Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Networks as Inference and Learning Engines

: This paper presents an abridgment of a neural network constructive methodology and applications with real data. The neural network can be considered as the learning core and inference engine of an expert system that produces either different network designs or simulations as output, its input being data sequences. Basically, it consists of additive structural learning, limiting it by a cross-validation technique. Considerations about uncertainty treatment in neural networks are also presented, including uncertainty in data, in neuron activation, in outputs, and combination of several uncertainty sources. Applications include three different sets of data, all of them related to the energy field. First, river streamflow estimation is discussed. Then CO2 concentration prediction from gas injection rate is studied. Finally, the program learns to imitate a feedwater control system in a nuclear reactor. All tests show good results, as can be seen when compared with other standard methods.
Separation of superimposed pattern and many-to-many associations by chaotic neural networks

We propose a chaotic associative memory (CAM). It has two distinctive features: 1) it can recall correct stored patterns from superimposed input; and 2) it can deal with many-to-many associations. As for the first feature, when a stored pattern is given to the conventional chaotic neural network as an external input, the input pattern is continuously searched. The proposed model makes use of the above property to separate the superimposed patterns. As for the second feature, most of the conventional associative memories cannot deal with many-to-many associations due to the superimposed pattern caused by the stored common data. However, since the proposed model can separate the superimposed pattern, it can deal with many-to-many associations. A series of computer simulations shows the effectiveness of the proposed model.
Modeling and Simulation of a PV Module Based Power System Using MATLAB/Simulink

Due to the variation of solar irradiance, temperature and shading conditions, the power generated by a photovoltaic (PV) module and hence the power delivered to the load changes drastically, which imposes the need for analysis of a complete PV system to get the maximum power under these natural variable conditions. In this paper, a complete off-grid PV module based power generation system has been designed and simulated using MATLAB/Simulink and performance has been scrutinized using the value of standard solar irradiance about 1 KW/m -2 for Bangladesh. The simulation model includes solar PV module, the converter power stage with MPPT control and charge controlling functions and here performance of each block has been examined conspicuously. Eventually, it has been found that the model is quite competent to simulate both the I-V and P-V characteristics of a PV module and based on the result it has been predicted that the performance of several modules or even PV array connected in series and/or in parallel with the delivery of maximum power can be tested under different solar irradiance and temperature conditions. DOI: http://dx.doi. org/10.3329/dujs.v62i2. 21977 Dhaka Univ. J. Sci. 62(2): 127-132, 2014 (July)
Modulation Spectral Features: In Pursuit of Invariant Representations of Music with Application to Unsupervised Source Identification

Abstract Modulation frequency analysis has been studied predominantly in research areas such as communications, filtering and coding of digital signals, and representations of neurons in the biomedical field. In turn, modulation frequency features have surfaced in the area of music data mining, also known as music information retrieval (MIR). The term ‘modulation spectral features’ has been used rather loosely, but we refer to them as temporal patterns in a signal that may be revealed via the modulation spectrum. This paper consists of a literature survey of modulation features in which we review the historical use of general modulation features along with evolving interest in exploring modulation spectral features for data mining in music signals. We also discuss challenges encountered in employing modulation spectral features with music signals and suggest new directions in this area. Lastly, we further exploit modulation spectral features by applying these motivated concepts, properties, and parameters of modulation spectra and its features to our preliminary, unsupervised source identification method for sound sources of periodic, temporal patterns.
A resonant signal driver for two-phase, almost-non-overlapping clocks

We describe a driver circuit for reducing the power dissipated when driving heavily loaded signals such as the clock lines of a VLSI chip. The design exhibits good power efficiency across a wide range of frequencies. We have tested the driver with a prototype shift-register chip which had a clock line load in the hundreds of picofarads. The worst-case overall dissipation was 35% of fCV/sup 2/ at 13 MHz and 5 V.
Impact of Semantic Interoperability in Enterprise Computing: A Report on the Use of Inter Enterprise Computing in An Actual Commercial Environment

While the complex enterprise computing environment continually evolves, technical resources are becoming more limited. More complicated systems demand more time invested in monitoring and system management. Standards for Business to business integration help to facilitate the interoperability between organizations. These standards, often called e-business frameworks, guide integration by specifying the details for business processes, business documents and secure messaging. The essential attribute of Semantic Interoperability is that the computer system receiving the information is able to use it properly in computation, without human intervention. With emerging trends in business many new tools and technologies have evolved that help e-Business develop. Extensible Mark-up Language is used in modern e-business frameworks. This framework analyses the many partially competing and overlapping e-business frameworks how they differ in support for business processes, documents and secure messaging. This paper provides the role models of how the technology can be deploy, exploit, and develop successful case studies.
How good are the best papers of JASIS

