Multi-label personality trait identification from text

Understanding the personality is beneficial for many purposes, e.g., it is natural to predict a user’s personality before offering him or her any services. The personality is intrinsic in the behavior of a person in all aspects, such as text writing. Some work has been proposed in recent times for correctly classifying a person’s personality from the text. However, it is still a significant challenge as the achieved accuracy is low; therefore, the proposed work addresses this issue. Effective feature selection techniques provide better classification accuracy in multi-label classification and personality traits identification as multi-label classification problem requires efficacy of feature selection methods. Therefore, to improve the accuracy using feature selection technique, this paper proposes a method for personality trait recognition from textual data called P ersonality T rait Classification based on L inguistic and F eature selection as M ulti-label classification (PTLFM). It combines analysis of variance’s F-statistic, Chi-square, and Mutual information with the sequential feature selection wrapper method to rank features. These three criteria apprehend different aspects of the dataset. The experimental results demonstrate that the proposed PTLFM method achieves higher accuracy across all the personality traits than the prevailing state-of-the-art machine learning and deep learning models. PTLFM provides an impressive absolute improvement of 2.23% and 3.84% of comparative improvement over the existing prevalent method, with more than 90% of features discarded. Furthemore, the proposed PTLFM achieves a percentage gain compared to the competitive methods across different personality traits Extraversion, Neuroticism, Agreeableness, Conscientiousness, and Openness in absolute terms 1.17, 1.94, 2.35, 1.64, and 0.35 respectively, and in comparative terms 2.01, 3.27, 4.14, 2.86, and 0.56 respectively. The results suggest that although deep learning is a popular paradigm, it does not always lead to a better predictive performance than machine learning models in all the problem domains.

     

Speech based automatic personality perception using spectral features

Automatic personality perception is the prediction of personality that others attribute to a person in a given situation. The aim of automatic personality perception is to forecast the behaviour of the speaker perceived by the listener from nonverbal behavior. Extroversion, Conscientiousness, Agreeableness, Neuroticism, and Openness are the speaker traits used for personality assessment. In this work, a speaker trait prediction approach for automatic personality assessment is proposed. This approach is based on modeling the relationship between speech signal and personality traits using spectral features. The experiments are achieved over the SSPNet Personality Corpus. The Frequency Domain Linear Prediction and Mel Frequency Cepstral Coefficient features are extracted for the prediction of speaker traits. The classification is done using Instance based k-Nearest neighbor and Support Vector Machine (SVM) classifiers. The experimental results show that k-Nearest Neighbor classifier outperforms SVM classifier. The classification accuracy is between 90 and 100%.     

User Personality and User Satisfaction with Recommender Systems

In this study, we show that individual users’ preferences for the level of diversity, popularity, and serendipity in recommendation lists cannot be inferred from their ratings alone. We demonstrate that we can extract strong signals about individual preferences for recommendation diversity, popularity and serendipity by measuring their personality traits. We conducted an online experiment with over 1,800 users for six months on a live recommendation system. In this experiment, we asked users to evaluate a list of movie recommendations with different levels of diversity, popularity, and serendipity. Then, we assessed users’ personality traits using the Ten-item Personality Inventory (TIPI). We found that ratings-based recommender systems may often fail to deliver preferred levels of diversity, popularity, and serendipity for their users (e.g. users with high-serendipity preferences). We also found that users with different personalities have different preferences for these three recommendation properties. Our work suggests that we can improve user satisfaction when we integrate users’ personality traits into the process of generating recommendations.     

Understanding the impact of personality traits on mobile app adoption – Insights from a large-scale field study

The sheer amount of available apps allows users to customize smartphones to match their personality and interests. As one of the first large-scale studies, the impact of personality traits on mobile app adoption was examined through an empirical study involving 2043 Android users. A mobile app was developed to assess each smartphone user's personality traits based on a state-of-the-art Big Five questionnaire and to collect information about her installed apps. The contributions of this work are two-fold. First, it confirms that personality traits have significant impact on the adoption of different types of mobile apps. Second, a machine-learning model is developed to automatically determine a user's personality based on her installed apps. The predictive model is implemented in a prototype app and shows a 65% higher precision than a random guess. Additionally, the model can be deployed in a non-intrusive, low privacy-concern, and highly scalable manner as part of any mobile app.     

