Miracle-dz/UniFAR-FTUs huggingface dataset:
OntoNotes: The 90% Solution

We describe the OntoNotes methodology and its result, a large multilingual richly-annotated corpus constructed at 90% interannotator agreement. An initial portion (300K words of English newswire and 250K words of Chinese newswire) will be made available to the community during 2007.
The Influence of Cultivars and Phenological Phases on the Accumulation of Nevadensin and Salvigenin in Basil {Ocimum basilicum)

According to the earlier literature the optimum harvest time for basil is at the full flowering stage if accumulation of essential oil is taken into account. In this research we have investigated our gene-bank stored basil accessions to determine whether the harvest timing is variety specific or not considering their flavonoid accumulation pattern. In our work we have determined by HPLC the content of two main flavonoid compounds, salvigenin and nevadensin, of eight different gene bank accessions from 2013 of Ocimum basilicum L. Data were analysed with the nonparametric Kruskal-Wallis test. Multiple pairwise comparisons were made using the Conover-Iman procedure where the significance level was 5%. We have observed that the optimum harvest time is at the full flowering stage in the case of accessions ‘Genovese’ and ‘Piros’, but this was not verified for the others. The result of our experiment has shown that the maximum salvigenin and nevadensin content was detected both at the full- and early flowering period. Almost in all phenological phases the accession ‘M. Grimes’ accumulated the highest level of nevadensin, while accession ‘Lengyel’ produced the lowest results in all phenological phases. Generally it could be observed that compared with nevadensin more salvigenin is accumulated, and it is independent of the phenological phases. In the case of salvigenin, ‘M. Grimes’ accession produced the largest quantity and accession ‘Dark Opal’ showed the lowest values. Our analyses demonstrated that harvest at different phenological phases may result in different amounts of active agents according to the cultivar.
Scalable And User-Friendly Simulation

Simulation is an important technique for integrating interacting models for predicting results of hypothetical scenarios. A typical application area for simulators is virtual prototyping (VP). In VP, simulators replace the real-world prototype. Hence, the quality of the virtual prototype depends on the quality of its simulations, which in turn are highly dependent on the quality of the models and the computational power, especially if visualization and/or real-time constraints are required. Unfortunately defining models is an error-prone activity which requires domain-experts to have knowledge about the implementation details and/or IT-technical concerns. In addition, the bigger the dataset, the more computational power is needed, which affects the cost, and in turn, the usability of today’s simulators. To address both of these aspects, we propose a user-friendly, adaptive and scalable agentbased modelling and simulation framework with a hybrid CPU/GPU/FPGA high performance computing platform. The solution we describe provides domain-experts with a a scalable, adaptive, and efficient simulator and enables domain-experts to define high quality models without indepth IT-knowledge. We use a running example from the particle transmission domain to illustrate our approach.
Some Promising Results of Communication-Based Automatic Measures of Team Cognition

Some have argued that the most appropriate measure of team cognition is a holistic measure directed at the entire team. In particular, communication data are useful for measuring team cognition because of the holistic nature of the data, and because of the connection between communication and declarative cognition. In order to circumvent the logistic difficulties of communication data, the present paper proposes several relatively automatic methods of analysis. Four data types are identified, with low-level physical data vs. content data being one dimension, and sequential vs. static data being the other. Methods addressing all four of these data types are proposed, with the exception of static physical data. Latent Semantic Analysis is an automatic method used to assess content, either statically or sequentially. PRONET is useful to address either physical or content-based sequential data, and we propose CHUMS to address sequential physical data. The usefulness of each method to predict team performance data is assessed.
Joint Adaptive Modulation and MIMO Transmission for Non-Ideal OFDMA Cellular Systems

In this paper, a joint adaptive modulation and multiple-input multiple-output (MIMO) transmission scheme is considered. An efficient use of radio resources shall be based on channel state information at the transmitter (CSIT) to adjust the modulation order and MIMO transmission mode. A new suboptimal algorithm is proposed for link adaptation in a MIMO-OFDMA cellular system under spatially correlated fading and imperfect CSIT due to user mobility. Performance results are provided for the proposed solution under a long term evolution (LTE) downlink scenario. A substantial spectral efficiency gain is observed in contrast to the standalone transmission schemes.
Use of Antibiotic Exposure to Detect Postoperative Infections

