Showing emotions through movement and symmetry

This paper proposes and explores a minimalist abstract approach to express emotions through movement and symmetry, which intends to minimize the user’s expectations. Emotions are represented in terms of arousal and valence dimensions and they are visually expressed in a simple way through the behaviour and appearance of a series of dynamic horizontal and vertical lines. In particular, the arousal value is expressed through the movement of these lines, while the valence value is expressed through their symmetry. The line movement is controlled by a flocking algorithm which is influenced by the arousal value, while the line symmetry/asymmetry is expressed through their rotation and length. Furthermore, the paper describes a user experiment which investigated whether the arousal and valence expressed by our model are appropriately perceived by the users or not. The results suggest that combinations of movement and symmetry are perceived correctly as particular emotions, that movement and symmetry are perceived as arousal and valence respectively and that they are independent of each other.     

A cost-aware auto-scaling approach using the workload prediction in service clouds

Service clouds are distributed infrastructures which deploys communication services in clouds. The scalability is an important characteristic of service clouds. With the scalability, the service cloud can offer on-demand computing power and storage capacities to different services. In order to achieve the scalability, we need to know when and how to scale virtual resources assigned to different services. In this paper, a novel service cloud architecture is presented, and a linear regression model is used to predict the workload. Based on this predicted workload, an auto-scaling mechanism is proposed to scale virtual resources at different resource levels in service clouds. The auto-scaling mechanism combines the real-time scaling and the pre-scaling. Finally experimental results are provided to demonstrate that our approach can satisfy the user Service Level Agreement (SLA) while keeping scaling costs low.     

Optimizing virtual machine allocation for parallel scientific workflows in federated clouds

Cloud computing has established itself as an interesting computational model that provides a wide range of resources such as storage, databases and computing power for several types of users. Recently, the concept of cloud computing was extended with the concept of federated clouds where several resources from different cloud providers are inter-connected to perform a common action (e.g. execute a scientific workflow). Users can benefit from both single-provider and federated cloud environment to execute their scientific workflows since they can get the necessary amount of resources on demand. In several of these workflows, there is a demand for high performance and parallelism techniques since many activities are data and computing intensive and can execute for hours, days or even weeks. There are some Scientific Workflow Management Systems (SWfMS) that already provide parallelism capabilities for scientific workflows in single-provider cloud. Most of them rely on creating a virtual cluster to execute the workflow in parallel. However, they also rely on the user to estimate the amount of virtual machines to be allocated to create this virtual cluster. Most SWfMS use this initial virtual cluster configuration made by the user for the entire workflow execution. Dimensioning the virtual cluster to execute the workflow in parallel is then a top priority task since if the virtual cluster is under or over dimensioned it can impact on the workflow performance or increase (unnecessarily) financial costs. This dimensioning is far from trivial in a single-provider cloud and specially in federated clouds due to the huge number of virtual machine types to choose in each location and provider. In this article, we propose an approach named GraspCC-fed to produce the optimal (or near-optimal) estimation of the amount of virtual machines to allocate for each workflow. GraspCC-fed extends a previously proposed heuristic based on GRASP for executing standalone applications to consider scientific workflows executed in both single-provider and federated clouds. For the experiments, GraspCC-fed was coupled to an adapted version of SciCumulus workflow engine for federated clouds. This way, we believe that GraspCC-fed can be an important decision support tool for users and it can help determining an optimal configuration for the virtual cluster for parallel cloud-based scientific workflows.     

A fuzzy physiological approach for continuously modeling emotion during interaction with play technologies

The popularity of computer games has exploded in recent years, yet methods of evaluating user emotional state during play experiences lag far behind. There are few methods of assessing emotional state, and even fewer methods of quantifying emotion during play. This paper presents a novel method for continuously modeling emotion using physiological data. A fuzzy logic model transformed four physiological signals into arousal and valence. A second fuzzy logic model transformed arousal and valence into five emotional states relevant to computer game play: boredom, challenge, excitement, frustration, and fun. Modeled emotions compared favorably with a manual approach, and the means were also evaluated with subjective self-reports, exhibiting the same trends as reported emotions for fun, boredom, and excitement. This approach provides a method for quantifying emotional states continuously during a play experience.     

Preparing computers for affective communication: a psychophysiological concept and preliminary results

Despite rapid advances in technology, computers remain incapable of responding to human emotions. An exploratory study was conducted to find out what physiological parameters might be useful to differentiate among 4 emotional states, based on 2 dimensions: pleasantness versus unpleasantness and arousal versus relaxation. The 4 emotions were induced by exposing 26 undergraduate students to different combinations of olfactory and auditory stimuli, selected in a pretest from 12 stimuli by subjective ratings of arousal and valence. Changes in electroencephalographic (EEG), heart rate variability, and electrodermal measures were used to differentiate the 4 emotions. EEG activity separates pleasantness from unpleasantness only in the aroused but not in the relaxed domain, where electrodermal parameters are the differentiating ones. All three classes of parameters contribute to a separation between arousal and relaxation in the positive valence domain, whereas the latency of the electrodermal response is the only differentiating parameter in the negative domain. We discuss how such a psychophysiological approach may be incorporated into a systemic model of a computer responsive to affective communication from the user.     