A citation analysis examined the 28 best articles published in JASIS (Journal of the American Society for Information Science) from 1969–1996. Best articles tend to be single‐authored works twice as long as the average article published in JASIS. They are cited and self‐cited much more often than the average article. The greatest source of references made to the best articles is from JASIS itself. The top five best papers focus largely on information retrieval and online searching.
A Best-Case Network Performance Model

Network performance measures usually focus on average through-put. We, however, were concerned with best-case behavior: how fast could a packet traverse the network if there were no contention for resources. By subtracting the path time to a node from the path time through the node, we were able to develop a simple best-case delay model. This model was sensitive enough to determine the board-level connguration of a router 750 miles away.
Synthetic interferometer radar for topographic mapping

The production of topographic maps requires two kinds of information. First, the detail to be placed on the map sheet must be identified. Second, the positions of the various objects and features must be measured in three dimensions. Current airborne radar technology provides the means to satisfy both of these requirements in adverse weather and at any time, day or night. Radar used specifically for this purpose employs synthetic-aperture techniques to obtain fine resolution measurement in two dimensions and interferometry to obtain the third measurement.
Ergosterol as an Indicator of Endophyte Biomass in Grass Seeds

Ergosterol can be used as a marker to estimate fungal biomass in soil and plant−fungal tissue samples. The following experiments assess the application of ergosterol analysis to endophyte determinations in grass seeds. Ergosterol was present in all endophytic fungi tested, ranging from 185.2 μg g-1 in Epichloe typhina to 1225.5 μg g-1 in Neotyphodium lolii. Significant differences in ergosterol content occurred both between and within species of endophytic fungi. Ergosterol content of grass seeds was highly correlated (r2 = 0.99) to the endophyte content of the seeds. Several extraction techniques were tested and found to yield variable results, but the method that gave the most complete extraction in seeds was methanol extraction under refluxing conditions. The endophyte content of fine fescue seed samples was predicted from ergosterol analysis and found to be well-correlated with microscopic analysis. It is concluded that ergosterol analysis can be used in both diagnostic and research applications to pr.
TextSumIt: a Semantic Single Document Summarization Model on Android Mobile Device

The explosion of information in the World Wide Web is overwhelming for readers with limitless information. Large internet articles or journals are often cumbersome to read as well as comprehend. More often than not, readers are immersed in a pool of information with limited time to assimilate all of the articles. As technology advances, it becomes more convenient to access information on-the-go, i.e., portability of information by utilizing mobile devices. In this research, a semantic and syntatic based summarization is implemented in a text summarizer to solve the information overload problem whilst providing a more coherent summary. The objective is to integrate WordNet into the proposed system aka TextSumIt which condenses lengthy documents into summarized text. The empirical experiments show that it produces satisfactory preliminary results on Android mobile phones.
The timing of phrase-initial tones in Seoul Korean: a weighted-constraint model*

This paper examines the timing of the L and H tonal targets that constitute the phrase-initial pitch rise in the Seoul Korean LHLH intonation pattern. Two hypotheses, the segmental anchoring hypothesis and the constant duration hypothesis, are examined, by varying speech rate. The segmental anchoring hypothesis states that the beginning and the end of a pitch rise are independently aligned with respect to segments. The alternative hypothesis holds that the interval between the L and H tones should remain stable. It was found that segmental alignment and duration targets simultaneously affect the timing of L and H tonal targets. The model proposed here, the ‘Alignment-Duration’ model, takes these two factors as weighted constraints, which require alignment and duration targets to be realised. The actual timing of the F0 events is determined as the values that minimise the summed cost of violations of these constraints, yielding a compromise between their demands.
Online tuning for retinal imaging adaptive optics systems

The imaging of the retina tissue in the back of the eye is important for a number of applications, such the early detection of eye disease. Adaptive optics systems are being increasingly used to provide clear images of the retina tissue by compensating for the eye's optical imperfections, also referred to as aberrations. Although current retinal imaging adaptive optics systems can compensate for some aberrations in the human eye, they are still unable to compensate for the unknown and time-varying higher order aberrations. This paper presents a control system design approach that allows the adaptive optics system to compensate for the unknown time-varying aberrations. The compensation for the aberrations is achieved by adjusting the shape of a deformable mirror in the adaptive optics system. Hence, the control problem addressed in this paper is that of tracking an unknown and time-varying desired shape for the membrane mirror. The proposed controller design method relies on tuning, online, a Q-parameterized stabilizing controller in such a way that the tuned controller converges to the desired controller needed to achieve regulation. The resulting system will allow for improved retinal images to be taken, resulting in the early detection of eye diseases
A New Medical Image Segmentation Method Based on Chan-Vese Model

