Miracle-dz/UniFAR-FTUs huggingface dataset:
A Higher-order data flow model for heterogeneous Big Data

We introduce a data flow model that supports highly parallelisable design patterns and also has useful properties for analysing data serially over extended time periods without requiring traditional Big Data computing facilities. The model ranges over a class of higher-order relations which are sufficiently expressive to represent a wide variety of unstructured, semi-structured and structured data. Using JSONMatch, our web service implementation of the model, we show that the combination of this model and higher-order representation provides a powerful and extensible framework that is particularly well suited to analysing Big Variety data in a web application context.
Exploiting capture effect in frameless ALOHA for massive wireless random access

The analogies between successive interference cancellation (SIC) in slotted ALOHA framework and iterative belief-propagation erasure-decoding, established recently, enabled the application of the erasure-coding theory and tools to design random access schemes. This approach leads to throughput substantially higher than the one offered by the traditional slotted ALOHA. In the simplest setting, SIC progresses when a successful decoding occurs for a single user transmission. In this paper we consider a more general setting of a channel with capture and explore how such physical model affects the design of the coded random access protocol. Specifically, we assess the impact of capture effect in Rayleigh fading scenario on the design of SIC-enabled slotted ALOHA schemes. We provide analytical treatment of frameless ALOHA, which is a special case of SIC-enabled ALOHA scheme. We demonstrate both through analytical and simulation results that the capture effect can be very beneficial in terms of achieved throughput.
The analysis of double hashing(Extended Abstract)

In this paper we analyze the performance of a well known algorithm known as double hashing [Knuth]. In this method we probe the hash table along arithmetic progressions, where both the initial element and the increment of the progression are chosen randomly and independently depending only on the key K of the search. We prove that double hashing is asymptotically equivalent to uniform probing, an idealized hashing technique that exhibits no clustering and is known to be optimal in a certain sense. Between steps of the extension process we can show that the effect of clustering is negligible, and that we therefore never depart too far from the truly random situation.
A magnetically isolated gate driver for high-speed voltage sharing in series-connected MOSFETs

A scalable resonant gate drive circuit is described, suitable for driving series-connected MOSFETs in high-voltage, high-speed inverter applications for resistive and capacitive loads. Galvanic isolation is provided by a loop of high voltage wire, which also serves as the resonant inductor in the circuit. Fast dynamic voltage sharing is achieved by delivering equal current to each gate. A prototype is built and tested, demonstrating a 75ns switching time at 5kV using 900V MOSFETs.
International Journal of Health Geographics Open Access Gideon: a Comprehensive Web-based Resource for Geographic Medicine

GIDEON (Global Infectious Diseases and Epidemiology Network) is a web-based computer program designed for decision support and informatics in the field of Geographic Medicine. The first of four interactive modules generates a ranked differential diagnosis based on patient signs, symptoms, exposure history and country of disease acquisition. Additional options include syndromic disease surveillance capability and simulation of bioterrorism scenarios. The second module accesses detailed and current information regarding the status of 338 individual diseases in each of 220 countries. Over 50,000 disease images, maps and user-designed graphs may be downloaded for use in teaching and preparation of written materials. The third module is a comprehensive source on the use of 328 anti-infective drugs and vaccines, including a listing of over 9,500 international trade names. The fourth module can be used to characterize or identify any bacterium or yeast, based on laboratory phenotype. GIDEON is an up-to-date and comprehensive resource for Geographic Medicine.
Innite Languages, Finite Minds Connectionism, Learning and Linguistic Structure

This thesis presents a connectionist theory of how infinite languages may fit within finite minds. Arguments are presented against the distinction between linguistic competence and observable language performance. It is suggested that certain kinds of finite state automata—i.e., recurrent neural networks—are likely to have sufficient computational power, and the necessary generalization capability, to serve as models for the processing and acquisition of linguistic structure. These arguments are further corroborated by a number of computer simulations, demonstrating that recurrent connectionist models are able to learn complex recursive regularities and have powerful generalization abilities. Importantly, the performance evinced by the networks are comparable with observed human behavior on similar aspects of language. Moreover, an evolutionary account is provided, advocating a learning and processing based explanation of the origin and subsequent phylogenetic development of language. This view construes language as a nonobligate symbiant, arguing that language has evolved to fit human learning and processing mechanisms, rather than vice versa. As such, this perspective promises to explain linguistic universals in functional terms, and motivates an account of language acquisition which incorporates innate, but not language-specific constraints on the learning process. The purported poverty of the stimulus is re-appraised in this light, and it is concluded that linguistic structure may be learnable by bottom-up statistical learning models, such as, connectionist neural networks.
An ordinal transformation framework for multi-fidelity simulation optimization