Electronic warfare: A personality model of cyber incivility

Cyber incivility is defined as communicative behavior exhibited in computer mediated interactions that violate workplace norms of mutual respect. This study examines the impact of personality traits on cyber incivility via work email. Specifically, by drawing on the abridged big-five dimensional circumplex (AB5C) model of personality and the extant literature on cyber incivility, this study proposes a personality model of cyber incivility and posits that the personality traits of extraversion and emotional stability can be linked to cyber incivility more closely when each of them is accompanied by the personality trait of conscientiousness than when without it. We test our model by conducting a two-phased online survey of 265 full-time employees in the country of India. Results indicate that the relationships of extraversion and emotional stability with cyber incivility are negatively moderated by conscientiousness. Our findings contribute to the knowledge base of both personality and cyber incivility by understanding their linkages.     

Prediction of places of visit using tweets

We study the problem of predicting likely places of visit of users using their past tweets. What people write on their microblogs reflects their intent and desire relating to most of their common day interests. Taking this as a strong evidence, we hypothesize that tweets of the person can also be treated as source of strong indicator signals for predicting their places of visits. In this paper, we propose a novel approach for predicting place of visit within a given geospatial range considering the past tweets and the time of visit. These predictions can be used for generating places recommendation or for promotions. In this approach, we analyze use of various features that can be extracted from the historical tweets—for example, personality traits estimated from the past tweets and the actual words mentioned in the tweets. We performed extensive empirical experiments involving, real data derived from twitter timelines of 4600 persons with multi-label classification as predictive model. The performances of proposed approach outperform the four baselines with accuracy reaching 90 % for top five predictions. Based on our experimental study, we come up with general guidelines on building the prediction model in terms of the type of features extracted from historical tweets, window size of historical tweets and on the optimal radius of query around the place of visit at a given time.     

Machine learning model for mapping of music mood and human emotion based on physiological signals

Emotion is considered a physiological state that appears whenever a transformation is observed by an individual in their environment or body. While studying the literature, it has been observed that combining the electrical activity of the brain, along with other physiological signals for the accurate analysis of human emotions is yet to be explored in greater depth. On the basis of physiological signals, this work has proposed a model using machine learning approaches for the calibration of music mood and human emotion. The proposed model consists of three phases (a) prediction of the mood of the song based on audio signals, (b) prediction of the emotion of the human-based on physiological signals using EEG, GSR, ECG, Pulse Detector, and finally, (c) the mapping has been done between the music mood and the human emotion and classifies them in real-time. Extensive experimentations have been conducted on the different music mood datasets and human emotion for influential feature extraction, training, testing and performance evaluation. An effort has been made to observe and measure the human emotions up to a certain degree of accuracy and efficiency by recording a person’s bio- signals in response to music. Further, to test the applicability of the proposed work, playlists are generated based on the user’s real-time emotion determined using features generated from different physiological sensors and mood depicted by musical excerpts. This work could prove to be helpful for improving mental and physical health by scientifically analyzing the physiological signals.

     

11K Hands: Gender recognition and biometric identification using a large dataset of hand images

Human hand not only possesses distinctive feature for gender information, it is also considered one of the primary biometric traits used to identify a person. Unlike face images, which are usually unconstrained, an advantage of hand images is they are usually captured under a controlled position. Most state-of-the-art methods, that rely on hand images for gender recognition or biometric identification, employ handcrafted features to train an off-the-shelf classifier or be used by a similarity metric for biometric identification. In this work, we propose a deep learning-based method to tackle the gender recognition and biometric identification problems. Specifically, we design a two-stream convolutional neural network (CNN) which accepts hand images as input and predicts gender information from these hand images. This trained model is then used as a feature extractor to feed a set of support vector machine classifiers for biometric identification. As part of this effort, we propose a large dataset of human hand images, 11K Hands, which contains dorsal and palmar sides of human hand images with detailed ground-truth information for different problems including gender recognition and biometric identification. By leveraging thousands of hand images, we could effectively train our CNN-based model achieving promising results. One of our findings is that the dorsal side of human hands is found to have effective distinctive features similar to, if not better than, those available in the palmar side of human hand images. To facilitate access to our 11K Hands dataset, the dataset, the trained CNN models, and our Matlab source code are available at (https://goo.gl/rQJndd).

