Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Morphological Tagging with Word Representations

We present a comparative investigation of word representations for part-of-speech (POS) and morphological tagging, focusing on scenarios with considerable differences between training and test data where a robust approach is necessary. Instead of adapting the model towards a specific domain we aim to build a robust model across domains. We developed a test suite for robust tagging consisting of six languages and different domains. We find that representations similar to Brown clusters perform best for POS tagging and that word representations based on linguistic morphological analyzers perform best for morphological tagging.
Importance-driven compositing window management

In this paper we present importance-driven compositing window management, which considers windows not only as basic rectangular shapes but also integrates the importance of the windows' content using a bottom-up visual attention model. Based on this information, importance-driven compositing optimizes the spatial window layout for maximum visibility and interactivity of occluded content in combination with see-through windows. We employ this technique for emerging window manager functions to minimize information overlap caused by popping up windows or floating toolbars and to improve the access to occluded window content. An initial user study indicates that our technique provides a more effective and satisfactory access to occluded information than the well-adopted Alt+Tab window switching technique and see-through windows without optimized spatial layout.
Learning Compositionality Functions on Word Embeddings for Modelling Attribute Meaning in Adjective-Noun Phrases

Word embeddings have been shown to be highly effective in a variety of lexical semantic tasks. They tend to capture meaningful relational similarities between individual words, at the expense of lacking the capabilty of making the underlying semantic relation explicit. In this paper, we investigate the attribute relation that often holds between the constituents of adjective-noun phrases. We use CBOW word embeddings to represent word meaning and learn a compositionality function that combines the individual constituents into a phrase representation, thus capturing the compositional attribute meaning. The resulting embedding model, while being fully interpretable, outperforms count-based distributional vector space models that are tailored to attribute meaning in the two tasks of attribute selection and phrase similarity prediction. Moreover, as the model captures a generalized layer of attribute meaning, it bears the potential to be used for predictions over various attribute inventories without re-training.
NLP and ontology based clustering — An integrated approach for optimal information extraction from social web

Social Web is a heterogeneous collection of both structured and unstructured dataprimarily composed of contents from various social networking sites, blogs, online shopping sites and much more. The knowledge extracted from such data can be valuable for accuracy of search results in light of present explosion of information exchange over social web. The extraction of information patterns from unstructured data sets available at social networking sites viz. facebook, twitter, linkedin is a challenging task as it cannot be understood by machine to robotically process the data. Also, the major data source in the form of naive users triggers the significance of filtration of the relevant results. For effectual analysis of social web contents, this paper proposes integrated NLP and ontology based clustering TVC algorithm that generates semantically meaningful concepts from the social web content. The algorithm promises to optimize the web search results and to provide accuracy in searching the well treated unstructured social web contents.
Lighting transfer functions using gradient aligned sampling

An important task in volume rendering is the visualization of boundaries between materials. This is typically accomplished using transfer functions that increase opacity based on a voxel's value and gradient. Lighting also plays a crucial role in illustrating surfaces. In this paper we present a multi-dimensional transfer function method for enhancing surfaces, not through the variation of opacity, but through the modification of surface shading. The technique uses a lighting transfer function that takes into account the distribution of values along a material boundary and features a novel interface for visualizing and specifying these transfer functions. With our method, the user is given a means of visualizing boundaries without modifying opacity, allowing opacity to be used for illustrating the thickness of homogeneous materials through the absorption of light.
Predictors of Participant Perceptions of Facilitated Individualized Education Program Meeting Success

Facilitated Individualized Education Program (FIEP) meetings present one option for early, alternative dispute resolution in special education. Although it has been suggested that this process may be useful in resolving disputes and improving relationships, these hypotheses have not been directly addressed. In this study, we used individual participant feedback data collected by a northwestern state over a 2-year period to answer the following research questions: (a) What are the perceived outcomes of FIEP meetings in terms of agreement, reduced future use of procedural safeguards, and improved relationships between school staff and family? and (b) What are the predictors of these positive participant perceptions of the outcomes of FIEP meetings? We found that respondents perceived FIEP meetings to be successful, with more than half of respondents reporting an outcome of full agreement by all team members, 44% reporting reduced future use of procedural safeguards, and 42% reporting an improved relationship between school staff and family following the meeting. Using multilevel models, we found that perceived facilitator quality was a significant predictor of all three positive outcomes, even after controlling for significant meeting characteristics such as region and year. Given these initial findings, we also provide implications for research, practice, and policy.
Economic evaluation of nurse practitioners versus GPs in treating common conditions.