Simulation models of different levels of fidelity are often available for evaluating alternative solutions of a complex system. High-fidelity simulations generate accurate predictions but can be very time-consuming to run. Therefore, they can only be applied to a small number of solutions. Low-fidelity simulations are much faster and can evaluate a large number of solutions. But simulation results may contain significant bias and variability. We propose a novel ordinal transformation framework to exploit the benefits of both high- and low-fidelity simulation models to efficiently identify a (near) optimal solution. A two-stage simulation optimization method under the ordinal transformation framework is described. Through preliminary theoretical analysis and numerical experiments, we demonstrate the promising performance of ordinal transformation, which opens up a new and potentially fruitful research avenue.
The use of paper in everyday student life

The information we encounter in modern life, in developed countries, is a hybrid of the physical and the digital. Personal archiving tools allow users to capture and retrieve aspects of their everyday lives in digital form. In this paper we use a diary study of students' interactions with paper-based information to inform the design of such archiving tools.
A novel fusion approach for multimodal medical images using Non-Subsampled contourlet transform

Image fusion is defined as the process of merging related information from two or more images captured using different sensors to produce a composite image for better medical diagnosis. Fusion of Computed Tomography (CT) and Magnetic Resonance (MR) images is considered. In the proposed work, trade-off between the spectral distortion and enhancement of spatial information is observed while fusing two images. The major drawbacks of wavelet based fusion techniques are incomplete directionality, deficiency of phase information and shift invariant and these are addressed by using Non-Subsampled Contourlet Transform. The Non-Subsampled contourlet retains the structural information while decomposing and reconstructing the images. The performance is analyzed by computing Root Mean Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR).
A manifold learning based feature extraction method for hyperspectral classification

T Manifold learning methods have widely used in ordinary image processing domain. It has many advantages, depending on the different formulation of the manifold. Hyperspectral images are kind of images acquired by air-borne or space-born platforms. This paper introduces a novel manifold learning method into hyperspectral classification. The purpose is to fully utilize the spectral and spatial information from hyperspectral images to get confidential landcover and land use class results.
Broadband programmable equalizer and limiting amplifier for an XFI interface in 45nm CMOS

An 11Gb/s Equalizer is designed in 45nm CMOS to compensate for the losses in a 12” PCB trace. Amplitude variations are compensated with a limiting amplifier that provides a constant swing to a digital phase detector. An offset cancellation loop is used to attenuate offset in the high gain stage.
A secure electronic diary and data collection tool

Handheld computerised data-collection tools have been shown to be snore accurate, less susceptible to recall errors and to encourage higher user-comnpliance than pen and paper alternatives. Pocket Interview is a configurable electronic diary and data collection tool that provides the ability to design and deliver questionnaires and collect data securely using handheld devices and desktop computers. The client software can be set to prompt the data contributor, via audible prompts, to provide entries at fixed times and preset intervals. The device also allows the users to record personally significant incidents out with the schedule times. Pocket Interview is designed for use, after minimal training, by both the data-provider(s) and the administrator. An administrator can define and/or amend the data they would like to be collected, via a graphical user interface. The system has been shown to be usable by both administrators and users.
A virtual water network of the Roman world

The Romans were perhaps the most impressive exponents of water resource management in preindustrial times with irrigation and virtual water trade facilitating unprecedented urbanization and socioeconomic stability for hundreds of years in a region of highly variable climate. To understand Roman water resource management in response to urbanization and climate variability, a Virtual Water Network of the Roman World was developed. Using this network we find that irrigation and virtual water trade increased Roman resilience to interannual climate variability. However, urbanization arising from virtual water trade likely pushed the Empire closer to the boundary of its water resources, led to an increase in import costs, and eroded its resilience to climate variability in the long term. In addition to improving our understanding of Roman water resource management, our cost–distance-based analysis illuminates how increases in import costs arising from climatic and population pressures are likely to be distributed in the future global virtual water network.
Neural network language models for conversational speech recognition

Recently there is growing interest in using neural networks for language modeling. In contrast to the well known backoff ngram language models (LM), the neural network approach tries to limit problems from the data sparseness by performing the estimation in a continuous space, allowing by these means smooth interpolations. Therefore this type of LM is interesting for tasks for which only a very limited amount of in-domain training data is available, such as the modeling of conversational speech. In this paper we analyze the generalization behavior of the neural network LM for in-domain training corpora varying from 7M to over 21M words. In all cases, significant word error reductions were observed compared to a carefully tuned 4-gram backoff language model in a state of the art conversational speech recognizer for the NIST rich transcription evaluations. We also apply ensemble learning methods and discuss their connections with LM interpolation.
Facial age estimation and gender classification using multi level local phase quantization

