Miracle-dz/UniFAR-FTUs huggingface dataset:
Supervised PP-Attachment Disambiguation for Swedish ; ( Combining Unsupervised & Supervised Training Data )

This paper is about the application of Machine Learningtechniques to the prepositional-phrase attachment ambiguity problem. Since Machine Learning requires large amounts of training instances, the mixture of unsupervised and restricted supervised acquisition of such data will be also reported. Training was performed both on a subset of the content of the Gothenburg Lexical Database(GLDB), and a combination of instances from large corpora. Testing was performed using a range of different algorithms and metrics. The application language is written Swedish.
Community Discovery in Social Networks via Heterogeneous Link Association and Fusion

Discovering social communities of web users through clustering analysis of heterogeneous link associations has drawn much attention. However, existing approaches typically require the number of clusters a prior, do not address the weighting problem for fusing heterogeneous types of links and have a heavy computational cost. In this paper, we explore the feasibility of a newly proposed heterogeneous data clustering algorithm, called Generalized Heterogeneous Fusion Adaptive Resonance Theory (GHF-ART), for discovering communities in heterogeneous social networks. Different from existing algorithms, GHF-ART performs real-time matching of patterns and one-pass learning which guarantee its low computational cost. With a vigilance parameter to restrain the intra-cluster similarity, GHF-ART does not need the number of clusters a prior. To achieve a better fusion of multiple types of links, GHF-ART employs a weighting function to incrementally assess the importance of all the feature channels. Extensive experiments have been conducted to analyze the performance of GHF-ART on two heterogeneous social network data sets and the promising results comparing with existing methods demonstrate the effectiveness and efficiency of GHF-ART.
A new multimodal recognition technique without subject's cooperation using neural network based self organizing maps

In this manuscript we present a new multimodal biometric system based on neural networks self organizing maps (SOM) for the detection and recognition of face, ear and hand geometry. We use combined principal component analysis (PCA) and SOMs for the dimensionality reduction and then use it for the combined search space optimization of ear, face and hand geometry. We name our method named RJSOM. We show that the proposed RJSOM method improves the performance and robustness of recognition when compared to methods proposed in literature. We apply the proposed method to a variety of datasets and show the results.
A new aperture admittance model for open-ended waveguides

A novel model for the aperture admittance of open-ended waveguide structures radiating into a homogeneous, lossy dielectric is presented. The model is based on the physical and mathematical properties of the driving point admittance of passive, stable one-port networks. The model parameters, which depend on the geometry of the waveguide and aperture, are determined from a relatively small number of computed admittances. These computed data are obtained by a full-wave moment method solution and, hence, include the effects of radiation and energy storage in the near-field and the evanescent waveguide modes. The accuracy of the model, for both the direct and the inverse problem, is verified. The proposed model has important applications in the field of dielectric spectroscopy. <<ETX>>
Performance Analysis with AODV Routing Protocol for Wireless Adhoc Network

Ad hoc networks are describe by multihop wireless connectivity, frequently varying network topology and the need for capable dynamic routing protocols. We evaluate the performance of two important ondemand routing protocols for portable ad hoc networks Dynamic source Routing (DSR) and Ad Hoc OnDemand Distance Vector Routing (AODV). A comprehensive simulation model with MAC and substantial layer models is used to study communication between layers and their presentation implications. We demonstrate that even while DSR and AODV share a similar ondemand performance, the differences in the protocol mechanics can lead to important performance differentials. The performance discrepancyis analyzed using unreliableload on network, node mobility and size of the network. Based on the performance, we can find out the superior protocol among DSR & AODV
A Hybrid Discourse Relation Parser in CoNLL 2015

The work presented here describes our participation in CoNLL 2015 shared task in the closed track. Here we have used a hybrid approach, where Machine Learning (ML) technique and linguistic rules are used to identify the discourse relations. We have developed this system with a view that it consistently works across all domains and all types of text corpus. We have obtained encouraging results. The performance on blind test data and test data were similar.
A method for longitudinal, transcranial imaging of blood flow and remodeling of the cerebral vasculature in postnatal mice

In the weeks following birth, both the brain and the vascular network that supplies it undergo dramatic alteration. While studies of the postnatal evolution of the pial vasculature and blood flow through its vessels have been previously done histologically or acutely, here we describe a neonatal reinforced thin‐skull preparation for longitudinally imaging the development of the pial vasculature in mice using two‐photon laser scanning microscopy. Starting with mice as young as postnatal day 2 (P2), we are able to chronically image cortical areas >1 mm2, repeatedly for several consecutive days, allowing us to observe the remodeling of the pial arterial and venous networks. We used this method to measure blood velocity in individual vessels over multiple days, and show that blood flow through individual pial venules was correlated with subsequent diameter changes. This preparation allows the longitudinal imaging of the developing mammalian cerebral vascular network and its physiology.
A simple method to derive bounds on the size and to train multilayer neural networks

