Miracle-dz/UniFAR-FTUs huggingface dataset:
Introduction to automation for librarians

From the Publisher: In a complete update of this classic text, author and automation expert William Saffady once again performs the vital function of surveying the types of technology used in libraries and discusses the library applications available to staff and patrons. He assesses the newest developments in operating systems, computer hardware, desktop and display technologies, and programming languages. "Written in layman's terms that even technophobes can understand, Introduction to Automation provides the keys to identifying the important characteristics of technology and understanding its many uses and applications.
A further note on the derivation of quarterly figures consistent with annual data

SUMMARY Several methods have been devised to deal with the problem of constructing quarterly data (a) either when related series are available or (b) when only annual totals exist. Methods (a) lead to quarterly figures inconsistent with annual totals. Method (b) adds nothing to the economic interpretation of the quarterly phenomenon. Very little has been done to construct quarterly data using related series such that consistency with annual figures is ensured. This note tries to improve and clarify the interpretation of the only existing method we know of.
Opinion Strength Identification in Customer Review Summarizing System Using Association Rule Technique

The purpose of this research is to study opinion strength identification in customer review summarizing system in Thai language. We expect that opinion strength identification will help refining results from customer review summarizing system and improving quality of the results. In this paper, we present results from our preliminary study that was conducted with an aim at creating rules for identifying opinion strength by using an association rule technique. We used a paper survey to collect human opinion towards words used in 180 reviews of three groups of cosmetic products: blush on, facial cleanser, and foundation (60 reviews per group). The survey was done with 225 samples and resulted in 11,686 phases that had effects on samples’ opinion towards the products. Association rule technique was applied to words in those phases to generate opinion strength identification rules. In our further study, those rules will be used to extend a capability of a customer review summarizing system. We plan to compare an effeciency of the system which is extended with the opinion strength identification rules to the one that is not, and also to a summary result obtained from human. Keywords—Opinion strength; Opinion strength identification; Customer review summarizing; Association rules.
Performance of punctured trellis coded modulation over fading channels

Punctured convolutional codes allow an easy implementation of variable-rate encoders/decoders and compared to conventional codes of the same rate, the decoding complexity is significantly reduced. The puncturing technique has been applied to trellis-coded modulation, yielding PSK and QAM codes. Most of these codes have a free squared-Euclidean distance which is almost as large as that of the best known Ungerboeck codes. Hence, the advantages provided by the puncturing technique with binary convolutional codes are essentially maintained with trellis-coded modulation over additive white Gaussian noise channels. In this paper, the performance of punctured QAM trellis codes over Rayleigh fading channels is presented. It is shown that compared to Ungerboeck codes, a significant coding gain of several dB can be achieved. We also show that our codes are nearly as good as Du and Vucetic (1991) 16-QAM codes which have been specifically designed for fading channels. Furthermore, they offer more flexibility and are simpler to decode than Du and Vucetic codes.
A Parallelizing Interface for K-Means Type Clustering Algorithms and Neural Network Batch Training

The k-means clustering algorithm and neural network batch training becomes computationally intensive when the manipulated data is large. One way to reduce the computational demand of such techniques is to execute them in a concurrent manner. Unfortunately, the effort required to implement these techniques in a distributed computing environment remains daunting. Much of the work takes place when partitioning and distributing workloads over processors in the distributed computing environment. To alleviate this task, we present a data parallel interface called Distributed Data Partitioning Interface (DDPI). Its simple interface permits parallel implementation of k-means type clustering algorithms and neural network batch training even by users with little understanding of parallel computing technicalities. In this work we demonstrate that it is possible to achieve near ideal speedups when k-means and k-harmonic means clustering algorithms and multilayer perceptron batch training are parallelized with DDPI.
An image coding technique using a human visual system model and image analysis criteria

Recently, ways to obtain a new generation of image-coding techniques have been proposed. The incorporation of the human visual system (HVS) models and tools of the image analysis, such as segmentation, are two defining features of these techniques. In this paper, a new image coding technique is presented which utilizes a human visual system model and a image analysis criteria together with the technique of projection onto convex sets (POCS). The human visual system model is used to determine the most probable perceptual information of an image and the removal of irrelevant information. The projection onto convex sets operation is used as a restoration technique aimed at correcting the degradations caused by the conventional coders in the transmitter part of the channel. And finally, the image analysis based technique is used as an interpolator in order to predict the unknown regions of the received image. This new technique can be used in single frame image coding applications to obtain acceptable images at extremely high compression rates.
Hierarchical Hidden Markov Models for Information Extraction

