助人情境中幼儿知恩图报意识的发展

研究选取北京市某幼儿园3~6岁幼儿共118名作为研究对象。以助人任务为实验情境,设置了慷慨施恩者VS吝啬者（情境一）、施恩者VS好人（情境二）和传承知恩图报意识（情境三）三种情境,研究幼儿在三种不同助人情境下知恩图报意识的发展关键年龄及发展趋势。结果表明,在情境一和情境二中的知恩图报意识的发展关键年龄为5岁;情境三中幼儿知恩图报意识的发展关键年龄推测为6岁以后。各情境下知恩图报意识随年龄增长而提升。     

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.     

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.     

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.     

心理病理学网络理论、方法与挑战

对于精神障碍这一概念的理解, 传统DSM-ICD分类诊断系统和研究领域标准RDoC均基于潜变量视角, 认为精神障碍的症状由其潜在共同原因所致。这2种观点都忽略了症状间的相互作用。不同于分类和维度视角, Borsboom在2008年对精神障碍的概念化提出了的全新视角——心理病理学网络理论。此理论的核心观点是症状之间的动态因果关系构成了精神障碍。基于心理病理学网络理论的网络分析方法, 主要以结合EBIC的glasso算法估计症状间的偏相关网络, 并通过网络中节点中心性与网络连接性等指标, 来考查精神障碍症状的不同特性。近几年来, 研究者发现心理病理学网络分析方法在对症状间因果关系的推断、核心症状的识别和网络结构的可靠性与可重复性方面仍面临一些挑战。这些挑战为心理病理学网络理论与方法指明了未来可能的发展方向。     

Normal executive attention but abnormal orienting attention in individuals with sluggish cognitive tempo

Background/objective: Sluggish Cognitive Tempo (SCT) is an attentional disorder characterized by the symptoms of slowness in behavior or thinking, a lack of en.ergy, difficulty initiating and sustaining effort, daydreaming, and drowsiness. The aim of the present study was to investigate the distinctive attentional characteristics of SCT as compared to Attention-Deficit/Hyperactivity Disorder (ADHD). Method: A total of 110 adults were recruited and divided into four groups: SCT+ADHD, SCT, ADHD, and healthy controls. The Revised version of Attention Networks Test was used to investigate each group’s attentional profile. Results: The results revealed that the two SCT groups (SCT+ADHD and SCT) showed a significantly weaker orienting network due to the problems of engaging and disengaging attention than the other two groups. Additionally, the two ADHD groups (SCT+ADHD and ADHD) showed a significantly weaker executive control network than the other two groups. Conclusions: The findings demonstrate an attentional distinction between the SCT and the ADHD groups with a greater dysfunction in the orienting network in the SCT group as compared to the ADHD group. Furthermore, a greater executive control dysfunction was observed in the ADHD group as compared to the SCT group.     

融合视角下创造力的认知神经机制

创造力的认知神经机制是近年来心理学研究领域的前沿和热点问题。通过融合创造力整体宏观视角和创造性产生过程的微观视角,对创造力的认知神经机制进行了综述。宏观视角下,创造力主要涉及α波和大脑前额叶、内外侧颞叶以及外侧顶叶; 微观视角下,在创造力产生过程中主要涉及α波序列位置效应以及默认网络和执行控制网络的功能耦合。未来研究方向应该结合多模态脑成像数据库,利用机器算法来探究创造力的本质; 关注青少年群体创造力的纵向发展趋势; 结合分子遗传学研究,探究与创造力有关的基因问题。     

Association between attachment insecurity and couple adjustment: Moderation by couple memory networks

This study investigated whether couple‐related memories and their organization in memory networks could act as cognitive resources to protect against the negative impact of insecure attachment on couple adjustment. In two studies (n1 = 153, n2 = 567), participants in a romantic relationship described a significant couple‐related memory and provided networked memories associated with their couple‐related memory, to assess its organization in the memory system, and rated each memory for its level of need satisfaction. Findings across the two studies revealed significant moderations of need satisfaction in couple‐related memory networks, such that a higher level of satisfaction need within couple‐related memory networks was associated with a reduced negative association of attachment anxiety and avoidance with couple adjustment. When examined separately, it was shown that need‐satisfying networked memories, but not main couple‐related memories, moderated the negative association of insecure attachment with couple adjustment.     

Earthquake disaster avoidance learning system using deep learning

The popularity of deep learning has influenced the field of surveillance and human safety. We adopt the advantages of deep learning techniques to recognize potentially harmful objects inside living rooms, offices, and dining rooms during earthquakes. In this study, we propose an educational system to teach earthquake risks using indoor object recognition based on deep learning algorithms. The system is based on the You Look Only Once (YOLO) deployed on our cloud-based server named Earthquake Situation Learning System (ESLS) for the detection of harmful objects associated with risk tags. ESLS is trained on our own indoor images dataset. The user interacts with the ESLS server through video or image files, and the object detection algorithm using YOLO recognizes the indoor objects with associated risk tags. Results show that the service time of ESLS is low enough to serve it to users in 0.8 s on average, including processing and communication times. Furthermore, the accuracy of the harmful object detection is 96% in the general indoor lighting situation. The results show that the proposed ESLS is applicable to real service for teaching the earthquake disaster avoidance.