A new derivation is presented for the bounds on the size of a multilayer neural network to exactly implement an arbitrary training set; namely the training set can be implemented with zero error with two layers and with the number of the hidden-layer neurons equal to #1>/= p-1. The derivation does not require the separation of the input space by particular hyperplanes, as in previous derivations. The weights for the hidden layer can be chosen almost arbitrarily, and the weights for the output layer can be found by solving #1+1 linear equations. The method presented exactly solves (M), the multilayer neural network training problem, for any arbitrary training set.
Mining issue tracking systems using topic models for trend analysis, corpus exploration, and understanding evolution

Issue Tracking systems (ITS) such as Google Code Hosting and Bugzilla facilitate software maintenance activities through bug reporting, archiving and fixing. The large number of bug reports and their unstructured text makes it impractical for developers to manually extract actionable intelligence to expedite bug fixing. In this paper, we present an application of mining bug report description and threaded discussion comments using Latent Dirichlet Allocation (LDA) which is a topic modeling technique. We apply LDA on the Chromium Browser Project bug archives (open-source) to extract topics (discovery of semantically related terms) and the latent semantic relationship between documents (bug reports) and extracted topics for corpus exploration, trend analysis and understanding evolution in maintenance domain. We conduct a series of experiments to uncover latent topics potentially useful for developers and testers based on the bug meta-data such as time, priority, type, category and status. The analysis of reopened and duplicate bugs in particular, has important inferences for the developers and can help in applications such as expertise modeling, resource allocation and knowledge management.
Lessons from Red Data Books: Plant Vulnerability Increases with Floral Complexity

The architectural complexity of flower structures (hereafter referred to as floral complexity) may be linked to pollination by specialized pollinators that can increase the probability of successful seed set. As plant—pollinator systems become fragile, a loss of such specialized pollinators could presumably result in an increased likelihood of pollination failure. This is an issue likely to be particularly evident in plants that are currently rare. Using a novel index describing floral complexity we explored whether this aspect of the structure of flowers could be used to predict vulnerability of plant species to extinction. To do this we defined plant vulnerability using the Red Data Book of Rare and Threatened Plants of Greece, a Mediterranean biodiversity hotspot. We also tested whether other intrinsic (e.g. life form, asexual reproduction) or extrinsic (e.g. habitat, altitude, range-restrictedness) factors could affect plant vulnerability. We found that plants with high floral complexity scores were significantly more likely to be vulnerable to extinction. Among all the floral complexity components only floral symmetry was found to have a significant effect, with radial-flower plants appearing to be less vulnerable. Life form was also a predictor of vulnerability, with woody perennial plants having significantly lower risk of extinction. Among the extrinsic factors, both habitat and maximum range were significantly associated with plant vulnerability (coastal plants and narrow-ranged plants are more likely to face higher risk). Although extrinsic and in particular anthropogenic factors determine plant extinction risk, intrinsic traits can indicate a plant’s proneness to vulnerability. This raises the potential threat of declining global pollinator diversity interacting with floral complexity to increase the vulnerability of individual plant species. There is potential scope for using plant—pollinator specializations to identify plant species particularly at risk and so target conservation efforts towards them.
Taxonomy for unsecure digital information processing

At a time when the media constantly reports about new sophisticated attacks, organizations of any business and size need to be prepared for such attacks against their IT infrastructures. To reflect the attacks organizations should have a properly designed information security (IS) management system with adequate documentation support. Among the most important documents, there are IS policies for different application areas, including an IS incident management policy. In order to create a truly effective IS policy it is necessary to adequately describe the organization's business environment from the IS viewpoint. The paper presents four most demand for these purposes classifications (taxonomy) of IS threats, vulnerabilities, attacks and IS incidents as the negative elements that should be avoided.
An energy efficient resource management method in virtualized cloud environment

Energy efficiency of cloud computing attracts a great deal of attention recently more than ever. In this paper, based on the characteristics of virtualized cloud environment and MapReduce workload, an energy efficiency management method called EECLOUD is presented. The method involves two strategies: VM allocation and dynamic VM placement strategies. The VM allocation strategy aims at maximizing an energy utility function making trade-off between job runtime and allocated virtual resources. And the VM placement strategy makes energy-aware scheme to place VMs on servers taking into account both VM duration and resource capacity. Additionally, a dynamic VM-host mapping algorithm is proposed to place VMs to the least amount of physical server, which uses live migration to enhance capacity utilization. The experimental results show that our approach achieves greater energy savings over existing methods.
A cluster grids based platform for digital copyright protection