Information extraction can be defined as the task of automatically extracting instances of specified classes or relations from text. We consider the case of using machine learning methods to induce models for extracting relation instances from biomedical articles. We propose and evaluate an approach that is based on using hierarchical hidden Markov models to represent the grammatical structure of the sentences being processed. Our approach first uses a shallow parser to construct a multi-level representation of each sentence being processed. Then we train hierarchical HMMs to capture the regularities of the parses for both positive and negative sentences. We evaluate our method by inducing models to extract binary relations in three biomedical domains. Our experiments indicate that our approach results in more accurate models than several baseline HMM approaches.
Capacity control in linear classifiers for pattern recognition

Achieving good performance in statistical pattern recognition requires matching the capacity of the classifier to the amount of training data. If the classifier has too many adjustable parameters (large capacity), it is likely to learn the training data without difficulty, but will probably not generalize properly to patterns that do not belong to the training set. Conversely, if the capacity of the classifier is not large enough, it might not be able to learn the task at all. In between, there is an optimal classifier capacity which ensures the best expected generalization for a given amount of training data. The method of structural risk minimization (SRM) refers to tuning the capacity of the classifier to the available amount of training data. This paper illustrates the method of SRM with several examples of algorithms. Experiments confirm theoretical predictions of performance improvement in application to handwritten digit recognition. <<ETX>>
English for the computer

Zirconium soaps are prepared by reacting precipitated zirconium basic sulphate and one or more organic monocarboxylic acids in the presence of at least one alkali metal hydroxide or carbonate or alkaline earth metal oxides and/or hydroxide in an amount stoichiometrically equivalent to the sulphate content of the zirconium basic sulphate. Acids mentioned are 2-ethyl hexoic, caprylic, nonylic, lauric, linoleic, stearic, palmitic, abietic, oleic, ricinoleic, naphthenic, phenyl-acetic, butoxy- and phenoxyacetic, and butyl- and dodecyl-thiopropionic acids and examples describe the preparation of zirconium naphthenate and 2-ethyl hexoic acid esters.
Digital speech enhancement based on DTOMP and adaptive quantile

Compressed Sensing (CS) that can effectively extract the information contained in the signal is a new sampling theory based on signal sparseness. This paper applies CS theory in digital speech signal enhancement processing, proposes an adaptive quantile method for the noise power estimation and combines the improved double-threshold orthogonal matching pursuit algorithm for speech reconstruction, then achieves speech enhancement processing. Compared with the simulation results of the spectral subtraction and the subspace algorithm, the experiment results verify the feasibility and effectiveness of the algorithm proposed in this paper applied to speech enhancement processing.
Tuning of Learning Algorithms for Use in Automated Product Recommendations

In this thesis, we study the problem of predicting users' media preferences based on their, as well as other users' historical rating data. The model used is that a user's rating for an item can be explained by a sum of bias terms and an inner product between two vectors in some multidimensional feature space that is specic to the product domain. This model is tted using a stochastic descent type method, the stochastic diagonal Levenberg-Marquardt method. The method is studied with respect to its stability and how fast it adapts its parameter estimates and it is found that these characteristics can be accurately predicted for linear regression problems, but that the dynamics become much more complex when training the nonlinear features. The concept of regularization and the tuning of regularization parameters with the Nelder-Mead simplex method is also discussed, as well as some methods for making the Python implementation faster by using Cython.
Fade: a vehicle detection and tracking system featuring monocular color vision and radar data fusion

This paper presents Fade, a vehicle detection and tracking system featuring monocular color vision and radar data fusion. Its main originality resides in its low-level fusion system. At each step and for each target, the fusion system fuses the results of four different image processing algorithms and radar information by automatically combining 12 different features and generating many possible target position proposals. It generates a belief network organized in three layers: sources, position proposals, and correlation between proposals. An inference algorithm is then used to find out the actual position of the target, by deducing which observations are wrong. The overall system runs in real time and has been evaluated on a variety of scenarios recorded in a real car using /sup RT/Maps. Fade yields very good detection results in most cases.
The iterative and incremental development on real-time database systems