     

Multimodal biometrics: Weighted score level fusion based on non-ideal iris and face images

The iris and face are among the most promising biometric traits that can accurately identify a person because their unique textures can be swiftly extracted during the recognition process. However, unimodal biometrics have limited usage since no single biometric is sufficiently robust and accurate in real-world applications. Iris and face biometric authentication often deals with non-ideal scenarios such as off-angles, reflections, expression changes, variations in posing, or blurred images. These limitations imposed by unimodal biometrics can be overcome by incorporating multimodal biometrics. Therefore, this paper presents a method that combines face and iris biometric traits with the weighted score level fusion technique to flexibly fuse the matching scores from these two modalities based on their weight availability. The dataset use for the experiment is self established dataset named Universiti Teknologi Malaysia Iris and Face Multimodal Datasets (UTMIFM), UBIRIS version 2.0 (UBIRIS v.2) and ORL face databases. The proposed framework achieve high accuracy, and had a high decidability index which significantly separate the distance between intra and inter distance.     

基于用户行为的社交网络人格特质识别方法

社交网络已成为人们获取和发布信息的一个重要平台,也是黑客发起网络诈骗的主要场地。大多数黑客在发起网络诈骗之前,首先会判别目标用户的主要人格特点,然后根据主要人格特点制定与其接触的策略。因此,面向社交网络用户的人格特质识别方法的研究对提高用户识别社交网络诈骗能力具有重要意义。提出基于用户的人格特质识别方法。通过构建面向社交网络的人格特质词典提取用户发表或转发文本信息中能反映用户主要人格特质类型的观测值,采用5个具有不同参数值的隐半马尔可夫模型刻画用户在社交网络上发表或转发文本信息的行为过程。在人格特质识别阶段,通过计算每个用户在发表或转发文本信息过程中产生的观测序列相对于模型的平均对数似然概率,以识别用户所属的人格特质类型。在采集的新浪微博数据集上进行实验,结果表明,当假正率为10%时,该方法的总真正率为93.18%,能准确识别用户的人格特质类型。     

Making Sense of Software Development and Personality Types

It's common sense to state that the production of any software product involves a human element, at least to some extent. We all have different personality traits, and the way we perceive, plan, and execute any activity is influenced by these characteristics. Typically, software development is a product of teamwork, involving several people performing various tasks. The success and failure of software projects reveal the human factor as one of vital importance. Not everyone can excel at every task, thus better results are achieved if people with particular personality traits are assigned to different aspects of a project, especially the roles best suited to their ability. The authors mapped some opposing psychological traits, such as extroversion-introversion, sensing-intuition, thinking-feeling, and judging-perceiving, to the main stages of a software development life cycle. Consequently, they concluded that assigning a person with specific psychological characteristics to the stage of the software life cycle best suited for his or her traits increases the chances of a successful outcome for the project.     

Understanding intention of movement from electroencephalograms

Abstract: In this paper, we propose a new framework for understanding intention of movement that can be used in developing non-invasive brain–computer interfaces. The proposed method is based on extracting salient features from brain signals recorded whilst the subject is actually (or imagining) performing a wrist movement in different directions. Our method focuses on analysing the brain signals at the time preceding wrist movement, i.e. while the subject is preparing (or intending) to perform the movement. Feature selection and classification of the direction is done using a wrapper method based on support vector machines (SVMs). The classification results show that we are able to discriminate the directions using features extracted from brain signals prior to movement. We then extract rules from the SVM classifiers to compare the features extracted for real and imaginary movements in an attempt to understand the mechanisms of intention of movement. Our new approach could be potentially useful in building brain–computer interfaces where a paralysed person could communicate with a wheelchair and steer it to the desired direction using a rule-based knowledge system based on understanding of the subject's intention to move through his/her brain signals.     

