Stevens Institute of Technology

About: Stevens Institute of Technology is a education organization based out in Hoboken, New Jersey, United States. It is known for research contribution in the topics: Cognitive radio & Wireless network. The organization has 5440 authors who have published 12684 publications receiving 296875 citations. The organization is also known as: Stevens & Stevens Tech.

Learning Process in New Product Development Teams and Effects on Product Success: A Socio-Cognitive Perspective

Abstract: The study purports to develop and empirically test a model of team learning process and its effects on team performance in new product development teams. Using the socio-cognitive theory of learning in groups and organizations, several hypotheses were tested to show that the primer components of social cognition (that is, information acquisition, information dissemination, information implementation, unlearning, thinking, intelligence, improvisation, sense-making, and memory) form an interactive process model of the team earning phenomenon. By studying 165 new product development projects, it was shown: (i) that the eight primer socio-cognitive factors of information acquisition, information dissemination, information implementation, memory, thinking, improvisation, unlearning, and sense-making constitute interrelated sub-components of a higher-order team information-processing construct; (ii) that team intelligence is positively related to components of team information-processing; and (iii) that information-processing facilitates new product success primarily through the positive effects of superior information implementation. Theoretical and managerial implications of the study findings are discussed.

Using multiple cues for hand tracking and model refinement

Abstract: We present a model based approach to the integration of multiple cues for tracking high degree of freedom articulated motions and model refinement. We then apply it to the problem of hand tracking using a single camera sequence. Hand tracking is particularly challenging because of occlusions, shading variations, and the high dimensionality of the motion. The novelty of our approach is in the combination of multiple sources of information, which come from edges, optical flow, and shading information in order to refine the model during tracking. We first use a previously formulated generalized version of the gradient-based optical flow constraint, that includes shading flow i.e., the variation of the shading of the object as it rotates with respect to the light source. Using this model we track its complex articulated motion in the presence of shading changes. We use a forward recursive dynamic model to track the motion in response to data derived 3D forces applied to the model. However, due to inaccurate initial shape, the generalized optical flow constraint is violated. We use the error in the generalized optical flow equation to compute generalized forces that correct the model shape at each step. The effectiveness of our approach is demonstrated with experiments on a number of different hand motions with shading changes, rotations and occlusions of significant parts of the hand.

The Concept of Reference Architectures

Abstract: The concept of Reference Architectures is novel in the business world However, many architects active in the creation of complex systems frequently use the term Reference Architecture Yet, these experienced architects do not collectively have a consistent notion of what constitutes a Reference Architecture, what is the value of maintaining the Reference Architecture, what is the best approach to visualizing a Reference Architecture, what is the most appropriate level of abstraction, and how should an architect make use of the Reference Architecture in their work? This paper examines current Reference Architectures and the driving forces behind development of them to come to a collective conclusion on what a Reference Architecture should truly be It will be shown that a Reference Architecture captures the accumulated architectural knowledge of thousands man-years of work This knowledge ranges from why (market segmentation, value chain, customer key drivers, application), what (systems, key performance parameters, system interfaces, functionality, variability), to how (design views and diagrams, essential design patterns, main concepts) The purpose of the Reference Architecture is to provide guidance for future developments The Reference Architecture incorporates the vision and strategy for the future The Reference Architecture is a reference for the hundreds of teams related to ongoing developments By providing this reference all these teams have a shared baseline of why, what and how It is the authors' goal that this paper will facilitate further research in the concepts and ideas presented herein © 2009 Wiley Periodicals, Inc Syst Eng

Anonymizing moving objects: how to hide a MOB in a crowd?

Abstract: Moving object databases (MOD) have gained much interest in recent years due to the advances in mobile communications and positioning technologies. Study of MOD can reveal useful information (e.g., traffic patterns and congestion trends) that can be used in applications for the common benefit. In order to mine and/or analyze the data, MOD must be published, which can pose a threat to the location privacy of a user. Indeed, based on prior knowledge of a user's location at several time points, an attacker can potentially associate that user to a specific moving object (MOB) in the published database and learn her position information at other time points.In this paper, we study the problem of privacy-preserving publishing of moving object database. Unlike in microdata, we argue that in MOD, there does not exist a fixed set of quasi-identifier (QID) attributes for all the MOBs. Consequently the anonymization groups of MOBs (i.e., the sets of other MOBs within which to hide) may not be disjoint. Thus, there may exist MOBs that can be identified explicitly by combining different anonymization groups. We illustrate the pitfalls of simple adaptations of classical k-anonymity and develop a notion which we prove is robust against privacy attacks. We propose two approaches, namely extreme-union and symmetric anonymization, to build anonymization groups that provably satisfy our proposed k-anonymity requirement, as well as yield low information loss. We ran an extensive set of experiments on large real-world and synthetic datasets of vehicular traffic. Our results demonstrate the effectiveness of our approach.