Reliable Indexing Research Articles

Ground-truth generation through crowdsourcing with probabilistic indexes

Automatic transcription of large series of historical handwritten documents generally aims at allowing to search for textual information in these documents. However, automatic transcripts often lack the level of accuracy needed for reliable text indexing and search purposes. Probabilistic Indexing (PrIx) offers a unique alternative to raw transcripts. Since it needs training data to achieve good search performance, PrIx-based crowdsourcing techniques are introduced in this paper to gather the required data. In the proposed approach, PrIx confidence measures are used to drive a correction process in which users can amend errors and possibly add missing text. In a further step, corrected data are used to retrain the PrIx models. Results on five large series are reported which show consistent improvements after retraining. However, it can be argued whether the overall costs of the crowdsourcing operation pay off for the improvements, or perhaps it would have been more cost-effective to just start with a larger and cleaner amount of professionally produced training transcripts.

Development of Simplified Recombinase Polymerase Amplification Assay for Rapid and Robust Detection of Citrus Yellow Vein Clearing Virus.

Citrus is an economically important fruit crop, belongs to family Rutaceae, cultivated commercially in over 130 countries, which holds a leading profitable position in the international market. The most important citrus varieties are mandarins, oranges, lemons, sweet limes, grapefruits and pomelos. Citrus yellow vein clearing virus (CYVCV) is an important graft transmissible plant pathogen known to reduce productivity of citrus fruits due to its predominant association and widespread occurrence. Requirement of fast, reliable, efficient & economical CYVCV indexing assay is a prerequisite for production of healthy planting material. Currently, nucleic acid isolation and thermal cycler-based assay available for CYVCV indexing is a cumbersome lab intensive method. The present study was undertaken to develop and validate reverse transcription-recombinase polymerase amplification (RT-RPA) assay requiring no tedious RNA isolation, separate cDNA synthesis and costlier instrument like thermo-cycler. Optimized RT-RPA assay was able to amplify CYVCV up to 10-7 dilution (equivalent to 0.1pg/μl) with the prepared templates of both RNA and crude saps and showed higher sensitivity in detection of CYVCV infection in field samples as compared to the conventional RT-PCR. Developed RT-RPA assay showed high specificity without any cross-reaction with other citrus pathogens (Indian citrus ringspot virus, citrus yellow mosaic virus, citrus tristeza virus, citrus exocortis viroid and huanglongbing). RT-RPA using crude leaf sap as template is quite simple, robust, highly sensitive, time and cost effective; therefore, it can be used in resource constrained laboratories as screening tool, for field surveys and on-site testing programs in farms, nurseries and biosecurity. Present study, first time reports the development, optimization and validation of crude sap-based RT-RPA assay for the detection of CYVCV infection in citrus plants namely; Kinnow mandarin, Mosambi and Grape fruit.

Mapping nanocrystal orientations via scanning Laue diffraction microscopy for multi-peak Bragg coherent diffraction imaging.

The recent commissioning of a movable monochromator at the 34-ID-C endstation of the Advanced Photon Source has vastly simplified the collection of Bragg coherent diffraction imaging (BCDI) data from multiple Bragg peaks of sub-micrometre scale samples. Laue patterns arising from the scattering of a polychromatic beam by arbitrarily oriented nanocrystals permit their crystal orientations to be computed, which are then used for locating and collecting several non-co-linear Bragg reflections. The volumetric six-component strain tensor is then constructed by combining the projected displacement fields that are imaged using each of the measured reflections via iterative phase retrieval algorithms. Complications arise when the sample is heterogeneous in composition and/or when multiple grains of a given lattice structure are simultaneously illuminated by the polychromatic beam. Here, a workflow is established for orienting and mapping nanocrystals on a substrate of a different material using scanning Laue diffraction microscopy. The capabilities of the developed algorithms and procedures with both synthetic and experimental data are demonstrated. The robustness is verified by comparing experimental texture maps obtained with Laue diffraction microscopy at the beamline with maps obtained from electron back-scattering diffraction measurements on the same patch of gold nanocrystals. Such tools provide reliable indexing for both isolated and densely distributed nanocrystals, which are challenging to image in three dimensions with other techniques.