While the advances in networking technologies actually enable mobility and ubiquity of access to computing resources located at different administrative domains, the progress in multimedia technologies has created large amounts of digital content that can be purchased and distributed on the Internet. Within this setting, the problem of the copyright protection of digital content has become a well-known research topic. This paper presents the architecture of a web software platform able to implement the copyright protection of digital content based on digital watermarking technology. The platform is structured as a service-oriented architecture composed of a federation of coordinated web entities and service providers which play distinct roles and interact within a trusted environment. Furthermore, the proposed architecture enables service providers to use a specific middleware to exploit the computing resources they need to provide their protection services. In fact, the middleware can aggregate computing resources directly available on the Internet as well as those belonging to multidomain, non-routable networks, thus enabling service providers to build internal computing environments arranged as cluster grids.
A Small-time Scale Netflow-based Anomaly Traffic Detecting Method Using MapReduce

Anomaly traffic detecting using Netflow data is one of important problems in the field of network security. In this paper, we proposed an approach using MapReduce model, which was realized by means of the entropy observation and DFN (Distinct feature number) distribution deviations of traffic features under anomalies at small time scales. The MapReduce was used to deal with huge amounts of data with the aid of computer cluster processing. Experimental results show the effectiveness of the proposed approach.
A new generalized sidelobe canceller with a compact array of microphones suitable for mobile terminals

This paper proposes a new generalized sidelobe canceller with a compact array of microphones suitable for mobile terminals. The output of the fixed beamformer (FBF) is further processed by a newly introduced decorrelation unit which has an auxiliary input signal to improve poor interference suppression of FBF in low frequencies. The output of the decorrelation unit is used as the reference signal for the adaptive blocking matrix and the input for the multi-input canceller. Because low and high frequency components are cancelled or suppressed by the decorrelation unit and FBF, better output-signal quality is obtained. Output signal comparison confirms approximately 12 dB higher interference cancellation by the new beamformer.
Efficient collision detection based on component technique using OBB trees in virtual assembly

An efficient collision detection scheme based on component technology using oriented bounding box trees is presented. Firstly, model is divided into parts and then a component hierarchical tree is built in a bottom-up manner using the OBB of these parts. In order to construct component OBB of two parts quickly, a simple way to compute two OBBs' convex hull is also proposed. As a result, not only parts' constraints are satisfied but also the issues of low speed in building and refitting the hierarchical bounding volume trees are solved.
Citation for Published Item: Use Policy Arm Position Does Not Attenuate Visual Loss in Patients with Homonymous Field Deficits

(2008) 'Arm position does not attenuate visual loss in patients with homonymous eld decits. The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that: • a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders. Please consult the full DRO policy for further details. Abstract One of the most common and debilitating consequences of stroke is the loss of vision in the contralesional visual field. Clinicians typically regard this loss as irreversible, and attempts at visual restoration have delivered only small and unreliable improvements. However, Schendel & Robertson (2004) reported that the visual abilities of a hemianopic patient (WM) were significantly improved when the left arm was extended into the blind field. They suggest that visual stimuli near the arm recruited bimodal visual-tactile neurons, and this activity was sufficient to bring the stimulus into awareness. This result has enormous potential therapeutic value, but given that it is a single case study there are a number of reasons to be cautious about interpreting the data. Here we investigate the effects of manipulating arm position on visual loss in a sample of 5 patients with homonymous field deficits and no visual sparing. None of our patients showed any evidence of improved implicit or explicit visual ability in the blind field as a consequence of moving the arm. We suggest that WMs improvement was the consequence of a spatial bias towards the space containing his extended arm rather than the recruitment of bimodal neurons, and conclude that manipulating arm position is of little therapeutic value to patients with dense hemianopia
Glial TLR4 receptor as new target to treat neuropathic pain: Efficacy of a new receptor antagonist in a model of peripheral nerve injury in mice