两路互补对称CNN结构的行人再识别

目的 行人再识别的任务是研究如何在海量监控数据中准确地识别出某个特定场合中曾经出现过的人,已成为公共安全领域中一项新的且具有挑战性的研究课题。其挑战在于,行人在图像中有较大的姿态、视角、光照等变化,这些复杂的变化会严重影响行人再识别性能。近年来,以卷积神经网络（CNN）为代表的深度学习方法在计算机视觉领域取得了巨大的成功,也带动了行人再识别领域的相关研究。CNN有效地克服了行人变化,取得较高的准确率。然而,由于行人再识别数据集中行人标注量小,利用现有的一路CNN模型,其训练过程并不够充分,影响了深度学习模型的鉴别能力。为了解决上述问题,通过对网络结构进行改进,提出一种两路互补对称的CNN结构用于行人再识别任务。方法 本文方法每次同时输入两路样本,其中每路样本之间具有互补特性,此时在有限的训练样本下,输入的组合会更加多样化,CNN模型的训练过程更加丰富。结果 对本文提出的方法在两个公开的大规模数据集（Market-1501和DukeMTMC-reID）上进行实验评估,相比于基线方法有稳定的提升,相比于现存的其他一些方法,其结果也有竞争力。在Market-1501数据集上,1选识别正确率和平均精度均值分别达到了73.25%和48.44%。在DukeMTMC-reID数据集上,1选识别正确率和平均精度均值分别达到了63.02%和41.15%。结论 本文提出的两路互补对称CNN结构的行人再识别方法,能够在现有的有限训练样本下,更加充分地训练CNN模型,学习得到鉴别能力更强的深度学习模型,从而有效地提升行人再识别的性能。     

Using self-organizing maps to model turnover of sales agents in a call center

This paper proposes an approach for modeling employee turnover in a call center using the versatility of supervised self-organizing maps. Two main distinct problems exist for the modeling employee turnover: first, to predict the employee turnover at a given point in the sales agent's trial period, and second to analyze the turnover behavior under different performance scenarios by using psychometric information about the sales agents. Identifying subjects susceptible to not performing well early on, or identifying personality traits in an individual that does not fit with the work style is essential to the call center industry, particularly when this industry suffers from high employee turnover rates. Self-organizing maps can model non-linear relations between different attributes and ultimately find conditions between an individual's performance and personality attributes that make him more predisposed to not remain long in an organization. Unlike other models that only consider performance attributes, this work successfully uses psychometric information that describes a sales agent's personality, which enables a better performance in predicting turnover and analyzing potential personality profiles that can identify agents with better prospects of a successful career in a call center. The application of our model is illustrated and real data are analyzed from an outbound call center.     

深度迭代融合的脑部磁共振图像颅骨去除网络

目的 去除颅骨是脑部磁共振图像处理和分析中的重要环节。由于脑部组织结构复杂以及采集设备噪声的影响导致现有方法不能准确分割出脑部区域,为此提出一种深度迭代融合的卷积神经网络模型实现颅骨的准确去除。方法 本文DIFNet（deep iteration fusion net）模型的主体结构由编码器和解码器组成,中间的跳跃连接方式由多个上采样迭代融合构成。其中编码器由残差卷积组成,以便浅层语义信息更容易流入深层网络,避免出现梯度消失的现象。解码器网络由双路上采样模块构成,通过具有不同感受野的反卷积操作,将输出的特征图相加后作为模块输出,有效还原更多细节上的特征。引入带有L2正则的Dice损失函数训练网络模型,同时采用内部数据增强方法,有效提高模型的鲁棒性和泛化能力。结果 为了验证本文模型的分割性能,分别利用两组数据集与传统分割算法和主流的深度学习分割模型进行对比。在训练数据集同源的NFBS（neurofeedback skull-stripped）测试数据集上,本文方法获得了最高的平均Dice值和灵敏度,分别为99.12%和99.22%。将在NFBS数据集上训练好的模型直接应用于LPBA40（loni probabilistic brain atlas 40）数据集,本文模型的Dice值可达98.16%。结论 本文提出的DIFNet模型可以快速、准确地去除颅骨,相比于主流的颅骨分割模型,精度有较高提升,并且模型具有较好的鲁棒性和泛化能力。     

内嵌人格分析的社交关系强度层次模型及算法

社交网络中用户关系强度计算对于个性化社交服务呈现具有重要意义.同时,心理学研究表明人格特征是影响用户关系强度的关键因素之一.基于社会心理学中人与人之间的关系产生原理,提出一种内嵌人格分析的社交关系强度层次模型及计算方法.通过社交网络行为建模,建立用户大五人格特征预测模型,实现用户人格倾向性演算.同时结合偏好相似性和交互熟悉性计算,实现嵌入人格特征的用户关系强度的求解算法.最后,本文通过构建人人网社交关系仿真实验平台,验证了该方法的合理性和有效性.     

