Miracle-dz/UniFAR-FTUs huggingface dataset:
Connected component in feature space to capture high level semantics in CBIR

An important problem in Content Based Image Retrieval (CBIR) systems is the gap between the human high-level semantics and the low-level machine features. In this paper, we develop a novel approach based on the intuition that a query along with the responses from the user during a relevance feedback session provides sufficient cues for learning multiple high-level concepts associated with the query image. For example, a single query image showing a yellow rose contains several high-level semantics such as yellow roses, any rose flower, any yellow coloured flower, a flower, a flower in front-view, etc. Unlike the past approaches that modelled positive responses from the user as a single class with a unimodal probability distribution function, we show that it is more appropriate to group them into multiple connected components in the feature space. It is demonstrated that these components capture and differentiate between the various semantics of an image. We also show that these components may be computed automatically by using a Gaussian Mixture Model. Results on several images illustrate the potential of these connected components to capture the multiple semantics of an image.
PEYMA: A Tagged Corpus for Persian Named Entities

The goal in the NER task is to classify proper nouns of a text into classes such as person, location, and organization. This is an important preprocessing step in many NLP tasks such as question-answering and summarization. Although many research studies have been conducted in this area in English and the state-of-the-art NER systems have reached performances of higher than 90 percent in terms of F1 measure, there are very few research studies for this task in Persian. One of the main important causes of this may be the lack of a standard Persian NER dataset to train and test NER systems. In this research we create a standard, big-enough tagged Persian NER dataset which will be distributed for free for research purposes. In order to construct such a standard dataset, we studied standard NER datasets which are constructed for English researches and found out that almost all of these datasets are constructed using news texts. So we collected documents from ten news websites. Later, in order to provide annotators with some guidelines to tag these documents, after studying guidelines used for constructing CoNLL and MUC standard English datasets, we set our own guidelines considering the Persian linguistic rules.
Effectiveness of Applying Codon Usage Bias for Translational Initiation Sites Prediction

The accurate recognition of translational initiation sites (TISs) in genomic, cDNA and mRNA sequences is crucial to identifying the primary structure of the functional gene products - proteins. Many computational methods have been proposed in the literature which apply one or more complicated models that examine a variety of sequence features. In this paper, we propose a novel TIS prediction approach, called codon usage bias agent, which operates solely based on the usage of codon preference; the algorithm only requires O(n) for execution time. The results of the experiments conducted on three benchmark data sets have shown that the proposed approach is very effective and well applicable to solving the problem of TIS recognition.
Semi-automatic extraction of digital surface model using ALOS/PRISM data with ENVI

Forest canopy height is an important indicator of standing biomass for management purposes as well as for the assessment of carbon storage. Theoretically, photogrammetry is one of remote sensing technologies which can be used to extract forest canopy height information. The Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) carried by the Advanced Land-Observing Satellite was designed to generate worldwide topographic data with its high-resolution and stereoscopic observation. A semi-automatic method for the extraction of digital surface model using PRISM data with ENVI is introduced. Tie points scattered evenly over the common area are manually selected on the stereo-image pair. Their ground coordinates are calculated using the geocoding parameters delivered along with the nadir image. Their elevations are extracted from SRTM by their ground coordinates. Ground control points (GCP) files needed in the stereo-processing of PRISM data can be composed by the image coordinates, ground coordinates and elevations. With the prepared tie points and GCP files, PRISM data can be stereo-processed automatically by ENVI. The results showed that the elevation from PRISM DSM is highly correlated with that from GLAS data and forest canopy height information is explicitly exhibited on it.
TruMatch - a BLAST post-processor that identifies bona fide sequence matches to genome assemblies

SUMMARY BLAST is a widely used alignment tool for detecting matches between a query sequence and entries in nucleotide sequence databases. Matches (high-scoring pairs, HSPs) are assigned a score based on alignment length and quality and, by default, are reported with the top-scoring matches listed first. For certain types of searches, however, this method of reporting is not optimal. This is particularly true when searching a genome sequence with a query that was derived from the same genome, or a closely related one. If the genome is complex and the assembly is far from complete, correct matches are often relegated to low positions in the results, where they may be easily overlooked. To rectify this problem, we developed TruMatch--a program that parses standard BLAST outputs and identifies HSPs that involve query segments with unique matches to the assembly. Candidates for bona fide matches between a query sequence and a genome assembly are listed at the top of the TruMatch output. AVAILABILITY TruMatch is written in Perl and is freely available to non-commercial users via web download at the URL: http://genome.kbrin. uky.edu/fungi_tel/TruMatch/
The p-value fallacy and how to avoid it.

