Miracle-dz/UniFAR-FTUs huggingface dataset:
Content Coverage of PNAS in 1982-2001

for the years 1982-2001. For each year the top 20 words taken from titles, publisher assigned keywords and controlled vocabulary Mesh terms were extracted. This paper reports results of determining changes in content coverage of the Proceedings of the National Academy of Science (PNAS) for the years 1982-2001. Dominant keywords were extracted to visualize major research topics and their evolution in the 20-year time frame. Two time slices namely 1995 and 2001 are examined in detail. Latent Semantic Analysis [6] was applied to determine the space of semantically similar documents for both years. The results of a coword analysis of terms used in titles and keywords of articles were visualized using graph layout algorithms available in the graphical visualization software Pajek [1]. Subsequently, the number of resulting terms was examined for major trends and eight keywords were selected for closer examination. Figure 1 plots the frequency of these words for years in which they were among the top 20 most frequently used words together with key events. In addition, Kleinberg’s burst detection algorithm [2] was applied to visualize bursts – major increases in the usage frequency – of those eight words. Correlating the graph with historical biotechnology events reported in [3] results in the interpretation below. Human studies are steadily increasing and burst occurred by the start of the Human Genome Project in 1988. The frequency of Transcription is coupled with the PCR technology conceived in 1983 and fully developed by 1986. The application of PCR in combination with reverse Data Set The utilized data set comprises the complete set of papers in the Proceedings of National Academy of Science
Watermarked 3D Object Quality Assessment

This work concerns the developing of new perceptual metrics for 3D watermarking quality assessment. Any watermarking algorithm, to be efiective, requires that the distortions it inevitably introduces into the watermarked media is imperceptible. This requirements is particularly severe for watermarking of 3D objects where the visual quality of the original model has to be preserved, i.e. the visual aspect of the watermarked object have to be the same of the original one. Several methods based on the knowledge of Human Visual System (HVS) have been developed to achieve this goal for still images and video watermarking. Since now, no similar techniques for watermarking of 3D objects exist. Here, we propose a novel experimental methodology for subjective evaluations of 3D objects and two perceptual metrics for quality assessment of watermarked 3D objects. Such metrics have been developed combining roughness estimation of model surface with psychophysical data collected by subjective experiments based on the proposed methodology. The performances of the proposed metrics are deeply analyzed.
Gesture-Based NUI Application for Real-Time Path Modification

Since the birth of Natural User Interface (NUI) concept, the NUI has become widely used. NUI-based applications have grown rapidly, particularly those using gestures, which have come to occupy a pivotal place in technology. The ever-popular Smartphone is one of the best examples. Recently, video conferencing has also begun adopting gesture-based NUIs with augmented reality (AR) technology. The NUI and AR have greatly enriched and facilitated human experience. In addition, path planning has been a popular topic in research area. Traditional path planning uses automatic navigation to solve problems, it cannot practically interact with people. Its algorithm calculates complexly, moreover, in certain extenuating circumstances, automatic real-time processing is much less efficient than human path modification. Therefore, considering such extenuating circumstances, we present a solution that employs NUI technology for 3D path modification in real time. In our proposed solution, users can manually operate and edit their own paths. The core method is based on 3D point detection to change paths. We did a simulation experiment about city path modification. Experiment resulted that computer can accurately identify a valid gesture. By using gesture it can effectively change the path. Among other applications, this solution can be used in virtual military maps and car navigation.
Excitation by geniculocortical synapses is not ‘vetoed’ at the level of dendritic spines in cat visual cortex.

1. We used anatomical methods to examine whether the geniculocortical afferent input to dendritic spines could be gated or ‘vetoed’ by an inhibitory input to the same spine. 2. Physiologically identified X‐ and Y‐type afferents were injected intra‐axonally with horseradish peroxidase (HRP), processed, and drawn under the light microscope. Selected regions of the terminal arbors were then serially sectioned for examination under the electron microscope. 3. Three‐dimensional reconstructions of thirty‐nine HRP‐filled terminal boutons forming fifty asymmetric (type 1) synapses showed that thirty‐one synapses were on the heads of dendritic spines. Only two of thirty‐one spine heads received an additional symmetric (type 2) synapse, which is presumed to be inhibitory. 4. Examination of twenty‐three boutons from two clutch cells (a GABA (gamma‐aminobutyric acid)‐ergic smooth cell) that form symmetric (type 2) synapses on spines indicated that their preferred location was opposite the asymmetric synapse on the head of the spine. Synaptic input to the necks of spines appears rare. 5. We conclude that most of the excitation provided by the geniculocortical afferent input to the heads of spines cannot be gated or vetoed by inhibition at the level of the spine.
A Novel Predictor of Posttransplant Portal Hypertension in Adult-To-Adult Living Donor Liver Transplantation: Increased Estimated Spleen/Graft Volume Ratio

