Miracle-dz/UniFAR-FTUs huggingface dataset:
Proximity Window Context Method for Term Extraction in Ontology Learning from Text

The ontology learning from text cycle consists of the consecutive phases of term, synonym, concept, taxonomy and relation extraction. The paper touches the problems of a low efficiency in the current term extraction methods which are handled by a combination of statistic (frequency-based) and linguistic approaches. We present a novel method to extract terms that uses only shallow linguistic information. It is proposed to explore a different set of linguistic layers and support a classic POS n-gram model with additional context information based on proximity window features. The method is evaluated on two substantially different corpora to produce better results than the classic measures, including standard n-gram models and frequency-based approaches.
Leg Perception of Object Heaviness

ABSTRACT Perceived heaviness is a function of both the muscle activity used to wield an object and the resulting movement. Wielding reveals invariant properties of the effector-object system, such as rotational inertia. Recent research has proposed a psychophysiological mechanism for perceiving the heaviness of a handheld object through dynamic touch that captures how arm muscle activity and angular movement combined reveal this invariance (Waddell, Fine, Likens, Amazeen & Amazeen, 2016). The current study extends this hypothesis by investigating the dynamics of heaviness perception with the leg. Participants lifted objects of varying mass with knee extension lifts while reporting perceived heaviness. During each lift, the electromyogram (EMG) was recorded from the quadriceps, and peak angular acceleration was recorded about the knee. The resulting psychophysiological function revealed the hypothesized ratio of muscle activity to movement, similar to that found in Waddell et al. (2016). This suggests that the dynamics for heaviness perception in the leg is similar to that shown in the arm in previous work.
Active Interest Management for Improving Flow Completion Time in Named-Data Networking

Interest aggregation is the primary feature of named-data networking (NDN). The interest aggregation reduces duplicated transmissions for multiple requests on the same data, but can delay loss recovery when data packets are lost. Since retransmitted interest packets for loss recovery cannot be distinguished from regular interest packets, the lost data packets can be recovered after the timeout of the interest aggregation. The delayed loss recovery can largely increase flow completion time, particularly on small content downloads. In this paper, we propose an active interest management (AIM) scheme based on random early NACK to avoid the delayed loss recovery as well as keeping the benefit of interest aggregation. Using NS-3, we show that our scheme significantly reduced flow completion time of both small and large content.
DNA-Redox Cation Interaction Improves the Sensitivity of an Electrochemical Immunosensor for Protein Detection

A simple DNA-redox cation interaction enhancement strategy has been developed to improve the sensitivity of electrochemical immunosensors for protein detection. Instead of labeling with fluorophores or redox-active groups, the detection antibodies were tethered with DNA single strands. Based on the electrostatic interaction between redox cations ([Ru(NH3)6]3+) and negatively charged DNA backbone, enhanced electrochemical signals were obtained. Human chorionic gonadotropin (hCG) detection has been performed as a trial analysis. A linear response range up to the concentration of 25 mIU/mL and a detection limit of 1.25 mIU/mL have been achieved, both are comparable with the ultrasensitive enzyme-linked immunosorbent assay (ELISA) tests. The method also shows great selectivity towards hCG over other hormones such as thyroid stimulating hormone (TSH) and follicle stimulating hormone (FSH). By and large, our approach bears the merits of cost effectiveness and simplicity of instrumentation in comparison with conventional optical detection methods.
Development of an Instrument for Enterprise Resource Planning (ERP) Implementation in Indian Small and Medium Enterprises (SMEs)