Null hypothesis significance tests are commonly used to provide a link between empirical evidence and theoretical interpretation. However, this strategy is prone to the "p-value fallacy" in which effects and interactions are classified as either "noise" or "real" based on whether the associated p value is greater or less than . 05. This dichotomous classification can lead to dramatic misconstruals of the evidence provided by an experiment. For example, it is quite possible to have similar patterns of means that lead to entirely different patterns of significance, and one can easily find the same patterns of significance that are associated with completely different patterns of means. Describing data in terms of an inventory of significant and nonsignificant effects can thus completely misrepresent the results. An alternative analytical technique is to identify competing interpretations of the data and then use likelihood ratios to assess which interpretation provides the better account. Several different methods of calculating the likelihood ratios are illustrated. It is argued that this approach satisfies a principle of "graded evidence," according to which similar data should provide similar evidence.
Finding Dependencies from Defect History

Dependency analysis is an essential part of various software engineering activities like integration testing, reliability analysis and defect prediction. In this paper, we propose a new approach to identify dependencies between components and associate a notion of ―importance‖ with each dependency by mining the defect history of the system, which can be used to complement traditional dependency detection approaches like static analysis. By using our tool Ladybug that implements the approach, we have been able to identify important dependencies for Microsoft Windows Vista and Microsoft Windows Server 2008 and rank them for prioritizing testing especially when the number of dependent components was large. We have validated the accuracy of the results with domain experts who have worked on designing, implementing or maintaining the components involved.
Vicinity Occlusion Maps: Enhanced Depth Perception of Volumetric Models

Volume models often show high depth complexity. This poses di±culties to the observer in judging the spatial relationships accurately. Illustrators usually use certain techniques such as halos or edge darkening in order to enhance depth perception of certain structures. Halos may be dark or light, and even colored. Halo construction on a volumetric basis impacts rendering performance due to the complexity of the construction process. In this paper we present Vicinity Occlusion Maps: a simple and fast method to compute the light occlusion due to neighboring voxels. Vicinity Occlusion Maps may be used to generate flexible halos around objects or selected structures in order to enhance depth perception or accentuate the presence of some structures in volumetric models at a low cost. The user may freely select the structure that requires the halos to be generated, its color and size, and our proposed application generates those in real time. They may also be used to perform vicinity shading in realtime, or even to combine both effects.
Tensor based source separation for single and multichannel signals

Blind source separation (BSS) techniques have the aim of separating original source signals from their mixtures without having or with a little knowledge about the source signals or the mixing process. Tensor based source separation techniques have become increasingly popular for various applications since they exploit different inherent diversities of the sources. Therefore, they can improve the estimation of desired sources and the identification of the mixing system. The proposed techniques in this thesis are the extensions of conventional tensor factorisation techniques. Our proposed methods can be categorised in two groups; single and multichannel source separation techniques. For single channel source separation a tensor based singular spectrum analysis (SSA) is proposed followed by a way to select the desired subspaces automatically. The proposed method is compared with conventional methods using both synthetic and real electroencephalography (EEG) sleep data to track different sleep stages. Another proposed method is symmetric tensor decomposition. The method has been applied to detect the beta rebound, as an indicator of movement related brain responses, in brain computer interfacing (BCI). In addition to single channel source separation, several multichannel BSS techniques have been proposed. The first method is a constrained BSS technique which uses the spatial information of data to improve the performance. This approach is particularly useful in separation of weak intermittent signal components. The results show that the proposed method performs better than the existing methods in terms of accuracy and quality. Complex tensor factorisation of correlated brain sources is attempted as well. The electrical brain responses due to motory, sensory, or cognitive stimuli, i.e. event related potentials (ERPs), particularly P300, have been used for cognitive information processing. P300 has two subcomponents, P3a and P3b, which are correlated, and therefore, the traditional BSS approaches fail in their separation. A complex-valued tensor factorisation of EEG signals is introduced with the aim of separating P300 subcomponents. The proposed method uses complex-valued statistics to exploit the data correlation. In this way, the variations of P3a and p3b can be tracked for the assessment of the brain state. The results of this work are compared with those of spatial principle component analysis (SPCA) method. Communication signals such as quadrature-phase shift keying (QPSK) often pose as complex waveforms and suffer from multipath and clutter problems. In this thesis therefore, a new convolutive complex tensor factorisation system is proposed to recover such signals in the receiver and estimate the communication channels. The proposed method is evaluated using simulated data with multiple multi-paths and various non-circularity and noise levels. Simulation results confirm the superiority of the proposed method over the existing popular techniques.
The supramolecular structure of melanin

