Paul Mc Kevitt

Bio: Paul Mc Kevitt is an academic researcher from Ulster University. The author has contributed to research in topics: Computer science & Affective computing. The author has an hindex of 13, co-authored 50 publications receiving 1939 citations. Previous affiliations of Paul Mc Kevitt include Aalborg University & University of California, Berkeley.

A new colour space for skin tone detection

Abstract: The majority of existing methods have one thing in common which is the de-correlation of luminance from the considered colour channels. It is believed that the luminance is underestimated here since it is seen as the least contributing colour component to skin colour detection. This work questions this claim by showing that luminance can be useful in separating skin and non-skin clusters. To this end, this work uses a new colour space which contains error signals derived from differentiating the grayscale map and the non-encoded-red grayscale version. The advantages of this approach are the reduction of space dimensionality from 3D to 1D space and the construction of a rapid classifier necessary for real time applications. This method is meant to assist digital image steganography to orient the embedding process since skin information is deemed to be psycho-visually redundant.

Visual semantics and ontology of eventive verbs

Abstract: Various English verb classifications have been analyzed in terms of their syntactic and semantic properties, and conceptual components, such as syntactic valency, lexical semantics, and semantic/syntactic correlations. Here the visual semantics of verbs, particularly their visual roles, somatotopic effectors, and level-of-detail, is studied. We introduce the notion of visual valency and use it as a primary criterion to recategorize eventive verbs for language visualization (animation) in our intelligent multimodal storytelling system, CONFUCIUS. The visual valency approach is a framework for modelling deeper semantics of verbs. In our ontological system we consider both language and visual modalities since CONFUCIUS is a multimodal system.

Adaptive interfaces and agents

Abstract: Introduction 434Concepts 434Functions: Supporting System Use 435Taking Over Parts of Routine Tasks 435Adapting the Interface 436Helping With System Use 437Mediating Interaction with the Real World 438Controlling a Dialogue 439Functions: Supporting InformationAcquisition 440Helping Users to Find Information 440Support for browsing 441Support for query-based search or filtering 442Spontaneous Provision of Information 442Recommending Products 442Tailoring Information Presentation 443Supporting Collaboration 444Supporting Learning 445Usability Challenges 446Threats to Predictability and Comprehensibility 447Threats to Controllability 447Obtrusiveness 448Threats to Privacy 448Breadth of Experience 448Dealing With Trade-offs 448Obtaining Information About Users 449Explicit Self-Reports and -Assessments 449Self-reports about objective personalcharacteristics 449Self-assessments of interests and knowledge 449Self-reports on specific evaluations 450Responses to test items 450Nonexplicit Input 450Naturally Occurring Actions 450Previously Stored Information 450Low-Level Indices of Psychological States 451Signals Concerning the Current Surroundings 451Special Considerations ConcerningEmpirical Methods 451Use of Data Collected With a Nonadaptive System 451Early Studies of Usage Scenarios andUser Requirements 452Wizard of Oz Studies 452Comparisons With the Work of Human Designers 453Experimental Comparisons of Adaptiveand Nonadaptive Systems 453Taking Into Account Individual Differences 454Checking Usability Under Realistic Conditions 454The Future of User-Adaptive Systems 454Growing Need for User-Adaptivity 454Diversity of users and contexts of use 454Number and complexity of interactive systems 454Scope of information to be dealt with 454Increasing Feasibility of Successful Adaptation 455Ways of acquiring information about users 455Advances in techniques for learning,inference, and decision 455Attention to empirical methods 455Acknowledgments 455References 455This chapter covers a broad range of interactive systems They all have one idea in common: It can be worthwhile for a system to learn something about each individual user and adapt its behavior to them in some nontrivial way

Deep residual learning for image steganalysis

Abstract: Image steganalysis is to discriminate innocent images and those suspected images with hidden messages. This task is very challenging for modern adaptive steganography, since modifications due to message hiding are extremely small. Recent studies show that Convolutional Neural Networks (CNN) have demonstrated superior performances than traditional steganalytic methods. Following this idea, we propose a novel CNN model for image steganalysis based on residual learning. The proposed Deep Residual learning based Network (DRN) shows two attractive properties than existing CNN based methods. First, the model usually contains a large number of network layers, which proves to be effective to capture the complex statistics of digital images. Second, the residual learning in DRN preserves the stego signal coming from secret messages, which is extremely beneficial for the discrimination of cover images and stego images. Comprehensive experiments on standard dataset show that the DRN model can detect the state of arts steganographic algorithms at a high accuracy. It also outperforms the classical rich model method and several recently proposed CNN based methods.

Image steganography in spatial domain: A survey

Abstract: This paper presents a literature review of image steganography techniques in the spatial domain for last 5 years. The research community has already done lots of noteworthy research in image steganography. Even though it is interesting to highlight that the existing embedding techniques may not be perfect, the objective of this paper is to provide a comprehensive survey and to highlight the pros and cons of existing up-to-date techniques for researchers that are involved in the designing of image steganographic system. In this article, the general structure of the steganographic system and classifications of image steganographic techniques with its properties in spatial domain are exploited. Furthermore, different performance matrices and steganalysis detection attacks are also discussed. The paper concludes with recommendations and good practices drawn from the reviewed techniques.