Miracle-dz/UniFAR-FTUs huggingface dataset:
LSA-Based Multi-Document Summarization

Text Summarization is a research area that attracts many research groups around the world. Its aim is to take a source text and present the most important content in a condensed form in a manner sensitive to the needs of the further task. Being able to produce summaries that would be close to human abstracts would affect many tasks in information retrieval. We developed a single-document summarization method [1] that is based on Latent Semantic Analysis (LSA [2]). The analysis captures main topics of a document. The sentences that contain the most important topics are selected for the summary. Further, we enriched the method by anaphoric information [3]. In the last years, the summarization field has turned to a more compex task multi-document summarization. It assumes that we have a set of documents related to a specific event/topic. The task is to create a short document that would summarize the event/topic. Moreover, the summary can be influenced by a user query. When turning to multi-document summarization some new problems arise. For example, we do not want in the summary very similar sentences from different documents. Our solutions of these problems are discussed in the paper. The structure of the paper is as follows. In Section II, some background information is presented. Then, the single-document LSA-based method is described and its extension to multi-dcoument summarization is proposed. In section IV, we firstly describe data and evaluation process and then the own results are discussed. In the end we conclude all.
Traditional markers of excessive alcohol use.

AIMS The blood tests used traditionally as markers of excessive drinking are the liver enzymes, gamma glutamyltransferase (GGT), aspartate aminotransferase (AST) and alanine aminotransferase (ALT), and the red blood cell volume (mean corpuscular volume: MCV). Here we review the nature of these markers' association with alcohol use, their practical application in detecting, assessing or monitoring drinking and increases in understanding of these markers in the past 10 years. DESIGN Articles were identified via Medline search and perusal of bibliographies. FINDINGS The conventional markers have imperfect sensitivity and specificity, but have an added clinical role in the detection of complications of drinking, and of comorbid conditions that may increase risk of drinking. GGT may in part be a marker of the oxidative stress associated with ethanol metabolism. Markers are more likely to be elevated in those aged more than 30 years and in regular drinkers with a longer drinking history. The markers may be useful in opportunistic case finding, in motivating patients to change drinking and in monitoring treatment response. Increased prevalence of obesity and hepatitis C must be considered in interpretation of liver enzyme results. The liver enzymes are prognostic indicators for several medical conditions and for mortality. CONCLUSIONS These conventional tests are widely available and relatively inexpensive. While having limited sensitivity and specificity in detection of excessive drinking, they also provide valuable data on complications of drinking, comorbid conditions that may be affected by drinking and, in some cases, prognosis.
Imagen corporal y riesgo de trastornos de la conducta alimentaria en población universitaria española: diferencias de género

This research analyzes body image and risk of eating disorders among Spanish university students (n=563), thorough the application of two questionnaires: EDI-3-RF and IMAGEN. Results showed a high proportion of students who are at risk of suffering one of these disorders. Furthermore, data showed significant scores in the scales related with drive for thinness, bulimic traits or body dissatisfaction, all of them clear risk factors associated to the development of these pathologies. Women showed higher levels in almost every variable analyzed, but differences between age groups were not significant. Results point out the need of implementing intervention and prevention strategies in this population.
Weighted Vote-Based Classifier Ensemble for Named Entity Recognition: A Genetic Algorithm-Based Approach

In this article, we report the search capability of Genetic Algorithm (GA) to construct a weighted vote-based classifier ensemble for Named Entity Recognition (NER). Our underlying assumption is that the reliability of predictions of each classifier differs among the various named entity (NE) classes. Thus, it is necessary to quantify the amount of voting of a particular classifier for a particular output class. Here, an attempt is made to determine the appropriate weights of voting for each class in each classifier using GA. The proposed technique is evaluated for four leading Indian languages, namely Bengali, Hindi, Telugu, and Oriya, which are all resource-poor in nature. Evaluation results yield the recall, precision and F-measure values of 92.08%, 92.22%, and 92.15%, respectively for Bengali; 96.07%, 88.63%, and 92.20%, respectively for Hindi; 78.82%, 91.26%, and 84.59%, respectively for Telugu; and 88.56%, 89.98%, and 89.26%, respectively for Oriya. Finally, we evaluate our proposed approach with the benchmark dataset of CoNLL-2003 shared task that yields the overall recall, precision, and F-measure values of 88.72%, 88.64%, and 88.68%, respectively. Results also show that the vote based classifier ensemble identified by the GA-based approach outperforms all the individual classifiers, three conventional baseline ensembles, and some other existing ensemble techniques. In a part of the article, we formulate the problem of feature selection in any classifier under the single objective optimization framework and show that our proposed classifier ensemble attains superior performance to it.
On reducing the artifacts of sonar images with image fusion technique

