Neural Emotion Detection via Personal Attributes

There has been a recent line of work to automatically detect the emotions of posts in social media. In literature, studies treat posts independently and detect their emotions separately. Different from previous studies, we explore the dependence among relevant posts via authors' backgrounds, since the authors with similar backgrounds, e.g., "gender", "location", tend to express similar emotions. However, personal attributes are not easy to obtain in most social media websites. Accordingly, we propose two approaches to determine personal attributes and capture personal attributes between different posts for emotion detection: the Joint Model with Personal Attention Mechanism (JPA) model is used to detect emotion and personal attributes jointly, and capture the attributes-aware words to connect similar people; the Neural Personal Discrimination (NPD) model is employed to determine the personal attributes from posts and connect the relevant posts with similar attributes for emotion detection. Experimental results show the usefulness of personal attributes in emotion detection, and the effectiveness of the proposed JPA and NPD approaches in capturing personal attributes over the state-of-the-art statistic and neural models.     

Analytical Modeling for Translating Statistical Changes to Circuit Variability by Ultra-Deep Submicron Digital Circuit Design

This paper presents a physics-based compact gate delay model that includes all short-channel phenomena prevalent at the ultra-deep submicron technology node of 32 nm. To simplify calculations, the proposed model is connected to a compact α-power law-based (Sakurai-Newton) model. The model has been tested on a wide range of supply voltages. The model accurately predicts nominal delays and the delays under process variations. It has been shown that at lower technology nodes, the delay is more sensitive to threshold voltage variations, specifically at the sub-threshold operating region as compared with effective channel length variations above the threshold region.     

A novel adaptive sampling based methodology for feasible region identification of compute intensive models using artificial neural network

Identification of feasible region of operations in multivariate processes is a problem of interest in several fields. This is particularly challenging when the process model is black-box in nature and/or is computationally expensive, as analytical solutions are not available and the number of possible model evaluations is limited. An efficient methodology is required to identify samples where the model is evaluated for developing a computationally efficient surrogate model. In this work, an artificial neural network based surrogate model is proposed which is integrated with a statistical-based approach (Jack-knifing) to estimate the variance of the surrogate model prediction. This allows implementation of an adaptive sampling approach where new samples are identified close to the feasible region boundary or in regions of high prediction uncertainty. The proposed approach performs better than a previously published kriging based method for different dimensionality case studies.     

Understanding the asymmetric perceptions of smartphone security from security feature perspective: A comparative study

Smartphones are being used and relied on by people more than ever before. The open connectivity brings with it great convenience and leads to a variety of risks that cannot be overlooked. Smartphone vendors, security policy designers, and security application providers have put a variety of practical efforts to secure smartphones, and researchers have conducted extensive research on threat sources, security techniques, and user security behaviors. Regrettably, smartphone users do not pay enough attention to mobile security, making many efforts futile. This study identifies this gap between technology affordance and user requirements, and attempts to investigate the asymmetric perceptions toward security features between developers and users, between users and users, as well as between different security features. These asymmetric perceptions include perceptions of quality, perceptions of importance, and perceptions of satisfaction. After scoping the range of smartphone security features, this study conducts an improved Kano-based method and exhaustively analyzes the 245 collected samples using correspondence analysis and importance satisfaction analysis. The 14 security features of the smartphone are divided into four Kano quality types and the perceived quality differences between developers and users are compared. Correspondence analysis is utilized to capture the relationship between the perceived importance of security features across different groups of respondents, and results of importance-satisfaction analysis provide the basis for the developmental path and resource reallocation strategy of security features. This article offers new insights for researchers as well as practitioners of smartphone security.     

磁性硅铁载铜吸附Hg0动力学机制及密度泛函研究

采用粉末冶金法将纳米单质铜(Cu⁰)、硅铁(FeSi)、四氧化三铁硅涂层(Fe₃O₄@SiO₂)混合煅烧并制备出新型磁性硅铁载铜吸附剂MagFeSi-Cu⁰。实验研究不同烟气温度下MagFeSi-Cu⁰的汞吸附能力基础上,结合颗粒内扩散模型、准二阶动力学模型、Elovich模型及Bangham模型分析了MagFeSi-Cu⁰吸附Hg⁰过程的主要控制步骤。在此基础上,依据密度泛函理论(DFT)研究了不同反应温度下FeSi表面Cu⁰原子与Hg⁰原子的汞齐作用机制。研究结果表明,Bangham模型的拟合值与MagFeSi-Cu⁰汞吸附实验值拟合度最高,MagFeSi-Cu⁰表面痕量Hg⁰吸附由汞的外扩散和表面铜汞齐吸附共同控制;通过密度泛函计算,发现Cu⁰颗粒表面Cu-Hg齐吸附能为-0.534 eV,当烟气温度从80℃上升至200℃时,Hg⁰原子与单质Cu原子的吸附自由能从-22.47 kJ/mol下降至-13.96 kJ/mol,这些结果为深入了解Hg⁰在Cu(111)表面的反应机理提供了理论基础。     

