A survey on mobile affective computing

The spontaneous recognition of emotional states and personality traits of individuals has been puzzling researchers for years whereas pertinent studies demonstrating the progress in the field, despite their diversity, are still encouraging. This work surveys the most well-known research studies and the state-of-the-art on affect recognition domain based on smartphone acquired data, namely smartphone embedded sensors and smartphone usage. Inevitably, supplementary modalities employed in many eminent studies are also reported here for the sake of completeness. Nevertheless, the intention of the survey is threefold; firstly to document all the to-date relevant literature on affect recognition through smartphone modalities, secondly to argue for the full potential of smartphone use in the inference of affect, and thirdly to demonstrate the current research trends towards mobile affective computing.     

A systematic review on affective computing: emotion models,databases, and recent advances

Affective computing conjoins the research topics of emotion recognition and sentiment analysis, and can be realized with unimodal or multimodal data, consisting primarily of physical information (e.g., text, audio, and visual) and physiological signals (e.g., EEG and ECG). Physical-based affect recognition caters to more researchers due to the availability of multiple public databases, but it is challenging to reveal one's inner emotion hidden purposefully from facial expressions, audio tones, body gestures, etc. Physiological signals can generate more precise and reliable emotional results; yet, the difficulty in acquiring these signals hinders their practical application. Besides, by fusing physical information and physiological signals, useful features of emotional states can be obtained to enhance the performance of affective computing models. While existing reviews focus on one specific aspect of affective computing, we provide a systematical survey of important components: emotion models, databases, and recent advances. Firstly, we introduce two typical emotion models followed by five kinds of commonly used databases for affective computing. Next, we survey and taxonomize state-of-the-art unimodal affect recognition and multimodal affective analysis in terms of their detailed architectures and performances. Finally, we discuss some critical aspects of affective computing and its applications and conclude this review by pointing out some of the most promising future directions, such as the establishment of benchmark database and fusion strategies. The overarching goal of this systematic review is to help academic and industrial researchers understand the recent advances as well as new developments in this fast-paced, high-impact domain.     

Quantum-inspired multimodal fusion for video sentiment analysis

We tackle the crucial challenge of fusing different modalities of features for multimodal sentiment analysis. Mainly based on neural networks, existing approaches largely model multimodal interactions in an implicit and hard-to-understand manner. We address this limitation with inspirations from quantum theory, which contains principled methods for modeling complicated interactions and correlations. In our quantum-inspired framework, the word interaction within a single modality and the interaction across modalities are formulated with superposition and entanglement respectively at different stages. The complex-valued neural network implementation of the framework achieves comparable results to state-of-the-art systems on two benchmarking video sentiment analysis datasets. In the meantime, we produce the unimodal and bimodal sentiment directly from the model to interpret the entangled decision.     

From signals to knowledge: A conceptual model for multimodal learning analytics

Multimodality in learning analytics and learning science is under the spotlight. The landscape of sensors and wearable trackers that can be used for learning support is evolving rapidly, as well as data collection and analysis methods. Multimodal data can now be collected and processed in real time at an unprecedented scale. With sensors, it is possible to capture observable events of the learning process such as learner's behaviour and the learning context. The learning process, however, consists also of latent attributes, such as the learner's cognitions or emotions. These attributes are unobservable to sensors and need to be elicited by human‐driven interpretations. We conducted a literature survey of experiments using multimodal data to frame the young research field of multimodal learning analytics. The survey explored the multimodal data used in related studies (the input space) and the learning theories selected (the hypothesis space). The survey led to the formulation of the Multimodal Learning Analytics Model whose main objectives are of (O1) mapping the use of multimodal data to enhance the feedback in a learning context; (O2) showing how to combine machine learning with multimodal data; and (O3) aligning the terminology used in the field of machine learning and learning science.     

