Miracle-dz/UniFAR-FTUs huggingface dataset:
Generating an Ontology from Scientific Works: Initial Results

Attempts to produce adequate and long-lived subject indexes of information systems and computer science research have failed. In this paper we report preliminary results of an approach by which the terms expressed in research literature, such as that in information systems, can be systematically and meaningfully categorised. The approach is based on Roman Ingarden’s ontological theory of the written scholarly work: its nature, existence, and categorisation, and builds on Grounded Theory: a rigorous grounded qualitative research method addressing how meaningful categories can be analysed from text and related to each other. We have found that the key guiding unit of analysis operationalising Ingarden’s approach through Grounded Theory is the “reported research activity” and that the process is possible although labour intensive. On the basis of using the approach, we propose simple steps to improve the quality of keywords in reported research.
A Survey: Content Based Image Retrieval Based On Color, Texture, Shape & Neuro Fuzzy

In current technology the acquisition, transmission, storing, and manipulation are allowed on the large collections of images. With the increase in popularity of the network and development of multimedia technologies, users are not satisfied with the traditional information retrieval techniques. So nowadays, the content based image retrieval is becoming a source of exact and fast retrieval. Content Based Image Retrieval (CBIR) is a technique which uses visual features of image such as color, shape, texture, etc. to search user required image from large image database according to user's requests in the form of a query image. Images are retrieved on the basis of similarity in features where features of the query specification are compared with features from the image database to determine which images match similarly with given features. Feature extraction is a crucial part for any of such retrieval systems. So far, the only way of searching these collections was based on keyword indexing, or simply by browsing. Literature survey is most important for understanding and gaining much more knowledge about specific area of a subject. In this paper we survey some technical aspects of current content-based image retrieval systems and described the image segmentation in image processing and the features like neuro fuzzy technique, color histogram, texture, and shape for accurate and effective Content Based Image Retrieval System after doing the deep study of related works.
Does fisheries management incentivize non-compliance? Estimated misreporting in the Swedish Baltic Sea pelagic fishery based on commercial fishing effort

Swedish Universityof Agricultural Sciences, Department of Aquatic Resources, Institute of Marine Research, Turistgatan 5, 453 30 Lysekil, Sweden*Corresponding author: Tel: +46 739 38 79 69; Fax: +46 8 15 95 22; Email: jonas.sundberg@stockholmresilience.su. seHentati-Sundberg, J., Hjelm, J., and O¨sterblom, H. Does ﬁsheries management incentivize non-compliance? Estimatedmisreporting in the Swedish Baltic Sea pelagic ﬁshery based on commercial ﬁshing effort. – ICES Journal of Marine Science,doi: 10.1093/icesjms/fsu036.Received10 December 2013; accepted 13 February 2014. Fisheriesmanagementagenciesandﬁshingindustryrepresentativesdependonreliableestimatesofﬁshbiomassandmortalityforthedetermin-ationofsustainablecatchlevels.Lackofdataormisreportingmaybereasonsforunreliablestockassessment,which,inturn,mayresultinadvicethatdoesnot reﬂect theavailabilityofﬁsheriesresources. IthasbeensuggestedthatthemixedpelagictrawlﬁsheriesintheBaltic representacaseofbiased estimatesof ﬁsh biomass and mortality resulting frommisreporting. Here,we estimate the degree of misreporting in the Swedish pelagicﬁshery (1996–2009) and propose an approach for reconstructing historical catchesbased on commercial effort data.The analysissuggeststhattotal catches have been underestimated during part of our study period and that systematic misreporting of species composition has takenplace over the whole study period. The analysis also suggests that there is overcapacity in the ﬁshery and that such economic incentive couldexplainthegeneralpatternsofmisreporting. Applyingourmethodforﬁsherieswithsuspectedmisreportingcouldsigniﬁcantlyimproveassessmentaccuracy, reduceuncertaintyand therebyallowfora better link between catches and resource levels.Keywords: Baltic Sea, herring, incentives, misreporting, non-compliance, reconstruction,sprat,stock assessment.
How do stakeholder groups vary in a Delphi technique about primary mental health care and what factors influence their ratings?

