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The traditional user-based collaborative filtering recommendation algorithm only considers the user's score when calculating the similarity between users, but ignores the differences between differ...

Programming with logic for sophisticated applications must deal with recursion and negation, which have created significant challenges in logic, leading to many different, conflicting semantics of rules. This paper describes a unified language, DA logic, for design and analysis logic, based on the unifying founded semantics and constraint semantics, that support the power and ease of programming with different intended semantics. The key idea is to provide meta-constraints, support the use of uncertain information in the form of either undefined values or possible combinations of values, and promote the use of knowledge units that can be instantiated by any new predicates, including predicates with additional arguments.

The paper deals with matters related to the adjustment training of the specialist who has specified competences in order to solve the specific staffing problems of enterprises, provide scheduled staff re-training, suggest additional specializations to graduates of educational institutions, etc. Defines a structure and content of the management system elements for training and adjustment training of specialists, including a shift from activity-related competence to desirable areas of knowledge, and therefrom to the curricula and programmes.A concept of the knowledge subject is defined. It allows perceiving a specific subject area. A concept of the aspects of the certain knowledge subject is proposed, and in this case it is assumed that the knowledge subject can be represented as a finite set of aspects of the given subject of knowledge. A concept of the knowledge section is defined as a body of knowledge, reflecting some aspect of the particular subject of knowledge. Thus, the entire body of knowledge to be offered by the particular Department may be represented by a variety of specific sections of knowledge. For each section of knowledge is introduced a concept of the training level, which corresponds to the level of detail and quality of the educational material delivered. To assess the level of training it is possible to use the method of expert’s estimates.The paper suggests a scheme that allows realization of the relevant order for target training of specialists. Requirements for training specialists are represented as a competency model of the specialist; an implemented study programme is characterized by the competences, obtained as the learning outcomes.The content of knowledge, corresponding to some competence, is represented as a total of all the sections of knowledge related to the aspects generated by the competence. Then the competence can be revealed through the body of knowledge. As a result, both an order for adjustment training and a Department can be defined as a set of the knowledge sections with indication of quality levels. The concepts of general knowledge, a lack of knowledge, knowledge residue were defined when comparing the weighted knowledge content of the order and the weighted knowledge content of the Department.The paper introduces an indicator of the level of requirements, which value shows a difference of the order-required level of training for the aspect from the level of training provided by the Department. The order-unclaimed knowledge forms the graduate’s general knowledge. This allows you to adjust the amount of additional knowledge to implement the order. To do this, we introduce the concept of a connectivity factor, which takes the value in the range [0,1]. The experts specify this factor, and the more is its value, the higher is the connectivity of the order materials and the knowledge residue.The obtained results make it possible to create an optimal technique (including automated) for adjustment or target training of specialists who possess basic education. In addition, this technique allows a customer to select the most desirable basic Department to organise target training.

Materials forming and processing technology is an important professional course in the undergraduate curriculum system of packaging engineering major. Based on the teaching requirements of the training program for undergraduate students of packaging engineering major in Beijing Institute of Graphic Communication, this paper constructs the knowledge network of the course with selected knowledge units and knowledge points and optimizes the teaching content. In the course teaching practice, multimedia teaching, inductive teaching, case study teaching, and other teaching methods are adopted to help students construct knowledge and develop skills. The course teaching can better meet the needs of discipline and specialty construction.

This paper presents a novel way to help match employers' knowledge requirements with students' knowledge earned using blockchain's smart contracts to assure credentials and track student records. This decentralized approach proposes using the micro-accreditation of topics from the CAE framework to courses and associated tasks, while introducing a revolutionary idea of a blockchain-based peer-reviewed rigor score assignment. Our work and result metrics were completed in Ethereum and connected test networks. We concluded this new approach is mostly efficient and scalable depending on the network load, with faster transaction times when the miners are properly incentivized. Future work will include further fine-tuning of the transaction algorithms to improve time, as well as an investigation into a better consensus model for peer review and rigor determination.

Knowledge flow between disciplines is typically measured through citations among publications. In this study, we quantify cross-disciplinary knowledge diffusion from the novel perspective of content by introducing knowledge memes, a special type of knowledge unit. Diffusion cascade is proposed to model the diffusion process of knowledge memes. By taking Medical Informatics (MI) as an exemplary interdisciplinary discipline, we measure the knowledge relationships between it and four related disciplines. The diffusion patterns of cross-disciplinary memes are also identified by analyzing the network structure of the diffusion cascade. The results present the knowledge relationships among disciplines measured by knowledge memes, which are different from those measured by citations. It is shown that preferential attachment takes effect in cross-disciplinary knowledge meme diffusion. In addition, cross-disciplinary knowledge memes generally originate earlier and have higher impact than the memes of MI. This study provides insights into new approaches to quantifying knowledge relationships among disciplines and furthers the understanding of content diffusion mechanisms through measurable knowledge units.