多模态情绪识别研究综述

本文针对多模态情绪识别这一新兴领域进行综述。首先从情绪描述模型及情绪诱发方式两个方面对情绪识别的研究基础进行了综述。接着针对多模态情绪识别中的信息融合这一重难点问题,从数据级融合、特征级融合、决策级融合、模型级融合4种融合层次下的主流高效信息融合策略进行了介绍。然后从多种行为表现模态混合、多神经生理模态混合、神经生理与行为表现模态混合这3个角度分别列举具有代表性的多模态混合实例,全面合理地论证了多模态相较于单模态更具情绪区分能力和情绪表征能力,同时对多模态情绪识别方法转为工程技术应用提出了一些思考。最后立足于情绪识别研究现状的分析和把握,对改善和提升情绪识别模型性能的方式和策略进行了深入的探讨与展望。     

A multimodal fusion method for sarcasm detection based on late fusion

Information on social media is multi-modal, most of which contains the meaning of sarcasm. In recent years, many people have studied the problem of sarcasm detection. Many traditional methods have been proposed in this field, but the study of deep learning methods to detect sarcasm is still insufficient. It is necessary to comprehensively consider the information of the text,the changes of the tone of the audio signal,the facial expressions and the body posture in the image to detect sarcasm. This paper proposes a multi-level late-fusion learning framework with residual connections, a more reasonable experimental data-set split and two model variants based on different experimental settings. Extensive experiments on the MUStARD show that our methods are better than other fusion models. In our speaker-independent experimental split, the multi-modality has a 4.85% improvement over the single-modality, and the Error rate reduction has an improvement of 11.8%. The latest code will be updated to this URL later: https://github.com/DingNing123/m_fusion

     

Random area-perimeter method for generation of unimodal and multimodal cancelable biometric templates

Applied Intelligence - In recent times, biometric based authentication systems have seen a tremendous growth in various applications. However, if databases in multiple applications are created...     

A novel approach for multimodal medical image fusion

Fusion of multimodal medical images increases robustness and enhances accuracy in biomedical research and clinical diagnosis. It attracts much attention over the past decade. In this paper, an efficient multimodal medical image fusion approach based on compressive sensing is presented to fuse computed tomography (CT) and magnetic resonance imaging (MRI) images. The significant sparse coefficients of CT and MRI images are acquired via multi-scale discrete wavelet transform. A proposed weighted fusion rule is utilized to fuse the high frequency coefficients of the source medical images; while the pulse coupled neural networks (PCNN) fusion rule is exploited to fuse the low frequency coefficients. Random Gaussian matrix is used to encode and measure. The fused image is reconstructed via Compressive Sampling Matched Pursuit algorithm (CoSaMP). To show the efficiency of the proposed approach, several comparative experiments are conducted. The results reveal that the proposed approach achieves better fused image quality than the existing state-of-the-art methods. Furthermore, the novel fusion approach has the superiority of high stability, good flexibility and low time consumption.     

From reaction-diffusion to Physarum computing

We experimentally demonstrate that computation of spanning trees and implementation of general purpose storage-modification machines can be executed by a vegetative state of the slime mold Physarum polycephalum. We advance theory and practice of reaction-diffusion computing by studying a biological model of reaction-diffusion encapsulated in a membrane.     

Function-based computing and parallelism: A review

One of today's most popular computing folktheorems states that true parallel processing and conventional computing techniques are mutually incompatible. The term Von Neumann bottleneck summarizes what many feel are the basic stumbling blocks preventing the successful application of parallelism in day-to-day computing. This paper reviews an alternative approach, based on function-based computing, that to a large degree eliminates or avoids much of the Von Neumann bottleneck, and offers opportunities for the exploitation of parallelism in ways not even conceivable in classical computing.Topics covered include a review of the Von Neumann bottleneck and imperative languages, the mathematical foundation of functional computing, namely lambda calculus, how this foundation provides opportunities for parallelism, and characteristics of the design space for implementation of these concepts in real computing hardware.     

Facial expression and body gesture emotion recognition: A systematic review on the use of visual data in affective computing

Emotion is an important driver of human decision-making and communication. With the recent rise of human–computer interaction, affective computing has become a trending research topic, aiming to develop computational systems that can understand human emotions and respond to them. A systematic review has been conducted to fill these gaps since previous reviews regarding machine-enabled automated visual emotion recognition neglect important methodological aspects, including emotion models and hardware usage. 467 relevant papers were initially found and examined. After the screening process with specific inclusion and exclusion criteria, 30 papers were selected. Methodological aspects including emotion models, devices, architectures, and classification techniques employed by the selected studies were analyzed, and the most popular techniques and current trends in visual emotion recognition were identified. This review not only offers a comprehensive and up-to-date overview of the topic but also provides researchers with insights regarding methodological aspects like emotion models employed, devices used, and classification techniques for automated visual emotion recognition. By identifying current trends, like the increased use of deep learning algorithms and the need for further study on body gestures, this review advocates the advantages of implementing emotion recognition with the use of visual data and builds a solid foundation for applying relevant techniques in different fields.     

