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.     

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.     

灾后中小学生创伤后应激障碍和抑郁症状的共存模式

为揭示灾后中小学生创伤后应激障碍(PTSD)和抑郁在症状层面的共存模式, 本研究分别在汶川地震和雅安地震1年后, 对灾区的中小学生进行问卷调查, 并基于高斯图形模型和贝叶斯爬山算法构建了二者的共存症状网络。结果发现, 在DSM-IV的框架下, PTSD和抑郁的重叠症状以及情绪麻木症状在二者的共存网络中起到了桥接作用; 子网络探测结果与DSM-IV划分的症状边界不同, PTSD中的闯入性症状和回避性症状是其区别于抑郁的特异性症状, 且多为闯入性症状激发回避性症状; 在二者的共存模式中, 多为抑郁症状激发PTSD症状。上述结果在汶川和雅安两个样本中均得到了交叉验证, 具有一定的可推广性。     

不同社交媒体平台上口碑信息是如何影响购买意愿的？——解释水平和加工流畅性的中介作用

研究基于解释水平理论,探讨了不同社交媒体平台上口碑信息对购买意愿的影响及机制。结果发现：（1）不同社交媒体平台可以激发不同社会距离感;（2）接收高解释水平口碑时,社会距离远平台的用户购买意愿更高,接收低解释水平口碑时,社会距离近平台的用户购买意愿更高,平台类型与口碑类型在解释水平上匹配时影响力最大;（3）其机制是：口碑类型通过调节用户心理表征的解释水平对加工流畅性的影响,进而调节了“平台类型→解释水平→加工流畅性→购买意愿”这一链式中介路径。     

A Network Approach to Affect Regulation Dynamics and Personality Trait‐Induced Variations: Extraversion and Neuroticism Moderate Reciprocal Influences between Affect and Affect Regulation Strategies

The objectives of the present study were twofold. First, we tested a new approach to affect regulation dynamics, conceptualized as a network made up of the reciprocal influences that affect and affect regulation strategies constantly exert on each other. Second, we attempted to gain a better understanding of these dynamics by examining how they vary according to broad personality traits. To this end, we adopted an experience sampling method, involving five daily assessments over a 2‐week period. In each assessment, participants indicated their current affective experience and the way they had implemented five well‐known affect regulation strategies (i.e. appreciation, positive reappraisal, distraction, expressive suppression, and rumination) since the previous assessment. At the sample level, the network of affect regulation dynamics was characterized by positive feedback loops between positive affect and so‐called broad‐minded strategies, and between negative affect and narrow‐minded strategies. The form of this network varied according to levels of extraversion and neuroticism. Our findings are discussed in light of current knowledge about personality and affect regulation. Copyright © 2017 European Association of Personality Psychology     

Vector Semiotic Model for Visual Question Answering

In this paper, we propose a Vector Semiotic Model as a possible solution to the symbol grounding problem in the context of Visual Question Answering. The Vector Semiotic Model combines the advantages of a Semiotic Approach implemented in the Sign-Based World Model and Vector Symbolic Architectures. The Sign-Based World Model represents information about a scene depicted on an input image in a structured way and grounds abstract objects in an agent’s sensory input. We use the Vector Symbolic Architecture to represent the elements of the Sign-Based World Model on a computational level. Properties of a high-dimensional space and operations defined for high-dimensional vectors allow encoding the whole scene into a high-dimensional vector with the preservation of the structure. That leads to the ability to apply explainable reasoning to answer an input question. We conducted experiments are on a CLEVR dataset and show results comparable to the state of the art. The proposed combination of approaches, first, leads to the possible solution of the symbol-grounding problem and, second, allows expanding current results to other intelligent tasks (collaborative robotics, embodied intellectual assistance, etc.).     

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。