Background: While mental health is a core part of primary care, there are few validated quality measures and little relevant internationally published research. Consensus panel methods are a useful means of developing quality measures where evidence is sparse and/or opinions are diverse. However, little is known about the dynamics of consensus techniques and the factors that influence the judgements and ratings of panels and individual panellists. Objectives: (1) To describe differences in panel ratings on the quality of primary mental health care services by patient, carer, professional and managerial panels within a Delphi procedure; and (2) to explore why different panels and panellists rate quality indicators of primary mental health care differently. Design: Two round postal Delphi technique and exploratory semi-structured interviews. Participants: 115 panellists across 11 panels. Eleven panellists were subsequently interviewed. Results: 87 of 334 indicators (26%) were rated face valid by all 11 panels. There was little disagreement within panel ratings but significant differences between panels. The GP panel rated the least number of indicators valid (n = 138, 41%) and carers the most (n = 304, 91%). The way in which panellists interpreted and conceptualised the indicators and their definition of quality of mental health care affected the way in which participants made their ratings. Conclusions: Stakeholders in primary mental health care have diverse views of quality of care and these differences translate into how they rate quality indicators. Exploratory interviews suggest that ratings are influenced by past experience, expectations, definitions of quality of care, and perceived power relationships between stakeholders.
A PTAS for the multiple knapsack problem

The Multiple Knapsack problem (MKP) is a natural and well known generalization of the single knapsack problem and is defined as follows. We are given a set of items and bins (knapsacks) such that each item has a profit and a size , and each bin has a capacity . The goal is to find a subset of items of maximum profit such that they have a feasible packing in the bins. MKP is a special case of the Generalized Assignment problem (GAP) where the profit and the size of an item can vary based on the specific bin that it is assigned to. GAP is APX-hard and a -approximation for it is implicit in the work of Shmoys and Tardos [26], and thus far, this was also the best known approximation for MKP. The main result of this paper is a polynomial time approximation scheme for MKP. Apart from its inherent theoretical interest as a common generalization of the well-studied knapsack and bin packing problems, it appears to be the strongest special case of GAP that is not APX-hard. We substantiate this by showing that slight generalizations of MKP are APX-hard. Thus our results help demarcate the boundary at which instances of GAP become APX-hard. An interesting aspect of our approach is a ptas-preserving reduction from an arbitrary instance of MKP to an instance with distinct sizes and profits.
Exploiting program-level masking and error propagation for constrained reliability optimization

Since embedded systems design involves stringent design constraints, designing a system for reliability requires optimization under tolerable overhead constraints. This paper presents a novel reliability-driven compilation scheme for software program reliability optimization under tolerable overhead constraints. Our scheme exploits program-level error masking and propagation properties to perform reliability-driven prioritization of instructions and selective protection during compilation. To enable this, we develop statistical models for estimating error masking and propagation probabilities. Our scheme provides significant improvement in reliability efficiency (avg. 30%-60%) compared to state-of-the-art program-level protection schemes.
Improving the performance of a Dutch CSR by modeling pronunciation variation

This paper describes how the performance of a continuous speech recognizer for Dutch has been improved by modeling pronunciation variation. We used three methods in order to model pronunciation variation. First, withinword variation was dealt with. Phonological rules were applied to the words in the lexicon, thus automatically generating pronunciation variants. Secondly, cross-word pronunciation variation was accounted for by adding multi2. METHOD AND MATERIAL words and their variants to the lexicon. Thirdly, probabilities of pronunciation variants were incorporated in the language model (LM), and thresholds were used to choose which pronunciation variants to add to the LMs. For each of the methods, recognition experiments were carried out. A significant improvement in error rates was measured.
The Anti-Inflammatory Effects of Ulinastatin in Trauma Patients with Hemorrhagic Shock

