同伴团体对青少年问题行为的影响：一项基于社会网络分析的研究

以675名初中和高中学生为被试,采用社会网络分析方法,获得506名青少年在其班级中的网络中心度,并确定他们所属的同伴团体,在此基础上考察同伴团体的行为规范对其问题行为的影响。结果发现：（1）在控制了班级层次的问题行为水平和其他相关变量后,同伴团体的问题行为水平能够正向预测青少年自身的问题行为;（2）青少年在同伴团体内部的地位能负向预测青少年的问题行为,青少年在班级社交网络中的度数中心度能正向预测其问题行为,而中介中心度能负向预测其问题行为;（3）交互作用分析表明：同伴团体的问题行为水平主要对低中介中心度的青少年产生显著影响;仅在问题行为水平较高的同伴团体中,青少年的度数中心度正向预测其问题行为。     

社交网站成瘾与青少年抑郁的关系:认知负载和核心自我评价的中介作用

为探讨社交网站成瘾对青少年抑郁的影响及其作用机制,在素质-压力模型的视角下,采用社交网站成瘾量表、认知负载量表、核心自我评价量表和流调中心抑郁量表,对武汉市三所全日制中学886名初中生进行调查。结果表明:(1)社交网站成瘾、认知负载、核心自我评价和抑郁两两间存在显著的相关,且社交网站成瘾对抑郁具有显著的正向预测作用;(2)认知负载和核心自我评价能在社交网站成瘾与抑郁的关系中起完全中介作用。具体而言,社交网站成瘾通过三条路径影响抑郁:一是认知负载的单独中介作用;二是核心自我评价的单独中介作用;三是认知负载-核心自我评价的链式中介作用。本研究揭示了社交网站成瘾与抑郁的关系及其作用机制,拓展了社交网站成瘾对个体心理社会适应的研究。     

Identifying behavioural change among drivers using Long Short-Term Memory recurrent neural networks

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.     

Convolutional neural network for bio-medical image segmentation with hardware acceleration

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.     

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.     

Wild facial expression recognition based on incremental active learning

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.     

Networks of self-defining memories as a contributing factor to emotional openness

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.     

A Network Analysis Investigation of Disordered Eating Across Demographic and Developmental Subpopulations Using a National Epidemiological Sample of High School Students

Disordered eating (DE) poses a large societal burden, yet limited research has examined DE from a developmental epidemiological perspective. It is important to consider how demographics influence DE symptoms to inform prevention and early intervention programs across diverse subpopulations. Therefore, we conducted network analyses using a large nationally representative epidemiological sample of high school students (Youth Risk Behavior Survey, United States; n = 59,582) to identify the most important symptoms and symptom relationships among six DE behaviors. We compared networks by sex, grade, and race to identify differences in symptom networks. Dieting for weight loss was highly central across networks. Networks significantly differed across sex, grade, and race. Our results suggest that dieting for weight loss may be an early intervention target for eating disorders, regardless of demographic and developmental factors. In addition, sex, race, and age should be accounted for when researching and developing prevention programs for DE and eating disorders. Public health officials, as well as mental health professionals, should present a more balanced message about dieting and weight loss to high school students to prevent the detrimental impact of DE on physical and mental health. Notably, this study is the first large, nationwide epidemiological sample using DE symptoms in network analysis.