Dataset: Exploring the Landscape of Cyber Harassment: A Data-driven Approach to Understanding the Legal Framework

This research focuses on the search for articles and literature reviews on cyber harassment. The search was conducted using Lens.org, a reliable indexing agency, by using keywords such as Cyber law and Cyber Harassment. Three keywords were selected and entered into the 'edit search' feature, and the search was filtered by selecting the law category in the subject matter and choosing journal articles as the document type. The data was exported in both CSV and BibTeX formats, and a screenshot was taken of the available data graphs. The goal of this research is to collect data on cyber harassment and its legal implications. The method involved searching scholarly works using specific keywords and filtering the results based on subject matter and document type. The results obtained are valuable as they provide a comprehensive understanding of the current state of cyber harassment and its legal implications.
 Highlights:
 
 Filtering through specific categories and document types is an effective way to obtain relevant literature on cyber harassment.
 The ten articles found provide a diverse range of topics related to cyber harassment, from the impact of technology on online sexual harassment to the legal response to cyberbullying.
 These articles offer valuable insights for future research and can be used as references for scholars studying cyber harassment.
 
 
 

An index based quality evaluation of groundwater—a case study of Whitefield area in Bangalore, India

ABSTRACT Conventional indices to describe the quality of water have been found to have several drawbacks. Hence, an attempt has been made to overcome these deficiencies by developing an improved system by incorporating a few modifications in the drinking water quality indices developed hitherto. Fifteen groundwater quality parameters were chosen for the study and categorised into four groups based on potability and hazard factor. These parameters were analysed for water samples drawn from 30 groundwater sources in the study area. A proposed drinking water quality index (PDWQI) was arrived at, and this was compared with the arithmetic and geometric indices as evaluated in this study, to depict its utility and adaptability. The results revealed that the geometric DWQI was higher than the arithmetic as well as proposed DWQI, while the geometric DWQI was lower than the proposed DWQI. All the indices had a high positive correlation (r = +0.99). The PDWQI clearly illustrated the degree of potability of groundwater for drinking purposes in regard of health threats, by indicating the degree of pollution caused by the various quality parameters. The comparison between the three indices clearly depicted that the PDWQI was the most reliable water quality indexing system, which enabled the reduction of issues of eclipsing as well as sensitivity, that is found to exist in conventional indexing systems.

WITHDRAWN: English vocabulary retrieval and recognition based on FPGA and machine learning

Abstract Digital audio spoken vocabulary retrieval and recognition have been developed in recent years. This growth has prompted extensive research into technology and reliable indexing. The English vocabulary content retrieval requires a combination of audio and speech processing technology and information retrieval. A study of English content search initially investigated units of planning to have a similar audio signal structure, and the subsequent focus outside the audio, naturally, more informal oral content. Shifted to generate conversational settings and the volume provided by this study outlines the component technical discipline of the relationship between lexical speech signal recognition and user interaction issues. The aim is for researchers with a background in speech technology to seek a deeper understanding of whether these fields are integrated into supporting research and development to solve the core challenges of vocabulary and content search. This study describes a content-based search method for Machine Learning based Neural Network classifiers that retrieve relevant documents efficiently and accurately. This overcomes the system limitations of characterization based arrangements regarding restricted vocabulary and preparing information accessibility. To analysis the system, it has designed a one-time learning neural network for classification and achieves better accuracy results.

Characterisation of super duplex stainless steel by optimisation of EBSD parameters

Electron backscatter diffraction (EBSD) was used to characterise the microstructure of a super duplex stainless steel (SDSS) alloy Zeron 100 and the intermetallic phases sigma and chi in this material. The material was analysed in three conditions; as-received, heat-treated at 900 °C for 8 min and heat-treated at 750 °C for 4 h. Heat treatments were performed to introduce the intermetallic phases in the steel. It is difficult to get reliable indexing of the sigma and chi phases due to noisy and overlapping patterns. To get the best results, the Hough transformation parameters were optimised and an averaging technique was used. The optimisation was done by removing some of the reflectors in the sigma- and chi-phases in the original TSL material files used for pattern indexing. By changing Hough parameters, considerable improvements in the indexing of the patterns were observed along with the increase of average confidence index (CI) value for both intermetallic phases. The applied SEM, Zeiss Ultra 55 FESEM was equipped with a NORDIF UF-1100 EBSD detector to acquire and stream patterns to HD. Indexing and data processing were performed by TSL/OIM 7.3. In the present study, at the most four different phases are present in the SDSS.

