Because of the difficulty of music feature recognition due to the complex and varied music theory knowledge influenced by music specialization, we designed a music feature recognition system based on Internet of Things technology. The physical sensing layer of the system places sound sensors at different locations to collect the original music signals and uses a digital signal processor to carry out music signal analysis and processing. The network transmission layer transmits the completed music signals to (...) the music signal database in the application layer of the system. The music feature analysis module of the application layer uses a dynamic time regularization algorithm to obtain the maximum similarity between the test template and the reference. The music feature analysis module of the application layer uses the dynamic time regularization algorithm to obtain the maximum similarity between the test template and the reference template to realize the feature recognition of the music signal and determine the music pattern and music emotion corresponding to the music feature content according to the recognition result. The experimental results show that the system operates stably, can capture high-quality music signals, and can correctly identify music style features and emotion features. The results of this study can meet the needs of composers’ assisted creation and music researchers’ analysis of a large amount of music data, and the results can be further transferred to deep music learning research, human-computer interaction music creation, application-based music creation, and other fields for expansion. (shrink)

This paper uses an improved deep learning algorithm to judge the rationality of the design of landscape image feature recognition. The preprocessing of the image is proposed to enhance the data. The deficiencies in landscape feature extraction are further addressed based on the new model. Then, the two-stage training method of the model is used to solve the problems of long training time and convergence difficulties in deep learning. Innovative methods for zoning and segmentation training of landscape (...) pattern features are proposed, which makes model training faster and generates more creative landscape patterns. Because of the impact of too many types of landscape elements in landscape images, traditional convolutional neural networks can no longer effectively solve this problem. On this basis, a fully convolutional neural network model is designed to perform semantic segmentation of landscape elements in landscape images. Through the method of deconvolution, the pixel-level semantic segmentation is realized. Compared with the 65% accuracy rate of the convolutional neural network, the fully convolutional neural network has an accuracy rate of 90.3% for the recognition of landscape elements. The method is effective, accurate, and intelligent for the classification of landscape element design, which better improves the accuracy of classification, greatly reduces the cost of landscape element design classification, and ensures that the technical method is feasible. This paper classifies landscape behavior based on this model for full convolutional neural network landscape images and demonstrates the effectiveness of using the model. In terms of landscape image processing, the image evaluation provides a certain basis. (shrink)

Face recognition is a computationally challenging classification task. Deep convolutional neural networks (DCNNs) are brain‐inspired algorithms that have recently reached human‐level performance in face and object recognition. However, it is not clear to what extent DCNNs generate a human‐like representation of face identity. We have recently revealed a subset of facial features that are used by humans for face recognition. This enables us now to ask whether DCNNs rely on the same facial information and whether this human‐like (...) representation depends on a system that is optimized for face identification. In the current study, we examined the representation of DCNNs of faces that differ in features that are critical or non‐critical for human face recognition. Our findings show that DCNNs optimized for face identification are tuned to the same facial features used by humans for face recognition. Sensitivity to these features was highly correlated with performance of the DCNN on a benchmark face recognition task. Moreover, sensitivity to these features and a view‐invariant face representation emerged at higher layers of a DCNN optimized for face recognition but not for object recognition. This finding parallels the division to a face and an object system in high‐level visual cortex. Taken together, these findings validate human perceptual models of face recognition, enable us to use DCNNs to test predictions about human face and object recognition as well as contribute to the interpretability of DCNNs. (shrink)

The immune system, to protect the body, must discriminate between the pathogenic and non-pathogenic microbes and respond to them in different ways. How the mucosal immune system manages to make this distinction is poorly understood. We suggest here that the distinction between pathogenic and non-pathogenic microbes is made by an integrated system rather than by single types of cells or single types of receptors; a systems biology approach is needed to understand immune recognition.