Our new idea can make the system classification technology requirements with value-oriented requirements more easily and less ambiguous. So, the new concept is platform to refine the value orientation for the requirements of iterative and incremental development real-time database system. This idea gives us life time keep a single platform of real-time database update user needs and full availability, guarantees the reliability of the real-time database system. The method of the value orientation is the evolution of proof that the batter support requirements and specifications. This new model more system structure of the definition and model is better than the existing iterative and incremental model. All kinds of relations attribute and traceability value the requirement to alleviate.
Ranked reservoir sampling: an extension to the reservoir sampling algorithm

This paper describes a simple extension to the reservoir sampling algorithm to allow its use with ranked records. Here the fixed‐sized sample must include records in order of rank, but a fair selection must occur to choose the lowest‐ranked records included. The result is produced after a single pass through the records. Copyright © 2007 John Wiley & Sons, Ltd.
The Application Level Multicast Technique Algorithms Oriented to P2P Video

Firstly challenges in P2P media streaming applications were pointed out, then some recent research results such as application-level multicast tree, heterogeneity of network and incentive mechanism and so on were introduced. This paper has proposed a new model of application level multicast named DHCM (density-based hierarchical clustering multicast)which has improved IHC arithmetic．DHCM divides the hosts into many hierarchies according to their density，and constructs a density tree to realize the shortest routing．The tree delivers the content of video server to each host in density tree and uses a P2P scheme in data transmission．By this way the application-level multicast has been realized．This density tree has the homogeneity and monotonic properties．The experiment result has proved that DHCM can transmit the video stream efficiently and robustly.
A design of a protocol for detecting a mobile agent clone and its correctness proof using Coloured Petri Nets

A mobile agent is an autonomous computer program which can migrate from machine to heterogeneous machine. An agent server which receives a mobile agent can easily make a copy of the mobile agent, because a mobile agent is just a program consisting of code and data. We deene a mobile agent clone as the copied agent. It is impossible to distinguish between a mobile agent clone and its original agent. This causes problems connected with agent authentication, unexpected multiple transactions, and other security issues. In this paper, we investigate the problems caused by mobile agent clones and we design a protocol, which detects agent clone executions and identiies the clone generating agent server. Finally, we prove the correctness of the protocol formally, through Coloured Petri Nets.
A defuzzification strategy for a fuzzy logic controller employing prohibitive information in command formulation

A defuzzification strategy is presented that can handle the possibility of conflicting control commands during the defuzzification process while still producing smooth transitions. The strategy, which is the centroid of the largest area, has parameters that can be chosen to make it behave like both the centroid strategy and the mean of the maximum membership strategy. The strategy addresses the problem of prohibitive information in fuzzy control command formulation. This strategy was applied to a mobile robot path execution system and it was shown that this strategy overcomes some of the deficiencies of other strategies. This strategy not only makes the best choice when several are given, but also avoids abrupt transitions in the control command. <<ETX>>
Handoff protocols in JD-CDMA

In the early 1990s investigations towards an air interface for UMTS and IMT-2000 started at the Research Group for RF Communications. The result is the JD-CDMA air interface, which formed the basis of the UMTS air interface mode TD-CDMA. JD-CDMA uses a combination of the multiple access schemes FDMA, TDMA and CDMA, in which signal separation is performed by joint detection (JD). To prove the performance and the feasibility of JD-CDMA a hardware demonstrator was build by the Research Group for RF Communications and Siemens AG. The JD-CDMA implementation for the demonstrator adopts the philosophy of the de facto world standard GSM to an extent as large as possible. The paper investigates which extensions of existing GSM handoff protocols are necessary to support a multiple cell JD-CDMA hardware demonstrator.
Active storage networks: topology, routing and application