Neuropathic pain remains a prevalent clinical problem because it is often poorly responsive to the currently used analgesics, thus it is crucial the identification of new potential targets and drugs. Recent evidence indicated that microglial cells in the spinal cord are critically involved in the development and maintenance of neuropathic pain, with a pivotal role of toll‐like receptor 4 (TLR4). Binding of an endogenous ligand to TLR4 might be considered an important step in the regulation of microglia activity in pain facilitation, suggesting that a mechanism aimed to inhibit such a binding could be effective against neuropathic pain. We have synthesized new ligands to TLR4 with antagonistic activity. In the present work we evaluated the efficacy of the most potent TLR4 antagonist synthesized by us (FP‐1), administered in mice with painful neuropathy. The repeated treatment of neuropathic mice with FP‐1 (5–10 mg/kg, i.p.) evoked a relief of both thermal hyperalgesia and mechanical allodynia, whereas the administration of the highest dose to TLR4 knockout neuropathic mice revealed that in the absence of TLR4 receptor, the compound lost its efficacy. As consequence of TLR4 binding, the repeated treatment with FP‐1 prevented the activation of the transcription factor NF‐kB and the TNFα overproduction in the spinal cord. Together, our findings support the previous evidence indicative for a contribution of glial TLR4 to the initiation of neuropathic pain, suggest it as potential innovative target to treat this debilitating disease, and propose FP‐1 as lead compound for the development of new effective drugs. © 2008 Wiley‐Liss, Inc.
Iterative figure-ground discrimination

Figure-ground discrimination is an important problem in computer vision. Previous work usually assumes that the color distribution of the figure can be described by a low dimensional parametric model such as a mixture of Gaussians. However, such approach has difficulty selecting the number of mixture components and is sensitive to the initialization of the model parameters. In this paper, we employ non-parametric kernel estimation for color distributions of both the figure and background. We derive an iterative sampling-expectation (SE) algorithm for estimating the color, distribution and segmentation. There are several advantages of kernel-density estimation. First, it enables automatic selection of weights of different cues based on the bandwidth calculation from the image itself. Second, it does not require model parameter initialization and estimation. The experimental results on images of cluttered scenes demonstrate the effectiveness of the proposed algorithm.
Sentiment Analysis Methodology for Social Web Intelligence Full

The rapid development and use of Web 2.0 and social media have stimulated user-generated big data. Social and opinion-based websites offer a lot of opportunities for organizations to gather information on their customers’ behavior and needs and sentiment analysis emerged as a technique for analyzing unstructured data. However, there are still problems related to its adoption by businesses. The current study proposes a comparison of two similar methodologies for sentiment analysis which can be utilized by any type of organization. The methods were demonstrated using secondary data from Cars.com, Yelp, and Twitter. The presented methodology provides a detailed guide for practitioners to incorporate sentiment analysis in business operations. It does not require significant investments in infrastructure or specialized software, which makes it much more affordable. Current knowledge on web social intelligence is extended by demonstrating how incorporating a number of methods and tools can be successful for analyzing unstructured text.
Bufferbloat: Dark Buffers in the Internet

We have conflated "speed" with "band width. " As Stuart Chesire wrote in "It's the Latency, Stupid" (http://rescomp.stanford.edu/~cheshire/rants/Latency.html), "Making more bandwidth is easy. Once you have bad latency, you're stuck with it. " Bufferbloat is the existence of excessively large (bloated) buffers in systems, particularly network communication systems. Bufferbloat is now (almost? ) everywhere. Today's routers, switches, gateways, broad band gear, and so on have bloated buffer sizes to where we often measure latency in seconds, rather than microseconds or milliseconds.
LASP: Look-ahead spatial protocol for vehicular multi-hop communication

Vehicular multi-hop protocols typically employ distance-based metrics, which do not capture the complexity of vehicular connectivity. In this work we present LASP, a geographic protocol that uses a more accurate spatial connectivity-based metric. Spatial connectivity describes the historical probability of successfully delivering a packet from one geographic area to another. Analysis of data collected from a vehicular testbed showed that, unlike other metrics, spatial connectivity indirectly captures all major factors affecting wireless connectivity. Moreover, it is temporally stable, which makes it useful in estimating the quality of future co-located links. When forwarding, LASP uses spatial connectivity information to pick a well-connected geographic forwarding zone, inside which multiple nodes cooperate in relaying through a distributed prioritization scheme. Compared with other techniques where the sender picks a specific next hop relay, cooperative forwarding improves resilience to losses through vehicle diversity. We evaluated LASP on a 30-node testbed, where it achieved a 30% increase in packet delivery ratio over the benchmark GPSR protocol.
A neural network model of causality

This paper proposes a model for commonsense causal reasoning, based on the basic idea of neural networks. After an analysis of the advantages and limitations of existing accounts of causality, a fuzzy logic based formalism FEL is proposed that takes into account the inexactness and the cumulative evidentiality of commonsense causal reasoning, overcoming the limitations of existing accounts. Analyses concerning how FEL handles various aspects of commonsense causal reasoning are performed, in an abstract way. FEL can be implemented (naturally) in a neural (connectionist) network. This work also serves to link rule-based reasoning with neural network models, in that a rule-encoding scheme (FEL) is equated directly to a neural network model.