Recommender Systems: An Introduction,by Dietmar Jannach,Markus Zanker,Alexander Felfernig,and Gerhard Friedrich

In addressing user experience issues, users’ perceptions and emotions need to be considered important. This study examines the relationships between perceived usability/aesthetics and emotional valence/arousal/engagement through an experiment using 15 existing websites from various domains and questionnaire items developed to measure users’ responses. According to the experimental results, both perceived usability and perceived aesthetics were positively correlated with emotional valence and negatively correlated with emotional engagement. No specific relationship was found between perceived usability/aesthetics and emotional arousal. Perceived aesthetics potentially had a greater impact on valence than perceived usability. Unlike valence, engagement could be more influenced by perceived usability than by perceived aesthetics. These findings can be utilized as bases for applying users’ emotional responses in each dimension to the product-use situations in the chain of perceptions, emotions, and behaviors.     

Understanding the impact of perceived visual aesthetics on user evaluations: An emotional perspective

Studies aimed at predicting user judgments have been dominated by the usability and efficiency perspective. An important assumption of this perspective is that higher order judgments such as quality perception and download intention are mainly cognitive processes. Increasingly, research has shown that this perspective is incapable of fully explaining user judgments. Emerging research posits that emotions and emotional subcomponents that arise from aesthetic-based design factors are at least equally important for understanding how users form higher order judgments such as quality perception and attractiveness. In this article, light is shed on the role of emotions in affecting these judgments. This is performed for the particular case of mobile apps. Specifically, the relationship between various aesthetic subdimensions (classical and expressive) and emotional subcomponents (valence and arousal) is explored. First, an explanatory model from theories of aesthetics, emotions, and visual perception is derived. Second, a laboratory experiment is conducted, and it provides empirical evidence for the relationships between visual aesthetics, emotions, and higher order evaluations such as users' quality perceptions and the intentions to download. Specifically, significant relationships were found between aesthetic subdimensions and valence, whereas arousal was partially significant. Selective emotional subdimensions also significantly impacted quality perceptions, attractiveness, and intention to download. Finally, implications for theory and practice are discussed.     

An online valuation-based sealed winner-bid auction game for resource allocation and pricing in clouds

Cloud computing is able to allocate different resources as virtual machines (VMs) to users, who need only pay for the amount of resources used. Two of the challenges in clouds are resource allocation and pricing in such a way to satisfy both cloud providers and users. Existing allocation and pricing mechanisms cannot guarantee increased profits due to various reasons. A better solution to increase the satisfaction of both parties, which is supported by economic theory, is the employment of auction-based allocation and pricing mechanisms. In these mechanisms, cloud resources and services are awarded based on the highest bids, while winners receive the quality of services expected. However, most existing auction-based mechanisms are inefficient and cannot be used in real clouds due to high computational or communication overhead, the bid function’s time complexity, and/or its inaccurate estimates. In the present paper, a lightweight mechanism is introduced which can be utilized in the real-world application of clouds. The currently proposed mechanism is a winner-bid auction game that seals users’ bids by a multi-criteria valuation-based bid function and sends them to the auctioneer. During scheduling, the auctioneer awards VMs exclusively to users with the highest bids. The presented approach is an online auction whose main aim is to increase the profits of the provider and user from different criteria. While determining the Nash equilibrium, the current study specifies the prices to be paid by users in various cases and proves the truthfulness of the proposed method. Finally, the effectiveness of the presented mechanism is examined through extensive experiments on different simulation scenarios and actual workload data.     

Using clouds to scale grid resources: An economic model

Infrastructure as a Service clouds are a flexible and fast way to obtain (virtual) resources as demand varies. Grids, on the other hand, are middleware platforms able to combine resources from different administrative domains for task execution. Clouds can be used by grids as providers of devices such as virtual machines, so they only use the resources they need. But this requires grids to be able to decide when to allocate and release those resources. Here we introduce and analyze by simulations an economic mechanism (a) to set resource prices and (b) resolve when to scale resources depending on the users’ demand. This system has a strong emphasis on fairness, so no user hinders the execution of other users’ tasks by getting too many resources.Our simulator is based on the well-known GridSim software for grid simulation, which we expand to simulate infrastructure clouds. The results show how the proposed system can successfully adapt the amount of allocated resources to the demand, while at the same time ensuring that resources are fairly shared among users.     

