The knowledge engineers’ oath

Reading Dan Ariely's book Predictably Irrational1, it struck me that Computer Science professionals would benefit from having an oath, something to play the role that the Hippocratic oath once held for doctors. Ariely's book describes an experiment in which contemplation of a ‘moral benchmark,’ such as an oath, raised the general level of responsibility – social, ethical and moral – in a group of people. Such an oath would need to be at the entry point of the Computing profession, so that it delimited the world of the CS student/apprentice from that of the Computing professional: the word professional is derived from the Latin for ‘public declaration’. “I have entered the serious pursuit of new knowledge as a member of the community of graduate students at the University of Toronto. I declare the following: Pride: I solemnly declare my pride in belonging to the international community of research scholars. Integrity: I promise never to allow financial gain, competitiveness, or ambition cloud my judgment in the conduct of ethical research and scholarship. Pursuit: I will pursue knowledge and create knowledge for the greater good, but never to the detriment of colleagues, supervisors, research subjects or the international community of scholars of which I am now a member. By pronouncing this Graduate Student Oath, I affirm my commitment to professional conduct and to abide by the principles of ethical conduct and research policies as set out by the University of Toronto.” Davis et al.s’ oath is a response to their perception, shared by this author, that during their education we, as educators, miss the opportunity to prime our student and discuss with them the social, ethical and moral responsibilities in science research. Their oath emphasises three aspects of scientific research training at the graduate level: community, professionalism, and ethical conduct. In the oath we see three critical characteristics of the research (computer) scientist coming through: pride, in standing alongside others who dedicate their lives to science; integrity, in removing as motives for progression those that are known to damage the community; and, at the core of our community, the pursuit of knowledge and knowledge creation. That last point is telling: and we should note that, as knowledge is the basis in science with its creation the goal, knowledge engineering is the modern key to the creation of knowledge. As such we have a special responsibility to the community in which we practice. Perhaps we should take a step in the right direction by developing and swearing together a Knowledge Engineer's Oath. There are six articles this issue: In ‘A complete chronicle discovery approach: application to activity analysis’, Damien Cram et al. provide insight into the discovery of temporal patterns hidden in a sequence of events, presenting the first chronicle discovery algorithm that is complete. Scheme Emerger has been developed in order to implement the algorithm and to provide real-time graphical support for an interactive chronicle discovery process. Noise attenuation of biomedical signals with a quasi-cyclical character can be achieved through arithmetic averaging. In ‘On application of input data partitioning to Bayesian weighted averaging of biomedical signals’, Alina Momot presents an improvement on traditional arithmetic averaging techniques which assume constant noise power. The techniques employed include Bayesian weighted averaging with traditional (sharp) and fuzzy partition of the input data in the presence of non-stationary noise. In “A decision support system for fund raising management based on the Choquet integral methodology” Barzanti and Giove describe techniques that allow small and medium size not-for-profits to improve their strategies for fund raising based on the organisation's profile, characterised as a hierarchically organised decision tree. The techniques are validated and effectiveness confirmed. In ‘Combining classifiers under probabilistic models: experimental comparative analysis of methods’ Kurzynski and Wozniak present a review of the concept of classifier combination based on the combined discriminant function, in which several recognition algorithms are described. In doing so they introduce the original concept of information unification, to enable the formation of rules on the basis of learning set and vice versa, and go on to formulate new project guidelines for this type of decision-making system. The new techniques are validated both on computer generated data and on data from the medical diagnostics field. Tsai and Tung, in ‘Modified Smith predictor with a robust disturbance reduction scheme for linear systems with small time delays’ present a robust disturbance reduction scheme for linear systems with small time delays that does not require information about unknown load disturbance frequencies. An artificial neural network is used to approximate the unknown load disturbances. Simulation results demonstrate the effectiveness of the scheme as applied to various linear delay systems. Last, but by no means least, ‘Discovering patterns of online purchasing behaviour and a new-product-launch strategy’ by Lun-Ping Hung develops a system to analyse customers’ purchasing behaviour and track shifts in their interests as the basis of recommendation. From this, the author suggests a new-product-launch strategy. The new strategy is tested, and shows almost 40% of potential customers respond to the recommendation positively.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.