堆本体抗震试验动力相似关系推导与验证

为确保堆本体抗震试验中流体对流效应、脉冲效应和堆本体结构响应的准确性，需保证重力、流体与固体惯性力、结构弹性力和结构应变的相似性。本文从固体结构的振动方程、不可压牛顿流体的动力学方程、流固交界面的边界条件和环形柱体域内液体线性晃动的动力学公式出发，基于控制方程的量纲分析法，推导了考虑液体晃动效应的堆本体地震响应动力相似关系。基于上述相似关系建立了堆容器堆内构件和堆容器内自由液面流体域的缩尺模型，通过有限体积法分析堆容器堆内构件原型和缩尺模型中液体的晃动固有频率、晃动波高、压力以及液体晃动对堆容器支承裙的倾覆力矩。结果表明本文动力相似关系具有合理性和准确性，可用于堆本体缩尺模型的抗震试验研究。     

Korean automatic spacing using pretrained transformer encoder and analysis

Automatic spacing in Korean is used to correct spacing units in a given input sentence. The demand for automatic spacing has been increasing owing to frequent incorrect spacing in recent media, such as the Internet and mobile networks. Therefore, herein, we propose a transformer encoder that reads a sentence bidirectionally and can be pretrained using an out-of-task corpus. Notably, our model exhibited the highest character accuracy (98.42%) among the existing automatic spacing models for Korean. We experimentally validated the effectiveness of bidirectional encoding and pretraining for automatic spacing in Korean. Moreover, we conclude that pretraining is more important than fine-tuning and data size.     

Saliency4ASD: Challenge,dataset and tools for visual attention modeling for autism spectrum disorder

The recent studies showing that gaze features can be useful in the identification of Autism Spectrum Disorder (ASD), have opened a new domain where Visual Attention (VA) modeling could be of great help. In this sense, this paper presents a report of the Grand Challenge “Saliency4ASD: Visual attention modeling for Autism Spectrum Disorder”, organized at IEEE ICME’19, aiming at supporting the research on VA modeling towards this healthcare societal challenge. In particular, this paper describes the workflow, obtained results, and datasets and tools that were used within this activity, in order to help on the development and evaluation of two types of VA models: (1) to predict saliency maps that fit gaze behavior of people with ASD, and (2) to identify individuals with ASD from typical development.     

Water vapor volatilization and oxidation induced surface cracking of environmental barrier coating systems: A numerical approach

A numerical model is developed for surface crack propagation in brittle ceramic coatings, aiming at the intrinsic failure of rare-earth silicate environmental barrier coating systems (EBCs) under combustion conditions in advanced gas turbines. The main features of progressive degradation of EBCs in such conditions are captured, including selective silica vaporization in the top coat due to exposure to water vapor, diffusion path-dependent bond coat oxidation, as well as crack propagation during cyclic thermal loading. In light of these features, user-defined subroutines are implemented in finite element analysis, where surface crack growth is simulated by node separation. Numerical results are validated by existing experimental data, in terms of monosilicate layer thickening, thermal oxide growth, and fracture behaviors. The experimentally observed quasi-linear oxidation in the early stage is also elucidated. Furthermore, it is suggested that surface crack undergoes rapid propagation in the late stage of extended thermal cycling in water vapor and leads to catastrophic failure, driven by both thermal mismatch and oxide growth stresses. The latter is identified as the dominant mechanism of penetration. Based on detailed analyses of failure mechanisms, the optimization strategy of EBCs composition is proposed, balancing the trade-off between mechanical compliance and erosion resistance.     

电商平台产品共同购买网络的影响因素研究

共同购买网络的推荐系统应用越加广泛，仅基于网络内生结构变量研究其共同购买链接的经济意义已有局限，故加入网络口碑这一外生变量，进行更全面的分析。采用了社会网络方法中指数随机图模型进行建模，主要围绕产品销售量、产品入度、差评率和评论数4个方面因素，探究其对共同购买网络中共同购买链接形成的影响。结果显示，销售量、产品入度和评论数对共同购买链接形成的影响呈正比关系，而差评率则会显著地降低产品共同购买的几率。该指数随机图构建出的共同购买网络为在线电商平台管理网络口碑和推荐系统优化设计提供有益参考。