Introducing the Wunderkammer as a tool for emotion research: Unconstrained gaze and movement patterns in three emotionally evocative virtual worlds

Here we introduce the “Wunderkammer”, a suite of immersive virtual worlds with different types of emotionally-charged content. We use these worlds to examine the effects of affective context on unconstrained gaze and movement. In the Affect Gallery, participants freely explored a virtual art museum filled with objects that varied in valence and arousal. Participants approached and gazed more at positively valenced objects. This preference was amplified by more arousing objects and was strongest among individuals with resilient emotion regulation tendencies. This bias of avoiding negative valence did not emerge in The Crowded Room, an environment in which participants encountered virtual humans expressing different emotions. Instead, participants gazed more at negative than neutral emotional displays although they physically avoided angry (but not sad or neutral) agents. When placed inside Room 101, an unpredictable environment filled with a series of disturbing events, frightened participants became relatively immobile in terms of both gaze and movement. This freezing-type response was particularly strong among dispositionally resilient individuals. Together these results demonstrate that distinct affective contexts elicit unique patterns in unconstrained gaze and movement. They further illustrate the benefits of using virtual reality to study affect as it naturally emerges.     

Modeling of operators' emotion and task performance in a virtual driving environment

Emotional human–computer interactions are attracting increasing interest with the improvement in the available technology. Through presenting affective stimuli and empathic communication, computer agents are able to adjust to users' emotional states. As a result, users may produce better task performance. Existing studies have mainly focused on the effect of only a few basic emotions, such as happiness and frustration, on human performance. Furthermore, most research explored this issue from the psychological perspective. This paper presents an emotion and performance relation model in the context of vehicle driving. This general emotion–performance model is constructed on an arousal–valence plane and is not limited to basic emotions. Fifteen paid participants took part in two driving simulation experiments that induced 115 pairs of emotion–performance sample. These samples revealed the following: (1) driving performance has a downward U-shaped relationship with both intensities of arousal and valence. It deteriorates at extreme arousal and valence. (2) Optimal driving performance, corresponding to the appropriate emotional state, matches the “sweet spot” phenomenon of the engagement psychology. (3) Arousal and valence are not perfectly independent across the entire 2-D emotion plane. Extreme valence is likely to stimulate a high level of arousal, which, in turn, deteriorates task performance. The emotion–performance relation model proposed in the paper is useful in designing emotion-aware intelligent systems to predict and prevent task performance degradation at an early stage and throughout the human–computer interactions.     

Data mining in emotion color with affective computing

This research applies an innovative way to measure and identify user’s emotion with different ingredient color. How to find an intuitive way to understand human emotion is the key point in this research. The RGB color system that is widely used of all forms computer system is an accumulative color system in which red, green, and blue light are added together showing entire color. This study was based on Thayer’s emotion model which classifies the emotions with two vectors, valence and arousal, and gathers the emotion color with RGB as input for calculating and forecasting user’s emotion. In this experiment, using 320 data divide to quarter into emotion groups to train the weight in the neural network and uses 160 data to prove the accuracy. The result reveals that this model can be valid reckon the emotion by reply color response from user. In other hand, this experiment found that trend of the different ingredient of color on Cartesian coordinate system figures out the distinguishing intensity in RGB color system. Via the foregoing detect emotion model is going to design an affective computing intelligence framework try to embed the emotion component in it.     

语音情感的维度特征提取与识别

研究了情绪的维度空间模型与语音声学特征之间的关系以及语音情感的自动识别方法。介绍了基本情绪的维度空间模型,提取了唤醒度和效价度对应的情感特征,采用全局统计特征减小文本差异对情感特征的影响。研究了生气、高兴、悲伤和平静等情感状态的识别,使用高斯混合模型进行4种基本情感的建模,通过实验设定了高斯混合模型的最佳混合度,从而较好地拟合了4种情感在特征空间中的概率分布。实验结果显示,选取的语音特征适合于基本情感类别的识别,高斯混合模型对情感的建模起到了较好的效果,并且验证了二维情绪空间中,效价维度上的情感特征对语音情感识别的重要作用。     

Continuous emotion estimation of facial expressions on JAFFE and CK+ datasets for human–robot interaction

Human–robot interaction was always based on estimation of human emotions from human facial expressions, voice and gestures. Human emotions were always categorized in a discretized manner, while we estimate facial images from common datasets for continuous emotions. Linear regression was used in this study which numerically quantizes human emotions as valence and arousal by displaying the raw images on the two-respective coordinate axis. The face image datasets from the Japanese female facial expression (JAFFE) dataset and the extended Cohn–Kanade (CK+) dataset were used in this experiment. Human emotions for the above-mentioned datasets were interpreted by 85 participants who were used in the experimentation. The best result from a series of experiments shows that the minimum of root mean square error for the JAFFE dataset was 0.1661 for valence and 0.1379 for arousal. The proposed method has been compared with previous methods such as songs, sentences, and it is observed that the proposed method for common datasets testing showed an outstanding emotion estimation performance.

