CAVIAR: Context-driven Active and Incremental Activity Recognition

Activity recognition on mobile device sensor data has been an active research area in mobile and pervasive computing for several years. While the majority of the proposed techniques are based on supervised learning, semi-supervised approaches are being considered to reduce the size of the training set required to initialize the model. These approaches usually apply self-training or active learning to incrementally refine the model, but their effectiveness seems to be limited to a restricted set of physical activities. We claim that the context which surrounds the user (e.g., time, location, proximity to transportation routes) combined with common knowledge about the relationship between context and human activities could be effective in significantly increasing the set of recognized activities including those that are difficult to discriminate only considering inertial sensors, and the highly context-dependent ones. In this paper, we propose CAVIAR, a novel hybrid semi-supervised and knowledge-based system for real-time activity recognition. Our method applies semantic reasoning on context-data to refine the predictions of an incremental classifier. The recognition model is continuously updated using active learning. Results on a real dataset obtained from 26 subjects show the effectiveness of our approach in increasing the recognition rate, extending the number of recognizable activities and, most importantly, reducing the number of queries triggered by active learning. In order to evaluate the impact of context reasoning, we also compare CAVIAR with a purely statistical version, considering features computed on context-data as part of the machine learning process

Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization

Artificial autonomous agents and robots interacting in complex environments are required to continually acquire and fine-tune knowledge over sustained periods of time. The ability to learn from continuous streams of information is referred to as lifelong learning and represents a long-standing challenge for neural network models due to catastrophic forgetting. Computational models of lifelong learning typically alleviate catastrophic forgetting in experimental scenarios with given datasets of static images and limited complexity, thereby differing significantly from the conditions artificial agents are exposed to. In more natural settings, sequential information may become progressively available over time and access to previous experience may be restricted. In this paper, we propose a dual-memory self-organizing architecture for lifelong learning scenarios. The architecture comprises two growing recurrent networks with the complementary tasks of learning object instances (episodic memory) and categories (semantic memory). Both growing networks can expand in response to novel sensory experience: the episodic memory learns fine-grained spatiotemporal representations of object instances in an unsupervised fashion while the semantic memory uses task-relevant signals to regulate structural plasticity levels and develop more compact representations from episodic experience. For the consolidation of knowledge in the absence of external sensory input, the episodic memory periodically replays trajectories of neural reactivations. We evaluate the proposed model on the CORe50 benchmark dataset for continuous object recognition, showing that we significantly outperform current methods of lifelong learning in three different incremental learning scenario

Adaptive Resonance Theory

SyNAPSE program of the Defense Advanced Projects Research Agency (Hewlett-Packard Company, subcontract under DARPA prime contract HR0011-09-3-0001, and HRL Laboratories LLC, subcontract #801881-BS under DARPA prime contract HR0011-09-C-0001); CELEST, an NSF Science of Learning Center (SBE-0354378

A situational approach for the definition and tailoring of a data-driven software evolution method

Successful software evolution heavily depends on the selection of the right features to be included in the next release. Such selection is difficult, and companies often report bad experiences about user acceptance. To overcome this challenge, there is an increasing number of approaches that propose intensive use of data to drive evolution. This trend has motivated the SUPERSEDE method, which proposes the collection and analysis of user feedback and monitoring data as the baseline to elicit and prioritize requirements, which are then used to plan the next release. However, every company may be interested in tailoring this method depending on factors like project size, scope, etc. In order to provide a systematic approach, we propose the use of Situational Method Engineering to describe SUPERSEDE and guide its tailoring to a particular context.Peer ReviewedPostprint (author's final draft

A half century of progress towards a unified neural theory of mind and brain with applications to autonomous adaptive agents and mental disorders