Robust Virome Profiling and Whole Genome Reconstruction of Viruses and Viroids Enabled by Use of Available mRNA and sRNA-Seq Datasets in Grapevine (Vitis vinifera L.).

Next-generation sequencing (NGS) based virome analyses of mRNA and sRNA have recently become a routine approach for reliable detection of plant viruses and viroids. In the present study we identified the viral/viroidal spectrum of several Indian grapevine cultivars and reconstructed their whole genomes using the publically available mRNAome and sRNAome datasets. Twenty three viruses and viroids (including two variants of grapevine leafroll associated virus 4) were identified from two tissues (fruit peels and young leaves) of three cultivars among which nine unique grapevine viruses and viroids were identified for the first time in India. Irrespective of the assemblers and tissues used, the mRNA based approach identified more acellular pathogens than the sRNA based approach across cultivars. Further, the mRNAome was on par with the whole transcriptome in viral identification. Through de novo assembly of transcriptomes followed by mapping against reference genome, we reconstructed 19 complete/near complete genomes of identified viruses and viroids. The reconstructed viral genomes included four larger RNA genomes (>13 kb), a DNA genome (RG grapevine geminivirus A), a divergent genome (RG grapevine virus B) and a genome for which no reference is available (RG grapevine virus L). A large number of SNPs detected in this study ascertained the quasispecies nature of viruses. Detection of three recombination events and phylogenetic analyses using reconstructed genomes suggested the possible introduction of viruses and viroids into India from several continents through the planting material. The whole genome sequences generated in this study can serve as a resource for reliable indexing of grapevine viruses and viroids in quarantine stations and certification programs.

Identifying Leadership Characteristics from Social Media Data during Natural Hazards using Personality Traits

With the availability of smart devices and affordable data plans, social media platforms have become the primary source of information dissemination across geographically dispersed users/locations. It has shown great potential across different application domains including event detection, opinion analysis, recommendation, and prediction. However, the process of extracting useful information from the collected voluminous social media data during natural hazards is a standing problem that needs significant attention from the research community. The fine-grained knowledge detailing users’ participation in information spreading could be advantageous in developing a reliable social network for the adverse events (Natural Hazards, Man-made attacks etc.). However, there has been no such findings related to identification of leader and their leadership characteristics associated with natural hazards in previous studies. We have collected 20.6 million tweets which were posted by 5.3 million users, during distinct devastating hazards namely - Floods, Hurricane, Earthquake and Typhoons. To achieve the goal, we divided our work in to three parts. Firstly, classify the collected crises data into four domains i.e resource, causality, news, and sympathy by employing deeper recurrent neural network model. Secondly, we used statistical physics of complex network to recognize local as well as global prominent leaders. At last, we curate leadership characteristics in terms of their big five personality traits and emotional traits. Our experimental, results find evidence that local leadership behaviour characteristics are significantly different from global potentials. Where as we also finds that some behaviour traits were certain to classified domains (resource, causality, news, and sympathy) and some were certain to hazard divisions, though emotional characteristics remained consistent. Later, we conclude that local potentials leaders have comparatively higher emotional strength. Furthermore, when the complete local network structure is unavailable, we find that the dynamic rank is reliable indexing proxy for local potentials. The current study, provide useful insight to understand how leadership characteristics are influenced to hazards, domains and centrality of users.