Recently, infrared human action recognition has attracted increasing attention for it has many advantages over visible light, that is, being robust to illumination change and shadows. However, the infrared action data is limited until now, which degrades the performance of infrared action recognition. Motivated by the idea of transfer learning, an infrared human action recognition framework using auxiliary data from visible light is proposed to solve the problem of limited infrared action data. In the proposed framework, we (...) first construct a novel Cross-Dataset Feature Alignment and Generalization framework to map the infrared data and visible light data into a common feature space, where Kernel Manifold Alignment and a dual aligned-to-generalized encoders model are employed to represent the feature. Then, a support vector machine is trained, using both the infrared data and visible light data, and can classify the features derived from infrared data. The proposed method is evaluated on InfAR, which is a publicly available infrared human action dataset. To build up auxiliary data, we set up a novel visible light action dataset XD145. Experimental results show that the proposed method can achieve state-of-the-art performance compared with several transfer learning and domain adaptation methods. (shrink)

Smart homes based on the Internet of Things have been rapidly developed. To improve the safety, comfort, and convenience of residents’ lives with minimal cost, daily activity recognition aims to know resident’s daily activity in non-invasive manner. The performance of daily activity recognition heavily depends on solving strategy of activity feature. However, the current common employed solving strategy based on statistical information of individual activity does not support well the activity recognition. To improve the common employed (...) solving strategy, an activity feature solving strategy based on TF-IDF is proposed in this paper. The proposed strategy exploits statistical information related to both individual activity and the whole of activities. Two distinct datasets have been commissioned, to mitigate against any possible effect of coupling between dataset and sensor configuration. Finally, a number of machine learning techniques and deep learning technique have been evaluated to assess their performance for residents activity recognition. (shrink)

Past research demonstrates that emotion recognition is influenced by social category cues present on faces. However, little research has investigated whether holistic processing is required to observe these influences of social category information on emotion perception, and no studies have investigated whether different visual sampling strategies (i.e. differences in the allocation of attention to different regions of the face) contribute to the interaction between social cues and emotional expressions. The current study aimed to address this. Participants categorised happy and (...) angry expressions on own- and other-race faces, and male and female faces. In Experiments 1 and 2, holistic processing was disrupted by presenting inverted faces (Experiment 1) or part faces (Experiment 2). In Experiments 3 and 4 participants’ eye-gaze to eye and mouth regions was also tracked. Disrupting holistic processing did not alter the moderating influence of sex and race cues on emotion recognition (Experiments 1, 2, 4). Gaze patterns differed as a function of emotional expression, and social category cues, however, eye-gaze patterns did not reflect response time patterns (Experiments 3 and 4). Results indicate that the interaction between social category cues and emotion does not require holistic processing and is not driven by differences in visual sampling. (shrink)

For unmanned aerial vehicle, object detection at different scales is an important component for the visual recognition. Recent advances in convolutional neural networks have demonstrated that attention mechanism remarkably enhances multiscale representation of CNNs. However, most existing multiscale feature representation methods simply employ several attention blocks in the attention mechanism to adaptively recalibrate the feature response, which overlooks the context information at a multiscale level. To solve this problem, a multiscale feature filtering network is proposed in (...) this paper for image recognition system in the UAV. A novel building block, namely, multiscale feature filtering module, is proposed for ResNet-like backbones and it allows feature-selective learning for multiscale context information across multiparallel branches. These branches employ multiple atrous convolutions at different scales, respectively, and further adaptively generate channel-wise feature responses by emphasizing channel-wise dependencies. Experimental results on CIFAR100 and Tiny ImageNet datasets reflect that the MFFNet achieves very competitive results in comparison with previous baseline models. Further ablation experiments verify that the MFFNet can achieve consistent performance gains in image classification and object detection tasks. (shrink)