BACKGROUND As studies evaluating substitution of care have revealed only limited evidence on cost-effectiveness, a trial was conducted to evaluate nurse practitioners as a first point of contact in Dutch general practices. AIM To estimate costs of GP versus nurse practitioner consultations from practice and societal perspectives. DESIGN OF STUDY An economic evaluation was conducted alongside a randomised controlled trial between May and October 2006, wherein 12 nurse practitioners and 50 GPs working in 15 general practices (study practices) participated. Consultations by study practices were also compared with an external reference group, with 17 GPs working in five general practices without the involvement of nurse practitioners. METHOD Direct costs within the healthcare sector included resource use, follow-up consultations, length of consultations, and salary costs. Costs outside the healthcare sector were productivity losses. Sensitivity analyses were performed. RESULTS Direct costs were lower for nurse practitioner consultations than for GP consultations at study practices. This was also the case for direct costs plus costs from a societal perspective for patients aged <65 years. Direct costs of consultations at study practices were lower than those of reference practices, while practices did not differ for direct costs plus costs from a societal perspective for patients aged <65 years. Cost differences are mainly caused by the differences in salary. CONCLUSION By involving nurse practitioners, substantial economic 'savings' could be used for redesigning primary care, to optimise the best skill mix, and to cover the full range of primary care activities.
Service Providers' Competences in Business Process Outsourcing for Delivering Successful Outcome: An Exploratory Study

Business Process Outsourcing (BPO) has become a very competitive industry with many players in the market. The challenge all the BPO companies are facing is how to build and sustain business with the clients in such a competitive industry. The service providers can hardly compete on cost arbitration. On the one hand, they have to respond to their existing clients, who have tasted and achieved labour cost arbitration benefits and have started looking beyond this. On the other hand, they have to win new customers by providing value more than simply cost arbitration. With this business environment in mind, we have undertaken a study of service providers operating in India to explore what competences they have developed for successfully delivering the outcome. This research explores two competences of service providers: business process management competence relationship management competence. These two competences have the theoretical grounding in resource-based view and relationship theories. The study also identifies the fundamental competences required to strengthen the above two competences as human resource management competence, information technology management competence, and outsourcing management competence. It further explores whether the service provider can, with the help of these competences, deliver the intended business process outsourcing outcome. In order to understand the successful business process outsourcing outcome, classification of business processes as viewed by both the client and the service provider is considered desirable. From the clients' perspective, processes are classified as traditional, peripheral, critical, and strategic; as core, critical, and non-core non-critical; and as critical, key, and support. Similarly, from the service providers� perspective, the business processes are classified on complexity and criticality dimensions. This classification further helps in defining the successful BPO outcome as intended by the client. For example, when a non-core non-critical process is outsourced, the successful BPO outcome would be process efficiency in terms of cost reduction and improvement in the quality, and at the most process transformation through some innovation. Thus the study concludes that the business process management competence and relationship management competence will definitely help the service providers to deliver the BPO outcome. As an expansion of BPO literature, this research makes two significant contributions: It puts forth a conceptual model that focuses on the competences of the service provider required to deliver successful BPO outcome It identifies the critical competences and the foundation competences for doing the same.
GPU-based iterative cone-beam CT reconstruction using tight frame regularization