ABSTRACT OBJECTIVE: To assess the utility of postoperative antibiotic exposure as an indicator of postoperative infection after coronary artery bypass graft surgery. DESIGN: We determined an optimal antibiotic exposure threshold by creating receiver operating characteristic curves. SETTING: Tertiary healthcare institution (United States); national sample (Israel). PATIENTS: 5,887 patients undergoing coronary artery bypass graft surgery. RESULTS: Postoperative antibiotic exposure with at least 9 days between the first and last dates of antibiotic administration, excluding the first postoperative day, had a sensitivity of 95% (261/276) and specificity of 85% (3,944/4,628) for identifying surgical-site infection, using as a gold standard surgical-site infections identified by conventional prospective surveillance or extrapolated from review of a sample of medical records. In contrast, using the same gold standard for surgical-site infections, the sensitivity of routine prospective surveillance alone was only 60%. The predictive value positive of the defined antibiotic exposure was 28% (261/945) for surgical-site infection and 60% (563/945) for any nosocomial infection. In the Israeli cohort, the sensitivity was 87% (74/85) and the specificity was 82% (735/898). CONCLUSION: Antibiotic exposure of sufficient duration and timing was more sensitive than conventional methods in detecting nosocomial infection and required substantially less effort to collect. Although the predictive value positive for surgical-site infection was only moderate, the majority of individuals identified this way had a nosocomial infection
Performance Analysis of Classification Techniques for Suspicious URL Detection in Social Networks

Social network services (SNSs) are increasing popular. Now a day’s most of the people in all over the world use Facebook, twitter for sharing their ideas. Though suspicious users collectively use by them to embed to harmful activities that may be tricky in securing user's personal information and data. This is challenge for social networks to rectify this type of security breach. The social networks or community websites must be able to identify phishing and suspicious urls. Machine learning techniques are proved an efficient tool in classifying benign and the suspicious urls from the set of many urls most of the solutions for training the classification models that supported all totally different sorts of feature sets. However, the most of the solutions does not provide good results as we expect from them on the basis of performance, behavior and some other criteria. In this study, a feature set is presented that combines the features of traditional heuristics and social networking. Furthermore, a suspicious URL identification system for use in social network environments is proposed which is based on comparative study of three algorithms named as Bayesian classification, KNN, SVM. The experimental results indicate that the proposed approach achieves a high detection rate.
Semantic Search Meets the Web

While semantic search technologies have been proven to work well in specific domains, they still have to confront two main challenges to scale up to the Web in its entirety. In this work we address this issue with a novel semantic search system that a) provides the user with the capability to query Semantic Web information using natural language, by means of an ontology-based Question Answering (QA) system [14] and b) complements the specific answers retrieved during the QA process with a ranked list of documents from the Web [3]. Our results show that ontology-based semantic search capabilities can be used to complement and enhance keyword search technologies.
Possible Nonconductive Role of Geobacter sulfurreducens Pilus Nanowires in Biofilm Formation

ABSTRACT Geobacter sulfurreducens required expression of electrically conductive pili to form biofilms on Fe(III) oxide surfaces, but pili were also essential for biofilm development on plain glass when fumarate was the sole electron acceptor. Furthermore, pili were needed for cell aggregation in agglutination studies. These results suggest that the pili of G. sulfurreducens also have a structural role in biofilm formation.
Automatic web pages hierarchical classification using dynamic domain ontologies

The use of ontologies for knowledge representation has had a fast increase in the last years and they are used in several application context. One of these challenging applications is the web. Managing large amount of information on internet needs more efficient and effective methods and techniques for mining and representing information. In this article, we present a methodology for automatic topic annotation of web pages. We describe an algorithm for words disambiguation using an apposite metric for measuring the semantic relatedness and we show a technique which allows to detect the topic of the analysed document using ontologies extracted from a knowledge base. The strategy is implemented in a system where these information are used to build a topic hierarchy automatically created and not a priori defined for classifying web pages. Experimental results are presented and discussed in order to measure the effectiveness of our approach.
Making programming more conversational

Accelerated by the Do-It-Yourself mindset of the Web 2.0 culture, end-user programming-programming by end users with limited or even no formal programming background-is growing rapidly. Especially in educational settings, children are exposed to computational thinking by making games, building scientific simulations and creating stories. Early educational programming languages such as Logo have made programming substantially more accessible to end-users. More recent approaches include visual programming with a drag-and-drop style of programming that makes it nearly impossible to compose syntactically incorrect programs. However, as the syntactic challenges of end-user programming are gradually fading into the past, the new frontier of semantic programming support emerges. This demonstration introduces Conversational Programming, a system to make programming more conversational. A conversational programming agent runs programs one step into the future in order to help end-users visualize discrepancies between the programs they intended to write and their actual programming results.
Inferring the dynamics of gene regulatory networks via optimized recurrent neural network and dynamic Bayesian network