     

Automated registration of multi-view point clouds using sphere targets

This paper addresses the problem of automated registration of multi-view point clouds generated by a 3D scanner using sphere targets. First, sphere targets are detected from each point cloud. The centroids of the detected targets in each point cloud are then used for rough registration. Congruent triangles are computed from the centroids for the correspondence among them, with which a rigid body transformation is obtained to bring the two point clouds together as closely as possible. After the initial registration, the two point clouds are further registered by refining the position and orientation of the point clouds using the underlying geometric shapes of the targets. These registration steps are integrated into one system that allows two input point clouds automatically registered with no user intervention. Real examples are used to demonstrate the performance of the point cloud registration.     

Affective video content analysis based on multimodal data fusion in heterogeneous networks

In heterogeneous networks, different modalities are coexisting. For example, video sources with certain lengths usually have abundant time-varying audiovisual data. From the users’ perspective, different video segments will trigger different kinds of emotions. In order to better interact with users in heterogeneous networks and improve their user experiences, affective video content analysis to predict users’ emotions is essential. Academically, users’ emotions can be evaluated by arousal and valence values, and fear degree, which provides an approach to quantize the prediction accuracy of the reaction of the audience and users towards videos. In this paper, we propose the multimodal data fusion method for integrating the visual and audio data in order to perform the affective video content analysis. Specifically, to align the visual and audio data, the temporal attention filters are proposed to obtain the time-span features of the entire video segments. Then, by using the two-branch network structure, matched visual and audio features are integrated in the common space. At last, the fused audiovisual feature is employed for the regression and classification subtasks in order to measure the emotional responses of users. Simulation results show that the proposed method can accurately predict the subjective feelings of users towards the video contents, which provides a way to predict users’ preferences and recommend videos according to their own demand.     

A field-based service management and discovery method in multiple clouds context

In diverse and self-governed multiple clouds context, the service management and discovery are greatly challenged by the dynamic and evolving features of services. How to manage the features of cloud services and support accurate and efficient service discovery has become an open problem in the area of cloud computing. This paper proposes a field model of multiple cloud services and corresponding service discovery method to address the issue. Different from existing researches, our approach is inspired by Bohr atom model. We use the abstraction of energy level and jumping mechanism to describe services status and variations, and thereby to support the service demarcation and discovery. The contributions of this paper are threefold. First, we propose the abstraction of service energy level to represent the status of services, and service jumping mechanism to investigate the dynamic and evolving features as the variations and re-demarcation of cloud services according to their energy levels. Second, we present user acceptable service region to describe the services satisfying users’ requests and corresponding service discovery method, which can significantly decrease services search scope and improve the speed and precision of service discovery. Third, a series of algorithms are designed to implement the generation of field model, user acceptable service regions, service jumping mechanism, and user-oriented service discovery.We have conducted an extensive experiments on QWS dataset to validate and evaluate our proposed models and algorithms. The results show that field model can well support the representation of dynamic and evolving aspects of services in multiple clouds context and the algorithms can improve the accuracy and efficiency of service discovery.     

Forbidden region virtual fixtures from streaming point clouds

Forbidden region virtual fixtures protect objects from unwanted contact with a robot. In this paper, we propose a method for creating forbidden region haptic virtual fixtures for teleoperation from streaming point clouds obtained by an RGB-D camera. Upon violating the protected area, the operator receives force feedback that opposes motion inside the forbidden region. Three architectures for creating virtual fixtures are presented and their advantages and disadvantages are described. The proposed methods have the ability to implement constraints and can handle dynamic environments in real-time. The effectiveness of the methods is demonstrated in experiments with a surgical robot.     

Visualization of volcanic ash clouds

Ash clouds from a volcanic eruption-invisible to radar and nearly indistinguishable from weather clouds-pose a serious hazard to aviation safety. This article describes a system developed by the Alaska Volcano Observatory and the Arctic Region Supercomputing Center for predicting and visualizing the movement of ash clouds. Using meteorological and geophysical data from volcanic eruptions, a supercomputer model provides predictions of ash cloud movements for up to 72 hours. The AVS (Ash-cloud Visualization System) controls the execution of the ash cloud model and displays the model output in 3D form, showing the location of the ash cloud over a digital terrain model     

交互式过程隐函数云建模方法

计算机生成云已成为虚拟环境中不可或缺的自然景观之一。提出了两层次结构的交互式云建模系统。利用隐式体函数定义的元球（blob）构建云的基本结构,同时采用过程噪声生成云细节部分。利用粒子系统创建元球的分裂和消亡规则,在基本结构的基础上自动生成层次更为丰富的云形状。方法适用于建模多种不同类型的云,并能交互式绘制建模结果。