Operations Management

Global competition has caused fundamental changes in the competitive environment of the manufacturing and service industries. Firms should develop strategic objectives that, upon achievement, result in a competitive advantage in the market place. The forces of globalization on one hand and rapidly growing marketing opportunities overseas, especially in emerging economies on the other, have led to the expansion of operations on a global scale. The book aims to cover the main topics characterizing operations management including both strategic issues and practical applications. A global environmental business including both manufacturing and services is analyzed. The book contains original research and application chapters from different perspectives. It is enriched through the analyses of case studies

Models and Analysis of Vocal Emissions for Biomedical Applications

The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies

Grounding semantic cognition using computational modelling and network analysis

The overarching objective of this thesis is to further the field of grounded semantics using a range of computational and empirical studies. Over the past thirty years, there have been many algorithmic advances in the modelling of semantic cognition. A commonality across these cognitive models is a reliance on hand-engineering “toy-models”. Despite incorporating newer techniques (e.g. Long short-term memory), the model inputs remain unchanged. We argue that the inputs to these traditional semantic models have little resemblance with real human experiences. In this dissertation, we ground our neural network models by training them with real-world visual scenes using naturalistic photographs. Our approach is an alternative to both hand-coded features and embodied raw sensorimotor signals. We conceptually replicate the mutually reinforcing nature of hybrid (feature-based and grounded) representations using silhouettes of concrete concepts as model inputs. We next gradually develop a novel grounded cognitive semantic representation which we call scene2vec, starting with object co-occurrences and then adding emotions and language-based tags. Limitations of our scene-based representation are identified for more abstract concepts (e.g. freedom). We further present a large-scale human semantics study, which reveals small-world semantic network topologies are context-dependent and that scenes are the most dominant cognitive dimension. This finding leads us to conclude that there is no meaning without context. Lastly, scene2vec shows promising human-like context-sensitive stereotypes (e.g. gender role bias), and we explore how such stereotypes are reduced by targeted debiasing. In conclusion, this thesis provides support for a novel computational viewpoint on investigating meaning - scene-based grounded semantics. Future research scaling scene-based semantic models to human-levels through virtual grounding has the potential to unearth new insights into the human mind and concurrently lead to advancements in artificial general intelligence by enabling robots, embodied or otherwise, to acquire and represent meaning directly from the environment

Brainwave-Based Human Emotion Estimation using Deep Neural Network Models for Biofeedback

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonEmotion is a state that comprehensively represents human feeling, thought and behavior, thus takes an important role in interpersonal human communication. Emotion estimation aims to automatically discriminate different emotional states by using physiological and nonphysiological signals acquired from human to achieve effective communication and interaction between human and machines. Brainwaves-Based Emotion Estimation is one of the most common used and efficient methods for emotion estimation research. The technology reveals a great role for human emotional disorder treatment, brain computer interface for disabilities, entertainment and many other research areas. In this thesis, various methods, schemes and frameworks are presented for Electroencephalogram (EEG) based human emotion estimation. Firstly, a hybrid dimension featurere duction scheme is presented using a total of 14 different features extracted from EEG recordings. The scheme combines these distinct features in the feature space using both supervised and unsupervised feature selection processes. Maximum Relevance Minimum Redundancy (mRMR) is applied to re-order the combined features into max-relevance with the emotion labels and min-redundancy of each feature. The generated features are further reduced with Principal Component Analysis (PCA) for extracting the principal components. Experimental results show that the proposed work outperforms the state-of-art methods using the same settings at the publicly available Database for Emotional Analysis using Physiological Signals (DEAP) data set. Secondly, a disentangled adaptive noise learning β-Variational autoencoder (VAE) combinewithlongshorttermmemory(LSTM)modelwasproposedfortheemotionrecognition based on EEG recordings. The experiment is also based on the EEG emotion public DEAPdataset. At first, the EEG time-series data are transformed into the Video-like EEG image data through the Azimuthal Equidistant Projection (AEP) to original EEG-sensor 3-D coordinates to perform 2-D projected locations of electrodes. Then Clough-Tocher scheme is applied for interpolating the scattered power measurements over the scalp and for estimating the values in-between the electrodes over a 32x32 mesh. After that, the βVAE LSTM algorithm is used to estimate the accuracy of the quadratic (arousal-valence) classification. The comparison between the β VAE-LSTM model and other classic methods is conducted at the same experimental setting that shows that the proposed model is effective. Finally, a novel real-time emotion detection system based on the EEG signals from a portable headband was presented, integrated into the interactive film ‘RIOT’. At first, the requirement of the interactive film was collected and the protocol for data collection using a portable EEG sensor (Emotiv Epoc) was designed. Then, a portable EEG emotion database (PEED) is built from 10 participants with the emotion labels using both self-reporting and video annotation tools. After that, various feature extraction, feature selection, validation scheme and classification methods are explored to build a practical system for the real-time detection. In the end, the emotion detection system is trained and integrated into the interactive film for real-time implementation and fully evaluated. The experimental results demonstrate the system with satisfied emotion detection accuracy and real-time performance

