Farmers’ knowledge and perceptions of potato pests and their management in Uganda

As we initiate entomological research on potato (Solanum tuberosum L.) in Uganda, there is need to understand farmers’ knowledge of existing insect pest problems and their management practices. Such information is important for designing a suitable intervention and successful integrated pest management (IPM) strategy. A farm household survey using a structured questionnaire was conducted among 204 potato farmers in six districts of Uganda (i.e., Kabale, Kisoro, Mbale, Kapchorwa, Mubende, and Kyegegwa) during August and September 2013. Diseases, insect pests, price fluctuations, and low market prices were the four highest ranked constraints in potato production, in order of decreasing importance. Cutworms (Agrotis spp.), aphids (Myzus persicae (Sulzer)), and potato tuber moth (Phthorimaea operculella (Zeller)) were the three most severe insect pests. Ants (Dorylis orantalis Westwood), whiteflies (Bemisia tabaci (Gennadius)), and leafminer flies (Liriomyza huidobrensis (Blanchard)) were pests of moderate importance. Major yield losses are predominantly due to late blight (Phytophthora infestans (Mont.) de Bary) and reached 100% without chemical control in the districts of Kabale, Kisoro, Mbale, and Kapchorwa. On average, farmers had little to moderate knowledge about pest characteristics. The predominant control methods were use of fungicides (72% of respondents) and insecticides (62% of respondents). On average, only 5% of the 204 farmers knew about insect pests and their natural enemies. This lack of knowledge calls for training of both farmers and extension workers in insect pest identification, their biology, and control. Empowering farmers with knowledge about insect pests is essential for the reduction of pesticide misuse and uptake of more environmentally friendly approaches like IPM. Field surveys would need follow-up in order to assess the actual field infestation rates and intensities of each insect pest and compare the results with the responses received from farmers.

ONTIC: A Knowledge Representation System for Mathematics

Ontic is an interactive system for developing and verifying mathematics. Ontic's verification mechanism is capable of automatically finding and applying information from a library containing hundreds of mathematical facts. Starting with only the axioms of Zermelo-Fraenkel set theory, the Ontic system has been used to build a data base of definitions and lemmas leading to a proof of the Stone representation theorem for Boolean lattices. The Ontic system has been used to explore issues in knowledge representation, automated deduction, and the automatic use of large data bases.

Using Analogy to Acquire Commonsense Knowledge from Human Contributors

The goal of the work reported here is to capture the commonsense knowledge of non-expert human contributors. Achieving this goal will enable more intelligent human-computer interfaces and pave the way for computers to reason about our world. In the domain of natural language processing, it will provide the world knowledge much needed for semantic processing of natural language. To acquire knowledge from contributors not trained in knowledge engineering, I take the following four steps: (i) develop a knowledge representation (KR) model for simple assertions in natural language, (ii) introduce cumulative analogy, a class of nearest-neighbor based analogical reasoning algorithms over this representation, (iii) argue that cumulative analogy is well suited for knowledge acquisition (KA) based on a theoretical analysis of effectiveness of KA with this approach, and (iv) test the KR model and the effectiveness of the cumulative analogy algorithms empirically. To investigate effectiveness of cumulative analogy for KA empirically, Learner, an open source system for KA by cumulative analogy has been implemented, deployed, and evaluated. (The site "1001 Questions," is available at http://teach-computers.org/learner.html). Learner acquires assertion-level knowledge by constructing shallow semantic analogies between a KA topic and its nearest neighbors and posing these analogies as natural language questions to human contributors. Suppose, for example, that based on the knowledge about "newspapers" already present in the knowledge base, Learner judges "newspaper" to be similar to "book" and "magazine." Further suppose that assertions "books contain information" and "magazines contain information" are also already in the knowledge base. Then Learner will use cumulative analogy from the similar topics to ask humans whether "newspapers contain information." Because similarity between topics is computed based on what is already known about them, Learner exhibits bootstrapping behavior --- the quality of its questions improves as it gathers more knowledge. By summing evidence for and against posing any given question, Learner also exhibits noise tolerance, limiting the effect of incorrect similarities. The KA power of shallow semantic analogy from nearest neighbors is one of the main findings of this thesis. I perform an analysis of commonsense knowledge collected by another research effort that did not rely on analogical reasoning and demonstrate that indeed there is sufficient amount of correlation in the knowledge base to motivate using cumulative analogy from nearest neighbors as a KA method. Empirically, evaluating the percentages of questions answered affirmatively, negatively and judged to be nonsensical in the cumulative analogy case compares favorably with the baseline, no-similarity case that relies on random objects rather than nearest neighbors. Of the questions generated by cumulative analogy, contributors answered 45% affirmatively, 28% negatively and marked 13% as nonsensical; in the control, no-similarity case 8% of questions were answered affirmatively, 60% negatively and 26% were marked as nonsensical.

Learning Commonsense Categorical Knowledge in a Thread Memory System

If we are to understand how we can build machines capable of broad purpose learning and reasoning, we must first aim to build systems that can represent, acquire, and reason about the kinds of commonsense knowledge that we humans have about the world. This endeavor suggests steps such as identifying the kinds of knowledge people commonly have about the world, constructing suitable knowledge representations, and exploring the mechanisms that people use to make judgments about the everyday world. In this work, I contribute to these goals by proposing an architecture for a system that can learn commonsense knowledge about the properties and behavior of objects in the world. The architecture described here augments previous machine learning systems in four ways: (1) it relies on a seven dimensional notion of context, built from information recently given to the system, to learn and reason about objects' properties; (2) it has multiple methods that it can use to reason about objects, so that when one method fails, it can fall back on others; (3) it illustrates the usefulness of reasoning about objects by thinking about their similarity to other, better known objects, and by inferring properties of objects from the categories that they belong to; and (4) it represents an attempt to build an autonomous learner and reasoner, that sets its own goals for learning about the world and deduces new facts by reflecting on its acquired knowledge. This thesis describes this architecture, as well as a first implementation, that can learn from sentences such as ``A blue bird flew to the tree'' and ``The small bird flew to the cage'' that birds can fly. One of the main contributions of this work lies in suggesting a further set of salient ideas about how we can build broader purpose commonsense artificial learners and reasoners.

Towards a knowledge economy

This paper provides a preliminary formulation of a new currency based on knowledge. Through a literature review of alternative currencies, various properties and benefits are selected that we hope will enable such a currency to be created. Nowadays not only money but also knowledge is necessary to do business. For instance, knowledge about markets and consumers is highly valuable but difficult to achieve, and even more difficult to store, transport or trade. The basic premise of this proposal is a knowledge measurement pattern that is formulated as a new alternative social currency. Therefore, it is an additional means of contributing to the worldwide evolution of a knowledge society. It is intended as a currency to facilitate the conservation and storage of knowledge, and its organization and categorization, but mainly its exploitation and transference

Knowledge integration within an applied mobile robot summer course

This paper presents the use of a mobile robot platform as an innovative educational tool in order to promote and integrate different curriculum knowledge. Hence, it is presented the acquired experience within a summer course named ldquoapplied mobile roboticsrdquo. The main aim of the course is to integrate different subjects as electronics, programming, architecture, perception systems, communications, control and trajectory planning by using the educational open mobile robot platform PRIM. The summer course is addressed to a wide range of student profiles. However, it is of special interests to the students of electrical and computer engineering around their final academic year. The summer course consists of the theoretical and laboratory sessions, related to the following topics: design & programming of electronic devices, modelling and control systems, trajectory planning and control, and computer vision systems. Therefore, the clues for achieving a renewed path of progress in robotics are the integration of several knowledgeable fields, such as computing, communications, and control sciences, in order to perform a higher level reasoning and use decision tools with strong theoretical base

Adding new components to the knowledge plane GMPLS over WDM networks

A recent study defines a new network plane: the knowledge plane. The incorporation of the knowledge plane over the network allows having more accurate information of the current and future network states. In this paper, the introduction and management of the network reliability information in the knowledge plane is proposed in order to improve the quality of service with protection routing algorithms in GMPLS over WDM networks. Different experiments prove the efficiency and scalability of the proposed scheme in terms of the percentage of resources used to protect the network

Context constraints, prior vocabulary knowledge and on-line inferences in reading

Resumen tomado de la publicaci??n

Knowledge and question asking

Resumen tomado de la publicaci??n

Talk the talk: Learner-generated podcasts as catalysts for knowledge creation

Instead of using the technology for the mere recording and dissemination of lectures and other instructor-centred information, the project reported on in this article focused on enabling students to create their own podcasts for distribution to their peers. The article describes how engaging in the podcasting exercise promoted collaborative knowledge building among the student-producers, as evidenced through focus-group interviewing and an analysis of the products of their shared dialogue and reflection.

Acknowledging knowledge: an academic guide to referencing

This guide explains why referencing in essays is so important, and provides clear examples of exactly how to reference a wide variety of sources from books to YouTube clips

The Spectrum of knowledge - starting off

A few files for background reading

Knowledge Technologies

An overview of COMP3028 Knowledge Technologies