Inferring gene regulatory networks (GRNs) from time-course expression data is a major challenge in systems biology and comprehensive understanding of its dynamics is difficult. Most temporal inference methods for the dynamics of GRNs assume linear dependencies among genes but this strong assumption of linearity among genes does not truly represent the dynamics of the GRNs which are inherently nonlinear. Other parametric and non-parametric methods for modeling nonlinear dynamical systems such as the S-systems and causal identification structure (CSI) have been proposed for modeling time-course nonlinearities in GRNs; however, these methods are statistically inefficient and analytically intractable especially in high dimensions. To overcome these problems, we propose an algorithm based on optimized recurrent neural network (RNN) and dynamic Bayesian (DBN) network called RNN-DBN. The inference algorithm for our DBN is based on nonlinear state space Elman recurrent neural network. Results on Drosophila Melanogaster nonlinear time-course benchmark dataset shows our method outperforms the G1DBN inference method based on linear model assumptions. The algorithm is further applied to time-course ovarian cancer dataset. The results show that the expression levels of three of five significant hub genes (flap structure-specific endonuclease 1, kinesin family member 11 and CDC6 cell division cycle 6 homolog (S. cerevisiae)) were decreased by oxaliplatin, but remained constant with cisplatin platinum drugs. These may therefore be potential drug candidates for ovarian cancer.
Provably secure browser-based user-aware mutual authentication over TLS

The standard solution for user authentication on the Web is to establish a TLS-based secure channel in server authenticated mode and run a protocol on top of TLS where the user enters a password in an HTML form. However, as many studies point out, the average Internet user is unable to identify the server based on a X.509 certificate so that impersonation attacks (e.g., phishing) are feasible. We tackle this problem by proposing a protocol that allows the user to identify the server based on human perceptible authenticators (e.g., picture, voice). We prove the security of this protocol by refining the game-based security model of Bellare and Rogaway and present a proof of concept implementation.
Geographical Distribution of Blogs in the United States

Blogging has diffused rapidly over the last several years in the United States, but that diffusion has not occurred evenly. In examining the distribution of 191,294 weblogs sampled in November 2003, we find that while blogging enjoys popularity throughout the U.S., bloggers appear more frequently within particular cities. This project indexes American bloggers by three-digit zip codes corresponding to their location, and identifies the demographic factors that appear to encourage blogging. We find that cities with populations that are young, urban, and more tolerant of difference are likely to host more bloggers.
Identification of a Novel LXXLL Motif in α-Actinin 4-spliced Isoform That Is Critical for Its Interaction with Estrogen Receptor α and Co-activators*

Background: ACTN4 potentiates nuclear receptor (NR)-mediated transcriptional activity. Results: The flanking sequences to the LXXLL motif in ACTN4 are important for both its association with ERα and co-activators. Conclusion: The ACTN4 (Iso) acts as a more potent co-activator of NRs than the corresponding ACTN4 (full length) through stronger interactions with known co-activators. Significance: This study describes a novel molecular mechanism by which ACTN4 (Iso) regulates transcription mediated by NRs. α-Actinins (ACTNs) are a family of proteins cross-linking actin filaments that maintain cytoskeletal organization and cell motility. Recently, it has also become clear that ACTN4 can function in the nucleus. In this report, we found that ACTN4 (full length) and its spliced isoform ACTN4 (Iso) possess an unusual LXXLL nuclear receptor interacting motif. Both ACTN4 (full length) and ACTN4 (Iso) potentiate basal transcription activity and directly interact with estrogen receptor α, although ACTN4 (Iso) binds ERα more strongly. We have also found that both ACTN4 (full length) and ACTN4 (Iso) interact with the ligand-independent and the ligand-dependent activation domains of estrogen receptor α. Although ACTN4 (Iso) interacts efficiently with transcriptional co-activators such as p300/CBP-associated factor (PCAF) and steroid receptor co-activator 1 (SRC-1), the full length ACTN4 protein either does not or does so weakly. More importantly, the flanking sequences of the LXXLL motif are important not only for interacting with nuclear receptors but also for the association with co-activators. Taken together, we have identified a novel extended LXXLL motif that is critical for interactions with both receptors and co-activators. This motif functions more efficiently in a spliced isoform of ACTN4 than it does in the full-length protein.
Recognition in-the-Tail: Training Detectors for Unusual Pedestrians with Synthetic Imposters

