@article {17203,
	title = {Human Responsibility for Autonomous Agents},
	journal = {IEEE Intelligent Systems},
	volume = {22},
	year = {2007},
	month = {2007/04//March},
	pages = {60 - 61},
	abstract = {Automated or autonomous systems can sometimes fail harmlessly, but they can also destroy data, compromise privacy, and consume resources, such as bandwidth or server capacity. What{\textquoteright}s more troubling is that automated systems embedded in vital systems can cause financial losses, destruction of property, and loss of life. Controlling these dangers will increase trust while enabling broader use of these systems with higher degrees of safety. Obvious threats stem from design errors and software bugs, but we can{\textquoteright}t overlook mistaken assumptions by designers, unanticipated actions by humans, and interference from other computerized systems. This article is part of a special issue on Interacting with Autonomy.},
	keywords = {Automatic control, Autonomous agents, autonomous systems, Bandwidth, Computer bugs, Computer errors, Control systems, data privacy, Human-computer interaction, HUMANS, Robots, Safety, Software design},
	isbn = {1541-1672},
	doi = {10.1109/MIS.2007.32},
	author = {Shneiderman, Ben}
}
@conference {11897,
	title = {Identifying and segmenting human-motion for mobile robot navigation using alignment errors},
	booktitle = {12th International Conference on Advanced Robotics, 2005. ICAR {\textquoteright}05. Proceedings},
	year = {2005},
	month = {2005/07//},
	pages = {398 - 403},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {This paper presents a new human-motion identification and segmentation algorithm, for mobile robot platforms. The algorithm is based on computing the alignment error between pairs of object images acquired from a moving platform. Pairs of images generating relatively small alignment errors are used to estimate the fundamental frequency of the object{\textquoteright}s motion. A decision criterion is then used to test the significance of the estimated frequency and to classify the object{\textquoteright}s motion. To verify the validity of the proposed approach, experimental results are shown on different classes of objects},
	keywords = {Computer errors, Educational institutions, Frequency estimation, human-motion identification, human-motion segmentation, HUMANS, Image motion analysis, Image segmentation, mobile robot navigation, Mobile robots, Motion estimation, Navigation, Object detection, robot vision, SHAPE},
	isbn = {0-7803-9178-0},
	doi = {10.1109/ICAR.2005.1507441},
	author = {Abd-Almageed, Wael and Burns,B. J and Davis, Larry S.}
}
@conference {17081,
	title = {Direct annotation: a drag-and-drop strategy for labeling photos},
	booktitle = {IEEE International Conference on Information Visualization, 2000. Proceedings},
	year = {2000},
	month = {2000///},
	pages = {88 - 95},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Annotating photographs is such a time-consuming, tedious and error-prone data entry task that it discourages most owners of personal photo libraries. By allowing the user to drag labels, such as personal names, from a scrolling list and drop them onto a photo, we believe we can make the task faster, easier and more appealing. Since the names are entered in a database, searching for all photos of a friend or family member is dramatically simplified. We describe the user interface design and the database schema to support direct annotation, as implemented in our PhotoFinder prototype},
	keywords = {Biomedical imaging, Cities and towns, Computer errors, Computer science, database indexing, database schema, Databases, digital libraries, direct annotation, direct manipulation, drag-and-drop strategy, Educational institutions, graphical user interface, Graphical user interfaces, History, hobby computing, image searching, label placement, Labeling, Laboratories, Libraries, personal information systems, personal names database, PhotoFinder prototype, photograph labelling, photographic libraries, Photography, scrolling list, user interface design, visual databases},
	isbn = {0-7695-0743-3},
	doi = {10.1109/IV.2000.859742},
	author = {Shneiderman, Ben and Kang,H.}
}
@article {16988,
	title = {Beyond intelligent machines: just do it},
	journal = {IEEE Software},
	volume = {10},
	year = {1993},
	month = {1993/01//},
	pages = {100 - 103},
	abstract = {The author argues that users want a sense of direct and immediate control over computers that differs from how they interact with people. He presents several examples of these predictable and controllable interfaces developed in the lab. The examples include tree maps and dynamic queries.<>},
	keywords = {Artificial intelligence, Computer aided instruction, Computer errors, controllable interfaces, Displays, dynamic queries, Educational institutions, History, HUMANS, intelligent machines, learning systems, Machine intelligence, predictable interfaces, Speech recognition, tree maps, User interfaces},
	isbn = {0740-7459},
	doi = {10.1109/52.207235},
	author = {Shneiderman, Ben}
}
@conference {16320,
	title = {Software metric classification trees help guide the maintenance of large-scale systems},
	booktitle = {, Conference on Software Maintenance, 1989., Proceedings},
	year = {1989},
	month = {1989/10/16/19},
	pages = {116 - 123},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The 80:20 rule states that approximately 20\% of a software system is responsible for 80\% of its errors. The authors propose an automated method for generating empirically-based models of error-prone software objects. These models are intended to help localize the troublesome 20\%. The method uses a recursive algorithm to automatically generate classification trees whose nodes are multivalued functions based on software metrics. The purpose of the classification trees is to identify components that are likely to be error prone or costly, so that developers can focus their resources accordingly. A feasibility study was conducted using 16 NASA projects. On average, the classification trees correctly identified 79.3\% of the software modules that had high development effort or faults},
	keywords = {automated method, automatic programming, classification, Classification tree analysis, classification trees, Computer errors, empirically-based models, error-prone software objects, Fault diagnosis, feasibility study, high development effort, Large-scale systems, multivalued functions, NASA, NASA projects, recursive algorithm, Software algorithms, software engineering, Software maintenance, Software measurement, software metrics, software modules, Software systems, trees (mathematics)},
	isbn = {0-8186-1965-1},
	doi = {10.1109/ICSM.1989.65202},
	author = {Selby,R. W and Porter, Adam}
}
@article {17090,
	title = {Display Strategies for Program Browsing: Concepts and Experiment},
	journal = {IEEE Software},
	volume = {3},
	year = {1986},
	month = {1986/05//},
	pages = {7 - 15},
	abstract = {The new, larger display screens can improve program comprehension{\textemdash}if the added space is used for mome effective presentation, not just more code or larger type.},
	keywords = {Computer errors, Content management, Fault detection, Information analysis, Large screen displays, Microscopy, Phase detection, Programming profession, User interfaces},
	isbn = {0740-7459},
	doi = {10.1109/MS.1986.233405},
	author = {Shneiderman, Ben and Shafer,P. and Simon,R. and Weldon,L.}
}