Storytelling, self, and affiliation : conversation analysis of interactions between neurotypical participants and participants with Asperger syndrome

https://helda.helsinki.fi/handle/10138/341931This dissertation examines interpersonal affiliation and the reciprocal protecting of selves and their worthiness, i.e., face-work, during conversational storytelling and story reception. The method utilized is Conversation Analysis (CA), which is a qualitative method for studying audio and video recorded interactions. CA’s purpose is unravelling recurring interactional practices through which social actions are constructed. The dataset analyzed in the study consists of ten video recordings of 45- to 60-minute dyadic conversations, where one participant has been diagnosed with Asperger syndrome (AS) and the other participant is neurotypical (NT), and nine video recordings, in which both participants are neurotypical. The participants were adult males, aged between 18-40 years. The participants received instructions to talk about happy events and losses in their lives in a freely chosen way. Storytelling and story reception practices have previously gained considerable attention in CA, as have the interactional practices of participants diagnosed with autism spectrum disorder or AS. The investigation in the current study, however, involves a unique combination of these elements. Studying AS–NT interactions can increase our understanding of the underlying structures and norms of conversational storytelling and help reveal the taken for granted aspects of ‘commonsense’ that usually go unquestioned. The aim for the study is thus twofold: to investigate the face-work, storytelling and story reception practices of individuals diagnosed with AS, and to increase our understanding of these phenomena in general. More specifically, the focus of the study is on the displays of (non-)affiliation and on the differing degrees of affiliation conveyed by different interactional practices. Since the study compares the interactional practices of NT and AS participants in the same interactional setting, it inherently involves categorizing the participants. CA has generally followed the policy of ‘ethnomethodological indifference’ toward the participants’ identities and predominantly focused on how participants themselves categorize each other in their talk. However, in this study the empirical observations of the participants’ talk have been interpreted in the light of different contextual factors, which include the participants’ neurological statuses. The dissertation consists of four research articles. The first concerns stories in which the AS participants are in the spontaneously assumed role of the recipient. The results are discussed in relation to earlier CA findings on story reception and affiliation in typical interaction, as well as on AS and its specific interactional features. The second article compares the affiliation and topicality of the questions that AS and NT story recipients ask after their co-participants’ tellings. The article shows that the affiliative import of story-responsive questions can only really be seen in retrospect, because the questioner can cast their action in an affiliative or non-affiliative light in subsequent turns. The third article investigates how story recipients manage to display the right level of access to the events the teller describes in order to achieve affiliation. The article describes two main ways to accomplish this in a responsive utterance: fine-tuning the strength of one’s access claim and adjusting the degree of generalization. The fourth article explores the differences in the ways in which the AS and NT participants recognize and manage face threats in interaction, in their role as both storytellers and story recipients. The study shows how affiliation and the establishment of empathic communion between participants has several intersecting levels, as refraining from endorsing the affective stance displayed in the co-participant’s telling can sometimes be a prosocial move that protects the selves of the participants. In addition, the study suggests that the difference between the NT and AS participants lies not in the amount of affiliation per se but in the subtle use of conversational practices to manage their non-affiliation. The study proposes that future CA studies of asymmetric interactions may consider more theory-laden approaches in addition to the traditional ‘ethnomethodologically indifferent’ perspectives

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers