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.     

From selling to managing strategic customers - a competency analysis

An increasing number of companies select strategic customers, who are then treated differently from standard customers. Thus, the customer-facing function has been divided into two separate functions: traditional selling and strategic account management. This article seeks to provide a comprehensive overview that compares the competencies of salespeople with the competencies of strategic account managers. The article's objective is to highlight a competency path that can lead salespeople to transition successfully into strategic account managers. Leveraging the two distinct competency classifications, we identify the key skills that traditional salespeople must be trained in or “unlearn” to successfully transition. From a theoretical perspective, this article bridges the gap between the literature on sales and the literature on strategic account management and pinpoints the major differences between those two customer-facing dimensions. From a managerial perspective, the article helps practitioners draw a competency grid to assess both salespeople's ability to succeed in their role transition and the training required to support this transition.     

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。     

小学教师职业倦怠维度发展顺序探究——来自结构方程模型和交叉滞后网络分析模型的证据

教师职业倦怠的情感衰竭、去人性化和个人成就感降低3个维度相对独立但也存在相互影响, 对其发展关系进行研究, 有助于深入理解职业倦怠的发展过程, 尽早识别倦怠症状。本研究对3837名小学教师进行追踪测试, 测试间隔为3年, 采用结构方程模型和交叉滞后网络分析模型进行分析, 结果发现小学教师职业倦怠维度最优发展模型为“T1的情感衰竭和个人成就感降低分别预测T2的情感衰竭和个人成就感降低, T1的去人性化预测T2的去人性化和个人成就感降低”, 且最优发展模型具有性别间的一致性和教龄段之间的一致性。这一结果支持并强调了去人性化在小学教师职业倦怠发展中的重要作用, 对识别教师职业倦怠早期症状并采取相应措施有效阻断教师职业倦怠的进一步发展具有一定的理论和现实意义。     

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。