Validity of decision mode analysis on an ROI determination problem in multichannel fNIRS data

Region of interest (ROI) determination is necessary when using functional near-infrared spectroscopy (fNIRS) data to detect brain activity. To extract ROIs from multiple fNIRS channels, we investigated the validity of applying decision mode analysis to the fNIRS dataset. This classifies a dataset into clusters with similar features. For each cluster, the dataset is decomposed into a mean vector and a linear combination of eigenvectors. Applying this to fNIRS signals, the mean vector can be used to represent change in hemoglobin (Hb), and the eigenvectors interpreted as a signal component constructing the arbitrary signal. Characterizing these vectors by correlating them with a theoretical model of brain function aids our understanding of where Hb changes occur and what type of Hb changes reflect brain activity in fNIRS data. Decision mode analysis of fNIRS data measured during viewing stereoscopic images identified ROIs around the right inferior frontal gyrus associated with attentional control, and frontal association area associated with decision on action and prediction. Our experimental results showed that information obtained from decision mode analysis can aid quantitative and qualitative ROI determination.     

The Impact of Personality on the Subjective Assessment of Usability

The system usability scale (SUS) has been widely employed in both the field and the laboratory as a valid and reliable measure of system usability. Although its psychometric properties are relatively well understood, the impact that differences in users’ personality traits have on their perceived usability of products, services, and systems has not been deeply explored—even though people’s scores on personality traits have been shown to be reliable and predict a staggering array of societally important outcomes in work, school, and life domains. In this study, 268 users assessed the usability of 20 different products retrospectively with the SUS. Five broad personality traits were then measured using the Mini-IPIP scale. Results indicated that measured personality traits do correlate with the rated usability of products, where measures of Openness to Experience and Agreeableness have the strongest positive correlations with subjective usability assessment. Implications for practitioners, designers, and researchers are discussed.     

基于LABVIEW的脑电信号虚拟采集系统设计

在神经科学研究领域,对大脑的观察主要来源于对脑电信号的收集与分析;当前对脑电信号收集的方法是通过专业脑电设备将信号收集保存,再由专业软件处理;由于这类仪器非常昂贵,系统体积也比较大,软件更新快,现在只能用在科学研究上,根本无法用于有规模的实验教学,更不可能一人一机;为此,提出了一种基于LABVIEW的脑电信号虚拟采集系统设计方法,使脑电收集与分析可以广泛地应用于教学;该方法首先对脑电信号虚拟采集系统的硬件进行构造,然后以硬件构造为依据,利用AR模型功率谱估计对脑电信号进行特征提取,在特征提取过程中,对模型类型与模型系数算法以及模型最佳阶数进行分析,最后通过将二阶低通滤波器与二阶高通滤波器进行串联,形成4阶Bessel带通滤波器,实现脑电信号的滤波,并以脑电信号传输电路的设计完成脑电信号虚拟采集系统的设计;实验结果证明,所提方法可以快速地对脑电信号虚拟采集系统进行设计,并为该领域的研究发展提供支撑。     

Predicting user personality by mining social interactions in Facebook

Adaptive applications may benefit from having models of users? personality to adapt their behavior accordingly. There is a wide variety of domains in which this can be useful, i.e., assistive technologies, e-learning, e-commerce, health care or recommender systems, among others. The most commonly used procedure to obtain the user personality consists of asking the user to fill in questionnaires. However, on one hand, it would be desirable to obtain the user personality as unobtrusively as possible, yet without compromising the reliability of the model built. On the other hand, our hypothesis is that users with similar personality are expected to show common behavioral patterns when interacting through virtual social networks, and that these patterns can be mined in order to predict the tendency of a user personality. With the goal of inferring personality from the analysis of user interactions within social networks, we have developed TP2010, a Facebook application. It has been used to collect information about the personality traits of more than 20,000 users, along with their interactions within Facebook. Based on all the collected data, automatic classifiers were trained by using different machine-learning techniques, with the purpose of looking for interaction patterns that provide information about the users? personality traits. These classifiers are able to predict user personality starting from parameters related to user interactions, such as the number of friends or the number of wall posts. The results show that the classifiers have a high level of accuracy, making the proposed approach a reliable method for predicting the user personality