The traditional segmentation method for medical image segmentation is difficult to achieve the accuracy requirement, and when the edges of the image are blurred, it will occurs incomplete segmentation problem, in order to solve this problem, we propose a medical image segmentation method which based on Chan-Vese model and mathematical morphology. The method integrates Chan-Vese model, mathematical morphology, composite multiphase level sets segmentation algorithm, first, through iterative etching operation to extract the outline of the medical image, and then the medical image is segmented by the Chan-Vese model based on the complex multiphase level sets, finally the medical image image is dilated iteratively by using morphological dilation to restore the image. The experimental results and analysis show that, this method improves the multi-region segmentation accuracy during the segmentation of medical image and solves the problem of incomplete segmentation.
Development of a fish-eye VR system with human visual functioning and biological signals

Present VR systems fall short of producing a true sense of reality. If we understand functioning on human senses, we will be able to generate imaginary space using minimum information. In the case of vision, the human eyesight fails from the center to the periphery of visual field. In this paper, we focus on this functioning, and propose a VR system, where one of biological signals, EOG (electro-oculogram), is used as a human interface.
The Application of Microform to Manual and Machine‐readable Catalogues

At Birmingham University Library it is proposed to implement in October 1972, a complete microfilm catalogue system. This system originated from two sources. Over the past two years various means have been evaluated of converting the library's card catalogues (which were closed at the end of 1971) to a more compact form. An interim report mentioned microfilming as one of a number of possibilities. At the time the production of hard copy was envisaged, but since then a true microform system has seemed preferable, in which the catalogue would exist as cassettes of film to be viewed by all users on reader machines. This system has been specified and costed, and details are included in this paper. The impetus in reaching this viewpoint was provided by the progress made over the last 18 months in the field of COM, which rapidly commended itself as the choice of output medium for the new mechanised MARC‐based catalogue, which covers all the library's serials and all monographs acquired after January 1972. BLCMP union catalogues in these categories will also be held in COM form at Birmingham University Library.
What are we talking about?—A taxonomy of computer simulations to support learning

In this article a proposal for a taxonomy of computer simulations to support learning processes is presented. To achieve this goal, distinguishing criteria of computer simulations are identified and categorized. The work has been done in order to facilitate research about the evaluation of learning tools. The taxonomy is mainly built to be suitable for this objective, but can be of general help in establishing a common terminology. Sharing of findings and discussions among researchers from different fields can therefore by stimulated. Copyright © 2000 John Wiley & Sons, Ltd.
Practical One-Step Synthesis of Koga′s Chiral Bases

Synthesis 2001, No. 5, 12 04 2001. Article Identifier: 1437-210X,E;2001,0,05,0693,0695,ftx,en;P20200SS.pdf. © Georg Thieme Verlag Stuttgart · New York ISSN 0039-7881 Abstract: A simple, efficient and practical method for the preparation of Koga's chiral bases is described. The method involves attack of an amine on a styrene oxide-derived aziridinium ion and has allowed the synthesis of novel diamines which cannot be prepared using Koga's original route.
Intelligent identification and classification of epileptic seizures using wavelet transform

Epilepsy is a common neurological disorder. The need of the hour is an automated analysis of the Electroencephalographs (EEGs), which enhances efficiency of diagnosis. This paper presents simple and new approach for classifying the types of epileptic seizures. A set of feed forward neural network with wavelet feature extraction are used to process time, frequency to detect and classify the type of seizure like absence, Tonic-clonic, Febrile and Complex partial seizures. Tests of the system on EEG indicate a success rate of 94.3%. This method makes it possible as a real-time detector, which will improve the clinical service of Electroencephalographic recording.
Social and Technological Issues Affecting Strategic Planning in University Libraries in Ghana

Social and technological factors that affect strategic planning in Ghanaian university libraries are explored in a qualitative study of sixty-five senior university officials and three civil servants. Social issues discussed include low status of library profession, the historical weaknesses of school libraries and other specific behaviours that correlate negatively with strategy. Technological points include national technological infrastructure and the lack of technology to monitor the environment.
ATM framing using CRC byte

Traditional framing methods search for a unique synchronization word. In this paper, we propose a new framing method for ATM cells which detects the CRC byte of the cell header. We also present an efficient logic design which implements a sliding window CRC check. The synchronization performance of cell delineation using CRC based framing has been predicted through analysis and the circuit operation has been verified in the laboratory. <<ETX>>
Imitation learning from unsegmented human motion based on N-gram statistics of linear prediction models