State-of-the-art facial expression methods outperform human beings, especially, thanks to the success of convolutional neural networks. However, most of the existing works focus mainly on analyzing an adult’s face and ignore the important problems: how can we recognize facial expression from a baby’s face image and how difficult is it? In this paper, we first introduce a new face image database, named BabyExp, which contains 12,000 images from babies younger than two years old, and each image is with one of (...) three facial expressions. To the best of our knowledge, the proposed dataset is the first baby face dataset for analyzing a baby’s face image, which is complementary to the existing adult face datasets and can shed some light on exploring baby face analysis. We also propose a feature guided CNN method with a new loss function, called distance loss, to optimize interclass distance. In order to facilitate further research, we provide the benchmark of expression recognition on the BabyExp dataset. Experimental results show that the proposed network achieves the recognition accuracy of 87.90% on BabyExp. (shrink)

There are complex posture changes in dance movements, which lead to the low accuracy of dance movement recognition. And none of the current motion recognition uses the dancer’s attributes. The attribute feature of dancer is the important high-level semantic information in the action recognition. Therefore, a dance movement recognition algorithm based on feature expression and attribute mining is designed to learn the complicated and changeable dancer movements. Firstly, the original image information is compressed by (...) the time-domain fusion module, and the information of action and attitude can be expressed completely. Then, a two-way feature extraction network is designed, which extracts the details of the actions along the way and takes the sequence image as the input of the network. Then, in order to enhance the expression ability of attribute features, a multibranch spatial channel attention integration module based on an attention mechanism is designed to extract the features of each attribute. Finally, using the semantic inference and information transfer function of the graph convolution network, the relationship between attribute features and dancer features can be mined and deduced, and more expressive action features can be obtained; thus, high-performance dance motion recognition is realized. The test and analysis results on the data set show that the algorithm can recognize the dance movement and improve the accuracy of the dance movement recognition effectively, thus realizing the movement correction function of the dancer. (shrink)

Handwritten text recognition is considered as the most challenging task for the research community due to slight change in different characters’ shape in handwritten documents. The unavailability of a standard dataset makes it vaguer in nature for the researchers to work on. To address these problems, this paper presents an optical character recognition system for the recognition of offline Pashto characters. The problem of the unavailability of a standard handwritten Pashto characters database is addressed by developing a (...) medium-sized database of offline Pashto characters. This database consists of 11352 character images. Enriched feature extraction techniques of histogram of oriented gradients and zoning-based density features are used for feature extraction of carved Pashto characters. K-nearest neighbors is considered as a classification tool for the proposed algorithm based on the proposed feature sets. A resultant accuracy of 80.34% is calculated for the histogram of oriented gradients, while for zoning-based density features, 76.42% is achieved using 10-fold cross validation. (shrink)

With the rapid emergence of the technology of deep learning, it was successfully used in different fields such as the aquatic product. New opportunities in addition to challenges can be created according to this change for helping data processing in the smart fish farm. This study focuses on deep learning applications and how to support different activities in aquatic like identification of the fish, species classification, feeding decision, behavior analysis, estimation size, and prediction of water quality. Power and performance of (...) computing with the analyzed given data are applied in the proposed DL method within fish farming. Results of the proposed method show the significance of contributions in deep learning and how automatic features are extracted. Still, there is a big challenge of using deep learning in an era of artificial intelligence. Training of the proposed method used a large number of labeled images got from the Fish4Knowledge dataset. The proposed method based on suitable feature extracted from the fish achieved good results in terms of recognition rate and accuracy. (shrink)

In speaker recognition systems, feature extraction is a challenging task under environment noise conditions. To improve the robustness of the feature, we proposed a multiscale chaotic feature for speaker recognition. We use a multiresolution analysis technique to capture more finer information on different speakers in the frequency domain. Then, we extracted the speech chaotic characteristics based on the nonlinear dynamic model, which helps to improve the discrimination of features. Finally, we use a GMM-UBM model to (...) develop a speaker recognition system. Our experimental results verified its good performance. Under clean speech and noise speech conditions, the ERR value of our method is reduced by 13.94% and 26.5% compared with the state-of-the-art method, respectively. (shrink)

