Estimation of word emotions based on part of speech and positional information

Recently, studies on emotion recognition technology have been conducted in the fields of natural language processing, speech signal processing, image data processing, and brain wave analysis, with the goal of letting the computer understand ambiguous information such as emotion or sensibility. This paper statistically studies the features of Japanese and English emotional expressions based on an emotion annotated parallel corpus and proposes a method to estimate emotion of the emotional expressions in the sentence. The proposed method identifies the words or phrases with emotion, which we call emotional expressions, and estimates the emotion category of the emotional expressions by focusing on the three kinds of features: part of speech of emotional expression, position of emotional expression, and part of speech of the previous/next morpheme of the target emotional expression.     

Exploring the relationship between emotions and the acquisition of computer knowledge

Most computer users have to deal with major software upgrades every 6–18 months. Given the pressure of having to adjust so quickly and so often, it is reasonable to assume that users will express emotional reactions such as anger, desperation, anxiety, or relief during the learning process. To date, the primary emotion studied with respect to computer knowledge has been anxiety or fear. The purpose of the following study was to explore the relationship among a broader range of emotions (anger, anxiety, happiness, and sadness) and the acquisition of nine computer related skills. Pre- and post-surveys were given to 184 preservice education students enrolled in 8 month, integrated laptop program. Happiness was expressed most of the time – anxiety, anger, and sadness were reported sometimes. Anxiety and anger levels decreased significantly, while computer knowledge increased. All four emotions were significantly correlated with all nine computer knowledge areas at the beginning of the program, but happiness and anxiety were the only emotions significantly related to change in computer knowledge.     

Dynamic estimation of worker reliability in crowdsourcing for regression tasks: Making it work

One of the biggest challenges in crowdsourcing is detecting noisy and incompetent workers. A possible way of handling this problem is to dynamically estimate the reliability of workers as they do work and accept only those workers who are deemed to be reliable to date. Although many approaches to dynamic estimation of rater reliability exist, they are often only appropriate for very specific categories of tasks, for example, only for binary classification. They also can make unrealistic assumptions such as requiring access to a large number of gold standard answers or relying on the constant availability of any rater. In this paper, we propose a novel approach to the dynamic estimation of rater reliability in regression (DER³) using multi-armed bandits. This approach is specifically suited for real-life crowdsourcing scenarios, where the task at hand is labelling or rating corpora to be used in supervised machine learning, and the annotations are continuous ratings, although it can be easily generalised to multi-class or binary classification tasks. We demonstrate that DER³ provides high-accuracy results and at the same time keeps the cost of the rating process low. Although our main motivating example is the recognition of emotion in speech, our approach shows similar results in other application areas.     

Crowdsourcing the perceived urban built environment via social media: The case of underutilized land

Crowdsourcing the public’s perceptions of the built environment in real time enables more responsive and agile infrastructure and land use planning. Social media has emerged to be an effective platform for citizens, engineers, and planners to communicate opinions and feelings transparently. However, a comprehensive terminological resource of the perceived built environment (BE) for consistent data collection and a specified analytical framework are still lacking, particularly for different underutilized land issues. To fill this knowledge gap, we demonstrate a BE-specific term construction and expansion method specifically for collecting Twitter data and propose a Geo-Topic-Sentiment analytical framework for retrieving and analyzing relevant tweets. We conduct a demonstrative study on un(der)utilized land-related BE terms across ten metropolitan statistical areas in the U.S. Findings reveal spatial variations in contents and sentiments about underutilized land environments, and more localized efforts may be required to address specific land use issues across different urban contexts. The research demonstrates Twitter as a useful platform in crowdsourcing perceived BE and sentiments at fine temporal and spatial scales in a timely manner. It contributes to engineering informatics by investigating the role of social media in environmental planning and proposing integrated domain-specific data analytic approaches for engineering practices.     

LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework

Automatically recognizing human emotions from spontaneous and non-prototypical real-life data is currently one of the most challenging tasks in the field of affective computing. This article presents our recent advances in assessing dimensional representations of emotion, such as arousal, expectation, power, and valence, in an audiovisual human–computer interaction scenario. Building on previous studies which demonstrate that long-range context modeling tends to increase accuracies of emotion recognition, we propose a fully automatic audiovisual recognition approach based on Long Short-Term Memory (LSTM) modeling of word-level audio and video features. LSTM networks are able to incorporate knowledge about how emotions typically evolve over time so that the inferred emotion estimates are produced under consideration of an optimal amount of context. Extensive evaluations on the Audiovisual Sub-Challenge of the 2011 Audio/Visual Emotion Challenge show how acoustic, linguistic, and visual features contribute to the recognition of different affective dimensions as annotated in the SEMAINE database. We apply the same acoustic features as used in the challenge baseline system whereas visual features are computed via a novel facial movement feature extractor. Comparing our results with the recognition scores of all Audiovisual Sub-Challenge participants, we find that the proposed LSTM-based technique leads to the best average recognition performance that has been reported for this task so far.     

Characterizing and Mining the Citation Graph of the Computer Science Literature

Citation graphs representing a body of scientific literature convey measures of scholarly activity and productivity. In this work we present a study of the structure of the citation graph of the computer science literature. Using a web robot we built several topic-specific citation graphs and their union graph from the digital library ResearchIndex. After verifying that the degree distributions follow a power law, we applied a series of graph theoretical algorithms to elicit an aggregate picture of the citation graph in terms of its connectivity. We discovered the existence of a single large weakly-connected and a single large biconnected component, and confirmed the expected lack of a large strongly-connected component. The large components remained even after removing the strongest authority nodes or the strongest hub nodes, indicating that such tight connectivity is widespread and does not depend on a small subset of important nodes. Finally, minimum cuts between authority papers of different areas did not result in a balanced partitioning of the graph into areas, pointing to the need for more sophisticated algorithms for clustering the graph.     

Coarse iris classification using box-counting to estimate fractal dimensions

This paper proposes a novel algorithm for the automatic coarse classification of iris images using a box-counting method to estimate the fractal dimensions of the iris. First, the iris image is segmented into sixteen blocks, eight belonging to an upper group and eight to a lower group. We then calculate the fractal dimension value of these image blocks and take the mean value of the fractal dimension as the upper and the lower group fractal dimensions. Finally, all the iris images are classified into four categories in accordance with the upper and the lower group fractal dimensions. This classification method has been tested and evaluated on 872 iris cases, and the proportions of these categories in our database are 5.50%, 38.54%, 21.79%, and 34.17%. The iris images are classified with two algorithms, the double threshold algorithm, which classifies iris images with an accuracy of 94.61%, and the backpropagation algorithm, which is 93.23% accurate. When we allow for the border effect, the double threshold algorithm is 98.28% accurate.     

Emotional design and positive emotions in multimedia learning: An eyetracking study on the use of anthropomorphisms

The present eyetracking study examined the influence of emotions on learning with multimedia. Based on a 2 × 2 experimental design, participants received experimentally induced emotions (positive vs. neutral) and then learned with a multimedia instructional material, which was varied in its design (with vs. without anthropomorphisms) to induce positive emotions and facilitate learning. Learners who were in a positive emotional state before learning had better learning outcomes in comprehension and transfer tests and showed longer fixation durations on the text information of the learning environment. Although anthropomorphisms in the learning environment did not induce positive emotions, the eyetracking data revealed that learners' attention was captured by this design element. Hence, learners in a positive emotional state who learned with the learning environment that included anthropomorphisms showed the highest learning outcome and longest fixation on the relevant information of the multimedia instruction. Results indicate an attention arousing effect of expressive anthropomorphisms and the relevance of emotional states before learning.     