Indexing documents with reliable indexing techniques using Apache Lucene in Hadoop

Mostly 85% of the data is presented in the form of text, which is the human-readable format. Present educational, business, medical organisations, etc. making use of big data analytics for storage of data and processing that stored data by using information retrieval. Often time's text documents have been transferred from one system to another system without any restrictions like, structured, unstructured and semi-structured data. Systems are well performed with high speed and less complexity only when it has all the data arranged in an orderly way. This paper describes how documents of text data are being Indexed using Apache Lucene with approaches in Hadoop. Most of the applications that deal with huge data over the internet are completely lacking. Use of effective analysis and techniques allow users in resulting high-performance and a challenging option in leading big data analytics.

Orientation mapping with Kikuchi patterns generated from a focused STEM probe and indexing with commercially available EDAX software

Relating a crystal's microscopic structure—such as orientation and size—to a material's macroscopic properties is of great importance in materials science. Although most crystal orientation microscopy is performed in the scanning electron microscope (SEM), transmission electron microscopy (TEM)-based methods have a number of benefits, including higher spatial resolution. Current TEM orientation methods have either specific hardware requirements or use software that has limited scope, utility, or availability. In this article, a technique is described for orientation mapping using Kikuchi diffraction patterns generated from a focused STEM probe. One key advantage is that indexing and analysis of the patterns and maps occurs in the robust OIM Analysis software, currently widely used for electron backscatter diffraction (EBSD) and transmission Kikuchi diffraction (TKD) analysis. It was found that with minimal to no image processing and by changing only a few software parameters, reliable indexing of Kikuchi patterns is possible. Three samples, a deformed β-Titanium (Ti), a medium carbon heat-treated steel, and BaCe0.8Y0.2O3-δ were tested to determine the effectiveness of the approach. In all three measurements the algorithms effectively and reliably determined the phases and the crystal orientations of the features measured. For the two orientation maps produced, less than 5% of the patterns were misindexed including boundary areas where overlapping patterns existed. An angular resolution of 0.15° was achieved while features <25 nm were able to be spatially resolved.

High pressure crystal structure of nitroethane.

Ambient temperature static high pressure compression of liquid nitroethane has been performed using the diamond anvil technique. The first transition to a crystalline powder around 4.2 GPa did not return reliable indexing solutions and could be the result of a mixture of phases. Subsequent cycling of the pressure apparatus with the same sample triggered a solid-solid transition around 4.9-4.3 GPa on the downstroke. Indexing returned a monoclinic structure with spacegroup P21 or P21/m which is consistent with the lowest enthalpy solution predicted from density functional theory (DFT) calculations performed in this study, namely, P21. Attempts to reproduce the first anomalous mixture of phases with other samples were not successful; rather, the numerically preferred monoclinic structure manifests itself after a liquid-solid transition around 4.3-3.6 GPa, a value consistent with the results of a previous study. The transition typically occurs more readily on the downstroke. Incomplete Debye rings resulting from preferred orientation complicates any Rietveld refinement, though the DFT simulations predict 2 molecules per unit cell. Unit cell volumes during the upstroke were within 3% of that predicted by DFT calculations. This departure increases to about 9% below that from DFT predictions during the downstroke suggesting the presence of residual stresses due to non-hydrostatic conditions. Finally, a sudden relief in d-spacing values around 3.7-4.3 GPa during the downstroke is thought to be due to the influence of intermolecular hydrogen bonding.

Machine learning systems open up access to large volumes of valuable information lying dormant in unstructured documents

Like many other types of data in the energy industry, well data stored electronically can be divided into two categories: (1) data stored in relational or object databases that are highly structured and (2) data located in documents in various formats (TIFF, JPG, PDF, XLS, etc.) that are typically gathered in folders in a semistructured or unstructured form. Typically, these data break down into 20% structured data versus 80% semi- or unstructured data; this figure is in line with what is observed for other types of data across the industry. This situation affects the ability to make informed decisions since geoscientific software and risk-assessment analytic systems only operate on structured data. Current practices to extract data and metadata from unstructured documents involve a mainly manual and costly process. Data model limitations of the most prevalent databases are a further hindrance to the capture of unstructured data. We discuss a feasibility study to access the 11,500 well headers and 450,000 documents from the United Kingdom Continental Shelf (UKCS) that were released by Common Data Access Limited (CDAL — a wholly owned subsidiary of Oil and Gas UK, funded by 55 operators to share subsurface E&amp;P data) as part of its 2016 Unstructured Data Challenge initiative. A cost-effective solution based on emerging machine learning technology “taught” and guided by data-management experts can support the reliable indexing and cataloging of these forms of data, paving the way for much more reliable E&amp;P business decisions in the future.