Presently, the State Grid Corporation of China has accumulated a large amount of maintenance records for power primary equipment. Unfortunately, most of these records are unstructured data which lead to difficultly analyze and utilize them. The emergence of natural language processing technology and deep learning methods provide a solution for unstructured text data. This paper proposes a progressive multitype feature fusion model to recognize Chinese named entity of unstructured maintenance records for power primary equipment. Firstly, the textual characteristics and (...) word separation difficulties of maintenance records are analyzed, then 7 main entity categories of power technical terms from unstructured maintenance records are chosen, and 3452 maintenance records are labeled by these categories, which is so called EPE-MR training dataset. Secondly, the standard test reports, standard maintenance, and fault analysis reports for three types of power primary equipment are employed as corpus to train character embedding in order to obtain certain words representation ability of maintenance records. After that, progressive multilevel radicals feature extraction module is designed to get detailed and fine semantic information in a hierarchical manner. Further, radicals feature representation and character embedding are concatenated and sent to BiLSTM module to extract contextual information in order to improve Chinese entity recognition ability. Moreover, CRF is introduced to handle the dependencies among prediction labels and to output the optimal prediction sequence, which can easily obtain structured data of maintenance records. Finally, comparative experiments on public MSRA dataset, China People’s Daily corpus, and EPE-MR dataset are implemented, respectively, which show the effectiveness of the proposed method. (shrink)

Communication and recognition are essential for social life. Social insects are good model systems to study social behavior and complexity because their societies are evolutionarily stable and ecologically successful. Ants, in particular, show a large variety of adaptations and are extremely diverse. In ants, social interactions are regulated by at least three levels of recognition. Nestmate recognition occurs between colonies, is very effective, and involves fast processing. Within a colony, division of labor is enhanced by recognition (...) of different classes of individuals. Ultimately, in particular circumstances, such as cooperative colony founding with stable dominance hierarchies, ants are capable of individual recognition. The underlying recognition cues and mechanisms appear to be specific to each recognition level, and their integrated understanding could contribute to the identification of the minimum requirements for the emergence of sociality. (shrink)

The study investigates the possibility of applying texture analysis (TA) for testing Duchenne Muscular Dystrophy (DMD) therapies. The work is based on the Golden Retriever Muscular Dystrophy (GRMD) canine model, in which 3 phases of canine growth and/or dystrophy development are identified: the first phase (0–4 months of age), the second phase (from over 4 to 6 months), and the third phase (from over 6 months to death). Two differentiation problems are posed: (i) the first phase vs. the second phase (...) and (ii) the second phase vs. the third phase. Textural features are derived from T2-weighted Magnetic Resonance Imaging (MRI) images. In total, 37 features provided by 8 different TA methods (statistical, filter-based, and model-based) have been tested. The work focuses on finding such textural features that evolve along with the dog’s growth. These features are indicated by means of statistical analyses and eliminated from further investigation, as they may disturb the correct assessment of response to treatment in dystrophy. The relative importance of each remaining feature is then assessed with the use of the Monte Carlo (MC) procedure. Furthermore, feature selection based on the MC procedure is employed to find the optimal subset of age-independent features. Finally, three classifiers are used for evaluating different sets of textural features: Adaptive Boosting (AB), back-propagation Neural Network (NN), and nonlinear Support Vector Machines (SVM). The best subsets of age-independent features ensure 80.0% and 78.5% of correctly identified phases of dystrophy progression, for the first (i) and second (ii) differentiation problem respectively. (shrink)

This chapter argues that emotion recognition is a skill. A skill perspective on emotion recognition draws attention to underappreciated features of this cornerstone of social cognition. Skills have a number of characteristic features. For example, they are improvable, practical, and flexible. Emotion recognition has these features as well. Leading theories of emotion recognition often draw inadequate attention to these features. The chapter advances a theory of emotion recognition that is better suited to this purpose. It (...) proposes that emotion recognition involves scripts. Emotion scripts describe how people are likely to behave in different emotional contexts. Scripts can be improved with the addition of richer social knowledge, they are practical in that they describe and guide social interaction, and they are flexible because they must accommodate the fact that emotions have different behavioral impact depending on the agents involved and the circumstances at play. Learning to recognize emotions through scripts qualifies as a skill. (shrink)

