7674871
Contextual vector quantization for speech recognition with discrete hidden Markov model
Abstract By using formulation of the finite mixture distribution identification, in this paper, several alternatives to the conventional LBG VQ method are investigated. A contextual VQ method based on the Markov Random Field (MRF) theory is proposed to model the speech feature vector space. Its superiority is confirmed by a series of comparative experiments in a speaker independent isolated word recognition task by using different VQ schemes as the front-end of DHMM. The motivation to use MRF to model the contextual dependence information in the underlying speech production process can be readily extended to acoustic modeling of the basic speech units in speech recognition.
18926397
Chinese Word Segmentation based on Maximum Matching and Word Binding Force
A Chinese word segmentation algorithm based on forward maximum matching and word binding force is proposed in this paper. This algorithm plays a key role in post-processing the output of a character or speech recognizer in determining the proper word sequence corresponding to an input line of character images or a speech waveform. To support this algorithm, a text corpus of over 63 millions characters is employed to enrich an 80,000-words lexicon in terms of its word entries and word binding forces. As it stands now, given an input line of text, the word segmentor can process on the average 210,000 characters per second when running on an IBM RISC System/6000 3BT workstation with a correct word identification rate of 99.74%.
5780401
Inverse modeling using multi-block PLS to determine the environmental conditions that provide optimal cellular function
MOTIVATIONS
Tissue engineering constitutes an important field with its potential of addressing the current shortage in organ availability. To successfully develop tissue-engineered organs, it is crucial to understand how to maintain the cells under conditions that maximize their ability to perform their physiological roles, regardless of the environment, whether the cells are part of an extracorporeal system, such as the bioartificial liver assist device, or an implantable tissue-engineered device. Our goals are to (1) provide insight into how cells will behave when confronted with changes in its environment and (2) determine the optimal environmental factors to achieve a desired level of cellular function.


RESULTS
Diverse sets of environmental factors were used to systematically perturb the metabolic behavior associated with pre-conditioning and plasma supplementation. To probe metabolic state of hepatocytes, metabolic flux analysis was used to obtain the metabolic profile. We applied a multi-block partial least square (MPLS) model to relate environmental factors and fluxes to levels of intracellular lipids and urea synthesis. The MPLS model identified: (1) the most influential environmental factors and (2) how the metabolic pathways are altered by these factors. Finally, we inverted the MPLS model to determine the concentrations and types of environmental factors required to obtain the most economical solution for achieving optimal levels of cellular function for practical situations.
5119128
From Data to Knowledge: an Integrated Rule-Based Data Mining System
This paper presents an integrated rule-based data mining system that is capable of creating rulebased classifiers with web-based user interface from data sets provided by end users. It provides a streamlined integration of three technologies, namely, database systems, machine learning systems , and rule-based systems. Rules generated by the system are based on rough set theory. Thus, it is capable of dealing with uncertain rules. The system generates user interface dynamically, therefore, end users are released from the burden of programming. The generated classifiers are stored in a database system and are delivered as web-based applications. Therefore, they can be managed and accessed easily by using web browsers.
18544921
Active User-Based and Ontology-Based Web Log Data Preprocessing for Web Usage Mining
User identification and session identification are two major steps in preprocessing Web log data for Web usage mining. This paper introduces a fast active user-based user identification algorithm with time complexity O(n). The algorithm uses both an IP address and a finite users' inactive time to identify different users in the Web log. Web site ontology is useful for identifying Web site structure and break points for browsing behavior. For session identification, we present an ontology-based method that utilizes the Web site structure and functionalities to identify different sessions
894881
Performance improvement of TCP Vegas over heterogeneous networks
Current IP network has become the dominant paradigm for all networking environments. The significant cause of packet losses in such heterogenous networks is no longer limited to network congestion. Traditional TCP interprets every packet loss as caused by congestion which may be not the case in the current Internet. Misinterpretation of wireless random loss as an indication of network congestion results in TCP slowing down its sending rate unnecessarily. We propose a new variant of TCP Vegas named RedVegas. By using the innate nature of Vegas and congestion indications marked by routers, RedVegas may detect random packet losses precisely. Through the packet loss differentiation, RedVegas reacts appropriately to the losses, and therefore the throughput of connection over heterogeneous networks can be significantly improved.
17880623
Conflicts in Knowledge Management: Visiting the Hidden Partner
As knowledge gains a reputation for being a critical resource in the information-intensive economy, organizations have doubled their efforts in trying to extract value from knowledge management policies. One particular aspect of knowledge management, which has gone unnoticed in academic research, is the presence of conflicts in knowledge activities. By adopting a conflict perspective of knowledge activities, this study arrives at a two-dimensional framework that defines knowledge conflicts in terms of its type and nature. Central to this paper is the fundamental idea that conflicts form an integral part of knowledge management and depending on how they are managed; conflicts may be formidable partners or dangerous adversaries in the corporate quest for knowledge-derived competitiveness.