A Digital Ecosystem-based Framework for Math Search Systems

Text-based search engines fall short in retrieving structured information. When searching for x(y+z) using those search engines, for example Google, it retrieves documents that contain xyz, x+y=z, (x+y+z) =xyz or any other document that contain x, y, and/or z but not x(y+z) as a standalone math expression. The reason behind this shortage; is that the text-based search engines ignore the structure of the mathematical expressions. Several issues are associated with designing and implementing math-based search systems. Those systems must be able to differentiate between a user query that contains a mathematical expression, and any other query that contains only a text term. A reliable indexing approach, along with a flexible and efficient representation technique are highly required. Eventually, text-based search systems must be able to process mathematical expressions that are well-structured and have properties that make them different from other forms of text. Here, in this context we take advantage from the concept of digital ecosystems to refine the text search process so it becomes applicable in searching for a mathematical expression. In this research, a framework that contains the basic building blocks of a math-based search system is designed.

An innovative approach of Drinking Water Quality Index—A case study from Southern Tamil Nadu, India

Abstract The aim of the present study is to exhaust the limitations of conventional reputed Water Quality Index (WQI) methodologies through the proposed reliable Drinking Water Quality Indexing (DWQI) system. Slight modifications were carried out in the methodology of the DWQI development which were parameter categorization, development of sub-index with regression statistics and aggregation function with Min–Max operator. Twenty-two water quality parameters were selected for quality evaluation. The proposed DWQI was compared with conventional methodologies of arithmetic DWQI and geometric DWQI and evaluated with a case study. A data set of 24 ground water samples collected from Southern Tamil Nadu, India was made to illustrate the application and feasibility of the index. The results of the case study revealed that arithmetic DWQI scores were higher than the proposed and geometric DWQI while geometric DWQI scores were lower than the proposed DWQI scores. All the indices were well correlated ( r ≥ +0.98) with each other. The arithmetic DWQI exhibited significant difference ( P P = 0.40) with geometric DWQI scores. The comparison of different forms of indices showed that the proposed DWQI was the most reliable indexing system than the others which diminished the sensitivity and eclipsing problems of conventional indexing system.

A novel hybridization approach for detection of citrus viroids

Citrus plants are natural hosts of several viroid species all belonging to the family Pospiviroidae. Previous attempts to detect viroids from field-grown species and cultivars yielded erratic results unless analyses were performed using Etrog citron a secondary bio-amplification host. To overcome the use of Etrog citron a number of RT-PCR approaches have been proposed with different degrees of success. Here we report the suitability of an easy to handle northern hybridization protocol for viroid detection of samples collected from field-grown citrus species and cultivars. The protocol involves: (i) Nucleic acid preparations from bark tissue samples collected from field-grown trees regardless of the growing season and storage conditions; (ii) Separation in 5% PAGE or 1% agarose, blotting to membrane and fixing; (iii) Hybridization with viroid-specific DIG-labelled probes and detection with anti-DIG-alkaline phosphatase conjugate and autoradiography with the CSPD substrate. The method has been tested with viroid-infected trees of sweet orange, lemon, mandarin, grapefruit, sour orange, Swingle citrumello, Tahiti lime and Mexican lime. This novel hybridization approach is extremely sensitive, easy to handle and shortens the time needed for reliable viroid indexing tests. The suitability of PCR generated DIG-labelled probes and the sensitivity achieved when the samples are separated and blotted from non-denaturing gels are discussed.