The melanins are an important class of multifunctional bio-macromolecules with fascinating and potentially useful electronic and optoelectronic properties. They are one of the last major bio-macromolecular systems where we do not know how the constituent molecules organise. This knowledge gap hinders attempts to map their properties and function and is a particular issue in understanding their roles in human photoprotection and melanoma cancer. In this article we provide strong and direct evidence of supramolecular organisation in both natural and synthetic eumelanins. Using low voltage–high resolution transmission electron microscopy (LVHRTEM) we show that sheets of protomolecules stack to form onion-like nanostructures. The inter-sheet spacings within these structures are between 3.7 and 4.0 A consistent with non-covalent π–π stacking in heteroaromatic systems. This type of arrangement has previously been suggested by reciprocal space scattering studies, but our real-space microscopic analysis provides the definitive evidence and dimensions. Furthermore, we show that key photophysical properties of melanin related to their role as photoprotectants are derived from the system's primary chemical structure rather than supramolecular organisation. We now have a framework for the secondary structure of melanins upon which biologically relevant structure–function relationships can be built and new bio-inspired optoelectronic materials and multifunctional coatings can be designed and realised.
A decision support system based on matrix representation for conflict resolution

A decision support system is designed for providing strategic advice to individuals involved in real-world conflict problems. Conflict is endemic in our world, from disputes within a family to wars between countries, so there is great demand for having a flexible decision support system for systematically investigating a rich range of real world conflicts. The graph model can provide a convenient and effective means to model and analyze a strategic conflict. To take advantage of this opportunity, a computer implemented decision support system based on the matrix representation of the graph model for conflict resolution (GMCR) is proposed in this paper. Although a decision support system, called GMCR II is available for conflict analysis, its coding is difficult to modify or adapt to new analysis techniques and new models. Compared with existing system, the proposed system is more effective and convenient for adapting to new analysis techniques and making codes.
TRANSESOPHAGEAL ECHOCARDIOGRAPHY, INSUFFICIENT CLEANING PRACTICES AND LAX EQUIPMENT MAINTENANCE, AND ESCHERICHIA COLI - A BREAKDOWN IN INFECTION CONTROL

Escherichia coli (E. coli) is a rod-shaped, gram-negative bacillus normally found in the lower gastrointestinal tract and is part of the normal intestinal flora. In hospital settings, E. coli most commonly causes urinary tract infections. Respiratory tract infections due to E. coli are uncommon, though there have been several published reports that chronicle E. coli pneumonia in the pediatric intensive care unit (ICU) [1]. Outbreaks of respiratory tract infections with gram-negative organisms have been increasingly reported due to contamination of medical equipment including bronchoscopes which are directly inserted into the respiratory tract. Transesophageal echocardiography (TEE) is normally done by inserting the instrument into the gastrointestinal tract (the esophagus) and is used during cardiac surgery to better visualize the posterior of the heart. The gastrointestinal tract is considered “dirty” and medical equipment should receive high-level disinfection.
How do nurse case managers care for older women with breast cancer?

PURPOSE/OBJECTIVES To describe how nurse case managers care for older women with breast cancer. DESIGN A randomized, prospective trial. SETTING Thirteen community hospitals and two public hospitals in southeastern Texas. SAMPLE 335 older women aged 60-89 years newly diagnosed with breast cancer and being cared for by 60 surgeons. Most participants were Caucasian. A total of 166 women were in the control group, and 159 were in the intervention nurse case management group. METHODS The nurses implemented multiple nursing interventions in each nursing process phase over a period of 12 months. MAIN RESEARCH VARIABLES Independent variables were participants' demographic characteristics, depressive symptomatology, and cognitive impairment. Nurse case management contact was a dependent variable. FINDINGS In each nursing phase, a greater number of nurse case management contacts were made in the first quarter. Bivariate analysis illustrated statistical differences among race, income, education, and living alone with respect to the mean amount of nurse case management. Multivariate analysis revealed that age, income, living alone, and stage of cancer predicted more nurse case management contact. CONCLUSIONS Nurse case managers may play a role in helping older women with breast cancer achieve positive health outcomes. IMPLICATIONS FOR NURSING Based on the findings of this study, nurses can develop specific nursing interventions to meet the needs of older women with breast cancer. Nurses can use the Model of Nurse Case Management to plan and manage care for older women with breast cancer.
Enhanced electrical property of Ni-doped CoOx hole transport layer for inverted perovskite solar cells

