The Divine Science

Peer reviewedPublisher PD

Productive Misreading in Intermedia Art: Four Approaches by a Musician

This discussion examines the evolution and lessons of four artistic performance works that engage with text and imagery with the mindset of a composer, rather than as an author or visual artist. The works involve computer music, improvisation, video art, generative art techniques, and challenging aesthetics. An analytical discussion reveals that different forms and mechanisms of reading are manifest in the artworks, and reflections upon these elucidate the intermedial nature of reading and the productive, expressive potential of interfering with these processes

Long-Run Success In The Accounting Profession: A Study Of Student Perceptions

Accounting students are generally well aware of the skills, education, and accomplishments needed to get that first job and initially enter the accounting profession. However, it is equally important that accounting students approaching graduation have a good understanding of the skills, education and accomplishments required for an experienced accountant (an accountant who is three, five, or even ten years into their career). Armed with this information, students will be better equipped to make the best decisions as they complete their undergraduate degree and begin their careers. This would include decisions about graduate studies, pursuing certifications, accepting a job in a particular industry or one which provides specific experiences or training. Unfortunately, many students do not have accurate or complete information regarding the requirements for continued, long-run success in the profession. This paper reports the results of a project which (1) measured accounting students perceptions of the education, training, knowledge and experience required for experienced accountants, (2) implemented a class project exposing students to employers requirements for experienced accountants, and (3) measured student perceptions after the project was completed

The global wood furniture value chain: what prospects for upgrading by developing countries? The case of South Africa

Because of its resource and labour intensity, the wood furniture sector presents an opportunity for developing countries and their firms to participate effectively in the global economy. This paper begins with a brief description of the global wood furniture industry and highlights the importance of exports wood furniture products for developing countries and emerging and transitional economies. The paper then maps the wood furniture value chain and opens-up the nature of the buying function, since this function represents the key form of control over global production networks in this sector (that is, the wood furniture chain is what is increasingly referred to as a "buyer-driven chain"). The paper then asks what producers need to do in order to upgrade their activities, particularly in developing countries. In order to address these issues the authors describe the evolution of an initiative designed to promote the upgrading of one segment of the wood furniture industry in a middle-income country, South Africa. This experience is then used to generate a series of generic policy challenges, which might be transferred to other countries and to other sectors

Water Pump Development for the EVA PLSS

This paper describes the effort by the Texas Engineering Experiment Station (TEES) and Honeywell for NASA to design, fabricate, and test a preflight prototype pump for use in the Extravehicular activity (EVA) portable life support subsystem (PLSS). Major design decisions were driven by the need to reduce the pump s mass, power, and volume compared to the existing PLSS pump. In addition, the pump will accommodate a much wider range of abnormal conditions than the existing pump, including vapor/gas bubbles and increased pressure drop when employed to cool two suits simultaneously. A positive displacement, external gear type pump was selected because it offers the most compact and highest efficiency solution over the required range of flow rates and pressure drops. An additional benefit of selecting a gear pump design is that it is self priming and capable of ingesting noncondensable gas without becoming "air locked." The chosen pump design consists of a 28 V DC, brushless, sealless, permanent magnet motor driven, external gear pump that utilizes a Honeywell development that eliminates the need for magnetic coupling. Although the planned flight unit will use a sensorless motor with custom designed controller, the preflight prototype to be provided for this project incorporates Hall effect sensors, allowing an interface with a readily available commercial motor controller. This design approach reduced the cost of this project and gives NASA more flexibility in future PLSS laboratory testing. The pump design was based on existing Honeywell designs, but incorporated features specifically for the PLSS application, including all of the key features of the flight pump. Testing at TEES will simulate the vacuum environment in which the flight pump will operate. Testing will verify that the pump meets design requirements for range of flow rates, pressure rise, power consumption, working fluid temperature, operating time, and restart capability. Pump testing is currently scheduled for March, 2009, after which the pump will be delivered to NASA for further testing

A deep active learning system for species identification and counting in camera trap images

1. A typical camera trap survey may produce millions of images that require slow, expensive manual review. Consequently, critical conservation questions may be answered too slowly to support decision‐making. Recent studies demonstrated the potential for computer vision to dramatically increase efficiency in image‐based biodiversity surveys; however, the literature has focused on projects with a large set of labeled training images, and hence many projects with a smaller set of labeled images cannot benefit from existing machine learning techniques. Furthermore, even sizable projects have struggled to adopt computer vision methods because classification models overfit to specific image backgrounds (i.e., camera locations). 2. In this paper, we combine the power of machine intelligence and human intelligence via a novel active learning system to minimize the manual work required to train a computer vision model. Furthermore, we utilize object detection models and transfer learning to prevent overfitting to camera locations. To our knowledge, this is the first work to apply an active learning approach to camera trap images. 3. Our proposed scheme can match state‐of‐the‐art accuracy on a 3.2 million image dataset with as few as 14,100 manual labels, which means decreasing manual labeling effort by over 99.5%. Our trained models are also less dependent on background pixels, since they operate only on cropped regions around animals. 4. The proposed active deep learning scheme can significantly reduce the manual labor required to extract information from camera trap images. Automation of information extraction will not only benefit existing camera trap projects, but can also catalyze the deployment of larger camera trap arrays

A deep active learning system for species identification and counting in camera trap images

1. A typical camera trap survey may produce millions of images that require slow, expensive manual review. Consequently, critical conservation questions may be answered too slowly to support decision‐making. Recent studies demonstrated the potential for computer vision to dramatically increase efficiency in image‐based biodiversity surveys; however, the literature has focused on projects with a large set of labeled training images, and hence many projects with a smaller set of labeled images cannot benefit from existing machine learning techniques. Furthermore, even sizable projects have struggled to adopt computer vision methods because classification models overfit to specific image backgrounds (i.e., camera locations). 2. In this paper, we combine the power of machine intelligence and human intelligence via a novel active learning system to minimize the manual work required to train a computer vision model. Furthermore, we utilize object detection models and transfer learning to prevent overfitting to camera locations. To our knowledge, this is the first work to apply an active learning approach to camera trap images. 3. Our proposed scheme can match state‐of‐the‐art accuracy on a 3.2 million image dataset with as few as 14,100 manual labels, which means decreasing manual labeling effort by over 99.5%. Our trained models are also less dependent on background pixels, since they operate only on cropped regions around animals. 4. The proposed active deep learning scheme can significantly reduce the manual labor required to extract information from camera trap images. Automation of information extraction will not only benefit existing camera trap projects, but can also catalyze the deployment of larger camera trap arrays