In India, majority of companies have had nothing but disaster with ERP implementation projects. Much has been written on implementation of Enterprise Resource Planning (ERP) system in organizations of various sizes. Implementing an ERP system is a difficult and high cost proposition as it places tremendous demands on organization‟s resources and time. Very few studies have scientifically developed and tested constructs that represents critical success factors for ERP implementation projects. Global competition and economic liberalization are creating opportunities and posing challenges in front of the Indian Industries. In this paper, we discover and classify critical success factors for ERP implementation in Indian Small & Medium size Enterprises (SMEs), presents a pilot study aim at developing an instrument for ERP in Indian small & medium scale Enterprises (SME‟s). An extensive literature review was carried out for identification of various attributes which grouped in various critical success factors (CSF‟s). The instrument consisting of 23 variables was identified after literature review. A 24 item questionnaire was developed from the relevant literature and distributed to 863 SME‟s. Data from 219 SMEs were collected for the measurement of effectiveness of these critical success factors. Through the study, four factors were identified that attempts to explain 84.203 % of variances that impact ERP Implementation. The factors are found to be reliable and valid. We believe that the comprehension of these factors will deepen the understanding of ERP implementation and will help to avoid implementation mistakes, thereby increasing the rate of success.
Constraining the concentration of the hydroxyl radical in a stratocumulus-topped marine boundary layer from sea-to-air eddy covariance flux measurements of dimethylsulfide

Abstract. The hydroxyl radical (OH) is an important oxidant in the troposphere due to its high reactivity and relative abundance. Measuring the concentration of OH in situ, however, is technically challenging. Here we present a simple method of estimating an OH-equivalent oxidant concentration ("effective OH") in the marine boundary layer (MBL) from the mass balance of dimethylsulfide (DMS). We use shipboard eddy covariance measurements of the sea-to-air DMS flux from the Vamos Ocean-Cloud-Atmosphere-Land Study Regional Experiment (VOCALS-REx) in October and November of 2008. The persistent stratocumulus cloud-cover off the west coast of South America and the associated strong inversion between MBL and the free troposphere (FT) greatly simplify the dynamics in this region and make our budget estimate possible. From the observed diurnal cycle in DMS concentration, the nighttime entrainment velocity at the inversion is estimated to be 4 mm s −1 . We calculate 1.4(±0.2)×10 6 OH molecules cm −3 from the DMS budget, which represents a monthly effective concentration and is well within the range of previous estimates. Furthermore, when linearly proportioned according to the intensity of solar flux, the resultant diel OH profile, together with DMS surface and entrainment fluxes, enables us to accurately replicate the observed diurnal cycle in DMS (correlation coefficient over 0.9). The nitrate radical (NO 3 ) is found to have little contribution to DMS oxidation during VOCALS-REx. An upper limit estimate of 1 pptv of bromine oxide radical (BrO) would account for 30% of DMS oxidation and lower the OH concentration to 1.0)×10 6 OH molecules cm −3 . Our effective OH estimate includes the oxidation of DMS by such radicals.
Using Clustering Technique to Restructure Programs

Program restructuring or refactoring is often required when a function becomes too large or is involved in multiple activities and therefore exhibits low cohesion. A critical factor in restructuring is to increase cohesion and decrease coupling. There are many existing methods which measure cohesion and coupling but do not provide much information as to how to restructure the program, while some other methods exist which only deals with restructuring the functions. The paper presents a simple but effective approach to function restructuring based on the experimental research on cohesion and coupling. Measure for software cohesion could be the first step of reengineering a software system to identify the functions with low cohesion. The next step is to restructure the identified functions. A clustering technique is presented in this paper which can assess the cohesiveness of a function and also gives indicaiton as to how to decompose a function to multiple high-cohesive functions. Several examples are presented to demonstrate the concept.
Three dimensional Terrain modeling and display for environmental assessment

A technique for compositing computer generated images of buildings and background images created from aerial photographs is described. The aerial photo data are mapped onto a terrain model based on cartographic data. The technique can be used to pre-evaluate visual impact of large scale construction by means of not only still images but also animations.The technique can be briefly described as follows: 1) an accurate, three-dimensional terrain model is created based on cartographic data. 2) a hierarchy of aerial photo data at various resolutions is mapped onto the terrain model as a texture. 3) the height of trees is taken into account in regions near the viewpoint. 4) allowing intersections of terrain models with constructions facilitates geometric adjustments and shadowing.
Nanomechanics of the Endothelial Glycocalyx in Experimental Sepsis