Linguistic-based emotion analysis and recognition for measuring consumer satisfaction: an application of affective computing

A growing body of research suggests that affective computing has many valuable applications in enterprise systems research and e-businesses. This paper explores affective computing techniques for a vital sub-area in enterprise systems—consumer satisfaction measurement. We propose a linguistic-based emotion analysis and recognition method for measuring consumer satisfaction. Using an annotated emotion corpus (Ren-CECps), we first present a general evaluation of customer satisfaction by comparing the linguistic characteristics of emotional expressions of positive and negative attitudes. The associations in four negative emotions are further investigated. After that, we build a fine-grained emotion recognition system based on machine learning algorithms for measuring customer satisfaction; it can detect and recognize multiple emotions using customers’ words or comments. The results indicate that blended emotion recognition is able to gain rich feedback data from customers, which can provide more appropriate follow-up for customer relationship management.     

From cloud computing to cloud manufacturing

Cloud computing is changing the way industries and enterprises do their businesses in that dynamically scalable and virtualized resources are provided as a service over the Internet. This model creates a brand new opportunity for enterprises. In this paper, some of the essential features of cloud computing are briefly discussed with regard to the end-users, enterprises that use the cloud as a platform, and cloud providers themselves. Cloud computing is emerging as one of the major enablers for the manufacturing industry; it can transform the traditional manufacturing business model, help it to align product innovation with business strategy, and create intelligent factory networks that encourage effective collaboration. Two types of cloud computing adoptions in the manufacturing sector have been suggested, manufacturing with direct adoption of cloud computing technologies and cloud manufacturing—the manufacturing version of cloud computing. Cloud computing has been in some of key areas of manufacturing such as IT, pay-as-you-go business models, production scaling up and down per demand, and flexibility in deploying and customizing solutions. In cloud manufacturing, distributed resources are encapsulated into cloud services and managed in a centralized way. Clients can use cloud services according to their requirements. Cloud users can request services ranging from product design, manufacturing, testing, management, and all other stages of a product life cycle.     

From volunteer to trustable computing: Providing QoS-aware scheduling mechanisms for multi-grid computing environments

The exploitation of service oriented technologies, such as Grid computing, is being boosted by the current service oriented economy trend, leading to a growing need of Quality of Service (QoS) mechanisms. However, Grid computing was created to provide vast amounts of computational power but in a best effort way. Providing QoS guarantees is therefore a very difficult and complex task due to the distributed and heterogeneous nature of their resources, specially the volunteer computing resources (e.g., desktop resources).The scope of this paper is to empower an integrated multi QoS support suitable for Grid Computing environments made of either dedicated and volunteer resources, even taking advantage of that fact. The QoS is provided through SLAs by exploiting different available scheduling mechanisms in a coordinated way, and applying appropriate resource usage optimization techniques. It is based on the differentiated use of reservations and scheduling in advance techniques, enhanced with the integration of rescheduling techniques that improve the allocation decisions already made, achieving a higher resource utilization and still ensuring the agreed QoS. As a result, our proposal enhances best-effort Grid environments by providing QoS aware scheduling capabilities.This proposal has been validated by means of a set of experiments performed in a real Grid testbed. Results show how the proposed framework effectively harnesses the specific capabilities of the underlying resources to provide every user with the desired QoS level, while, at the same time, optimizing the resources’ usage.     

A corpus-based approach to the multimodal analysis of specialized knowledge

Modern communication environments have changed the cognitive patterns of individuals, who are now used to the interaction of information encoded in different semiotic modalities, especially visual and linguistic. Despite this, the main premise of Corpus Linguistics is still ruling: our perception of and experience with the world is conveyed in texts, which nowadays need to be studied from a multimodal perspective. Therefore, multimodal corpora are becoming extremely useful to extract specialized knowledge and explore the insights of specialized language and its relation to non-language-specific representations of knowledge. It is our assertion that the analysis of the image-text interface can help us understand the way visual and linguistic information converge in subject-field texts. In this article, we use Frame-based terminology to sketch a novel proposal to study images in a corpus rich in pictorial representations for their inclusion in a terminological resource on the environment. Our corpus-based approach provides the methodological underpinnings to create meaning within terminographic entries, thus facilitating specialized knowledge transfer and acquisition through images.