Facial demographic classification is an attractive topic in computer vision. Attributes such as age and gender can be used in many real life application such as face recognition and internet safety for minors. In this paper, we present a novel approach for age estimation and gender classification under uncontrolled conditions following the standard protocols for fair comparaison. Our proposed approach is based on Multi Level Local Phase Quantization (ML-LPQ) features which are extracted from normalized face images. Two different Support Vector Machines (SVM) models are used to predict the age group and the gender of a person. The experimental results on the benchmark Image of Groups dataset showed the superiority of our approach compared to the state-of-the-art.
Re-engineering Based Feature Model Management for Software Product Line

Nowadays, Software Product Line Engineering (SPLE) is an emerging software engineering paradigm, which is based on the concept of reusing software artifacts gaining from the previous software development lifecycle. Researches concerning with domain analyzing, feature modeling (FM), common and variability analyzing processes have being developed for SPLE. So, this system proposes re-engineering based feature model management system for software product line about mobile phone environment. This system is intended not only to develop an FM integration method for meta-modeling but also to develop a method for extracting Common & Variability from the versioned products. Firstly the features of products will be reversely extracted from the technical description file with matrix format. After that, these features will be used by FM Integration and Common & Variability Extraction. This system is useful for mobile phone environment. The proposed system is implemented by using Java programming language.
Development, evaluation and introduction of a lane keeping assistance system

A driver assistance system has been developed which simultaneously supports the driver with two driving tasks that are most frequently required during normal driving - keeping in lane and maintaining vehicle headway. A new concept of joint driving by the driver and the assistance system is introduced to achieve a driver workload reduction without decreasing the driver's driving motivation. The system has been approved by the Japanese Ministry of Land, Infrastructure and Transport as a fundamental driver assistance system, which can be used on expressways in Japan. The system has been introduced on the Japanese market as the Honda intelligent driver support system (HIDS).
Carcinogenic Effects in a Phenylketonuria Mouse Model

Phenylketonuria (PKU) is a metabolic disorder caused by impaired phenylalanine hydroxylase (PAH). This condition results in hyperphenylalaninemia and elevated levels of abnormal phenylalanine metabolites, among which is phenylacetic acid/phenylacetate (PA). In recent years, PA and its analogs were found to have anticancer activity against a variety of malignancies suggesting the possibility that PKU may offer protection against cancer through chronically elevated levels of PA. We tested this hypothesis in a genetic mouse model of PKU (PAHenu2) which has a biochemical profile that closely resembles that of human PKU. Plasma levels of phenylalanine in homozygous (HMZ) PAHenu2 mice were >12-fold those of heterozygous (HTZ) littermates while tyrosine levels were reduced. Phenylketones, including PA, were also markedly elevated to the range seen in the human disease. Mice were subjected to 7,12 dimethylbenz[a]anthracene (DMBA) carcinogenesis, a model which is sensitive to the anticancer effects of the PA derivative 4-chlorophenylacetate (4-CPA). Tumor induction by DMBA was not significantly different between the HTZ and HMZ mice, either in total tumor development or in the type of cancers that arose. HMZ mice were then treated with 4-CPA as positive controls for the anticancer effects of PA and to evaluate its possible effects on phenylalanine metabolism in PKU mice. 4-CPA had no effect on the plasma concentrations of phenylalanine, phenylketones, or tyrosine. Surprisingly, the HMZ mice treated with 4-CPA developed an unexplained neuromuscular syndrome which precluded its use in these animals as an anticancer agent. Together, these studies support the use of PAHenu2 mice as a model for studying human PKU. Chronically elevated levels of PA in the PAHenu2 mice were not protective against cancer.
A Multi-domain Adaptation for sentiment classification algorithm based on Class Distribution

At present, most multi-domain adaptation for sentiment classification algorithms use all source domains to train the classifier with no selecting and dynamic dealing with the diferent source domains. This will result in those source domains very dissimilar to target have negative impact on domain In this paper, we propose Multi-domain Adaptation algorithm based on the Class Distribution (MACD). First, the information of class distribution is used to select some adaptive base classifiers from all source domains. Then add the 'self-labeled' samples into training data, in which, the selection of samples is dynamically adjusted with the similarity between the source and target domain. Last, the final ensemble classifier is constructed using the information of class distribution. The experimental results have shown that the MACD algorithm is effective and superior to some existing approaches in accuracy.
THE ROAD TO NETWORK SECURITY OR THE VALUE OF SMALL COBBLESTONES