The x-ray imaging dose from serial cone-beam computed tomography (CBCT) scans raises a clinical concern in most image-guided radiation therapy procedures. It is the goal of this paper to develop a fast graphic processing unit (GPU)-based algorithm to reconstruct high-quality CBCT images from undersampled and noisy projection data so as to lower the imaging dose. For this purpose, we have developed an iterative tight-frame (TF)-based CBCT reconstruction algorithm. A condition that a real CBCT image has a sparse representation under a TF basis is imposed in the iteration process as regularization to the solution. To speed up the computation, a multi-grid method is employed. Our GPU implementation has achieved high computational efficiency and a CBCT image of resolution 512 × 512 × 70 can be reconstructed in ∼5 min. We have tested our algorithm on a digital NCAT phantom and a physical Catphan phantom. It is found that our TF-based algorithm is able to reconstruct CBCT in the context of undersampling and low mAs levels. We have also quantitatively analyzed the reconstructed CBCT image quality in terms of the modulation-transfer function and contrast-to-noise ratio under various scanning conditions. The results confirm the high CBCT image quality obtained from our TF algorithm. Moreover, our algorithm has also been validated in a real clinical context using a head-and-neck patient case. Comparisons of the developed TF algorithm and the current state-of-the-art TV algorithm have also been made in various cases studied in terms of reconstructed image quality and computation efficiency.
A Novel Multihop Energy Efficient Heterogeneous Clustered Scheme for Wireless Sensor Networks

Research on wireless sensor networks has been studied and employed in many applications such as medical monitoring, automotive safety, and many more. Typically, sensor nodes have several issues such as limited battery life, short radio transmission range and small memory speed. However, the most severe constraint of the nodes is their limited battery energy because they cease to function when their battery deplete. In this paper, we have proposed a new cluster based energy efficient routing protocol to obtain the optimal path for data transmission between cluster heads and the base station for sparse heterogeneous wireless sensor networks. To analyze the lifetime of the network, we have assumed three types of sensor nodes, primarily with different energy levels. We have evaluated and compared the performance of protocols through simulations. Simulation results show that our protocol offers a much better performance than the existing protocols in terms of stability, network lifetime and energy efficiency.
The Role of Modifier and Head Properties in Predicting the Compositionality of English and German Noun-Noun Compounds: A Vector-Space Perspective

In this paper, we explore the role of constituent properties in English and German noun-noun compounds (corpus frequencies of the compounds and their constituents; productivity and ambiguity of the constituents; and semantic relations between the constituents), when predicting the degrees of compositionality of the compounds within a vector space model. The results demonstrate that the empirical and semantic properties of the compounds and the head nouns play a significant role.
The Location of a Closed Channel Gate in the GABAA Receptor Channel

Considerable controversy surrounds the location of the closed channel gate in members of the Cys-loop receptor family of neurotransmitter-gated ion channels that includes the GABAA, glycine, acetylcholine, and 5-HT3 receptors. Cysteine-accessibility studies concluded that the gate is near the cytoplasmic end of the channel in acetylcholine and GABAA receptors but in the middle of the 5-HT3A receptor channel. Zn2+ accessibility studies in a chimeric 5-HT3-ACh receptor suggested the gate is near the channel's cytoplasmic end. In the 4-Å resolution structure of the acetylcholine receptor closed state determined by cryoelectron microscopy, the narrowest region, inferred to be the gate, is in the channel's midsection from 9' to 14' but the M1–M2 loop residues at the channel's cytoplasmic end were not resolved in that structure. We used blocker trapping experiments with picrotoxin, a GABAA receptor open channel blocker, to determine whether a gate exists at a position more extracellular than the picrotoxin binding site, which is in the vicinity of α1Val257 (2') near the channel's cytoplasmic end. We show that picrotoxin can be trapped in the channel after removal of GABA. By using the state-dependent accessibility of engineered cysteines as reporters for the channel's structural state we infer that after GABA washout, with picrotoxin trapped in the channel, the channel appears to be in the closed state. We infer that a gate exists between the picrotoxin binding site and the channel's extracellular end, consistent with a closed channel gate in the middle of the channel. Given the homology with acetylcholine and 5-HT3 receptors there is probably a similar gate in those channels as well. This does not preclude the existence of an additional gate at a more cytoplasmic location.
Stabilité de la sélection de variables pour la classification de données en grande dimension