We investigated the use of ulinastatin in association with the suppression of polymorphonuclear leukocyte elastase (PMNE), tumor necrosis factor alpha (TNF-α) and interleukin 6 (IL-6), and its effects on the prognosis of patients with traumatic hemorrhagic shock. Nineteen patients who visited the emergency department for traumatic hemorrhagic shock were enrolled. Eleven patients were randomly selected to receive a total of 300,000 IU of ulinastatin. Measurements of serum PMNE, TNF-α and IL-6 were taken before ulinastatin treatment at 24 hr, two days, three days, and seven days after admission. We compared the Systemic Inflammatory Response Syndrome scores, Multiple Organ Dysfunction Syndrome scores and Acute Physiology, age, Chronic Health Evaluation III scores of the control and ulinastatin groups. There were no significant differences in baseline values, laboratory data, treatment or mortality between the two groups. The serum PMNE levels in the ulinastatin group were lower than in the control group on the second hospitalized day. Serum TNF-α and IL-6 levels in the ulinastatin group decreased 24 hr after admission but had no significance. It is suggested that ulinastatin treatment could decrease the serum PMNE levels in trauma patients with hemorrhagic shock at 48 hr after treatment.
Analysis of and observations from a Bangla News Corpus

In this paper we present the compilation methodology and some statistical analysis on a Bangla news corpus“Prothom-Alo”, which is the first of its kind for Bangla. We compare some of the statistics with the CIIL Bangla corpus and also present our observation of atypical behavior of Zipf’s curve for Prothom-Alo corpus.
Analytic network process applied to R&D project selection

Research and development (R&D) project selection is a complex decision-making process. Risks and uncertainties are also associated with the investments and returns of R&D projects. This research paper illustrates an application of ANP (analytic network process). The analytic network process (ANP) is presented as a potentially valuable method to support the selection of projects in a research and development (R&D) environment. The AHP (Analytic Hierarchy Process) is employed to break down large unstructured decision problems into manageable and measurable components. The ANP, as the general form of AHP, is powerful to deal with complex decisions where interdependence exists in a decision model. The research paper discusses the use of the ANP, a general form of Saaty's analytic Network process, as a model to evaluate the value of competing R&D projects.
Multidimensional supernova simulations with approximative neutrino transport. I. Neutron star kicks and the anisotropy of neutrino-driven explosions in two spatial dimensions

We study hydrodynamic instabilities during the first seconds of core-collapse supernovae by means of 2D simulations with approximative neutrino transport and boundary conditions that parameterize the effects of the contracting neutron star and allow us to obtain sufficiently strong neutrino heating and, hence, neutrino-driven explosions. Confirming more idealised studies, as well as supernova simulations with spectral transport, we find that random seed perturbations can grow by hydrodynamic instabilities to a globally asymmetric mass distribution in the region between the nascent neutron star and the accretion shock, leading to a dominance of dipole ($l=1$) and quadrupole ($l=2$) modes in the explosion ejecta, provided the onset of the supernova explosion is sufficiently slower than the growth time scale of the low-mode instability. By gravitational and hydrodynamic forces, the anisotropic mass distribution causes an acceleration of the nascent neutron star, which lasts for several seconds and can propel the neutron star to velocities of more than 1000 km s -1 . Because the explosion anisotropies develop chaotically and change by small differences in the fluid flow, the magnitude of the kick varies stochastically. No systematic dependence of the average neutron star velocity on the explosion energy or the properties of the considered progenitors is found. Instead, the anisotropy of the mass ejection, and hence of the kick, seems to increase when the nascent neutron star contracts more quickly, and thus low-mode instabilities can grow more rapidly. Our more than 70 models separate into two groups, one with high and the other with low neutron star velocities and accelerations after one second of post-bounce evolution, depending on whether the $l=1$ mode is dominant in the ejecta or not. This leads to a bimodality of the distribution when the neutron star velocities are extrapolated to their terminal values. Establishing a link to the measured distribution of pulsar velocities, however, requires a much larger set of calculations and ultimately 3D modelling.
Seroconversion rate to positivity for antibodies against core antigen of hepatitis B virus and duration of renal replacement therapy.

