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Proton Induced X-ray Emission (PIXE) and Energy Dispersive X-ray Fluorescence (EDXRF) techniques have been used for elemental characterization of offset printing ink tagged with rare-earth taggants. The offset printing ink was tagged with rare-earth (La, Pr, Nd, Sm, Eu and Gd) thenoyltrifluoroacetonate chelates at about 1000-ppm level for each element separately. Small aliquots (approximately 20 mg) of tagged inks were coated on paper supports in the form of small circles having diameter 10–15 mm each and then analyzed. In the case of PIXE, a proton beam of energy 4 MeV and in the case of EDXRF a radioisotope source of ²⁴¹Am (100 mCi) was used to excite the samples. The PIXE analysis showed well-resolved rare-earth L X-rays and EDXRF analysis showed the K X-rays of rare earths. Satisfactory results to identify and quantify the taggants were achieved.

This paper presents extensions to a previously developed logic form representation and a method for transforming WordNet glosses into logic forms using a set of high-precision rules combined with a set of high recall heuristics. An almost 3% increase in POS tagging accuracy is achieved over state-of-the art results at the expense of user intervention on only 7.52% of words. We apply a nearest neighbor solution of parser switching that leads to 6.43% increase in exact sentence accuracy for glosses. Logic Forms are derived with an accuracy of 89.46%.

In recent times, bacterial Antimicrobial Resistance (AMR) analyses becomes a hot study topic. The AMR comprises information related to the antibiotic product name, class name, subclass name, type, subtype, gene type, etc., which can fight against the illness. However, the tagging language used to determine the data is of free context. These contexts often contain ambiguous data, which leads to a hugely challenging issue in retrieving, organizing, merging, and finding the relevant data. Manually reading this text and labelling is not time-consuming. Hence, topic modeling overcomes these challenges and provides efficient results in categorizing the topic and in determining the data. In this view, this research work designs an ensemble of artificial intelligence for categorizing the AMR gene data and determine the relationship between the antibiotics. The proposed model includes a weighted voting based ensemble model by the incorporation of Latent Dirichlet Allocation (LDA) and Hierarchical Recurrent Neural Networks (HRNN), shows the novelty of the work. It is used for determining the amount of “topics” that cluster utilizing a multidimensional scaling approach. In addition, the proposed model involves the data pre-processing stage to get rid of stop words, punctuations, lower casing, etc. Moreover, an explanatory data analysis uses word cloud which assures the proper functionality and to proceed with the model training process. Besides, three approaches namely perplexity, Harmonic mean, and Random initialization of K are employed to determine the number of topics. For experimental validation, an openly accessible Bacterial AMR reference gene database is employed. The experimental results reported that the perplexity provided the optimal number of topics from the AMR gene data of more than 6500 samples. Therefore, the proposed model helps to find the appropriate antibiotic for bacterial and viral spread and discover how to increase the proper antibiotic in human bodies

Through interviews with representatives from GE Healthcare, we determined that a central problem of their current knowledge management system is users' inability to effectively retrieve the documents they need in order to carry out the everyday business activities of the corporation. We propose a three-pronged approach to resolving the problem, from a user-, data-, and system-centred perspective. From the users' perspective, we conducted user-based usability studies to confirm and identify the key problem. For our data-centred approach, we developed a new Dublin Core-compatible metadata schema that has the following four key characteristics: compatibility with existing practices, specificity to the corporation's needs, simplicity to facilitate easy adoption and flexibility to evolve as the corporation grows and changes. From a system-centred perspective, we developed a new search interface for GE Healthcare that incorporates our proposed metadata schema to improve the effectiveness of the information search.

A beam channel of polarized protons and antiprotons produced from decays of $\mathrm{\Lambda }$- and anti-$\mathrm{\Lambda }$-hyperons for the SPASCHARM experiment is to be built at IHEP U-70 accelerator in Protvino, Russia. The methods for tagging and measuring polarization of the beam (anti)protons are discussed in this report. The fast on-line beam tagging exploits the correlations between polarization and kinematics of (anti)protons originated from (anti)$\mathrm{\Lambda }$-decays. In the intermediate focus of primary target, decay (anti)protons of different transverse polarizations are spatially dispersed transversely with respect to the beam axis. The tagging system, consisting of fast beam detectors with good spatial resolution, measures the momentum and trajectory of each beam particle, including its position at the intermediate focus, thus allowing instant (on-line) assignment of the transverse polarization value to each (anti)proton. This system is also extremely useful for the beam channel tuning. While being fast and convenient, the polarization tagging fully relies on computing of particle transportation in the beam channel. In order to verify the real beam polarization and operating of the tagging system and beam channel, the independent absolute beam polarimetry is to be used. It is based on measuring the spin asymmetries in elastic scattering of beam (anti)protons in Coulomb-Nuclear Interference (CNI) and diffractive kinematic regions. It is estimated that less than one week of data taking would allow measuring an absolute beam polarization at the statistical accuracy of $\sim$4–5%.

The greatest use of the Internet and new online technologies today is for constructive purposes. However, the use of the same technologies to spread illegal and objectionable content has been increasing dramatically during the last years. Internet users have begun to protect themselves and their wards by using so-called web content filters, which allow access to legitimate content and block access to objectionable and unwanted content. In this paper, we describe the design and anatomy of an open architecture for creating a social filtering service and show how it can be implemented. The system is designed to overcome the deficiencies of today's content rating and filtering systems and frameworks by empowering and involving end users, and to actively support user collaboration and cooperation by using techniques and methodologies with a low cost of participation combined with ease of use. The proposed work improves the quality of information shared by existing collaborative filtering systems by integrating tagging and folksonomy techniques, which we have extended in such a way that relations between document and rating metadata can be better expressed with only a minimal additional amount of required information. Data structures and workflows are optimized for scalability and fast service response times and allow the efficient computation and processing of even very large volumes of data. We show how such a service can be used by designing and implementing two client applications for filtering pornographic web content.

There are millions of digital images available and their use has been increasing. Image users are very diverse, from professional users to users searching for fun. An image can be seen and described in a different way by different users. What the user sees depends on his perception and cognitive processes. The purpose of this paper is to examine how users describe images. Students of the University of Ljubljana are surveyed on a random sample of images taken from Digital Library of Slovenia and Flickr in order to assign tags to each image. The tags are grouped into categories of image attributes such as objects, place, time, etc. and compared to tags assigned by Slovene library professionals and Flickr visitors. Finally, the obtained data are investigated to ascertain whether differences in tagging practices exist among students, library professionals and Flickr users.