Common-sense knowledge for a computer vision system for human action recognition

This work presents a novel approach for human action recognition based on the combination of computer vision techniques and common-sense knowledge and reasoning capabilities. The emphasis of this work is on how common sense has to be leveraged to a vision-based human action recognition so that nonsensical errors can be amended at the understanding stage. The proposed framework is to be deployed in a realistic environment in which humans behave rationally, that is, motivated by an aim or a reason. © 2012 Springer-Verlag.

Common-sense reasoning for human action recognition

This paper presents a novel method that leverages reasoning capabilities in a computer vision system dedicated to human action recognition. The proposed methodology is decomposed into two stages. First, a machine learning based algorithm - known as bag of words - gives a first estimate of action classification from video sequences, by performing an image feature analysis. Those results are afterward passed to a common-sense reasoning system, which analyses, selects and corrects the initial estimation yielded by the machine learning algorithm. This second stage resorts to the knowledge implicit in the rationality that motivates human behaviour. Experiments are performed in realistic conditions, where poor recognition rates by the machine learning techniques are significantly improved by the second stage in which common-sense knowledge and reasoning capabilities have been leveraged. This demonstrates the value of integrating common-sense capabilities into a computer vision pipeline. © 2012 Elsevier B.V. All rights reserved.

Episodic Reasoning for Vision-Based Human Action Recognition

Smart Spaces, Ambient Intelligence, and Ambient Assisted Living are environmental paradigms that strongly depend on their capability to recognize human actions. While most solutions rest on sensor value interpretations and video analysis applications, few have realized the importance of incorporating common-sense capabilities to support the recognition process. Unfortunately, human action recognition cannot be successfully accomplished by only analyzing body postures. On the contrary, this task should be supported by profound knowledge of human agency nature and its tight connection to the reasons and motivations that explain it. The combination of this knowledge and the knowledge about how the world works is essential for recognizing and understanding human actions without committing common-senseless mistakes. This work demonstrates the impact that episodic reasoning has in improving the accuracy of a computer vision system for human action recognition. This work also presents formalization, implementation, and evaluation details of the knowledge model that supports the episodic reasoning.

Low-rank representation based action recognition

Human action recognition is an important problem in computer vision, which has been applied to many applications. However, how to learn an accurate and discriminative representation of videos based on the features extracted from videos still remains to be a challenging problem. In this paper, we propose a novel method named low-rank representation based action recognition to recognize human actions. Given a dictionary, low-rank representation aims at finding the lowestrank representation of all data, which can capture the global data structures. According to its characteristics, low-rank representation is robust against noises. Experimental results demonstrate the effectiveness of the proposed approach on several publicly available datasets.

Kinect and Episodic Reasoning for Human Action Recognition

This paper presents a method for rational behaviour recognition that combines vision-based pose estimation with knowledge modeling and reasoning. The proposed method consists of two stages. First, RGB-D images are used in the estimation of the body postures. Then, estimated actions are evaluated to verify that they make sense. This method requires rational behaviour to be exhibited. To comply with this requirement, this work proposes a rational RGB-D dataset with two types of sequences, some for training and some for testing. Preliminary results show the addition of knowledge modeling and reasoning leads to a significant increase of recognition accuracy when compared to a system based only on computer vision.

Learning and organization in the knowledge-based information economy: Initial findings from a participatory action research case study

This paper reports on an ongoing, multiphase, project-based action learning and research project. In particular, it summarizes some aspects of the learning climate and outcomes for a case study company In the software industry, Using a participatory action research approach, the learning company framework developed by Pedler et al, (1997) is used to initiate critical reflection in the company at three levels: managing director, senior management team and technical and professional staff. As such, this is one of the first systematic attempts to apply this framework to the entire organization and to a company in the knowledge-based learning economy. Two sets of issues are of general concern to the company: internal issues surrounding the company's reward and recognition policies and practices and the provision of accounting and control information in a business relevant way to all levels of staff; and external issues concerning the extent to which the company and its members actively learn from other companies and effectively capture, disseminate and use information accessed by staff in boundary-spanning roles. The paper concludes with some illustrations of changes being introduced by the company as a result of the feedback on and discussion of these issues.

DNA-dependent Protein Kinase-mediated Phosphorylation of Protein Kinase B Requires a Specific Recognition Sequence in the C-terminal Hydrophobic Motif

DNA-dependent protein kinase (DNA-PK) has been implicated in a variety of nuclear processes including DNA double strand break repair, V(D)J recombination, and transcription. A recent study showed that DNA-PK is responsible for Ser-473 phosphorylation in the hydrophobic motif of protein kinase B (PKB/Akt) in genotoxic-stressed cells, suggesting a novel role for DNA-PK in cell signaling. Here, we report that DNA-PK activity toward PKB peptides is impaired in DNA-PK knock-out mouse embryonic fibroblast cells when compared with wild type. In addition, human glioblastoma cells expressing a mutant form of DNA-PK (M059J) displayed a lower DNA-PK activity when compared with glioblastoma cells expressing wild-type DNA- PK (M059K) when PKB peptide substrates were tested. DNA- PK preferentially phosphorylated PKB on Ser-473 when compared with its known in vitro substrate, p53. A consensus hydrophobic amino acid surrounding the Ser-473 phospho-acceptor site in PKB containing amino acids Phe at position +1 and +4 and Tyr at position -1 are critical for DNA- PK activity. Thus, these data define the specificity of DNA- PK action as a Ser-473 kinase for PKB in DNA repair signaling.

Empowering the Shamed Self: Recognition and Critical Social Work

Summary: This article provides a review of the contribution of Axel Honneth’s model of recognition for critical social work. While Honneth’s tripartite conceptualisation of optimal identity-formation is positively appraised, his analysis of the link between misrecognition, the experience of shame and eventual sense of moral outrage, is contested. Drawing on a range of sources, including the sociology of shame, Honneth’s ideas about the emotional antecedents of emancipatory action are revised to guide critical social work with misrecognised service users.

Findings: The intellectual background to Honneth’s recognition model, emanating from leading German philosophers, is described and its application to social work set out. Even so, Honneth’s model is found to be deficient in one primary regard: its assumption about the emotional antecedents to quests for withheld recognition is misapprehended. In particular, the argument in this article is that the ubiquitous emotion of shame, which Honneth argues flows from misrecognition, must be carefully addressed through the medium of relationship, otherwise it might lead to repressed shame and frustrated attempts at social struggle. To this end, a social work process is delineated for dealing with shame, following episodes of misrecognition.

Applications: Honneth’s model of recognition, along with revised ideas about how to recognise and manage shame, is incorporated into a conceptual framework for critical social work practice. With this renewed understanding of the impact of shame, following misrecognition, social workers should be better equipped conceptually to enable service users to take action for empowerment.