The work considers the artifacts compensation in the context of airborne ultrasonic image generation. Almost all airborne ultrasonic images used in robotics have artifacts and distortions, when comparing the real objects to those presented or identified in ultrasonic images. The differences between the real and reference images can have various causes, such as asymmetries in the directivity of the ultrasonic transducers, reflections in the explored environment, and nonlinearities of the signal processing blocks. A fusion-based method of reducing artifacts of sonar images is proposed and partially implemented. The object image from the ultrasonic image is fused with a reference image, selected from a database after an identification procedure. The selection rule used in image fusion is considered a supervised process. The results are encouraging and suggest a trade-off between complexity of fusion technique and quality of the result.
Using a semantic tagger as dictionary search tool

The USAS semantic tagger is a powerful language technology tool that has proven to be very effective in various applications such as content analysis, discourse analysis and information extraction. In the Benedict project, we intend to use the semantic taggers for the English and Finnish languages as search tools in electronic dictionaries, thereby enabling users to carry out context-sensitive dictionary searches. The aim of this paper is to envisage ways in which a semantic tagger can help users find the ”right” answer from a dictionary (i.e. the answer that the user needs). We begin with a brief introduction of the semantic taggers for the English and Finnish languages. Thereafter, we focus on the presentation of the context-sensitive dictionary look-up, and show how the dictionary software will be able (i) to determine the correct sense in the context at hand, and (ii) to highlight that sense for the user. The new search tool will be commercially available in the Benedict software.
Interconnections Between RNA-Processing Pathways Revealed by a Sequencing-Based Genetic Screen for Pre-mRNA Splicing Mutants in Fission Yeast

Pre-mRNA splicing is an essential component of eukaryotic gene expression and is highly conserved from unicellular yeasts to humans. Here, we present the development and implementation of a sequencing-based reverse genetic screen designed to identify nonessential genes that impact pre-mRNA splicing in the fission yeast Schizosaccharomyces pombe, an organism that shares many of the complex features of splicing in higher eukaryotes. Using a custom-designed barcoding scheme, we simultaneously queried ∼3000 mutant strains for their impact on the splicing efficiency of two endogenous pre-mRNAs. A total of 61 nonessential genes were identified whose deletions resulted in defects in pre-mRNA splicing; enriched among these were factors encoding known or predicted components of the spliceosome. Included among the candidates identified here are genes with well-characterized roles in other RNA-processing pathways, including heterochromatic silencing and 3ʹ end processing. Splicing-sensitive microarrays confirm broad splicing defects for many of these factors, revealing novel functional connections between these pathways.
Uncorrelated Multilinear Discriminant Analysis With Regularization and Aggregation for Tensor Object Recognition

This paper proposes an uncorrelated multilinear discriminant analysis (UMLDA) framework for the recognition of multidimensional objects, known as tensor objects. Uncorrelated features are desirable in recognition tasks since they contain minimum redundancy and ensure independence of features. The UMLDA aims to extract uncorrelated discriminative features directly from tensorial data through solving a tensor-to-vector projection. The solution consists of sequential iterative processes based on the alternating projection method, and an adaptive regularization procedure is incorporated to enhance the performance in the small sample size (SSS) scenario. A simple nearest-neighbor classifier is employed for classification. Furthermore, exploiting the complementary information from differently initialized and regularized UMLDA recognizers, an aggregation scheme is adopted to combine them at the matching score level, resulting in enhanced generalization performance while alleviating the regularization parameter selection problem. The UMLDA-based recognition algorithm is then empirically shown on face and gait recognition tasks to outperform four multilinear subspace solutions (MPCA, DATER, GTDA, TR1DA) and four linear subspace solutions (Bayesian, LDA, ULDA, R-JD-LDA).
Foraging energetics and diving behavior of lactating New Zealand sea lions, Phocarctos hookeri.