Class-specific multiple classifiers scheme to recognize emotions from speech signals

Automatic emotion recognition from speech signals is one of the important research areas, which adds value to machine intelligence. Pitch, duration, energy and Mel-frequency cepstral coefficients (MFCC) are the widely used features in the field of speech emotion recognition. A single classifier or a combination of classifiers is used to recognize emotions from the input features. The present work investigates the performance of the features of Autoregressive (AR) parameters, which include gain and reflection coefficients, in addition to the traditional linear prediction coefficients (LPC), to recognize emotions from speech signals. The classification performance of the features of AR parameters is studied using discriminant, k-nearest neighbor (KNN), Gaussian mixture model (GMM), back propagation artificial neural network (ANN) and support vector machine (SVM) classifiers and we find that the features of reflection coefficients recognize emotions better than the LPC. To improve the emotion recognition accuracy, we propose a class-specific multiple classifiers scheme, which is designed by multiple parallel classifiers, each of which is optimized to a class. Each classifier for an emotional class is built by a feature identified from a pool of features and a classifier identified from a pool of classifiers that optimize the recognition of the particular emotion. The outputs of the classifiers are combined by a decision level fusion technique. The experimental results show that the proposed scheme improves the emotion recognition accuracy. Further improvement in recognition accuracy is obtained when the scheme is built by including MFCC features in the pool of features.     

数据挖掘在计算机等级考试中的应用

文章阐述如何采用ID3算法对学生计算机等级考试数据进行分类,并对分类结果进行分析,从而得出通过计算机等级考试的规则。以期待学生在学习过程中,借鉴这些规则,有规律、有目的地进行学习,从而提高等级考试合格率,实现优化教学。     

Using cocitation information to estimate political orientation in web documents

This paper introduces a simple method for estimating cultural orientation, the affiliation of online entities in a polarized field of discourse. In particular, cocitation information is used to estimate the political orientation of hypertext documents. A type of cultural orientation, the political orientation of a document is the degree to which it participates in traditionally left- or right-wing beliefs. Estimating documents' political orientation is of interest for personalized information retrieval and recommender systems. In its application to politics, the method uses a simple probabilistic model to estimate the strength of association between a document and left- and right-wing communities. The model estimates the likelihood of cocitation between a document of interest and a small number of documents of known orientation. The model is tested on three sets of data, 695 partisan web documents, 162 political weblogs, and 198 nonpartisan documents. Accuracy above 90% is obtained from the cocitation model, outperforming lexically based classifiers at statistically significant levels.     

网络控制系统中的调度问题

网络控制系统（NCS）的控制性能受限于共享的计算资源和网络资源,如何对NCS中的周期和非周期的复杂信息流进行有效的调度成为影响系统控制性能的关键因素。从实时控制角度,分析了NCS中的时态特性和NCS的实时调度问题。同时从实时CPU调度策略、NCS网络调度方法、调度诱导问题及其补偿方法、可调度性分析和调度优化、控制与调度协同设计等方面综述了现有的NCS中的调度理论和方法,并讨论了今后进一步的研究方向。     

Investigating perceived emotions and affects of a scene,and the user satisfaction with motion effects in 4D movies

The primary objective of this study was to investigate the intensity of perceived affect and emotion of a scene in relation to individual empathy ability and the rotation angle of a seat's motion caused by 4D movie motion effect. The secondary objective was to identify the causal factors that affected satisfaction with a motion effect. Although 4D movies have received increasing attention from the users of audiovisual content, user-centered research related to motion effects has rarely been conducted. Thirty-six participants who were grouped according to their empathy ability, viewed 10 4D movie clips and answered 2 questions related to 1) the perceived affect and emotion felt from the scenes and 2) satisfaction with the motion effects. The participants who had a high empathy ability indicated a stronger perceived affect and emotion during a scene than the participants who had a low empathy ability. A motion effect elicited different perceived emotions and affects between the two groups. The perceived temporal appropriateness, physical appropriateness, understandability, and disturbance of a motion effect affected the participants' satisfaction. This is a pioneering research related to the motion effects of 4D movie; therefore, these results could be helpful in providing insight for 4D effect designers of contents industry.     