ABSTRACT This paper examines the question of whether recognition relations are based on trust. Theorists of recognition have acknowledged the ways in which recognition relations make us vulnerable to others but have largely neglected the underlying ‘webs of trust’ in which such relations are embedded. In this paper, I consider the ways in which the theories of recognition developed by Jürgen Habermas and Axel Honneth, not only point to our mutual vulnerability but also implicitly rely upon (...) mutual relations of trust. The paper first offers a novel examination of the relation between recognition, vulnerability and trust in Habermas’ account of communicative action with the aim of arguing that such a consideration helps to elucidate important features of recognition. My claim is that a consideration of the dynamics of recognition and vulnerability in language-use, leads to an acknowledgment of the forms of trust that not only underpin communicative action, but recognition more generally. I conclude by considering the elements that are underplayed in Habermas’ account by turning to an examination of Axel Honneth’s alternative affective theory of recognition, specifically considering the interrelation between vulnerability and recognition. In doing so, I also turn to a consideration of the kind of trust that must be assumed in Honneth’s account of mutual recognition and point to a recognitive notion of trust. (shrink)

In this chapter I distinguish between a) recognition of persons, b) normative acknowledgement and c) institution-creating acceptance. All of these go beyond a fourth, merely descriptive sense of the word “recognition,” namely identification or re-identification of something as something. I distinguish four aspects of "taking someone as a person": R1 A Belief that the other is a person, and can engage in agency-regarding relations.R2 Moral Opinion that the choice whether and when to engage with persons is ethically significant.R3 (...) Willingness to refrain from wronging the other person, and to respond adequately to the normatively relevant features of the other (regardless of whether the willingness is ultimately selfish or not).R4 Unselfish Recognitive Attitudes explaining such willingness; such as genuine respect or genuine concern or solidarity. The second section asks: is mutual recognition between individuals necessary, sufficient, paradigmatic or desirable for group agency? I also ask: is interaction or communication necessary for mutual recognition? I also ask what kinds of groups emerge from mutual recognition as persons? The third section studies more briefly acknowledgement and normativity (Reasons, Values, and Principles). The fourth section discusses the nature of acceptance necessary for the existence of institutions. -/- . (shrink)

This paper implements the continuous Hindi Automatic Speech Recognition (ASR) system using the proposed integrated features vector with Recurrent Neural Network (RNN) based Language Modeling (LM). The proposed system also implements the speaker adaptation using Maximum-Likelihood Linear Regression (MLLR) and Constrained Maximum likelihood Linear Regression (C-MLLR). This system is discriminatively trained by Maximum Mutual Information (MMI) and Minimum Phone Error (MPE) techniques with 256 Gaussian mixture per Hidden Markov Model(HMM) state. The training of the baseline system has been done (...) using a phonetically rich Hindi dataset. The results show that discriminative training enhances the baseline system performance by up to 3%. Further improvement of ~7% has been recorded by applying RNN LM. The proposed Hindi ASR system shows significant performance improvement over other current state-of-the-art techniques. (shrink)

I describe a distinctive kind of fear that is generated by a vivid recognition of one’s mortal nature. I name it ‘existential shock’. This special fear does not take our future annihilation as any kind of harm, whether intrinsic or extrinsic. One puzzling feature of existential shock is that it is experienced as disclosing an important truth, yet attempts to specify this revelatory content bring us back to familiar facts about one’s inevitable death. But how can I discover (...) something that I already knew? I argue that in our everyday lives, we are in the grip of deeply entrenched patterns of thought and feeling that prevent the knowledge of our mortality from being fully assimilated. Rather, we merely ‘pay lip service’ to the facts of our mortality. I propose that existential shock involves a distinctive mode of presentation of oneself as a mortal being, in a way that cuts through subtle layers of denial that govern our lives. I develop this thesis by utilizing and synthesizing ideas from several traditions, including the work of Samuel Scheffler, Mark Johnston, Martin Heidegger, and Jay L. Garfield. (shrink)