The endothelial glycocalyx (eGC), a carbohydrate-rich layer lining the luminal side of the endothelium, regulates vascular adhesiveness and permeability. Although central to the pathophysiology of vascular barrier dysfunction in sepsis, glycocalyx damage has been generally understudied, in part because of the aberrancy of in vitro preparations and its degradation during tissue handling. The aim of this study was to analyze inflammation-induced damage of the eGC on living endothelial cells by atomic-force microscopy (AFM) nanoindentation technique. AFM revealed the existence of a mature eGC on the luminal endothelial surface of freshly isolated rodent aorta preparations ex vivo, as well as on cultured human pulmonary microvascular endothelial cells (HPMEC) in vitro. AFM detected a marked reduction in glycocalyx thickness (266 ± 12 vs. 137 ± 17 nm, P<0.0001) and stiffness (0.34 ± 0.03 vs. 0.21 ± 0.01 pN/mn, P<0.0001) in septic mice (1 mg E. coli lipopolysaccharides (LPS)/kg BW i.p.) compared to controls. Corresponding in vitro experiments revealed that sepsis-associated mediators, such as thrombin, LPS or Tumor Necrosis Factor-α alone were sufficient to rapidly decrease eGC thickness (-50%, all P<0.0001) and stiffness (-20% P<0.0001) on HPMEC. In summary, AFM nanoindentation is a promising novel approach to uncover mechanisms involved in deterioration and refurbishment of the eGC in sepsis.
PPNOCS: Performance and Power Network on Chip Simulator based on SystemC

As technology moves towards multi-core system-on-chips (SoCs), networks-on-chip (NoCs) are emerging as the scalable fabric for interconnecting the cores. Network-on-Chip architectures have a wide variety of parameters that can be adapted to the designer’s requirements. This paper proposes a performance and power network on chip simulator (PPNOCS) based on SystemC to explore the impact of various architectural level parameters of the on-chip interconnection network elements on its performance and power. PPNOCS supports an arbitrary size of mesh and torus topology, adopts five classic routing algorithms and seven synthetic traffic patterns. Developers also can develop and verify their own network design by modifying the corresponding modules. Experiments of using this simulator are carried out to study the power, latency and throughput of a 4x4 multi-core mesh network topology. Results show that PPNOCS provides a fast and convenient platform for researching and verification of
Combining Statistical and Knowledge-Based Spoken Language Understanding in Conditional Models

Spoken Language Understanding (SLU) addresses the problem of extracting semantic meaning conveyed in an utterance. The traditional knowledge-based approach to this problem is very expensive --- it requires joint expertise in natural language processing and speech recognition, and best practices in language engineering for every new domain. On the other hand, a statistical learning approach needs a large amount of annotated data for model training, which is seldom available in practical applications outside of large research labs. A generative HMM/CFG composite model, which integrates easy-to-obtain domain knowledge into a data-driven statistical learning framework, has previously been introduced to reduce data requirement. The major contribution of this paper is the investigation of integrating prior knowledge and statistical learning in a conditional model framework. We also study and compare conditional random fields (CRFs) with perceptron learning for SLU. Experimental results show that the conditional models achieve more than 20% relative reduction in slot error rate over the HMM/CFG model, which had already achieved an SLU accuracy at the same level as the best results reported on the ATIS data.
Monolithic Integration of Arrays of Single‐Walled Carbon Nanotubes and Sheets of Graphene

Sheets of graphene and arrays of single-walled carbon nanotubes (SWNTs) are formed separately using chemical vapor deposition techniques onto different optimized growth substrates. Techniques of transfer printing provide a route to integration, yielding two terminal devices and transistors in which patterned structures of graphene form the electrodes and the SWNTs arrays serve as the semiconducting channels.
Know-who/know-how Navigation Using Development Project-related Taxonomies

