Conferences Books

I think this is a good book for persons interested to know how make a good presentation. My special interest it's learn a good method to write and speak a better english and improve my communication.

This conference proceeding is quite focussed on fuzzy logic applications and theoretical considerations for applying fuzzy logic for some real applications.

This book's most interesting sections describe the state of the art in 1999 of automatic speech recognition. The dominance of Hidden Markov Models and Linear Predictive Coding had been clear by then, for practical, near-realtime ASRs.

There is reference in several sections to the high speed microprocessors and cheap memory and disk space that made all this possible. Given that this is 2004 and the book came out in 1999, it means that in the described research, typically the CPU clocks were under 1 GHz, and memory was maybe less than 100 Mb. In other words, with current hardware, you should be able to implement most of the described algorithms with possibly greater efficacy than achieved in the text. The other implication is that now there might be greater commercial applications possible using these methods.

There is some discussion of how to improve ASR recognition. But this can run into the currently intractable problem of trying to infer semantics from an arbitrary utterance. Hard AI.

It's nice to know some of the theory behind p2p networks. This book shows that serious research is being conducted in the field, for ever better p2p nets.

What is interesting is how perhaps some of the research described could lead to a p2p network whose main usage is not the unauthorised copying of copyrighted material. Right now [2004], most nontechnical articles on p2p focus on this issue, because there is yet not widespread p2p net whose main usage has not become exactly this.

Most of the research deals with ways to improve a p2p network. Like easier discovery mechanisms, or more robust downloading. Which is especially germane if one is downloading large files and the duration of which increases the chance of a hiccup on the network. The larger issue is that if one solves these problems and builds a better p2p network, that just increases its allure for downloading music and video.

This book goes hand-in-hand with Laura Robb's, "Teaching Reading in Middle School." The activities and lessons that she presents in the mentioned book are available in this book. All you have to do is copy the page - everthing else is done for you! Although you do not need to purchase "Teaching Reading in Middle School" it will be helpful to see how Laura Robb implements the assessments and record keeping forms.

The papers at this conference look at pushing the limits of current file and storage methods. Several scrutinise the popular NFS. This is common in many LAN environments. But performances can degrade for certain data intensive workflows. Some researchers document these boundaries.

One useful paper discusses the problems with trying to trace file system usage. Both to analyse user behaviour and also that of the operating system and software packages. The drawbacks are that specific methods often cannot be adapted for other uses, and that results typically go stale with time. So one proposal was a "tracefs", that is a "thin, stackable file system". This sits atop an existing file system (hence "stackable"). Crudely akin to running that latter file system in a virtual machine that is tracefs. While one shouldn't push the analogy too far, the current popularity of VMs can suggest also the appeal of tracefs.

Artificial intelligence (AI) is exploding, both in theory and in applications, as a perusal of the research literature and the availability of commercial products will reveal. The progress has become so rapid that the literature on it has become staggering. This collection of articles is representative of some of the most important developments in the field, with emphasis on theory as well as 'emerging' applications. Readers will probably not be interested in every article in the book, but will instead peruse the ones that are more geared toward their interests or speciality. The section on learning was the one of primary interest to this reviewer, in particular the article on hierarchical hidden Markov models by H. H. Bui, D. Q. Phung, and S. Venkatesh.

As these authors view it, a hierarchical hidden Markov model (HHMM) is a generalization of the ordinary hidden Markov model wherein a hierarchy of hidden states is included. The authors propose a more general HHMM than what has been done in the literature, and one which allows 'sharing of substructures'. This sharing they argue results in more manageable models, and decreases the sample complexity needed for learning. In fact, the complexity of their model is linear in the number of states, making it much more palatable for use in real applications. They illustrate the advantanges of using their version of the HHMM using simulations.

For a particular model depth D, the HHMM consists of a collection of states at each level, with the top level having one state, and the bottom level having D states. There is of course also an observation model consisting of Y observation symbols. One can also speak of the parents and children of a particular state. The parameters of the HHMM give it a definition as a joint probability distribution over a collection of variables that represent its stochastic evolution over time. The authors represent this probability distribution as a dynamic Bayesian network and the HHMM defines a joint probablity distribution over the set of all variables (I.e. the "data") following the factorization of the Bayesian belief network. The conditional probability of a variable given its parents models the state evolution over time, and a state can end only when the state below it has ended. The probability of this occurring is given by a 'termination parameter'. A state will stay the same if it does not end, and if it does end and its parent state stays the same, it will make a transition to a new child-state with the same parent. If it does not end, it is initialized by a new parent state.

The authors then show how to calculate the 'expected sufficient statistics' of the HHMM, where it is assumed as usual that the full set of variables cannot be observed. The algorithm that they use to do this is called 'asymmetric inside-outside', and differs from the usual factorization algorithms used to decompose HMMs. They also tackle the numerical scaling problem that is present in ordinary HMMs, namely the numerical underflow that occurs as the length of the observation sequence increases. The authors use simulations to check that the parameters can be recovered accurately using their approach, and to compare their method with the linear time method. They also give a very short overview of an application of the proposed HHMM dealing with the learning of an HHMM for movement trajectories in a simulated airport environment. They mention an application to human movement but do not elaborate on it in any detail.

Most of the developments in artificial intelligence have been restricted to one domain, such as chess, medical diagnostics, or network event correlation. It is of great interest to be able to design an approach to learning that will allow the machine to reason in more than one domain with little or no change to the structure of its learning algorithms. The article on 'fibring' neural networks by A.S.A Garcez and D.M. Gabbay seems to show promise in this regard, even though the authors' goal is to develop a hybrid neural network that can perform symbolic processing. The allusion to 'fibring' in their approach refers to the ability of single neuron to behave like a complete embedded network according to a particular 'fibring' function. The output of the embedded network depends on the fibring function. The fibring function is not a complicated one in terms of its bare definition. For two neural networks it is merely a function that takes input potentials in one to the weights of the other. A neural network B is then said to be 'embedded' in another neural network A if there is a fibring function from A to B and the output of a neuron in A is given by the output of B. The network composed of A and B is then called a fibring neural network.

The authors give a detailed example of a fibring neural network, emphasizing how several neural networks can be embedded into a single network, and how they can be used to approximate polynomials of arbitrary degree. They also define 'nested' fibred networks as being essentially a series of embedded networks threaded together with a fibring function, and the dynamics of fibred networks is discussed in detail. They then show that fibring neural networks are not only univeral approximators but that they can also approximate any polynomial function to any desired degree of accuracy.

There are 8 sections, and 70 papers.
1. Foundations and Epistemology
2. Origins of Life and Evolution
3. Adaptive and Cognitive Systems
4. Artificial Worlds
5. Robotics and Emulation of Animal Behavior
6. Aoxiwriwa ns Xollwxricw Vwhcioe
7. Biocomputing
8. Applications and Common Tools.
I did not know all of them.
I checked "Evaluation of Learning Performance of Situated Embodied Agents." by Maja J Mataric in Robotics sections.

This hard cover volume contains the papers presented at an international conference on matrix analytic methods. Matrix analytic methods are a body of techniques developed for algorithmic analysis of wide classes of applied probability models.

The papers in the volume are all of excellent quality and attest to the thorough refereeing/selection process the submissions should have been subjected to. Several of the papers report on what one may consider to be signficant and major advances in the field. In addition, there are several papers reporting on practical applications of the method.

The book is certainly a useful addition to the literature and has signficant value as a reference. The hard cover volume has a price that is commensurate with the quality of both its content and presentation.

Affective Computing and Intelligent Interaction: First International Conference - if you want to know recent researches in this field you have to buy this book!