Background In adult living donor liver transplantation (ALDLT), graft-to-recipient weight ratio of less than 0.8 is incomplete for predicting portal hypertension (>20 mm Hg) after reperfusion. We aimed to identify preoperative factors contributing to portal venous pressure (PVP) after reperfusion and to predict portal hypertension, focusing on spleen volume-to-graft volume ratio (SVGVR). Methods In 73 recipients with ALDLT between 2002 and 2013, first we analyzed survival according to PVP of 20 mm Hg as the threshold, evaluating the efficacy of splenectomy. Second, we evaluated various preoperative factors contributing to portal hypertension after reperfusion. Results All of the recipients with PVP greater than 20 mm Hg (n = 19) underwent PVP modulation by splenectomy, and their overall survival was favorable compared with 54 recipients who did not need splenectomy (PVP ⩽ 20 mm Hg). Graft-to-recipient weight ratio had no correlation with PVP. Multivariate analysis revealed that estimated graft and spleen volume were significant factors contributing to PVP after reperfusion (P < 0.0001 and P < 0.0001, respectively). Furthermore, estimated SVGVR showed a significant negative correlation to PVP after reperfusion (R = 0.652), and the best cutoff value for portal hypertension was 0.95. Conclusions In ALDLT, preoperative assessment of SVGVR is a good predictor of portal hypertension after reperfusion can be used to indicate the need for splenectomy before reperfusion.
Conversational Assessment of Hypnotic Ability to Promote Hypnotic Responsiveness

Abstract In this article, hypnotic responsiveness is conceptualized as the byproduct of hypnotic ability, which is largely unalterable, plus hypnotic participation, which is highly subject to manipulation in the therapeutic context. This framework constitutes the basis of a model for the conversational assessment of hypnotic ability and hypnotic participation, as well as the subsequent tailoring of therapeutic interventions based on that assessment. Specific clinical steps for implementing activities implied by the model are explained and then demonstrated by way of a case example.
A Unified Paradigm for the Accuracy of Classification Based on Granular Computing

Accuracy is a very important criterion for the classifier in the process of classification. In this paper, a unified paradigm for the calculation of accuracy evaluated different classifier, using topological covering-based granular computing, is presented under the given sample space and different ideal classification assumptions. And corresponding examples for the calculation of accuracy in different classification situations are given.
Collaborative filtering algorithm based on rating distance

Collaborative filtering, a successful and wildly used technique in personalized recommender systems, generates recommendations by similar users. Cosine similarity and Pearson correlation coefficient are widely used in collaborative filtering to calculate the similarity; however, the similarity is not accurate in some cases because of the defects of the algorithm. To solve these issues, this paper proposes a novel similarity calculation method which combined information entropy with compressive distance weight based on the probability distribution of rating distance. Experiment results show that the proposed method get better performance than conventional Pearson correlation coefficient method.
Discover Dependencies from Data—A Review

Functional and inclusion dependency discovery is important to knowledge discovery, database semantics analysis, database design, and data quality assessment. Motivated by the importance of dependency discovery, this paper reviews the methods for functional dependency, conditional functional dependency, approximate functional dependency, and inclusion dependency discovery in relational databases and a method for discovering XML functional dependencies.
Introduction to the Special Issue on Arabic Computational Linguistics

In recent years there has been a significant increase in the amount of research on the computational analysis of the Arabic language being carried out around the world. This includes both a steady growth of the computational linguistics research community in the Arab-speaking world, as evidenced by the emergence of scientific societies such as the Egyptian Society for Language Engineering1, as well as the existence of major initiatives on Arabic natural language processing (NLP) in Europe and North America (particularly in the United States by the DARPA-funded GALE program2). In addition, the past year alone saw the publication of two monographs on Arabic Computational Linguistics [Farghaly 2010; Habash 2010], a special issue of this journal [Shaalan and Farghaly 2009], a new release by the Linguistics Data Consortium of the Arabic Treebank [Maamouri et al. 2009] (among other Arabic-language resources), as well as a number of conferences and workshops on Arabic NLP around the world. At the 2010 Georgetown University Roundtable on Language3 (GURT), whose theme was Arabic Language and Linguistics, linguists from around the world and from a wide range of different linguistic traditions and sub-disciplines came together in Washington, D.C. to share their research on the Arabic language. A two-day special session dedicated to Arabic computational linguistics and a half-day preconference tutorial on Arabic NLP drew researchers from both academia and industry. The presentations in the special session spanned the range from applied NLP to natural language understanding, from automatic diacritization to speech act classification, providing attendees with a snapshot of the current state of Arabic NLP. The papers in this volume were selected from those presentations to represent the different approaches and techniques currently being used in the field of Arabic computational linguistics. Arabic NLP research faces a number of well-known challenges, due to both the morphological complexity of the Arabic language and to the particularities of Arabic orthography. These challenges have been addressed extensively in Arabic NLP research which has focused on Modern Standard Arabic (MSA) [Habash and Rambow 2005; Diab et al. 2007; Larkey et al. 2007; Buckwalter 2007; Dichy and Farghaly 2007]. Significant recent attention, however, has been paid to the problems for NLP
Measuring structure parameters and pulse equivalents of a double-chained visual mechanism on a circular orbit