Multisensor Input for CPG-Based Sensory---Motor Coordination

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> This paper describes a method for providing in real time a reliable synchronization signal for cyclical motions such as steady-state walking. The approach consists in estimating online a phase variable on the basis of several implicit central pattern generator associated with a set of sensors. These sensors can be of any kind, provided their output strongly reflects the timed motion of a link. They can be, for example, spatial position or orientation sensors, or foot sole pressure sensors. The principle of the method is to use their outputs as inputs to nonlinear observers of modified Van der Pol oscillators that provide us with several independent estimations of the overall phase of the system. These estimations are then combined within a dynamical filter constituted of a Hopf oscillator. The resulting phase is a reliable indexing of the cyclic behavior of the system, which can finally be used as input to low-level controllers of a robot. Some results illustrate the efficiency of the approach, which can be used to control robots. </para>

Phasic Emotional Reactions to Video Game Events: A Psychophysiological Investigation

We examined emotional valence- and arousal-related phasic psychophysiological responses to different video game events among 36 young adults who played Super Monkey Ball 2 (Sega Corporation, Tokyo, Japan). Event-related changes in zygomaticus major, corrugator supercilii, and orbicularis oculi electromyographic activity, skin conductance level, and cardiac interbeat intervals were recorded. Instantaneous game events elicited reliable psychophysiological responses indexing valence and arousal. A largely linear, positive dose-response relationship between rewards obtained in the game and phasic increases in arousal was revealed. The valence of the emotional response varied as a function of the player's active participation (active coping). In addition, not only positive events, but also some putatively negative events elicited positively valenced arousal. The findings extend our understanding of the phasic changes in the emotional state during video games and a dynamic flow of events and action and may have several applied implications (e.g., for game design).

Evaluation of reverse transcription – polymerase chain reaction assays for detecting Apple chlorotic leaf spot virus in certification and quarantine programs

Apple chlorotic leaf spot virus (ACLSV), the type member of the genus Trichovirus, family Flexiviridae, occurs worldwide in a wide range of rosaceous hosts and is an important pathogen targeted in certification and quarantine programs. Detection of ACLSV in infected hosts has been based on inoculation by grafting (indexing), enzyme-linked immunosorbent assays (ELISA), and more recently, on reverse transcription – polymerase chain reaction (RT–PCR). The latter has the potential to become a reliable molecular indexing tool. However, its suitability for routine diagnosis needs to be verified for a broad spectrum of virus isolates in a wide range of hosts. In this study, oligonucleotide primers targeting the coat-protein (CP) region and the replicase region of ACLSV were compared in RT–PCR assays and applied to detection of virus isolates from natural hosts originating from domestic and imported sources of plant germplasm. Primers CLS6860 and CLS7536 amplifying a 676-bp (base pair) fragment in the CP-coding region of ACLSV were the most reliable, detecting all isolates, in all tested hosts. The forward/reverse primer pair 4F/4R, which amplifies a 390-bp fragment in the replicase-coding region, detected most of the tested ACLSV isolates, but generated some nonspecific amplifications with several other fruit-tree viruses in the family Flexiviridae. Alignment of the associated nucleotide sequences of the respective viruses indicated high levels of identity with the targeted replicase region, supporting the use of this region for broad-spectrum detection of distantly related viruses. However, this region is less useful for accurate virus identification. Comparison of the RT–PCR data with the ELISA and indexing results highlighted the advantages of a reliable molecular assay for problematic hosts.

Query by image and video content: a colored-based stochastic model approach

For efficient image retrieval, the image database should be processed to extract a representing feature vector for each member image in the database. A reliable and robust statistical image indexing technique based on a stochastic model of an image color content has been developed. Based on the developed stochastic model, a compact 12-dimensional feature vector was defined to tag images in the database system. The entries of the defined feature vector are the mean, variance, and skewness of the image color histogram distributions as well as correlation factors between color components of the RGB color space. It was shown using statistical analysis that the feature vector provides sufficient knowledge about the histogram distribution. The reliability and robustness of the proposed technique against common intensity artifacts and noise was validated through several experiments conducted for that purpose. The proposed technique outperforms traditional and other histogram based techniques in terms of feature vector size and properties, as well as performance.