Strategies of alignment: Organizational identity management and strategic change at Bang & Olufsen

During periods of strategic change, maintaining the congruence between new configurations of resources and activities (strategic investments) and how these new configurations are communicated to external organizational constituents (strategic projections) is an important task facing organizational leaders. One part of this activity is to manage organizational identity to ensure that the various strategic projections produced by organizational members are coherent and support the new strategic investments. Little is known, however, about how organizational leaders accomplish this crucial task. This study of strategic change at Bang & Olufsen highlights the different strategies available to organizational leaders to ensure members’ identity beliefs are aligned with their own beliefs about the distinctive and appealing organizational features that result from the new strategic investments and result in appropriate strategic projections. The study’s findings highlight the internal identity work – or identity management – that organizational leaders engage in to preserve this congruence. The findings also complement the current emphasis in the literature on the social validation of organizational identities by pointing to the importance of a connection between identity claims and beliefs, strategic projections and the material reality of organizational products, practices and structures

Implementing a Business Process Management System Using ADEPT: A Real-World Case Study

This article describes how the agent-based design of ADEPT (advanced decision environment for processed tasks) and implementation philosophy was used to prototype a business process management system for a real-world application. The application illustrated is based on the British Telecom (BT) business process of providing a quote to a customer for installing a network to deliver a specified type of telecommunication service. Particular emphasis is placed upon the techniques developed for specifying services, allowing heterogeneous information models to interoperate, allowing rich and flexible interagent negotiation to occur, and on the issues related to interfacing agent-based systems and humans. This article builds upon the companion article (Applied Artificial Intelligence Vol.14, no 2, pgs. 145-189) that provides details of the rationale and design of the ADEPT technology deployed in this application

Laboratories of Reform? Human Resource Management Strategies in Illinois Charter Schools

The purpose of this study was to investigate how Illinois charter schools are leveraging the flexibility they are provided by law to innovate in the area of human resource management, and to explore the relationships between HR practices and school outcomes. To do this, we conducted surveys and interviews with administrators from 27 Illinois charter schools to describe the ways they recruitment, develop, and retain teachers. We create a typology of four broad HR strategies that are utilized to a greater or lesser extent at each school: 1) incentivist reforms; 2) teacher support and empowerment; 3) information-rich decision-making, and 4) mission-driven practice. Next, we compare these HR strategies with data on teacher retention, school climate, and student achievement to measure the relationship between human resources practices and school outcomes. The analysis reveals evidence suggesting that incentivist practices may be associated with increased math achievement, but this is dependent on how achievement growth is measured. The study also shows that the newest charter schools were considerably less likely to use incentivist practices than their more established counterparts, and that teacher empowerment and information-rich decision-making practices were associated with certain measures to school climate.https://spark.siue.edu/ierc_pub/1001/thumbnail.jp

Learning Curricula in Open-Ended Worlds

Deep reinforcement learning (RL) provides powerful methods for training optimal sequential decision-making agents. As collecting real-world interactions can entail additional costs and safety risks, the common paradigm of sim2real conducts training in a simulator, followed by real-world deployment. Unfortunately, RL agents easily overfit to the choice of simulated training environments, and worse still, learning ends when the agent masters the specific set of simulated environments. In contrast, the real-world is highly open-ended—featuring endlessly evolving environments and challenges, making such RL approaches unsuitable. Simply randomizing across a large space of simulated environments is insufficient, as it requires making arbitrary distributional assumptions, and as the design space grows, it can become combinatorially less likely to sample specific environment instances that are useful for learning. An ideal learning process should automatically adapt the training environment to maximize the learning potential of the agent over an open-ended task space that matches or surpasses the complexity of the real world. This thesis develops a class of methods called Unsupervised Environment Design (UED), which seeks to enable such an open-ended process via a principled approach for gradually improving the robustness and generality of the learning agent. Given a potentially open-ended environment design space, UED automatically generates an infinite sequence or curriculum of training environments at the frontier of the learning agent’s capabilities. Through both extensive empirical studies and theoretical arguments founded on minimax-regret decision theory and game theory, the findings in this thesis show that UED autocurricula can produce RL agents exhibiting significantly improved robustness and generalization to previously unseen environment instances. Such autocurricula are promising paths toward open-ended learning systems that approach general intelligence—a long sought-after ambition of artificial intelligence research—by continually generating and mastering additional challenges of their own design

The Development of a Scrip Management & Order System: A Case Study

This paper describes the development of a scrip management application system to support the fund-raising needs of a youth swim team. A scrip program is a common fund raising activity used by many not-for-profit organizations and offers a long-term method of raising funds without asking people to spend more money than they normally would in their daily routine. A scrip program requires that people purchase coupons, certificates or cards (scrip) from the not-for-profit organization or directly from a national scrip organization. The scrip cards or certificates can then be used as cash. Each merchant or vendor then contributes a set percentage back to the not-for-profit. Utilizing an action research approach, the organizational and technical decisions will be discussed as well as the lessons learned. This should be of interest to practitioners interested in developing Web-based applications, as well as researchers who hope to engage in action research

Independent- Nov. 23, 1999

https://neiudc.neiu.edu/independent/1224/thumbnail.jp

'Maybe I can take you by the hand and we can do this': Transitions, Translation and Transformation in Creative Dance.