Double-chained visual mechanism on a circular orbit is a novel binocular visual platform which the length, location and direction of the baseline can be adjusted within a circular region. Measuring structure parameters and pulse Equivalents of motors is a key question to ensure the accuracy of metric reconstruction of the scene. This paper describes an indirect strategy based on vision measurement for it. Firstly, the geometric model of the mechanism is build. Secondly, the optimal estimation model of the camera center from a target image is proposed. Then the method to compute structure parameters and pulse Equivalents from the fitted trajectory of the camera center is presented. Finally, experiment results show the effectiveness of the method.
An Analysis of Adenovirus Genomes Using Whole Genome Software Tools

The evolution of sequencing technology has lead to an enormous increase in the number of genomes that have been sequenced. This is especially true in the field of virus genomics. In order to extract meaningful biological information from these genomes, whole genome data mining software tools must be utilized. Hundreds of tools have been developed to analyze biological sequence data. However, only some of these tools are user-friendly to biologists. Several of these tools that have been successfully used to analyze adenovirus genomes are described here. These include Artemis, EMBOSS, pDRAW, zPicture, CoreGenes, GeneOrder, and PipMaker. These tools provide functionalities such as visualization, restriction enzyme analysis, alignment, and proteome comparisons that are extremely useful in the bioinformatics analysis of adenovirus genomes.
A Dynamic Transfer Ensemble Model for Customer Churn Prediction

It is difficult to get satisfactory churn prediction results by traditional models, because the available customer samples in target domain are usually few and the class distribution of customer data is imbalanced. This study proposes a group method of data handling (GMDH) based dynamic transfer ensemble (GDTE) model for churn pre-diction. It first transfers the data in related source domains to the target domain by transfer learning technique, and then adopts resampling technique to balance the class distribution of the training data. Finally, it trains a series of base classifiers and dynamically selects a proper classifier ensemble for each test sample by GMDH. The experimental results in two datasets show that the performance of GDTE is better than that of one traditional churn prediction strategy, as well as three transfer learning strategies.
Complexities and Algorithms for Glycan Structure Sequencing using Tandem Mass Spectrometry

Determining glycan structures is vital to comprehend cell-matrix, cell-cell, and even intracellular biological events. Glycan structure sequencing, which is to determine the primary structure of a glycan using MS/MS spectrometry, remains one of the most important tasks in proteomics. Analogous to the peptide de novo sequencing, the glycan de novo sequencing is to determine the structure without the aid of a known glycan database. We show in this paper that glycan de novo sequencing is NP-hard. We then provide a heuristic algorithm and develop a software program to solve the problem in practical cases. Experiments on real MS/MS data of glycopeptides demonstrate that our heuristic algorithm gives satisfactory results on practical data.
Bitextor’s participation in WMT’16: shared task on document alignment

This paper describes the participation of Prompsit Language Engineering and the Universitat d’Alacant in the shared task on document alignment at the First Conference on Machine Translation (WMT 2016). Two systems have been submitted, corresponding to two different versions of the tool Bitextor: the last stable release, version 4.1, and the newest one, version 5.0. The paper describes the main features of each version of the tool and discusses the results obtained on the data sets published for the shared task.
Exploration of Music Emotion Recognition Based on MIDI

Audio and lyric features are commonly considered in the research of music emotion recognition, whereas MIDI features are rarely used. Some research revealed that among the features employed in music emotion recognition, lyric has the best performance on valence, MIDI takes the second place, and audio is the worst. However, lyric cannot be found in some music types, such as instrumental music. In this case, MIDI features can be considered as a choice for music emotion recognition on valence dimension. In this presented work, we systematically explored the effect and value of using MIDI features for music emotion recognition. Emotion recognition was treated as a regression problem in this paper. We also discussed the emotion regression performance of three aspects of music in terms of edited MIDI: chorus, melody, and accompaniment. We found that the MIDI features performed better than audio features on valence. And under the realistic conditions, converted MIDI performed better than edited MIDI on valence. We found that melody was more important to valence regression than accompaniment, which was in contrary to arousal. We also found that the chorus part of an edited MIDI might contain as sufficient information as the entire edited MIDI for valence regression.
Novel Algorithm for Sparse Solutions to Linear Inverse Problems with Multiple Measurements

