Research and application of virtual user context information security strategy based on group intelligent computing

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.     

Linking patterns of infant eye movements to a neural network model of the ventral stream using representational similarity analysis

Little is known about the development of higher-level areas of visual cortex during infancy, and even less is known about how the development of visually guided behavior is related to the different levels of the cortical processing hierarchy. As a first step toward filling these gaps, we used representational similarity analysis (RSA) to assess links between gaze patterns and a neural network model that captures key properties of the ventral visual processing stream. We recorded the eye movements of 4- to 12-month-old infants (N = 54) as they viewed photographs of scenes. For each infant, we calculated the similarity of the gaze patterns for each pair of photographs. We also analyzed the images using a convolutional neural network model in which the successive layers correspond approximately to the sequence of areas along the ventral stream. For each layer of the network, we calculated the similarity of the activation patterns for each pair of photographs, which was then compared with the infant gaze data. We found that the network layers corresponding to lower-level areas of visual cortex accounted for gaze patterns better in younger infants than in older infants, whereas the network layers corresponding to higher-level areas of visual cortex accounted for gaze patterns better in older infants than in younger infants. Thus, between 4 and 12 months, gaze becomes increasingly controlled by more abstract, higher-level representations. These results also demonstrate the feasibility of using RSA to link infant gaze behavior to neural network models. A video abstract of this article can be viewed at https://youtu.be/K5mF2Rw98Is     

人们为什么会羞怯：认知机制及神经基础

羞怯指个体在社交情境下的抑制, 是个体参与社交的阻碍。近年来, 关于羞怯的认知神经科学研究增多, 学者基于元认知模型、社会适应模型、侧化脑-体情绪模型等理论, 探讨了大脑结构和功能以及几种与感知觉和注意相关的ERP成分与羞怯的关系。但当前关于羞怯认知神经科学的理论和实证研究尚处于起步阶段。基于上述提出羞怯的心理发展模型; 未来应从人格和情绪双角度研究羞怯, 并在开发研究范式的基础上, 加大羞怯认知神经机制的探讨。     

The flow of narrative in the mind unmoored: An account of narrative processing

Verbal narratives provide incomplete information and can be very long, yet readers and hearers often effortlessly fill in the gaps and make connections across long stretches of text, sometimes even finding this immersive. How is this done? In the last few decades, event-indexing situation modeling and complementary accounts of narrative emotion have suggested answers. Despite this progress, comparisons between real-life perception and narrative experience might underplay the way narrative processing modifies our world model, as well as the role of the emotions that do not relate to characters. I reframe narrative experience in predictive processing and neural networks, capturing continuity between fiction, perception, and states like dreaming and imagination, enabled by the flexible instantiation of concepts. In this framework, narrative experience is more clearly revealed as a creative experience that can share some of the phenomenology of dreams.     

Explainability of neural networks for child language: Agent-First strategy in comprehension of Korean active transitive construction

This study investigates how neural networks address the properties of children's linguistic knowledge, with a focus on the Agent-First strategy in comprehension of an active transitive construction in Korean. We develop various neural-network models and measure their classification performance on the test stimuli used in a behavioural experiment involving scrambling and omission of sentential components at varying degrees. Results show that, despite some compatibility of these models’ performance with the children's response patterns, their performance does not fully approximate the children's utilisation of this strategy, demonstrating by-model and by-condition asymmetries. This study's findings suggest that neural networks can utilise information about formal co-occurrences to access the intended message to a certain degree, but the outcome of this process may be substantially different from how a child (as a developing processor) engages in comprehension. This implies some limits of neural networks on revealing the developmental trajectories of child language.

Research Highlights

• This study investigates how neural networks address properties of child language.
• We focus on the Agent-First strategy in comprehension of Korean active transitive.
• Results show by-model/condition asymmetries against children's response patterns.
• This implies some limits of neural networks on revealing properties of child language.

     

Age-related hearing loss associated with altered response efficiency and variability on a visual sustained attention task

ABSTRACT

This study investigated the association between age-related hearing loss (ARHL) and differences in response efficiency and variability on a sustained attention task. The study population comprised 32 participants in a hearing loss group (HLG) and 34 controls without hearing loss (CG). Mean reaction time (RT) and accuracy were recorded to assess response efficiency. RT variability was decomposed to examine temporal aspects of variability associated with neural arousal and top-down executive control of vigilant attention. The HLG had a significantly longer mean RT, possibly reflecting a strategic approach to maintain accuracy. The HLG also demonstrated altered variability (indicative of greater decline in neural arousal) but maintained executive control that was significantly predictive of poorer response efficiency. Adults with ARHL may rely on higher-order attention networks to compensate for decline in both peripheral sensory function and in subcortical arousal systems which mediate lower-order automatic neurocognitive processes.