The Profiling Potential of Computer Vision and the Challenge of Computational Empiricism

Computer vision and other biometrics data science applications have commenced a new project of profiling people. Rather than using 'transaction generated information', these systems measure the 'real world' and produce an assessment of the 'world state' - in this case an assessment of some individual trait. Instead of using proxies or scores to evaluate people, they increasingly deploy a logic of revealing the truth about reality and the people within it. While these profiling knowledge claims are sometimes tentative, they increasingly suggest that only through computation can these excesses of reality be captured and understood. This article explores the bases of those claims in the systems of measurement, representation, and classification deployed in computer vision. It asks if there is something new in this type of knowledge claim, sketches an account of a new form of computational empiricism being operationalised, and questions what kind of human subject is being constructed by these technological systems and practices. Finally, the article explores legal mechanisms for contesting the emergence of computational empiricism as the dominant knowledge platform for understanding the world and the people within it

Crowdsourcing in Computer Vision

Computer vision systems require large amounts of manually annotated data to properly learn challenging visual concepts. Crowdsourcing platforms offer an inexpensive method to capture human knowledge and understanding, for a vast number of visual perception tasks. In this survey, we describe the types of annotations computer vision researchers have collected using crowdsourcing, and how they have ensured that this data is of high quality while annotation effort is minimized. We begin by discussing data collection on both classic (e.g., object recognition) and recent (e.g., visual story-telling) vision tasks. We then summarize key design decisions for creating effective data collection interfaces and workflows, and present strategies for intelligently selecting the most important data instances to annotate. Finally, we conclude with some thoughts on the future of crowdsourcing in computer vision.Comment: A 69-page meta review of the field, Foundations and Trends in Computer Graphics and Vision, 201

Management information systems professionals—The requirement to be true Renaissance people within the organization

Management Information Systems (MIS) is not simply data processing, nor computer science, nor computer services, nor computer information systems, nor computer information services, nor information systems. Management Information Systems is a comprehensive, academic and business discipline requiring a knowledge of business operations as a whole, the human decision making cognitive process, plus automated processing of data into information. This paper suggests that, as opposed to only being highly technical individuals as they are often viewed, MIS professionals must have a knowledge of various business functions and the nature of the related decision making timeframes plus draw on and blend knowledge and skills originating in a number of academic areas. The paper discusses the breadth and depth of these facets

Penggunaan Data Mining dalam Kegiatan Sistem Pembelajaran Berbantuan Komputer

Web-based educational systems and intelligent guidance systems collect large amounts of student data, from web logs to student models. Data mining applications on such data can help find relevant knowledge to improve computer-aided learning systems. Using knowledge, teachers can better understand [how students learn by studying a group of students in order to improve teaching and learning. In this paper, the process of data mining will be separated into data collection, data transformation, and data analysis. Association rules, classification, and clustering of data mining algorithms that are explored in data analysis for computer-assisted learning systems

Challenges of developing an electro-optical system for measuring man's operational envelope

In designing work stations and restraint systems, and in planning tasks to be performed in space, a knowledge of the capabilities of the operator is essential. Answers to such questions as whether a specific control or work surface can be reached from a given restraint and how much force can be applied are of particular interest. A computer-aided design system has been developed for designing and evaluating work stations, etc., and the Anthropometric Measurement Laboratory (AML) has been charged with obtaining the data to be used in design and modeling. Traditional methods of measuring reach and force are very labor intensive and require bulky equipment. The AML has developed a series of electro-optical devices for collecting reach data easily, in computer readable form, with portable systems. The systems developed, their use, and data collected with them are described

Knowledge-based Biomedical Data Science 2019

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table

Factors shaping the evolution of electronic documentation systems

The main goal is to prepare the space station technical and managerial structure for likely changes in the creation, capture, transfer, and utilization of knowledge. By anticipating advances, the design of Space Station Project (SSP) information systems can be tailored to facilitate a progression of increasingly sophisticated strategies as the space station evolves. Future generations of advanced information systems will use increases in power to deliver environmentally meaningful, contextually targeted, interconnected data (knowledge). The concept of a Knowledge Base Management System is emerging when the problem is focused on how information systems can perform such a conversion of raw data. Such a system would include traditional management functions for large space databases. Added artificial intelligence features might encompass co-existing knowledge representation schemes; effective control structures for deductive, plausible, and inductive reasoning; means for knowledge acquisition, refinement, and validation; explanation facilities; and dynamic human intervention. The major areas covered include: alternative knowledge representation approaches; advanced user interface capabilities; computer-supported cooperative work; the evolution of information system hardware; standardization, compatibility, and connectivity; and organizational impacts of information intensive environments