In this demonstration, we introduce a corporate knowledge management system. Our system uses develeoment project-specific taxonomies to extract know-who/know-how information from e-mails automatically. After that, it visualizes knowledge on taxonomies in an understandable way. Accordingly, the system lets us easily learn about business keywords and knowledge that each developer has.
Murine Model Imitating Chronic Wound Infections for Evaluation of Antimicrobial Photodynamic Therapy Efficacy

It is generally acknowledged that the age of antibiotics could come to an end, due to their widespread, and inappropriate use. Particularly for chronic wounds alternatives are being thought. Antimicrobial Photodynamic Therapy (APDT) is a potential candidate, and while approved for some indications, such as periodontitis, chronic sinusitis and other niche indications, its use in chronic wounds is not established. To further facilitate the development of APDT in chronic wounds we present an easy to use animal model exhibiting the key hallmarks of chronic wounds, based on full-thickness skin wounds paired with an optically transparent cover. The moisture-retaining wound exhibited rapid expansion of pathogen colonies up to 8 days while not jeopardizing the host survival. Use of two bioluminescent pathogens; methicillin resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa permits real time monitoring of the pathogens. The murine model was employed to evaluate the performance of four different photosensitizers as mediators in Photodynamic Therapy. While all four photosensitizers, Rose Bengal, porphyrin TMPyP, New Methylene Blue, and TLD1411 demonstrated good to excellent antimicrobial efficacy in planktonic solutions at 1 to 50 μM concentrations, whereas in in vivo the growth delay was limited with 24–48 h delay in pathogen expansion for MRSA, and we noticed longer growth suppression of P. aeruginosa with TLD1411 mediated Photodynamic Therapy. The murine model will enable developing new strategies for enhancement of APDT for chronic wound infections.
From Chirps to Whistles: Discovering Event-specific Informative Content from Twitter

Twitter has brought a paradigm shift in the way we produce and curate information about real-life events. Huge volumes of user-generated tweets are produced in Twitter, related to events. Not, all of them are useful and informative. A sizable amount of tweets are spams and colloquial personal status updates, which does not provide any useful information about an event. Thus, it is necessary to identify, rank and segregate event-specific informative content from the tweet streams. In this paper, we develop a novel generic framework based on the principle of mutual reinforcement, for identifying event-specific informative content from Twitter. Mutually reinforcing relationships between tweets, hashtags, text units, URLs and users are defined and represented using TwitterEventInfoGraph. An algorithm - TwitterEventInfoRank is proposed, that simultaneously ranks tweets, hashtags, text units, URLs and users producing them, in terms of event-specific informativeness by leveraging the semantics of relationships between each of them as represented by TwitterEventInfoGraph. Experiments and observations are reported on four million (approx) tweets collected for five real-life events, and evaluated against popular baseline techniques showing significant improvement in performance.
Scaling of suction-feeding kinematics and dynamics in the African catfish, Clarias gariepinus

SUMMARY Scaling effects on the kinematics of suction feeding in fish remain poorly understood, at least partly because of the inconsistency of the results of the existing experimental studies. Suction feeding is mechanically distinct from most other type of movements in that negative pressure inside the buccal cavity is thought to be the most important speed-limiting factor during suction. However, how buccal pressure changes with size and how this influences the speed of buccal expansion is unknown. In this paper, the effects of changes in body size on kinematics of suction feeding are studied in the catfish Clarias gariepinus. Video recordings of prey-capturing C. gariepinus ranging in total length from 111 to 923 mm were made, from which maximal displacements, velocities and accelerations of several elements of the cranial system were determined. By modelling the observed expanding head of C. gariepinus as a series of expanding hollow elliptical cylinders, buccal pressure and power requirement for the expansive phase of prey capture were calculated for an ontogenetic sequence of catfish. We found that angular velocities decrease approximately proportional with increasing cranial size, while linear velocities remain more or less constant. Although a decreasing (angular) speed of buccal expansion with increasing size could be predicted (based on calculations of power requirement and the expected mass-proportional scaling of available muscular power in C. gariepinus), the observed drop in (angular) speed during growth exceeds these predictions. The calculated muscle-mass-specific power output decreases significantly with size, suggesting a relatively lower suction effort in the larger catfish compared with the smaller catfish.
Early referral and planned initiation of dialysis: what impact on quality of life?