BACKGROUND Prevalence of total antibodies against core antigen (anti-HBc) of hepatitis B virus (HBV) is greater in longer dialysed patients, but there are no data indicating a relationship between the higher seroconversion rate to anti-HBc positivity and longer renal replacement therapy (RRT) vintage prior to seroconversion. Our aim was to evaluate the association between RRT duration and seroconversion to anti-HBc positivity. METHODS An incidence of anti-HBc was evaluated in 425 anti-HBc-negative intermittent haemodialysis (IHD) patients: Group I included patients who underwent first anti-HBc testing 31 days from first IHD session, and Group II or III included patients with RRT duration < 3 or ≥ 3 years, respectively. Anti-HBc testing was repeated every 6-12 months. Sex, age, RRT duration, anti-HCV, HCV RNA, ALT, ASP, GGT, full vaccination series against HBV with developed anti-HBs titre > 10 IU/L, hepatitis history and underlying kidney diseases were used as independent variables predicting seroconversion to anti-HBc positivity. RESULTS Seroconversion rate to anti-HBc positivity was 2.59, 2.12 and 2.44 episodes/100 patient-years in Group I (n = 174), II (n = 170) and III (n = 80), respectively. In the entire group, there were 15 seroconversions to anti-HBc and one seroconversion to HBsAg positivity. The only variable predicting seroconversion in all HBsAg-negative patients (n = 424) was the lack of full vaccination series against HBV with developed anti-HBs titre > 10 IU/L maintained during the study (β - 0.112, P = 0.04). CONCLUSIONS Seroconversion rate to anti-HBc positivity is not related to duration of RRT treatment but to ineffective vaccination against HBV.
Knowledge Dilemmas within Organizations: Resolutions from Game Theory

The knowledge management literature suggests that an organization’s knowledge ecosystem is comprised of strategic situations in which the individual behaviors of its knowledge workers show potential conflicts with what would be optimal for the organization. This paper aims to explore how such behaviors in terms of knowledge flows may be modeled and analyzed using a game theoretic approach. While prior research has investigated some use of game theory in knowledge management, a comprehensive understanding of the organizational eco-system remains unexplored. Hence, a qualitative inductive approach was adopted in order to pursue the exploratory nature of the research question. Critical reviews of key literature in both knowledge management and organization theory identified four organizational knowledge dilemmas – ‘‘silos of knowledge’’, ‘‘tragedy of the knowledge commons’’, ‘‘knowledge friction’’ and ‘‘knowledge toxicity’’. These dilemmas were used in various combinations to generate five commonly occurring scenarios in organizations. A game theoretic analysis of these scenarios using the PARTS framework provided a useful understanding of knowledge flows within organizational eco-systems. More specifically, the analysis led to key insights and prescriptive guiding principles in formulating knowledge strategies and policies to combat the major knowledge dilemmas that inhibit effective knowledge flows within organizations.
Effect of organic fertilizers on soil improvement in Mab Bon , Tha Yang , Satuk and Renu series for corn cultivation in Thailand