High performance computing systems are often inhibited by the performance of their storage systems and their ability to deliver data. Active Storage Networks (ASN) provide an opportunity to optimize storage system and computational performance by offloading some computation to the network switch. An ASN is based around an intelligent network switch that allows data processing to occur on data as it flows through the storage area network from storage nodes to client nodes. A key design element for an ASN is the switching topology. In this thesis, we present an ASN switching topology named 2-Dilated flattened butterfly (2DFB) which is a nonblocking, low latency, low cost network compared to other nonblocking interconnecting networks. We have implemented this network topology using the NetFPGA as the basic building block of the switching network. The ASN 2DFB architecture has been used in a variety of applications including data sort, data search, data clustering, and min-max. We have also developed an adaptive load balanced routing scheme (ALDFB) which exploits the topological properties of 2DFB network. ALDFB always gives priority to forwarding packets through the minimal path and therefore, for local and benign traffic the performance of this routing scheme is equal to that of the minimal routing. In adversarial traffic, ALDFB provides better load balance by one non minimal forwarding in each dimension. ALDFB provides high throughput on adversarial traffic patterns and provides better latency on benign traffic patterns. We have compared the performance of ALDFB on a 2DFB network with non-minimal global adaptive routing (UGAL), Minimal Adaptive and Adaptive Clos routing algorithm for different traffic patterns. We observed that a 2DFB network with ALDFB routing provides high throughput with reduced latency compared to other routing schemes for all the traffic patterns. Finally, we show how a 2DFB-based ASN can be used to improve parallel file system performance. We have done simulations of striping files and file writes using file locking protocols in a parallel file system. In an ASN we offload some operations from the end-terminals to the ASN switch. In the case of file striping, the splitting of files and the parity calculations are done on the run inside ASN switch. In the file locking case we offload the file locking protocol to the ASN switch. In both cases we observe a significant reduction in traffic through the network and this helps an ASN based parallel file system to offer significant performance improvement.
Helping spanish SMEs staff to develop their competence in writing business letters

This paper reports on the development of a website tool aimed at helping Spanish small and medium enterprises (SMEs) staff to write their commercial correspondence in English. It describes the steps involved in the tool system design process, making an emphasis on the methodological criteria and rational that guided us to develop the site. In order to obtain the material, a corpus of commercial correspondence written in English was collected and later analysed, applying a genre-based approach as established by Bhatia (1993) and Swales (1990). The findings provided the move structure and strategies of each type of letter as well as the lexico-grammatical realizations of each move which later would be standardised and presented to be used systematically in the composition of letters with the same communicative purpose. This work illustrates how a genre-based approach can successfully be applied to ESP materials development.
An Improved Direct Current Prediction Method for Intra Coding

Direct current mode is one of the primary prediction modes for intra coding. In the coding standard, it has not concerned the spatial correlations between neighboring pixels, which limits its coding performance. To address this problem, an improved prediction method is proposed in this paper. In the prediction scheme, prediction pixel uses its available neighbor samples and its value is set to be the mean of the references. The reference samples are not only from other encoder blocks but also pixels predicted in current block. Experimental results demonstrate that the proposed method advances in both coding performance and coding efficiency, compared with the existing method.
Vision system for on-loom fabric inspection

This paper describes a vision-based fabric inspection system that accomplishes on-loom inspection of the fabric under construction with 100% coverage. The inspection system, which offers a scalable, open architecture, can be manufactured at relatively low cost using off-the-shelf components. While synchronized to the motion of the loom, the developed system first acquires very high-quality, vibration-free images of the fabric using either front or backlighting. Then the acquired images are subjected to a novel defect segmentation algorithm, which is based on the concepts of wavelet transform, image fusion and the correlation dimension. The essence of this segmentation algorithm is the localization of those events (i.e., defects) in the input images that disrupt the global homogeneity of the background texture. The efficacy of this algorithm, as well as the overall inspection system, has been tested thoroughly under realistic conditions. The system was used to acquire and to analyze more than 3700 images of fabrics that were constructed with two different types of yarn. In each case, the performance of the system was evaluated as an operator introduced defects from 26 categories into the weaving process. The overall detection rate of the presented approach was found to be 89% with a localization accuracy of 0.2 in. (i.e., the minimum defect size) and a false alarm rate of 2.5%.
Computing Term-Concept Association in Semantic-Based Query Expansion: Computing Term-Concept Association in Semantic-Based Query Expansion

In semantic-based query expansion, computing term-concept association is a key step in finding associated concepts to describe the needed query. A method called K2CM (keyword to concept method) is proposed to compute the term-concept association. In K2CM, the attaching relationship among term, document and concept together with term-concept co-occurrence relationship are introduced to compute term-concept association. The attaching relationship derives from the fact that a term is attached to some concepts in annotated corpus, where a term is in some documents and the documents are labeled with some concepts. For term-concept co-occurrence relationship, it is enhanced by the text distance and the distribution feature of term-concept pair in corpus. Experimental results of semantic-based search on three different corpuses show that compared with classical methods, semantic-based query expansion on the basis of K2CM can improve search effectiveness.