In this paper I analyze recognition as a kind of power. I analyze the notion of power in the general sense as some sort of causal capacity, and introduce the distinction between the active power of doing something and the passive power of undergoing something. Such a distinction is needed in order to capture some central features of the phenomenon of recognition, and in particular the way that ‘being recognized’ and ‘recognizing’ are intertwined. I then argue in favor (...) of both the conceptual and genetic priority of the passive power of being recognized over the active power of recognizing. Furthermore, I introduce the notion of ‘attractor’ of recognition as a way to analyze some relevant features of the phenomenon of ‘being-recognized’. My approach to recognition as some kind of power, should here offer some tools not only to bridge the gap between the theory of recognition and the theory of power, but also to get the passive side of both power and recognition right. This could provide a more plausible and phenomenologically adequate understanding of both, and be useful to help capture and read anew within a recognitive grounding of critical theory some aspects of the contemporary debate on biopolitics. (shrink)

Recognition is one of the most debated concepts in contemporary social and political thought. Its proponents, such as Axel Honneth, hold that to be recognized by others is a basic human need that is central to forming an identity, and the denial of recognition deprives individuals and communities of something essential for their flourishing. Yet critics including Judith Butler have questioned whether recognition is implicated in structures of domination, arguing that the desire to be recognized can motivative (...) individuals to accept their assigned place in the social order by conforming to oppressive norms or obeying repressive institutions. Is there a way to break this impasse? -/- Recognition and Ambivalence brings together leading scholars in social and political philosophy to develop new perspectives on recognition and its role in social life. It begins with a debate between Honneth and Butler, the first sustained engagement between these two major thinkers on this subject. Contributions from both proponents and critics of theories of recognition further reflect upon and clarify the problems and challenges involved in theorizing the concept and its normative desirability. Together, they explore different routes toward a critical theory of recognition, departing from wholly positive or negative views to ask whether it is an essentially ambivalent phenomenon. Featuring original, systematic work in the philosophy of recognition, this book also provides a useful orientation to the key debates on this important topic. (shrink)

Speech recognition plays an important role in human–computer interaction. The higher the accuracy and efficiency of speech recognition are, the larger the improvement of human–computer interaction performance. This article briefly introduced the hidden Markov model -based English speech recognition algorithm and combined it with a back-propagation neural network to further improve the recognition accuracy and reduce the recognition time of English speech. Then, the BPNN-combined HMM algorithm was simulated and compared with the HMM algorithm and (...) the BPNN algorithm. The results showed that increasing the number of test samples increased the word error rate and recognition time of the three speech recognition algorithms, among which the word error rate and recognition time of the BPNN-combined HMM algorithm were the lowest. In conclusion, the BPNN-combined HMM can effectively recognize English speeches, which provides a valid reference for intelligent recognition of English speeches by computers. (shrink)

[Comment, pp 759-780] Lockhead (1992) [Target Article] is undoubtedly right to attack so-called intensity scaling or the estimation of subjective magnitudes as an invalid perversion of tasks requiring quantitative judgments of aspects of objects, stuffs, and situations. He goes too far, however, in claiming that feature scales do not exist... ... A perceived physical pattern (sensory feature or channel) and the cognitive process that integrates it with its context are characterized by determining to which particular combination of specified (...) stimulus patterns the response is most sensitive. This is the principle of causal system analysis that is at the basis of all theory-building science. (shrink)