A model to estimate and interpret the energy-efficiency of movement patterns in urban road traffic

Urban road traffic is highly dynamic. Traffic conditions vary in time and with location and so do the movement patterns of individual road users. In this article, a movement pattern is the behaviour of a car when traversing a road link in an urban road network. A movement pattern can be recorded with a global navigation satellite system (GNSS), such as the Global Positioning System (GPS). A movement pattern has a specific energy-efficiency, which is a measure of how fuel-intensively the car is moving. For example, a car driving uniformly at medium speed consumes little fuel and, therefore, is energy-efficient, whereas stop-and-go driving consumes much fuel and is energy-inefficient. In this article we introduce a model to estimate the energy-efficiency of movement patterns in urban road traffic from GNSS data. First, we derived statistical features about the car's movement along the road. Then, we compared these to fuel consumption data from the car's controller area network (CAN) bus, normalized to the car's overall range of fuel consumption. We identified the optimal feature set for prediction. With the optimal feature set we trained, tested and verified a model to estimate energy-efficiency, with the fuel consumption serving as ground truth. Existing fuel consumption models usually view movement as a snapshot. Thus, the behaviour of the car remains unknown that causes a movement pattern to be energy-efficient or energy-inefficient. Our model views movement as a process and allows to interpret this process. A movement pattern can, for example, be energy-inefficient because the car is driving in stop-and-go traffic, because it is travelling at high speed, or because it is accelerating. Our model allows to distinguish between these different types of behaviours. Thus, it can provide new insights into the dynamics of urban road traffic and its energy-efficiency.     

蒙特卡罗法在局域网交通信息估计中的应用

为解决理论计算需要事先已知车辆行驶路径的问题,提出采用蒙特卡罗法设计局域路网交通信息估计集成仿真模型,可利用有限交通采集设备,实现全面掌握城市路网交通运行状况、快速有效制定管理措施的目的。在实际路网上的研究结果表明,其具有良好的可靠性,可为先进交通管理系统（ATMS）相关交通模型的建立和交通采集设备的布设提供依据。     

数据挖掘技术在个人信用评估模型中的应用

为了能够及时、恰当地进行个人信用评估分析,加快信用卡发卡机构的决策速度,介绍了数据挖掘技术在信用卡公司对用户评估中的应用,对比分析了数理统计模型、分类-聚类个人信用评估模型等几种个人信用评估模型建模方法的优缺点。建立了一种决策树-神经网络个人信用评估模型,针对该模型提出了一种近邻聚类算法。该算法不需要事先给定聚类的类别数,可以进行无监督学习。通过对比分析可知,该算法在个人信用评估应用中可以得到较理想的结果。     

数据挖掘分类算法研究与探讨

数据挖掘技术是一个年轻且充满希望的研究领域,商业利益的强大驱动力将会不停地促进它的发展。随着数据库应用的不断深化,数据库的规模急剧膨胀,数据挖掘已成为当今研究的热点,每年都有新的数据挖掘方法和模型问世,特别是其中的分类问题,引起了越来越多的关注。     

人工免疫系统在数据挖掘中的应用

对自然免疫系统机制、人工免疫系统及数据挖掘进行简要介绍。对人工免疫系统在数据挖掘领域中的应用进行详细分析综述。主要阐述人工免疫系统在分类规则、聚类规则等中的应用现状,并对其方法进行详细分析并指出其优缺点。     

The interactional effects of atmospherics and perceptual curiosity on emotions and online shopping intention

With increasing importance of online stores, a great number of studies have focused on extending our knowledge related to successful functional aspects increasing ease of use and usefulness. More recent studies have focused on identifying the effects produced by hedonic aspects of online store environment such as web atmospherics on emotional responses of customers. However, previous studies have been somewhat deficient in their investigation of studying diverse aspects of online consumer characteristics, which may have an impact on customer evaluation of atmospheric cues. Building on this research tradition, the present study addresses two critical issues. The present study adopting a well validated S–O–R framework tests the effect of atmospheric cues of online stores on the intervening affective emotional states of consumers, which have a subsequent impact on behavioral intention. Additionally, the model hypothesizes that perceptual curiosity (PC) moderates the relationships between atmospheric cues and shoppers’ emotional reactions. Structure equation model confirmed that online atmospherics such as graphics, colors, and links have an impact on customer emotions such as pleasure and arousal, both of which have subsequent effects on intention. The moderating effect of perceptual curiosity has also been supported. Theoretical and practical implications, limitations, and directions for future research are discussed in conclusion.