This chapter introduces three teaching modules centered on parallel performance concepts. Performance related topics embody many fundamental ideas in parallel computing. In the ACM/IEEE curricular guidelines (ACM2013), an entire knowledge unit has been devoted to parallel performance. In addition, performance topics pervade every knowledge area within PDC and can be found across other knowledge areas including Algorithms, Architecture and Systems Fundamentals. The three modules presented in this chapter cover a range of parallel performance topics. Since power savings have become an important consideration from hand-held devices to supercomputers, energy efficiency is also emphasized in each module. The modules focus more on architectural and algorithmic issues rather than the programming aspects. The modules are constructed to illustrate parallel performance issues primarily through code examples and experimental studies. This approach makes the modules accessible to students who do not yet have a strong background in parallel programming. Thus, the target audience for this chapter are instructors who are teaching CS1, with or without parallel programming, and also instructors who are teaching upper-level electives where their students may already have taken a semester of parallel programming.

Curricula are not static, especially in Computer Science. Curricula specifications are updated and it is not always clear what the exact updates are nor the impact these updated may have on a curriculum which has been developed to comply with the specification. In this paper, the results of a comparison on the knowledge unit and topic-level between the ACM/IEEE Computer Science curriculum volumes of 2001, 2008 and 2013 is presented.

Model-tracing tutors (MTTs) have proven effective for the tutoring of well-defined tasks, but the pedagogical interventions they produce are limited and usually require the inclusion of pedagogical content, such as text message templates, in the model of the task. The capability to generate pedagogical content would be beneficial to MTT frameworks, as it would lessen the task-specific efforts and could lead to the capability of providing more sophisticated pedagogical interventions. In this paper, we show how Astus, as an MTT framework, strive to attain a higher level of automation when generating pedagogical interventions compared to other MTT frameworks such as TDK and CTAT’s MTTs. This is achieved by designing a knowledge representation approach in which each type of knowledge unit has a clearly defined semantic on which the MTT’s pedagogical module can rely on. We explain how this knowledge representation approach is implemented as a knowledge-based system in ASTUS and show how it allows the development of MTTs that can automatically generate the pedagogical content required to provide next-step hints and negative feedback on errors. Multiple small-scale experiments were conducted with computer science undergraduate students in order to obtain a preliminary assessment of the effectiveness of Astus’s pedagogical interventions.

We introduce closed sets, which we will call knowledge units, to represent tight collections of experience, facts, or skills, etc. Associated with each knowledge unit is the notion of its generators consisting of those attributes which characterize it. Using these closure concepts, we then provide a rigorous mathematical model of learning in terms of continuous transformations. We illustrate the behavior of transformations by means of closure lattices, and provide necessary and sufficient criteria for simple transformations to be continuous. By using a rigorous definition, one can derive necessary alternative properties of cognition which may be more easily observed in experimental situations.

In view of such a common phenomenon that the CDIO-based learning method is unable to guarantee high learning quality, this paper proposes a knowledge unit-based learning quality evaluation model in English major that is adapted to the CDIO environment based on the fuzzy comprehensive evaluation method with the combination of the characteristics of the classroom- based learning pattern. The result of the teaching experiment proves that this model in English major is able to enhance efficiently the ability of the learning feedback acquisition and the processing capacity of an e-learning system, promoting the collaboration between the on-line educators and strengthening the learning quality monitoring function provided by the educator. It has played a significant role in the improvement of the knowledge unit-based learning quality. Hence, this model in English major will have a promising prospect of application with the rapid development of the CDIO-based learning pattern.

This paper presents a method for calculating the boiling point rise of Bayer plant caustic-aluminate liquors within about ±0.1°C from the chemical analyses of the liquors. The effect of impurities present in significant amounts is included.

Identifying the pre-requisite relationships among learning objects is a crucial step for faculty and instructional designers when they try to adapt them for delivery in their general education distance courses. We propose a general-purpose content-based approach for facilitating this step by means of semantic analysis techniques: the learning objects are associated to WikiPedia pages (topics), and their dependency is obtained using the classification of those topics supported by Wikipedia Miner.

"Scenario-Respond" are the most efficient model for unconventional emergency management, and the scenarios are the foundation to deal with the unconventional emergency, therefore how to build and deduce the scenarios are important research parts of "scenario-respond". From the respective of cognitive science, the paper built a scenario deduction framework based on the knowledge-unit model, then discussed the factor representation, relationships of factors and the deduction algorithm in details. Finally, the debris flow event is used to testify the feasibility and scientific of the proposed framework.

This poster session examines the ACM/IEEE undergraduates programs for computer science and information technology with a focus on the learning outcomes. The IT guidelines were completed in 2008 and are due to be redone in the next couple of years. A database was created for each of the guidelines storing the Knowledge Area (KA), Knowledge Unit (KU) and the Learning Outcome (LO). Queries were generated to allow comparison between the two programs. The computer science guidelines are recent with some significant changes made compared to the older version. This poster will also examine the various knowledge areas and units within each of guidelines showing similarities and differences. Added into the database was the institution's IT and CS programs' student learning outcomes as well which gives us the opportunity to see how we compare as well.