Face recognition depends upon the uniqueness of each human face. This is accomplished by the patterns formed by the unique relationship among face features. Unique face-patterns are produced by the intrusion of random factors into the process of biological growth and development. Processes are described which enable a unique face-pattern to be represented as a percept in the visual sensory system. The components of the face recognition system are analyzed as is the manner in which the precept is (...) connected through microcircuits to a memory file so that the history of a perceiver’s encounters with a familiar face enables the perceiver to access a memory store that is a record of the outcome of past encounters with the perceived. The importance of the face recognition system in enabling humans to individuate members the social group is discussed, as well as the importance of face recognition in the development of the individual’s social identity and ability to be a collaborative member of the social groups to which it belongs. The role of prosopagnosia—the inability to recognize familiar faces—in furthering an understanding of the face recognition system is examined, as is its importance in demonstrating the crucial nature of face recognition in human social functions. It is proposed that human face recognition is not a unique phenomenon but is an elaboration of processes existing in nonhuman primates as well as in lower animals. (shrink)

Trust is critical for social life, and yet it is alarmingly fragile. It is easily damaged and difficult to repair. Philosophers studying trust have often noted that basic kind of trust needs to be in place in order for social life to be possible. Although philosophers have suggested that basic trust must exist, they have not tried to describe in explicit terms what this basic trust looks like, or how it comes to be. In this article I will identify and (...) describe a basic form of trust that I call recognition trust. It is a latent expectation we have in others to respect our moral status as persons. By conceptualizing recognition trust, I can explain why trust is not just a useful tool for social cooperation: having our personhood recognized is necessary for living a minimally decent human life, and trust is a necessary element of recognition. First, I motivate the need for conceptualizing a basic form of trust by looking at cases of moral injury and our inherent vulnerability. Such injuries involve the betrayal of an expectation we have in others to recognize us as persons with moral status. Second, I dig deeper into this basic human need of recognition that is thwarted in moral injuries. I argue that fulfilling this basic need requires a particular kind of trust that others will not morally injure us. Third, I give shape to recognition trust by describing its core features, showing why it is a candidate for basic trust. Finally, I defend my concept of recognition trust against a pair of objections. (shrink)

We develop a version of a direct perception account of emotion recognition on the basis of a metaphysical claim that emotions are individuated as patterns of characteristic features. On our account, emotion recognition relies on the same type of pattern recognition as is described for object recognition. The analogy allows us to distinguish two forms of directly perceiving emotions, namely perceiving an emotion in the absence of any top-down processes, and perceiving an emotion in a way (...) that significantly involves some top-down processes ; and, in addition, an inference-based evaluation of an emotion. Our model clarifies the epistemology of emotion recognition. (shrink)

The objective of this article is to see whether desire for recognition might contain an emancipatory aspect. Could this desire be a political ally? The argumentative strategy is to fully acknowledge the oppressive mechanisms at work before trying to find a way to other outcomes, including emancipation, with which desire for recognition has been associated in the tradition from Hegel. Through a re-interpretation of the master-and-slave dialectic, supplemented by sociological research on status expectations, I suggest a way out (...) of the vicious circle, where desire produced by power finds satisfaction through a preexisting other, resulting in an endless dynamic of compulsive conformity and regressive assertion of status. The hold of this dynamic must not be underestimated, yet, as I argue, both desire and what satisfies desire are liable to change, through struggle. The transformation of the generalized other, which provides recognition, is seen to be a crucial feature of all collective struggle. The very source of recognition is transformed, behind the backs of the actors, in turn affecting their desire. This can but must not lead to the emancipatory outcomes which are traditionally tied to recognition in the Hegelian tradition. (shrink)

1 Matching Templates and Feature Analysers. 2 Modes of Perception in Left and Right Cerebral Hemispheres. 3 Identification and Recognition. 4 Chess Plying Machines.