In this report, a novel efficient algorithm for recovery of jointly sparse signals (sparse matrix) from multiple incomplete measurements has been presented, in particular, the NESTA-based MMV optimization method. In a nutshell, the jointly sparse recovery is obviously superior to applying standard sparse reconstruction methods to each channel individually. Moreover several efforts have been made to improve the NESTA-based MMV algorithm, in particular, (1) the NESTA-based MMV algorithm for partially known support to greatly improve the convergence rate, (2) the detection of partial (or all) locations of unknown jointly sparse signals by using so-called MUSIC algorithm; (3) the iterative NESTA-based algorithm by combing hard thresholding technique to decrease the numbers of measurements. It has been shown that by using proposed approach one can recover the unknown sparse matrix X with () Spark A -sparsity from () Spark A measurements, predicted in Ref. [1], where the measurement matrix denoted by A satisfies the so-called restricted isometry property (RIP). Under a very mild condition on the sparsity of X and characteristics of the A, the iterative hard threshold (IHT)-based MMV method has been shown to be also a very good candidate.
HOW WELL PERFORMS FREE-FORM 3D OBJECT RECOGNITION FROM RANGE IMAGES?

This paper investigates the recognition performance of a geometric matching approach to the recognition of free-form objects obtained from range images. The heart of this approach is a closest point matching algorithm which, starting from an initial configuration of two rigid objects, iteratively finds their best correspondence. While the effective performance of this algorithm is known to depend largely on the chosen set of initial configurations, this paper investigates the quantitative nature of this dependence. In essence, we experimentally measure the range of successful configurations for a set of test objects and derive quantitative rules for the recognition strategy. These results show the conditions under which the closest point matching algorithm can be successfully applied to free-form 3D object recognition and help to design a reliable and costeffective recognition system.
At the crossroads between literature, culture, linguistics, and cognition: local character-based metaphors in fairy tales

This paper resumes the series devoted to metaphors in fairy tales (cf. Herrero 2005a, 2006, 2007, 2008, and 2010). We attempt to show how five conceptual metaphors (PEOPLE ARE ANIMALS, PEOPLE ARE PLANTS, IMPERFECT IS IRREGULAR, LOVE IS MAGIC, and REAL PEOPLE ARE FICTITIOUS CHARACTERS) and their variants may occur at a local level in the narration, allowing us to understand the magical depiction of characters and some of the relationships they may establish in the tales under analysis. The tales, which were compiled by the British author Andrew Lang (1844-1912), are representative of different cultures and have been downloaded from the Project Gutenberg online library. Our research also supports Herrero’s claims that (1) conceptual metaphor may serve as a taxonomic criterion for tales, and that (2) although many of these stories belong to different socio-cultural settings, they are coincident with the same plots and local metaphors employed, which may be a result of their strong experiential basis.
Simple and Efficient Projective Clustering

We describe a new Monte Carlo algorithm for projective clustering that is both simple and efficient. Like previous Monte Carlo algorithms, we perform trials that sample a small subset of the data points to determine the dimensions in which the points are sufficiently close to form a cluster and then search the rest of the data for data points that are part of the cluster. However, our algorithm differs from previous algorithms in the method by which the dimensions of the cluster are determined and the method for determining the points in the cluster. This allows us to use smaller subsets of the data to determine the cluster dimensions and achieve improved efficiency over previous algorithms. The complexity of our algorithm is O(nd1+log α/ logβ), where n is the number of data points, d is the number of dimensions in the space, and α and β are parameters that specify which clusters should be found. To our knowledge, this is the lowest published complexity for an algorithm that is able to place high bounds on the probability of success. We present experiments that show that our algorithm outperforms previous algorithms on real and synthetic data.
Computer-Aided Analysis of Nailfold Capillaroscopy Images

Nailfold capillaroscopy (NC) is a non-invasive imaging technique employed to assess the condition of blood capillaries in the nailfold, and is routinely used for the detection of scleroderma spectral disorders, Raynaud’s phenomenon and other connective tissue diseases. In this chapter, we present computer-aided approaches for capillary inspection, in particular focussing on the tasks of image enhancement, binarisation and skeletonisation. We evaluate the performance of a number image enhancement/noise removal techniques for NC images, as a pre-cursor to edge detection aimed at identifying capillaries. Results show that bilateral filters and enhancers provide the best overall image quality. Following noise removal, NC images typically get converted into binary form. For this purpose, we employ a difference-of-Gaussian approach before thresholding. The final stage is that of skeletonisation, which can be effectively performed using a rule-based thinning algorithm. Thus the complete imaging pipeline of pre-processing, binarisation and skeletonisation is represented in this chapter.