The effect of serveral kinds of organic fertilizers on soil improvement was studied in 4 soil series, all of them were silt loam soil and low fertility namely Mab Bon, Tha Yang, Satuk and Renu series for super sweet corn cultivation. Sites of the research were conducted at Rayong , Ratchaburi and Chiang Mai Land Development Station and Khao Suan Kwang demonstration farm, Khon Kean province respectively. Randomized complete block design was used in this experiment, and totally 6 treatments with 3 replications which consisted of no addition of fertilizer , application of compost , animal manure and chemical fertilizer, incorporation of green manure and rice straw on change of soil microbiological and chemical properties, corn yield and quality. The result showed that application of several organic fertilizer effected on increasing total bacteria, actinomycetes and fungi higher than control and chemical fertilizer treatments. Application of compost, animal manure and rice straw incorporation could raise soil organic matter content in Mab Bon, Tha Yang, Satuk and Renu series when compared to chemical fertilizer application from 0.50 to 0.86-1.02%, from 1.95 to 2.8-3.62%, from 0.42 to 0.65-0.71% and from 0.74% to 0.91-1.05% respectively. Most organic fertilizer application increased phosphorus content except incorporation of green manure in Tha Yang and Satuk series due to low biomass of Sesbania rostrata. Furthermore, all of four soil series, potassium content of organic fertilizer addition was increased greater than chemical fertilizer addition. The growth of super sweet corn in Mab Bon series was more responsive on organic fertilizer than the others led to high increment of yield from 733 to 3,300-7,111 kg/ha especially incorporation of green manure. Tha Yang and Satuk series, incorporation of rice straw increased super sweet corn yield to 3,592 and 2,134 kg/ha respectively, greater than other organic fertilizers, but corn yield in Renu series was increased in the compost-amended soil. Corn yield of addition chemical fertilizer was higher than unamended soil, but was lower than organic fertilizer application. The sweetness of corn mostly increased when applied with several organic fertilizers all four soil series compare to unamended soil and chemical fertilizer treatment, but there was no great difference among each organic fertilizer application.
Using Attentive Focus to Discover Action Ontologies from Perception

The word “symbol”, as it is used in logic and computational theory, is considerably different from its usage in cognitive linguistics and in everyday life. Formal approaches that define symbols in terms of other symbols ultimately need to be grounded in perceptualmotor terms. Based on cognitive evidence that the earliest action structures may be learned from perception alone, we propose to use attentive focus to identify the agents participating in an action, map the characteristics of their interaction, and ultimately discover actions as clusters in perceptuo-temporal space. We demonstrate its applicability by learning actions from simple 2D image sequences, and then demonstrate the learned predicate by recognizing 3D actions. This mapping, which also identifies the objects involved in the interaction, informs us on the argument structure of the verb, and may help guide syntax. Ontologies in such systems are learned as different granularities in the clustering space; action hierarchies emerge as membership relations between actions.
Explicit feature mapping via multi-layer perceptron and its application to Mine-Like Objects detection

In this paper, a novel learning method is introduced that borrows simultaneously from the principles of kernel methods and multi-layer perceptron. Specifically, the method implements the feature mapping idea of kernel methods into a multi-layer perceptron. Unlike in kernel learning where the feature space is usually invisible and inaccessible, the multilayer perceptron based mapping is explicit. Therefore, the proposed model can be learned directly in feature space. Together with the inherent sparse representation, the proposed approach will thus be much faster and easier to train even in the event of a large network size. The proposed approach is applied in the context of an Autonomous Underwater Vehicle Mine-Like Objects detection task. The results show that the proposed approach is able to improve upon the generalization performance of neural network based methods. Its prediction results are also close to or better than those obtained by kernel machines. Its learning and classification speed is shown to far surpass those of kernel machines. These results are confirmed on a number of experiments involving benchmarking UCI domains.
Visual Cryptography for Black and White Images

A visual cryptography scheme is a method to encode a secret image SI into shadowimages called shares such that, certain qualified subsets of shares enable the “visual"recovery of the secret image. The “visual" recovery consists of xeroxing the shares ontotransparencies, and then stacking them. The shares of a qualified set will reveal the secretimage without any cryptographic computation. Here, we analyze the construction of k outof n visual cryptography schemes for black and white images(In such a scheme any kshares out of n will reveal the secret image, but any k -1 shares give no information aboutthe image). The important parameters of a scheme are its contrast, i.e., the clarity of thedecoded image, and the number of pixels needed to encode one pixel of the originalimage. We discuss some methods of construction of (2, n)-schemes having optimalrelative contrast using Hadamard matrices and some combinatorial block designs. Westudy the construction of an efficient (3, n)-scheme. We also study the construction of (3,n)-schemes using 3 designs and (t, n)-schemes using t-designs.Some constructions for the general (k, k) and (k, n) schemes are also discussed.