This article suggests first that the concept of interpersonal recognition be understood in a multidimensional (as opposed to one-dimensional), practical (as opposed to symbolic), and strict (as opposed to broad) way. Second, it is argued that due recognition be seen as a reason-governed response to evaluative features, rather than all normativity and reasons being seen as generated by recognition. This can be called a response-model, or, more precisely, a value-based model of due recognition. A further suggestion (...) is that there is a systematic basis for distinguishing three dimensions of recognition, depending on whether recognition is given to someone qua a person, qua a certain kind of person, or qua a certain person. Finally, it is argued that recognition is a necessary condition of personhood, but whether it is of direct or indirect relevance depends on our theories of personhood (social vs. capacity-theory) and practical identity (dialogical definition model vs. feature-model). Despite the apparent opposition, it is shown that interpersonal recognition is both a response to value and a precondition of personhood. (shrink)

Witnessing the fate of the various definitions of truth, Donald Davidson has recently called the very drive to define truth a “folly.” Before him, Kant and Frege had given independent arguments why a general definition of truth is impossible. After a quick summary of their arguments, I recount several reasons that Gangeśa gave for not counting truth as a genuine natural universal. I argue that in spite of defining truth as a feature of personal and ephemeral awareness episodes, the (...) Nyāya ya realists such as Gangeśa could maintain that truth is independent of recognition of truth. In the course of my argument, I also show that Roy Perrett’s alleged proof against realism does not succeed. I conclude that realism does not need nonmental atemporal truth-bearers (propositions) which are eternally wholly true (or wholly false), and that knowledge-independence from truths and things can be shown without admitting the existence of unknowable things or truths. (shrink)

Ludwig Feuerbach is famous for his critical hermeneutics of religion. At the heart of it lie arguments of philosophical anthropology that directly anticipate contemporary developments in the theory of recognition. He counts amongst the great philosophers who, immediately following Kant, emphasised the constitutive importance for human beings of interpersonal and social relations. Indeed, his theory of intersubjectivity contains features that are highly original, notably the link between individual and community, and between recognition and recollection.

In this article I describe and defend an account of social freedom grounded in intersubjective recognition. I term this the ‘normative authorisation’ account. It holds that a person enjoys social freedom if she is recognised as a discursive equal able to engage in justificatory dialogue with other social agents about the appropriateness of her reasons for action. I contrast this with Axel Honneth’s theory of social freedom, which I term the ‘self-realisation’ account. According to this view, the affirmative (...) class='Hi'>recognition of others is necessary for obtaining a positive relation-to-self and hence freedom. I identify several problems with this account, which challenge the connection Honneth draws between social recognition and freedom. I show how the normative authorisation account avoids these problems and captures some basic features of our everyday, normative interactions. Finally, I suggest that the account fits well with recent work on epistemic injustice. Specifically, it shows that securing the social conditions of freedom requires ensuring epistemically-just social relations. Thus, the normative authorisation account is an explanatorily powerful, inclusive theory of social freedom that fits well with wider accounts of justice and freedom. Thus, it represents the most promising way of understanding social freedom in terms of interpersonal recognition. (shrink)

Neuromarketing has gained attention to bridge the gap between conventional marketing studies and electroencephalography -based brain-computer interface research. It determines what customers actually want through preference prediction. The performance of EEG-based preference detection systems depends on a suitable selection of feature extraction techniques and machine learning algorithms. In this study, We examined preference detection of neuromarketing dataset using different feature combinations of EEG indices and different algorithms for feature extraction and classification. For EEG feature extraction, we (...) employed discrete wavelet transform and power spectral density, which were utilized to measure the EEG-based preference indices that enhance the accuracy of preference detection. Moreover, we compared deep learning with other traditional classifiers, such as k-nearest neighbor, support vector machine, and random forest. We also studied the effect of preference indicators on the performance of classification algorithms. Through rigorous offline analysis, we investigated the computational intelligence for preference detection and classification. The performance of the proposed deep neural network outperforms KNN and SVM in accuracy, precision, and recall; however, RF achieved results similar to those of the DNN for the same dataset. (shrink)