Software subsystems that implement cryptographic security features can be built from small modules using uniform interfaces. The methods demonstrated in this paper illustrate how configuration flexibility can be achieved and how complex services can be constructed, all using the same building block modules. These allow the configuration process to be independent of algorithm details, while the algorithms used in the subsystem are obvious.1
ColLex.en: Automatically Generating and Evaluating a Full-form Lexicon for English

The paper describes a procedure for the automatic generation of a large full-form lexicon of English. We put emphasis on two statistical methods to lexicon extension and adjustment: in terms of a letter-based HMM and in terms of a detector of spelling variants and misspellings. The resulting resource, \collexen, is evaluated with respect to two tasks: text categorization and lexical coverage by example of the SUSANNE corpus and the \openanc.
The role of active and passive motion staffâs emotional intelligence on entrepreneurship behavior

This study has investigated the role of active and passive motion staff’s emotional intelligence on their entrepreneurship in Islamic Azad university Of Maraghehat2011. The statistical sample of this study includes all employees of the 177 subjects (108men and 69 women and 147 married and 30unmarried), from which 54 people were with no special physical activity or exercise training, and the 123 person of them were taking a part in at least two sessions of friendly sport companies in a week . They were to answer two questionnaires: Wang and Lau Emotional Intelligence questionnaire and questionnaire of Pierce on entrepreneurial behavior. There is a significant relationship between emotional intelligence and corporate entrepreneurship.
Advanced Maximum Likelihood statistical estimation for incomplete data sets in CT system

Reconstruction of image from incomplete data set in computed tomography (CT) system is a problem with immense importance. The various approaches reported so far are based on extrapolation or interpolation, iterative methods, imputation and so on. The algorithms proposed under these approaches are limited to specific incomplete problems. A generic algorithm addressing interior, exterior and limited angle view situation is desired. In this work we present analysis of existing algorithms and propose design of new algorithm for the same. In this report the algorithms analyzed with respect to time complexity and space complexity are: Bayesian algorithm, Expectation maximization algorithm, Regression techniques such as interpolation and extrapolation, Imputation methods like maximum a posteriori, maximum likelihood and a-priori algorithm. We propose a new method with some modification to maximum likelihood statistical estimation method which promises better efficiency as compared to existing one.
Histone deacetylase inhibition reduces cardiac connexin43 expression and gap junction communication

Histone deacetylase inhibitors (HDACIs) are being investigated as novel therapies for cancer, inflammation, neurodegeneration, and heart failure. The effects of HDACIs on the functional expression of cardiac gap junctions (GJs) are essentially unknown. The purpose of this study was to determine the effects of trichostatin A (TSA) and vorinostat (VOR) on functional GJ expression in ventricular cardiomyocytes. The effects of HDAC inhibition on connexin43 (Cx43) expression and functional GJ assembly were examined in primary cultured neonatal mouse ventricular myocytes. TSA and VOR reduced Cx43 mRNA, protein expression, and immunolocalized Cx43 GJ plaque area within ventricular myocyte monolayer cultures in a dose-dependent manner. Chromatin immunoprecipitation experiments revealed altered protein interactions with the Cx43 promoter. VOR also altered the phosphorylation state of several key regulatory Cx43 phospho-serine sites. Patch clamp analysis revealed reduced electrical coupling between isolated ventricular myocyte pairs, altered transjunctional voltage-dependent inactivation kinetics, and steady state junctional conductance inactivation and recovery relationships. Single GJ channel conductance was reduced to 54 pS only by maximum inhibitory doses of TSA (≥ 100 nM). These two hydroxamate pan-HDACIs exert multiple levels of regulation on ventricular GJ communication by altering Cx43 expression, GJ area, post-translational modifications (e.g., phosphorylation, acetylation), gating, and channel conductance. Although a 50% downregulation of Cx43 GJ communication alone may not be sufficient to slow ventricular conduction or induce arrhythmias, the development of class-selective HDACIs may help avoid the potential negative cardiovascular effects of pan-HDACI.
Linkage Discovery through Data Mining [Research Frontier]

Genetic algorithms (GAs) are extensively adopted in various aspects of data mining, e.g., association rules, clustering, and classification. Instead of applying GAs for data mining, this study addresses linkage discovery, an essential topic in GAs, by using data mining methods. Inspired by natural evolution, GAs utilize selection, crossover, and mutation operations to evolve candidate solutions into global optima. This evolutionary scheme can effectively resolve many search and optimization problems. As the most salient feature of GAs, crossover enables the recombination of good parts of two selected chromosomes, yet, in doing so, may disrupt the collected promising segments.