BACKGROUND Early patient referral correlates with improved patient survival on dialysis. We examine whether early referral and a planned first dialysis affect quality of life (QoL). METHODS All patients commencing dialysis in nine centres in seven European countries between 1 July 1998 and 31 October 1999 were recruited. DEFINITIONS early referral=followed by a nephrologist >1 month before first dialysis (<1 month=late referral); planned=early referral and previous serum creatinine >300 micro mol/l and non-urgent first dialysis (early referral and no creatinine >300 micro mol/l or urgent first dialysis=unplanned). QoL was measured at 8 weeks using a visual analogue scale (VAS) and Short Form 36 (SF-36). RESULTS VAS was significantly higher in early referral patients [mean (SD) 58.4 (20) vs 50.4 (19), P=0.005], particularly if the first dialysis was planned [60.7 (20) vs 54.2 (20), P=0.03]. Planned patients also had higher SF-36 mental summary scores [45.4 (12) vs 39.7 (11), P=0.003], role emotional scores [58.0 (43) vs 30.9 (38), P=0.003], and mental health scores [63.7 (24) vs 54.6 (22), P=0.01] than unplanned patients. Adjusting for centre and other confounding variables showed that having a planned first dialysis had an independent effect on QoL (VAS, and the SF-36's mental summary score, physical functioning, role physical, general health, role emotional and mental health). Early referral had no independent effect on QoL. Socio-economic status had an important positive effect on physical QoL. CONCLUSIONS While the effect of early referral to a nephrologist on QoL appeared centre dependent, a smooth transition onto dialysis was associated with significantly better early QoL, independent of other variables.
Toric degenerations of toric varieties and tropical curves

We show that the counting of rational curves on a complete toric variety that are in general position to the toric prime divisors coincides with the counting of certain tropical curves. The proof is algebraic-geometric and relies on degeneration techniques and log deformation theory. This generalizes results of Mikhalkin obtained by different methods in the surface case to arbitrary dimensions.
Background subtraction via coherent trajectory decomposition

Background subtraction, the task to detect moving objects in a scene, is an important step in video analysis. In this paper, we propose an efficient background subtraction method based on coherent trajectory decomposition. We assume that the trajectories from background lie in a low-rank subspace, and foreground trajectories are sparse outliers in this background subspace. Meanwhile, the Markov Random Field (MRF) is used to encode the spatial coherency and trajectory consistency. With the low-rank decomposition and the MRF, our method can better handle videos with moving camera and obtain coherent foreground. Experimental results on a video dataset show our method achieves very competitive performance.
Collaborating between Local and Global Learning for Distributed Online Multiple Tasks

This paper studies the novel learning scenarios of Distributed Online Multi-tasks (DOM), where the learning individuals with continuously arriving data are distributed separately and meanwhile they need to learn individual models collaboratively. It has three characteristics: distributed learning, online learning and multi-task learning. It is motivated by the emerging applications of wearable devices, which aim to provide intelligent monitoring services, such as health emergency alarming and movement recognition. To the best of our knowledge, no previous work has been done for this kind of problems. Thus, in this paper a collaborative learning scheme is proposed for this problem. Specifically, it performs local learning and global learning alternately. First, each client performs online learning using the increasing data locally. Then, DOM switches to global learning on the server side when some condition is triggered by clients. Here, an asynchronous online multi-task learning method is proposed for global learning. In this step, only this client's model, which triggers the global learning, is updated with the support of the difficult local data instances and the other clients' models. The experiments from 4 applications show that the proposed method of global learning can improve local learning significantly. DOM framework is effective, since it can share knowledge among distributed tasks and obtain better models than learning them separately. It is also communication efficient, which only requires the clients send a small portion of raw data to the server.