This is an explorative and qualitative research study that uses critical and reflexive ethnographic methods (Denzin and Lincoln, 2002). It explores the dance artist’s role in participative and somatic dance, recognising dance as a culturally constructed mode of human action (Buckland, 1999, p. 4). The dance artist is often under-represented and largely invisible in the dance-health literature. This study contributes to this gap in knowledge by exploring the embodied and intersubjective experiences of a group of independent dance artists practicing in a specific, creative, social, and cultural dance domain. Data was collected by adopting an ethnographic attitude of ‘being there’ (Geertz, 1988, p. 1) and embedding myself, as the researcher, in the field of study for a 12-month period. I engaged in a self-critical and self-conscious analysis (Etherington, 2004), thereby making explicit my orientations and assumptions as a researcher with a background in health care and occupational therapy. This is therefore an inter-disciplinary study exploring the potential transfer of knowledge between arts and health sectors. Data gathering methods included participant listening, observation and felt body experience to further understanding of the dance world experienced by this group of dance artists. Data was analysed using primarily ethnographic content (Glaser and Strauss, 1967), and some narrative thematic analysis (Riessman, 2003, 2008) of the dance artists’ partial life stories. The study findings suggest that social and intersubjective relations are key in this dance-health practice in enabling the dance artists, acting as Guides, to facilitate a heightened awareness of somatic and subjective lived body experience. The dance artists empower others to translate and find meaning, as they transition between different mind-body experiences. Corporeal learning and acculturation take place by participating in the creative dance practice, which both reflects and influences everyday life (Koff, 2005). This experience of embodied transformation is understood from the perspective of a salutogenic approach to health (Antonovsky, 1996) with an emphasis on capability, meaning and a sense of coherence. This is exemplified by a sense of congruence between the dance participants’ inner and outer physical experience (Blackburn and Price, 2007, p. 69). Essentially, the body is explored within a specific group of dance artists situated in a particular social and cultural dance-health setting. This doctoral research therefore seeks to bring embodiment before the sociological gaze (Crossley, 2007, p. 80) exploring the subjective and intersubjective lived body experience from a social perspective

NEIU News- Sep. 1986

https://neiudc.neiu.edu/neiunews/1029/thumbnail.jp

Understanding the Dynamic Visual World: From Motion to Semantics

We live in a dynamic world, which is continuously in motion. Perceiving and interpreting the dynamic surroundings is an essential capability for an intelligent agent. Human beings have the remarkable capability to learn from limited data, with partial or little annotation, in sharp contrast to computational perception models that rely on large-scale, manually labeled data. Reliance on strongly supervised models with manually labeled data inherently prohibits us from modeling the dynamic visual world, as manual annotations are tedious, expensive, and not scalable, especially if we would like to solve multiple scene understanding tasks at the same time. Even worse, in some cases, manual annotations are completely infeasible, such as the motion vector of each pixel (i.e., optical flow) since humans cannot reliably produce these types of labeling. In fact, living in a dynamic world, when we move around, motion information, as a result of moving camera, independently moving objects, and scene geometry, consists of abundant information, revealing the structure and complexity of our dynamic visual world. As the famous psychologist James J. Gibson suggested, “we must perceive in order to move, but we also must move in order to perceive”. In this thesis, we investigate how to use the motion information contained in unlabeled or partially labeled videos to better understand and synthesize the dynamic visual world. This thesis consists of three parts. In the first part, we focus on the “move to perceive” aspect. When moving through the world, it is natural for an intelligent agent to associate image patterns with the magnitude of their displacement over time: as the agent moves, far away mountains don’t move much; nearby trees move a lot. This natural relationship between the appearance of objects and their apparent motion is a rich source of information about the relationship between the distance of objects and their appearance in images. We present a pretext task of estimating the relative depth of elements of a scene (i.e., ordering the pixels in an image according to distance from the viewer) recovered from motion field of unlabeled videos. The goal of this pretext task was to induce useful feature representations in deep Convolutional Neural Networks (CNNs). These induced representations, using 1.1 million video frames crawled from YouTube within one hour without any manual labeling, provide valuable starting features for the training of neural networks for downstream tasks. It is promising to match or even surpass what ImageNet pre-training gives us today, which needs a huge amount of manual labeling, on tasks such as semantic image segmentation as all of our training data comes almost for free. In the second part, we study the “perceive to move” aspect. As we humans look around, we do not solve a single vision task at a time. Instead, we perceive our surroundings in a holistic manner, doing visual understanding using all visual cues jointly. By simultaneously solving multiple tasks together, one task can influence another. In specific, we propose a neural network architecture, called SENSE, which shares common feature representations among four closely-related tasks: optical flow estimation, disparity estimation from stereo, occlusion detection, and semantic segmentation. The key insight is that sharing features makes the network more compact and induces better feature representations. For real-world data, however, not all an- notations of the four tasks mentioned above are always available at the same time. To this end, loss functions are designed to exploit interactions of different tasks and do not need manual annotations, to better handle partially labeled data in a semi- supervised manner, leading to superior understanding performance of the dynamic visual world. Understanding the motion contained in a video enables us to perceive the dynamic visual world in a novel manner. In the third part, we present an approach, called SuperSloMo, which synthesizes slow-motion videos from a standard frame-rate video. Converting a plain video into a slow-motion version enables us to see memorable moments in our life that are hard to see clearly otherwise with naked eyes: a difficult skateboard trick, a dog catching a ball, etc. Such a technique also has wide applications such as generating smooth view transition on a head-mounted virtual reality (VR) devices, compressing videos, synthesizing videos with motion blur, etc