Invited article for the book Artificial Intelligence in the Age of Neural Networks and Brain Computing R. Kozma, C. Alippi, Y. Choe, and F. C. Morabito, Eds. Cambridge, MA: Academic PressThis article surveys some of the main design principles, mechanisms, circuits, and architectures that have been discovered during a half century of systematic research aimed at developing a unified theory that links mind and brain, and shows how psychological functions arise as emergent properties of brain mechanisms. The article describes a theoretical method that has enabled such a theory to be developed in stages by carrying out a kind of conceptual evolution. It also describes revolutionary computational paradigms like Complementary Computing and Laminar Computing that constrain the kind of unified theory that can describe the autonomous adaptive intelligence that emerges from advanced brains. Adaptive Resonance Theory, or ART, is one of the core models that has been discovered in this way. ART proposes how advanced brains learn to attend, recognize, and predict objects and events in a changing world that is filled with unexpected events. ART is not, however, a “theory of everything” if only because, due to Complementary Computing, different matching and learning laws tend to support perception and cognition on the one hand, and spatial representation and action on the other. The article mentions why a theory of this kind may be useful in the design of autonomous adaptive agents in engineering and technology. It also notes how the theory has led to new mechanistic insights about mental disorders such as autism, medial temporal amnesia, Alzheimer’s disease, and schizophrenia, along with mechanistically informed proposals about how their symptoms may be ameliorated

Information technology as boundary object for transformational learning

Collaborative work is considered as a way to improve productivity and value generation in construction. However, recent research demonstrates that socio-cognitive factors related to fragmentation of specialized knowledge may hinder team performance. New methods based on theories of practice are emerging in Computer Supported Collaborative Work and organisational learning to break these knowledge boundaries, facilitating knowledge sharing and the generation of new knowledge through transformational learning. According to these theories, objects used in professional practice play a key role in mediating interactions. Rules and methods related to these practices are also embedded in these objects. Therefore changing collaborative patterns demand reconfiguring objects that are at the boundary between specialized practices, namely boundary objects. This research is unique in presenting an IT strategy in which technology is used as a boundary object to facilitate transformational learning in collaborative design work

The Role of Corporate HR Functions In Multinational Corporations: The Interplay Between Corporate, Regional/National And Plant Level

The HR literature has been abundant in providing typologies of the roles of HR professionals in their organisation. These typologies are largely related to the changing nature of HRM over time, and the context in which empirical work was carried out. In this paper we focus on the context of the increasing internationalisation of firms and how this has an effect upon modern-day typologies of HR roles. We explore these roles by focusing on the way in which HRM practices come about. Especially in a MNC setting of increasing internationalisation of firms the issues of coordination, shared learning and standardisation versus leeway for adapting to the local context (customisation) are prominent. These issues present themselves both at the corporate and regional level and at the national and local (plant) level. On all these levels HR practitioners are active and find themselves amidst the interplay of both (de-)centralisation and standardisation versus customisation processes. This paper thus explores the way in which HR practices come into being and how they are implemented and coordinated. These insights help us understand further the roles of international corporate HR functions that are being identified. Our data is based on 65 interviews, which were held (as part of larger study of HR-function excellence) with HR managers, line managers and senior executives of six multinational companies in eight countries from September to December 2004. This data reveals new classifications of processes by which HR activities are developed, implemented and coordinated, both in terms of who is involved and how these processes are carried out

The Role of Corporate HR Funcitons in MNCs: The Interplay Between Corporate, Regional/National and Plant Level

The HR literature has been abundant in providing typologies of the roles of HR professionals in their organisation. These typologies are largely related to the changing nature of HRM over time, and the context in which empirical work was carried out. In this paper we focus on the context of the increasing internationalisation of firms and how this has an effect upon modern-day typologies of HR roles. We explore these roles by focusing on the way in which HRM practices come about. Especially in a MNC setting of increasing internationalisation of firms the issues of coordination, shared learning and standardisation versus leeway for adapting to the local context (customisation) are prominent. These issues present themselves both at the corporate and regional level and at the national and local (plant) level. On all these levels HR practitioners are active and find themselves amidst the interplay of both (de-)centralisation and standardisation versus customisation processes. This paper thus explores the way in which HR practices come into being and how they are implemented and coordinated. These insights help us understand further the roles of international corporate HR functions that are being identified. Our data is based on 65 interviews, which were held (as part of larger study of HR-function excellence) with HR managers, line managers and senior executives of six multinational companies in eight countries from September to December 2004. This data reveals new classifications of processes by which HR activities are developed, implemented and coordinated, both in terms of who is involved and how these processes are carried out