As autonomous vehicles become an every-day reality, high-accuracy pedestrian detection is of paramount practical importance. Pedestrian detection is a highly researched topic with mature methods, but most datasets focus on common scenes of people engaged in typical walking poses on sidewalks. But performance is most crucial for dangerous scenarios, such as children playing in the street or people using bicycles/skateboards in unexpected ways. Such “inthe-tail” data is notoriously hard to observe, making both training and testing difficult. To analyze this problem, we have collected a novel annotated dataset of dangerous scenarios called the Precarious Pedestrian dataset. Even given a dedicated collection effort, it is relatively small by contemporary standards (≈ 1000 images). To explore largescale data-driven learning, we explore the use of synthetic data generated by a game engine. A significant challenge is selected the right “priors” or parameters for synthesis: we would like realistic data with realistic poses and object configurations. Inspired by Generative Adversarial Networks, we generate a massive amount of synthetic data and train a discriminative classifier to select a realistic subset, which we deem Synthetic Imposters. We demonstrate that this pipeline allows one to generate realistic training data by making use of rendering/animation engines. Interestingly, we also demonstrate that such data can be used to rank algorithms, suggesting that Synthetic Imposters can also be used for “in-the-tail” validation at test-time, a notoriously difficult challenge for real-world deployment.
Making links between theory and practice : an investigation into pre-service teacher learning experiences with an online simulation

Current research in teacher education identifies a gap between what teachers are taught during their pre-service teacher education and what they are expected to do at the ‘chalk-face’ in their professional career (Cole & Knowles, 2000; Ramsey, 2000; Sorin, 2004). In response to the perception that theory is often irrelevant to practice, various studies assert that there is a need to integrate the various components of pre-service teacher education (for relevant studies see Brady, Seagal, Bamford & Deer, 1998; Lanier & Little, 1986). An integrated approach is advocated to enable pre-service teachers to gain a better understanding of the theory/practice nexus (Educational Training Committee, 2005; MACQT, 1998). In response to such studies, teacher educators are motivated to investigate strategies that might more effectively and relevantly bridge the gap between theory and practice. One approach has been to use simulations as a tool to experiment with practical scenarios while providing explicit links to the theory of pre-service teacher training. Simulations have the potential to enhance a learning experience by providing authentic and relevant scenarios in which learners apply their newly-acquired knowledge by experimenting and making decisions within a safe, virtual environment in which mistakes have no consequences, such as they have in the real world (Carrington, Ferry & Kervin, 2006; Aldrich, 2004; Limited research has been conducted on the use of simulations in pre-service teacher education and how engagement with simulations can support the learning of pre-service teachers. The purpose of this inquiry was to investigate how pre-service teachers make use of a virtual learning environment provided by an online simulation - ClassSim - link knowledge from university coursework with field experiences, particular, whether and how experience with an online simulation contributed to the development of pre-service teachers’ emerging professional case
Voronoi-Based Compact Image Descriptors: Efficient Region-of-Interest Retrieval With VLAD and Deep-Learning-Based Descriptors

We investigate the problem of image retrieval based on visual queries when the latter comprise arbitrary regions-of-interest (ROI) rather than entire images. Our proposal is a compact image descriptor that combines the state-of-the-art in content-based descriptor extraction with a multilevel, Voronoi-based spatial partitioning of each dataset image. The proposed multilevel Voronoi-based encoding uses a spatial hierarchical K-means over interest-point locations, and computes a content-based descriptor over each cell. In order to reduce the matching complexity with minimal or no sacrifice in retrieval performance: 1) we utilize the tree structure of the spatial hierarchical K-means to perform top-to-bottom pruning for local similarity maxima; 2) we propose a new image similarity score that combines relevant information from all partition levels into a single measure for similarity; 3) we combine our proposal with a novel and efficient approach for optimal bit allocation within quantized descriptor representations. By deriving both a Voronoi-based VLAD descriptor (called Fast-VVLAD) and a Voronoi-based deep convolutional neural network (CNN) descriptor (called Fast-VDCNN), we demonstrate that our Voronoi-based framework is agnostic to the descriptor basis, and can easily be slotted into existing frameworks. Via a range of ROI queries in two standard datasets, it is shown that the Voronoi-based descriptors achieve comparable or higher mean average precision against conventional grid-based spatial search, while offering more than twofold reduction in complexity. Finally, beyond ROI queries, we show that Voronoi partitioning improves the geometric invariance of compact CNN descriptors, thereby resulting in competitive performance to the current state-of-the-art on whole image retrieval.
A bee-inspired algorithm for optimal data clustering

The amount of data generated in different knowledge areas has made it necessary the use of data mining tools capable of automatically analyzing and extracting knowledge from datasets. Clustering is one of the most important tasks in data mining and can be defined as the process of partitioning objects into groups or clusters, such that objects in the same group are more similar to one another than to objects belonging to other groups. In this context, this paper aims to propose an adaptation of a bee-inspired optimization algorithm so that it is able to solve data clustering problems. The algorithm was run for different datasets and the results obtained showed high quality clusters and diversity of solutions, whilst a suitable number of clusters was automatically determined.