Ultrathin Ni doped CoOx films were prepared by direct current co-sputtering at room temperature as inorganic hole transport materials for inverted perovskite solar cells. P-type doping was designed to adjust the valence band position of CoOx to match the that of CH3NH3PbI3, which would effectively eliminate the interface barrier. Moreover, the hole extraction ability would be enhanced and the power conversion efficiency of the devices hence increased from 3.68% to 9.60%. The optimized performance was also accompanied by decent stability as a result of its intrinsic stability.
NFIDS: a neuro-fuzzy intrusion detection system

Heavy reliance on the Internet and worldwide connectivity has greatly increased the potential damage that can be inflicted by remote attacks launched over the Internet. Since it is not technically feasible to build a system with no vulnerabilities, intrusion detection has become an important area of research. The neuro-fuzzy intrusion detection system (NFIDS) is an anomaly based intrusion detection system that uses fuzzy logic and neural networks to detect if malicious activity is taking place on a network. This paper describes the architecture of the NFIDS and its components. The sample fuzzy rules are developed for some kinds of attacks and the testing results with actual network data are described.
Stability Analysis and Controller Design for Linear Time Delay Systems with Actuator Saturation

This paper deals with the stability analysis and controller design of linear systems with state delay and input saturation. First, a delay-dependent stability criterion is derived within the framework of linear matrix inequalities by using some free matrices to express the relationship of the terms in the Leibniz-Newton formula. Then the problem of designing linear state feedback laws which guarantees the stability of the closed-loop system and enlarging the domain of attraction is formulated and solved as an optimization problem with LMI constraints. A numerical example is used to demonstrate the effectiveness of the proposed design technique comparing with the previous results.
Metabolomics approaches for characterizing metabolic interactions between host and its commensal microbes

It is increasingly evident that the gut microbiota is involved in the regulation of multiple mammalian metabolic pathways through a series of interactive host‐microbiota metabolic, signaling, and immune‐inflammatory axes that physiologically connect the gut, liver, brain, and other organs. Correlation of the metabotypes with the gut microbial profiles derived from culture‐independent molecular techniques is increasingly useful for deciphering inherent and intimate host–microbe relationships. Real‐time analysis of the small molecule metabolites derived from gut microbial‐host co‐metabolism is essential for understanding the metabolic functions of the gut microbiome and has tremendous implications for personalized healthcare strategies. Metabolomics, an array of analytical techniques that includes high resolution NMR spectroscopy and chromatography‐MS in conjunction with chemometrics and bioinformatics tools, enables characterization of the metabolic footprints of mammalian hosts that correlate with the microbial community in the intestinal tract. The metabolomics approach provides important information of a complete spectrum of metabolites produced from the gut microbial‐mammalian co‐metabolism and is improving our understanding of the molecular mechanisms underlying multilevel host–microbe interactions. In this review, the interactions of gut microbiota with their host are discussed and some examples of NMR‐ or MS‐based metabolomics applications for characterizing the metabolic footprints of gut microbial‐host co‐metabolism are described. Advances in the metabolomic analysis of bile acids, short‐chain fatty acids, and choline metabolism are also summarized.
Defect-based Quality Assurance with Defect Models

This thesis proposes a systematic approach using defect models for defect-based quality assurance to formally describe defects and create (semi-)automatic test case / check list generators operationalizing this description. The defect model lifecycle framework yields activities for practical application. Defects are suitably elicited and classified in DELICLA and effective, efficient and reproducible operationalizations are created on each test level (i.e. 8Cage, OUTFIT and Controller Tester).
Voided cyclo-olefin polymer films: ferroelectrets with high thermal stability

Recently, new ferroelectrets were developed from foams of polyethylene terephthalate and of cyclo-olefin polymers and copolymers. Here, we focus on ferroelectret films based on compounds of cyclo-olefin polymers. Suitable compounding and preparation led to cyclo-olefin polymer ferroelectrets which show an electromechanical activity of around 15 pC/N that is thermally stable at least up to 110/spl deg/C. We discuss the preparation, the electrical charging and the electromechanical activity, the short-and long-term thermal stability as well as the electro-acoustical properties of an eight-layer cyclo-olefin film stack. The results obtained on the cyclo-olefin foams represent a significant improvement of the service temperature of ferroelectret transducer materials.