The New Zealand sea lion, Phocarctos hookeri, is the deepest- and longest-diving sea lion. We were interested in whether the diving ability of this animal was related to changes in its at-sea and diving metabolic rates. We measured the metabolic rate, water turnover and diving behavior of 12 lactating New Zealand sea lions at Sandy Bay, Enderby Island, Auckland Islands Group, New Zealand (50 degrees 30'S, 166 degrees 17'E), during January and February 1997 when their pups were between 1 and 2 months old. Metabolic rate (rate of CO(2) production) and water turnover were measured using the (18)O doubly-labeled water technique, and diving behavior was measured with time/depth recorders (TDRs). Mean total body water was 66.0+/-1.1 % (mean +/- s.d.) and mean rate of CO(2) production was 0. 835+/-0.114 ml g(-)(1 )h(-)(1), which provides an estimated mass-specific field metabolic rate (FMR) of 5.47+/-0.75 W kg(-)(1). After correction for time on shore, the at-sea FMR was estimated to be 6.65+/-1.09 W kg(-)(1), a value 5.8 times the predicted standard metabolic rate of a terrestrial animal of equal size. The mean maximum dive depth was 353+/-164 m, with a mean diving depth of 124+/-36 m. The mean maximum dive duration was 8.3+/-1.7 min, with an average duration of 3.4+/-0.6 min. The deepest, 550 m, and longest, 11.5 min, dives were made by the largest animal (155 kg). Our results indicate that the deep and long-duration diving ability of New Zealand sea lions is not due to a decreased diving metabolic rate. Individual sea lions that performed deeper dives had lower FMRs, which may result from the use of energetically efficient burst-and-glide locomotion. There are differences in the foraging patterns of deep and shallow divers that may reflect differences in surface swimming, time spent on the surface and/or diet. Our data indicate that, although New Zealand sea lions have increased their O(2) storage capacity, they do not, or cannot, significantly reduce their at-sea metabolic rates and are therefore likely to be operating near their physiological maximum.
Social referral: leveraging network connections to deliver recommendations

Much work has been done to study the interplay between recommender systems and social networks. This creates a very powerful coupling in presenting highly relevant recommendations to the users. However, to our knowledge, little attention has been paid to leverage a user's social network to deliver these recommendations. We present a novel approach to aid delivery of recommendations using the recipient's friends or connections. Our contributions with this study are 1) A novel recommendation delivery paradigm called Social Referral, which utilizes a user's social network for the delivery of relevant content. 2) An implementation of the paradigm is described in a real industrial production setting of a large online professional network. 3) A study of the interaction between the trifecta of the recommender system, the trusted connections and the end consumer of the recommendation by comparing and contrasting the proposed approach's performance with the direct recommender system. Our experiments indicate that Social Referral is a promising mechanism for recommendation delivery. The experiments show that a significant portion of users are receptive to passing along relevant recommendations to their social networks, and that recommendations delivered through users' social networks are much more likely to be accepted than those directly delivered to users.
Binding Interaction between Tet(M) and the Ribosome: Requirements for Binding

ABSTRACT Tet(M) protein interacts with the protein biosynthesis machinery to render this process resistant to tetracycline by a mechanism which involves release of the antibiotic from the ribosome in a reaction dependent on GTP hydrolysis. To clarify this resistance mechanism further, the interaction of Tet(M) with the ribosome has been examined by using a gel filtration assay with radioactively labelled Tet(M) protein. The presence of GTP and 5′-guanylyl imido diphosphate, but not GDP, promoted Tet(M)-ribosome complex formation. Furthermore, thiostrepton, which inhibits the activities of elongation factor G (EF-G) and EF-Tu by binding to the ribosome, blocks stable Tet(M)-ribosome complex formation. Direct competition experiments show that Tet(M) and EF-G bind to overlapping sites on the ribosome.
Hypoxic Tumor Environments Exhibit Disrupted Collagen I Fibers and Low Macromolecular Transport

Hypoxic tumor microenvironments result in an aggressive phenotype and resistance to therapy that lead to tumor progression, recurrence, and metastasis. While poor vascularization and the resultant inadequate drug delivery are known to contribute to drug resistance, the effect of hypoxia on molecular transport through the interstitium, and the role of the extracellular matrix (ECM) in mediating this transport are unexplored. The dense mesh of fibers present in the ECM can especially influence the movement of macromolecules. Collagen 1 (Col1) fibers form a key component of the ECM in breast cancers. Here we characterized the influence of hypoxia on macromolecular transport in tumors, and the role of Col1 fibers in mediating this transport using an MDA-MB-231 breast cancer xenograft model engineered to express red fluorescent protein under hypoxia. Magnetic resonance imaging of macromolecular transport was combined with second harmonic generation microscopy of Col1 fibers. Hypoxic tumor regions displayed significantly decreased Col1 fiber density and volume, as well as significantly lower macromolecular draining and pooling rates, than normoxic regions. Regions adjacent to severely hypoxic areas revealed higher deposition of Col1 fibers and increased macromolecular transport. These data suggest that Col1 fibers may facilitate macromolecular transport in tumors, and their reduction in hypoxic regions may reduce this transport. Decreased macromolecular transport in hypoxic regions may also contribute to poor drug delivery and tumor recurrence in hypoxic regions. High Col1 fiber density observed around hypoxic regions may facilitate the escape of aggressive cancer cells from hypoxic regions.
The Robot Vision Track at ImageCLEF 2010