本稿では人間の非分節な動作提示からロボットが自律的に模倣学習を行う為の新たな手法を提案する．ロボットが人間と関わり合う中で見真似を通じて動作獲得と行う為には，連続的な時系列情報として提示される人間の動作から真似る対象となる動作区間を切り出さなければならない．言い換えるると「何を真似るか」という問題を解かねばならない．提案手法では模倣の対象となる一連の動作を複数の線形ダイナミクスの切り替わりで表現しうるとしてモデル化する．具体的には提示された高次元の動作時系列を特異値分解を用いて低次元化した後に，Switching AR modelに基づいてARモデルがマルコフ遷移により切り替わるダイナミクスとしてモデル化する．これにより各動作の要素がARモデルとして学習されるとともに，どのARモデルによってその時系列が生成されているかが推定され，対象時系列は各ARモデルを表わす隠れ状態の遷移として表わされる事になる．対象時系列は隠れ状態を表わす文字の列として表現されるが，これに対してN-gram統計量に基づき記述量を最小化する辞書生成を通じて単語抽出を行うことにより，人間の特徴的な動作区間を抽出する．実験では等手法の有効性を人間の上半身動作を対象にして検証したところ，人間の非分節な動作系列の中に特徴的動作として埋め込んだ “Hi” “Shrug” の二つの動作を自動的に抽出することができた．また，その抽出性能を人間の抽出能力と比較することで提案手法の有効性検討した．
Content-based image retrieval with ontological ranking

Images are a much more powerful medium of expression than text, as the adage says: "One picture is worth a thousand words. " It is because compared with text consisting of an array of words, an image has more degrees of freedom and therefore a more complicated structure. However, the less limited structure of images presents researchers in the computer vision community a tough task of teaching machines to understand and organize images, especially when a limit number of learning examples and background knowledge are given. The advance of internet and web technology in the past decade has changed the way human gain knowledge. People, hence, can exchange knowledge with others by discussing and contributing information on the web. As a result, the web pages in the internet have become a living and growing source of information. One is therefore tempted to wonder whether machines can learn from the web knowledge base as well. Indeed, it is possible to make computer learn from the internet and provide human with more meaningful knowledge. In this work, we explore this novel possibility on image understanding applied to semantic image search. We exploit web resources to obtain links from images to keywords and a semantic ontology constituting human's general knowledge. The former maps visual content to related text in contrast to the traditional way of associating images with surrounding text; the latter provides relations between concepts for machines to understand to what extent and in what sense an image is close to the image search query. With the aid of these two tools, the resulting image search system is thus content-based and moreover, organized. The returned images are ranked and organized such that semantically similar images are grouped together and given a rank based on the semantic closeness to the input query. The novelty of the system is twofold: first, images are retrieved not only based on text cues but their actual contents as well; second, the grouping is different from pure visual similarity clustering. More specifically, the inferred concepts of each image in the group are examined in the context of a huge concept ontology to determine their true relations with what people have in mind when doing image search.
Wavelet-based multiscale statistical process monitoring: A literature review

Data that represent complex and multivariate processes are well known to be multiscale due to the variety of changes that could occur in a process with different localizations in time and frequency. Examples of changes may include mean shift, spikes, drifts and variance shifts all of which could occur in a process at different times and at different frequencies. Acoustic emission signals arising from machining, images representing MRI scans and musical audio signals are some examples that contain these changes and are not suited for single scale analysis. The recent literature contains several wavelet-decomposition-based multiscale process monitoring approaches including many real life process monitoring applications. These approaches are shown to be effective in handling different data types and, in concept, are likely to perform better than existing single scale approaches. There also exists a vast literature on the theory of wavelet decomposition and other statistical elements of multiscale monitoring methods, such as principal components analysis, denoising and charting. To our knowledge, no comprehensive review of the work relevant to multiscale monitoring of both univariate and multivariate processes has been presented to the literature. In this paper, over 150 both published and unpublished papers are cited for this important subject, and some extensions of the current research are also discussed.
Effects of Trust and Cognitive Absorption on Smart Phone Use and User Satisfaction

The purpose of this study is to explore determinants which affect the significant increase in the user acceptance of smart phone. This study also analyzes the effect of each variable on the actual acceptance by empirical methods. In this study, first, the system quality and the service quality are defined as independent variables based on developed IS success model of DeLone & McLean(2003). Second, we proposed the research model by providing trust and perceptual immersion as intermediate variables, and user satisfaction and actual use as dependent variables by the proceeding research for accepting information technology and new service. Third, the statistical analysis is conducted by surveying to 200 smart phone users for verifying a validity of research models and hypotheses. As a result, almost hypotheses are accepted in confidence interval except for the hypothesis between security and trust variable.