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Fundamental ideas of computer science
 Bull. European Assoc. for Theoretical Computer Science
, 1994
"... The current situation of computer science education at the university level is characterized by two aspects: 1. Even with a historical background of more than 40 years computer science stills develops ..."
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Cited by 10 (0 self)
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The current situation of computer science education at the university level is characterized by two aspects: 1. Even with a historical background of more than 40 years computer science stills develops
The fundamental idea of opportunistic...
"... We consider a multichannel opportunistic communication system where the states of these channels evolve as independent and statistically identical Markov chains (the GilbertElliot channel model). A user chooses one channel to sense and access in each slot and collects a reward determined by the st ..."
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We consider a multichannel opportunistic communication system where the states of these channels evolve as independent and statistically identical Markov chains (the GilbertElliot channel model). A user chooses one channel to sense and access in each slot and collects a reward determined by the state of the chosen channel. The problem is to design a sensing policy for channel selection to maximize the average reward, which can be formulated as a multiarm restless bandit process. In this paper, we study the structure, optimality, and performance of the myopic sensing policy. We show that the myopic sensing policy has a simple robust structure that reduces channel selection to a roundrobin procedure and obviates the need for knowing the channel transition probabilities. The optimality of this simple policy is established for the twochannel case and conjectured for the general case based on numerical results. The performance of the myopic sensing policy is analyzed, which, based on the optimality of myopic sensing, characterizes the maximum throughput of a multichannel opportunistic communication system and its scaling behavior with respect to the number of channels. These results apply to cognitive radio networks, opportunistic transmission in fading environments, and resourceconstrained jamming and antijamming.
Some fundamental ideas in probability
"... In this panel discussion I want to focus your attention to three – of many – aspects of probability, which influence our individual capacity and the way how we perceive situations and their standard mathematical treatment – these are: thinking probabilistically, weighing the evidence, and the parado ..."
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In this panel discussion I want to focus your attention to three – of many – aspects of probability, which influence our individual capacity and the way how we perceive situations and their standard mathematical treatment – these are: thinking probabilistically, weighing the evidence, and the paradox of stabilizing and fluctuating of relative frequencies. 1 Thinking probabilistically While mathematicians would define thinking probabilistically in terms of adequate use of probabilistic models, individuals are faced with the context of the situations to be modelled. And the ingredients of these situations could lead to directions completely different from standard mathematical models and their solutions. For didactical purpose it has to be clarified in which respects thinking probabilistically could be characterized. Some features are illustrated by context and comprise amongst others: i. Feedback in probability items is indirect – you may win with the wrong strategy (Figure 1). ii. Interference with causal perception might lead astray (Figure 2). iii. Our criteria in probabilistic situations might be completely non probabilistic and emotionally laden (Figure 3). The spinner with a special margin. Which is the better choice if the spinner is twisted and the
Philosophical Aspects of Fundamental Ideas: Ideas and Concepts
"... Abstract: We consider the term ªidea from a philosophical point of view. In particular we are interested in the concept s origins, its relevance to human thinking and in particular in its pedagogical value for computer science lessons in schools as well as universities. Since the concept of fundamen ..."
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of fundamental ideas in computer science has been seized, extended, and reviewed by other authors and applied to lessons, often with a different understanding of the defined terms, we wish to explain some of the objectives of the approach in more detail and in particular clarify the relation between concept
Computer science education based on fundamental ideas
 In D. Passey & B. Samways (Eds.), Information
, 1997
"... We sketch a pedagogical ‚theory ‘ based on Bruner’s educational psychology that might set computer science education for students and teachers on a wellfounded basis and integrate different approaches to teach it, stress its longlasting fundamentals and give a feeling of its essence. For it we (1) ..."
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Cited by 6 (1 self)
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) define the notion of fundamental ideas more precisely by several criteria, (2) present a collection of fundamental ideas of computer science, and (3) show how to develop a curriculum that centers around these ideas.
Analyses and Fundamental ideas for a Relation Extraction Approach
"... Relation extraction is a difficult open research problem with important applications in several fields such as knowledge management, web mining, ontology building, intelligent systems, etc. In our research, we focus on extracting relations among the ontological concepts in order to build a domain on ..."
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ontology. In this paper, firstly, we answer some crucial questions related to the text analyses, the word features and the various relation types. Secondly, we use this theoretical analysis and some issues to define the fundamental ideas of our new approach. Our objective is to extract multitype relations
Hierarchical phrasebased translation
 Computational Linguistics
, 2007
"... We present a statistical machine translation model that uses hierarchical phrases—phrases that contain subphrases. The model is formally a synchronous contextfree grammar but is learned from a parallel text without any syntactic annotations. Thus it can be seen as combining fundamental ideas from b ..."
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Cited by 597 (9 self)
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We present a statistical machine translation model that uses hierarchical phrases—phrases that contain subphrases. The model is formally a synchronous contextfree grammar but is learned from a parallel text without any syntactic annotations. Thus it can be seen as combining fundamental ideas from
2000: Clear identification of fundamental idea of Nakamura’s technique and its applications
 In: Proc
"... A method which employs microtremor has been introduced for estimating dynamic characteristics of surface layers, in early 1950. Then usage of this method has received lots of criticism considering uncertainty about source of microtremor. After an introduction of the Nakamura’s technique (H/V or QTS ..."
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Cited by 37 (1 self)
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in microtremor and checking the applicability of the QTS technique. And some of them are suggested that the peak on H/V ratio can be explained with the fundamental peak of Rayleigh waves. From the output of these researches, explanation of microtremor with Rayleigh waves caused some confusion between users
Quantization
 IEEE TRANS. INFORM. THEORY
, 1998
"... The history of the theory and practice of quantization dates to 1948, although similar ideas had appeared in the literature as long ago as 1898. The fundamental role of quantization in modulation and analogtodigital conversion was first recognized during the early development of pulsecode modula ..."
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Cited by 884 (12 self)
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The history of the theory and practice of quantization dates to 1948, although similar ideas had appeared in the literature as long ago as 1898. The fundamental role of quantization in modulation and analogtodigital conversion was first recognized during the early development of pulsecode
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
, 2001
"... Variable selection is fundamental to highdimensional statistical modeling, including nonparametric regression. Many approaches in use are stepwise selection procedures, which can be computationally expensive and ignore stochastic errors in the variable selection process. In this article, penalized ..."
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Cited by 948 (62 self)
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Variable selection is fundamental to highdimensional statistical modeling, including nonparametric regression. Many approaches in use are stepwise selection procedures, which can be computationally expensive and ignore stochastic errors in the variable selection process. In this article, penalized
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