by increasing feelings of worthlessness). We apply the theory to neuroticism and major depressive disorder. Through simulations, we show that the model can accommodate important phenomena, such as the strong relation between neuroticism and depression and individual differences in the change of neuroticism levels and development of depression over time. The results of the simulation are implemented in an online, interactive simulation tool. Implications for research into the relationship between personality and psychopathology are discussed. © 2020 The Authors. European Journal of Personality published by John Wiley & Sons Ltd on behalf of European Association of Personality Psychology 相似文献