This paper describes the robot vision track that has been proposed to the ImageCLEF 2010 participants. The track addressed the problem of visual place classication, with a special focus on general- ization. Participants were asked to classify rooms and areas of an oce environment on the basis of image sequences captured by a stereo cam- era mounted on a mobile robot, under varying illumination conditions. The algorithms proposed by the participants had to answer the question \where are you? " (I am in the kitchen, in the corridor, etc) when pre- sented with a test sequence, acquired within the same building but at a dierent oor than the training sequence. The test data contained images of rooms seen during training, or additional rooms that were not imaged in the training sequence. The participants were asked to solve the prob- lem separately for each test image (obligatory task). Additionally, results could also be reported for algorithms exploiting the temporal continuity of the image sequences (optional task). A total of seven groups partic- ipated to the challenge, with 42 runs submitted to the obligatory task, and 13 submitted to the optional task. The best result in the obligatory task was obtained by the Computer Vision and Geometry Laboratory, ETHZ, Switzerland, with an overall score of 677. The best result in the optional task was obtained by the Idiap Research Institute, Martigny, Switzerland, with an overall score of 2052.
Technology and the Gendering of Music Education

Critical of technologically determinist assumptions underpinning current educational policy, Victoria Armstrong argues that this growing technicism has grave implications for the music classroom where composition is often synonymous with the music technology suite. The use of computers and associated compositional software in music education is frequently decontextualized from cultural and social relationships, thereby ignoring the fact that new technologies are used and developed within existing social spaces that are always already delineated along gender lines. Armstrong suggests these gender-technology relations have a profound effect on the ways adolescents compose music as well as how gendered identities in the technologized music classroom are constructed. Drawing together perspectives from the sociology of science and technology studies (STS) and the sociology of music, Armstrong examines the gendered processes and practices that contribute to how students learn about technology, the repertoire of teacher and student talk, its effect on student confidence and the issue of male control of technological knowledge. Even though girls and female teachers have technological knowledge and skill, the continuing material and symbolic associations of technology with men and masculinity contribute to the perception of women as less able and less interested in all things technological. In light of the fact that music technology is now central to many music-making practices across all sectors of education from primary, secondary through to higher education, this book provides a timely critical analysis that powerfully demonstrates why the relationship between gender and music technology should remain an important empirical consideration.
The community is where the rapport is -- on sense and structure in the youtube community

YouTube is a video sharing repository, enabling users to post, share and discuss videos. Its stated mission is to create "an online video community"; however, YouTube is not commonly thought of as a community. Our aim in this study is to answer the question whether users have a "sense of community" towards YouTube, and if such feelings exist are they reflected in the explicit ties among members. To accomplish this, YouTube was examined using two different and complementing methods. Using Grounded Theory, we performed a detailed analysis of more than 30 videos and their corresponding textual comments, which discussed two topics: users' feelings about the YouTube community, and users' accounts of interaction within the community. We then performed a structural analysis on the ties these users display on their YouTube channels. This analysis showed that although users perceive YouTube to be a cohesive community, the explicit relationships in the friendship and subscription network are almost random. We suggest that users' sense of community is not necessarily related to the structure of the YouTube network, and may result from subjective affinity towards other users. This study also points out the importance of triangulating qualitative and quantitative data to get a deeper understanding of the nature of an online community.
Production risk in multi-output industries: estimates from Norwegian dairy farms

Farmers who produce multiple outputs are portfolio managers in the sense that they use inputs to balance expected economic return and variance of return. This article estimates the structure of the stochastic multioutput production technology in Norwegian dairy farming, allowing for a more flexible specification of the technology than previous studies. We find that an increase in input levels leads primarily to higher output variability, and that inputs also influence the covariance of shocks between outputs. Risk reducing effects of inputs on outputs are primarily present in the covariance functions. Technical change leads to shifts in the profit distribution over the data period, but no welfare improvement for risk averse farmers.
A learning framework for generic sensory-motor maps

We present a new approach to cope with unknown redundant systems. For this we present i) an online algorithm that learns general input-output restrictions and, ii) a method that, given a partial set of input-output variables, provides an estimate of the remaining ones, using the learned restrictions. We show applications of the algorithm using examples of direct and inverse robot kinematics.
A method to evaluate Web Services Anomaly Detection using Hidden Markov Models

In spite of the existence of security challenges to develop informational and servicing structures, web services are important for organizations. There are so many things done to compensate for their security shortcomings, including replacing security standards in their structure to provide defensive tools such as web services firewall. Due to the is attempting inefficiency of the current Intrusion Detection Systems (IDS) in recognizing web services attacks, current research to enhance these systems' level of security for the web services' activities. The efficiency of the prevalent Intrusion Detection System can be compared and assessed using different parameters. But these methods are inefficient in the area of web systems. In this article, according to the implemented chores in the area of anomaly detection in the web services, we are going to evaluate the efficiency of Hidden Markov Models as a being-used method in the Web Services Anomaly Detection (WSAD) groundwork. The assessments' methods and results can be used as an efficient and important step in guaranteeing web services.