Les donnees a haut-debit ont motive le developpement de methodes statistiques pour la selection de variables. Ces donnees sont caracterisees par leur grande dimension et par leur heterogeneite car le signal est souvent observe simultanement a plusieurs facteurs de confusion. Les approches habituelles sont ainsi remises en question car elles peuvent conduire a des decisions erronees. Efron (2007), Leek and Storey (2007, 2008), Friguet et al (2009) montrent l'impact negatif de l'heterogeneite des donnees sur le nombre de faux-positifs des tests multiples. La selection de variables est une etape importante de la construction d'un modele de classification en grande dimension car elle reduit la dimension du probleme aux variables les plus predictives. On s'interesse ici aux performances de classification de la selection de variables, via la procedure LASSO (Tibshirani (1996)) et a la reproductibilite des ensembles de variables selectionnes. Des simulations montrent que l'ensemble des variables selectionnees par le LASSO n'est pas celui des meilleurs predicteurs theoriques. Aussi, d'interessantes performances de classification ne sont atteintes que pour un grand nombre de variables selectionnees. Notre methode s'appuie sur la description de la dependance entre covariables grâce a un petit nombre de variables latentes (Friguet et al. (2009)). La strategie proposee consiste a appliquer les procedures sur les donnees conditionnellement a cette structure de dependance. Cette strategie permet de stabiliser les variables selectionnees : d'interessantes performances de classification sont atteintes pour de plus petits ensembles de variables et les variables les plus predictives sont detectees.
Creating Cryptographic Challenges Using Multi-Party Computation: The LWE Challenge

Practical hardness results are necessary to select parameters for cryptographic schemes. Cryptographic challenges proved to be useful for determining the practical hardness of computational problems that are used to build public-key cryptography. However, several of these problems have the drawback that it is not known how to create a challenge for them without knowing the solutions. Hence, for these problems the creators of the challenges are excluded from participating. In this work, we present a method to create cryptographic challenges without excluding anyone from participating. This method is based on secure multi-party computation (MPC). We demonstrate that the MPC-based approach is indeed feasible by using it to build a challenge for the learning with errors (LWE) problem. The LWE problem is one of the most important problems in lattice-based cryptography. The security of many cryptographic schemes that have been proposed in the last decade is directly based on it. We identify parameters for LWE instances that provide the appropriate hardness level for a challenge while representing instances used to instantiate encryption schemes as close as possible. The LWE challenge is designed to determine the practical hardness of LWE, to gain an overview of the best known LWE solvers, and to motivate additional research effort in this direction.
Denotational semantics of dynamic fuzzy logic programming language

In reference (1), we have given the operational semantics model of dynamic fuzzy logic (DFL) programming language which has an excellent leading meaning to realizers. However, it is hard to see whether the language is well defined. That led us to pay attention to the denotational semantics because it is suitable for testing the validity of the implement of a language. In this paper we research the denotationl semantics of dynamic fuzzy logic programming language, which includes modifying the classical lambda calculus to introduce the character of dynamic fuzzy, the abstract syntax, the descriptions of semantic objects and the handling functions of semantics.
Step Frequency Radar Using Compressed Sensing

The current and advent progress in semiconductor technologies will allow the development of integrated circuits operating in frequency ranges well above 100 GHz. This development will the utilization of the unallocated bandwidth available in this frequency range for a multitude of applications including high data rate wireless communications as well as radars for civilian applications. For example, even today, the 77 GHz automotive collision avoidance radar represents a landmark progress due to each potential to save thousands of lives every year, by providing to the driver of a vehicle early warning of a possible collision with another vehicle or a pedestrian. Following the collision avoidance example, radars for other commercial applications can be developed. In this report, the possibility of a radar for non-contact respiration rate monitoring as graphically illustrated in figure 1 is investigated. As shown in the figure, a radar transceiver is placed in a position that has Line-of-Sight contact with the thorax of a sleeping patient. By accurately measuring the distance to the patient’s thorax, several times a second, the minute displacements caused due to respiration are recorded. These data are used in a later stage for calculation and evaluation of the patient’s respiration pattern. By knowing this pattern for a period of several days, a medical doctor can evaluate whether the patient suffers from a sleep disorder such as sleep apnea. The range accuracy (resolution) requirement for this application, as well as many other similar applications, is of the order of 1 mm. As will be shown in the following sections, this accuracy requirement is particularly challenging to meet using standard radar processing. Therefore, an alternative technique using basis pursuit reconstruction is explored. Various target separation and noise scenarios are explored and different reconstruction methods are evaluated.